├── .gitignore ├── README.md ├── BFS.ipynb ├── DoubleLinkedListSearch.ipynb ├── Outline.txt ├── DoubleLinkedListSearch-InClass.ipynb ├── BFS-InClass.ipynb └── Computational-Complexity.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints/ 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PDL-Interview 2 | 3 | https://docs.google.com/presentation/d/1Qe7Ro9LZe91HCSRNxmEH5wRAlV7unSDiDZ9BrQvPv4g/ 4 | -------------------------------------------------------------------------------- /BFS.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 5, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/plain": [ 11 | "Node(5)" 12 | ] 13 | }, 14 | "execution_count": 5, 15 | "metadata": {}, 16 | "output_type": "execute_result" 17 | } 18 | ], 19 | "source": [ 20 | "class Node:\n", 21 | " def __init__(self, value, left=None, right=None):\n", 22 | " self.value = value\n", 23 | " self.left = left\n", 24 | " self.right = right\n", 25 | " \n", 26 | " def __repr__(self):\n", 27 | " return f'Node({self.value})'\n", 28 | "\n", 29 | "# 5\n", 30 | "# 3 10\n", 31 | "# 1 4 7 15\n", 32 | "\n", 33 | "# We want: 5, 3, 10, 1, 4, 7, 15\n", 34 | "\n", 35 | "root = Node(\n", 36 | " 5,\n", 37 | " left=Node(\n", 38 | " 3, \n", 39 | " left=Node(1), \n", 40 | " right=Node(4)\n", 41 | " ),\n", 42 | " right=Node(\n", 43 | " 10, \n", 44 | " left=Node(7), \n", 45 | " right=Node(15)\n", 46 | " )\n", 47 | ")\n", 48 | "\n", 49 | "root" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 6, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [ 58 | "def breadth_first_search_print(root) -> list:\n", 59 | " queue = [root]\n", 60 | " return_list = []\n", 61 | " while queue:\n", 62 | " node = queue.pop(0)\n", 63 | "\n", 64 | " if node.left:\n", 65 | " queue.append(node.left)\n", 66 | " \n", 67 | " if node.right:\n", 68 | " queue.append(node.right)\n", 69 | " \n", 70 | " return_list.append(node)\n", 71 | " \n", 72 | " return return_list" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 7, 78 | "metadata": {}, 79 | "outputs": [ 80 | { 81 | "data": { 82 | "text/plain": [ 83 | "[Node(5), Node(3), Node(10), Node(1), Node(4), Node(7), Node(15)]" 84 | ] 85 | }, 86 | "execution_count": 7, 87 | "metadata": {}, 88 | "output_type": "execute_result" 89 | } 90 | ], 91 | "source": [ 92 | "breadth_first_search_print(root)" 93 | ] 94 | } 95 | ], 96 | "metadata": { 97 | "kernelspec": { 98 | "display_name": "Python 3 (ipykernel)", 99 | "language": "python", 100 | "name": "python3" 101 | }, 102 | "language_info": { 103 | "codemirror_mode": { 104 | "name": "ipython", 105 | "version": 3 106 | }, 107 | "file_extension": ".py", 108 | "mimetype": "text/x-python", 109 | "name": "python", 110 | "nbconvert_exporter": "python", 111 | "pygments_lexer": "ipython3", 112 | "version": "3.11.4" 113 | } 114 | }, 115 | "nbformat": 4, 116 | "nbformat_minor": 4 117 | } 118 | -------------------------------------------------------------------------------- /DoubleLinkedListSearch.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "alert-liquid", 7 | "metadata": {}, 8 | "outputs": [ 9 | { 10 | "name": "stdout", 11 | "output_type": "stream", 12 | "text": [ 13 | "Node(5) <-> Node(10) <-> Node(1) <-> Node(7) <-> Node(8) <-> Node(2)\n", 14 | "head: Node(5)\n", 15 | "tail: Node(2)\n" 16 | ] 17 | } 18 | ], 19 | "source": [ 20 | "class Node:\n", 21 | " def __init__(self, value, before=None, after=None):\n", 22 | " self.value = value\n", 23 | " self.before = before\n", 24 | " self.after = after\n", 25 | " \n", 26 | " def __repr__(self):\n", 27 | " return f'Node({self.value})'\n", 28 | " \n", 29 | "\n", 30 | "class DoubleLinkedList:\n", 31 | " def __init__(self):\n", 32 | " self.head = None\n", 33 | " self.tail = None\n", 34 | " \n", 35 | " @property\n", 36 | " def nodes(self):\n", 37 | " node_list = []\n", 38 | " current_node = self.head\n", 39 | " tail = None\n", 40 | " while current_node:\n", 41 | " node_list.append(current_node)\n", 42 | " current_node = current_node.after\n", 43 | " if current_node:\n", 44 | " tail = current_node\n", 45 | " return node_list\n", 46 | " \n", 47 | " def __repr__(self):\n", 48 | " return ' <-> '.join(repr(n) for n in self.nodes)\n", 49 | " \n", 50 | " def append(self, value):\n", 51 | " if len(self.nodes) == 0:\n", 52 | " self.head = Node(value)\n", 53 | " self.tail = self.head\n", 54 | " else:\n", 55 | " self.tail.after = Node(value, before=self.tail)\n", 56 | " self.tail = self.tail.after\n", 57 | "\n", 58 | "\n", 59 | "dll = DoubleLinkedList()\n", 60 | "dll.append(5)\n", 61 | "dll.append(10)\n", 62 | "dll.append(1)\n", 63 | "dll.append(7)\n", 64 | "dll.append(8)\n", 65 | "dll.append(2)\n", 66 | "\n", 67 | "print(dll)\n", 68 | "print('head:', dll.head)\n", 69 | "print('tail:', dll.tail)" 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": 5, 75 | "id": "satellite-disclosure", 76 | "metadata": {}, 77 | "outputs": [ 78 | { 79 | "data": { 80 | "text/plain": [ 81 | "[Node(1), Node(7)]" 82 | ] 83 | }, 84 | "execution_count": 5, 85 | "metadata": {}, 86 | "output_type": "execute_result" 87 | } 88 | ], 89 | "source": [ 90 | "def get_middle_nodes(dll: DoubleLinkedList) -> []:\n", 91 | " p1 = dll.head\n", 92 | " p2 = dll.tail\n", 93 | "\n", 94 | " while p1 and p2:\n", 95 | " if p1 == p2:\n", 96 | " return [p1]\n", 97 | " elif p1.after == p2:\n", 98 | " return [p1, p2]\n", 99 | "\n", 100 | " else:\n", 101 | " p1 = p1.after\n", 102 | " p2 = p2.before\n", 103 | "\n", 104 | "get_middle_nodes(dll)" 105 | ] 106 | } 107 | ], 108 | "metadata": { 109 | "kernelspec": { 110 | "display_name": "Python 3 (ipykernel)", 111 | "language": "python", 112 | "name": "python3" 113 | }, 114 | "language_info": { 115 | "codemirror_mode": { 116 | "name": "ipython", 117 | "version": 3 118 | }, 119 | "file_extension": ".py", 120 | "mimetype": "text/x-python", 121 | "name": "python", 122 | "nbconvert_exporter": "python", 123 | "pygments_lexer": "ipython3", 124 | "version": "3.11.4" 125 | } 126 | }, 127 | "nbformat": 4, 128 | "nbformat_minor": 5 129 | } 130 | -------------------------------------------------------------------------------- /Outline.txt: -------------------------------------------------------------------------------- 1 | - Hellos 2 | - What I've been asked to teach: How to do a tech interview 3 | - Lots of books out there but the industry has leaned towards "Cracking the Coding Interview" by GAYLE LAAKMANN MCDOWELL as the defacto best practice guide. 4 | 5 | - Lots of different sizes of companies and styles of interviewing 6 | - I'm going to focus on comparing the stereo typical large company and start up company interviews 7 | 8 | The flows: 9 | 10 | LARGE 11 | - Phone interview 12 | - a week or two 13 | - coding interview 14 | - a week or two 15 | - manager interview 16 | - potentially repeat for several interviewers 17 | - offer. 18 | 19 | SMALL 20 | - In person meeting 21 | - coding challenge 22 | - Personality check 23 | - offer 24 | 25 | When in doubt about the flow, kindly ask. 26 | 27 | 28 | How to get the first interview: 29 | 30 | - Build projects 31 | - Classes 32 | - Interships 33 | - Your own free time (GitHub) 34 | - Put yourself out there 35 | - Publish on the internet (Medium, etc) 36 | - Give talks or lightning talks at conferences 37 | 38 | RESUME 39 | - Get help with the Resume (simple and to the point is better than anything else in my opinion). 40 | - Put only the bigger milestones to avoid noise 41 | 42 | THE CODING INTERVIEW 43 | 44 | - Show up on time, even a bit early is best. 45 | - For me, one of the best predictors of how easy it will be to work with any person is if they show up on time to an interview. 46 | - Do some light chit chat. Not fake but interested in the interviewer. 47 | - Get the coding challenge and before you start, draw the problem. 48 | - 30% of our compute power is in our occipital lobe. Don't waist it. 49 | - Ask lots of clarifying questions. 50 | - This shows you can probe for corner cases prior to starting the work. Good engineering. 51 | - Take a wack at it. Solve it in the first way that comes to mind. Usually this is a very inefficient solution. BUT! It's a starting place. Then refine, refine, refine. 52 | - Remove particularly inefficient code: Replace linked list if a map will do the same job better. 53 | - Try your algo with a small simple problem. 54 | - Try your algo with edge cases. 55 | - Gayle uses the accronym BUD (iterate by removing Bottelnecks, Unneeded work, and Duplicated work) 56 | 57 | - Talk through the problem. Don't just do it in your head. Same thing from school when they told you don't just write down the answer. Write down each step. This shows you thought process and it is what an interviewer wants. 58 | - An incomplete answer with great lead up is much better than a complete answer with no lead up. Plus you get partial credits :) 59 | - Break The Problem Down! 60 | - Each part of the problem can be broken down into multiple smaller chuncks. 61 | - Its smaller chunks that make up each tool in our eco system. 62 | 63 | Finally write code. Leave this for last so that you don't spend your time correcting coding mistakes and focus your time on algo performance. 64 | 65 | - Big companies work with big data. They need solutions that scale. 66 | - Thus they will want to make sure you understand Big-O concepts. 67 | - Particular language is not important because they will use that lang some where. They use them all. 68 | 69 | - Smaller teams / companies will focus more on specifics of a language and framework. 70 | - You should know the language and frameworks they are using by asking before the interview or during phone screen. 71 | - Usually you don't need to be a wiz in the language but need to be able to tackle a basic Big-O problem. 72 | - Ask if you can use your language of choice if its not clear which language you need to code in. 73 | - Usually a fishy sign if the company doesn't allow you to use your language of choice. If not, they are grading you 74 | on the "wrong" things. 75 | 76 | - Talk Big-O for a while. Do complexity lecture. 77 | 78 | - End of interview. Say your thanks and follow up with POC, but not the coding interviewer. 79 | They are usually engineers with just enough time to do the interview. 80 | If you have a contact for your interviewer, a simple "Thank you" email is fine in the following days but nothinig more and do not expect a response. 81 | 82 | Practice. Go on interviews. Do interview problems at home over your favorite beverage. Don't let your first interview be the frist time you see an interview like question. This will help you stay in tune with the demands of the market. -------------------------------------------------------------------------------- /DoubleLinkedListSearch-InClass.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "alert-liquid", 7 | "metadata": {}, 8 | "outputs": [ 9 | { 10 | "name": "stdout", 11 | "output_type": "stream", 12 | "text": [ 13 | "Node(5) <-> Node(10) <-> Node(1) <-> Node(7) <-> Node(8) <-> Node(2)\n", 14 | "head: Node(5)\n", 15 | "tail: Node(2)\n" 16 | ] 17 | } 18 | ], 19 | "source": [ 20 | "class Node:\n", 21 | " def __init__(self, value, before=None, after=None):\n", 22 | " self.value = value\n", 23 | " self.before = before\n", 24 | " self.after = after\n", 25 | " \n", 26 | " def __repr__(self):\n", 27 | " return f'Node({self.value})'\n", 28 | " \n", 29 | " def __eq__(self, other):\n", 30 | " return self.value == other.value\n", 31 | "\n", 32 | " \n", 33 | "\n", 34 | "class DoubleLinkedList:\n", 35 | " def __init__(self):\n", 36 | " self.head = None\n", 37 | " self.tail = None\n", 38 | " \n", 39 | " @property\n", 40 | " def nodes(self):\n", 41 | " node_list = []\n", 42 | " current_node = self.head\n", 43 | " tail = None\n", 44 | " while current_node:\n", 45 | " node_list.append(current_node)\n", 46 | " current_node = current_node.after\n", 47 | " if current_node:\n", 48 | " tail = current_node\n", 49 | " return node_list\n", 50 | " \n", 51 | " def __repr__(self):\n", 52 | " return ' <-> '.join(repr(n) for n in self.nodes)\n", 53 | " \n", 54 | " def append(self, value):\n", 55 | " if len(self.nodes) == 0:\n", 56 | " self.head = Node(value)\n", 57 | " self.tail = self.head\n", 58 | " else:\n", 59 | " self.tail.after = Node(value, before=self.tail)\n", 60 | " self.tail = self.tail.after\n", 61 | " \n", 62 | "\n", 63 | "\n", 64 | "dll = DoubleLinkedList()\n", 65 | "dll.append(5)\n", 66 | "dll.append(10)\n", 67 | "dll.append(1)\n", 68 | "dll.append(7)\n", 69 | "dll.append(8)\n", 70 | "dll.append(2)\n", 71 | "\n", 72 | "print(dll)\n", 73 | "print('head:', dll.head)\n", 74 | "print('tail:', dll.tail)" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 2, 80 | "id": "satellite-disclosure", 81 | "metadata": {}, 82 | "outputs": [], 83 | "source": [ 84 | "def get_middle_nodes(dll_) -> list:\n", 85 | " return []" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 3, 91 | "id": "281b0dec", 92 | "metadata": {}, 93 | "outputs": [ 94 | { 95 | "data": { 96 | "text/plain": [ 97 | "[]" 98 | ] 99 | }, 100 | "execution_count": 3, 101 | "metadata": {}, 102 | "output_type": "execute_result" 103 | } 104 | ], 105 | "source": [ 106 | "get_middle_nodes(dll)" 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "execution_count": 4, 112 | "id": "dece682d", 113 | "metadata": {}, 114 | "outputs": [ 115 | { 116 | "ename": "AssertionError", 117 | "evalue": "", 118 | "output_type": "error", 119 | "traceback": [ 120 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 121 | "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", 122 | "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m get_middle_nodes(dll) \u001b[38;5;241m==\u001b[39m [Node(\u001b[38;5;241m1\u001b[39m), Node(\u001b[38;5;241m7\u001b[39m)]\n", 123 | "\u001b[0;31mAssertionError\u001b[0m: " 124 | ] 125 | } 126 | ], 127 | "source": [ 128 | "assert get_middle_nodes(dll) == [Node(1), Node(7)]" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": 5, 134 | "id": "95132510", 135 | "metadata": {}, 136 | "outputs": [ 137 | { 138 | "name": "stdout", 139 | "output_type": "stream", 140 | "text": [ 141 | "Node(5) <-> Node(10) <-> Node(1) <-> Node(8) <-> Node(2)\n" 142 | ] 143 | }, 144 | { 145 | "data": { 146 | "text/plain": [ 147 | "[]" 148 | ] 149 | }, 150 | "execution_count": 5, 151 | "metadata": {}, 152 | "output_type": "execute_result" 153 | } 154 | ], 155 | "source": [ 156 | "dll = DoubleLinkedList()\n", 157 | "dll.append(5)\n", 158 | "dll.append(10)\n", 159 | "dll.append(1)\n", 160 | "dll.append(8)\n", 161 | "dll.append(2)\n", 162 | "print(dll)\n", 163 | "\n", 164 | "get_middle_nodes(dll)" 165 | ] 166 | } 167 | ], 168 | "metadata": { 169 | "kernelspec": { 170 | "display_name": "Python 3 (ipykernel)", 171 | "language": "python", 172 | "name": "python3" 173 | }, 174 | "language_info": { 175 | "codemirror_mode": { 176 | "name": "ipython", 177 | "version": 3 178 | }, 179 | "file_extension": ".py", 180 | "mimetype": "text/x-python", 181 | "name": "python", 182 | "nbconvert_exporter": "python", 183 | "pygments_lexer": "ipython3", 184 | "version": "3.11.4" 185 | } 186 | }, 187 | "nbformat": 4, 188 | "nbformat_minor": 5 189 | } 190 | -------------------------------------------------------------------------------- /BFS-InClass.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 4, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/plain": [ 11 | "5" 12 | ] 13 | }, 14 | "execution_count": 4, 15 | "metadata": {}, 16 | "output_type": "execute_result" 17 | } 18 | ], 19 | "source": [ 20 | "# 5\n", 21 | "# 3 10\n", 22 | "# 1 4 7 15\n", 23 | "\n", 24 | "\n", 25 | "# We want: [Node(5), 3, 10, 1, 4, 7, 15]\n", 26 | "\n", 27 | "\n", 28 | "class Node:\n", 29 | " def __init__(self, value, left=None, right=None):\n", 30 | " self.value = value\n", 31 | " self.left = left\n", 32 | " self.right = right\n", 33 | " \n", 34 | " def __repr__(self):\n", 35 | " return f'{self.value}'\n", 36 | "\n", 37 | "assert repr(Node(4)) == '4'\n", 38 | "\n", 39 | "root = Node(\n", 40 | " 5,\n", 41 | " left=Node(\n", 42 | " 3,\n", 43 | " left=Node(1), \n", 44 | " right=Node(4),\n", 45 | " ),\n", 46 | " right=Node(\n", 47 | " 10, \n", 48 | " left=Node(7), \n", 49 | " right=Node(15),\n", 50 | " )\n", 51 | ")\n", 52 | "\n", 53 | "root" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": null, 59 | "metadata": {}, 60 | "outputs": [], 61 | "source": [ 62 | "# What should the output be? Printed? Returned?\n", 63 | "# What time complexity are you looking for?\n", 64 | "# What optimizations should we employ?\n", 65 | "# What are the constraints? \n", 66 | "# How should null cases be handled? Error, or just return None?\n", 67 | "# Networkx (3rd-party library), ABC, bisect (bin search), can use? \n", 68 | "\n", 69 | "\n", 70 | "# INTERVIEWER\n", 71 | "# Can you walk me through the code that you want write?\n", 72 | "\n", 73 | "# Interviewee\n", 74 | "# " 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 27, 80 | "metadata": {}, 81 | "outputs": [], 82 | "source": [ 83 | "def nodes_by_layer(root) -> list:\n", 84 | " result = []\n", 85 | "\n", 86 | " if root is None:\n", 87 | " return []\n", 88 | "\n", 89 | " queue = [root]\n", 90 | "\n", 91 | " while queue:\n", 92 | " item = queue.pop(0)\n", 93 | " \n", 94 | " # print(item)\n", 95 | "\n", 96 | " if item.left:\n", 97 | " queue.append(item.left)\n", 98 | "\n", 99 | " if item.right:\n", 100 | " queue.append(item.right)\n", 101 | "\n", 102 | " result.append(item)\n", 103 | " \n", 104 | " return result" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 34, 110 | "metadata": {}, 111 | "outputs": [], 112 | "source": [ 113 | "ret_val = nodes_by_layer(root)\n", 114 | "assert [n.value for n in ret_val] == [5, 3, 10, 1, 4, 7, 15], ret_val\n", 115 | "\n", 116 | "assert nodes_by_layer(None) == []\n", 117 | "\n", 118 | "ret_val = nodes_by_layer(Node(75))\n", 119 | "assert [n.value for n in ret_val] == [75]" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 29, 125 | "metadata": {}, 126 | "outputs": [ 127 | { 128 | "data": { 129 | "text/plain": [ 130 | "[5, 3, 10, 1, 4, 7, 15]" 131 | ] 132 | }, 133 | "execution_count": 29, 134 | "metadata": {}, 135 | "output_type": "execute_result" 136 | } 137 | ], 138 | "source": [ 139 | "nodes_by_layer(root)" 140 | ] 141 | }, 142 | { 143 | "cell_type": "code", 144 | "execution_count": null, 145 | "metadata": {}, 146 | "outputs": [], 147 | "source": [] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 10, 152 | "metadata": {}, 153 | "outputs": [ 154 | { 155 | "ename": "KeyboardInterrupt", 156 | "evalue": "", 157 | "output_type": "error", 158 | "traceback": [ 159 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 160 | "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", 161 | "Cell \u001b[0;32mIn[10], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, item \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(data):\n\u001b[0;32m----> 4\u001b[0m data\u001b[38;5;241m.\u001b[39mappend(i)\n", 162 | "\u001b[0;31mKeyboardInterrupt\u001b[0m: " 163 | ] 164 | } 165 | ], 166 | "source": [ 167 | "data = [1]\n", 168 | "\n", 169 | "for i, item in enumerate(data):\n", 170 | " data.append(i)" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": null, 176 | "metadata": {}, 177 | "outputs": [], 178 | "source": [] 179 | } 180 | ], 181 | "metadata": { 182 | "kernelspec": { 183 | "display_name": "Python 3 (ipykernel)", 184 | "language": "python", 185 | "name": "python3" 186 | }, 187 | "language_info": { 188 | "codemirror_mode": { 189 | "name": "ipython", 190 | "version": 3 191 | }, 192 | "file_extension": ".py", 193 | "mimetype": "text/x-python", 194 | "name": "python", 195 | "nbconvert_exporter": "python", 196 | "pygments_lexer": "ipython3", 197 | "version": "3.11.4" 198 | } 199 | }, 200 | "nbformat": 4, 201 | "nbformat_minor": 4 202 | } 203 | -------------------------------------------------------------------------------- /Computational-Complexity.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Computational Complexity" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "How do we use less compute resources (CPU cycles and bytes in memory) to tackle bigger and bigger problems?\n", 15 | "\n", 16 | "What's an algorithm?\n", 17 | "\n", 18 | "- Series of steps to reach a goal\n", 19 | "- Examples:\n", 20 | " - Algorithm for brushing teeth?\n", 21 | " - Algorithm for ordering a meal?\n", 22 | " \n", 23 | "Many different algorithms exist for each one of these goals. Some will take more compute resources and some will take less. \n", 24 | "\n", 25 | "When we talk about Computational Complexity we usually want to talk about worst case scenarios. We want to know how will this algorithm perform in the worst case. We refer to this measure of computational requirements with a special notation known as Big-O or\n", 26 | "\n", 27 | " O()\n", 28 | " \n", 29 | "How much time will it take me to brush 2 billion teeth?\n", 30 | " \n", 31 | "In industry we are usually less interested in best case scenarios but when we are we refer to that with Big-Omega notation or\n", 32 | "\n", 33 | " Ω()\n", 34 | " \n", 35 | "Big-Ɵ (Big-Theta) is the notation used to describe the runtime of an algorithm if Big-O == Big-Ω. And that's the last time you'll ever hear about Big-Ɵ in your life. (It's extremely infrequently used).\n", 36 | "\n", 37 | "This Big-Something notation is purposefully vague and general. This way we can use it to describe the runtime requirements of all sorts of different problems." 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "To calculate the Big-O or Big-Ω for an algorithm, we need to dive a bit deeper. \n", 45 | "\n", 46 | "For Big-O, we need to think about what would cause an algorithm to take the most steps it possibly could. Are there some if conditional that lead to many more instructions? \n", 47 | "\n", 48 | "Big-Ω, we need to think about what would cause an algorithm to take the fewest steps it possibly could. Could a set of data in a particular order signal to the algorithm that no work is required?\n", 49 | "\n", 50 | "\n", 51 | "\n", 52 | "###### Examples\n", 53 | "\n", 54 | "What is the Big-O and Big-Ω for the following function:\n", 55 | "\n", 56 | "```python\n", 57 | "def return_true(data: list) -> bool:\n", 58 | " return True\n", 59 | "```\n", 60 | "\n", 61 | "Answer\n", 62 | "
\n", 63 | "- O(just return true) or O(1)\n", 64 | "
\n", 65 | "- Ω(just return true) or Ω(1)\n", 66 | "
\n", 67 | "\n", 68 | "```python\n", 69 | "def return_true_if_x_in_list(x: int, list_: list) -> bool:\n", 70 | " for item in list_:\n", 71 | " if x == item:\n", 72 | " return True\n", 73 | " return False\n", 74 | "```\n", 75 | "\n", 76 | "Answer\n", 77 | "
\n", 78 | "- O(number of items to check) or O(N)\n", 79 | "
\n", 80 | "- Ω(check first item and we find x) or Ω(1)\n", 81 | "
" 82 | ] 83 | }, 84 | { 85 | "cell_type": "markdown", 86 | "metadata": {}, 87 | "source": [ 88 | "So what if our Big-O function is...\n", 89 | "\n", 90 | " Big-O(N^2 + 2*N + 45)\n", 91 | " \n", 92 | "Well, we are only concerned with most drastic changes to the computation complexity as the data set grows, so we only need to care about the highest order term. \n", 93 | "\n", 94 | "For example, let's say we have the following three algorithms:\n", 95 | "\n", 96 | "- Big-O(N^2 + 2*N + 45)\n", 97 | "- Big-O(2N^2 + 2*N)\n", 98 | "- Big-O(N^2)\n", 99 | "\n", 100 | "How do these perform as the data grows?" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 1, 106 | "metadata": {}, 107 | "outputs": [ 108 | { 109 | "data": { 110 | "text/html": [ 111 | "
\n", 112 | "\n", 125 | "\n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | "
1101001000
n**2 + 2*n + 4548165102451002045
n**2 + 2*n3120102001002000
n**21100100001000000
\n", 159 | "
" 160 | ], 161 | "text/plain": [ 162 | " 1 10 100 1000\n", 163 | "n**2 + 2*n + 45 48 165 10245 1002045\n", 164 | "n**2 + 2*n 3 120 10200 1002000\n", 165 | "n**2 1 100 10000 1000000" 166 | ] 167 | }, 168 | "execution_count": 1, 169 | "metadata": {}, 170 | "output_type": "execute_result" 171 | } 172 | ], 173 | "source": [ 174 | "functions = {\n", 175 | " 'n**2 + 2*n + 45': lambda n: n**2 + 2*n + 45,\n", 176 | " 'n**2 + 2*n': lambda n: n**2 + 2*n,\n", 177 | " 'n**2': lambda n: n**2,\n", 178 | "}\n", 179 | "\n", 180 | "data_sizes = [1, 10, 100, 1000]\n", 181 | "\n", 182 | "results = {}\n", 183 | "for data_size in data_sizes:\n", 184 | " results[data_size] = {} \n", 185 | " for function_str, function in functions.items():\n", 186 | " results[data_size][function_str] = function(data_size)\n", 187 | "\n", 188 | "# You don't need to know how to use Pandas yet. \n", 189 | "# This is just to make the table of data look pretty.\n", 190 | "import pandas as pd \n", 191 | "pd.DataFrame(results)" 192 | ] 193 | }, 194 | { 195 | "cell_type": "markdown", 196 | "metadata": {}, 197 | "source": [ 198 | "As the data grows, only the highest order term matters. Thus the runtime complexity for each of the functions above is `O(N^2)`." 199 | ] 200 | }, 201 | { 202 | "cell_type": "markdown", 203 | "metadata": {}, 204 | "source": [ 205 | "- O(1) - Constant time\n", 206 | "- O(log(n)) - logarithmic time\n", 207 | "- O(n) - linear time\n", 208 | "- O(n * log(n)) - linearithmic time\n", 209 | "- O(n^2) - quadratic time\n", 210 | "- O(n^c) - polynomial time\n", 211 | "- O(c^n) - exponential time\n", 212 | "- O(n!) - factorial time\n", 213 | "- O(∞) - infinite time" 214 | ] 215 | }, 216 | { 217 | "cell_type": "code", 218 | "execution_count": 2, 219 | "metadata": {}, 220 | "outputs": [ 221 | { 222 | "data": { 223 | "text/html": [ 224 | "
\n", 225 | " \n", 226 | " Loading BokehJS ...\n", 227 | "
\n" 228 | ] 229 | }, 230 | "metadata": {}, 231 | "output_type": "display_data" 232 | }, 233 | { 234 | "data": { 235 | "application/javascript": [ 236 | "(function(root) {\n", 237 | " function now() {\n", 238 | " return new Date();\n", 239 | " }\n", 240 | "\n", 241 | " const force = true;\n", 242 | "\n", 243 | " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", 244 | " root._bokeh_onload_callbacks = [];\n", 245 | " root._bokeh_is_loading = undefined;\n", 246 | " }\n", 247 | "\n", 248 | "const JS_MIME_TYPE = 'application/javascript';\n", 249 | " const HTML_MIME_TYPE = 'text/html';\n", 250 | " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", 251 | " const CLASS_NAME = 'output_bokeh rendered_html';\n", 252 | "\n", 253 | " /**\n", 254 | " * Render data to the DOM node\n", 255 | " */\n", 256 | " function render(props, node) {\n", 257 | " const script = document.createElement(\"script\");\n", 258 | " node.appendChild(script);\n", 259 | " }\n", 260 | "\n", 261 | " /**\n", 262 | " * Handle when an output is cleared or removed\n", 263 | " */\n", 264 | " function handleClearOutput(event, handle) {\n", 265 | " const cell = handle.cell;\n", 266 | "\n", 267 | " const id = cell.output_area._bokeh_element_id;\n", 268 | " const server_id = cell.output_area._bokeh_server_id;\n", 269 | " // Clean up Bokeh references\n", 270 | " if (id != null && id in Bokeh.index) {\n", 271 | " Bokeh.index[id].model.document.clear();\n", 272 | " delete Bokeh.index[id];\n", 273 | " }\n", 274 | "\n", 275 | " if (server_id !== undefined) {\n", 276 | " // Clean up Bokeh references\n", 277 | " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", 278 | " cell.notebook.kernel.execute(cmd_clean, {\n", 279 | " iopub: {\n", 280 | " output: function(msg) {\n", 281 | " const id = msg.content.text.trim();\n", 282 | " if (id in Bokeh.index) {\n", 283 | " Bokeh.index[id].model.document.clear();\n", 284 | " delete Bokeh.index[id];\n", 285 | " }\n", 286 | " }\n", 287 | " }\n", 288 | " });\n", 289 | " // Destroy server and session\n", 290 | " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", 291 | " cell.notebook.kernel.execute(cmd_destroy);\n", 292 | " }\n", 293 | " }\n", 294 | "\n", 295 | " /**\n", 296 | " * Handle when a new output is added\n", 297 | " */\n", 298 | " function handleAddOutput(event, handle) {\n", 299 | " const output_area = handle.output_area;\n", 300 | " const output = handle.output;\n", 301 | "\n", 302 | " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", 303 | " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", 304 | " return\n", 305 | " }\n", 306 | "\n", 307 | " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", 308 | "\n", 309 | " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", 310 | " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", 311 | " // store reference to embed id on output_area\n", 312 | " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", 313 | " }\n", 314 | " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", 315 | " const bk_div = document.createElement(\"div\");\n", 316 | " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", 317 | " const script_attrs = bk_div.children[0].attributes;\n", 318 | " for (let i = 0; i < script_attrs.length; i++) {\n", 319 | " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", 320 | " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", 321 | " }\n", 322 | " // store reference to server id on output_area\n", 323 | " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", 324 | " }\n", 325 | " }\n", 326 | "\n", 327 | " function register_renderer(events, OutputArea) {\n", 328 | "\n", 329 | " function append_mime(data, metadata, element) {\n", 330 | " // create a DOM node to render to\n", 331 | " const toinsert = this.create_output_subarea(\n", 332 | " metadata,\n", 333 | " CLASS_NAME,\n", 334 | " EXEC_MIME_TYPE\n", 335 | " );\n", 336 | " this.keyboard_manager.register_events(toinsert);\n", 337 | " // Render to node\n", 338 | " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", 339 | " render(props, toinsert[toinsert.length - 1]);\n", 340 | " element.append(toinsert);\n", 341 | " return toinsert\n", 342 | " }\n", 343 | "\n", 344 | " /* Handle when an output is cleared or removed */\n", 345 | " events.on('clear_output.CodeCell', handleClearOutput);\n", 346 | " events.on('delete.Cell', handleClearOutput);\n", 347 | "\n", 348 | " /* Handle when a new output is added */\n", 349 | " events.on('output_added.OutputArea', handleAddOutput);\n", 350 | "\n", 351 | " /**\n", 352 | " * Register the mime type and append_mime function with output_area\n", 353 | " */\n", 354 | " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", 355 | " /* Is output safe? */\n", 356 | " safe: true,\n", 357 | " /* Index of renderer in `output_area.display_order` */\n", 358 | " index: 0\n", 359 | " });\n", 360 | " }\n", 361 | "\n", 362 | " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", 363 | " if (root.Jupyter !== undefined) {\n", 364 | " const events = require('base/js/events');\n", 365 | " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", 366 | "\n", 367 | " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", 368 | " register_renderer(events, OutputArea);\n", 369 | " }\n", 370 | " }\n", 371 | " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", 372 | " root._bokeh_timeout = Date.now() + 5000;\n", 373 | " root._bokeh_failed_load = false;\n", 374 | " }\n", 375 | "\n", 376 | " const NB_LOAD_WARNING = {'data': {'text/html':\n", 377 | " \"
\\n\"+\n", 378 | " \"

\\n\"+\n", 379 | " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", 380 | " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", 381 | " \"

\\n\"+\n", 382 | " \"
    \\n\"+\n", 383 | " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", 384 | " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", 385 | " \"
\\n\"+\n", 386 | " \"\\n\"+\n", 387 | " \"from bokeh.resources import INLINE\\n\"+\n", 388 | " \"output_notebook(resources=INLINE)\\n\"+\n", 389 | " \"\\n\"+\n", 390 | " \"
\"}};\n", 391 | "\n", 392 | " function display_loaded() {\n", 393 | " const el = document.getElementById(\"1002\");\n", 394 | " if (el != null) {\n", 395 | " el.textContent = \"BokehJS is loading...\";\n", 396 | " }\n", 397 | " if (root.Bokeh !== undefined) {\n", 398 | " if (el != null) {\n", 399 | " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", 400 | " }\n", 401 | " } else if (Date.now() < root._bokeh_timeout) {\n", 402 | " setTimeout(display_loaded, 100)\n", 403 | " }\n", 404 | " }\n", 405 | "\n", 406 | " function run_callbacks() {\n", 407 | " try {\n", 408 | " root._bokeh_onload_callbacks.forEach(function(callback) {\n", 409 | " if (callback != null)\n", 410 | " callback();\n", 411 | " });\n", 412 | " } finally {\n", 413 | " delete root._bokeh_onload_callbacks\n", 414 | " }\n", 415 | " console.debug(\"Bokeh: all callbacks have finished\");\n", 416 | " }\n", 417 | "\n", 418 | " function load_libs(css_urls, js_urls, callback) {\n", 419 | " if (css_urls == null) css_urls = [];\n", 420 | " if (js_urls == null) js_urls = [];\n", 421 | "\n", 422 | " root._bokeh_onload_callbacks.push(callback);\n", 423 | " if (root._bokeh_is_loading > 0) {\n", 424 | " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", 425 | " return null;\n", 426 | " }\n", 427 | " if (js_urls == null || js_urls.length === 0) {\n", 428 | " run_callbacks();\n", 429 | " return null;\n", 430 | " }\n", 431 | " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", 432 | " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", 433 | "\n", 434 | " function on_load() {\n", 435 | " root._bokeh_is_loading--;\n", 436 | " if (root._bokeh_is_loading === 0) {\n", 437 | " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", 438 | " run_callbacks()\n", 439 | " }\n", 440 | " }\n", 441 | "\n", 442 | " function on_error(url) {\n", 443 | " console.error(\"failed to load \" + url);\n", 444 | " }\n", 445 | "\n", 446 | " for (let i = 0; i < css_urls.length; i++) {\n", 447 | " const url = css_urls[i];\n", 448 | " const element = document.createElement(\"link\");\n", 449 | " element.onload = on_load;\n", 450 | " element.onerror = on_error.bind(null, url);\n", 451 | " element.rel = \"stylesheet\";\n", 452 | " element.type = \"text/css\";\n", 453 | " element.href = url;\n", 454 | " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", 455 | " document.body.appendChild(element);\n", 456 | " }\n", 457 | "\n", 458 | " for (let i = 0; i < js_urls.length; i++) {\n", 459 | " const url = js_urls[i];\n", 460 | " const element = document.createElement('script');\n", 461 | " element.onload = on_load;\n", 462 | " element.onerror = on_error.bind(null, url);\n", 463 | " element.async = false;\n", 464 | " element.src = url;\n", 465 | " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", 466 | " document.head.appendChild(element);\n", 467 | " }\n", 468 | " };\n", 469 | "\n", 470 | " function inject_raw_css(css) {\n", 471 | " const element = document.createElement(\"style\");\n", 472 | " element.appendChild(document.createTextNode(css));\n", 473 | " document.body.appendChild(element);\n", 474 | " }\n", 475 | "\n", 476 | " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n", 477 | " const css_urls = [];\n", 478 | "\n", 479 | " const inline_js = [ function(Bokeh) {\n", 480 | " Bokeh.set_log_level(\"info\");\n", 481 | " },\n", 482 | "function(Bokeh) {\n", 483 | " }\n", 484 | " ];\n", 485 | "\n", 486 | " function run_inline_js() {\n", 487 | " if (root.Bokeh !== undefined || force === true) {\n", 488 | " for (let i = 0; i < inline_js.length; i++) {\n", 489 | " inline_js[i].call(root, root.Bokeh);\n", 490 | " }\n", 491 | "if (force === true) {\n", 492 | " display_loaded();\n", 493 | " }} else if (Date.now() < root._bokeh_timeout) {\n", 494 | " setTimeout(run_inline_js, 100);\n", 495 | " } else if (!root._bokeh_failed_load) {\n", 496 | " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", 497 | " root._bokeh_failed_load = true;\n", 498 | " } else if (force !== true) {\n", 499 | " const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n", 500 | " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", 501 | " }\n", 502 | " }\n", 503 | "\n", 504 | " if (root._bokeh_is_loading === 0) {\n", 505 | " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", 506 | " run_inline_js();\n", 507 | " } else {\n", 508 | " load_libs(css_urls, js_urls, function() {\n", 509 | " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", 510 | " run_inline_js();\n", 511 | " });\n", 512 | " }\n", 513 | "}(window));" 514 | ], 515 | "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1002\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" 516 | }, 517 | "metadata": {}, 518 | "output_type": "display_data" 519 | }, 520 | { 521 | "data": { 522 | "text/html": [ 523 | "\n", 524 | "
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R80Lfo/XiWNastV+j/np7y1Yn76P3Eq7AD6pvo/+qwbTJHP+j+EL0uXKPj6Pw2yeuK/IPs/lzSqLVdJ+z8gt9l47nH7P6o5CcSFmvs/M7w4Dx3D+z+9PmhatOv7P0bBl6VLFPw/z0PH8OI8/D9ZxvY7emX8P+JIJocRjvw/bMtV0qi2/D/1TYUdQN/8P3/QtGjXB/0/CFPks24w/T+S1RP/BVn9PxtYQ0qdgf0/pdpylTSq/T8uXaLgy9L9P7jf0Stj+/0/QWIBd/oj/j/K5DDCkUz+P1RnYA0pdf4/3emPWMCd/j9nbL+jV8b+P/Du7u7u7v4/enEeOoYX/z8D9E2FHUD/P412fdC0aP8/FvmsG0yR/z+ge9xm47n/Pyn+C7J64v8/WcCd/ogFAECegTWk1BkAQOJCzUkgLgBAJwRl72tCAEBsxfyUt1YAQLGGlDoDawBA9Ucs4E5/AEA6CcSFmpMAQH/KWyvmpwBAxIvz0DG8AEAJTYt2fdAAQE0OIxzJ5ABAks+6wRT5AEDXkFJnYA0BQBtS6gysIQFAYBOCsvc1AUCl1BlYQ0oBQOqVsf2OXgFALldJo9pyAUBzGOFIJocBQLjZeO5xmwFA/ZoQlL2vAUBBXKg5CcQBQIYdQN9U2AFAy97XhKDsAUAQoG8q7AACQFRhB9A3FQJAmSKfdYMpAkDe4zYbzz0CQCKlzsAaUgJAZ2ZmZmZmAkCsJ/4LsnoCQPHolbH9jgJANaotV0mjAkB6a8X8lLcCQL8sXaLgywJABO70RyzgAkBIr4ztd/QCQI1wJJPDCANA0jG8OA8dA0AW81PeWjEDQFu064OmRQNAoHWDKfJZA0DlNhvPPW4DQCn4snSJggNAbrlKGtWWA0CzeuK/IKsDQPg7emVsvwNAPP0RC7jTA0CBvqmwA+gDQMZ/QVZP/ANAC0HZ+5oQBEBPAnGh5iQEQJTDCEcyOQRA2YSg7H1NBEAdRjiSyWEEQGIH0DcVdgRAp8hn3WCKBEDsif+CrJ4EQDBLlyj4sgRAdQwvzkPHBEC6zcZzj9sEQP+OXhnb7wRAQ1D2viYEBUCIEY5kchgFQM3SJQq+LAVAEZS9rwlBBUBWVVVVVVUFQJsW7fqgaQVA4NeEoOx9BUAkmRxGOJIFQGlatOuDpgVArhtMkc+6BUDz3OM2G88FQDeee9xm4wVAfF8TgrL3BUDBIKsn/gsGQAXiQs1JIAZASqPacpU0BkCPZHIY4UgGQNQlCr4sXQZAGOehY3hxBkBdqDkJxIUGQKJp0a4PmgZA5yppVFuuBkAr7AD6psIGQHCtmJ/y1gZAtW4wRT7rBkD6L8jqif8GQD7xX5DVEwdAg7L3NSEoB0DIc4/bbDwHQAw1J4G4UAdAUfa+JgRlB0CWt1bMT3kHQNt47nGbjQdAHzqGF+ehB0Bk+x29MrYHQKm8tWJ+ygdA7n1NCMreB0AyP+WtFfMHQHcAfVNhBwhAvMEU+awbCEAAg6ye+C8IQEVERERERAhAigXc6Y9YCEDPxnOP22wIQBOICzUngQhAWEmj2nKVCECdCjuAvqkIQOLL0iUKvghAJo1qy1XSCEBrTgJxoeYIQLAPmhbt+ghA9dAxvDgPCUA5kslhhCMJQH5TYQfQNwlAwxT5rBtMCUAH1pBSZ2AJQEyXKPiydAlAkVjAnf6ICUDWGVhDSp0JQBrb7+iVsQlAX5yHjuHFCUCkXR80LdoJQOket9l47glALeBOf8QCCkByoeYkEBcKQLdifspbKwpA+yMWcKc/CkBA5a0V81MKQIWmRbs+aApAymfdYIp8CkAOKXUG1pAKQFPqDKwhpQpAmKukUW25CkDdbDz3uM0KQCEu1JwE4gpAZu9rQlD2CkCrsAPomwoLQPBxm43nHgtANDMzMzMzC0B59MrYfkcLQL61Yn7KWwtAAnf6IxZwC0BHOJLJYYQLQIz5KW+tmAtA0brBFPmsC0AVfFm6RMELQFo98V+Q1QtAn/6IBdzpC0DkvyCrJ/4LQCiBuFBzEgxAbUJQ9r4mDECyA+ibCjsMQPbEf0FWTwxAO4YX56FjDECAR6+M7XcMQMUIRzI5jAxACcre14SgDEBOi3Z90LQMQJNMDiMcyQxA2A2myGfdDEAczz1us/EMQGGQ1RP/BQ1AplFtuUoaDUDrEgVfli4NQC/UnATiQg1AdJU0qi1XDUC5VsxPeWsNQP0XZPXEfw1AQtn7mhCUDUCHmpNAXKgNQMxbK+anvA1AEB3Di/PQDUBV3loxP+UNQJqf8taK+Q1A32CKfNYNDkAjIiIiIiIOQGjjucdtNg5AraRRbblKDkDxZekSBV8OQDYngbhQcw5Ae+gYXpyHDkDAqbAD6JsOQARrSKkzsA5ASSzgTn/EDkCO7Xf0ytgOQNOuD5oW7Q5AF3CnP2IBD0BcMT/lrRUPQKHy1or5KQ9A5bNuMEU+D0AqdQbWkFIPQG82nnvcZg9AtPc1ISh7D0D4uM3Gc48PQD16ZWy/ow9Agjv9EQu4D0DH/JS3VswPQAu+LF2i4A9AUH/EAu70D0BKIC7UnAQQQO0A+qbCDhBAj+HFeegYEEAxwpFMDiMQQNSiXR80LRBAdoMp8lk3EEAYZPXEf0EQQLtEwZelSxBAXSWNastVEED/BVk98V8QQKLmJBAXahBARMfw4jx0EEDmp7y1Yn4QQImIiIiIiBBAK2lUW66SEEDNSSAu1JwQQHAq7AD6phBAEgu40x+xEEC064OmRbsQQFfMT3lrxRBA+awbTJHPEECcjecet9kQQD5us/Hc4xBA4E5/xALuEECDL0uXKPgQQCUQF2pOAhFAx/DiPHQMEUBq0a4PmhYRQAyyeuK/IBFArpJGteUqEUBRcxKICzURQPNT3loxPxFAljSqLVdJEUA4FXYAfVMRQNr1QdOiXRFAfdYNpshnEUAft9l47nERQMGXpUsUfBFAZHhxHjqGEUAGWT3xX5ARQKk5CcSFmhFASxrVlqukEUDt+qBp0a4RQJDbbDz3uBFAMrw4Dx3DEUDUnATiQs0RQHd90LRo1xFAGV6ch47hEUC7PmhatOsRQF4fNC3a9RFAAAAAAAAAEkCj4MvSJQoSQEXBl6VLFBJA56FjeHEeEkCKgi9LlygSQCxj+x29MhJAzkPH8OI8EkBxJJPDCEcSQBMFX5YuURJAteUqaVRbEkBYxvY7emUSQPqmwg6gbxJAnYeO4cV5EkA/aFq064MSQOFIJocRjhJAhCnyWTeYEkAmCr4sXaISQMjqif+CrBJAa8tV0qi2EkANrCGlzsASQK+M7Xf0yhJAUm25ShrVEkD0TYUdQN8SQJcuUfBl6RJAOQ8dw4vzEkDb7+iVsf0SQH7QtGjXBxNAILGAO/0RE0DCkUwOIxwTQGVyGOFIJhNAB1Pks24wE0CpM7CGlDoTQEwUfFm6RBNA7vRHLOBOE0CR1RP/BVkTQDO239ErYxNA1ZarpFFtE0B4d3d3d3cTQBpYQ0qdgRNAvDgPHcOLE0BfGdvv6JUTQAH6psIOoBNApNpylTSqE0BGuz5oWrQTQOibCjuAvhNAi3zWDabIE0AtXaLgy9ITQM89brPx3BNAch46hhfnE0AU/wVZPfETQLbf0Stj+xNAWcCd/ogFFED7oGnRrg8UQJ6BNaTUGRRAQGIBd/ojFEDiQs1JIC4UQIUjmRxGOBRAJwRl72tCFEDJ5DDCkUwUQGzF/JS3VhRADqbIZ91gFECwhpQ6A2sUQFNnYA0pdRRA9Ucs4E5/FECYKPiydIkUQDoJxIWakxRA3OmPWMCdFEB/ylsr5qcUQCGrJ/4LshRAw4vz0DG8FEBmbL+jV8YUQAhNi3Z90BRAqi1XSaPaFEBNDiMcyeQUQO/u7u7u7hRAks+6wRT5FEA0sIaUOgMVQNaQUmdgDRVAeXEeOoYXFUAbUuoMrCEVQL0ytt/RKxVAYBOCsvc1FUAC9E2FHUAVQKTUGVhDShVAR7XlKmlUFUDplbH9jl4VQIx2fdC0aBVALldJo9pyFUDQNxV2AH0VQHMY4UgmhxVAFfmsG0yRFUC32XjucZsVQFq6RMGXpRVA/JoQlL2vFUCee9xm47kVQEFcqDkJxBVA4zx0DC/OFUCGHUDfVNgVQCj+C7J64hVAyt7XhKDsFUBtv6NXxvYVQA+gbyrsABZAsYA7/RELFkBUYQfQNxUWQPZB06JdHxZAmSKfdYMpFkA7A2tIqTMWQN3jNhvPPRZAgMQC7vRHFkAipc7AGlIWQMSFmpNAXBZAZ2ZmZmZmFkAJRzI5jHAWQKsn/guyehZATgjK3teEFkDw6JWx/Y4WQJPJYYQjmRZANaotV0mjFkDXivkpb60WQHprxfyUtxZAHEyRz7rBFkC+LF2i4MsWQGENKXUG1hZAA+70RyzgFkClzsAaUuoWQEivjO139BZA6o9YwJ3+FkCNcCSTwwgXQC9R8GXpEhdA0TG8OA8dF0B0EogLNScXQBbzU95aMRdAuNMfsYA7F0BbtOuDpkUXQP2Ut1bMTxdAn3WDKfJZF0BCVk/8F2QXQOQ2G889bhdAhxfnoWN4F0Ap+LJ0iYIXQMvYfkevjBdAbrlKGtWWF0AQmhbt+qAXQLJ64r8gqxdAVVuukka1F0D3O3plbL8XQJkcRjiSyRdAPP0RC7jTF0De3d3d3d0XQIG+qbAD6BdAI591gynyF0DFf0FWT/wXQGhgDSl1BhhACkHZ+5oQGECsIaXOwBoYQE8CcaHmJBhA8eI8dAwvGECUwwhHMjkYQDak1BlYQxhA2ISg7H1NGEB7ZWy/o1cYQB1GOJLJYRhAvyYEZe9rGEBiB9A3FXYYQATomwo7gBhApshn3WCKGEBJqTOwhpQYQOuJ/4KsnhhAjmrLVdKoGEAwS5co+LIYQNIrY/sdvRhAdQwvzkPHGEAX7fqgadEYQLnNxnOP2xhAXK6SRrXlGED+jl4Z2+8YQKBvKuwA+hhAQ1D2viYEGUDlMMKRTA4ZQIgRjmRyGBlAKvJZN5giGUDM0iUKviwZQG+z8dzjNhlAEZS9rwlBGUCzdImCL0sZQFZVVVVVVRlA+DUhKHtfGUCaFu36oGkZQD33uM3GcxlA39eEoOx9GUCCuFBzEogZQCSZHEY4khlAxnnoGF6cGUBpWrTrg6YZQAs7gL6psBlArRtMkc+6GUBQ/Bdk9cQZQPLc4zYbzxlAlL2vCUHZGUA3nnvcZuMZQNl+R6+M7RlAfF8TgrL3GUAeQN9U2AEaQMAgqyf+CxpAYwF3+iMWGkAF4kLNSSAaQKfCDqBvKhpASqPacpU0GkDsg6ZFuz4aQI5kchjhSBpAMUU+6wZTGkDTJQq+LF0aQHYG1pBSZxpAGOehY3hxGkC6x202nnsaQF2oOQnEhRpA/4gF3OmPGkChadGuD5oaQERKnYE1pBpA5ippVFuuGkCJCzUngbgaQCvsAPqmwhpAzczMzMzMGkBwrZif8tYaQBKOZHIY4RpAtG4wRT7rGkBXT/wXZPUaQPkvyOqJ/xpAmxCUva8JG0A+8V+Q1RMbQODRK2P7HRtAg7L3NSEoG0Alk8MIRzIbQMdzj9tsPBtAalRbrpJGG0AMNSeBuFAbQK4V81PeWhtAUfa+JgRlG0Dz1or5KW8bQJW3VsxPeRtAOJgin3WDG0DaeO5xm40bQH1ZukTBlxtAHzqGF+ehG0DBGlLqDKwbQGT7Hb0ythtABtzpj1jAG0CovLVifsobQEudgTWk1BtA7X1NCMreG0CPXhnb7+gbQDI/5a0V8xtA1B+xgDv9G0B3AH1TYQccQBnhSCaHERxAu8EU+awbHEBeouDL0iUcQACDrJ74LxxAomN4cR46HEBFREREREQcQOckEBdqThxAiQXc6Y9YHEAs5qe8tWIcQM7Gc4/bbBxAcac/YgF3HEATiAs1J4EcQLVo1wdNixxAWEmj2nKVHED6KW+tmJ8cQJwKO4C+qRxAP+sGU+SzHEDhy9IlCr4cQISsnvgvyBxAJo1qy1XSHEDIbTaee9wcQGtOAnGh5hxADS/OQ8fwHECvD5oW7focQFLwZekSBR1A9NAxvDgPHUCWsf2OXhkdQDmSyWGEIx1A23KVNKotHUB+U2EH0DcdQCA0Ldr1QR1AwhT5rBtMHUBl9cR/QVYdQAfWkFJnYB1AqbZcJY1qHUBMlyj4snQdQO539MrYfh1AkFjAnf6IHUAzOYxwJJMdQNUZWENKnR1AePojFnCnHUAa2+/olbEdQLy7u7u7ux1AX5yHjuHFHUABfVNhB9AdQKNdHzQt2h1ARj7rBlPkHUDoHrfZeO4dQIr/gqye+B1ALeBOf8QCHkDPwBpS6gweQHKh5iQQFx5AFIKy9zUhHkC2Yn7KWyseQFlDSp2BNR5A+yMWcKc/HkCdBOJCzUkeQEDlrRXzUx5A4sV56BheHkCEpkW7PmgeQCeHEY5kch5AyWfdYIp8HkBsSKkzsIYeQA4pdQbWkB5AsAlB2fuaHkBT6gysIaUeQPXK2H5Hrx5Al6ukUW25HkA6jHAkk8MeQNxsPPe4zR5Afk0Iyt7XHkAhLtScBOIeQMMOoG8q7B5AZu9rQlD2HkAI0DcVdgAfQKqwA+ibCh9ATZHPusEUH0DvcZuN5x4fQJFSZ2ANKR9ANDMzMzMzH0DWE/8FWT0fQHn0yth+Rx9AG9WWq6RRH0C9tWJ+ylsfQGCWLlHwZR9AAnf6IxZwH0CkV8b2O3ofQEc4kslhhB9A6RhenIeOH0CL+SlvrZgfQC7a9UHToh9A0LrBFPmsH0Bzm43nHrcfQBV8WbpEwR9At1wljWrLH0BaPfFfkNUfQPwdvTK23x9Anv6IBdzpH0BB31TYAfQfQOO/IKsn/h9AQ1D2viYEIECUQFyoOQkgQOUwwpFMDiBANyEoe18TIECIEY5kchggQNkB9E2FHSBAKvJZN5giIEB74r8gqycgQMzSJQq+LCBAHsOL89AxIEBvs/Hc4zYgQMCjV8b2OyBAEZS9rwlBIEBihCOZHEYgQLN0iYIvSyBABGXva0JQIEBWVVVVVVUgQKdFuz5oWiBA+DUhKHtfIEBJJocRjmQgQJoW7fqgaSBA7AZT5LNuIEA997jNxnMgQI7nHrfZeCBA39eEoOx9IEAwyOqJ/4IgQIG4UHMSiCBA06i2XCWNIEAkmRxGOJIgQHWJgi9LlyBAxnnoGF6cIEAXak4CcaEgQGlatOuDpiBAukoa1ZarIEALO4C+qbAgQFwr5qe8tSBArRtMkc+6IED+C7J64r8gQFD8F2T1xCBAoex9TQjKIEDy3OM2G88gQEPNSSAu1CBAlL2vCUHZIEDmrRXzU94gQDeee9xm4yBAiI7hxXnoIEDZfkevjO0gQCpvrZif8iBAe18TgrL3IEDNT3lrxfwgQB5A31TYASFAbzBFPusGIUDAIKsn/gshQBERERERESFAYwF3+iMWIUC08dzjNhshQAXiQs1JICFAVtKotlwlIUCnwg6gbyohQPmydImCLyFASqPacpU0IUCbk0BcqDkhQOyDpkW7PiFAPXQML85DIUCOZHIY4UghQOBU2AH0TSFAMUU+6wZTIUCCNaTUGVghQNMlCr4sXSFAJBZwpz9iIUB2BtaQUmchQMf2O3plbCFAGOehY3hxIUBp1wdNi3YhQLrHbTaeeyFAC7jTH7GAIUBdqDkJxIUhQK6Yn/LWiiFA/4gF3OmPIUBQeWvF/JQhQKFp0a4PmiFA81k3mCKfIUBESp2BNaQhQJU6A2tIqSFA5ippVFuuIUA3G889brMhQIgLNSeBuCFA2vuaEJS9IUAr7AD6psIhQHzcZuO5xyFAzczMzMzMIUAevTK239EhQHCtmJ/y1iFAwZ3+iAXcIUASjmRyGOEhQGN+ylsr5iFAtG4wRT7rIUAFX5YuUfAhQFdP/Bdk9SFAqD9iAXf6IUD5L8jqif8hQEogLtScBCJAmxCUva8JIkDtAPqmwg4iQD7xX5DVEyJAj+HFeegYIkDg0Stj+x0iQDHCkUwOIyJAgrL3NSEoIkDUol0fNC0iQCWTwwhHMiJAdoMp8lk3IkDHc4/bbDwiQBhk9cR/QSJAalRbrpJGIkC7RMGXpUsiQAw1J4G4UCJAXSWNastVIkCuFfNT3loiQP8FWT3xXyJAUfa+JgRlIkCi5iQQF2oiQPPWivkpbyJARMfw4jx0IkCVt1bMT3kiQOenvLVifiJAOJgin3WDIkCJiIiIiIgiQNp47nGbjSJAK2lUW66SIkB8WbpEwZciQM5JIC7UnCJAHzqGF+ehIkBwKuwA+qYiQMEaUuoMrCJAEgu40x+xIkBk+x29MrYiQLXrg6ZFuyJABtzpj1jAIkBXzE95a8UiQKi8tWJ+yiJA+awbTJHPIkBLnYE1pNQiQJyN5x632SJA7X1NCMreIkA+brPx3OMiQI9eGdvv6CJA4U5/xALuIkAyP+WtFfMiQIMvS5co+CJA1B+xgDv9IkAlEBdqTgIjQHYAfVNhByNAyPDiPHQMI0AZ4UgmhxEjQGrRrg+aFiNAu8EU+awbI0AMsnrivyAjQF6i4MvSJSNAr5JGteUqI0AAg6ye+C8jQFFzEogLNSNAomN4cR46I0DzU95aMT8jQEVERERERCNAljSqLVdJI0DnJBAXak4jQDgVdgB9UyNAiQXc6Y9YI0Db9UHTol0jQCzmp7y1YiNAfdYNpshnI0DOxnOP22wjQB+32XjucSNAcac/YgF3I0DCl6VLFHwjQBOICzUngSNAZHhxHjqGI0C1aNcHTYsjQAZZPfFfkCNAWEmj2nKVI0CpOQnEhZojQPopb62YnyNASxrVlqukI0CcCjuAvqkjQO76oGnRriNAP+sGU+SzI0CQ22w897gjQOHL0iUKviNAMrw4Dx3DI0CDrJ74L8gjQNWcBOJCzSNAJo1qy1XSI0B3fdC0aNcjQMhtNp573CNAGV6ch47hI0BrTgJxoeYjQLw+aFq06yNADS/OQ8fwI0BeHzQt2vUjQK8Pmhbt+iNAAAAAAAAAJEA=\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1000]}},\"selected\":{\"id\":\"1116\"},\"selection_policy\":{\"id\":\"1115\"}},\"id\":\"1095\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"1034\"}},\"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Application\",\"version\":\"2.4.3\"}};\n", 536 | " const render_items = [{\"docid\":\"a1bf5b56-4b0e-4928-874d-1cbe45bd618b\",\"root_ids\":[\"1003\"],\"roots\":{\"1003\":\"a6e5e77d-ee05-4f52-9f6b-dbe68211e238\"}}];\n", 537 | " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", 538 | " }\n", 539 | " if (root.Bokeh !== undefined) {\n", 540 | " embed_document(root);\n", 541 | " } else {\n", 542 | " let attempts = 0;\n", 543 | " const timer = setInterval(function(root) {\n", 544 | " if (root.Bokeh !== undefined) {\n", 545 | " clearInterval(timer);\n", 546 | " embed_document(root);\n", 547 | " } else {\n", 548 | " attempts++;\n", 549 | " if (attempts > 100) {\n", 550 | " clearInterval(timer);\n", 551 | " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", 552 | " }\n", 553 | " }\n", 554 | " }, 10, root)\n", 555 | " }\n", 556 | "})(window);" 557 | ], 558 | "application/vnd.bokehjs_exec.v0+json": "" 559 | }, 560 | "metadata": { 561 | "application/vnd.bokehjs_exec.v0+json": { 562 | "id": "1003" 563 | } 564 | }, 565 | "output_type": "display_data" 566 | } 567 | ], 568 | "source": [ 569 | "import math\n", 570 | "import numpy as np\n", 571 | "\n", 572 | "functions = {\n", 573 | " 'O(1)': lambda n: 1,\n", 574 | " 'O(log(n))': lambda n: np.log2(n),\n", 575 | " 'O(n)': lambda n: n,\n", 576 | " 'O(n * log(n))': lambda n: n * np.log2(n),\n", 577 | "# 'O(n^2)': lambda n: n ** 2,\n", 578 | "# 'O(c^n)': lambda n: 2 ** n,\n", 579 | "# 'O(n!)': lambda n: [np.math.factorial(int(x)) for x in n],\n", 580 | "}\n", 581 | "\n", 582 | "from bokeh.plotting import figure, output_notebook, show\n", 583 | "from bokeh import palettes\n", 584 | "output_notebook()\n", 585 | "\n", 586 | "x_points = np.linspace(0.1, 10, 1000)\n", 587 | "fig = figure(plot_width=900, plot_height=400)\n", 588 | "for i, (function_str, function) in enumerate(functions.items()):\n", 589 | " y_points = function(x_points)\n", 590 | " fig.line(\n", 591 | " x=x_points, \n", 592 | " y=y_points, \n", 593 | " color=palettes.Category10[10][i], \n", 594 | " legend_label=function_str,\n", 595 | " line_width=2,\n", 596 | " )\n", 597 | "\n", 598 | "fig.legend.location = \"top_left\"\n", 599 | "show(fig)" 600 | ] 601 | }, 602 | { 603 | "cell_type": "markdown", 604 | "metadata": {}, 605 | "source": [ 606 | "Helpful Big-O links:\n", 607 | "- https://towardsdatascience.com/the-big-o-notation-d35d52f38134\n", 608 | "- https://towardsdatascience.com/a-data-scientists-guide-to-data-structures-algorithms-1176395015a0\n", 609 | "- https://stackoverflow.com/questions/2307283/what-does-olog-n-mean-exactly\n", 610 | "- https://stackoverflow.com/questions/13467674/determining-complexity-for-recursive-functions-big-o-notation" 611 | ] 612 | } 613 | ], 614 | "metadata": { 615 | "kernelspec": { 616 | "display_name": "Python 3 (ipykernel)", 617 | "language": "python", 618 | "name": "python3" 619 | }, 620 | "language_info": { 621 | "codemirror_mode": { 622 | "name": "ipython", 623 | "version": 3 624 | }, 625 | "file_extension": ".py", 626 | "mimetype": "text/x-python", 627 | "name": "python", 628 | "nbconvert_exporter": "python", 629 | "pygments_lexer": "ipython3", 630 | "version": "3.9.13" 631 | } 632 | }, 633 | "nbformat": 4, 634 | "nbformat_minor": 2 635 | } 636 | --------------------------------------------------------------------------------