From 658eca81162c1b4c6769fd4d3fbec8eb6c600a1c Mon Sep 17 00:00:00 2001 From: Pietro Berkes Date: Tue, 27 Aug 2024 16:33:26 +0300 Subject: [PATCH] Add gitignore, remove files that were not ignored --- .DS_Store | Bin 6148 -> 0 bytes .gitignore | 166 +++ ...tebook-1-sorting-examples-checkpoint.ipynb | 386 ------ ...otebook-2-numpy_SOLUTIONS-checkpoint.ipynb | 1070 ----------------- .../numpy_views_and_copies-checkpoint.ipynb | 693 ----------- ...hen_copying_is_convenient-checkpoint.ipynb | 6 - ...hich_data_structure_intro-checkpoint.ipynb | 103 -- .../big_O_example_with_plots-checkpoint.ipynb | 446 ------- 8 files changed, 166 insertions(+), 2704 deletions(-) delete mode 100644 .DS_Store create mode 100644 .gitignore delete mode 100644 notebooks/.ipynb_checkpoints/notebook-1-sorting-examples-checkpoint.ipynb delete mode 100644 notebooks/.ipynb_checkpoints/notebook-2-numpy_SOLUTIONS-checkpoint.ipynb delete mode 100644 notebooks/.ipynb_checkpoints/numpy_views_and_copies-checkpoint.ipynb delete mode 100644 notebooks/.ipynb_checkpoints/when_copying_is_convenient-checkpoint.ipynb delete mode 100644 notebooks/.ipynb_checkpoints/which_data_structure_intro-checkpoint.ipynb delete mode 100644 notebooks/010_data_structures/.ipynb_checkpoints/big_O_example_with_plots-checkpoint.ipynb diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 632526be922af63254e899a13305eb08109b3919..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHK!EVz)5S>i}*c7C+0ts#_agB;o1*uV0WI{M_;eb&&6cp^(v{r4e6*~keisU;S z_!$oT00+*9pWq{4W_K$^k80<jeNdoP|Ar4ggrFgzd}NGzg88 zu1QPxTtQ?q#~3CcAcY)~HE+{l7%&X{Z4A)b-GUH&C{gR{H^QC>c5p3+@bY0ICP5+s z^ro%i9_r3FDQE%M==mCTit9gy0<9vL7rBo35adZ*l#cU-wKm!pE?%-MYtwq}ort+t z`sJ(``qLwNdLl#;oY{VG7)SGQ=h}Udm42K>QyCD45xP8k8fT%H55+7C3mNOF0n4`S zap&e@(ckNLd9S~_?D9p=8SHlX9cQpy+Sc`3xAz~9Pm?qgZ{*7Z!_7;|RgGux7LA6| zlH;B?5os>n_7A@{d!=GD(gaxV2cJcL_ao8PkS`VCZ+hk@wV4kbdVz*~ue@oNi0l|g zsWKZf+?46Z3=jpJLa|c8*3qM8uX+WaTD1z+^3^N&wdYC&+wXAVruX}y0`f9)?hNR^ zy|sDnnN32&fMMWYXMpwxj!NiitQ5+v0~>h-Kx|{T6tw9BfnvBuS7W6RM^KmwMO2|o zUon^p$9}H;T#c1N6%I^aKA4`F=^F}@vtxcP-GR9ZO=}o146HJ+p&mPQ{y+Hn{eLyc zJQ)TI1OF8RtUdBZLo7+3t!u^6S<6vhppuYZrBH@oqmN^0&{2E?RSJ%AG7w#jl|t-6 QF&_ew2GbY@{wM?A0V9mA;{X5v diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..b6c03e5 --- /dev/null +++ b/.gitignore @@ -0,0 +1,166 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ + + +# OSX +.DS_Store diff --git a/notebooks/.ipynb_checkpoints/notebook-1-sorting-examples-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/notebook-1-sorting-examples-checkpoint.ipynb deleted file mode 100644 index 39070d5..0000000 --- a/notebooks/.ipynb_checkpoints/notebook-1-sorting-examples-checkpoint.ipynb +++ /dev/null @@ -1,386 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "8685ea3a", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import timeit\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "048881d0", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Example: Find common words" - ] - }, - { - "cell_type": "markdown", - "id": "2464a282", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "Problem: given two lists of words, extract all the words that are in common" - ] - }, - { - "cell_type": "markdown", - "id": "71740eab", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Implementation with 2x for-loops" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f175c775", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "%%timeit\n", - "\n", - "scaling_factor = 1 #10, 100\n", - "\n", - "words1 = ['apple', 'orange', 'banana', 'melon', 'peach'] * scaling_factor\n", - "words2 = ['orange', 'kiwi', 'avocado', 'apple', 'banana'] * scaling_factor\n", - "\n", - "common_for = []\n", - "for w in words1:\n", - " if w in words2:\n", - " common_for.append(w) # 612 ns, 12.3 us, 928 us " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "affab857", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "input_size = [1, 10, 100]\n", - "results_for_loop = [(612/10**9)/(612/10**9), (12.4 /10**6)/(612/10**9), (928/10**6)/(612/10**9)] # in seconds\n", - "\n", - "x = np.linspace(0,100,100)\n", - "fit1 = np.polyfit(input_size,results_for_loop,2)\n", - "eval1 = np.polyval(fit1, x)\n", - "\n", - "plt.plot(x,eval1,c = 'orange')\n", - "plt.scatter(input_size, results_for_loop, c = 'orange', s = 100, label = '2 for loops')\n", - "\n", - "plt.xlabel('input size')\n", - "plt.ylabel('processing time')\n", - "plt.yticks(results_for_loop, ['T', str(int((12.4 /10**6)/(513/10**9)))+ 'x T', str(int((928/10**6)/(513/10**9))) + 'x T'])\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a61bf38", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "print('Data increase 1x, 10x, 100x')\n", - "print('Time increase 513 ns, 12.4 µs, 928 µs')\n", - "print('time1, ~ 24x time1, ~ 1800x time1')" - ] - }, - { - "cell_type": "markdown", - "id": "38e47397", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "What is the big-O complexity of this implementation? " - ] - }, - { - "cell_type": "markdown", - "id": "4118b38d", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "n * n ~ O(n2)" - ] - }, - { - "cell_type": "markdown", - "id": "31cd0e74", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Implementation with sorted lists" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c13a24f4", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "%%timeit\n", - "scaling_factor = 100 #10, 100\n", - "words1 = ['apple', 'orange', 'banana', 'melon', 'peach'] * scaling_factor\n", - "words2 = ['orange', 'kiwi', 'avocado', 'apple', 'banana'] *scaling_factor\n", - "words1 = sorted(words1)\n", - "words2 = sorted(words2)\n", - "\n", - "common_sort_list = []\n", - "idx2 = 0\n", - "for w in words1:\n", - " while idx2 < len(words2) and words2[idx2] < w:\n", - " idx2 += 1\n", - " if idx2 >= len(words2):\n", - " break\n", - " if words2[idx2] == w:\n", - " common_sort_list.append(w) #1.94 ns, 17.3 us, 204 us" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1e8fed2", - "metadata": { - "slideshow": { - "slide_type": "notes" - } - }, - "outputs": [], - "source": [ - "# 1.9 * 10**6\n", - "# 17.9 * 10**6\n", - "# 205 * 10**6" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ce798ab", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "input_size = [1, 10, 100]\n", - "results_sorted_lists = [(1.9 * 10**6)/(1.9 * 10**6), (17.9 * 10**6)/(1.9 * 10**6), (205 * 10**6)/(1.9 * 10**6)]\n", - "fit2 = np.polyfit(input_size, results_sorted_lists, 2)\n", - "eval2 = np.polyval(fit2, x)\n", - "plt.plot(x,eval1,c = 'orange')\n", - "plt.plot(x,eval2,c = 'pink')\n", - "plt.scatter(input_size, results_for_loop, c = 'orange', s = 100, label = '2 for loops')\n", - "plt.scatter(input_size, results_sorted_lists, c = 'pink', s = 100, label = 'sorted lists')\n", - "plt.xlabel('input size')\n", - "plt.ylabel('processing time')\n", - "plt.yticks(results_for_loop + results_sorted_lists[1:], ['T', str(int((12.4 /10**6)/(513/10**9)))+ 'x T', str(int((928/10**6)/(513/10**9))) + 'x T',\n", - " str(int((17.9 * 10**6)/(1.9 * 10**6)))+ 'x T', str(int((205 * 10**6)/(1.9 * 10**6))) + 'x T',])\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "1da4c22f", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "What is the big-O complexity of this implementation? " - ] - }, - { - "cell_type": "markdown", - "id": "4b068a1b", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "2 * sorting + traversing two lists = 2*n log2 + 2*n ~ O(n * logn)" - ] - }, - { - "cell_type": "markdown", - "id": "13c96239", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Implementation with sets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61edb9f3", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "%%timeit\n", - "\n", - "scaling_factor = 1\n", - "\n", - "words1 = ['apple', 'orange', 'banana', 'melon', 'peach'] * scaling_factor\n", - "words2 = ['orange', 'kiwi', 'avocado', 'apple', 'banana'] *scaling_factor\n", - "\n", - "words2 = set(words2)\n", - "\n", - "common_sets = []\n", - "for w in words1:\n", - " if w in words2:\n", - " common_sets.append(w) # 630 ns, 3.13 us, 28.6 us" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c90d8e68", - "metadata": { - "slideshow": { - "slide_type": "notes" - } - }, - "outputs": [], - "source": [ - "# 630 * 10**9\n", - "# 3.13 * 10**6\n", - "# 28.6 * 10**6" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "236c132d", - "metadata": { - "scrolled": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "results_sets = [(630 * 10**9)/(630 * 10**9), (3.13 * 10**6)/(630 * 10**9), (28.6 * 10**6)/(630 * 10**9)]\n", - "fit3 = np.polyfit(input_size, results_sets, 2)\n", - "eval3 = np.polyval(fit3, x)\n", - "plt.plot(x,eval1,c = 'orange')\n", - "plt.plot(x,eval2,c = 'pink')\n", - "plt.plot(x, eval3, c = 'blue')\n", - "plt.scatter(input_size, results_for_loop, c = 'orange', s = 100, label = '2 for loops')\n", - "plt.scatter(input_size, results_sorted_lists, c = 'pink', s = 100, label = 'sorted lists')\n", - "plt.scatter(input_size, results_sets, c = 'blue', s = 100, label = 'sets')\n", - "plt.xlabel('input size')\n", - "plt.ylabel('processing time')\n", - "plt.yticks(results_for_loop + results_sorted_lists[1:], ['T', str(int((12.4 /10**6)/(513/10**9)))+ 'x T', str(int((928/10**6)/(513/10**9))) + 'x T', str(int((17.9 * 10**6)/(1.9 * 10**6)))+ 'x T', str(int((205 * 10**6)/(1.9 * 10**6))) + 'x T'])\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c9780532", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "What is the big-O complexity of this implementation? " - ] - }, - { - "cell_type": "markdown", - "id": "297bcd7d", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "transforming one list to set + 1 for loop = 2 * n ~ O(n)\n", - "\n", - "It’s the exact same code as for lists, but now looking up an element in sets \u000b", - "(if w in words2) takes constant time!\n", - "How could you have known that set lookup is fast? Learning about data structures!" - ] - } - ], - "metadata": { - "celltoolbar": "Slideshow", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/.ipynb_checkpoints/notebook-2-numpy_SOLUTIONS-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/notebook-2-numpy_SOLUTIONS-checkpoint.ipynb deleted file mode 100644 index 13ce2b1..0000000 --- a/notebooks/.ipynb_checkpoints/notebook-2-numpy_SOLUTIONS-checkpoint.ipynb +++ /dev/null @@ -1,1070 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "81bfa588", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Numpy arrays" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "87f078ef", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "id": "4670f195", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Numpy arrays in memory (representation)\n", - "**reminder**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f006625e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0 1 2]\n", - " [3 4 5]\n", - " [6 7 8]]\n" - ] - } - ], - "source": [ - "X = np.arange(0,9).reshape(3,3)\n", - "print(X)" - ] - }, - { - "cell_type": "markdown", - "id": "0dfd22f3", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "![memory_lists1](images/memory_layout_array3.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "08bf5b5e", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 1, 2, 3, 4, 5, 6, 7, 8])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# flatten\n", - "X.ravel()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "45e1988e", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 3, 6],\n", - " [1, 4, 7],\n", - " [2, 5, 8]])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# transpose\n", - "X.T" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "0aab72b3", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 2],\n", - " [6, 8]])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# slice\n", - "X[::2, ::2]" - ] - }, - { - "cell_type": "markdown", - "id": "d9518a25", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "![numpy_meta](images/numpy_metadata.png)" - ] - }, - { - "cell_type": "markdown", - "id": "7d938ab8", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "![numpy_magic](images/numpy_views.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "5c8ad61e", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "cecb3364", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Views and Copies: an important distinction!\n", - "\n", - "\n", - "**View**\n", - "\n", - "- accessing the array without changing the databuffer \n", - "- **regular indexing** and **slicing** give views\n", - "- *in-place* operations can be done in views\n", - "\n", - "\n", - "**Copy**\n", - "- when a new array is created by duplicating the data buffer as well as the array metadata\n", - "- **fancy indexing** give always copies\n", - "- a copy can be forced by method **.copy()**\n", - "\n", - "How to know? with ```base```" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a169f158", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "def is_view(a, x): #checks if the base of a is the same as the base of x\n", - " return a.base is x" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "23f95dca", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a = [1 2 3 4 5 6]\n" - ] - } - ], - "source": [ - "a = np.arange(1,7)\n", - "print('a = ',a)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c3150638", - "metadata": { - "lines_to_next_cell": 2, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a_slice = [3 4 5]\n", - "The base of a_slice is [1 2 3 4 5 6]\n", - "Is a_slice a view of a? True\n" - ] - } - ], - "source": [ - "# create slice of a and print its base\n", - "a_slice = a[2:5]\n", - "\n", - "print('a_slice = ', a_slice)\n", - "print('The base of a_slice is ', a_slice.base)\n", - "\n", - "print('Is a_slice a view of a?', is_view(a_slice, a))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "19d2ae59", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a_copy = [[1 2 3]\n", - " [4 5 6]]\n", - "the base of a_copy None\n", - "a and a_copy have the same base False\n" - ] - } - ], - "source": [ - "# create a copy of a and print its base\n", - "\n", - "a_copy = np.reshape(a, (2,3)).copy()\n", - "\n", - "print('a_copy = ', a_copy)\n", - "print('the base of a_copy ', a_copy.base)\n", - "print('a and a_copy have the same base ', is_view(a_copy, a))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "c2e2e7ab", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1 2 3]\n", - " [4 5 6]]\n", - "True\n", - "True\n", - "False\n" - ] - } - ], - "source": [ - "## ! DON'T understand\n", - "\n", - "# create a copy of a and print its base\n", - "a_2_3 = np.reshape(a, (2,3))\n", - "\n", - "b = np.reshape(a_2_3, (2,3))\n", - "print(b)\n", - "print(is_view(b, a))\n", - "print(is_view(a_2_3, a))\n", - "print(is_view(b, a_2_3)) #??????" - ] - }, - { - "cell_type": "markdown", - "id": "6446c6a7", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "As a copy is a different array in memory, modifiying it will *not* change the base array" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "728a2740", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a [1 2 3 4 5 6]\n", - "a_copy [[ 1 2 3]\n", - " [ 4 666 6]]\n" - ] - } - ], - "source": [ - "a = np.arange(1, 7)\n", - "\n", - "# create a copy\n", - "a_copy = np.reshape(a, (2,3)).copy()\n", - "\n", - "a_copy[1,1] = 666\n", - "\n", - "print('a ', a)\n", - "print('a_copy ', a_copy)" - ] - }, - { - "cell_type": "markdown", - "id": "3978394b", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "# change an element in the copy, print original array\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "4763cc05", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a [ 1 2 3 4 101 6]\n", - "a_view: [[ 1 2 3]\n", - " [ 4 101 6]]\n", - "a_view strides: (24, 8)\n", - "a_copy [[ 1. 2. 3. ]\n", - " [ 4. 666.44 6. ]]\n", - "a_copy strides: (24, 8)\n", - "a_copy base: None\n" - ] - } - ], - "source": [ - "a = np.arange(1, 7)\n", - "\n", - "# create a copy\n", - "a_copy = np.reshape(a, (2,3)).astype('float64')\n", - "a_view = np.reshape(a, (2,3))\n", - "\n", - "a_copy[1,1] = 666.44\n", - "a_view[1,1] = 101.6555 # the data type in the original array (int) stays the same \n", - "\n", - "print('a ', a)\n", - "print('a_view: ', a_view)\n", - "print('a_view strides: ', a_view.strides)\n", - "print('a_copy ', a_copy)\n", - "print('a_copy strides: ', a_view.strides)\n", - "print('a_copy base: ', a_copy.base)" - ] - }, - { - "cell_type": "markdown", - "id": "adaf1f45", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "The same operation with a *view*, however, will carry the change " - ] - }, - { - "cell_type": "markdown", - "id": "94fc8724", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "**Take-away**: you **do** need to know if you are using a **view** or a **copy**, particularly when you are operating on the array **in-place**" - ] - }, - { - "cell_type": "markdown", - "id": "512588d1", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### 1.2.1 Strides - why some indexing gives copies and others views?\n", - "\n", - "- how does numpy arrange data in memory? - When you create an array, numpy allocates certain memory that depends on the type you choose" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "f94ffd04", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0 1 2]\n", - " [3 4 5]\n", - " [6 7 8]]\n" - ] - } - ], - "source": [ - "a = np.arange(9).reshape(3,3)\n", - "print(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "2f3e051a", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "8" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a.itemsize" - ] - }, - { - "cell_type": "markdown", - "id": "b141572c", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "In this example the array has 8 bytes allocated per item.\n", - "\n", - "Memory is *linear*, that means, the 2-D array will look in memory something like this (blue boxes) \n", - "\n", - "![linear_mem](images/memory_linear.png)\n", - "\n", - "However, the user 'sees' the array in 2D (green boxes).\n", - "\n", - "How does numpy accomplishes this? By defining ```strides```.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "f8dffd3a", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(24, 8)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a.strides" - ] - }, - { - "cell_type": "markdown", - "id": "10883b65", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "Strides tell you by how many bytes you should move in memory when moving one step in that dimension.\n", - "\n", - "![strides](images/strides.png)\n", - "\n", - "To go from the first item in the first row to the first item in the second row, you need to move (3*8) 24 bytes. To move from the column-wise, you just need to move 8 bytes." - ] - }, - { - "cell_type": "markdown", - "id": "7be8064c", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Views** are created when you use other strides to read your data. Slicing and regular indexing allows that, as you know how many byte steps you need to take to get the data.\n", - "\n", - "**Fancy indexing** does not allow that, because the data you are asking **cannot** be obtained by just changing the strides. Thus, numpy need to make a **copy** of it in memory." - ] - }, - { - "cell_type": "markdown", - "id": "66a2711b", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "Now, you can change the strides of an array at will." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "541bb33c", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 3, 6],\n", - " [1, 4, 7],\n", - " [2, 5, 8]])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a.strides=(8,24)\n", - "a" - ] - }, - { - "cell_type": "markdown", - "id": "05a3e933", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - " But be careful! Changing the strides to something non-sensical will also **give you non-sense**. And numpy will not complain. " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "8c20fea5", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "a.strides=(8, 9)" - ] - }, - { - "cell_type": "markdown", - "id": "e7bccdc1", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Exercises on indexing, views/copies\n" - ] - }, - { - "cell_type": "markdown", - "id": "694ed250", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Exercise 1: indexing, dimensionality of the output, view or copy?\n", - "\n", - "Look at the following code examples and before running it, try to answer for each case: \\\n", - "(1) what is the dimensionality of v? \\\n", - "(2) is v a view or a copy?" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "cc625fdd", - "metadata": {}, - "outputs": [], - "source": [ - "x = np.arange(0,12).reshape(3,4)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "02319316", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2, 3],\n", - " [ 8, 9, 10, 11]])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x[::2, :] #dim, view or copy\n", - "\n", - "#is_view(x[::2, :], x.base)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "c3336d1e", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([4, 5, 6, 7])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x[1, :]\n", - "\n", - "#is_view(x[1, :], x.base)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "1c8449f8", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x[1]\n", - "\n", - "#is_view(x[1], x.base)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "b635ea0e", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0 1 2 3]\n", - " [ 4 5 6 7]\n", - " [ 8 9 10 11]]\n" - ] - }, - { - "data": { - "text/plain": [ - "array([5, 9, 2])" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(x)\n", - "x[[1, 2, 0], [1, 1, 2]]\n", - "\n", - "#is_view(x[[1, 2, 0], [1, 1, 2]], x.base)" - ] - }, - { - "cell_type": "markdown", - "id": "ab7c3609", - "metadata": {}, - "source": [ - "### Fancy indexing\n", - "\n", - "![fancy](images/fancy_indexing_lookup.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "1371c65a", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x[[0, 2], :]\n", - "\n", - "#is_view(x[[0, 2], :], x.base)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "9db68cf6", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.reshape((6, 2))\n", - "\n", - "#is_view(x.reshape((6, 2)), x.base)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9374de16", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "x.ravel()\n", - "\n", - "#is_view(x.ravel(), x.base)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4dce1b10", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "x.T.ravel()\n", - "\n", - "#is_view(x.T.ravel(), x.base)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "fd4bd129", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(32, 8)" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x[(x % 2) == 1]\n", - "\n", - "#is_view(x[(x % 2) == 1], x.base)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "baf5b337", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y = x + 2\n", - "\n", - "##### Is this because \n", - "\n", - "#is_view(y, x)" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "a5e79e50", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y = np.sort(x, axis=1)\n", - "\n", - "#is_view(y, x)" - ] - }, - { - "cell_type": "markdown", - "id": "08b9c593", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Sources + other resources\n", - "\n", - "\n", - "ASPP Bilbao 2022 - Lisa Schwetlick & Aina Frau-Pascual\n", - "https://github.com/ASPP/2022-bilbao-advanced-numpy\n", - "\n", - "\n", - "Scipy lecture notes, 2022.1\n", - "- Basic Numpy: http://scipy-lectures.org/intro/numpy/index.html\n", - "- Advanced Numpy: http://scipy-lectures.org/advanced/advanced_numpy/index.html\n", - "\n", - "Numpy chapter in \"Python Data Science Handbook\"\n", - "https://jakevdp.github.io/PythonDataScienceHandbook/02.00-introduction-to-numpy.html\n", - "\n", - "\n", - "\n", - "Further resources on strides: \n", - "- https://scipy-lectures.org/advanced/advanced_numpy/#indexing-scheme-strides\n", - "- https://ajcr.net/stride-guide-part-1/\n" - ] - } - ], - "metadata": { - "celltoolbar": "Slideshow", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.3" - }, - "rise": { - "scroll": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/.ipynb_checkpoints/numpy_views_and_copies-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/numpy_views_and_copies-checkpoint.ipynb deleted file mode 100644 index c785239..0000000 --- a/notebooks/.ipynb_checkpoints/numpy_views_and_copies-checkpoint.ipynb +++ /dev/null @@ -1,693 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "20df51b1", - "metadata": {}, - "source": [ - "# NumPy views and copies\n", - "\n", - "- Operations that only require changing the metadata always do so, and return a **view**\n", - "- Operations that cannot be executed by changing the metadata create a new memory block, and return a **copy**" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4ed67e38", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "\n", - "def print_info(a):\n", - " \"\"\" Print the content of an array, and its metadata. \"\"\"\n", - " \n", - " txt = f\"\"\"\n", - "dtype\\t{a.dtype}\n", - "ndim\\t{a.ndim}\n", - "shape\\t{a.shape}\n", - "strides\\t{a.strides}\n", - " \"\"\"\n", - "\n", - " print(a)\n", - " print(txt)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "53bd92f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0 1 2 3]\n", - " [ 4 5 6 7]\n", - " [ 8 9 10 11]]\n", - "\n", - "dtype\tint64\n", - "ndim\t2\n", - "shape\t(3, 4)\n", - "strides\t(32, 8)\n", - " \n" - ] - } - ], - "source": [ - "x = np.arange(12).reshape(3, 4).copy()\n", - "print_info(x)" - ] - }, - { - "cell_type": "markdown", - "id": "d2ee43d7", - "metadata": {}, - "source": [ - "# Views" - ] - }, - { - "cell_type": "markdown", - "id": "f4838e77", - "metadata": {}, - "source": [ - "Operations that only require changing the metadata always do so, and return a **view**" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "f1b82845", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1 3]\n", - " [ 9 11]]\n", - "\n", - "dtype\tint64\n", - "ndim\t2\n", - "shape\t(2, 2)\n", - "strides\t(64, 16)\n", - " \n" - ] - } - ], - "source": [ - "y = x[0::2, 1::2]\n", - "print_info(y)" - ] - }, - { - "cell_type": "markdown", - "id": "3199b45b", - "metadata": {}, - "source": [ - "A view shares the same memory block as the original array. \n", - "\n", - "CAREFUL: Modifying the view changes the original array and all an other views of that array as well!" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "28ea1c71", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0 1 2 3 4 5 6 7 8 9 10 11]]\n", - "\n", - "dtype\tint64\n", - "ndim\t2\n", - "shape\t(1, 12)\n", - "strides\t(96, 8)\n", - " \n" - ] - } - ], - "source": [ - "z = x.reshape(1, 12)\n", - "print_info(z)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "46822b5a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[101 103]\n", - " [109 111]]\n", - "\n", - "dtype\tint64\n", - "ndim\t2\n", - "shape\t(2, 2)\n", - "strides\t(64, 16)\n", - " \n" - ] - } - ], - "source": [ - "y += 100\n", - "print_info(y)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "ad9a7950", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0 101 2 103]\n", - " [ 4 5 6 7]\n", - " [ 8 109 10 111]]\n", - "\n", - "dtype\tint64\n", - "ndim\t2\n", - "shape\t(3, 4)\n", - "strides\t(32, 8)\n", - " \n", - "[[ 0 101 2 103 4 5 6 7 8 109 10 111]]\n", - "\n", - "dtype\tint64\n", - "ndim\t2\n", - "shape\t(1, 12)\n", - "strides\t(96, 8)\n", - " \n" - ] - } - ], - "source": [ - "print_info(x)\n", - "print_info(z)" - ] - }, - { - "cell_type": "markdown", - "id": "4fc789c1", - "metadata": {}, - "source": [ - "Functions that take an array as an input should avoid modifying it in place! \n", - "\n", - "Always make a copy or be super extra clear in the docstring." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "aa25ac4b", - "metadata": {}, - "outputs": [], - "source": [ - "def robust_log(a, cte=1e-10):\n", - " \"\"\" Returns the log of an array, avoiding troubles when a value is 0.\n", - " \n", - " Add a tiny constant to the values of `a` so that they are not 0. \n", - " `a` is expected to have non-negative values.\n", - " \"\"\"\n", - " a[a == 0] += cte\n", - " return np.log(a)\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "471d9d6b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_48764/1018405258.py:2: RuntimeWarning: divide by zero encountered in log\n", - " np.log(a)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[-1.2039728 , -4.60517019],\n", - " [ -inf, 0. ]])" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.array([[0.3, 0.01], [0, 1]])\n", - "np.log(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "6c05d356", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0. 1.]\n", - "\n", - "dtype\tfloat64\n", - "ndim\t1\n", - "shape\t(2,)\n", - "strides\t(8,)\n", - " \n" - ] - } - ], - "source": [ - "# This is a view of `a`\n", - "b = a[1, :]\n", - "print_info(b)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "9d96fb61", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ -1.2039728 , -4.60517019],\n", - " [-23.02585093, 0. ]])" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "robust_log(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "35d0327d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[3.e-01, 1.e-02],\n", - " [1.e-10, 1.e+00]])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "4a2b95c5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.e-10, 1.e+00])" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b" - ] - }, - { - "cell_type": "markdown", - "id": "fa8cf77a", - "metadata": {}, - "source": [ - "Better to make a copy!" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "c5359eac", - "metadata": {}, - "outputs": [], - "source": [ - "def robust_log(a, cte=1e-10):\n", - " \"\"\" Returns the log of an array, avoiding troubles when a value is 0.\n", - " \n", - " Add a tiny constant to the values of `a` so that they are not 0. \n", - " `a` is expected to have non-negative values.\n", - " \"\"\"\n", - " a = a.copy()\n", - " a[a == 0] += cte\n", - " return np.log(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "0bf9b2d5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ -1.2039728 , -4.60517019],\n", - " [-23.02585093, 0. ]])" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.array([[0.3, 0.01], [0, 1]])\n", - "b = a[1, :]\n", - "\n", - "robust_log(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "895209ce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.3 , 0.01],\n", - " [0. , 1. ]])" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "18004050", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0., 1.])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b" - ] - }, - { - "cell_type": "markdown", - "id": "d664b462", - "metadata": {}, - "source": [ - "# Copies\n", - "\n", - "Operations that cannot be executed by changing the metadata create a new memory block, and return a **copy**" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "8c8f77e1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0 1 2 3]\n", - " [ 4 5 6 7]\n", - " [ 8 9 10 11]]\n", - "\n", - "dtype\tint64\n", - "ndim\t2\n", - "shape\t(3, 4)\n", - "strides\t(32, 8)\n", - " \n" - ] - } - ], - "source": [ - "x = np.arange(12).reshape(3, 4).copy()\n", - "print_info(x)" - ] - }, - { - "cell_type": "markdown", - "id": "716aec53", - "metadata": {}, - "source": [ - "Choosing row, columns, or individual elements of an array by giving explicitly their indices (a.k.a \"fancy indexing\") it's an operation that in general cannot be executed by changing the metadata alone.\n", - "\n", - "Therefore, **fancy indexing always returns a copy**." - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "40fb1777", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0 1]\n", - " [4 5]\n", - " [8 9]]\n", - "\n", - "dtype\tint64\n", - "ndim\t2\n", - "shape\t(3, 2)\n", - "strides\t(8, 24)\n", - " \n" - ] - } - ], - "source": [ - "# Get the first and second column\n", - "y = x[:, [0, 1]]\n", - "print_info(y)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "b8ed81d5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[2000 2001]\n", - " [2004 2005]\n", - " [2008 2009]]\n", - "\n", - "dtype\tint64\n", - "ndim\t2\n", - "shape\t(3, 2)\n", - "strides\t(8, 24)\n", - " \n", - "[[ 0 1 2 3]\n", - " [ 4 5 6 7]\n", - " [ 8 9 10 11]]\n", - "\n", - "dtype\tint64\n", - "ndim\t2\n", - "shape\t(3, 4)\n", - "strides\t(32, 8)\n", - " \n" - ] - } - ], - "source": [ - "y += 1000\n", - "print_info(y)\n", - "# the original array is unchanged => not a view!\n", - "print_info(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "6c50e46e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 1 0 11]\n", - "\n", - "dtype\tint64\n", - "ndim\t1\n", - "shape\t(3,)\n", - "strides\t(8,)\n", - " \n" - ] - } - ], - "source": [ - "y = x[[0, 0, 2], [1, 0, 3]]\n", - "print_info(y)" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "9d65a5c3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1001 1000 1011]\n", - "\n", - "dtype\tint64\n", - "ndim\t1\n", - "shape\t(3,)\n", - "strides\t(8,)\n", - " \n", - "[[ 0 1 2 3]\n", - " [ 4 5 6 7]\n", - " [ 8 9 10 11]]\n", - "\n", - "dtype\tint64\n", - "ndim\t2\n", - "shape\t(3, 4)\n", - "strides\t(32, 8)\n", - " \n" - ] - } - ], - "source": [ - "y += 1000\n", - "print_info(y)\n", - "# the original array is unchanged => not a view!\n", - "print_info(x)" - ] - }, - { - "cell_type": "markdown", - "id": "5e76ea7a", - "metadata": {}, - "source": [ - "Any operation that computes new values also returns a copy." - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "b8a3d44c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0. 7.1 14.2 21.3]\n", - " [28.4 35.5 42.6 49.7]\n", - " [56.8 63.9 71. 78.1]]\n", - "\n", - "dtype\tfloat64\n", - "ndim\t2\n", - "shape\t(3, 4)\n", - "strides\t(32, 8)\n", - " \n" - ] - } - ], - "source": [ - "y = x * 7.1\n", - "print_info(y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e50edfd", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "022e7b98", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/.ipynb_checkpoints/when_copying_is_convenient-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/when_copying_is_convenient-checkpoint.ipynb deleted file mode 100644 index 363fcab..0000000 --- a/notebooks/.ipynb_checkpoints/when_copying_is_convenient-checkpoint.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/.ipynb_checkpoints/which_data_structure_intro-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/which_data_structure_intro-checkpoint.ipynb deleted file mode 100644 index 38856a6..0000000 --- a/notebooks/.ipynb_checkpoints/which_data_structure_intro-checkpoint.ipynb +++ /dev/null @@ -1,103 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "3ae332a0", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "aa7bbab6", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "sound_data = np.random.rand(100)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "626eafc7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.66709183, 0.55973494, 0.95416669, 0.60810949, 0.05188879,\n", - " 0.58619063, 0.25555136, 0.72451477, 0.2646681 , 0.08694215,\n", - " 0.75592186, 0.67261696, 0.62847452, 0.06232598, 0.20549438,\n", - " 0.11718457, 0.25184725, 0.48625729, 0.8103058 , 0.18100915,\n", - " 0.81113341, 0.62055231, 0.9046905 , 0.56664205, 0.73235338,\n", - " 0.74382869, 0.64856368, 0.80644398, 0.46199345, 0.78516632,\n", - " 0.91298397, 0.48290914, 0.20847714, 0.99162659, 0.26374781,\n", - " 0.3602381 , 0.07173351, 0.8584085 , 0.32248766, 0.39167573,\n", - " 0.67944923, 0.00930429, 0.21714217, 0.58810089, 0.17668711,\n", - " 0.57444803, 0.25760187, 0.43785728, 0.39119371, 0.68268063,\n", - " 0.95954499, 0.45934239, 0.03616905, 0.23896063, 0.61872801,\n", - " 0.76332531, 0.96272817, 0.57169277, 0.50225193, 0.01361629,\n", - " 0.15357459, 0.8057233 , 0.0642748 , 0.95013941, 0.38712684,\n", - " 0.97231498, 0.20261775, 0.74184693, 0.26629893, 0.84672705,\n", - " 0.67662718, 0.96055977, 0.64942314, 0.66487937, 0.86867536,\n", - " 0.40815661, 0.1139344 , 0.95638066, 0.87436447, 0.18407227,\n", - " 0.64457074, 0.19233097, 0.24012179, 0.90399279, 0.39093908,\n", - " 0.26389161, 0.97537645, 0.14209784, 0.75261696, 0.10078122,\n", - " 0.87468408, 0.77990102, 0.92983283, 0.45841805, 0.61470669,\n", - " 0.87939755, 0.09266009, 0.41177209, 0.46973971, 0.43152144])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sound_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ef55bee9", - "metadata": {}, - "outputs": [], - "source": [ - "synonyms = {\n", - " 'hot': ['blazing', 'boiling', 'heated'],\n", - " 'airplane': ['aircraft', 'airliner', \n", - " 'cab', 'jet', 'plane'],\n", - " 'beach': ['coast', 'shore', 'waterfront'],\n", - " # ...\n", - "}" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/010_data_structures/.ipynb_checkpoints/big_O_example_with_plots-checkpoint.ipynb b/notebooks/010_data_structures/.ipynb_checkpoints/big_O_example_with_plots-checkpoint.ipynb deleted file mode 100644 index 8e9ac97..0000000 --- a/notebooks/010_data_structures/.ipynb_checkpoints/big_O_example_with_plots-checkpoint.ipynb +++ /dev/null @@ -1,446 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "id": "8685ea3a", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import timeit" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "id": "cd404fc3", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_style(figsize=(12, 6), labelsize=20, titlesize=24, ticklabelsize=14, **kwargs):\n", - " basic_style = {\n", - " 'figure.figsize': figsize,\n", - " 'axes.labelsize': labelsize,\n", - " 'axes.titlesize': titlesize,\n", - " 'xtick.labelsize': ticklabelsize,\n", - " 'ytick.labelsize': ticklabelsize,\n", - " 'axes.spines.top': False,\n", - " 'axes.spines.right': False,\n", - " 'axes.spines.left': True,\n", - " 'axes.grid': True,\n", - " 'axes.grid.axis': 'both',\n", - " }\n", - " basic_style.update(kwargs)\n", - " return plt.rc_context(rc=basic_style)\n", - "\n", - "\n", - "def plot_time_increase(multipliers, timing, ax=None, color='red', ls='-'): \n", - " if ax is None:\n", - " ax = plt.gca()\n", - " ax.plot(multipliers, timing, lw=3, ls=ls, color=color)\n", - " ax.set_xticks(list(range(1, multipliers[-1] + 2, 2)))\n", - " ax.set_xlabel('N')\n", - " ax.set_ylabel('Time (s)')\n", - " ax.set_xlim(0, multipliers[-1] + 1.1)\n", - " ax.set_ylim(0, 0.06)\n", - "\n", - "\n", - "def measure_time_increase(func, words1, words2, max_mult=20, z=1):\n", - " timing = []\n", - " multipliers = list(range(1, max_mult + 1))\n", - " for mult in multipliers:\n", - " mult_words1 = words1 * mult\n", - " mult_words2 = words2 * mult\n", - " t = timeit.repeat(\n", - " 'func(words1, words2)', \n", - " globals={'func': func, 'words1': mult_words1, 'words2': mult_words2}, \n", - " repeat=3,\n", - " number=3000\n", - " )\n", - " timing.append(min(t) * z)\n", - " return multipliers, timing" - ] - }, - { - "cell_type": "markdown", - "id": "048881d0", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Example: Find common words" - ] - }, - { - "cell_type": "markdown", - "id": "2464a282", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "Problem: given two lists of words, extract all the words that are in common" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "id": "b8d7a1eb", - "metadata": {}, - "outputs": [], - "source": [ - "results = {}" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "id": "01d8c3d7", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def two_loops(words1, words2):\n", - " common = []\n", - " for w in words1:\n", - " if w in words2:\n", - " common.append(w)\n", - " return common\n", - "\n", - "\n", - "words1 = ['apple', 'orange', 'banana', 'melon', 'peach']\n", - "words2 = ['orange', 'kiwi', 'avocado', 'apple', 'banana']\n", - "multipliers, timing = measure_time_increase(two_loops, words1, words2)\n", - "\n", - "results['Two loops'] = (multipliers, timing)\n", - "with plot_style(figsize=(6, 6), ticklabelsize=16, labelsize=22):\n", - " plot_time_increase(*results['Two loops'], ax=None, color='tab:blue', ls='-')\n", - " plt.legend(['Two loops', 'Sorted lists', 'Sets'], \n", - " title=None, fontsize=18, loc='center left', \n", - " bbox_to_anchor=(1, 0.5), frameon=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "944f65b2", - "metadata": {}, - "outputs": [], - "source": [ - "common = []\n", - "for w in words1: # O(N)\n", - " if w in words2: # O(N)\n", - " common.append(w) # O(1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "id": "68088136", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def sorted_lists(words1, words2):\n", - " words1 = sorted(words1)\n", - " words2 = sorted(words2)\n", - "\n", - " common = []\n", - " idx2 = 0\n", - " for w in words1:\n", - " while idx2 < len(words2) and words2[idx2] < w:\n", - " idx2 += 1\n", - "\n", - " if idx2 >= len(words2):\n", - " break\n", - "\n", - " if words2[idx2] == w:\n", - " common.append(w)\n", - " \n", - " return common\n", - "\n", - "words1 = ['apple', 'orange', 'banana', 'melon', 'peach']\n", - "words2 = ['orange', 'kiwi', 'avocado', 'apple', 'banana']\n", - "multipliers, timing = measure_time_increase(sorted_lists, words1, words2)\n", - "\n", - "results['Sorted lists'] = (multipliers, timing)\n", - "with plot_style(figsize=(6, 6), ticklabelsize=16, labelsize=22):\n", - " plot_time_increase(*results['Two loops'], ax=None, color='tab:blue', ls='-')\n", - " plot_time_increase(*results['Sorted lists'], ax=None, color='tab:orange', ls='-')\n", - " plt.legend(['Two loops', 'Sorted lists', 'Sets'], \n", - " title=None, fontsize=18, loc='center left', \n", - " bbox_to_anchor=(1, 0.5), frameon=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13625669", - "metadata": {}, - "outputs": [], - "source": [ - "words1 = sorted(words1) # O(N * log(N))\n", - "words2 = sorted(words2) # O(N * log(N))\n", - "\n", - "common = []\n", - "idx2 = 0\n", - "for w in words1: # O(N)\n", - " while idx2 < len(words2) and words2[idx2] < w: # O(N) in total\n", - " idx2 += 1\n", - "\n", - " if idx2 >= len(words2): # O(1)\n", - " break\n", - "\n", - " if words2[idx2] == w: # O(1)\n", - " common.append(w)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "id": "d7a6a1be", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def sets(words1, words2):\n", - " words2 = set(words2)\n", - "\n", - " common = []\n", - " for w in words1:\n", - " if w in words2:\n", - " common.append(w)\n", - "\n", - " return common\n", - "\n", - "\n", - "words1 = ['apple', 'orange', 'banana', 'melon', 'peach']\n", - "words2 = ['orange', 'kiwi', 'avocado', 'apple', 'banana']\n", - "multipliers, timing = measure_time_increase(sets, words1, words2)\n", - "\n", - "results['Sets'] = (multipliers, timing)\n", - "with plot_style(figsize=(6, 6), ticklabelsize=16, labelsize=22):\n", - " plot_time_increase(*results['Two loops'], ax=None, color='tab:blue', ls='-')\n", - " plot_time_increase(*results['Sorted lists'], ax=None, color='tab:orange', ls='-')\n", - " plot_time_increase(*results['Sets'], ax=None, color='tab:green', ls='-')\n", - " plt.legend(['Two loops', 'Sorted lists', 'Sets'], \n", - " title=None, fontsize=18, loc='center left', \n", - " bbox_to_anchor=(0.05, 0.85), frameon=True, facecolor='white', framealpha=0.8, edgecolor='white')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca3700a5", - "metadata": {}, - "outputs": [], - "source": [ - "words2 = set(words2) # O(N)\n", - "\n", - "common = []\n", - "for w in words1: # O(N)\n", - " if w in words2: # O(1)\n", - " common.append(w) # O(1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "93c8e164", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 172, - "id": "2c760f1c", - "metadata": {}, - "outputs": [], - "source": [ - "words1 = ['apple', 'orange', 'banana', 'melon', 'peach']\n", - "words2 = ['orange', 'kiwi', 'avocado', 'apple', 'banana']\n", - "multipliers, timing = measure_time_increase(two_loops, words1, words2, z=0.6)\n", - "\n", - "results['Two loops (fast)'] = (multipliers, timing)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "id": "57cff0ad", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "with plot_style(figsize=(6, 6), ticklabelsize=16, labelsize=22):\n", - " plot_time_increase(*results['Two loops'], ax=None, color='tab:blue', ls='-')\n", - " plot_time_increase(*results['Sorted lists'], ax=None, color='tab:orange', ls='-')\n", - " plot_time_increase(*results['Sets'], ax=None, color='tab:green', ls='-')\n", - " plot_time_increase(*results['Two loops (fast)'], ax=None, color='tab:blue', ls='--')\n", - " plt.legend(['Two loops', 'Sorted lists', 'Sets'], \n", - " title=None, fontsize=18, loc='center left', \n", - " bbox_to_anchor=(0.05, 0.85), frameon=True, facecolor='white', framealpha=0.8, edgecolor='white')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "id": "0496ee17", - "metadata": {}, - "outputs": [], - "source": [ - "words1 = ['apple', 'orange', 'banana', 'melon', 'peach']\n", - "words2 = ['orange', 'kiwi', 'avocado', 'apple', 'banana']\n", - "results_longer = {}\n", - "max_mult = 40\n", - "results_longer['Two loops'] = measure_time_increase(two_loops, words1, words2, max_mult=max_mult)\n", - "results_longer['Two loops (fast)'] = measure_time_increase(two_loops, words1, words2, z=0.6, max_mult=max_mult)\n", - "results_longer['Sorted lists'] = measure_time_increase(sorted_lists, words1, words2, max_mult=max_mult)\n", - "results_longer['Sets'] = measure_time_increase(sets, words1, words2, max_mult=max_mult)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "id": "de3857e9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "with plot_style(figsize=(6, 6), ticklabelsize=16, labelsize=22):\n", - " plot_time_increase(*results_longer['Two loops'], ax=None, color='tab:blue', ls='-')\n", - " plot_time_increase(*results_longer['Sorted lists'], ax=None, color='tab:orange', ls='-')\n", - " plot_time_increase(*results_longer['Sets'], ax=None, color='tab:green', ls='-')\n", - " plot_time_increase(*results_longer['Two loops (fast)'], ax=None, color='tab:blue', ls='--')\n", - " plt.legend(['Two loops', 'Sorted lists', 'Sets'], \n", - " title=None, fontsize=18, loc='center left', \n", - " bbox_to_anchor=(0.05, 0.85), frameon=True, facecolor='white', framealpha=0.8, edgecolor='white')\n", - " plt.ylim(0, 0.2)\n", - " plt.xticks(list(range(1, max_mult + 2, 4)))" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "id": "b512aa6d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "with plot_style(figsize=(12, 6), ticklabelsize=16, labelsize=22):\n", - " plot_time_increase(*results_longer['Two loops'], ax=None, color='tab:blue', ls='-')\n", - " plot_time_increase(*results_longer['Sorted lists'], ax=None, color='tab:orange', ls='-')\n", - " plot_time_increase(*results_longer['Sets'], ax=None, color='tab:green', ls='-')\n", - " plot_time_increase(*results_longer['Two loops (fast)'], ax=None, color='tab:blue', ls='--')\n", - " plt.legend(['Two loops', 'Sorted lists', 'Sets'], \n", - " title=None, fontsize=18, loc='center left', \n", - " bbox_to_anchor=(0.05, 0.85), frameon=True, facecolor='white', framealpha=0.8, edgecolor='white')\n", - " plt.ylim(0, 0.2)\n", - " plt.xticks(list(range(1, max_mult + 2, 2)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0263826e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}