Python_Math_OpenCV_Dic.ipynb

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   "source": [
    "<a id='0.0'></a>\n",
    "* [6. OpenCV](#6)\n",
    "    * [6.1 Image Read](#6.1)\n",
    "    * [6.2 Colour and Save images](#6.2)\n",
    "    * [6.3 Resize image and Annotation](#6.3)\n",
    "    * [6.4 Brightness, Contrast and Thresholding](#6.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe319439-8436-4fa3-ada5-2ec6f48d2103",
   "metadata": {},
   "source": [
    "# 1. Interview Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "668f705c-bc21-4aac-b9f5-a787c97d8b5a",
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    "jupyter": {
     "source_hidden": true
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    "tags": []
   },
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   "source": [
    "from tkinter import *\n",
    "from tkinter import messagebox\n",
    "import random\n",
    "tkWindow = Tk()  \n",
    "tkWindow.geometry('400x150')  \n",
    "tkWindow.title('PhD interview Questions')\n",
    "interviews={}\n",
    "interviews[1]=[['Can you tell us about your research experience?'],[1]]\n",
    "interviews[2]=[['Can you describe your current research project?'],[2]]\n",
    "interviews[3]=[['What motivated you to pursue a PhD in this field?'],[3]]\n",
    "interviews[4]=[['What are your long-term career goals?'],[4]]\n",
    "interviews[5]=[['Can you describe the methodology you plan to use for your PhD research?'],[5]]\n",
    "interviews[6]=[['How do you plan to contribute to the field with your research?'],[6]]\n",
    "interviews[7]=[['Can you discuss any challenges you have faced in your research so far, and how you overcame them?'],[7]]\n",
    "interviews[8]=[['Can you explain a recent publication in your field and its significance?'],[8]]\n",
    "interviews[9]=[['Can you discuss a time when you had to collaborate with others on a research project?'],[9]]\n",
    "interviews[10]=[['Can you tell us about any research funding opportunities you have applied for and their outcomes?'],[10]]\n",
    "interviews[11]=[['Can you tell us about your research interests and how they align with the work being done in our department?'],[11]]\n",
    "interviews[12]=[['Can you walk us through your previous research experience and how it has prepared you for this PhD program?'],[12]]\n",
    "interviews[13]=[['Can you explain the methodology and techniques you used in your previous research projects?'],[13]]\n",
    "interviews[14]=[['How do you approach problem-solving in your research?'],[14]]\n",
    "interviews[15]=[['Can you discuss any current trends or developments in your field of study and how you plan to contribute to them?'],[15]]\n",
    "interviews[16]=[['Can you describe a specific research project you have worked on and the results you achieved?'],[16]]\n",
    "interviews[17]=[['How do you plan to collaborate with other researchers in the department and contribute to the larger academic community?'],[17]]\n",
    "interviews[18]=[['Can you discuss any publications you have had or conferences you have presented at?'],[18]]\n",
    "interviews[19]=[['How do you plan to balance your coursework, research, and other responsibilities during the PhD program?'],[19]]\n",
    "interviews[20]=[['Can you talk about any specific professors or researchers in our department whose work you admire and why?'],[20]]\n",
    "interviews[21]=[['Tell me about yourself'],[21]]\n",
    "interviews[22]=[['Tell me about a experience that you faced challenge and How to overcome?'],[22]]\n",
    "interviews[23]=[['What is your strengths and weaknesses?'],[23]]\n",
    "interviews[24]=[['Explain about your previous ML algorithm for work or research'],[24]]\n",
    "interviews[25]=[['What is Cross-validation?'],[25]]\n",
    "interviews[26]=[['What is Bias and Variance?'],[26]]\n",
    "interviews[27]=[['What is Probability distribution?'],[27]]\n",
    "interviews[28]=[[' What is Bayes Theorem?'],[28]]\n",
    "interviews[29]=[['What are K-Means and KNN algorithm?'],[29]]\n",
    "interviews[30]=[['What are Decision Tree and Random Forest?'],[30]]\n",
    "interviews[31]=[['What is Supportive Vector Machine?'],[31]]\n",
    "interviews[32]=[['What is the difference between Regression and Classification?'],[32]]\n",
    "interviews[33]=[['What is the difference between Supervised and Unsupervised learning?'],[33]]\n",
    "interviews[34]=[['What is Regularisation (L1: Ridge, L2: Lasso)?'],[34]]\n",
    "interviews[35]=[['What are CNN and RNN?'],[35]]\n",
    "interviews[36]=[['What are GAN and Autoencoder?'],[36]]\n",
    "interviews[37]=[['What are Ensemble, Bagging and Boosting?'],[37]]\n",
    "interviews[38]=[['What are Filter, Kernel, Pooling, Padding and Stride?'],[38]]\n",
    "interviews[39]=[['Explain Spectral Graph Theory'],[39]]\n",
    "interviews[40]=[['Explain Photometric Method'],[40]]\n",
    "interviews[41]=[['Explain Structure from Motion'],[41]]\n",
    "interviews[42]=[['Explain Multi-View Stereopsis'],[42]]\n",
    "#interviews[43]=[[''],[43]]\n",
    "def showQues():\n",
    "    r=random.randint(1, len(interviews)+1)\n",
    "    messagebox.showinfo(f'Question{r}', interviews[r][0][0])\n",
    "    messagebox.showinfo(f'Sample Answer{r}', interviews[r][1])\n",
    "button = Button(tkWindow,\n",
    "\ttext = 'Questions_Answer',\n",
    "\tcommand = showQues)  \n",
    "button.pack()\n",
    "tkWindow.mainloop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c70269f-dd30-4f2d-8401-dec1250f7c5a",
   "metadata": {},
   "source": [
    "# List and Array\n",
    "\n",
    "List is sequential. Array is Vector.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a537352-c3a7-46fb-86b8-9c4b2ae2162e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a='dictionary'\n",
    "print('String to list',list(a)) # Make string into list\n",
    "b=10284765\n",
    "print('Single integer to list by each digits',list(str(b))) # Make number into list\n",
    "c=[1,2,3,4,5,6,7,8,9,10]\n",
    "print('One line to make list',[i for i in c if i%2 ==0]) # Make one line iterate list\n",
    "xy = lambda x, y : x * y\n",
    "print('Map function of list',list(map(xy,c,c))) # Apply function in list\n",
    "print('Plus and Multiplication of list',c*2, c+c) # Plus and multiply of list\n",
    "print('Plus and Multiplication of array',np.array(c)*2, np.array(c)+np.array(c)) # Plus and Multiply of Array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0c29bc-66f2-4c24-aa97-4a2bf823204f",
   "metadata": {},
   "source": [
    "# ListNode\n",
    "A ListNode is a basic building block of a singly-linked list data structure. It usually contains a value and a reference to the next node in the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6c5e8f7-c1f6-4e47-b9aa-c73813249208",
   "metadata": {},
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   "source": [
    "class ListNode: # Defining a ListNode Class\n",
    "    def __init__(self, val=0, next=None):\n",
    "        self.val = val\n",
    "        self.next = next\n",
    "# Creating a linked list with three nodes\n",
    "node1 = ListNode(1) # Initialize nodes\n",
    "node2 = ListNode(2)\n",
    "node3 = ListNode(3)\n",
    "\n",
    "node1.next = node2 # Link nodes together\n",
    "node2.next = node3\n",
    "# Printing a linked list\n",
    "def print_linked_list(head):\n",
    "    current = head\n",
    "    while current is not None:\n",
    "        print(current.val, end=\" -> \")\n",
    "        current = current.next\n",
    "    print(\"None\")\n",
    "print_linked_list(node1)\n",
    "# Inserting a new node at the beginning of the list\n",
    "new_head = ListNode(0)\n",
    "new_head.next = node1\n",
    "print_linked_list(new_head)\n",
    "# Inserting a new node after a specific node\n",
    "def insert_after_node(node, value):\n",
    "    new_node = ListNode(value)\n",
    "    new_node.next = node.next\n",
    "    node.next = new_node\n",
    "insert_after_node(node1, 1.5)\n",
    "print_linked_list(new_head)\n",
    "# Deleting a node with specific value\n",
    "def delete_node(head, value):\n",
    "    dummy = ListNode(0)\n",
    "    dummy.next = head\n",
    "    current = dummy\n",
    "    while current.next is not None:\n",
    "        if current.next.val == value:\n",
    "            current.next = current.next.next\n",
    "            break\n",
    "        current = current.next\n",
    "    return dummy.next\n",
    "new_head = delete_node(new_head, 1.5)\n",
    "print_linked_list(new_head)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c5b6bb6-0466-4143-ba3d-21c5351dcc2c",
   "metadata": {},
   "source": [
    "# Recursive Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "476f7370-8660-4870-8a42-22edf607e572",
   "metadata": {},
   "outputs": [],
   "source": [
    "nums=[1,2,3]\n",
    "def permute(nums):\n",
    "    if len(nums) == 1:\n",
    "        return [nums]\n",
    "    result = []\n",
    "    for i in range(len(nums)):\n",
    "        others = nums[:i] + nums[i+1:]\n",
    "        other_permutations = permute(others)\n",
    "        for permutation in other_permutations:\n",
    "            result.append([nums[i]] + permutation)\n",
    "    return result\n",
    "print(permute(nums))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08bb6a8f-9186-4454-aded-802a604c936f",
   "metadata": {},
   "source": [
    "# Binary Search Algorithm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0804b7cb-a405-4ab7-b9a1-80e4c8ea7be0",
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   "source": [
    "def binary_search(arr, x):\n",
    "    low = 0\n",
    "    high = len(arr) - 1\n",
    "    while low <= high:\n",
    "        mid = (low + high) // 2\n",
    "        if arr[mid] == x:\n",
    "            return mid\n",
    "        elif arr[mid] < x:\n",
    "            low = mid + 1\n",
    "        else:\n",
    "            high = mid - 1\n",
    "    return -1\n",
    "arr = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "x = 6\n",
    "result = binary_search(arr, x)\n",
    "if result != -1:\n",
    "    print(\"Element is present at index\", str(result))\n",
    "else:\n",
    "    print(\"Element is not present in the array\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b5397f-2577-48ab-a3ae-c9b085244253",
   "metadata": {},
   "source": [
    "# Depth First Search Algorithm\n",
    "Have the function SearchingChallenge(strArr) take the array of strings stored in strArr, which will be a 2D matrix of 0 and 1's, and determine how many holes, or contiguous regions of 0's, exist in the matrix. A contiguous region is one where there is a connected group of 0's going in one or more of four directions: up, down, left, or right. For example: if strArr is [\"10111\", \"10101\", \"11101\", \"11111\"], then this looks like the following matrix: \n",
    "\n",
    "1 0 1 1 1\n",
    "1 0 1 0 1\n",
    "1 1 1 0 1\n",
    "1 1 1 1 1 \n",
    " 1 1 \n",
    "\n",
    "For the input above, your program should return 2 because there are two separate contiguous regions of 0's, which create \"holes\" in the matrix. You can assume the input will not be empty.\n",
    "Examples\n",
    "Input: [\"01111\", \"01101\", \"00011\", \"11110\"] \n",
    "Output: 3Input: [\"1011\", \"0010\"] \n",
    "Output: 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc56e3f4-fc3c-4966-a618-b92849dadb1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "def SearchingChallenge(strArr):\n",
    "    matrix = [[int(ch) for ch in row] for row in strArr] # string to int matrix\n",
    "    num_rows = len(matrix) # size\n",
    "    num_cols = len(matrix[0]) # size\n",
    "    def dfs(i,j): # deep first search\n",
    "        if i<0 or j<0 or i>= num_rows or j>=num_cols or matrix[i][j] !=0: # out when its not possible\n",
    "            return\n",
    "        matrix[i][j] = 1 # mark as 1 to erase previous 0\n",
    "        # search up, down, left, right\n",
    "        dfs(i-1,j) \n",
    "        dfs(i+1,j)\n",
    "        dfs(i,j-1)\n",
    "        dfs(i,j+1)\n",
    "    num_holes =0 # initialise number of holes\n",
    "    for i in range(num_rows):\n",
    "        for j in range(num_cols): # search from 0,0\n",
    "            if matrix[i][j]==0: # find 0\n",
    "                num_holes +=1 # find hole\n",
    "                dfs(i,j) # do deep first search\n",
    "    return num_holes\n",
    "input=[\"01111\", \"01101\", \"00011\", \"11110\"] \n",
    "print(SearchingChallenge(input))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65772d8b-1121-4393-9ce6-5fe5e14dac13",
   "metadata": {},
   "source": [
    "# Sorting algorithms\n",
    "These algorithms arrange elements of a list in a particular order, for example, bubble sort, insertion sort, merge sort, quick sort, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "787dcd84-c1ee-49e7-b49a-b15a3a0374d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import random\n",
    "def bubble_sort(arr):\n",
    "    n = len(arr)\n",
    "    for i in range(n):\n",
    "        for j in range(0, n - i - 1):\n",
    "            if arr[j] > arr[j + 1]:\n",
    "                arr[j], arr[j + 1] = arr[j + 1], arr[j]\n",
    "    return arr\n",
    "def insertion_sort(arr):\n",
    "    for i in range(1, len(arr)):\n",
    "        key = arr[i]\n",
    "        j = i - 1\n",
    "        while j >= 0 and key < arr[j]:\n",
    "            arr[j + 1] = arr[j]\n",
    "            j -= 1\n",
    "        arr[j + 1] = key\n",
    "    return arr\n",
    "def merge_sort(arr):\n",
    "    if len(arr) > 1:\n",
    "        mid = len(arr)//2\n",
    "        L = arr[:mid]\n",
    "        R = arr[mid:]\n",
    "        merge_sort(L)\n",
    "        merge_sort(R)\n",
    "        i = j = k = 0\n",
    "        while i < len(L) and j < len(R):\n",
    "            if L[i] < R[j]:\n",
    "                arr[k] = L[i]\n",
    "                i += 1\n",
    "            else:\n",
    "                arr[k] = R[j]\n",
    "                j += 1\n",
    "            k += 1\n",
    "        while i < len(L):\n",
    "            arr[k] = L[i]\n",
    "            i += 1\n",
    "            k += 1\n",
    "        while j < len(R):\n",
    "            arr[k] = R[j]\n",
    "            j += 1\n",
    "            k += 1\n",
    "    return arr\n",
    "def quick_sort(arr, low, high):\n",
    "    if low < high:\n",
    "        pi = partition(arr, low, high)\n",
    "        quick_sort(arr, low, pi-1)\n",
    "        quick_sort(arr, pi+1, high)\n",
    "    return arr\n",
    "def partition(arr, low, high):\n",
    "    pivot = arr[high]\n",
    "    i = low - 1\n",
    "    for j in range(low, high):\n",
    "        if arr[j] <= pivot:\n",
    "            i = i + 1\n",
    "            arr[i], arr[j] = arr[j], arr[i]\n",
    "    arr[i + 1], arr[high] = arr[high], arr[i + 1]\n",
    "    return i + 1\n",
    "if __name__ == \"__main__\":\n",
    "    arr = random.sample(range(1, 10001), 1000)\n",
    "    start = time.time()\n",
    "    bubble_sort(arr.copy())\n",
    "    print(\"Bubble sort: \", time.time() - start)\n",
    "    start = time.time()\n",
    "    insertion_sort(arr.copy())\n",
    "    print(\"Insertion sort: \", time.time() - start)\n",
    "    start = time.time()\n",
    "    merge_sort(arr.copy())\n",
    "    print(\"Merge sort: \", time.time() - start)\n",
    "    start = time.time()\n",
    "    quick_sort(arr.copy(), 0, len(arr) - 1)\n",
    "    print(\"Quick sort: \", time.time() - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa00c3a6-2e20-4715-9878-728b233fc377",
   "metadata": {},
   "source": [
    "# Dynamic Programming\n",
    "Dynamic programming is a technique used to solve problems by breaking them down into simpler subproblems and storing the results of these subproblems to avoid redundant computations. Here's an advanced example using dynamic programming to solve the 0/1 knapsack problem, which is a classic optimization problem.\n",
    "\n",
    "The 0/1 knapsack problem is defined as follows:\n",
    "Given a set of items, each with a weight and a value, determine the items to include in a knapsack such that the total weight is less than or equal to a given limit and the total value is as large as possible.\n",
    "\n",
    "This code defines a function knapSack that solves the 0/1 knapsack problem using dynamic programming. It creates a memoization table dp to store the optimal solution for each subproblem and builds the table in a bottom-up manner. After constructing the table, it backtracks to find the items that were included in the optimal solution. The function returns the maximum value that can be obtained and the items included in the knapsack.\n",
    "\n",
    "You can customize the values, weights, and W variables to test the code with different inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "178eaeb2-a621-4cc5-bd87-0f3ae382b18b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def knapSack(W, weights, values, n):\n",
    "    # Create a memoization table to store the optimal solution for each subproblem\n",
    "    dp = [[0 for w in range(W + 1)] for i in range(n + 1)]\n",
    "\n",
    "    # Build the table in a bottom-up manner\n",
    "    for i in range(n + 1):\n",
    "        for w in range(W + 1):\n",
    "            if i == 0 or w == 0:\n",
    "                dp[i][w] = 0\n",
    "            elif weights[i-1] <= w:\n",
    "                dp[i][w] = max(values[i-1] + dp[i-1][w-weights[i-1]], dp[i-1][w])\n",
    "            else:\n",
    "                dp[i][w] = dp[i-1][w]\n",
    "\n",
    "    # Now backtrack to find the items that were included in the optimal solution\n",
    "    items_included = []\n",
    "    i, w = n, W\n",
    "    while i > 0 and w > 0:\n",
    "        if dp[i][w] != dp[i-1][w]:\n",
    "            items_included.append(i-1)\n",
    "            w -= weights[i-1]\n",
    "        i -= 1\n",
    "\n",
    "    # Return the optimal value and the items included\n",
    "    return dp[n][W], items_included\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    values = [60, 100, 120]\n",
    "    weights = [10, 20, 30]\n",
    "    W = 50\n",
    "    n = len(values)\n",
    "\n",
    "    max_value, items = knapSack(W, weights, values, n)\n",
    "    print(\"The maximum value that can be obtained is:\", max_value)\n",
    "    print(\"Items included (indexed from 0):\", items)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b36fcb6b-8600-4f8b-ac7d-560b1b533615",
   "metadata": {},
   "source": [
    "# Graph algorithms\n",
    "Algorithms for working with graphs, such as depth-first search, breadth-first search, shortest path algorithms, and minimum spanning tree algorithms.\n",
    "Depth-First Search (DFS), Breadth-First Search (BFS), Dijkstra's shortest path algorithm, and Kruskal's minimum spanning tree algorithm using an adjacency list representation for the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d23d8338-d0ef-41fd-9a93-951ad002114a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict, deque\n",
    "from heapq import heappop, heappush\n",
    "from functools import total_ordering\n",
    "\n",
    "class Graph:\n",
    "    def __init__(self, vertices, directed=False):\n",
    "        self.graph = defaultdict(list)\n",
    "        self.directed = directed\n",
    "        self.vertices = vertices\n",
    "\n",
    "    def add_edge(self, u, v, weight):\n",
    "        self.graph[u].append((v, weight))\n",
    "        if not self.directed:\n",
    "            self.graph[v].append((u, weight))\n",
    "\n",
    "    def dfs(self, start):\n",
    "        visited = set()\n",
    "        stack = [start]\n",
    "\n",
    "        while stack:\n",
    "            vertex = stack.pop()\n",
    "            if vertex not in visited:\n",
    "                visited.add(vertex)\n",
    "                stack.extend(neighbor for neighbor, _ in self.graph[vertex] if neighbor not in visited)\n",
    "\n",
    "        return visited\n",
    "\n",
    "    def bfs(self, start):\n",
    "        visited = set()\n",
    "        queue = deque([start])\n",
    "\n",
    "        while queue:\n",
    "            vertex = queue.popleft()\n",
    "            if vertex not in visited:\n",
    "                visited.add(vertex)\n",
    "                queue.extend(neighbor for neighbor, _ in self.graph[vertex] if neighbor not in visited)\n",
    "\n",
    "        return visited\n",
    "\n",
    "    def dijkstra(self, start, end):\n",
    "        heap = [(0, start)]\n",
    "        visited = set()\n",
    "        while heap:\n",
    "            (cost, current) = heappop(heap)\n",
    "            if current in visited:\n",
    "                continue\n",
    "            visited.add(current)\n",
    "            if current == end:\n",
    "                return cost\n",
    "            for neighbor, weight in self.graph[current]:\n",
    "                if neighbor not in visited:\n",
    "                    heappush(heap, (cost + weight, neighbor))\n",
    "\n",
    "        return -1\n",
    "\n",
    "    def kruskal(self):\n",
    "        edges = [(u, v, weight) for u in self.graph for v, weight in self.graph[u]]\n",
    "        edges.sort(key=lambda x: x[2])\n",
    "\n",
    "        parent = {i: i for i in range(self.vertices)}\n",
    "        rank = {i: 0 for i in range(self.vertices)}\n",
    "\n",
    "        def find(vertex):\n",
    "            if parent[vertex] != vertex:\n",
    "                parent[vertex] = find(parent[vertex])\n",
    "            return parent[vertex]\n",
    "\n",
    "        def union(u_root, v_root):\n",
    "            if rank[u_root] > rank[v_root]:\n",
    "                parent[v_root] = u_root\n",
    "            else:\n",
    "                parent[u_root] = v_root\n",
    "                if rank[u_root] == rank[v_root]:\n",
    "                    rank[v_root] += 1\n",
    "\n",
    "        minimum_spanning_tree = []\n",
    "        for u, v, weight in edges:\n",
    "            u_root, v_root = find(u), find(v)\n",
    "            if u_root != v_root:\n",
    "                minimum_spanning_tree.append((u, v, weight))\n",
    "                union(u_root, v_root)\n",
    "\n",
    "        return minimum_spanning_tree\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    g = Graph(9)\n",
    "    edges = [\n",
    "        (0, 1, 4), (0, 7, 8), (1, 7, 11), (1, 2, 8), (2, 8, 2),\n",
    "        (2, 3, 7), (2, 5, 4), (3, 5, 14), (3, 4, 9), (4, 5, 10),\n",
    "        (5, 6, 2), (6, 8, 6), (6, 7, 1), (7, 8, 7)\n",
    "    ]\n",
    "\n",
    "    for edge in edges:\n",
    "        g.add_edge(*edge)\n",
    "\n",
    "    # DFS\n",
    "    visited_dfs = g.dfs(0)\n",
    "    print(\"DFS traversal:\", visited_dfs)\n",
    "\n",
    "    # BFS\n",
    "    visited_bfs = g.bfs(0)\n",
    "    print(\"BFS traversal:\", visited_bfs)\n",
    "\n",
    "    # Dijkstra's Shortest Path\n",
    "    shortest_path = g.dijkstra(0, 4)\n",
    "    print(\"Shortest path from 0 to 4 using Dijkstra's algorithm:\", shortest_path)\n",
    "\n",
    "    # Kruskal's Minimum Spanning Tree\n",
    "    min_spanning_tree = g.kruskal()\n",
    "    print(\"Minimum spanning tree using Kruskal's algorithm:\", min_spanning_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf8b311a-57f7-4047-8390-07de6516450a",
   "metadata": {},
   "source": [
    "# Greedy algorithm\n",
    "\n",
    "Algorithms that make the locally optimal choice at each stage in the hope of finding a global optimum.\n",
    "Given a set of items, each with a weight and a value, and a knapsack with a maximum weight capacity, determine the maximum value that can be obtained by including fractions of items, such that the total weight does not exceed the knapsack capacity.\n",
    "\n",
    "This code defines a function fractional_knapsack that solves the fractional knapsack problem using a greedy algorithm. It first sorts the items by their value-to-weight ratio in descending order. Then, it iterates through the items and takes the maximum possible fraction of each item, updating the total value and remaining capacity accordingly. The function returns the maximum value that can be obtained.\n",
    "\n",
    "You can customize the items and capacity variables to test the code with different inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d0eff82-37a9-44c8-a082-a588ab42cf80",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fractional_knapsack(items, capacity):\n",
    "    # Sort items by value-to-weight ratio in descending order\n",
    "    items.sort(key=lambda x: x['value'] / x['weight'], reverse=True)\n",
    "\n",
    "    total_value = 0\n",
    "    for item in items:\n",
    "        if capacity == 0:\n",
    "            break\n",
    "\n",
    "        weight_to_take = min(item['weight'], capacity)\n",
    "        fraction = weight_to_take / item['weight']\n",
    "        total_value += item['value'] * fraction\n",
    "        capacity -= weight_to_take\n",
    "\n",
    "    return total_value\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    items = [\n",
    "        {'value': 60, 'weight': 10},\n",
    "        {'value': 100, 'weight': 20},\n",
    "        {'value': 120, 'weight': 30},\n",
    "    ]\n",
    "    capacity = 50\n",
    "\n",
    "    max_value = fractional_knapsack(items, capacity)\n",
    "    print(\"The maximum value that can be obtained is:\", max_value)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25e1a82b-2b7a-4a4b-93c3-eb317d28f642",
   "metadata": {},
   "source": [
    "# Backtracking algorithm\n",
    "Backtracking: A general algorithmic technique that incrementally builds candidates to the solutions, and abandons a candidate (\"backtracks\") as soon as it determines that the candidate cannot possibly be completed to a valid solution.\n",
    "\n",
    "Place N chess queens on an N x N chessboard so that no two queens threaten each other (i.e., they should not be in the same row, column, or diagonal).\n",
    "\n",
    "This code defines a function n_queens that solves the N-Queens problem using a backtracking algorithm. It uses helper functions is_safe and n_queens_util to check whether a queen can be placed safely in a given position and to recursively place queens on the board, respectively. The n_queens function returns the resulting chessboard with queens placed safely, or None if no solution is found.\n",
    "\n",
    "You can customize the n variable to test the code with different board sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2292e23c-7304-4e96-b57b-424ff2e0f8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_safe(board, row, col, n):\n",
    "    # Check the same column\n",
    "    for i in range(row):\n",
    "        if board[i][col] == 1:\n",
    "            return False\n",
    "\n",
    "    # Check upper diagonal on the left side\n",
    "    for i, j in zip(range(row, -1, -1), range(col, -1, -1)):\n",
    "        if board[i][j] == 1:\n",
    "            return False\n",
    "\n",
    "    # Check upper diagonal on the right side\n",
    "    for i, j in zip(range(row, -1, -1), range(col, n)):\n",
    "        if board[i][j] == 1:\n",
    "            return False\n",
    "\n",
    "    return True\n",
    "\n",
    "def n_queens_util(board, row, n):\n",
    "    if row == n:\n",
    "        return True\n",
    "\n",
    "    for col in range(n):\n",
    "        if is_safe(board, row, col, n):\n",
    "            board[row][col] = 1\n",
    "\n",
    "            if n_queens_util(board, row + 1, n):\n",
    "                return True\n",
    "\n",
    "            board[row][col] = 0\n",
    "\n",
    "    return False\n",
    "\n",
    "def n_queens(n):\n",
    "    board = [[0 for _ in range(n)] for _ in range(n)]\n",
    "\n",
    "    if not n_queens_util(board, 0, n):\n",
    "        return None\n",
    "\n",
    "    return board\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    n = 8\n",
    "    result = n_queens(n)\n",
    "\n",
    "    if result is not None:\n",
    "        for row in result:\n",
    "            print(row)\n",
    "    else:\n",
    "        print(\"No solution found.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb20b000-c09d-4e08-9ea2-be7b58c88be0",
   "metadata": {},
   "source": [
    "# Randomize algorithm\n",
    "Randomized algorithms: Algorithms that use randomness as part of their logic.\n",
    "Reservoir Sampling algorithm, which is used to select k items randomly from a list of items of unknown size. This algorithm is particularly useful when the size of the list is too large or expensive to bring into memory.\n",
    "\n",
    "the reservoir_sampling function takes a stream (an iterator) and an integer k as input, and returns k items randomly selected from the stream using the Reservoir Sampling algorithm. The generate_stream function is a generator function that simulates a stream of items from 0 to n-1.\n",
    "\n",
    "You can customize the k and n variables to test the code with different sample sizes and stream sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "831ccdcb-dcd3-4615-90d5-f3ca29580bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def reservoir_sampling(stream, k):\n",
    "    reservoir = []\n",
    "\n",
    "    for i, item in enumerate(stream):\n",
    "        if i < k:\n",
    "            reservoir.append(item)\n",
    "        else:\n",
    "            random_index = random.randint(0, i)\n",
    "            if random_index < k:\n",
    "                reservoir[random_index] = item\n",
    "\n",
    "    return reservoir\n",
    "\n",
    "def generate_stream(n):\n",
    "    for i in range(n):\n",
    "        yield i\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    k = 10\n",
    "    n = 100\n",
    "\n",
    "    stream = generate_stream(n)\n",
    "    sampled_items = reservoir_sampling(stream, k)\n",
    "    print(\"Sampled items:\", sampled_items)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "784f26c1-45eb-4228-b6d0-d0a182e9645a",
   "metadata": {},
   "source": [
    "# Two Pinter\n",
    "\n",
    "Have the function ArrayChallenge(arr) take the array of non-negative integers stored in arr, and determine the largest amount of water that can be trapped. The numbers in the array represent the height of a building (where the width of each building is 1) and if you imagine it raining, water will be trapped between the two tallest buildings. For example: if arr is [3, 0, 0, 2, 0, 4] then this array of building heights looks like the following picture if we draw it out: \n",
    " \n",
    "\n",
    "Now if you imagine it rains and water gets trapped in this picture, then it'll look like the following (the x's represent water)\n",
    "\n",
    "  \n",
    "\n",
    "This is the most water that can be trapped in this picture, and if you calculate the area you get 10, so your program should return 10.\n",
    "Examples\n",
    "Input: [1, 2, 1, 2] \n",
    "\n",
    "<br>\n",
    "\n",
    "Output: 1Input: [0, 2, 4, 0, 2, 1, 2, 6] \n",
    "Output: 11\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f1e9563-d53a-47df-8fe7-4dd2a10633d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "def ArrayChallenge(arr):\n",
    "    l, r=0, len(arr)-1 # initialise variables from first index and last index\n",
    "    l_max, r_max = arr[l], arr[r] # find the maximum level of water\n",
    "    water=0 # initial water level\n",
    "    while l <= r: # until index reaching right\n",
    "        if arr[l] <= arr[r]: # divide current building level based on the right most building\n",
    "            if arr[l] > l_max: # check current building level from maximum water level\n",
    "                l_max=arr[l]\n",
    "            else:\n",
    "                water+=l_max-arr[l]\n",
    "            l+=1 # move to next building\n",
    "        else:\n",
    "            if arr[r]>r_max:\n",
    "                r_max=arr[r]\n",
    "            else:\n",
    "                water+=r_max-arr[r]\n",
    "            right-=1  # move right standard to left\n",
    "    return water\n",
    "\n",
    "# keep this function call here \n",
    "input=[0, 2, 4, 0, 2, 1, 2, 6] \n",
    "print(ArrayChallenge(input))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f9470e-570f-4ca6-b22e-e9f2bfe1dc98",
   "metadata": {},
   "source": [
    "# Game Theory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e1bf8c5-26d6-4d4f-9c54-14f1e958fd77",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def evaluate(board):\n",
    "    for row in range(3):\n",
    "        if board[row, 0] == board[row, 1] == board[row, 2]:\n",
    "            return board[row, 0]\n",
    "\n",
    "    for col in range(3):\n",
    "        if board[0, col] == board[1, col] == board[2, col]:\n",
    "            return board[0, col]\n",
    "\n",
    "    if board[0, 0] == board[1, 1] == board[2, 2]:\n",
    "        return board[0, 0]\n",
    "\n",
    "    if board[0, 2] == board[1, 1] == board[2, 0]:\n",
    "        return board[0, 2]\n",
    "\n",
    "    return 0\n",
    "\n",
    "def minimax(board, depth, maximizing_player):\n",
    "    evaluation = evaluate(board)\n",
    "    if evaluation != 0 or depth == 0:\n",
    "        return evaluation * (1 if maximizing_player else -1)\n",
    "\n",
    "    if maximizing_player:\n",
    "        max_eval = float('-inf')\n",
    "        for row in range(3):\n",
    "            for col in range(3):\n",
    "                if board[row, col] == 0:\n",
    "                    board[row, col] = 1\n",
    "                    eval = minimax(board, depth - 1, False)\n",
    "                    board[row, col] = 0\n",
    "                    max_eval = max(max_eval, eval)\n",
    "        return max_eval\n",
    "    else:\n",
    "        min_eval = float('inf')\n",
    "        for row in range(3):\n",
    "            for col in range(3):\n",
    "                if board[row, col] == 0:\n",
    "                    board[row, col] = -1\n",
    "                    eval = minimax(board, depth - 1, True)\n",
    "                    board[row, col] = 0\n",
    "                    min_eval = min(min_eval, eval)\n",
    "        return min_eval\n",
    "\n",
    "def best_move(board):\n",
    "    max_eval = float('-inf')\n",
    "    move = (-1, -1)\n",
    "\n",
    "    for row in range(3):\n",
    "        for col in range(3):\n",
    "            if board[row, col] == 0:\n",
    "                board[row, col] = 1\n",
    "                eval = minimax(board, 2, False)\n",
    "                board[row, col] = 0\n",
    "\n",
    "                if eval > max_eval:\n",
    "                    max_eval = eval\n",
    "                    move = (row, col)\n",
    "\n",
    "    return move\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    board = np.array([\n",
    "        [ 1, -1,  0],\n",
    "        [ 0,  1, -1],\n",
    "        [ 0,  0,  0]\n",
    "    ])\n",
    "\n",
    "    optimal_move = best_move(board)\n",
    "    print(\"Optimal move:\", optimal_move)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3bed3e3-b458-4472-a59c-3995bb8d6e6d",
   "metadata": {},
   "source": [
    "# Divide and Conquer\n",
    "the Fast Fourier Transform (FFT), which is an efficient algorithm for computing the Discrete Fourier Transform (DFT) of a sequence.\n",
    "\n",
    "we define the fft function to compute the Fast Fourier Transform of a sequence x using the Cooley-Tukey algorithm, which is a divide and conquer approach. We also define the ifft function to compute the inverse Fast Fourier Transform.\n",
    "\n",
    "The fft function works by recursively dividing the input sequence into even and odd indexed subsequences until the length is 1. It then combines the results of the even and odd subsequences using complex exponential functions.\n",
    "\n",
    "You can test the code by modifying the length and values of the input sequence x. Make sure the length of x is a power of 2, as the Cooley-Tukey algorithm assumes this for simplicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31c2e813-470e-45ce-ac6f-fab891bb4757",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def fft(x):\n",
    "    N = len(x)\n",
    "    if N <= 1:\n",
    "        return x\n",
    "\n",
    "    even = fft(x[0::2])\n",
    "    odd = fft(x[1::2])\n",
    "\n",
    "    T = [np.exp(-2j * np.pi * k / N) * odd[k] for k in range(N // 2)]\n",
    "    return [even[k] + T[k] for k in range(N // 2)] + [even[k] - T[k] for k in range(N // 2)]\n",
    "\n",
    "def ifft(x):\n",
    "    N = len(x)\n",
    "    x_conj = np.conjugate(x)\n",
    "    y_conj = fft(x_conj)\n",
    "    return np.conjugate(y_conj) / N\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    x = np.random.rand(8)\n",
    "    print(\"Original sequence:\", x)\n",
    "\n",
    "    X = fft(x)\n",
    "    print(\"Fourier Transform (FFT):\", X)\n",
    "\n",
    "    x_reconstructed = ifft(X)\n",
    "    print(\"Reconstructed sequence (IFFT):\", x_reconstructed.real)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbba1b1e-8a8b-4689-bab2-6ec08c4d3db8",
   "metadata": {},
   "source": [
    "# Q-Q Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "560b06cb-cfba-4841-a1ba-25062e8aab05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "84dab67d-089a-4c5a-a313-0483bf7be22f",
   "metadata": {},
   "source": [
    "# Erase all Warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cde1717-98df-4760-aebc-dbbd24a061dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9e0a6db-a221-410b-afdf-4a97aac265dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e1f1b855-583e-4dfa-b31b-fa546e6b2117",
   "metadata": {},
   "source": [
    "# Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28ce901a-273b-496b-bae5-df4b1d7d009a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1e9465db-cf79-4b52-9938-875ce4a5da66",
   "metadata": {},
   "source": [
    "# Tetris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53a53889-5214-4a0f-b7a4-229c5ce4ac20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd70e238-a260-4dbf-8db4-06ae9ee518ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31716aa1-e3dd-4603-976d-9bce91a17145",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3712ba4f-4528-4eaf-b7fd-34143ed8d7b5",
   "metadata": {},
   "source": [
    "# Fast Fourier Transform (FFT)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b690363a-432e-4d3c-8979-554cf1e3163b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def fft(x):\n",
    "    N = len(x)\n",
    "    if N <= 1:\n",
    "        return x\n",
    "\n",
    "    even = fft(x[0::2])\n",
    "    odd = fft(x[1::2])\n",
    "\n",
    "    T = [np.exp(-2j * np.pi * k / N) * odd[k] for k in range(N // 2)]\n",
    "    return [even[k] + T[k] for k in range(N // 2)] + [even[k] - T[k] for k in range(N // 2)]\n",
    "\n",
    "def ifft(x):\n",
    "    N = len(x)\n",
    "    x_conj = np.conjugate(x)\n",
    "    y_conj = fft(x_conj)\n",
    "    return np.conjugate(y_conj) / N\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    x = np.random.rand(128)\n",
    "    print(\"Original sequence:\", x)\n",
    "\n",
    "    X = fft(x)\n",
    "    print(\"Fourier Transform (FFT):\", X)\n",
    "\n",
    "    x_reconstructed = ifft(X)\n",
    "    print(\"Reconstructed sequence (IFFT):\", x_reconstructed.real)\n",
    "\n",
    "    # Plot original sequence\n",
    "    plt.subplot(3, 1, 1)\n",
    "    plt.plot(x)\n",
    "    plt.title(\"Original Sequence\")\n",
    "\n",
    "    # Plot the magnitude of the FFT\n",
    "    plt.subplot(3, 1, 2)\n",
    "    plt.plot(np.abs(X))\n",
    "    plt.title(\"Magnitude of Fourier Transform (FFT)\")\n",
    "\n",
    "    # Plot the reconstructed sequence\n",
    "    plt.subplot(3, 1, 3)\n",
    "    plt.plot(x_reconstructed.real)\n",
    "    plt.title(\"Reconstructed Sequence (IFFT)\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffc88c88-b0e7-4446-8a59-59fa7ed42c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate a complex wave\n",
    "N = 1024\n",
    "t = np.linspace(0, 1, N)\n",
    "f1, f2, f3 = 5, 15, 30\n",
    "y = np.sin(2 * np.pi * f1 * t) + np.sin(2 * np.pi * f2 * t) + np.sin(2 * np.pi * f3 * t)\n",
    "\n",
    "# Add noise\n",
    "noise = np.random.normal(0, 1, N)\n",
    "y_noisy = y + noise\n",
    "\n",
    "# Compute FFT of the complex wave\n",
    "Y = np.fft.fft(y_noisy)\n",
    "\n",
    "# Find the magnitude and normalize\n",
    "magnitude = np.abs(Y) / N\n",
    "\n",
    "# Compute the frequencies\n",
    "frequencies = np.fft.fftfreq(N, d=t[1] - t[0])\n",
    "\n",
    "# Plot the time domain signal\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(t, y_noisy)\n",
    "plt.title(\"Unknown Complex Wave (Time Domain)\")\n",
    "\n",
    "# Plot the magnitude of the FFT\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(frequencies[:N // 2], magnitude[:N // 2])\n",
    "plt.title(\"Magnitude of Fourier Transform (FFT)\")\n",
    "\n",
    "# Filter the signal by removing low magnitude components\n",
    "threshold = 0.1\n",
    "Y_filtered = Y * (magnitude > threshold)\n",
    "y_filtered = np.fft.ifft(Y_filtered)\n",
    "\n",
    "# Plot the filtered signal in the time domain\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(t, y_filtered.real)\n",
    "plt.title(\"Filtered Signal (Time Domain)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1569da7a-b938-42a6-b5f6-e7ef15cc3961",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def reconstruct_signal(Y, freq_idx):\n",
    "    Y_single_freq = np.zeros_like(Y)\n",
    "    Y_single_freq[freq_idx] = Y[freq_idx]\n",
    "    Y_single_freq[-freq_idx] = Y[-freq_idx]\n",
    "    return np.fft.ifft(Y_single_freq)\n",
    "\n",
    "# Generate a complex wave\n",
    "N = 1024\n",
    "t = np.linspace(0, 1, N)\n",
    "f1, f2, f3 = 5, 15, 30\n",
    "y = np.sin(2 * np.pi * f1 * t) + np.sin(2 * np.pi * f2 * t) + np.sin(2 * np.pi * f3 * t)\n",
    "\n",
    "# Add noise\n",
    "noise = np.random.normal(0, 1, N)\n",
    "y_noisy = y + noise\n",
    "\n",
    "# Compute FFT of the complex wave\n",
    "Y = np.fft.fft(y_noisy)\n",
    "\n",
    "# Find the magnitude and normalize\n",
    "magnitude = np.abs(Y) / N\n",
    "\n",
    "# Compute the frequencies\n",
    "frequencies = np.fft.fftfreq(N, d=t[1] - t[0])\n",
    "\n",
    "# Filter the signal by removing low magnitude components\n",
    "threshold = 0.1\n",
    "significant_freq_indices = np.where(magnitude > threshold)[0]\n",
    "\n",
    "# Reconstruct each periodic signal\n",
    "reconstructed_signals = [reconstruct_signal(Y, freq_idx) for freq_idx in significant_freq_indices]\n",
    "\n",
    "# Plot each periodic signal\n",
    "for i, reconstructed_signal in enumerate(reconstructed_signals, 1):\n",
    "    plt.subplot(len(reconstructed_signals), 1, i)\n",
    "    plt.plot(t, reconstructed_signal.real)\n",
    "    plt.title(f\"Periodic Signal {i}\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2dcb866-ec3b-4dc3-9f11-b70a557c9de5",
   "metadata": {},
   "source": [
    "# Singular Value Decomposition (SVD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08ffb5b9-50e5-436c-afd0-5b15d338282d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Create a random matrix with 4 rows and 3 columns\n",
    "A = np.random.rand(4, 3)\n",
    "\n",
    "# Perform singular value decomposition on the matrix A\n",
    "U, s, Vt = np.linalg.svd(A)\n",
    "\n",
    "# Reconstruct the original matrix from the SVD components\n",
    "Sigma = np.zeros_like(A)\n",
    "Sigma[:min(A.shape), :min(A.shape)] = np.diag(s)\n",
    "B = np.dot(U, np.dot(Sigma, Vt))\n",
    "\n",
    "# Print the original matrix A, reconstructed matrix B, and SVD components\n",
    "print(\"Matrix A:\\n\", A)\n",
    "print(\"\\nReconstructed matrix B:\\n\", B)\n",
    "print(\"\\nLeft singular vectors (U):\\n\", U)\n",
    "print(\"\\nSingular values (s):\\n\", s)\n",
    "print(\"\\nRight singular vectors (Vt):\\n\", Vt)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e8bbba0-c6a9-47d7-8777-cc7d9ab05554",
   "metadata": {},
   "source": [
    "# Runge-Kutta Second and Forth order (RK2, RK4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d86f8568-c111-4966-b924-9c280c9f0461",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "class DEq_Solver:\n",
    "    def solve(self,x0,t0,t1,delta_t,terminateCondition=None,circular_index=None):\n",
    "        self.initialise(x0,t0,t1,delta_t)\n",
    "        while self.t <= self.t1:\n",
    "            self.record(self.t,self.x)\n",
    "            if terminateCondition is not None:\n",
    "                if terminateCondition(self.x):\n",
    "                    break\n",
    "            self.makeStep()\n",
    "        return self.history\n",
    "    def initialise(self,x0,t0,t1,delta_t):\n",
    "        self.x       = x0.copy()\n",
    "        self.t       = t0\n",
    "        self.t1      = t1\n",
    "        self.delta_t = delta_t\n",
    "        self.history = []\n",
    "    def record(self,t,x):\n",
    "        self.history.append(  ( t, x.copy() )  )\n",
    "    def timeSteps(self):\n",
    "        ts,vs=list(zip(*self.history))\n",
    "        return np.array(ts).T\n",
    "    def coordinateSteps(self):\n",
    "        ts,vs=list(zip(*self.history))\n",
    "        return np.array(vs).T\n",
    "    def makeStep(self):\n",
    "        raise NotImplemented\n",
    "class RK2Solver(DEq_Solver):\n",
    "    def __init__(self,kernel):\n",
    "        self.kernel  = kernel\n",
    "    def makeStep(self):\n",
    "        self.x=self.x+self.kernel.dx_dt(self.x+self.delta_t*self.kernel.dx_dt(self.x,self.t)/2,self.t+self.delta_t/2)*self.delta_t\n",
    "        self.t=self.t+self.delta_t \n",
    "class RK4Solver(DEq_Solver):\n",
    "    def __init__(self,kernel):\n",
    "        self.kernel  = kernel\n",
    "    def makeStep(self):\n",
    "        x1=self.x\n",
    "        t1=self.t\n",
    "        x2=self.x+self.kernel.dx_dt(x1,t1)*self.delta_t/2\n",
    "        t2=self.t+self.delta_t/2\n",
    "        x3=self.x+self.kernel.dx_dt(x2,t2)*self.delta_t/2\n",
    "        t3=self.t+self.delta_t/2\n",
    "        x4=self.x+self.kernel.dx_dt(x3,t3)*self.delta_t\n",
    "        t4=self.t+self.delta_t\n",
    "        self.x=self.x+self.delta_t/6*(self.kernel.dx_dt(x1,t1)+2*self.kernel.dx_dt(x2,t2)+2*self.kernel.dx_dt(x3,t3)+self.kernel.dx_dt(x4,t4))\n",
    "        self.t=self.t+self.delta_t\n",
    "class EulersMethod(DEq_Solver):\n",
    "    def __init__(self,kernel):\n",
    "        self.kernel  = kernel\n",
    "    def makeStep(self):\n",
    "        self.x=self.x+self.kernel.dx_dt(self.x,self.t)*self.delta_t\n",
    "        self.t=self.t+self.delta_t\n",
    "class DifferentialEquation:\n",
    "    def dx_dt(self,x,t):\n",
    "        raise NotImplemented\n",
    "class TestFunction(DifferentialEquation):\n",
    "    def dx_dt(self,x,t):\n",
    "        return np.sin(t)*np.cos(x)\n",
    "t0 = 0\n",
    "t1 = 10\n",
    "delta_t = 0.1\n",
    "x0 = np.array([0.5])\n",
    "solver_euler = EulersMethod(TestFunction())\n",
    "solver_rk2 = RK2Solver(TestFunction())\n",
    "solver_rk4 = RK4Solver(TestFunction())\n",
    "t = np.linspace(t0, t1, int((t1 - t0) / delta_t) + 1)\n",
    "ground_truth = np.sin(t) + 0.5\n",
    "class SimpleHarmonicOscillator: # define the differential equation\n",
    "    def dx_dt(self, x, t):\n",
    "        k = 1\n",
    "        m = 1\n",
    "        return np.array([x[1], -k/m*x[0]])\n",
    "# set up the initial conditions and time interval\n",
    "x0 = np.array([1, 0]) # initial position and velocity\n",
    "t0 = 0 # start time\n",
    "t1 = 10 # end time\n",
    "delta_t = 0.1 # time step\n",
    "def ground_truth(t): #define the ground truth function\n",
    "    return np.array([np.cos(t), -np.sin(t)])\n",
    "SHO = SimpleHarmonicOscillator() # create the solver objects\n",
    "euler_cromer = EulersMethod(SHO)\n",
    "rk2 = RK2Solver(SHO)\n",
    "rk4 = RK4Solver(SHO)\n",
    "euler_cromer_soln = euler_cromer.solve(x0, t0, t1, delta_t) #solve the differential equation using each solver\n",
    "rk2_soln = rk2.solve(x0, t0, t1, delta_t)\n",
    "rk4_soln = rk4.solve(x0, t0, t1, delta_t)\n",
    "fig, ax = plt.subplots() #plot the results\n",
    "t = np.linspace(t0, t1, int((t1-t0)/delta_t+1)) \n",
    "ax.plot(t, ground_truth(t)[0], label='Ground Truth') #plot ground truth\n",
    "ax.scatter(euler_cromer.timeSteps(), euler_cromer.coordinateSteps()[0], c='orange', s=10, label='Euler-Cromer') #plot Euler-Cromer\n",
    "ax.scatter(rk2.timeSteps(), rk2.coordinateSteps()[0], c='r', s=10, label='RK2') #plot RK2\n",
    "ax.scatter(rk4.timeSteps(), rk4.coordinateSteps()[0], c='g', s=10, label='RK4') #plot RK4\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Position')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca06a912-4858-437b-b08e-69b3e977e1c6",
   "metadata": {},
   "source": [
    "# Monte-Carlo Integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c867d7db-39b9-4643-8e43-4772f37d64f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "def func(x): # Define the function to be integrated\n",
    "    return x**2\n",
    "a, b, N= 0, 2, 100000 # Limit 0~2 and number of random points 100000 \n",
    "c = b**2\n",
    "x = np.random.uniform(a, b, N) # Generate random x and y values between the limits of integration\n",
    "y = np.random.uniform(a, c, N)\n",
    "f = func(x) # Evaluate the function at the random x values\n",
    "below = np.sum(y < f) # Count the number of points below the function\n",
    "integral = b*c * (below/N) # Calculate the area under the function\n",
    "print(\"Monte Carlo Estimate of the Integral: \", integral, 'Analytic Integral:', 1/3*b**3) # Print the result\n",
    "plt.scatter(x, y, alpha=0.1, s=3, c='g', label='Random Points') # Plot the results\n",
    "plt.scatter(x, f, c='b', label='Function')\n",
    "plt.fill_between(np.linspace(a,b,num=20), 0, func(np.linspace(a,b,num=20)), alpha=0.8, color='r', label='Area Under Function')\n",
    "plt.legend()\n",
    "plt.xlim(0,b)\n",
    "plt.ylim(0,c)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eadf657-01b7-4489-8170-d15860ce1baa",
   "metadata": {},
   "source": [
    "# Random Walk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "177ab5d4-1c96-45e2-9dcc-188f471ad390",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "num_walks = 3 # Set up the parameters\n",
    "num_steps = 500\n",
    "step_size = 1\n",
    "start_positions = [[0, 0, 0], [1, 1, 1], [2, -1, 3]] # Define the starting positions for each walker\n",
    "walks = [] # Generate the random walks\n",
    "for i in range(num_walks):\n",
    "    x, y, z = start_positions[i]\n",
    "    walk = [[x], [y], [z]]\n",
    "    for j in range(num_steps):\n",
    "        step = step_size * np.random.normal(0, 4, 1).astype(int)[0]\n",
    "        xyz=np.random.randint(0,3)\n",
    "        if xyz == 0:\n",
    "            x+=step\n",
    "        elif xyz == 1:\n",
    "            y+=step\n",
    "        else :\n",
    "            z+=step\n",
    "        walk[0].append(x)\n",
    "        walk[1].append(y)\n",
    "        walk[2].append(z)\n",
    "    walks.append(walk)\n",
    "fig = plt.figure(figsize=(8, 8)) # Plot the random walks\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "for i in range(num_walks):\n",
    "    x, y, z = walks[i]\n",
    "    ax.plot(x, y, z)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee20fe7c-b51a-4eef-9e57-0d55368c0940",
   "metadata": {},
   "source": [
    "# Black-Scholes Equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90f074f7-393f-4c47-ad1b-7c0d093012ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "def black_scholes(S, K, T, r, sigma, option_type):\n",
    "    d1 = (np.log(S/K) + (r + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))\n",
    "    d2 = d1 - sigma*np.sqrt(T)\n",
    "    if option_type == 'call':\n",
    "        price = S*norm.cdf(d1) - K*np.exp(-r*T)*norm.cdf(d2)\n",
    "    elif option_type == 'put':\n",
    "        price = K*np.exp(-r*T)*norm.cdf(-d2) - S*norm.cdf(-d1)\n",
    "    return price\n",
    "S = 100   # current stock price\n",
    "K = 110   # strike price\n",
    "T = 1     # time to maturity (in years)\n",
    "r = 0.05  # risk-free interest rate\n",
    "sigma = 0.2  # volatility\n",
    "option_type = 'call'  # type of option ('call' or 'put')\n",
    "price = black_scholes(S, K, T, r, sigma, option_type)\n",
    "print(price)  # print the option price"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5dc7785-69c3-45c9-95f0-2adada863711",
   "metadata": {},
   "source": [
    "# Numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab76d9e6-26de-41a7-acf0-ff14f25dc90e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from numba import jit, njit\n",
    "import os\n",
    "from mpl_toolkits import mplot3d\n",
    "import matplotlib.pyplot as plt\n",
    "import timeit\n",
    "os.environ['NUMBA_WARNINGS'] = '0'\n",
    "@jit(nopython=True)\n",
    "#@njit(parallel = False)\n",
    "def computedisparity2(left, right, deep, window):\n",
    "    max_disparity = deep\n",
    "    size = left.shape\n",
    "    kernel_size = window\n",
    "    disp = np.zeros(size)    \n",
    "    for y in range(size[0]):    \n",
    "        y_kernel = (max(0, y-int(kernel_size/2)), min(size[0]-1, y+int(kernel_size/2)))\n",
    "        for x in range(size[1]):\n",
    "            x_kernel = (max(0, x-int(kernel_size/2)), min(size[1]-1, x+int(kernel_size/2)))\n",
    "            max_disp = min([max_disparity, size[1]-x_kernel[1]])\n",
    "            template = right[y_kernel[0]:y_kernel[1], x_kernel[0]:x_kernel[1]]\n",
    "            SSD = np.zeros(max_disp + 1)\n",
    "            for i in range(max_disp+1):\n",
    "                block = left[y_kernel[0]:y_kernel[1], x_kernel[0]+i:x_kernel[1]+i]\n",
    "                SSD[i] = np.sum((template-block)**2)\n",
    "            disparity = np.amin(SSD)\n",
    "            dis_index = np.argmin(SSD)\n",
    "            if ((dis_index == 0) or (dis_index == max_disp)):\n",
    "                disp[y, x] = dis_index\n",
    "            else:\n",
    "                C3 = SSD[dis_index - 1]\n",
    "                C2 = SSD[dis_index]\n",
    "                C1 = SSD[dis_index + 1]\n",
    "                disp[y, x] = dis_index - (0.5 * (C3 - C1) / (C1 + C3 - (2*C2)))\n",
    "    return disp\n",
    "left = cv2.imread('img/left.png')\n",
    "left = cv2.cvtColor(left, cv2.COLOR_BGR2GRAY)\n",
    "right = cv2.imread('img/right.png')\n",
    "right = cv2.cvtColor(right, cv2.COLOR_BGR2GRAY)\n",
    "_, image_cells = plt.subplots(1, 2, figsize=(20, 20))\n",
    "image_cells[0].imshow(left)\n",
    "image_cells[0].set_title('left image')\n",
    "image_cells[1].imshow(right)\n",
    "image_cells[1].set_title('right image')\n",
    "plt.show()\n",
    "start = timeit.default_timer()\n",
    "stereo = computedisparity2(left, right, 50, 20)\n",
    "stop = timeit.default_timer()\n",
    "print('Time: ', stop - start)  \n",
    "plt.imshow(stereo, cmap='gray')\n",
    "z = stereo\n",
    "x = np.arange(stereo.shape[1])\n",
    "y = np.arange(stereo.shape[0])\n",
    "X, Y = np.meshgrid(x, y)\n",
    "fig = plt.figure(figsize = (12, 7))\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.plot_surface(-X, Y, z, cmap='viridis', alpha=0.7)\n",
    "ax.view_init(80, 90, 0)\n",
    "ax.set_title('3D reconstruction')\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "ax.set_zlabel('stereo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e7f9a2-c1d4-46a2-bc35-3a866670f7ba",
   "metadata": {},
   "source": [
    "# Importing C++ program on python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1b74e16-9a6a-4a5a-aff4-59d23d411cfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "C++ program\n",
    "// calculation.cpp\n",
    "#include <iostream>\n",
    "\n",
    "extern \"C\" {\n",
    "    int add(int a, int b) {\n",
    "        return a + b;\n",
    "    }\n",
    "\n",
    "    int subtract(int a, int b) {\n",
    "        return a - b;\n",
    "    }\n",
    "}\n",
    "'''\n",
    "\n",
    "'''\n",
    "bash\n",
    "g++ -shared -o calculation.so calculation.cpp\n",
    "\n",
    "'''\n",
    "# Load the shared library\n",
    "lib_name = \"calculation.so\" if os.name == \"posix\" else \"calculation.dll\"\n",
    "calculation_lib = ctypes.CDLL(lib_name)\n",
    "\n",
    "# Define the argument and return types of the C++ functions\n",
    "calculation_lib.add.argtypes = [ctypes.c_int, ctypes.c_int]\n",
    "calculation_lib.add.restype = ctypes.c_int\n",
    "calculation_lib.subtract.argtypes = [ctypes.c_int, ctypes.c_int]\n",
    "calculation_lib.subtract.restype = ctypes.c_int\n",
    "\n",
    "# Call the C++ functions from Python\n",
    "a = 10\n",
    "b = 5\n",
    "\n",
    "result_add = calculation_lib.add(a, b)\n",
    "result_subtract = calculation_lib.subtract(a, b)\n",
    "\n",
    "print(f\"{a} + {b} = {result_add}\")\n",
    "print(f\"{a} - {b} = {result_subtract}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88be01b9-7aa4-4492-9d4d-4990790c016e",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ce2e0e6-4e5f-4709-90f8-2b167ebe7d92",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "479e406b-8867-4ae3-8c5f-38812deafd23",
   "metadata": {},
   "source": [
    "# Colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b95651f8-02fd-4e46-aa85-96b571d65143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.patches as mpatch\n",
    "import matplotlib.pyplot as plt\n",
    "overlap = {name for name in mcolors.CSS4_COLORS\n",
    "           if f'xkcd:{name}' in mcolors.XKCD_COLORS}\n",
    "\n",
    "fig = plt.figure(figsize=[9, 5])\n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    "\n",
    "n_groups = 3\n",
    "n_rows = len(overlap) // n_groups + 1\n",
    "\n",
    "for j, color_name in enumerate(sorted(overlap)):\n",
    "    css4 = mcolors.CSS4_COLORS[color_name]\n",
    "    xkcd = mcolors.XKCD_COLORS[f'xkcd:{color_name}'].upper()\n",
    "\n",
    "    # Pick text colour based on perceived luminance.\n",
    "    rgba = mcolors.to_rgba_array([css4, xkcd])\n",
    "    luma = 0.299 * rgba[:, 0] + 0.587 * rgba[:, 1] + 0.114 * rgba[:, 2]\n",
    "    css4_text_color = 'k' if luma[0] > 0.5 else 'w'\n",
    "    xkcd_text_color = 'k' if luma[1] > 0.5 else 'w'\n",
    "\n",
    "    col_shift = (j // n_rows) * 3\n",
    "    y_pos = j % n_rows\n",
    "    text_args = dict(fontsize=10, weight='bold' if css4 == xkcd else None)\n",
    "    ax.add_patch(mpatch.Rectangle((0 + col_shift, y_pos), 1, 1, color=css4))\n",
    "    ax.add_patch(mpatch.Rectangle((1 + col_shift, y_pos), 1, 1, color=xkcd))\n",
    "    ax.text(0.5 + col_shift, y_pos + .7, css4,\n",
    "            color=css4_text_color, ha='center', **text_args)\n",
    "    ax.text(1.5 + col_shift, y_pos + .7, xkcd,\n",
    "            color=xkcd_text_color, ha='center', **text_args)\n",
    "    ax.text(2 + col_shift, y_pos + .7, f'  {color_name}', **text_args)\n",
    "\n",
    "for g in range(n_groups):\n",
    "    ax.hlines(range(n_rows), 3*g, 3*g + 2.8, color='0.7', linewidth=1)\n",
    "    ax.text(0.5 + 3*g, -0.3, 'X11/CSS4', ha='center')\n",
    "    ax.text(1.5 + 3*g, -0.3, 'xkcd', ha='center')\n",
    "\n",
    "ax.set_xlim(0, 3 * n_groups)\n",
    "ax.set_ylim(n_rows, -1)\n",
    "ax.axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e735d8e3-1708-454e-89dd-e33d7b8fc9b1",
   "metadata": {},
   "source": [
    "<a id='6'></a>\n",
    "# 6. OpenCV\n",
    "<a id='6.1'></a>\n",
    "## 6.1 Image read\n",
    "\n",
    "* [List](#0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6347c857-b488-4b02-b364-4495c28ad8e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from IPython.display import Image\n",
    "cb_img = cv2.imread(\"img/checkerboard_fuzzy_18x18.jpg\",0)\n",
    "plt.imshow(cb_img, cmap='gray')\n",
    "print(cb_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b1ab93-e82c-40d4-b8a8-d08aac1f8aba",
   "metadata": {},
   "source": [
    "<a id='6.2'></a>\n",
    "## 6.2 Color and save image\n",
    "\n",
    "* [List](#0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e81cd536-63d4-4d63-a8df-f90feaf63815",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_bgr = cv2.imread(\"img/right.png\",cv2.IMREAD_COLOR)\n",
    "img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\n",
    "r,g,b = cv2.split(img_rgb)\n",
    "# Show the channels\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.subplot(141);plt.imshow(r,cmap='gray');plt.title(\"Red Channel\");\n",
    "plt.subplot(142);plt.imshow(g,cmap='gray');plt.title(\"Green Channel\");\n",
    "plt.subplot(143);plt.imshow(b,cmap='gray');plt.title(\"Blue Channel\");\n",
    "# Merge the individual channels into a BGR image\n",
    "imgMerged = cv2.merge((r,g,b))\n",
    "# Show the merged output\n",
    "plt.subplot(144);plt.imshow(imgMerged);plt.title(\"Merged Output\");\n",
    "\n",
    "img_hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)\n",
    "# Split the image into the B,G,R components\n",
    "h,s,v = cv2.split(img_hsv)\n",
    "# Show the channels\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.subplot(141);plt.imshow(h,cmap='gray');plt.title(\"H Channel\");\n",
    "plt.subplot(142);plt.imshow(s,cmap='gray');plt.title(\"S Channel\");\n",
    "plt.subplot(143);plt.imshow(v,cmap='gray');plt.title(\"V Channel\");\n",
    "plt.subplot(144);plt.imshow(img_rgb);plt.title(\"Original\");\n",
    "\n",
    "# save the image\n",
    "#cv2.imwrite(\"dd.png\", img_bgr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a605e224-e2eb-442d-a8e3-6bdbcb95430f",
   "metadata": {},
   "source": [
    "<a id='6.3'></a>\n",
    "## 6.3 Resize image and Annotation\n",
    "\n",
    "* [List](#0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b96afa5a-b8dd-48ba-aff5-5e0145d1c865",
   "metadata": {},
   "outputs": [],
   "source": [
    "resized_cropped_region_2x = cv2.resize(img_rgb,None,fx=2, fy=2)\n",
    "\n",
    "\n",
    "resized_cropped_region = cv2.resize(img_rgb, dsize=(300,600), interpolation=cv2.INTER_AREA)\n",
    "\n",
    "plt.figure(figsize=[10,5])\n",
    "plt.subplot(121);plt.imshow(resized_cropped_region_2x);plt.title('2 times')\n",
    "plt.subplot(122);plt.imshow(resized_cropped_region);plt.title('resize')\n",
    "\n",
    "img_rgb_flipped_horz = cv2.flip(img_rgb, 1)\n",
    "img_rgb_flipped_vert = cv2.flip(img_rgb, 0)\n",
    "img_rgb_flipped_both = cv2.flip(img_rgb, -1)\n",
    "\n",
    "# Show the images\n",
    "plt.figure(figsize=[18,5])\n",
    "plt.subplot(141);plt.imshow(img_rgb_flipped_horz);plt.title(\"Horizontal Flip\");\n",
    "plt.subplot(142);plt.imshow(img_rgb_flipped_vert);plt.title(\"Vertical Flip\");\n",
    "plt.subplot(143);plt.imshow(img_rgb_flipped_both);plt.title(\"Both Flipped\");\n",
    "plt.subplot(144);plt.imshow(img_rgb);plt.title(\"Original\");\n",
    "\n",
    "imageRectangle = img_rgb.copy()\n",
    "\n",
    "cv2.rectangle(imageRectangle, (200, 110), (370,350), (255, 0, 255), thickness=5, lineType=cv2.LINE_8)\n",
    "cv2.putText(imageRectangle, 'mask', (250, 370), cv2.FONT_HERSHEY_PLAIN, 2.3, (0,255,0), 2, cv2.LINE_AA)\n",
    "plt.figure(figsize=[6,5])\n",
    "plt.plot();plt.imshow(imageRectangle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84378edf-3bff-4865-a0cf-3b99352d71ed",
   "metadata": {},
   "source": [
    "<a id='6.4'></a>\n",
    "## 6.4 Brightness, Contrast, Thresholding\n",
    "\n",
    "* [List](#0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe4737c4-3710-4ddb-841a-e5fa42d0f1c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rgb = cv2.cvtColor(cv2.imread(\"img/right.png\",cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB)\n",
    "matrix = np.ones(img_rgb.shape, dtype = \"uint8\") * 50\n",
    "img_rgb_brighter = cv2.add(img_rgb, matrix)\n",
    "img_rgb_darker   = cv2.subtract(img_rgb, matrix)\n",
    "# Show the images\n",
    "plt.figure(figsize=[18,5])\n",
    "plt.subplot(131); plt.imshow(img_rgb_darker);  plt.title(\"Darker\");\n",
    "plt.subplot(132); plt.imshow(img_rgb);         plt.title(\"Original\");\n",
    "plt.subplot(133); plt.imshow(img_rgb_brighter);plt.title(\"Brighter\");\n",
    "\n",
    "matrix1 = np.ones(img_rgb.shape) * .8\n",
    "matrix2 = np.ones(img_rgb.shape) * 1.2\n",
    "img_rgb_darker   = np.uint8(cv2.multiply(np.float64(img_rgb), matrix1))\n",
    "img_rgb_brighter = np.uint8(cv2.multiply(np.float64(img_rgb), matrix2))\n",
    "# Show the images\n",
    "plt.figure(figsize=[18,5])\n",
    "plt.subplot(131); plt.imshow(img_rgb_darker);  plt.title(\"Lower Contrast\");\n",
    "plt.subplot(132); plt.imshow(img_rgb);         plt.title(\"Original\");\n",
    "plt.subplot(133); plt.imshow(img_rgb_brighter);plt.title(\"Higher Contrast\");\n",
    "\n",
    "img_read = cv2.imread(\"img/Piano_Sheet_Music.png\", cv2.IMREAD_GRAYSCALE) # Read the original image\n",
    "retval, img_thresh_gbl_1 = cv2.threshold(img_read,50, 255, cv2.THRESH_BINARY) # Perform global thresholding\n",
    "retval, img_thresh_gbl_2 = cv2.threshold(img_read,130, 255, cv2.THRESH_BINARY) # Perform global thresholding\n",
    "img_thresh_adp = cv2.adaptiveThreshold(img_read, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 7) # Perform adaptive thresholding\n",
    "# Show the images\n",
    "plt.figure(figsize=[18,15])\n",
    "plt.subplot(221); plt.imshow(img_read,        cmap=\"gray\");  plt.title(\"Original\");\n",
    "plt.subplot(222); plt.imshow(img_thresh_gbl_1,cmap=\"gray\");  plt.title(\"Thresholded (global: 50)\");\n",
    "plt.subplot(223); plt.imshow(img_thresh_gbl_2,cmap=\"gray\");  plt.title(\"Thresholded (global: 130)\");\n",
    "plt.subplot(224); plt.imshow(img_thresh_adp,  cmap=\"gray\");  plt.title(\"Thresholded (adaptive)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db10bca9-1619-4bbc-9be7-21354eec6b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import svgwrite\n",
    "import svgpathtools\n",
    "def vectorize_image(image_path, output_path):\n",
    "    # Read the image\n",
    "    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "    # Threshold the image\n",
    "    _, binary_image = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY_INV)\n",
    "\n",
    "    # Find contours\n",
    "    contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "    # Create an SVG canvas\n",
    "    dwg = svgwrite.Drawing(output_path, profile='tiny', size=(image.shape[1], image.shape[0]))\n",
    "\n",
    "    # Add contours to the SVG canvas\n",
    "    for contour in contours:\n",
    "        points = [tuple(map(float, point[0])) for point in contour]\n",
    "        polyline = dwg.polyline(points, stroke='black', fill='none', stroke_width=1)\n",
    "        dwg.add(polyline)\n",
    "\n",
    "    # Save the SVG file\n",
    "    dwg.save()\n",
    "\n",
    "image_path = 'img/left.png'\n",
    "output_path = 'img/output.svg'\n",
    "vectorize_image(image_path, output_path)\n",
    "paths, attributes = svgpathtools.svg2paths('img/output.svg')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f7814e0-2fd0-41aa-a344-9edb8be64c84",
   "metadata": {},
   "source": [
    "## 6.5 Image stitching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcb63c7f-d131-40ae-abb0-8227450948ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "imagefiles = glob.glob(\"img/boat/*\")\n",
    "imagefiles.sort()\n",
    "images = []\n",
    "for filename in imagefiles:\n",
    "    img = cv2.imread(filename)\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    images.append(img)\n",
    "num_images = len(images)\n",
    "# Display Images\n",
    "plt.figure(figsize=[30,10]) \n",
    "num_cols = 3\n",
    "num_rows = math.ceil(num_images / num_cols)\n",
    "for i in range(0, num_images):\n",
    "    plt.subplot(num_rows, num_cols, i+1) \n",
    "    plt.axis('off')\n",
    "    plt.imshow(images[i])\n",
    "# Stitch Images\n",
    "stitcher = cv2.Stitcher_create()\n",
    "status, result = stitcher.stitch(images)\n",
    "if status == 0:\n",
    "    plt.figure(figsize=[30,10]) \n",
    "    plt.imshow(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3380adeb-ace3-4970-a26a-5929639dbc32",
   "metadata": {},
   "source": [
    "## 6.6 High Dynamic Range Image (HDR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adc2d6e0-8d4e-44c5-b33b-f73127841664",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "def readImagesAndTimes():\n",
    "    filenames = [\"img/img_0.033.jpg\", \"img/img_0.25.jpg\", \"img/img_2.5.jpg\", \"img/img_15.jpg\"]\n",
    "    times = np.array([ 1/30.0, 0.25, 2.5, 15.0 ], dtype=np.float32)\n",
    "    images = []\n",
    "    for filename in filenames:\n",
    "        im = cv2.imread(filename)\n",
    "        im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n",
    "        images.append(im)\n",
    "    return images, times\n",
    "# Read images and exposure times\n",
    "images, times = readImagesAndTimes()\n",
    "\n",
    "# Align Images\n",
    "alignMTB = cv2.createAlignMTB()\n",
    "alignMTB.process(images, images)\n",
    "# Merge images into an HDR linear image\n",
    "calibrateDebevec = cv2.createCalibrateDebevec()\n",
    "mergeDebevec = cv2.createMergeDebevec()\n",
    "responseDebevec = calibrateDebevec.process(images, times)\n",
    "hdrDebevec = mergeDebevec.process(images, times, responseDebevec)\n",
    "# Tonemap using Drago's method to obtain 24-bit color image\n",
    "tonemapDrago = cv2.createTonemapDrago(1.0, 0.7)\n",
    "ldrDrago = tonemapDrago.process(hdrDebevec)\n",
    "ldrDrago = 3 * ldrDrago\n",
    "plt.figure(figsize=(20,10)); plt.imshow(np.clip(ldrDrago,0,1)); plt.axis('off');\n",
    "cv2.imwrite(\"img/ldr-Drago.jpg\", ldrDrago * 255)\n",
    "print(\"img.saved_Drago_Tonemap.jpg\")\n",
    "# Tonemap using Reinhard's method to obtain 24-bit color image\n",
    "tonemapReinhard = cv2.createTonemapReinhard(1.5, 0,0,0)\n",
    "ldrReinhard = tonemapReinhard.process(hdrDebevec)\n",
    "plt.figure(figsize=(20,10)); plt.imshow(np.clip(ldrReinhard,0,1)); plt.axis('off');\n",
    "cv2.imwrite(\"img/ldr-Reinhard.jpg\", ldrReinhard * 255)\n",
    "print(\"saved_Reinhard_Tonemap.jpg\")\n",
    "# Tonemap using Mantiuk's method to obtain 24-bit color image\n",
    "tonemapMantiuk = cv2.createTonemapMantiuk(2.2,0.85, 1.2)\n",
    "ldrMantiuk = tonemapMantiuk.process(hdrDebevec)\n",
    "ldrMantiuk = 3 * ldrMantiuk\n",
    "plt.figure(figsize=(20,10)); plt.imshow(np.clip(ldrMantiuk,0,1)); plt.axis('off');\n",
    "cv2.imwrite(\"img/ldr-Mantiuk.jpg\", ldrMantiuk * 255)\n",
    "print(\"img/saved_Mantiuk_Tonemap.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a5f8c96-9067-4800-b16c-88ff252abed9",
   "metadata": {},
   "source": [
    "## 6.7 Image alignment\n",
    "1. A **Homography** transforms a square to arbitrary quad. \n",
    "\n",
    "<img src=\"img/motion-models.jpg\" alt=\"drawing\" width=\"800\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a251ec07-675a-442b-a737-efd1cdff9b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "def extract_features(image, method, features):\n",
    "    if method == 'SURF':\n",
    "        surf = cv2.xfeatures2d.SURF_create(features) #patent\n",
    "        kp, des = surf.detectAndCompute(image, None)\n",
    "    elif method == 'SIFT':\n",
    "        sift = cv2.SIFT_create(features)\n",
    "        kp, des = sift.detectAndCompute(image, None)\n",
    "    elif method == 'ORB':\n",
    "        orb = cv2.ORB_create(features)\n",
    "        kp, des = orb.detectAndCompute(image, None)\n",
    "    return kp, des\n",
    "\n",
    "def match_features(des1, des2, method):\n",
    "    if method == 'BF':\n",
    "        bf = cv2.BFMatcher(crossCheck=True)\n",
    "        matches = bf.knnMatch(des1, des2, k=2)\n",
    "        matches = sorted(matches, key=lambda x: x.distance)\n",
    "    elif method == 'SIFT':\n",
    "        bf = cv2.BFMatcher()\n",
    "        raw_matches = bf.knnMatch(des1, des2, k=2)\n",
    "        matches = []\n",
    "        for m, n in raw_matches:\n",
    "            if m.distance < 0.55 * n.distance:  # Ratio test\n",
    "                matches.append(m)\n",
    "        matches = sorted(matches, key=lambda x: x.distance)\n",
    "    elif method == 'ORB':\n",
    "        matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)\n",
    "        matches = matcher.match(des1, des2, None)\n",
    "        numGoodMatches = int(len(matches) * 0.01)\n",
    "        matches = matches[:numGoodMatches]\n",
    "    else:\n",
    "        FLANN_INDEX_KDTREE = 1\n",
    "        index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)\n",
    "        search_params = dict(checks=50)\n",
    "        flann = cv2.FlannBasedMatcher(index_params, search_params)\n",
    "        matches = flann.knnMatch(des1, des2, k=2)\n",
    "    return matches\n",
    "\n",
    "def visualize_matches(image1, kp1, image2, kp2, matches):\n",
    "    image_matches = cv2.drawMatches(image1, kp1, image2, kp2, matches, None, flags=2)\n",
    "    plt.figure(figsize=(16, 6), dpi=100)\n",
    "    plt.imshow(image_matches)\n",
    "\n",
    "# Read reference image\n",
    "refFilename = \"img/form.jpg\"\n",
    "im1 = cv2.imread(refFilename, cv2.IMREAD_COLOR)\n",
    "im1 = cv2.cvtColor(im1, cv2.COLOR_BGR2RGB)\n",
    "im1_gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# Read image to be aligned\n",
    "imFilename = \"img/scanned-form.jpg\"\n",
    "im2 = cv2.imread(imFilename, cv2.IMREAD_COLOR)\n",
    "im2 = cv2.cvtColor(im2, cv2.COLOR_BGR2RGB)\n",
    "im2_gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# Detect ORB features and compute descriptors.\n",
    "MAX_NUM_FEATURES = 500\n",
    "key1, des1 = extract_features(im1_gray, 'SIFT', MAX_NUM_FEATURES)\n",
    "key2, des2 = extract_features(im2_gray, 'SIFT', MAX_NUM_FEATURES)\n",
    "\n",
    "# Match features.\n",
    "matches = match_features(des1, des2, 'SIFT')\n",
    "\n",
    "# Visualize matches\n",
    "visualize_matches(im1_gray, key1, im2_gray, key2, matches)\n",
    "plt.show()\n",
    "\n",
    "# Extract location of good matches\n",
    "points1 = np.zeros((len(matches), 2), dtype=np.float32)\n",
    "points2 = np.zeros((len(matches), 2), dtype=np.float32)\n",
    "\n",
    "for i, match in enumerate(matches):\n",
    "    points1[i, :] = key1[match.queryIdx].pt\n",
    "    points2[i, :] = key2[match.trainIdx].pt\n",
    "  \n",
    "# Find homography\n",
    "h, mask = cv2.findHomography(points2, points1, cv2.RANSAC, 5.0)\n",
    "\n",
    "# Use homography to warp image\n",
    "height, width, channels = im1.shape\n",
    "im2_reg = cv2.warpPerspective(im2, h, (width, height))\n",
    "# Display results \n",
    "plt.figure(figsize=[20,10]); \n",
    "plt.subplot(121); plt.imshow(im1); plt.axis('off'); plt.title(\"Original Form\");\n",
    "plt.subplot(122); plt.imshow(im2_reg); plt.axis('off'); plt.title(\"Scanned Form\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21704d76-cca7-495a-965c-6643cc63ec91",
   "metadata": {},
   "source": [
    "## 6.8 Extract meta data of image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06ae5afc-99b1-4a06-ab66-c03b9a9d805f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium\n",
    "from PIL import Image\n",
    "from PIL.ExifTags import TAGS\n",
    "\n",
    "def get_exif_data(image_path):\n",
    "    image = Image.open(image_path)\n",
    "    exif_data = {}\n",
    "    if image._getexif() is not None:\n",
    "        for tag, value in image._getexif().items():\n",
    "            if tag in TAGS:\n",
    "                exif_data[TAGS[tag]] = value\n",
    "    return exif_data\n",
    "\n",
    "def convert_to_decimal(gps_data):\n",
    "    degrees, minutes, seconds = gps_data\n",
    "    decimal = degrees + minutes / 60 + seconds / 3600\n",
    "    return decimal\n",
    "\n",
    "def get_gps_decimal(gps_info):\n",
    "    latitude = convert_to_decimal(gps_info[2])\n",
    "    longitude = convert_to_decimal(gps_info[4])\n",
    "    if gps_info[1] == \"S\":\n",
    "        latitude = -latitude\n",
    "    if gps_info[3] == \"W\":\n",
    "        longitude = -longitude\n",
    "    return latitude, longitude\n",
    "\"\"\"def create_map(latitude, longitude, date_info):\n",
    "    map = folium.Map(location=[latitude, longitude], zoom_start=16)\n",
    "    folium.Marker([latitude, longitude], popup=f\"Date: {date_info}\").add_to(map)\n",
    "    return map\"\"\"\n",
    "def create_map_with_markers(image_paths):\n",
    "    # Initialize an empty map centered at the first image's location\n",
    "    first_image_exif_data = get_exif_data(image_paths[0])\n",
    "    first_image_gps_info = first_image_exif_data[\"GPSInfo\"]\n",
    "    first_image_latitude, first_image_longitude = get_gps_decimal(first_image_gps_info)\n",
    "    map = folium.Map(location=[first_image_latitude, first_image_longitude], zoom_start=5)\n",
    "\n",
    "    # Loop through the image paths and add markers to the map\n",
    "    for img_path in image_paths:\n",
    "        exif_data = get_exif_data(img_path)\n",
    "\n",
    "        if \"GPSInfo\" in exif_data:\n",
    "            gps_info = exif_data[\"GPSInfo\"]\n",
    "            latitude, longitude = get_gps_decimal(gps_info)\n",
    "\n",
    "            # Get the date information from the EXIF data\n",
    "            date_info = exif_data.get(\"DateTimeOriginal\", \"Date not found\")\n",
    "\n",
    "            # Add the marker with the date popup to the map\n",
    "            folium.Marker([latitude, longitude], popup=f\"Date: {date_info}\").add_to(map)\n",
    "        else:\n",
    "            print(f\"No GPS information found in the image: {img_path}\")\n",
    "\n",
    "    return map\n",
    "\n",
    "image_path =[\"img/IMG_0983.JPG\", 'img/IMG_0926.JPG']\n",
    "\n",
    "map_with_markers = create_map_with_markers(image_path)\n",
    "map_with_markers.save(\"outputs/Image_map.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8667128-912e-4eaa-90d0-8d7dab911bf5",
   "metadata": {},
   "source": [
    "## 6.9 Video edit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "604a9fa1-5bbf-43e6-bf85-5e4e4a843b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the library\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from IPython.display import HTML\n",
    "source = 'vid/race_car.mp4'  # source = 0 for webcam\n",
    "cap = cv2.VideoCapture(source)\n",
    "if (cap.isOpened()== False): \n",
    "    print(\"Error opening video stream or file\")\n",
    "ret, frame = cap.read()\n",
    "plt.imshow(frame[...,::-1])\n",
    "HTML(\"\"\"<video width=1024 controls><source src=\"race_car.mp4\" type=\"video/mp4\"></video>\"\"\")\n",
    "# Default resolutions of the frame are obtained.\n",
    "# Convert the resolutions from float to integer.\n",
    "frame_width = int(cap.get(3))\n",
    "frame_height = int(cap.get(4))\n",
    "# Define the codec and create VideoWriter object.\n",
    "out_avi = cv2.VideoWriter('vid/race_car_out.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 10, (frame_width,frame_height))\n",
    "out_mp4 = cv2.VideoWriter('vid/race_car_out.mp4',cv2.VideoWriter_fourcc(*'XVID'), 10, (frame_width,frame_height))\n",
    "# Read until video is completed\n",
    "while(cap.isOpened()):\n",
    "    # Capture frame-by-frame\n",
    "    ret, frame = cap.read()\n",
    "    if ret == True:\n",
    "        # Write the frame to the output files\n",
    "        out_avi.write(frame)\n",
    "        out_mp4.write(frame)\n",
    "    # Break the loop\n",
    "    else: \n",
    "        break\n",
    "# When everything done, release the VideoCapture and VideoWriter objects\n",
    "cap.release()\n",
    "out_avi.release()\n",
    "out_mp4.release()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1584fc5",
   "metadata": {},
   "source": [
    "# Merge Sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "580c30bd-7837-43a4-9e00-c701cdb82057",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.029061079025268555\n",
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      "0.0\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import random\n",
    "\n",
    "def generate_unique_random_numbers(n, range_start, range_end):\n",
    "    # Ensure the range can accommodate the requested number of unique numbers\n",
    "    if n > (range_end - range_start + 1):\n",
    "        raise ValueError(\"Range is too small for the requested number of unique numbers.\")\n",
    "    \n",
    "    # Generate n unique random numbers within the specified range\n",
    "    random_numbers = random.sample(range(range_start, range_end + 1), n)\n",
    "    return random_numbers\n",
    "\n",
    "# Example usage\n",
    "n = 1000  # Length of the list\n",
    "range_start = 1  # Start of the range (inclusive)\n",
    "range_end = 1000  # End of the range (inclusive)\n",
    "\n",
    "a = generate_unique_random_numbers(n, range_start, range_end)\n",
    "print(a)\n",
    "\n",
    "def recursive_bubble_sort(arr, n=None):\n",
    "    if n is None:\n",
    "        n = len(arr)\n",
    "    # Base case: If the array size is 1, return.\n",
    "    if n == 1:\n",
    "        return arr\n",
    "    # Perform one pass of bubble sort. After this pass, the largest element is moved (or bubbled) to the end.\n",
    "    for i in range(n - 1):\n",
    "        if arr[i] > arr[i + 1]:\n",
    "            arr[i], arr[i + 1] = arr[i + 1], arr[i]\n",
    "    # Recursive call to bubble sort the rest of the array\n",
    "    recursive_bubble_sort(arr, n - 1)\n",
    "    return arr\n",
    "\n",
    "def merge_sort(arr):\n",
    "    if len(arr) > 1:\n",
    "        mid = len(arr) // 2  # Finding the mid of the array\n",
    "        L = arr[:mid]  # Dividing the array elements into 2 halves\n",
    "        R = arr[mid:]\n",
    "\n",
    "        merge_sort(L)  # Sorting the first half\n",
    "        merge_sort(R)  # Sorting the second half\n",
    "\n",
    "        i = j = k = 0\n",
    "\n",
    "        # Copy data to temp arrays L[] and R[]\n",
    "        while i < len(L) and j < len(R):\n",
    "            if L[i] < R[j]:\n",
    "                arr[k] = L[i]\n",
    "                i += 1\n",
    "            else:\n",
    "                arr[k] = R[j]\n",
    "                j += 1\n",
    "            k += 1\n",
    "\n",
    "        # Checking if any element was left\n",
    "        while i < len(L):\n",
    "            arr[k] = L[i]\n",
    "            i += 1\n",
    "            k += 1\n",
    "\n",
    "        while j < len(R):\n",
    "            arr[k] = R[j]\n",
    "            j += 1\n",
    "            k += 1\n",
    "    return arr\n",
    "t1=time.time()\n",
    "print(merge_sort(a))\n",
    "t2=time.time()\n",
    "print(t2-t1)\n",
    "t3=time.time()\n",
    "print(recursive_bubble_sort(a))\n",
    "t4=time.time()\n",
    "print(t4-t3)\n",
    "t5=time.time()\n",
    "print(sorted(a))\n",
    "t6=time.time()\n",
    "print(t6-t5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dc93403c-578f-4b7d-ab7d-f29fbc74c3df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorted Array: [1, 2, 3, 4, 5, 6]\n",
      "Total Inversions: 2\n"
     ]
    }
   ],
   "source": [
    "def merge_sort_count_inversions(arr):\n",
    "    # Base case: if the list is empty or consists of a single element,\n",
    "    # it is already sorted, and there are no inversions.\n",
    "    if len(arr) <= 1:\n",
    "        return arr, 0\n",
    "    \n",
    "    # Divide the array into two halves\n",
    "    middle = len(arr) // 2\n",
    "    left_half, left_inversions = merge_sort_count_inversions(arr[:middle])\n",
    "    right_half, right_inversions = merge_sort_count_inversions(arr[middle:])\n",
    "    \n",
    "    # Merge the sorted halves and count inversions\n",
    "    merged_arr, split_inversions = merge_and_count_inversions(left_half, right_half)\n",
    "    \n",
    "    # The total inversions is the sum of inversions in left, right, and split inversions\n",
    "    total_inversions = left_inversions + right_inversions + split_inversions\n",
    "    \n",
    "    return merged_arr, total_inversions\n",
    "\n",
    "def merge_and_count_inversions(left, right):\n",
    "    sorted_arr = []\n",
    "    i = j = 0\n",
    "    inversions = 0\n",
    "    \n",
    "    # Merge the two arrays and count inversions\n",
    "    while i < len(left) and j < len(right):\n",
    "        if left[i] <= right[j]:\n",
    "            sorted_arr.append(left[i])\n",
    "            i += 1\n",
    "        else:\n",
    "            # Since left[i] is greater than right[j], all remaining elements in left\n",
    "            # are also greater than right[j]. Thus, count inversions.\n",
    "            sorted_arr.append(right[j])\n",
    "            inversions += (len(left) - i)\n",
    "            j += 1\n",
    "            \n",
    "    # Append the remaining elements of left and right (if any)\n",
    "    sorted_arr.extend(left[i:])\n",
    "    sorted_arr.extend(right[j:])\n",
    "    \n",
    "    return sorted_arr, inversions\n",
    "\n",
    "# Example usage\n",
    "arr = [1, 3, 2, 5, 4, 6]\n",
    "sorted_arr, total_inversions = merge_sort_count_inversions(arr)\n",
    "print(\"Sorted Array:\", sorted_arr)\n",
    "print(\"Total Inversions:\", total_inversions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a371079",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorted points: [(2, 3), (3, 4), (5, 1), (12, 30), (12, 10), (40, 50)]\n",
      "Dividing [(2, 3), (3, 4), (5, 1), (12, 30), (12, 10), (40, 50)]\n",
      "Dividing [(2, 3), (3, 4), (5, 1)]\n",
      "Brute force result: 1.4142135623730951\n",
      "Dividing [(12, 30), (12, 10), (40, 50)]\n",
      "Brute force result: 20.0\n",
      "Point added to strip: (12, 30)\n",
      "Point added to strip: (12, 10)\n",
      "Closest in strip: 20.0\n",
      "The smallest distance is 1.4142135623730951\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# Calculate the Euclidean distance between two points\n",
    "def dist(p1, p2):\n",
    "    return math.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)\n",
    "\n",
    "# A brute force method to calculate the smallest distance between any two points\n",
    "def bruteForce(P, n):\n",
    "    min_val = float('inf')\n",
    "    for i in range(n):\n",
    "        for j in range(i+1, n):\n",
    "            if dist(P[i], P[j]) < min_val:\n",
    "                min_val = dist(P[i], P[j])\n",
    "    print(\"Brute force result:\", min_val)\n",
    "    return min_val\n",
    "\n",
    "# Find the distance between the closest points of strip of given size. All points in strip[] are sorted according to y coordinate.\n",
    "def stripClosest(strip, size, d):\n",
    "    min_val = d \n",
    "    for i in range(size):\n",
    "        j = i + 1\n",
    "        while j < size and (strip[j][1] - strip[i][1]) < min_val:\n",
    "            min_val = dist(strip[i], strip[j])\n",
    "            j += 1\n",
    "\n",
    "    print(\"Closest in strip:\", min_val)\n",
    "    return min_val\n",
    "\n",
    "# A recursive function to find the smallest distance. The array P contains all points sorted according to x coordinate\n",
    "def closestUtil(P, n):\n",
    "    print(\"Dividing\", P[:n])\n",
    "    # If there are 2 or 3 points, then use brute force\n",
    "    if n <= 3:\n",
    "        return bruteForce(P, n)\n",
    "\n",
    "    # Find the middle point\n",
    "    mid = n // 2\n",
    "    midPoint = P[mid]\n",
    "\n",
    "    # Consider the vertical line passing through the middle point calculate the smallest distance dl on left of middle point and dr on right side\n",
    "    dl = closestUtil(P[:mid], mid)\n",
    "    dr = closestUtil(P[mid:], n - mid)\n",
    "\n",
    "    # Find the smaller of two distances\n",
    "    d = min(dl, dr)\n",
    "\n",
    "    # Build an array strip[] that contains points close (closer than d) to the line passing through the middle point\n",
    "    strip = []\n",
    "    for i in range(n):\n",
    "        if abs(P[i][0] - midPoint[0]) < d:\n",
    "            strip.append(P[i])\n",
    "            print(\"Point added to strip:\", P[i])\n",
    "\n",
    "    # Find the closest points in strip. Return the minimum of d and closest distance is strip[]\n",
    "    return min(d, stripClosest(strip, len(strip), d))\n",
    "\n",
    "# The main function that finds the smallest distance. This method mainly uses closestUtil()\n",
    "def closest(P, n):\n",
    "    P.sort(key = lambda point: point[0])\n",
    "    print(\"Sorted points:\", P)\n",
    "    return closestUtil(P, n)\n",
    "\n",
    "# Example\n",
    "P = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]\n",
    "n = len(P)\n",
    "print(\"The smallest distance is\", closest(P, n))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2b03aa0",
   "metadata": {},
   "source": [
    "Q1. You are given as input an unsorted array of n distinct numbers, where n is a power of 2. Give an algorithm that identifies the second-largest number in the array, and that uses at most n+log_2(n)-2 comparisons.\n",
    "\n",
    "\n",
    "A1. Finding the Second-Largest Number with Minimum Comparisons\n",
    "The idea is to find the second-largest number by first identifying the largest number through a tournament-like method (similar to a knockout round in sports), where each element is compared with each other in pairs. This way, we only need n-1 comparisons to find the largest number. During this process, we keep track of the losers against the largest number, as the second-largest number must be among those. Then, we find the maximum among these log_2(n) elements, requiring log_2(n)-1 additional comparisons. This results in a total of n+log_2(n)-2 comparisons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f84b89e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 1, 14, 8, 7, 2, 9, 10, 4, 6]\n",
      "[3, 1, 14, 8, 7]\n",
      "[3, 1]\n",
      "[14, 8, 7]\n",
      "[8, 7]\n",
      "[2, 9, 10, 4, 6]\n",
      "[2, 9]\n",
      "[10, 4, 6]\n",
      "[4, 6]\n",
      "[1, 1, 2, 4, 6, 6, 6, 10, 4, 6]\n"
     ]
    }
   ],
   "source": [
    "arr = [3, 1, 14, 8, 7, 2, 9, 10, 4, 6]\n",
    "def order(arr):\n",
    "    if len(arr) <= 1:\n",
    "        return arr\n",
    "    else:\n",
    "        print(arr)\n",
    "        right=arr[:int(len(arr)/2)]\n",
    "        left=arr[int(len(arr)/2):]\n",
    "        order(right)\n",
    "        order(left)\n",
    "        i = j = k = 0\n",
    "        while i < len(right) and j < len(left):\n",
    "            if right[i] < left[j]:\n",
    "                arr[k] = right[i]\n",
    "                i+=1\n",
    "            else:\n",
    "                arr[k] = left[j]\n",
    "                j+=1\n",
    "            k+=1\n",
    "        return arr\n",
    "\n",
    "print(order(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18c74e0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tournament_method(arr):\n",
    "    def tournament(arr, left, right):\n",
    "        # If there is only one element\n",
    "        if left == right:\n",
    "            return arr[left], []\n",
    "\n",
    "        # If there are two elements\n",
    "        if right - left == 1:\n",
    "            if arr[left] > arr[right]:\n",
    "                return arr[left], [arr[right]]\n",
    "            else:\n",
    "                return arr[right], [arr[left]]\n",
    "\n",
    "        # If there are more than two elements, split the array and find the largest in each half\n",
    "        mid = (left + right) // 2\n",
    "        left_max, left_losers = tournament(arr, left, mid)\n",
    "        right_max, right_losers = tournament(arr, mid + 1, right)\n",
    "\n",
    "        # The winner goes to the next round, and the loser is added to the list of losers\n",
    "        if left_max > right_max:\n",
    "            return left_max, left_losers + [right_max] + right_losers\n",
    "        else:\n",
    "            return right_max, right_losers + [left_max] + left_losers\n",
    "\n",
    "    # Start the tournament\n",
    "    overall_max, losers = tournament(arr, 0, len(arr) - 1)\n",
    "\n",
    "    # The second largest is the maximum among the losers\n",
    "    second_largest = max(losers)\n",
    "\n",
    "    return second_largest\n",
    "\n",
    "# Example usage\n",
    "arr = [3, 1, 14, 8, 7, 2, 9, 10, 4, 6]\n",
    "print(\"Second largest:\", tournament_method(arr))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a75e15a",
   "metadata": {},
   "source": [
    "Q2. You are a given a unimodal array of n distinct elements, meaning that its entries are in increasing order up until its maximum element, after which its elements are in decreasing order. Give an algorithm to compute the maximum element that runs in O(log n) time.\n",
    "\n",
    "\n",
    "A2. Finding the Maximum Element in a Unimodal Array\n",
    "For a unimodal array, we can use binary search to find the maximum element in O(log n) time. If the middle element is greater than both its neighbors, it is the maximum. If it's increasing, the maximum is in the right half; if decreasing, the maximum is in the left half."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b4f9b90",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_maximum_in_unimodal_array(arr):\n",
    "    left, right = 0, len(arr) - 1\n",
    "    while left < right:\n",
    "        mid = (left + right) // 2\n",
    "        if arr[mid] < arr[mid + 1]:\n",
    "            left = mid + 1\n",
    "        else:\n",
    "            right = mid\n",
    "    return arr[left]\n",
    "\n",
    "# Example usage\n",
    "arr = [1, 3, 5, 7, 9, 11, 8, 6, 4, 2]\n",
    "print(\"Maximum element:\", find_maximum_in_unimodal_array(arr))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024b0398",
   "metadata": {},
   "source": [
    "Q3. You are given a sorted (from smallest to largest) array A of n distinct integers which can be positive, negative, or zero. You want to decide whether or not there is an index i such that A[i] = i. Design the fastest algorithm that you can for solving this problem.\n",
    "\n",
    "\n",
    "A3. Finding if There Exists an Index i Such That A[i] = i in a Sorted Array\n",
    "This can be solved with a binary search approach since the array is sorted. We compare the middle element with its index. If they are equal, we've found such an index. If the element is greater than its index, we search the left half; otherwise, we search the right half."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "506ca818",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_fixed_point(arr):\n",
    "    left, right = 0, len(arr) - 1\n",
    "    while left <= right:\n",
    "        mid = (left + right) // 2\n",
    "        if arr[mid] == mid:\n",
    "            return mid\n",
    "        elif arr[mid] < mid:\n",
    "            left = mid + 1\n",
    "        else:\n",
    "            right = mid - 1\n",
    "    return None\n",
    "\n",
    "# Example usage\n",
    "arr = [-10, -5, 0, 3, 7]\n",
    "print(\"Fixed point:\", find_fixed_point(arr))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54b965ef",
   "metadata": {},
   "source": [
    "## Master Method"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fbd980e",
   "metadata": {},
   "source": []
  }
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