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automatically generated notebooks
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‎episodes/notebooks/optimisation-data-structures-algorithms.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3b8b3bc6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from timeit import timeit\n",
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"\n",
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"def list_append():\n",
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" li = []\n",
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" for i in range(100000):\n",
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" li.append(i)\n",
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"\n",
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"def list_preallocate():\n",
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" li = [0]*100000\n",
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" for i in range(100000):\n",
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" li[i] = i\n",
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"\n",
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"def list_comprehension():\n",
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" li = [i for i in range(100000)]\n",
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"\n",
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"repeats = 1000\n",
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"print(f\"Append: {timeit(list_append, number=repeats):.2f}ms\")\n",
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"print(f\"Preallocate: {timeit(list_preallocate, number=repeats):.2f}ms\")\n",
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"print(f\"Comprehension: {timeit(list_comprehension, number=repeats):.2f}ms\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "31a74be6",
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"metadata": {},
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"outputs": [],
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"source": [
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"class MyKey:\n",
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"\n",
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" def __init__(self, _a, _b, _c):\n",
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" self.a = _a\n",
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" self.b = _b\n",
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" self.c = _c\n",
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"\n",
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" def __eq__(self, other):\n",
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" return (isinstance(other, type(self))\n",
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" and (self.a, self.b, self.c) == (other.a, other.b, other.c))\n",
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"\n",
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" def __hash__(self):\n",
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" return hash((self.a, self.b, self.c))\n",
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"\n",
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"dict = {}\n",
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"dict[MyKey(\"one\", 2, 3.0)] = 12"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8bbf7001",
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"from timeit import timeit\n",
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"\n",
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"def generateInputs(N = 25000):\n",
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" random.seed(12) # Ensure every list is the same \n",
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" return [random.randint(0,int(N/2)) for i in range(N)]\n",
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" \n",
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"def uniqueSet():\n",
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" ls_in = generateInputs()\n",
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" set_out = set(ls_in)\n",
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" \n",
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"def uniqueSetAdd():\n",
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" ls_in = generateInputs()\n",
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" set_out = set()\n",
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" for i in ls_in:\n",
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" set_out.add(i)\n",
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" \n",
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"def uniqueList():\n",
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" ls_in = generateInputs()\n",
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" ls_out = []\n",
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" for i in ls_in:\n",
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" if not i in ls_out:\n",
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" ls_out.append(i)\n",
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"\n",
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"def uniqueListSort():\n",
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" ls_in = generateInputs()\n",
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" ls_in.sort()\n",
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" ls_out = [ls_in[0]]\n",
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" for i in ls_in:\n",
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" if ls_out[-1] != i:\n",
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" ls_out.append(i)\n",
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" \n",
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"repeats = 1000\n",
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"gen_time = timeit(generateInputs, number=repeats)\n",
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"print(f\"uniqueSet: {timeit(uniqueSet, number=repeats)-gen_time:.2f}ms\")\n",
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"print(f\"uniqueSetAdd: {timeit(uniqueSetAdd, number=repeats)-gen_time:.2f}ms\")\n",
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"print(f\"uniqueList: {timeit(uniqueList, number=repeats)-gen_time:.2f}ms\")\n",
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"print(f\"uniqueListSort: {timeit(uniqueListSort, number=repeats)-gen_time:.2f}ms\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "63d3eb19",
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"from timeit import timeit\n",
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"from bisect import bisect_left\n",
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"\n",
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"N = 25000 # Number of elements in list\n",
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"M = 2 # N*M == Range over which the elements span\n",
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"\n",
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"def generateInputs():\n",
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" random.seed(12) # Ensure every list is the same\n",
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" st = set([random.randint(0, int(N*M)) for i in range(N)])\n",
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" ls = list(st)\n",
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" ls.sort() # Sort required for binary\n",
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" return st, ls # Return both set and list\n",
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" \n",
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"def search_set():\n",
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" st, _ = generateInputs()\n",
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" j = 0\n",
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" for i in range(0, int(N*M), M):\n",
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" if i in st:\n",
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" j += 1\n",
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" \n",
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"def linear_search_list():\n",
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" _, ls = generateInputs()\n",
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" j = 0\n",
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" for i in range(0, int(N*M), M):\n",
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" if i in ls:\n",
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" j += 1\n",
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" \n",
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"def binary_search_list():\n",
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" _, ls = generateInputs()\n",
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" j = 0\n",
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" for i in range(0, int(N*M), M):\n",
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" k = bisect_left(ls, i)\n",
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" if k != len(ls) and ls[k] == i:\n",
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" j += 1\n",
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"\n",
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" \n",
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"repeats = 1000\n",
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"gen_time = timeit(generateInputs, number=repeats)\n",
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"print(f\"search_set: {timeit(search_set, number=repeats)-gen_time:.2f}ms\")\n",
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"print(f\"linear_search_list: {timeit(linear_search_list, number=repeats)-gen_time:.2f}ms\")\n",
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"print(f\"binary_search_list: {timeit(binary_search_list, number=repeats)-gen_time:.2f}ms\")"
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]
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}
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],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎episodes/notebooks/optimisation-introduction.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6438a865",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Python code\n",
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"a = 1\n",
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"b = 2\n",
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"c = a + b"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b38dca50",
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"metadata": {},
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"outputs": [],
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"source": [
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"# file: test_demonstration.py\n",
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"\n",
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"# A simple function to be tested, this could instead be an imported package\n",
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"def squared(x):\n",
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" return x**2\n",
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"\n",
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"# A simple test case\n",
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"def test_example():\n",
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" assert squared(5) == 24"
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]
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}
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],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎episodes/notebooks/optimisation-latency.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f620fe00",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os, time\n",
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"\n",
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"# Generate 10MB\n",
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"data_len = 10000000\n",
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"data = os.urandom(data_len)\n",
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"file_ct = 1000\n",
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"file_len = int(data_len/file_ct)\n",
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"# Write one large file\n",
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"start = time.perf_counter()\n",
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"large_file = open(\"large.bin\", \"wb\")\n",
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"large_file.write(data)\n",
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"large_file.close ()\n",
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"large_write_s = time.perf_counter() - start\n",
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"# Write multiple small files\n",
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"start = time.perf_counter()\n",
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"for i in range(file_ct):\n",
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" small_file = open(f\"small_{i}.bin\", \"wb\")\n",
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" small_file.write(data[file_len*i:file_len*(i+1)])\n",
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" small_file.close()\n",
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"small_write_s = time.perf_counter() - start\n",
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"# Read back the large file\n",
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"start = time.perf_counter()\n",
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"large_file = open(\"large.bin\", \"rb\")\n",
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"t = large_file.read(data_len)\n",
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"large_file.close ()\n",
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"large_read_s = time.perf_counter() - start\n",
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"# Read back the small files\n",
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"start = time.perf_counter()\n",
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"for i in range(file_ct):\n",
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" small_file = open(f\"small_{i}.bin\", \"rb\")\n",
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" t = small_file.read(file_len)\n",
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" small_file.close()\n",
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"small_read_s = time.perf_counter() - start\n",
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"# Print Summary\n",
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"print(f\"{1:5d}x{data_len/1000000}MB Write: {large_write_s:.5f} seconds\")\n",
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"print(f\"{file_ct:5d}x{file_len/1000}KB Write: {small_write_s:.5f} seconds\")\n",
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"print(f\"{1:5d}x{data_len/1000000}MB Read: {large_read_s:.5f} seconds\")\n",
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"print(f\"{file_ct:5d}x{file_len/1000}KB Read: {small_read_s:.5f} seconds\")\n",
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"print(f\"{file_ct:5d}x{file_len/1000}KB Write was {small_write_s/large_write_s:.1f} slower than 1x{data_len/1000000}MB Write\")\n",
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"print(f\"{file_ct:5d}x{file_len/1000}KB Read was {small_read_s/large_read_s:.1f} slower than 1x{data_len/1000000}MB Read\")\n",
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"# Cleanup\n",
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"os.remove(\"large.bin\")\n",
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"for i in range(file_ct):\n",
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" os.remove(f\"small_{i}.bin\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "217267af",
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"metadata": {},
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"outputs": [],
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"source": [
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"grid_shape = (512, 512)\n",
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"\n",
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"def update(grid, a_dt):\n",
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" x_max, y_max = grid_shape\n",
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" out_grid = [[0.0 for x in range(y_max)] * y_max for x in range(x_max)]\n",
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" for i in range(x_max):\n",
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" for j in range(y_max):\n",
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" out_xx = grid[(i-1)%x_max][j] - 2 * grid[i][j] + grid[(i+1)%x_max][j]\n",
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" out_yy = grid[i][(j-1)%y_max] - 2 * grid[i][j] + grid[i][(j+1)%y_max]\n",
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" out_grid[i][j] = grid[i][j] + (out_xx + out_yy) * a_dt \n",
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" return out_grid\n",
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" \n",
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"def heat_equation(steps):\n",
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" x_max, y_max = grid_shape\n",
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" grid = [[0.0] * y_max for x in range(x_max)]\n",
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" # Init central point to diffuse\n",
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" grid[int(x_max/2)][int(y_max/2)] = 1.0\n",
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" # Run steps\n",
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" for i in range(steps):\n",
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" grid = update(grid, 0.1)\n",
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"\n",
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"heat_equation(100)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d0809621",
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"metadata": {},
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"outputs": [],
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"source": [
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"grid_shape = (512, 512)\n",
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"\n",
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"def update(grid, a_dt, out_grid):\n",
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" x_max, y_max = grid_shape\n",
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" for i in range(x_max):\n",
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" for j in range(y_max):\n",
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" out_xx = grid[(i-1)%x_max][j] - 2 * grid[i][j] + grid[(i+1)%x_max][j]\n",
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" out_yy = grid[i][(j-1)%y_max] - 2 * grid[i][j] + grid[i][(j+1)%y_max]\n",
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" out_grid[i][j] = grid[i][j] + (out_xx + out_yy) * a_dt \n",
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" \n",
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"def heat_equation(steps):\n",
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" x_max, y_max = grid_shape\n",
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" grid = [[0.0 for x in range(y_max)] for x in range(x_max)]\n",
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" out_grid = [[0.0 for x in range(y_max)] for x in range(x_max)] # Allocate a second buffer once\n",
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" # Init central point to diffuse\n",
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" grid[int(x_max/2)][int(y_max/2)] = 1.0\n",
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" # Run steps\n",
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" for i in range(steps):\n",
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" update(grid, 0.1, out_grid) # Pass the output buffer\n",
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" grid, out_grid = out_grid, grid # Swap buffers\n",
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"\n",
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"heat_equation(100)"
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]
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}
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],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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