DS_and_Algorithms_tutorials.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/epythonlab/PythonLab/blob/master/DS_and_Algorithms_tutorials.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kk1rGAOCA8Ep"
      },
      "source": [
        "# Python Dictionary `filter()`"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Intro"
      ],
      "metadata": {
        "id": "pR9AQ73FxloW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Welcome to our Python tutorial series! In this episode, I will delve into one of the fundamental tools for data manipulation in Python - the filter() function. Whether you're a beginner or an experienced developer, mastering the filter() function is crucial for effective data filtering and manipulation. Stay tuned to grasp its power in simplifying complex data handling tasks.\n",
        "\n",
        "Don't forget to hit the subscribe button and the bell icon to stay updated with our latest Python tutorials. Let's dive in!\"\n",
        "\n",
        "Python filter function, filter() method, Python filtering tutorial, how to use filter() in Python, Python data filtering, Python data manipulation, Python programming tutorial, Python coding tips, Python data science, Python programming examples"
      ],
      "metadata": {
        "id": "VqYLuRoGxn08"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introduction"
      ],
      "metadata": {
        "id": "5zAkFXXTx-wR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Filtering** dictionaries in Python involves selecting specific key-value pairs based on certain criteria.\n",
        "\n",
        " You can achieve this using the **filter()** function.  Here are some examples of using the **filter()** function to filter dictionaries in Python:"
      ],
      "metadata": {
        "id": "33JfDEYWo2hR"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_-uNrmRnA8Ep"
      },
      "source": [
        "- filter keys\n",
        "- filter by value\n",
        "- filter none values\n",
        "- filter key based on value\n",
        "- filter dictionary from list\n",
        "- filter empty values"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Syntax"
      ],
      "metadata": {
        "id": "DkYFV7gqtwJE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **filter()** function in Python is used to filter elements of an iterable based on a function. It takes two parameters:\n",
        "\n",
        "- A function that defines the filtering condition.\n",
        "- An iterable (like a **list**, **tuple**, **dictionary**, etc.) that needs to be filtered.\n",
        "- The syntax of the filter() function is as follows:"
      ],
      "metadata": {
        "id": "1EgrYJ_etyef"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "filter(function, iterable)\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "eKjcyfPUt9Pc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define the syntax:"
      ],
      "metadata": {
        "id": "4HQSybjou6QZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, the **function** parameter is a function that returns a Boolean value (**True or False**). The **function** is applied to each element of the iterable. If the function returns **True** for an element, that element is included in the result. If the function returns False, the element is excluded.\n",
        "\n",
        "The **iterable** parameter is the sequence to be filtered. It can be a list, tuple, dictionary, or any other iterable object.\n",
        "\n",
        "The **filter()** function returns an iterator that contains the elements from the iterable for which the function returns **True**."
      ],
      "metadata": {
        "id": "IF-GGQ4xu-Zr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Examples"
      ],
      "metadata": {
        "id": "aSLujjwyvQnj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Filter Keys:\n",
        "Filtering keys from a dictionary based on a specific condition using the **filter()** function:"
      ],
      "metadata": {
        "id": "TSF8LvJxrkUH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# let's define sample dictionary\n",
        "sample_dict = {'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}"
      ],
      "metadata": {
        "id": "yjWhRY18s0-h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sample code here\n",
        "filtered_keys = dict(filter(lambda item: item[0] in {'a', 'b', 'c'}, sample_dict.items()))\n",
        "print(filtered_keys)  # Output: {'a': 1, 'b': 2, 'c': 3}"
      ],
      "metadata": {
        "id": "WxE2QQdi2O8V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Filter by Value:\n",
        "Filtering the dictionary based on the values using the **filter()** function:"
      ],
      "metadata": {
        "id": "amnPSf7GssWs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# sample code here\n",
        "filtered_values = dict(filter(lambda item: item[1] > 3, sample_dict.items()))\n",
        "print(filtered_values)  # Output: {'d': 4, 'e': 5}"
      ],
      "metadata": {
        "id": "f-cJxd6psvkJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFxEwF5vA8Ep"
      },
      "source": [
        "How to filter values of even and odd keys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gz8QEoKZA8Eq"
      },
      "outputs": [],
      "source": [
        "# example: let's define the dictionary\n",
        "my_dict = {8:'u',4:'t',9:'z',10:'j',5:'k',3:'s'}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cg4nfuYQA8Eq"
      },
      "outputs": [],
      "source": [
        "# Filter values for even keys\n",
        "even_values = {k: v for k, v in sample_dict.items() if k % 2 == 0}\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter values for odd keys\n",
        "odd_values = {k: v for k, v in sample_dict.items() if k % 2 != 0}"
      ],
      "metadata": {
        "id": "Ox1RC5ad2qXh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion"
      ],
      "metadata": {
        "id": "uTQRK2EJtTUJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "These examples demonstrate the use of the **filter()** function to filter dictionaries in Python based on various conditions. You can customize the **lambda** functions or conditions within the filter to suit your specific filtering requirements."
      ],
      "metadata": {
        "id": "LbElgpc3tWe2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "Og4xGRMkBGOS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Time Complexity Analysis"
      ],
      "metadata": {
        "id": "xBkarVDVBHPZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Intro"
      ],
      "metadata": {
        "id": "7IYQBo5EJiLZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Welcome to this comprehensive guide on understanding time complexity in algorithms. In the world of programming and computer science, the efficiency of an algorithm is crucial for its practicality and scalability. A fundamental concept that aids in this evaluation is 'time complexity.'\n",
        "\n",
        "In this video, I will explore the basics of time complexity and delve into its various types, using simple Python examples and visual representations. By the end, you'll have a clear understanding of different time complexities such as **O(1)**, **O(n)**, **O(n^2)**, and **O(log n)**, and their implications on algorithm performance.\n",
        "\n",
        "Let's dive in and unravel the secrets behind the efficiency of algorithms, making complex concepts accessible to everyone, regardless of your programming background. Join me on this enlightening journey through the intricacies of time complexity in algorithms. Let's get started!"
      ],
      "metadata": {
        "id": "dkYqOY8bIei-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What is Time Complexity"
      ],
      "metadata": {
        "id": "lv1ExoKbBMey"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Time complexity** is a way of expressing the amount of time an algorithm takes to run as a function of the size of its input. It helps us understand how an algorithm's execution time grows as the input size increases."
      ],
      "metadata": {
        "id": "gO-D_xTsBVbT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Big O Notation"
      ],
      "metadata": {
        "id": "zmBUXYxnBZC6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Big O notation** is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. In the context of time complexity, it describes the upper bound of the time taken by an algorithm in terms of the input size."
      ],
      "metadata": {
        "id": "596mDd7NBfji"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analyzing Time Complexity:\n",
        "Let's analyze the time complexities of some common algorithms."
      ],
      "metadata": {
        "id": "9IUvtr01B-zq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Constant Time Complexity **O(1)**"
      ],
      "metadata": {
        "id": "vqWrNRsZCBsf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "An algorithm is said to have constant time complexity (denoted as O(1)) if the execution time of the algorithm does not depend on the size of the input.\n",
        "\n",
        "This means that the time taken to run the algorithm remains constant, regardless of the input size.\n",
        "\n",
        "In the example provided, the `constant_algo` function always returns the first item from the list items, regardless of the size of the list. It is evident that the time taken to retrieve the first element remains the same, irrespective of the length of the list.\n",
        "\n"
      ],
      "metadata": {
        "id": "A4boD6BzCfHV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "n = np.linspace(1, 100, 100)\n",
        "y = np.ones_like(n)\n",
        "\n",
        "plt.plot(n, y, label=\"O(1)\")\n",
        "plt.xlabel('Input Size')\n",
        "plt.ylabel('Time Complexity')\n",
        "plt.title('Constant Time Complexity O(1)')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "cellView": "form",
        "id": "2uEzaLs7Gx1W",
        "outputId": "7f54aa73-f7ca-4231-f08f-c8556d73439a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAHHCAYAAABXx+fLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABFl0lEQVR4nO3dd3gU5f7//9emJ0ASIiQhlNANNdLkBDgUwRMVURRRECF0ONI50qSDiOAHBAEp4hfwiBTPQVRUikGaIkgJiiCgVCEh9NBLcv/+8Mce1wTI4m6WMM/Hde11uffcM/OeG2VfztwzYzPGGAEAAFiIl6cLAAAAyGkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAuV7x4cbVt29bTZdw3Dh48KJvNprlz57ptH23btlXx4sXdtn1XGD9+vGJiYpSRkeH0ujNmzFCxYsV09epVN1SG3IgABEv69ddf1aVLF5UsWVIBAQEKDg5W7dq1NXnyZF2+fNmjtX3xxRcaMWJEjuzr22+/1YgRI3T27Nnb9luzZo1sNlu2PveypKQkvfTSSypatKj8/f0VFhamRo0aac6cOUpPT/d0efeUS5cuacSIEVqzZo1btv/TTz/ppZdeUuHCheXv76+oqCi1atVKP/30U5b909LSNG7cOA0YMEBeXv/76Vq0aJFeeukllSlTRjabTfXr189y/bZt2+ratWuaOXOmOw4HuZCPpwsActrnn3+u5s2by9/fX23atFHFihV17do1bdiwQf369dNPP/2kWbNmeay+L774QtOmTcuREPTtt99q5MiRatu2rUJDQ2/Zr1y5cvr3v//t0DZo0CDlzZtXgwcPztR/z549Dj9S94LZs2era9euioiIUOvWrVWmTBmdP39eiYmJ6tChg5KTk/Xqq696ukyPeffddx3OrFy6dEkjR46UpFuGiru1ZMkStWzZUmFhYerQoYNKlCihgwcP6r333tN//vMfLVy4UM8884zDOv/v//0/3bhxQy1btnRonz59urZu3aoaNWro1KlTt9xnQECAEhISNHHiRPXo0eOeD+vIAQawkP3795u8efOamJgYc+zYsUzL9+3bZyZNmuSByv6nW7duJqf+03zzzTeNJHPgwAGn161QoYKpV6+ey2tyh40bNxpvb29Tp04dk5aWlmn5999/b+bMmZPzhWXTgQMHjKQcrfHEiRNGkhk+fLhLt/vLL7+YoKAgExMTY1JTUzPtMyYmxuTJk8f8+uuvDssqV65sXnrppUzbO3z4sElPTzfG3PnfyS1bthhJJjEx8a8fCHI9AhAspWvXrkaS+eabb7LV//r162bUqFGmZMmSxs/Pz0RHR5tBgwaZK1euOPSLjo42jRs3NuvXrzc1atQw/v7+pkSJEmbevHkO/a5du2ZGjBhhSpcubfz9/U1YWJipXbu2WblypTHGmISEBCMp0+emN99808TFxZmwsDATEBBgqlataj766KNMdUsy3bp1Mx9//LGpUKGC8fPzM+XLlzdffvmlvc/w4cOz3Fd2w9Dtfmyio6NNQkKC/fucOXOMJLN+/XrTo0cPU6BAARMSEmI6d+5srl69as6cOWNat25tQkNDTWhoqOnXr5/JyMhw2GZ6erp56623TPny5Y2/v78JDw83nTt3NqdPn75jrY899pjx8fExhw4dytaxXbhwwfTt29cUKVLE+Pn5mbJly5o333wzU003x3nx4sWmXLlyJiAgwPztb38zP/zwgzHGmBkzZphSpUoZf39/U69evUxjW69ePVOhQgWzZcsWExcXZwICAkzx4sXN9OnTHfrdKgDt3r3bNGvWzOTPn9/4+/ubatWqmU8++cS+/Pjx46ZAgQKmXr16DrXv27fPBAUFmeeff97elpCQYKKjox329+fP8OHDzf/7f//PSDLbtm3LNG5jxowxXl5e5rfffrvl2Hbp0sVIMuvWrcty+dq1a40k06VLF3vb/v37jSQzd+7cW27XmOyF8rCwMNOzZ8/b9oE1EIBgKYULFzYlS5bMdv+bgeS5554z06ZNM23atDGSTNOmTR36RUdHmwcffNBERESYV1991UydOtVUrVrV2Gw2s3PnTnu/V1991dhsNtOpUyfz7rvvmgkTJpiWLVuaN954wxhjzLfffmseffRRI8n8+9//tn9uKlKkiHn55ZfN1KlTzcSJE83DDz9sJJlly5Y51CPJxMbGmkKFCpnRo0ebSZMmmZIlS5qgoCBz8uRJY4wxO3bsMC1btjSSzFtvvWXf14ULF7I1NncTgB566CHz2GOPmWnTppnWrVsbSaZ///6mTp065sUXXzTvvPOOefLJJ42kTOGxY8eOxsfHx3Tq1MnMmDHDDBgwwOTJk8fUqFHDXLt27ZZ1Xrx40fj6+ppHHnkkW8eVkZFhHnnkEWOz2UzHjh3N1KlTTZMmTYwk07t3b4e+kkzlypVN0aJFzRtvvGHeeOMNExISYooVK2amTp1qypcvbyZMmGCGDBli/Pz8TIMGDRzWr1evnomKijLh4eGme/fu5u233zZ16tQxksx7771n75dVANq5c6cJCQkx5cuXN+PGjTNTp041devWNTabzSxZssTe76OPPjKSzOTJk40xvwfJ2rVrm4iICPu/C8Y4BqALFy6Y6dOnG0nmmWeesf+7sWPHDpOWlmYCAwPNv/71r0xjV758+TuOc1RUlClevPht+xQvXtwUKVLE/v2DDz4wkuzB8layE4AaNWpkqlWrdts+sAYCECzj3LlzRpJ5+umns9U/KSnJSDIdO3Z0aH/llVeMJLN69Wp7W3R0dKb/q01NTTX+/v4OPxSxsbGmcePGt93v7S6BXbp0yeH7tWvXTMWKFTP96Egyfn5+5pdffrG37dixw0gyU6ZMsbe56xLYrQJQfHy8w5mIuLg4Y7PZTNeuXe1tN27cMEWKFHHY9vr1640kM3/+fIf9LF++PMv2P7p53L169crWcS1dutRIMq+99ppD+3PPPWdsNpvDmEoy/v7+DuM3c+ZMI8lERkY6XG4bNGhQprGuV6+ekWQmTJhgb7t69ap56KGHTHh4uD3YZRWAGjZsaCpVquRwNjIjI8PUqlXLlClTxqH2li1bmqCgILN37177n/nSpUsd+vwxABlz+0tgLVu2NFFRUfZLT8YYs23btjtepjt79my2/ht86qmnjCT7+A0ZMsRIMufPn7/tetkJQJ07dzaBgYG37QNruLdmKQJulJaWJknKly9ftvp/8cUXkqS+ffs6tP/rX/+S9Ptk6j8qX768/v73v9u/FyxYUA8++KD2799vbwsNDdVPP/2kffv2OX8AkgIDA+3/fObMGZ07d05///vftW3btkx9GzVqpFKlStm/V65cWcHBwQ715LQOHTo4TD6tWbOmjDHq0KGDvc3b21vVq1d3qPOjjz5SSEiIHn30UZ08edL+qVatmvLmzauvv/76lvu8mz93b29v9ezZ06H9X//6l4wx+vLLLx3aGzZs6HD7eM2aNSVJzZo1c9jnzfY/j7+Pj4+6dOli/+7n56cuXbooNTVVW7duzbLG06dPa/Xq1Xr++ed1/vx5+3icOnVK8fHx2rdvn44ePWrvP3XqVIWEhOi5557T0KFD1bp1az399NPZGo+stGnTRseOHXMY9/nz5yswMFDNmjW75Xrnz5+XdOc/i5vLb/7ZnTp1Sj4+PsqbN+9d13xT/vz5dfnyZV26dOkvbwu5GwEIlhEcHCzpf38J38mhQ4fk5eWl0qVLO7RHRkYqNDRUhw4dcmgvVqxYpm3kz59fZ86csX8fNWqUzp49q7Jly6pSpUrq16+ffvjhh2wfw7Jly/S3v/1NAQEBCgsLU8GCBTV9+nSdO3cuU9/s1JPT/lxTSEiIJKlo0aKZ2v9Y5759+3Tu3DmFh4erYMGCDp8LFy4oNTX1lvu8mz/3qKioTD/S5cqVsy+/22OSlGn8o6KilCdPHoe2smXLSvr9+T9Z+eWXX2SM0dChQzONx/DhwyXJYUzCwsL09ttv64cfflBISIjefvvtrA8+mx599FEVKlRI8+fPlyRlZGRowYIFevrpp28bbm4uu9OfRXaD0t0wxkgSd4GB2+BhHcHBwYqKitLOnTudWi+7f1F6e3tn2X7zL1xJqlu3rn799Vd98sknWrlypWbPnq233npLM2bMUMeOHW+7/fXr1+upp55S3bp19c4776hQoULy9fXVnDlz9OGHH95VPTntVjVl1f7HOjMyMhQeHm7/wf2zggUL3nKfpUuXlo+Pj3788Ucnq80eZ45Jcs3437xd/ZVXXlF8fHyWff4c3FesWCHp9wD222+/3faxB3fi7e2tF198Ue+++67eeecdffPNNzp27Jheeuml264XEhKiQoUK3TH0//DDDypcuLA9vD7wwAO6ceOGzp8//5dD0ZkzZxQUFORwNhXWRACCpTz55JOaNWuWNm7cqLi4uNv2jY6OVkZGhvbt22f/v39JOn78uM6ePavo6Oi7qiEsLEzt2rVTu3btdOHCBdWtW1cjRoywB6BbBa7//ve/CggI0IoVK+Tv729vnzNnzl3Vcbt93WtKlSqlr776SrVr13b6hysoKEiPPPKIVq9erSNHjmQ6M/Nn0dHR+uqrrzL92P7888/25a507NgxXbx40eEs0N69eyXplk9mLlmypCTJ19dXjRo1uuM+li9frtmzZ6t///6aP3++EhIStGnTJvn43Pon4E7/brRp00YTJkzQZ599pi+//FIFCxa8ZRj7oyeffFLvvvuuNmzYoDp16mRavn79eh08eNDhsmBMTIwk6cCBA6pcufId93E7Bw4ccPjvGdbFJTBYSv/+/ZUnTx517NhRx48fz7T8119/1eTJkyVJTzzxhCRp0qRJDn0mTpwoSWrcuLHT+//zg9ry5s2r0qVLOzye/+YP4Z+fzuzt7S2bzebwxOKDBw9q6dKlTtdxp33da55//nmlp6dr9OjRmZbduHHjjvUPHz5cxhi1bt1aFy5cyLR869atmjdvnqTf/9zT09M1depUhz5vvfWWbDabHn/88bs/kCzcuHHD4enEN59WXLBgQVWrVi3LdcLDw1W/fn3NnDlTycnJmZafOHHC/s9nz55Vx44d9fDDD+v111/X7NmztW3bNr3++uu3rSsoKMi+flYqV66sypUra/bs2frvf/+rFi1a3DZQ3dSvXz8FBgaqS5cumf57OH36tLp27aqgoCD169fP3n7zf1a2bNlyx+3fybZt21SrVq2/vB3kfpwBgqWUKlVKH374oV544QWVK1fO4UnQ3377rT766CP7O6xiY2OVkJCgWbNm6ezZs6pXr542b96sefPmqWnTpmrQoIHT+y9fvrzq16+vatWqKSwsTFu2bNF//vMfde/e3d7n5o9ez549FR8fL29vb7Vo0UKNGzfWxIkT9dhjj+nFF19Uamqqpk2bptKlSzs1j+iPbu5r8ODBatGihXx9fdWkSZNMc1I8rV69eurSpYvGjh2rpKQk/eMf/5Cvr6/27dunjz76SJMnT9Zzzz13y/Vr1aqladOm6eWXX1ZMTIzDk6DXrFmjTz/9VK+99pokqUmTJmrQoIEGDx6sgwcPKjY2VitXrtQnn3yi3r17O0wsd4WoqCiNGzdOBw8eVNmyZbVo0SIlJSVp1qxZ8vX1veV606ZNU506dVSpUiV16tRJJUuW1PHjx7Vx40b99ttv2rFjhySpV69eOnXqlL766it5e3vrscceU8eOHfXaa6/p6aefVmxsbJbbDwwMVPny5bVo0SKVLVtWYWFhqlixoipWrGjv06ZNG73yyiuSdMfLXzeVKVNG8+bNU6tWrVSpUqVMT4I+efKkFixY4DDOJUuWVMWKFfXVV1+pffv2Dttbt26d1q1bJ+n34Hfx4kX7n2XdunVVt25de9+tW7fq9OnTf2kCOO4jHrv/DPCgvXv3mk6dOpnixYsbPz8/ky9fPlO7dm0zZcoUh9uKr1+/bkaOHGlKlChhfH19TdGiRW/7IMQ/q1evnsNtua+99pp5+OGHTWhoqAkMDDQxMTFmzJgxDs+xuXHjhunRo4cpWLCgsdlsDrfEv/fee6ZMmTLG39/fxMTEmDlz5tgfaPhH+v8f0Pdnf7493RhjRo8ebQoXLmy8vLzc/iDE77//3qHfzdpPnDjh0J6QkGDy5MmTabuzZs0y1apVM4GBgSZfvnymUqVKpn///lk+1TsrW7duNS+++KKJiooyvr6+Jn/+/KZhw4Zm3rx5Drd0nz9/3vTp08fer0yZMrd9EOIf3bxl/c0333Ro//rrr40khwdXZvUgxOjoaDN16tQst/nnW8x//fVX06ZNGxMZGWl8fX1N4cKFzZNPPmn+85//GGOM+eSTTzLdZm+MMWlpaSY6OtrExsba/937823wxvz+XKpq1aoZPz+/LG+JT05ONt7e3qZs2bJZjPbt/fDDD6Zly5amUKFCxtfX10RGRpqWLVuaH3/8Mcv+EydONHnz5s30KIhbPdAzq3oHDBhgihUrlunPEdZkM8aDMyIBwMLq16+vkydPOj0x/15x8uRJFSpUSMOGDdPQoUPduq9z586pZMmSGj9+vMNjE7Lr6tWrKl68uAYOHKhevXq5oULkNswBAgDclblz5yo9PV2tW7d2+75CQkLUv39/vfnmmw4vbc2uOXPmyNfXV127dnVDdciNOAMEAB6SW88ArV69Wrt27dLQoUPVoEEDLVmyxNMlAU5jEjQAwCmjRo3St99+q9q1a2vKlCmeLge4K5wBAgAAlsMcIAAAYDkEIAAAYDnMAcpCRkaGjh07pnz58uWaVwUAAGB1xhidP39eUVFR8vK6/TkeAlAWjh07dsf3BQEAgHvTkSNHVKRIkdv2IQBl4eYLEI8cOWJ/GzEAALi3paWlqWjRog4vMr4VAlAWbl72Cg4OJgABAJDLZGf6CpOgAQCA5RCAAACA5RCAAACA5TAHCAAAD8jIyNC1a9c8XUau4uvrK29vb5dsiwAEAEAOu3btmg4cOHBXb7a3utDQUEVGRv7l5/QRgAAAyEHGGCUnJ8vb21tFixa94wP78DtjjC5duqTU1FRJUqFChf7S9ghAAADkoBs3bujSpUuKiopSUFCQp8vJVQIDAyVJqampCg8P/0uXw4idAADkoPT0dEmSn5+fhyvJnW6GxuvXr/+l7RCAAADwAN41eXdcNW4EIAAAYDkEIAAA4HJ79uxRZGSkzp8/n+11ZsyYoSZNmrixqv8hAAEAgGw7cuSI2rdvr6ioKPn5+Sk6Olq9evXSqVOnHPoNGjRIPXr0sL+Y9MqVK2rbtq0qVaokHx8fNW3aNNO227dvr23btmn9+vVuPw4CEAAAyJb9+/erevXq2rdvnxYsWKBffvlFM2bMUGJiouLi4nT69GlJ0uHDh7Vs2TK1bdvWvm56eroCAwPVs2dPNWrUKMvt+/n56cUXX9Tbb7/t9mPhNngAAJAt3bp1k5+fn1auXGm/Jb1YsWKqUqWKSpUqpcGDB2v69OlavHixYmNjVbhwYfu6efLk0fTp0yVJ33zzjc6ePZvlPpo0aaJHH31Uly9ftu/DHQhAAAB4kDFGl6+ne2Tfgb7e2b6r6vTp01qxYoXGjBmTKZhERkaqVatWWrRokd555x2tX79e1atXv6uaqlevrhs3bmjTpk2qX7/+XW0jOwhAAAB40OXr6So/bIVH9r1rVLyC/LIXBfbt2ydjjMqVK5fl8nLlyunMmTM6ceKEDh06dNcBKCgoSCEhITp06NBdrZ9dzAECAADZZoy5Y5/Lly8rICDgrvcRGBioS5cu3fX62cEZIAAAPCjQ11u7RsV7bN/ZVbp0adlsNu3evVvPPPNMpuW7d+9W/vz5VbBgQRUoUEBnzpy567pOnz6tggUL3vX62UEAAgDAg2w2W7YvQ3nSAw88oEcffVTvvPOO+vTp4zAPKCUlRfPnz1ebNm1ks9lUpUoV7dq166728+uvv+rKlSuqUqWKq0rPEpfAAABAtkydOlVXr15VfHy81q1bpyNHjmj58uV69NFHVbhwYY0ZM0aSFB8fr40bN9rfe3bTrl27lJSUpNOnT+vcuXNKSkpSUlKSQ5/169erZMmSKlWqlFuP5d6PnAAA4J5QpkwZbdmyRcOHD9fzzz+v06dPKzIyUk2bNtXw4cMVFhYmSXr88cfl4+Ojr776SvHx/7u898QTTzhMbr55lueP84oWLFigTp06uf1YCEAAACDboqOjNXfu3Nv28fHx0auvvqqJEyc6BKCDBw/edr2ffvpJSUlJWrx4sQsqvT0CEAAAcLkuXbro7NmzOn/+vP11GHeSnJys999/XyEhIW6ujgAEAADcwMfHR4MHD3ZqnVu9IsMdmAQNAAAshwAEAAAshwAEAIAHZOeJysjMVeNGAAIAIAd5e//+9OVr1655uJLc6eYrMnx9ff/SdpgEDQBADvLx8VFQUJBOnDghX19feXlxLiI7jDG6dOmSUlNTFRoaag+Sd4sABABADrLZbCpUqJAOHDjg9jee349CQ0MVGRn5l7dDAAIAIIf5+fmpTJkyXAZzkq+v718+83MTAQgAAA/w8vJSQECAp8uwLC48AgAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAy/FoAFq3bp2aNGmiqKgo2Ww2LV269I7rrFmzRlWrVpW/v79Kly6tuXPn3rLvG2+8IZvNpt69e7usZgAAkPt5NABdvHhRsbGxmjZtWrb6HzhwQI0bN1aDBg2UlJSk3r17q2PHjlqxYkWmvt9//71mzpypypUru7psAACQy3n0bfCPP/64Hn/88Wz3nzFjhkqUKKEJEyZIksqVK6cNGzborbfeUnx8vL3fhQsX1KpVK7377rt67bXXXF43AADI3XLVHKCNGzeqUaNGDm3x8fHauHGjQ1u3bt3UuHHjTH0BAAAkD58BclZKSooiIiIc2iIiIpSWlqbLly8rMDBQCxcu1LZt2/T9999ne7tXr17V1atX7d/T0tJcVjMAALj35KozQHdy5MgR9erVS/Pnz1dAQEC21xs7dqxCQkLsn6JFi7qxSgAA4Gm5KgBFRkbq+PHjDm3Hjx9XcHCwAgMDtXXrVqWmpqpq1ary8fGRj4+P1q5dq7fffls+Pj5KT0/PcruDBg3SuXPn7J8jR47kxOEAAAAPyVWXwOLi4vTFF184tK1atUpxcXGSpIYNG+rHH390WN6uXTvFxMRowIAB8vb2znK7/v7+8vf3d0/RAADgnuPRAHThwgX98ssv9u8HDhxQUlKSwsLCVKxYMQ0aNEhHjx7V+++/L0nq2rWrpk6dqv79+6t9+/ZavXq1Fi9erM8//1ySlC9fPlWsWNFhH3ny5NEDDzyQqR0AAFiXRy+BbdmyRVWqVFGVKlUkSX379lWVKlU0bNgwSVJycrIOHz5s71+iRAl9/vnnWrVqlWJjYzVhwgTNnj3b4RZ4AACAO7EZY4yni7jXpKWlKSQkROfOnVNwcLCnywEAANngzO93rpoEDQAA4AoEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkeDUDr1q1TkyZNFBUVJZvNpqVLl95xnTVr1qhq1ary9/dX6dKlNXfuXIflY8eOVY0aNZQvXz6Fh4eradOm2rNnj3sOAAAA5EoeDUAXL15UbGyspk2blq3+Bw4cUOPGjdWgQQMlJSWpd+/e6tixo1asWGHvs3btWnXr1k3fffedVq1apevXr+sf//iHLl686K7DAAAAuYzNGGM8XYQk2Ww2ffzxx2ratOkt+wwYMECff/65du7caW9r0aKFzp49q+XLl2e5zokTJxQeHq61a9eqbt262aolLS1NISEhOnfunIKDg506DgAA4BnO/H7nqjlAGzduVKNGjRza4uPjtXHjxluuc+7cOUlSWFiYW2sDAAC5h4+nC3BGSkqKIiIiHNoiIiKUlpamy5cvKzAw0GFZRkaGevfurdq1a6tixYq33O7Vq1d19epV+/e0tDTXFg4AAO4pueoMkLO6deumnTt3auHChbftN3bsWIWEhNg/RYsWzaEKAQCAJ+SqABQZGanjx487tB0/flzBwcGZzv50795dy5Yt09dff60iRYrcdruDBg3SuXPn7J8jR464vHYAAHDvyFWXwOLi4vTFF184tK1atUpxcXH278YY9ejRQx9//LHWrFmjEiVK3HG7/v7+8vf3d3m9AADg3uTRM0AXLlxQUlKSkpKSJP1+m3tSUpIOHz4s6fczM23atLH379q1q/bv36/+/fvr559/1jvvvKPFixerT58+9j7dunXTBx98oA8//FD58uVTSkqKUlJSdPny5Rw9NgAAcO/y6G3wa9asUYMGDTK1JyQkaO7cuWrbtq0OHjyoNWvWOKzTp08f7dq1S0WKFNHQoUPVtm1b+3KbzZblvubMmePQ73a4DR4AgNzHmd/ve+Y5QPcSAhAAALnPffscIAAAAFcgAAEAAMshAAEAAMshAAEAAMtxOgDNmTNHly5dckctAAAAOcLpADRw4EBFRkaqQ4cO+vbbb91REwAAgFs5HYCOHj2qefPm6eTJk6pfv75iYmI0btw4paSkuKM+AAAAl3M6APn4+OiZZ57RJ598oiNHjqhTp06aP3++ihUrpqeeekqffPKJMjIy3FErAACAS/ylSdARERGqU6eO4uLi5OXlpR9//FEJCQkqVaqUw9ObAQAA7iV3FYCOHz+u//u//1OFChVUv359paWladmyZTpw4ICOHj2q559/XgkJCa6uFQAAwCWcfhVGkyZNtGLFCpUtW1YdO3ZUmzZtFBYW5tAnNTVVkZGRufZSGK/CAAAg93Hm99vH2Y2Hh4dr7dq1iouLu2WfggUL6sCBA85uGgAAIEc4fQmsXr16qlq1aqb2a9eu6f3335f0+xvZo6Oj/3p1AAAAbuD0JTBvb28lJycrPDzcof3UqVMKDw9Xenq6Swv0BC6BAQCQ+7j1bfDGGNlstkztv/32m0JCQpzdHAAAQI7L9hygKlWqyGazyWazqWHDhvLx+d+q6enpOnDggB577DG3FAkAAOBK2Q5ATZs2lSQlJSUpPj5eefPmtS/z8/NT8eLF1axZM5cXCAAA4GrZDkDDhw+XJBUvXlwvvPCCAgIC3FYUAACAOzl9GzwPOAQAALldtgJQWFiY9u7dqwIFCih//vxZToK+6fTp0y4rDgAAwB2yFYDeeust5cuXz/7PtwtAAAAA9zqnnwNkBTwHCACA3MetzwGaO3dulu03btzQoEGDnN0cAABAjnM6APXs2VPNmzfXmTNn7G179uxRzZo1tWDBApcWBwAA4A5OB6Dt27frt99+U6VKlbRq1SpNmzZNVatWVUxMjHbs2OGOGgEAAFzK6dvgS5UqpW+++Ua9e/fWY489Jm9vb82bN08tW7Z0R30AAAAu5/QZIEn6/PPPtXDhQsXFxSk0NFTvvfeejh075uraAAAA3MLpANSlSxc1b95cAwYM0Pr16/XDDz/Iz89PlSpV0uLFi91RIwAAgEs5fRt8xYoVNX/+fMXGxjq0T5s2TQMGDNCFCxdcWqAncBs8AAC5jzO/304HoKtXr8rf3z/LZXv27NGDDz7ozObuSQQgAAByH7c+B8jf31+//vqrhgwZopYtWyo1NVWS9OWXX+rGjRt3VzEAAEAOcjoArV27VpUqVdKmTZu0ZMkS+yWvHTt22N8YDwAAcC9zOgANHDhQr732mlatWiU/Pz97+yOPPKLvvvvOpcUBAAC4g9MB6Mcff9QzzzyTqT08PFwnT550SVEAAADu5HQACg0NVXJycqb27du3q3Dhwi4pCgAAwJ2cDkAtWrTQgAEDlJKSIpvNpoyMDH3zzTd65ZVX1KZNG3fUCAAA4FJOB6DXX39dMTExKlq0qC5cuKDy5curbt26qlWrloYMGeKOGgEAAFzK6ecA3XT48GHt3LlTFy5cUJUqVVSmTBlX1+YxPAcIAIDcx5nfb6dfhnpTsWLFVKxYsbtdHQAAwGOyFYD69u2b7Q1OnDjxrosBAADICdkKQNu3b8/Wxmw2218qBgAAICdkKwB9/fXX7q4DAAAgxzh9F9gfHTlyREeOHHFVLQAAADnC6QB048YNDR06VCEhISpevLiKFy+ukJAQDRkyRNevX3dHjQAAAC7l9F1gPXr00JIlSzR+/HjFxcVJkjZu3KgRI0bo1KlTmj59usuLBAAAcCWnnwMUEhKihQsX6vHHH3do/+KLL9SyZUudO3fOpQV6As8BAgAg93Hm99vpS2D+/v4qXrx4pvYSJUo4vB0eAADgXuV0AOrevbtGjx6tq1ev2tuuXr2qMWPGqHv37i4tDgAAwB2cngO0fft2JSYmqkiRIoqNjZUk7dixQ9euXVPDhg317LPP2vsuWbLEdZUCAAC4iNMBKDQ0VM2aNXNoK1q0qMsKAgAAcDenA9CcOXPcUQcAAECO+UsPQgQAAMiNnD4DdOrUKQ0bNkxff/21UlNTlZGR4bD89OnTLisOAADAHZwOQK1bt9Yvv/yiDh06KCIighegAgCAXMfpALR+/Xpt2LDBfgcYAABAbuP0HKCYmBhdvnzZHbUAAADkCKcD0DvvvKPBgwdr7dq1OnXqlNLS0hw+AAAA9zqnA1BoaKjS0tL0yCOPKDw8XPnz51f+/PkVGhqq/PnzO7WtdevWqUmTJoqKipLNZtPSpUvvuM6aNWtUtWpV+fv7q3Tp0po7d26mPtOmTVPx4sUVEBCgmjVravPmzU7VBQAA7m9OzwFq1aqVfH199eGHH/7lSdAXL15UbGys2rdv7/AE6Vs5cOCAGjdurK5du2r+/PlKTExUx44dVahQIcXHx0uSFi1apL59+2rGjBmqWbOmJk2apPj4eO3Zs0fh4eF3XSsAALh/OP02+KCgIG3fvl0PPvigawux2fTxxx+radOmt+wzYMAAff7559q5c6e9rUWLFjp79qyWL18uSapZs6Zq1KihqVOnSpIyMjJUtGhR9ejRQwMHDsxWLe56G7wxRpevp7tsewAA5GaBvt4uvZvcmd9vp88AVa9eXUeOHHF5AMqOjRs3qlGjRg5t8fHx6t27tyTp2rVr2rp1qwYNGmRf7uXlpUaNGmnjxo233O7Vq1cdXu7qrrlMl6+nq/ywFW7ZNgAAuc2uUfEK8nM6iriE03vt0aOHevXqpX79+qlSpUry9fV1WF65cmWXFfdnKSkpioiIcGiLiIhQWlqaLl++rDNnzig9PT3LPj///PMttzt27FiNHDnSLTUDAIB7j9MB6IUXXpAktW/f3t5ms9lkjJHNZlN6eu67xDNo0CD17dvX/j0tLc0tL3gN9PXWrlHxLt8uAAC5UaCvt8f27XQAOnDggDvqyJbIyEgdP37coe348eMKDg5WYGCgvL295e3tnWWfyMjIW27X399f/v7+bqn5j2w2m8dO9QEAgP9x+tc4OjraHXVkS1xcnL744guHtlWrVikuLk6S5Ofnp2rVqikxMdE+mTojI0OJiYnq3r17TpcLAADuUXd1OuLXX3/VpEmTtHv3bklS+fLl1atXL5UqVcqp7Vy4cEG//PKL/fuBAweUlJSksLAwFStWTIMGDdLRo0f1/vvvS5K6du2qqVOnqn///mrfvr1Wr16txYsX6/PPP7dvo2/fvkpISFD16tX18MMPa9KkSbp48aLatWt3N4cKAADuQ04HoBUrVuipp57SQw89pNq1a0uSvvnmG1WoUEGfffaZHn300Wxva8uWLWrQoIH9+815OAkJCZo7d66Sk5N1+PBh+/ISJUro888/V58+fTR58mQVKVJEs2fPtj8DSPp9jtKJEyc0bNgwpaSk6KGHHtLy5cszTYwGAADW5fRzgKpUqaL4+Hi98cYbDu0DBw7UypUrtW3bNpcW6Anueg4QAABwH2d+v51+Fcbu3bvVoUOHTO3t27fXrl27nN0cAABAjnM6ABUsWFBJSUmZ2pOSknjVBAAAyBWcngPUqVMnde7cWfv371etWrUk/T4HaNy4cQ7P0gEAALhXOT0HyBijSZMmacKECTp27JgkKSoqSv369VPPnj1d+k4PT2EOEAAAuY8zv99OB6A/On/+vCQpX758d7uJexIBCACA3Mctk6AvX76sTz/91B56pN+DT758+ZSWlqZPP/3U4YWiAAAA96psB6BZs2Zp8uTJWZ7tCQ4O1ttvv63Zs2e7tDgAAAB3yHYAmj9/vnr37n3L5b1799a8efNcURMAAIBbZTsA7du3T7GxsbdcXrlyZe3bt88lRQEAALhTtgPQjRs3dOLEiVsuP3HihG7cuOGSogAAANwp2wGoQoUK+uqrr265fOXKlapQoYJLigIAAHCnbAeg9u3ba/To0Vq2bFmmZZ999pnGjBmj9u3bu7Q4AAAAd8j2k6A7d+6sdevW6amnnlJMTIwefPBBSdLPP/+svXv36vnnn1fnzp3dVigAAICrOPUusA8++EALFy5U2bJltXfvXu3Zs0cPPvigFixYoAULFrirRgAAAJf6S0+Cvl/xJGgAAHIftzwJGgAA4H5BAAIAAJZDAAIAAJZDAAIAAJZz1wHol19+0YoVK3T58mVJEnOpAQBAbuF0ADp16pQaNWqksmXL6oknnlBycrIkqUOHDvrXv/7l8gIBAABczekA1KdPH/n4+Ojw4cMKCgqyt7/wwgtavny5S4sDAABwh2w/CfqmlStXasWKFSpSpIhDe5kyZXTo0CGXFQYAAOAuTp8BunjxosOZn5tOnz4tf39/lxQFAADgTk4HoL///e96//337d9tNpsyMjI0fvx4NWjQwKXFAQAAuIPTl8DGjx+vhg0basuWLbp27Zr69++vn376SadPn9Y333zjjhoBAABcyukzQBUrVtTevXtVp04dPf3007p48aKeffZZbd++XaVKlXJHjQAAAC7Fy1CzwMtQAQDIfZz5/Xb6EpgkXblyRT/88INSU1OVkZHhsOypp566m00CAADkGKcD0PLly9WmTRudPHky0zKbzab09HSXFAYAAOAuTs8B6tGjh5o3b67k5GRlZGQ4fAg/AAAgN3A6AB0/flx9+/ZVRESEO+oBAABwO6cD0HPPPac1a9a4oRQAAICc4fRdYJcuXVLz5s1VsGBBVapUSb6+vg7Le/bs6dICPYG7wAAAyH3cehfYggULtHLlSgUEBGjNmjWy2Wz2ZTab7b4IQAAA4P7mdAAaPHiwRo4cqYEDB8rLy+kraAAAAB7ndIK5du2aXnjhBcIPAADItZxOMQkJCVq0aJE7agEAAMgRTl8CS09P1/jx47VixQpVrlw50yToiRMnuqw4AAAAd3A6AP3444+qUqWKJGnnzp0Oy/44IRoAAOBe5XQA+vrrr91RBwAAQI5hJjMAALCcbJ0BevbZZzV37lwFBwfr2WefvW3fJUuWuKQwAAAAd8lWAAoJCbHP7wkJCXFrQQAAAO6W7VdhjBo1Sq+88oqCgoLcXZPH8SoMAAByH2d+v7M9B2jkyJG6cOHCXy4OAADA07IdgJx8ZyoAAMA9y6m7wHjODwAAuB849RygsmXL3jEEnT59+i8VBAAA4G5OBaCRI0dyFxgAAMj1nApALVq0UHh4uLtqAQAAyBHZngPE/B8AAHC/4C4wAABgOdm+BJaRkeHOOgAAAHIML0MFAACWQwACAACW4/EANG3aNBUvXlwBAQGqWbOmNm/efMu+169f16hRo1SqVCkFBAQoNjZWy5cvd+iTnp6uoUOHqkSJEgoMDFSpUqU0evRo5jABAAA7jwagRYsWqW/fvho+fLi2bdum2NhYxcfHKzU1Ncv+Q4YM0cyZMzVlyhTt2rVLXbt21TPPPKPt27fb+4wbN07Tp0/X1KlTtXv3bo0bN07jx4/XlClTcuqwAADAPS7bb4N3h5o1a6pGjRqaOnWqpN8nWhctWlQ9evTQwIEDM/WPiorS4MGD1a1bN3tbs2bNFBgYqA8++ECS9OSTTyoiIkLvvffeLfvcCW+DBwAg93HL2+Bd7dq1a9q6dasaNWr0v2K8vNSoUSNt3Lgxy3WuXr2qgIAAh7bAwEBt2LDB/r1WrVpKTEzU3r17JUk7duzQhg0b9Pjjj9+ylqtXryotLc3hAwAA7l9OPQnalU6ePKn09HRFREQ4tEdEROjnn3/Ocp34+HhNnDhRdevWValSpZSYmKglS5YoPT3d3mfgwIFKS0tTTEyMvL29lZ6erjFjxqhVq1a3rGXs2LEaOXKkaw4MAADc8zw+CdoZkydPVpkyZRQTEyM/Pz91795d7dq1k5fX/w5j8eLFmj9/vj788ENt27ZN8+bN0//93/9p3rx5t9zuoEGDdO7cOfvnyJEjOXE4AADAQzx2BqhAgQLy9vbW8ePHHdqPHz+uyMjILNcpWLCgli5dqitXrujUqVOKiorSwIEDVbJkSXuffv36aeDAgWrRooUkqVKlSjp06JDGjh2rhISELLfr7+8vf39/Fx0ZAAC413nsDJCfn5+qVaumxMREe1tGRoYSExMVFxd323UDAgJUuHBh3bhxQ//973/19NNP25ddunTJ4YyQJHl7e/MkawAAYOexM0CS1LdvXyUkJKh69ep6+OGHNWnSJF28eFHt2rWTJLVp00aFCxfW2LFjJUmbNm3S0aNH9dBDD+no0aMaMWKEMjIy1L9/f/s2mzRpojFjxqhYsWKqUKGCtm/frokTJ6p9+/YeOUYAAHDv8WgAeuGFF3TixAkNGzZMKSkpeuihh7R8+XL7xOjDhw87nM25cuWKhgwZov379ytv3rx64okn9O9//1uhoaH2PlOmTNHQoUP18ssvKzU1VVFRUerSpYuGDRuW04cHAADuUR59DtC9iucAAQCQ++SK5wABAAB4CgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYjscD0LRp01S8eHEFBASoZs2a2rx58y37Xr9+XaNGjVKpUqUUEBCg2NhYLV++PFO/o0eP6qWXXtIDDzygwMBAVapUSVu2bHHnYQAAgFzEowFo0aJF6tu3r4YPH65t27YpNjZW8fHxSk1NzbL/kCFDNHPmTE2ZMkW7du1S165d9cwzz2j79u32PmfOnFHt2rXl6+urL7/8Urt27dKECROUP3/+nDosAABwj7MZY4yndl6zZk3VqFFDU6dOlSRlZGSoaNGi6tGjhwYOHJipf1RUlAYPHqxu3brZ25o1a6bAwEB98MEHkqSBAwfqm2++0fr16++6rrS0NIWEhOjcuXMKDg6+6+0AAICc48zvt8fOAF27dk1bt25Vo0aN/leMl5caNWqkjRs3ZrnO1atXFRAQ4NAWGBioDRs22L9/+umnql69upo3b67w8HBVqVJF77777m1ruXr1qtLS0hw+AADg/uWxAHTy5Emlp6crIiLCoT0iIkIpKSlZrhMfH6+JEydq3759ysjI0KpVq7RkyRIlJyfb++zfv1/Tp09XmTJltGLFCv3zn/9Uz549NW/evFvWMnbsWIWEhNg/RYsWdc1BAgCAe5LHJ0E7Y/LkySpTpoxiYmLk5+en7t27q127dvLy+t9hZGRkqGrVqnr99ddVpUoVde7cWZ06ddKMGTNuud1Bgwbp3Llz9s+RI0dy4nAAAICHeCwAFShQQN7e3jp+/LhD+/HjxxUZGZnlOgULFtTSpUt18eJFHTp0SD///LPy5s2rkiVL2vsUKlRI5cuXd1ivXLlyOnz48C1r8ff3V3BwsMMHAADcvzwWgPz8/FStWjUlJiba2zIyMpSYmKi4uLjbrhsQEKDChQvrxo0b+u9//6unn37avqx27dras2ePQ/+9e/cqOjratQcAAAByLR9P7rxv375KSEhQ9erV9fDDD2vSpEm6ePGi2rVrJ0lq06aNChcurLFjx0qSNm3apKNHj+qhhx7S0aNHNWLECGVkZKh///72bfbp00e1atXS66+/rueff16bN2/WrFmzNGvWLI8cIwAAuPd4NAC98MILOnHihIYNG6aUlBQ99NBDWr58uX1i9OHDhx3m91y5ckVDhgzR/v37lTdvXj3xxBP697//rdDQUHufGjVq6OOPP9agQYM0atQolShRQpMmTVKrVq1y+vAAAMA9yqPPAbpX8RwgAAByn1zxHCAAAABPIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADL8fF0AfciY4wkKS0tzcOVAACA7Lr5u33zd/x2CEBZOH/+vCSpaNGiHq4EAAA46/z58woJCbltH5vJTkyymIyMDB07dkz58uWTzWa76+2kpaWpaNGiOnLkiIKDg11YIf6Msc45jHXOYaxzFuOdc9w11sYYnT9/XlFRUfLyuv0sH84AZcHLy0tFihRx2faCg4P5jymHMNY5h7HOOYx1zmK8c447xvpOZ35uYhI0AACwHAIQAACwHAKQG/n7+2v48OHy9/f3dCn3PcY65zDWOYexzlmMd865F8aaSdAAAMByOAMEAAAshwAEAAAshwAEAAAshwAEAAAshwDkRtOmTVPx4sUVEBCgmjVravPmzZ4uKVcbO3asatSooXz58ik8PFxNmzbVnj17HPpcuXJF3bp10wMPPKC8efOqWbNmOn78uIcqvn+88cYbstls6t27t72NsXato0eP6qWXXtIDDzygwMBAVapUSVu2bLEvN8Zo2LBhKlSokAIDA9WoUSPt27fPgxXnTunp6Ro6dKhKlCihwMBAlSpVSqNHj3Z4dxRjfXfWrVunJk2aKCoqSjabTUuXLnVYnp1xPX36tFq1aqXg4GCFhoaqQ4cOunDhglvqJQC5yaJFi9S3b18NHz5c27ZtU2xsrOLj45Wamurp0nKttWvXqlu3bvruu++0atUqXb9+Xf/4xz908eJFe58+ffros88+00cffaS1a9fq2LFjevbZZz1Yde73/fffa+bMmapcubJDO2PtOmfOnFHt2rXl6+urL7/8Urt27dKECROUP39+e5/x48fr7bff1owZM7Rp0yblyZNH8fHxunLligcrz33GjRun6dOna+rUqdq9e7fGjRun8ePHa8qUKfY+jPXduXjxomJjYzVt2rQsl2dnXFu1aqWffvpJq1at0rJly7Ru3Tp17tzZPQUbuMXDDz9sunXrZv+enp5uoqKizNixYz1Y1f0lNTXVSDJr1641xhhz9uxZ4+vraz766CN7n927dxtJZuPGjZ4qM1c7f/68KVOmjFm1apWpV6+e6dWrlzGGsXa1AQMGmDp16txyeUZGhomMjDRvvvmmve3s2bPG39/fLFiwICdKvG80btzYtG/f3qHt2WefNa1atTLGMNauIsl8/PHH9u/ZGdddu3YZSeb777+39/nyyy+NzWYzR48edXmNnAFyg2vXrmnr1q1q1KiRvc3Ly0uNGjXSxo0bPVjZ/eXcuXOSpLCwMEnS1q1bdf36dYdxj4mJUbFixRj3u9StWzc1btzYYUwlxtrVPv30U1WvXl3NmzdXeHi4qlSponfffde+/MCBA0pJSXEY75CQENWsWZPxdlKtWrWUmJiovXv3SpJ27NihDRs26PHHH5fEWLtLdsZ148aNCg0NVfXq1e19GjVqJC8vL23atMnlNfEyVDc4efKk0tPTFRER4dAeERGhn3/+2UNV3V8yMjLUu3dv1a5dWxUrVpQkpaSkyM/PT6GhoQ59IyIilJKS4oEqc7eFCxdq27Zt+v777zMtY6xda//+/Zo+fbr69u2rV199Vd9//7169uwpPz8/JSQk2Mc0q79TGG/nDBw4UGlpaYqJiZG3t7fS09M1ZswYtWrVSpIYazfJzrimpKQoPDzcYbmPj4/CwsLcMvYEIORK3bp1086dO7VhwwZPl3JfOnLkiHr16qVVq1YpICDA0+Xc9zIyMlS9enW9/vrrkqQqVapo586dmjFjhhISEjxc3f1l8eLFmj9/vj788ENVqFBBSUlJ6t27t6Kiohhri+ESmBsUKFBA3t7eme6IOX78uCIjIz1U1f2je/fuWrZsmb7++msVKVLE3h4ZGalr167p7NmzDv0Zd+dt3bpVqampqlq1qnx8fOTj46O1a9fq7bfflo+PjyIiIhhrFypUqJDKly/v0FauXDkdPnxYkuxjyt8pf12/fv00cOBAtWjRQpUqVVLr1q3Vp08fjR07VhJj7S7ZGdfIyMhMNwrduHFDp0+fdsvYE4DcwM/PT9WqVVNiYqK9LSMjQ4mJiYqLi/NgZbmbMUbdu3fXxx9/rNWrV6tEiRIOy6tVqyZfX1+Hcd+zZ48OHz7MuDupYcOG+vHHH5WUlGT/VK9eXa1atbL/M2PtOrVr1870SIe9e/cqOjpaklSiRAlFRkY6jHdaWpo2bdrEeDvp0qVL8vJy/Onz9vZWRkaGJMbaXbIzrnFxcTp79qy2bt1q77N69WplZGSoZs2ari/K5dOqYYwxZuHChcbf39/MnTvX7Nq1y3Tu3NmEhoaalJQUT5eWa/3zn/80ISEhZs2aNSY5Odn+uXTpkr1P165dTbFixczq1avNli1bTFxcnImLi/Ng1fePP94FZgxj7UqbN282Pj4+ZsyYMWbfvn1m/vz5JigoyHzwwQf2Pm+88YYJDQ01n3zyifnhhx/M008/bUqUKGEuX77swcpzn4SEBFO4cGGzbNkyc+DAAbNkyRJToEAB079/f3sfxvrunD9/3mzfvt1s377dSDITJ04027dvN4cOHTLGZG9cH3vsMVOlShWzadMms2HDBlOmTBnTsmVLt9RLAHKjKVOmmGLFihk/Pz/z8MMPm++++87TJeVqkrL8zJkzx97n8uXL5uWXXzb58+c3QUFB5plnnjHJycmeK/o+8ucAxFi71meffWYqVqxo/P39TUxMjJk1a5bD8oyMDDN06FATERFh/P39TcOGDc2ePXs8VG3ulZaWZnr16mWKFStmAgICTMmSJc3gwYPN1atX7X0Y67vz9ddfZ/l3dEJCgjEme+N66tQp07JlS5M3b14THBxs2rVrZ86fP++Wem3G/OHxlwAAABbAHCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAcFL9+vXVu3dvT5cB4C8gAAHIUW3btlXTpk1zfL9z585VaGjoHfulp6frjTfeUExMjAIDAxUWFqaaNWtq9uzZ9j5LlizR6NGj3VgtAHfz8XQBAHAvGTlypGbOnKmpU6eqevXqSktL05YtW3TmzBl7n7CwMA9WCMAVOAMEwKPq16+vnj17qn///goLC1NkZKRGjBjh0Mdms2n69Ol6/PHHFRgYqJIlS+o///mPffmaNWtks9l09uxZe1tSUpJsNpsOHjyoNWvWqF27djp37pxsNptsNlumfdz06aef6uWXX1bz5s1VokQJxcbGqkOHDnrllVccar55Cezmvv/8adu2rb3/J598oqpVqyogIEAlS5bUyJEjdePGjb86dAD+AgIQAI+bN2+e8uTJo02bNmn8+PEaNWqUVq1a5dBn6NChatasmXbs2KFWrVqpRYsW2r17d7a2X6tWLU2aNEnBwcFKTk5WcnKyQ6D5o8jISK1evVonTpzI9rZvbjM5OVmrV69WQECA6tatK0lav3692rRpo169emnXrl2aOXOm5s6dqzFjxmRr+wDcgwAEwOMqV66s4cOHq0yZMmrTpo2qV6+uxMREhz7NmzdXx44dVbZsWY0ePVrVq1fXlClTsrV9Pz8/hYSEyGazKTIyUpGRkcqbN2+WfSdOnKgTJ04oMjJSlStXVteuXfXll1/edts3t+nr66uOHTuqffv2at++vaTfL6kNHDhQCQkJKlmypB599FGNHj1aM2fOzOboAHAHAhAAj6tcubLD90KFCik1NdWhLS4uLtP37J4Bckb58uW1c+dOfffdd2rfvr1SU1PVpEkTdezY8bbrXb9+Xc2aNVN0dLQmT55sb9+xY4dGjRqlvHnz2j+dOnVScnKyLl265PL6AWQPk6ABeJyvr6/Dd5vNpoyMjGyv7+X1+//LGWPsbdevX7/rery8vFSjRg3VqFFDvXv31gcffKDWrVtr8ODBKlGiRJbr/POf/9SRI0e0efNm+fj876/WCxcuaOTIkXr22WczrRMQEHDXNQL4awhAAHKF7777Tm3atHH4XqVKFUlSwYIFJUnJycnKnz+/pN8nQf+Rn5+f0tPT72rf5cuXlyRdvHgxy+UTJ07U4sWL9e233+qBBx5wWFa1alXt2bNHpUuXvqt9A3APAhCAXOGjjz5S9erVVadOHc2fP1+bN2/We++9J0kqXbq0ihYtqhEjRmjMmDHau3evJkyY4LB+8eLFdeHCBSUmJio2NlZBQUEKCgrKtJ/nnntOtWvXVq1atRQZGakDBw5o0KBBKlu2rGJiYjL1/+qrr9S/f39NmzZNBQoUUEpKiiQpMDBQISEhGjZsmJ588kkVK1ZMzz33nLy8vLRjxw7t3LlTr732mhtGCkB2MAcIQK4wcuRILVy4UJUrV9b777+vBQsW2M/M+Pr6asGCBfr5559VuXJljRs3LlO4qFWrlrp27aoXXnhBBQsW1Pjx47PcT3x8vD777DM1adJEZcuWVUJCgmJiYrRy5UqHS1s3bdiwQenp6eratasKFSpk//Tq1cu+vWXLlmnlypWqUaOG/va3v+mtt95SdHS0i0cIgDNs5o8XzQHgHmSz2fTxxx975AnSAO5PnAECAACWQwACAACWwyRoAPc8rtQDcDXOAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMv5/wB/EcuvtqNNuAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example"
      ],
      "metadata": {
        "id": "zRQWlrc5CrEg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def constant_algo(items):\n",
        "    return items[0]\n"
      ],
      "metadata": {
        "id": "8YuW3j30CqO-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Linear Time Complexity **O(n)**"
      ],
      "metadata": {
        "id": "nnv65-MNCxiG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "An algorithm has linear time complexity (denoted as O(n)) if the time taken for the algorithm to run is directly proportional to the size of the input.\n"
      ],
      "metadata": {
        "id": "mZZUo9OPElKB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "n = np.linspace(1, 100, 100)\n",
        "y = n\n",
        "\n",
        "plt.plot(n, y, label=\"O(n)\")\n",
        "plt.xlabel('Input Size')\n",
        "plt.ylabel('Time Complexity')\n",
        "plt.title('Linear Time Complexity O(n)')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "cellView": "form",
        "id": "H-MblNiuHHV5",
        "outputId": "1ec1fdc5-7bb3-49f6-c07d-618fca06474f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example"
      ],
      "metadata": {
        "id": "I8MYImn7HINQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def linear_algo(items):\n",
        "    for item in items:\n",
        "        print(item)"
      ],
      "metadata": {
        "id": "af9EATH1DTA-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the **linear_algo** example, the function iterates through each element in the list items and prints the item. The time taken to complete this operation increases linearly with the number of elements in the list"
      ],
      "metadata": {
        "id": "rnQUgg4gEpg-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Quadratic Time Complexity **O(n^2)**"
      ],
      "metadata": {
        "id": "xV75io_vExEA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "An algorithm is said to have quadratic time complexity (denoted as O(n^2)) if the time taken by the algorithm is proportional to the square of the size of the input."
      ],
      "metadata": {
        "id": "vh6TfKKuE2qw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "n = np.linspace(1, 10, 100)\n",
        "y = n**2\n",
        "\n",
        "plt.plot(n, y, label=\"O(n^2)\")\n",
        "plt.xlabel('Input Size')\n",
        "plt.ylabel('Time Complexity')\n",
        "plt.title('Quadratic Time Complexity O(n^2)')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "cellView": "form",
        "id": "yBJvujtcF4Jo",
        "outputId": "7cbcce40-24fd-45e5-a8c0-6d2b53d6e7e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example"
      ],
      "metadata": {
        "id": "uo9tgMgTHThF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def quadratic_algo(items):\n",
        "    for item1 in items:\n",
        "        for item2 in items:\n",
        "            print(item1, item2)\n"
      ],
      "metadata": {
        "id": "-uZU1q2pHXbT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the **quadratic_algo** function, for each element in the list items, there is another nested loop that iterates through each element in the list again. As a result, the function has a quadratic time complexity as the time taken increases quadratically with the number of elements in the list."
      ],
      "metadata": {
        "id": "b5Dny1tlF19K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Logarithmic Time Complexity **O(log n)**:"
      ],
      "metadata": {
        "id": "Wn7BeJaYF8Mv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "An algorithm is said to have logarithmic time complexity (denoted as O(log n)) if the time taken to complete the algorithm increases logarithmically with the size of the input."
      ],
      "metadata": {
        "id": "xGTYzK8EF-6p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "n = np.linspace(1, 100, 100)\n",
        "y = np.log2(n)\n",
        "\n",
        "plt.plot(n, y, label=\"O(log n)\")\n",
        "plt.xlabel('Input Size')\n",
        "plt.ylabel('Time Complexity')\n",
        "plt.title('Logarithmic Time Complexity O(log n)')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "cellView": "form",
        "id": "p8L-9fbuHqDB",
        "outputId": "a424878d-2bbe-4c73-846e-6f503896fda2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example"
      ],
      "metadata": {
        "id": "ZBpzPkcrHq2l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def logarithmic_algo(n):\n",
        "    i = 1\n",
        "    while i < n:\n",
        "        print(i)\n",
        "        i = i * 2\n"
      ],
      "metadata": {
        "id": "QdqKlf4TGB9G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the **logarithmic_algo** function, the value of **i** is multiplied by `2` in each iteration of the while loop. This results in the loop running for a logarithmic number of times based on the input `n`. The time complexity remains relatively low even as the input size increases significantly."
      ],
      "metadata": {
        "id": "cTOOpWj0GCHY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Measure Time Complexity\n",
        "Now let's time these functions using the `time` module in Python"
      ],
      "metadata": {
        "id": "CQdfRHmBGD4C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "# Function to measure the execution time of a given function\n",
        "def measure_time(func, *args):\n",
        "    start_time = time.time()\n",
        "    func(*args)\n",
        "    end_time = time.time()\n",
        "    return end_time - start_time\n",
        "\n",
        "# Usage Example:\n",
        "items = [1, 2, 3, 4, 5]\n",
        "print(\"Constant Time:\", measure_time(constant_algo, items))\n",
        "print(\"Linear Time:\", measure_time(linear_algo, items))\n",
        "print(\"Quadratic Time:\", measure_time(quadratic_algo, items))\n",
        "\n",
        "logarithmic_algo(100)  # Printing the output of logarithmic_algo function for demonstration\n"
      ],
      "metadata": {
        "id": "45KL7MqKGRm-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Conclusion"
      ],
      "metadata": {
        "id": "R0v-pN_NIQH5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Understanding time complexity is crucial for designing efficient algorithms. It helps in identifying potential bottlenecks and improving the performance of the algorithms.\n",
        "\n",
        "Make sure to test your algorithms with different input sizes to get a clearer understanding of their time complexities."
      ],
      "metadata": {
        "id": "kGxV7oNmISRm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Constant Time Complexity O(1)\n",
        "n1 = np.linspace(1, 100, 100)\n",
        "y1 = np.ones_like(n1)\n",
        "\n",
        "# Linear Time Complexity O(n)\n",
        "n2 = np.linspace(1, 100, 100)\n",
        "y2 = n2\n",
        "\n",
        "# Quadratic Time Complexity O(n^2)\n",
        "n3 = np.linspace(1, 10, 100)\n",
        "y3 = n3**2\n",
        "\n",
        "# Logarithmic Time Complexity O(log n)\n",
        "n4 = np.linspace(1, 100, 100)\n",
        "y4 = np.log2(n4)\n",
        "\n",
        "# Plotting all the time complexities\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plt.plot(n1, y1, label=\"O(1)\")\n",
        "plt.plot(n2, y2, label=\"O(n)\")\n",
        "plt.plot(n3, y3, label=\"O(n^2)\")\n",
        "plt.plot(n4, y4, label=\"O(log n)\")\n",
        "\n",
        "plt.xlabel('Input Size')\n",
        "plt.ylabel('Time Complexity')\n",
        "plt.title('Time Complexity Analysis')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "XXuZuFGWKxNx",
        "outputId": "34d9d450-fdde-442e-c9b4-852f1590c83c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}