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100644 index a7fcb1d..0000000 Binary files a/content/data/matplotlib.png and /dev/null differ diff --git a/content/javascript.ipynb b/content/javascript.ipynb deleted file mode 100644 index a5e7487..0000000 --- a/content/javascript.ipynb +++ /dev/null @@ -1,86 +0,0 @@ -{ - "metadata": { - "language_info": { - "codemirror_mode": { - "name": "javascript" - }, - "file_extension": ".js", - "mimetype": "text/javascript", - "name": "javascript", - "nbconvert_exporter": "javascript", - "pygments_lexer": "javascript", - "version": "es2017" - }, - "kernelspec": { - "name": "javascript", - "display_name": "JavaScript", - "language": "javascript" - }, - "toc-showcode": true - }, - "nbformat_minor": 4, - "nbformat": 4, - "cells": [ - { - "cell_type": "markdown", - "source": "# JavaScript in `JupyterLite`\n\n![](https://jupyterlite.readthedocs.io/en/latest/_static/kernelspecs/javascript.svg)", - "metadata": {} - }, - { - "cell_type": "markdown", - "source": "## Standard streams", - "metadata": {} - }, - { - "cell_type": "code", - "source": "console.log('hello world')", - "metadata": { - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": "console.error('error')", - "metadata": { - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": "## JavaScript specific constructs", - "metadata": {} - }, - { - "cell_type": "code", - "source": "const delay = 2000;\n\nsetTimeout(() => {\n console.log('done');\n}, delay);", - "metadata": { - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": "var str = \"hello world\"\nstr.split('').forEach(c => {\n console.log(c)\n})", - "metadata": { - "trusted": true - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": "## Markdown cells", - "metadata": {} - }, - { - "cell_type": "markdown", - "source": "Lorenz system of differential equations\n\n$$\n\\begin{aligned}\n\\dot{x} & = \\sigma(y-x) \\\\\n\\dot{y} & = \\rho x - y - xz \\\\\n\\dot{z} & = -\\beta z + xy\n\\end{aligned}\n$$\n", - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/content/lab00/SOSY-Logo.png b/content/lab00/SOSY-Logo.png new file mode 100644 index 0000000..e34beea Binary files /dev/null and b/content/lab00/SOSY-Logo.png differ diff --git a/content/lab00/ex00-01-0-intro.ipynb b/content/lab00/ex00-01-0-intro.ipynb new file mode 100644 index 0000000..b5e2baf --- /dev/null +++ b/content/lab00/ex00-01-0-intro.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "@deathbeds/jupyterlab-fonts": { + "styles": { + "": { + "body[data-jp-deck-mode='presenting'] &": { + "z-index": "5" + } + } + } + }, + "slideshow": { + "slide_type": "skip" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import sys\n", + "import platform" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "@deathbeds/jupyterlab-fonts": { + "styles": { + "": { + "body[data-jp-deck-mode='presenting'] &": { + "height": "2.2532695538932446%", + "left": "18.645701232677496%", + "position": "fixed", + "top": "96.02853455243014%", + "width": "68.00931860258498%" + } + } + } + }, + "jupyterlab-deck": { + "layer": "deck" + }, + "slideshow": { + "slide_type": "skip" + }, + "tags": [] + }, + "source": [ + "
Search-Based Software Engineering
Johannes Dorn
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "@deathbeds/jupyterlab-fonts": { + "styles": { + "": { + "body[data-jp-deck-mode='presenting'] &": { + "height": null, + "left": null, + "opacity": "100%", + "position": null, + "top": null, + "width": null, + "zoom": "500%" + } + } + } + }, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "# Search-Based Software Lab Class\n", + "## Organization\n", + "
\n", + " \n", + " Johannes Dorn (johannes.dorn@informatik.uni-leipzig.de)\n", + " \n", + " Softwaresysteme - Summer Term 2024\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "## Organization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "- Discussion of exercise sheets\n", + "- In between: use [Moodle ressources](https://moodle2.uni-leipzig.de/course/view.php?id=48562)\n", + "- Groups of 2 ok" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Exercise dates for this semester\n", + "\n", + "Übungen mittwochs 09:15 Uhr und 11:15 Uhr\n", + "\n", + "Bearbeitungszeit 2 Wochen\n", + "\n", + "Geplante Termine:\n", + "- Ü00 17.04. (1 Woche)\n", + "- Ü01 24.04.\n", + "- Ü02 08.05.\n", + "- Ü03 22.05.\n", + "- Ü04 05.06. (3 Wochen)\n", + "- Ü05 26.06." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "### Übungsleiter\n", + " - Johannes Dorn\n", + " - Max Weber\n", + " - Stefan Jahns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Tasks\n", + "- Using Python" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "print(\"Python version:\", platform.python_version())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "- No formal submission\n", + "- Discussion in excercise" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Setup - Get Python\n", + "### Anaconda\n", + "- get [Anaconda](https://anaconda.org/anaconda/python)\n", + "- [Tutorial on Windows](https://medium.com/@GalarnykMichael/install-python-on-windows-anaconda-c63c7c3d1444)\n", + "- [Tutorial on Ubuntu](https://www.digitalocean.com/community/tutorials/how-to-install-the-anaconda-python-distribution-on-ubuntu-16-04)\n", + "\n", + "\n", + "### Python Standalone\n", + "- Packaged:\n", + "```\n", + "sudo apt install python3 python3-pip\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Setup - Install Libraries\n", + "### Numpy \n", + "pip:\n", + "``` pip3 install numpy ```\n", + "\n", + "Anaconda: ``` conda install -c anaconda numpy ```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "### Pandas\n", + "pip: ``` pip3 install pandas ```\n", + "\n", + "Anaconda: ``` conda install -c anaconda pandas ```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Setup - Environment\n", + "### Jupyter\n", + "pip: \n", + "```\n", + "pip3 install jupyter\n", + "```\n", + "Anaconda:\n", + "```\n", + "conda install -c anaconda jupyter\n", + "```\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Contact Info\n", + "- Office: P624" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "- Via email (see [SWS Website](https://sws.informatik.uni-leipzig.de/team/))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "- Course material [on moodle](https://moodle2.uni-leipzig.de/course/view.php?id=42306)" + ] + } + ], + "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": "3.11.9" + }, + "rise": { + "footer": "
Search-Based Software Engineering
Johannes Dorn
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Search-Based Software Engineering
Johannes Dorn
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "@deathbeds/jupyterlab-fonts": { + "styles": { + "": { + "body[data-jp-deck-mode='presenting'] &": { + "height": "19.475401453225007%", + "left": "2.540895887084863%", + "position": "fixed", + "top": "40.26230042239269%", + "width": "94.91686648355792%" + } + } + } + }, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "# Search-Based Software Engineering Exercise\n", + "## Exercise 01 - Traveling Salesman Problem Introduction\n", + "
\n", + " \n", + " Johannes Dorn (johannes.dorn@informatik.uni-leipzig.de)\n", + " \n", + " Softwaresysteme - Summer Term 2024\n", + " \n", + "
\n", + "
\n", + " \n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "The Traveling Salesperson Problem (TSP) is given by the following question:\n", + "\n", + "*“Given is a list of cities and distances between each pair of cities - what is the shortest route that visits each city and returns to the original city?”*\n", + "\n", + "\n", + "
\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "The TSP is an **NP-Hard-Problem** which does not mean an instance of the problem will be hard to solve.\n", + "\n", + "It means, there does not exist an algorithm that produces the best solution in polynomial time.\n", + "\n", + "We can not make predictions about how long it might take to find the best solution.\n", + "\n", + "However, we can find a good solution which might not be the best solution.\n", + "\n", + "It is ok to find a route amongst 1000 cities that is only few miles longer than the best route.\n", + "\n", + "Particularly, if it would take an inordinate amount amount of computing time to get from our good solution to the best solution." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "The Traveling Salesman Problem (TSP) is given by the following question: *“Given is a list of cities and distances between each pair of cities - what is the shortest route that visits each city and returns to the original city?”*\n", + "\n", + "The TSP is an **NP-Hard-Problem** which does not mean an instance of the problem will be hard to solve. It means, there does not exist an algorithm that produces the best solution in polynomial time. We can not make predictions about how long it might take to find the best solution.\n", + "\n", + "But, we can find a good solution which might not be the best solution. It is ok to find a route amongst 1000 cities that is only few miles longer than the best route. Particularly, if it would take an inordinate amount amount of computing time to get from our good solution to the best solution." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Representation of the Problem" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "
\n", + "\n", + "
\n", + "\n", + "\n", + "A TSP can be modelled as an undirected weighted graph:\n", + "* cities = vertices\n", + "* paths between cities = edges\n", + "* distance of a path = weight of an edge" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "This graph can be represented as an **adjacency matrix**:\n", + "\n", + "\n", + "| \\ | A | B | C | D |\n", + "| :---: | :---: | :---: | :---: | :---: |\n", + "| **A** | 0 | 20 | 42 | 35 |\n", + "| **B** | 20 | 0 | 30 | 34 |\n", + "| **C** | 42 | 30 | 0 | 12 |\n", + "| **D** | 35 | 34 | 12 | 0 |" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "But how do we get the distances between cities if we only got the coordinates for each city?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Euclidean Distance" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Each city is represented by a cartesian koordinate P\n", + "\n", + "$ P = (p_x, p_y) $" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Euclidean distance between two points P1 = (x1, y1) and P2 = (x2, y2) is:\n", + "\n", + "$d(P_{1},P_{2}) = \\sqrt{(x_{1} - x_{2})^2 + (y_{1} - y_{2})^2}$" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "from itertools import permutations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "def distance(p1, p2):\n", + " \"\"\"\n", + " Returns the Euclidean distance of two points in the Cartesian Plane.\n", + "\n", + " >>> distance([3,4],[0,0])\n", + " 5.0\n", + " \n", + " \"\"\"\n", + " return ((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2) ** 0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "13.038404810405298" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "distance([20,5],[7,6])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "def total_distance(points):\n", + " \"\"\"\n", + " Returns the length of the path passing throught\n", + " all the points in the given order.\n", + "\n", + " >>> total_distance([[1,2],[4,6]])\n", + " 5.0\n", + " >>> total_distance([[3,6],[7,6],[12,6]])\n", + " 9.0\n", + " \"\"\"\n", + " return sum([distance(point, points[index + 1]) for index, point in enumerate(points[:-1])])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "- keep in mind that \\[:-1\\] means \"all elements if the list without the last\"\n", + "- *enumerate* is a function to enumerate all elements of a given sequence" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# enumerate example\n", + "seasons = ['spring', 'summer', 'fall', 'winter']\n", + "enumerate(seasons)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Generators: can be lazily evaluated once" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, 'spring'), (1, 'summer'), (2, 'fall'), (3, 'winter')]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_seasons_with_numbers = enumerate(seasons)\n", + "list(all_seasons_with_numbers)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(all_seasons_with_numbers)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "def traveling_salespers(points, start = None):\n", + " \"\"\"\n", + " Finds the shortest route to visit all the cities by bruteforce.\n", + " Time complexity is O(N!), so never use on long lists.\n", + "\n", + " >>> traveling_salesman([[0,0],[10,0],[6,0]])\n", + " ([0, 0], [6, 0], [10, 0])\n", + " >>> traveling_salesman([[0,0],[6,0],[2,3],[3,7],[0.5,9],[3,5],[9,1]])\n", + " ([0, 0], [6, 0], [9, 1], [2, 3], [3, 5], [3, 7], [0.5, 9])\n", + " \"\"\"\n", + " if start is None:\n", + " start = points[0]\n", + " return min([perm for perm in permutations(points) if perm[0] == start], key = total_distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "**keep in mind that the permutation without repetition creates $ \\frac{n!}{(n-r)!} $ examples**\n", + "\n", + "where n is the amount of elements in the set and r is the number of elements we choose from the set\n", + "\n", + "That means if we have a list of 4 elements we have 24 possible combinations:\n", + "\n", + "$ |perm([a,b,c,d])| = \\frac{4!}{(4-4)!} = \\frac{4!}{0!} = \\frac{4!}{1} = 24 $" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('a', 'b', 'c', 'd')\n", + "('a', 'b', 'd', 'c')\n", + "('a', 'c', 'b', 'd')\n", + "('a', 'c', 'd', 'b')\n", + "('a', 'd', 'b', 'c')\n", + "('a', 'd', 'c', 'b')\n", + "('b', 'a', 'c', 'd')\n", + "('b', 'a', 'd', 'c')\n", + "('b', 'c', 'a', 'd')\n", + "('b', 'c', 'd', 'a')\n", + "('b', 'd', 'a', 'c')\n", + "('b', 'd', 'c', 'a')\n", + "('c', 'a', 'b', 'd')\n", + "('c', 'a', 'd', 'b')\n", + "('c', 'b', 'a', 'd')\n", + "('c', 'b', 'd', 'a')\n", + "('c', 'd', 'a', 'b')\n", + "('c', 'd', 'b', 'a')\n", + "('d', 'a', 'b', 'c')\n", + "('d', 'a', 'c', 'b')\n", + "('d', 'b', 'a', 'c')\n", + "('d', 'b', 'c', 'a')\n", + "('d', 'c', 'a', 'b')\n", + "('d', 'c', 'b', 'a')\n" + ] + } + ], + "source": [ + "# Example for permutations\n", + "test_list = [\"a\", \"b\", \"c\", \"d\"]\n", + "test_permutations = permutations(test_list)\n", + "for test_perm in test_permutations:\n", + " print(test_perm)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "- *permutations* returns tuples with all possible orderings without repeat\n", + "- function returns minimum of all possible tuples by the help of the function *total_distance* from above" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# How bad could it possibly be?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "13" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "many_points = [\n", + " [0, 0],\n", + " [1, 5.7],\n", + " [2, 3],\n", + " [3, 7],\n", + " [0.5, 9],\n", + " [3, 5],\n", + " [9, 1],\n", + " [10, 5],\n", + " [20, 5],\n", + " [12, 12],\n", + " [20, 19],\n", + " [25, 6],\n", + " [23, 7]\n", + "] \n", + "len(many_points)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calculation time 0:00:00.084947\n", + "\n", + "The minimum distance to visit all \n", + "of the following points:\n", + "([[0, 0], [1, 5.7], [2, 3], [3, 7], [0.5, 9], [3, 5], [9, 1], [10, 5], [20, 5]],)\n", + "\n", + "starting at\n", + "[0, 0] is 36.358221434159375 and takes this route:\n", + "([0, 0], [2, 3], [1, 5.7], [0.5, 9], [3, 7], [3, 5], [9, 1], [10, 5], [20, 5])\n" + ] + } + ], + "source": [ + "import datetime \n", + "points = many_points[:9] # try out more than 9 points\n", + "start = datetime.datetime.now()\n", + "result = traveling_salespers(points)\n", + "distance_result = total_distance(result)\n", + "now = datetime.datetime.now()\n", + "print(\"calculation time\", now - start)\n", + "print(f\"\"\"\n", + "The minimum distance to visit all \n", + "of the following points:\n", + "{points,}\n", + "\n", + "starting at\n", + "{points[0]} is {distance_result} and takes this route:\n", + "{result}\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Solving TSP with Hill Climbing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "#### Recap: Hill Climbing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Idea:\n", + "- use only your local solution and evaluate your \n", + "neighbors to find a better one\n", + "- repeat this step until no better neighbour exists" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "
\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Pros:\n", + "- requires few resources (current state and neighbors)\n", + "- finds local optimum (global is possible)\n", + "- useful if the search space is huge (even unlimited)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Cons:\n", + "- is prone to get stuck at the top of local maximum and on plateaus\n", + "- strongly depends on “good” initialization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "# Essential Steps for each Meta-Heuristic" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "- **Initialization procedure**: Provide one or more initial candidate\n", + "solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "- **Assessment procedure**: Assess the quality of a candidate\n", + "solution" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "- Make a **copy** of a candidate solution" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "- **Modification procedure**: Tweak a candidate solution to\n", + "produce a randomly slightly different candidate solution" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Initialization procedure" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import random\n", + "def init_random_tour(tour_length):\n", + " tour = list(range(tour_length))\n", + " random.shuffle(tour)\n", + " return tour" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 3, 2, 1]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "init_random_tour(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Copy procedure" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "from copy import deepcopy # Copy procedure" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Assessment procedure" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Adjacency Matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "We will use standard Python lists to represent a route through our collection of cities. Each city will simply be assigned to a number from 0 to N-1 where N is the number of cities. Therefore, our list of cities will be a list of unique numbers between 0 and N-1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "We also need to specify a \"distance matrix\" that we can use to keep track of distances between cities. To generate a distance matrix for a set of (x,y) coordinates we will use the following function:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "def cartesian_matrix(coordinates):\n", + " '''\n", + " Creates a distance matrix for the city coords using straight line distances\n", + " computed by the Euclidean distance of two points in the Cartesian Plane.\n", + " '''\n", + " matrix = {}\n", + " for i, p1 in enumerate(coordinates):\n", + " for j, p2 in enumerate(coordinates):\n", + " matrix[i,j] = distance(p1,p2)\n", + " return matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "This function takes a list of (x,y) tuples and outputs a dictionary that contains the distance between any pair of cities:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0, 0) 0.0\n", + "(0, 1) 1.0\n", + "(0, 2) 1.4142135623730951\n", + "(1, 0) 1.0\n", + "(1, 1) 0.0\n", + "(1, 2) 1.0\n", + "(2, 0) 1.4142135623730951\n", + "(2, 1) 1.0\n", + "(2, 2) 0.0\n" + ] + } + ], + "source": [ + "m = cartesian_matrix([(0,0), (1,0), (1,1)])\n", + "for k, v in m.items():\n", + " print(k, v)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.4142135623730951" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m[0,2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "\\[2,0\\] gives the distance between the city with number 2 and the city with number 0.\n", + "In our case the result of \\[2,0\\] is the same for \\[0,2\\], but for other TSPs this may not be the case (for example if a street between two cities is only one way - we have to take another route)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Read City Coordinates from File" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "def read_coords(file_handle):\n", + " coords = []\n", + " for line in file_handle:\n", + " x,y = line.strip().split(',')\n", + " coords.append((float(x), float(y)))\n", + " return coords\n", + "\n", + "with open('city100.txt', 'r') as coord_file:\n", + " coords = read_coords(coord_file)\n", + "matrix = cartesian_matrix(coords)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "On real world problems it may be more complicated to generate a distance matrix - you might need to take a map and calculate the real distances between cities." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Compute the Total Distance" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "def tour_length(matrix, tour):\n", + " \"\"\"Sum up the total length of the tour based on the distance matrix\"\"\"\n", + " result = 0\n", + " num_cities = len(list(tour))\n", + " for i in range(num_cities):\n", + " j = (i+1) % num_cities\n", + " city_i = tour[i]\n", + " city_j = tour[j]\n", + " result += matrix[city_i, city_j]\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "26648.918302894548" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "rnd_route = init_random_tour(100)\n", + "tour_length(matrix, rnd_route)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Modification procedure" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "We will implement the two tweak operators as generator functions that will return all possible versions of a route that can be made in one step of the generator (in a random order)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Generators are iterators which can be only iterated once.\n", + "They generate values on the fly and do not store them in memory.\n", + "By using a generator function, we can get each possiblility and perhaps decide to not generate any more variations.\n", + "This saves the overhead of generating all combinations at once." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "def all_pairs(size, shuffle = random.shuffle):\n", + " r1 = list(range(size))\n", + " r2 = list(range(size))\n", + " if shuffle:\n", + " shuffle(r1)\n", + " shuffle(r2)\n", + " for i in r1:\n", + " for j in r2:\n", + " yield(i,j) # yield is an iterator function\n", + " # for each call of the generator it returns the next value in yield" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "import random\n", + "\n", + "def all_pairs(size, shuffle = random.shuffle):\n", + " r1 = list(range(size))\n", + " r2 = list(range(size))\n", + " if shuffle:\n", + " shuffle(r1)\n", + " shuffle(r2)\n", + " for i in r1:\n", + " for j in r2:\n", + " yield(i,j) # yield is an iterator function\n", + " # for each call of the generator it returns the next value in yield" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 5)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_pairs(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, 0), (0, 2), (0, 1), (2, 0), (2, 2), (2, 1), (1, 0), (1, 2), (1, 1)]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(all_pairs(3))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "# Tweak 1\n", + "def swapped_cities(tour):\n", + " \"\"\"\n", + " Generator to create all possible variations where two \n", + " cities have been swapped\n", + " \"\"\"\n", + " ap = all_pairs(len(tour))\n", + " for i,j in ap:\n", + " if i < j:\n", + " copy = deepcopy(tour)\n", + " copy[i], copy[j] = tour[j], tour[i]\n", + " yield copy" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "start tour swap: [1, 2, 3, 4]\n", + "[1, 3, 2, 4]\n", + "[1, 4, 3, 2]\n", + "[1, 2, 4, 3]\n", + "[3, 2, 1, 4]\n", + "[4, 2, 3, 1]\n", + "[2, 1, 3, 4]\n" + ] + } + ], + "source": [ + "print(\"start tour swap:\",[1,2,3,4])\n", + "for tour in swapped_cities([1,2,3,4]):\n", + " print(tour)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "# Tweak 2\n", + "def reversed_sections(tour):\n", + " \"\"\"\n", + " Generator to return all possible variations where the\n", + " section between two cities are swapped.\n", + " It preserves entire sections of a route,\n", + " yet still affects the ordering of multiple cities in one go.\n", + " \"\"\"\n", + " ap = all_pairs(len(tour))\n", + " for i,j in ap:\n", + " if i != j:\n", + " #print(\"indices from:\",i, \"to\", j)\n", + " copy = deepcopy(tour)\n", + " if i < j:\n", + " copy[i:j+1] = reversed(tour[i:j+1])\n", + " else:\n", + " copy[i+1:] = reversed(tour[:j])\n", + " copy[:j] = reversed(tour[i+1:])\n", + " if copy != tour: # not returning same tour\n", + " yield copy" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "start tour reverse section: [1, 2, 3, 4]\n", + "[4, 2, 3, 1]\n", + "[4, 1, 2, 3]\n", + "[1, 2, 4, 3]\n", + "[4, 3, 1, 2]\n", + "[1, 3, 2, 4]\n", + "[1, 4, 3, 2]\n", + "[2, 3, 4, 1]\n", + "[3, 4, 2, 1]\n", + "[2, 1, 3, 4]\n", + "[3, 2, 1, 4]\n", + "[4, 3, 2, 1]\n" + ] + } + ], + "source": [ + "print(\"start tour reverse section:\",[1,2,3,4])\n", + "for tour in reversed_sections([1,2,3,4]):\n", + " print(tour)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Getting Started with Hill Climbing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "To start with Hill Climbing, we need two functions:\n", + "- init function that returns a random solution\n", + "- objective function that tells us how \"good\" a solution is\n", + "\n", + "For the TSP, an init function will just return a tour of correct length that has cities aranged in random order.\n", + "\n", + "The objective function will return the length of a tour." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "\n", + "We need to ensure that init function takes no arguments and returns a tour of the correct length and the objective function takes one argument (the solution tour) and returns its length.\n", + "\n", + "Assume we have the city coordinates in a variable *coords* and our distance matrix in *matrix*, we can define the objective function and init function by using *init_random_tour*:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "init_function = lambda: init_random_tour(len(coords))\n", + "objective_function = lambda tour: tour_length(matrix, tour)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Short Explanation of Lambda Functions\n", + "is the creation of an anonymous function\n", + "- lambda definition does not include a return statement\n", + "- it always contains an expression which is returned\n", + "- you can put a lambda definition anywhere a function is expected\n", + "- you don't have to assign it to a variable" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25\n", + "25\n" + ] + } + ], + "source": [ + "# normal function definition\n", + "def f(x): return x**2\n", + "\n", + "# lambda function definition\n", + "g = lambda x: x**2\n", + "print(f(5))\n", + "print(g(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Basic Hill Climbing" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "def hc(init_function, move_operator, objective_function, max_evaluations, history=None):\n", + " '''\n", + " Hillclimb until either max_evaluations is \n", + " reached or we are at a local optimum.\n", + " '''\n", + " best = init_function()\n", + " best_score = objective_function(best)\n", + " \n", + " num_evaluations = 1\n", + " \n", + " while num_evaluations < max_evaluations:\n", + " # move around the current position\n", + " move_made = False\n", + " for candidate in move_operator(best):\n", + " if num_evaluations >= max_evaluations:\n", + " break\n", + " \n", + " candidate_score = objective_function(candidate)\n", + " num_evaluations += 1\n", + " if candidate_score < best_score:\n", + " best = candidate\n", + " best_score = candidate_score\n", + " move_made = True\n", + " break # depth first search\n", + " if history:\n", + " history.log(best, candidate)\n", + " if not move_made:\n", + " break # couldn't find better move - must be a local max\n", + " return (num_evaluations, best_score, best)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "### Basic plotting\n", + "do not change unless you want to" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "from IPython.display import HTML, display, clear_output, update_display\n", + "from PIL import Image, ImageDraw, ImageFont\n", + "import random\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "class History:\n", + " def __init__(self, plot_slowdown=0.0001):\n", + " self.tours = []\n", + " self.last_plot = time.time()\n", + " self.plot_slowdown = plot_slowdown\n", + " \n", + " def first_log(self):\n", + " return len(self.tours) == 0\n", + " \n", + " def log(self, best, candidate):\n", + " if not self.tours or time.time() - self.last_plot > 1:\n", + " filename = \"test\"+str(max_evaluations)+\".PNG\"\n", + " tours={\"black\":candidate, \"red\": best}\n", + " self.write_tours_to_img(coords, tours, filename, open(filename, \"ab\"))\n", + " self.tours.append(best)\n", + " self.last_plot = time.time()\n", + " elif self.plot_slowdown:\n", + " time.sleep(self.plot_slowdown)\n", + "\n", + " def write_tours_to_img(self, coords, tours, title, img_file):\n", + " coords = deepcopy(coords)\n", + " padding = 20\n", + " # shift all coords a bit inwards\n", + " coords = [(x+padding,y+padding) for (x,y) in coords]\n", + " maxx, maxy = 0,0\n", + " for x,y in coords:\n", + " maxx = max(x,maxx)\n", + " maxy = max(y,maxy)\n", + " maxx += padding\n", + " maxy += padding\n", + " img = Image.new(\"RGB\",(int(maxx), int(maxy)), color=(255,255,255))\n", + "\n", + " font=ImageFont.load_default()\n", + " d=ImageDraw.Draw(img);\n", + " for color, tour in tours.items():\n", + " num_cities = len(tour)\n", + " for i in range(num_cities):\n", + " j = (i+1) % num_cities\n", + " city_i = tour[i]\n", + " city_j = tour[j]\n", + " x1,y1 = coords[city_i]\n", + " x2,y2 = coords[city_j]\n", + " d.line((int(x1), int(y1), int(x2), int(y2)), fill=color)\n", + " #d.text((int(x1)+7, int(y1)-5), str(i), font=font, fill=(32,32,32))\n", + "\n", + " for x,y in coords:\n", + " x,y = int(x), int(y)\n", + " d.ellipse((x-5, y-5, x+5, y+5), outline=(0,0,0), fill=(196,196,196))\n", + "\n", + " del d\n", + " display_id = title\n", + " if self.first_log():\n", + " display(img, display_id=display_id)\n", + " else:\n", + " update_display(img, display_id=display_id, clear=False)\n", + " if self.plot_slowdown:\n", + " time.sleep(self.plot_slowdown) " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "def do_hc_evaluations(evaluations , move_operator = swapped_cities, plot_slowdown=0):\n", + " max_evaluations = evaluations\n", + " then = datetime.datetime.now()\n", + " history = History(plot_slowdown=plot_slowdown)\n", + " num_evaluations, best_score, best = hc(init_function, move_operator, objective_function, max_evaluations, history=history)\n", + " now = datetime.datetime.now()\n", + " print(\"computation time \", now - then)\n", + " print(\"best score:\", best_score)\n", + " print(\"best route:\", best)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Time to test it all out!" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computation time 0:00:05.701691\n", + "best score: 17336.925645025058\n", + "best route: [97, 66, 79, 71, 88, 49, 39, 44, 86, 16, 14, 48, 64, 85, 4, 11, 54, 55, 31, 0, 68, 18, 21, 34, 24, 7, 61, 32, 52, 51, 2, 89, 70, 33, 20, 62, 95, 76, 30, 56, 22, 43, 74, 47, 8, 9, 1, 90, 26, 25, 83, 94, 72, 13, 45, 87, 82, 50, 99, 17, 91, 93, 29, 27, 46, 98, 67, 96, 92, 5, 35, 73, 77, 69, 38, 19, 84, 75, 40, 81, 28, 42, 59, 57, 60, 10, 23, 36, 41, 63, 80, 58, 53, 15, 12, 6, 78, 3, 37, 65]\n" + ] + } + ], + "source": [ + "move_operator = swapped_cities\n", + "#move_operator = reversed_sections\n", + "max_evaluations = 500\n", + "#do_hc_evaluations(max_evaluations,move_operator) \n", + "do_hc_evaluations(max_evaluations,move_operator, plot_slowdown=10e-3)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computation time 0:00:03.980816\n", + "best score: 9030.03390637316\n", + "best route: [40, 46, 89, 17, 48, 47, 58, 37, 91, 33, 7, 57, 59, 27, 70, 24, 29, 98, 25, 71, 84, 3, 54, 11, 80, 85, 28, 43, 90, 97, 1, 14, 8, 82, 69, 10, 96, 30, 79, 9, 22, 18, 63, 41, 23, 73, 86, 68, 5, 92, 77, 99, 81, 42, 4, 12, 75, 62, 83, 49, 38, 19, 31, 20, 50, 34, 88, 67, 74, 64, 13, 15, 53, 95, 72, 76, 55, 94, 32, 6, 93, 2, 87, 60, 44, 66, 0, 39, 35, 56, 21, 61, 16, 36, 26, 78, 51, 65, 52, 45]\n" + ] + } + ], + "source": [ + "move_operator = swapped_cities\n", + "#move_operator = reversed_sections\n", + "max_evaluations = 5000\n", + "#do_hc_evaluations(max_evaluations,move_operator)\n", + "do_hc_evaluations(max_evaluations,move_operator, plot_slowdown=50e-5)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computation time 0:00:05.765240\n", + "best score: 4146.106958651244\n", + "best route: [94, 32, 15, 71, 53, 13, 11, 52, 47, 65, 48, 84, 54, 3, 51, 97, 90, 78, 26, 41, 61, 16, 36, 22, 18, 34, 21, 10, 96, 88, 67, 23, 63, 1, 49, 74, 43, 38, 8, 14, 19, 31, 79, 59, 20, 99, 50, 30, 9, 56, 73, 86, 39, 35, 0, 68, 5, 92, 66, 44, 77, 69, 60, 82, 87, 81, 57, 7, 46, 70, 33, 24, 89, 29, 17, 27, 91, 40, 98, 25, 2, 93, 12, 75, 4, 42, 28, 85, 83, 80, 62, 64, 45, 6, 37, 58, 72, 95, 76, 55]\n" + ] + } + ], + "source": [ + "#move_operator = swapped_cities\n", + "move_operator = reversed_sections\n", + "max_evaluations = 100000\n", + "do_hc_evaluations(max_evaluations,move_operator, plot_slowdown=5e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Bonus: Helper Methods & Declarations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "The following cells help deciding if an algorithm A is better than another algorithm B." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "import time\n", + "def get_reps(alg, n_reps, *args, **kwargs):\n", + " times = []\n", + " scores = []\n", + " for i in range(n_reps):\n", + " np.random.seed(i)\n", + " then = time.time()\n", + " num_evaluations, best_score, best = alg(*args, **kwargs)\n", + " now = time.time()\n", + " time_cost = now - then\n", + " times.append(time_cost)\n", + " scores.append(best_score)\n", + " return times, scores" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "def a12(lst1,lst2,rev=True):\n", + " \"how often is x in lst1 better than y in lst2?\"\n", + " more = same = 0.0\n", + " for x in lst1:\n", + " for y in lst2:\n", + " if x==y : same += 1\n", + " elif rev and x > y : more += 1\n", + " elif not rev and x < y : more += 1\n", + " return (more + 0.5*same) / (len(lst1)*len(lst2))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# decrease these values for quicker results\n", + "max_evaluations = 2000\n", + "n_reps = 20" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "hc_swapping_times, hc_swapping_scores = get_reps(hc, n_reps, init_function, swapped_cities, objective_function, max_evaluations)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "hc_reversing_times, hc_reversing_scores = get_reps(hc, n_reps, init_function, reversed_sections, objective_function, max_evaluations)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import scipy.stats as sps\n", + "from matplotlib import pyplot as plt\n", + "def short_stats(a,b):\n", + " plt.hist(a, color=\"k\", label=\"first\")\n", + " plt.hist(b, color=\"r\", label=\"second\")\n", + " plt.legend()\n", + " _, p = sps.mannwhitneyu(a, b)\n", + " if p > 0.05:\n", + " print(\"No significant difference (p= {} > 0.05)\".format(p))\n", + " else:\n", + " print(\"Found significant difference with p={} (lower ist better)\".format(p))\n", + " chance = a12(a, b, rev=False)\n", + " print(\"First is better with chance {}\".format(chance))" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found significant difference with p=6.795615128173357e-08 (lower ist better)\n", + "First is better with chance 0.0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "short_stats(hc_swapping_scores, hc_reversing_scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Next: your turn" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "myenv", + "language": "python", + "name": "myenv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/content/lab01/rise.css b/content/lab01/rise.css new file mode 100644 index 0000000..ad0dff5 --- /dev/null +++ b/content/lab01/rise.css @@ -0,0 +1,4 @@ +#rise-footer { + height:2em; + width:100%; +} \ No newline at end of file diff --git a/content/lab01/uni-leipzig.png b/content/lab01/uni-leipzig.png new file mode 100644 index 0000000..9cf527b Binary files /dev/null and b/content/lab01/uni-leipzig.png differ diff --git a/content/p5.ipynb b/content/p5.ipynb deleted file mode 100644 index 78be9cb..0000000 --- a/content/p5.ipynb +++ /dev/null @@ -1,150 +0,0 @@ -{ - "metadata":{ - "kernelspec":{ - "name":"p5js", - "display_name":"p5.js", - "language":"javascript" - }, - "language_info":{ - "codemirror_mode":{ - "name":"javascript" - }, - "file_extension":".js", - "mimetype":"text/javascript", - "name":"p5js", - "nbconvert_exporter":"javascript", - "pygments_lexer":"javascript", - "version":"es2017" - } - }, - "nbformat_minor":4, - "nbformat":4, - "cells":[ - { - "cell_type":"markdown", - "source":"# p5 notebook\n\nA minimal Jupyter notebook UI for [p5.js](https://p5js.org) kernels.", - "metadata":{ - - } - }, - { - "cell_type":"markdown", - "source":"First let's define a couple of variables:", - "metadata":{ - - } - }, - { - "cell_type":"code", - "source":"var n = 4;\nvar speed = 1;", - "metadata":{ - "trusted":true - }, - "execution_count":null, - "outputs":[ - - ] - }, - { - "cell_type":"markdown", - "source":"## The `setup` function\n\nThe usual p5 setup function, which creates the canvas.", - "metadata":{ - - } - }, - { - "cell_type":"code", - "source":"function setup () {\n createCanvas(innerWidth, innerHeight);\n rectMode(CENTER);\n}", - "metadata":{ - "trusted":true - }, - "execution_count":null, - "outputs":[ - - ] - }, - { - "cell_type":"markdown", - "source":"## The `draw` function\n\nFrom the [p5.js documentation](https://p5js.org/reference/#/p5/draw):\n\n> The `draw()` function continuously executes the lines of code contained inside its block until the program is stopped or `noLoop()` is called.", - "metadata":{ - - } - }, - { - "cell_type":"code", - "source":"function draw() {\n background('#ddd');\n translate(innerWidth / 2, innerHeight / 2);\n for (let i = 0; i < n; i++) {\n push();\n rotate(frameCount * speed / 1000 * (i + 1));\n fill(i * 5, i * 100, i * 150);\n const s = 200 - i * 10;\n rect(0, 0, s, s);\n pop();\n }\n}", - "metadata":{ - "trusted":true - }, - "execution_count":null, - "outputs":[ - - ] - }, - { - "cell_type":"markdown", - "source":"## Show the sketch\n\nNow let's show the sketch by using the `%show` magic:", - "metadata":{ - - } - }, - { - "cell_type":"code", - "source":"%show", - "metadata":{ - "trusted":true - }, - "execution_count":null, - "outputs":[ - - ] - }, - { - "cell_type":"markdown", - "source":"## Tweak the values\n\nWe can also tweak some values in real time:", - "metadata":{ - - } - }, - { - "cell_type":"code", - "source":"speed = 3", - "metadata":{ - "trusted":true - }, - "execution_count":null, - "outputs":[ - - ] - }, - { - "cell_type":"code", - "source":"n = 20", - "metadata":{ - "trusted":true - }, - "execution_count":null, - "outputs":[ - - ] - }, - { - "cell_type":"markdown", - "source":"We can also show the sketch a second time taking into account the new values:", - "metadata":{ - - } - }, - { - "cell_type":"code", - "source":"%show", - "metadata":{ - "trusted":true - }, - "execution_count":null, - "outputs":[ - - ] - } - ] -} \ No newline at end of file diff --git a/content/pyodide/altair.ipynb b/content/pyodide/altair.ipynb deleted file mode 100644 index d7b8780..0000000 --- a/content/pyodide/altair.ipynb +++ /dev/null @@ -1,231 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Altair in `JupyterLite`\n", - "\n", - "**Altair** is a declarative statistical visualization library for Python.\n", - "\n", - "Most of the examples below are from: https://altair-viz.github.io/gallery" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import the dependencies:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%pip install -q altair" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simple Bar Chart" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import altair as alt\n", - "import pandas as pd\n", - "\n", - "source = pd.DataFrame({\n", - " 'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],\n", - " 'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]\n", - "})\n", - "\n", - "alt.Chart(source).mark_bar().encode(\n", - " x='a',\n", - " y='b'\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simple Heatmap" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import altair as alt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Compute x^2 + y^2 across a 2D grid\n", - "x, y = np.meshgrid(range(-5, 5), range(-5, 5))\n", - "z = x ** 2 + y ** 2\n", - "\n", - "# Convert this grid to columnar data expected by Altair\n", - "source = pd.DataFrame({'x': x.ravel(),\n", - " 'y': y.ravel(),\n", - " 'z': z.ravel()})\n", - "\n", - "alt.Chart(source).mark_rect().encode(\n", - " x='x:O',\n", - " y='y:O',\n", - " color='z:Q'\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Install the Vega Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%pip install -q vega_datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interactive Average" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import altair as alt\n", - "from vega_datasets import data\n", - "\n", - "source = data.seattle_weather()\n", - "brush = alt.selection(type='interval', encodings=['x'])\n", - "\n", - "bars = alt.Chart().mark_bar().encode(\n", - " x='month(date):O',\n", - " y='mean(precipitation):Q',\n", - " opacity=alt.condition(brush, alt.OpacityValue(1), alt.OpacityValue(0.7)),\n", - ").add_selection(\n", - " brush\n", - ")\n", - "\n", - "line = alt.Chart().mark_rule(color='firebrick').encode(\n", - " y='mean(precipitation):Q',\n", - " size=alt.SizeValue(3)\n", - ").transform_filter(\n", - " brush\n", - ")\n", - "\n", - "alt.layer(bars, line, data=source)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Locations of US Airports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import altair as alt\n", - "from vega_datasets import data\n", - "\n", - "airports = data.airports.url\n", - "states = alt.topo_feature(data.us_10m.url, feature='states')\n", - "\n", - "# US states background\n", - "background = alt.Chart(states).mark_geoshape(\n", - " fill='lightgray',\n", - " stroke='white'\n", - ").properties(\n", - " width=500,\n", - " height=300\n", - ").project('albersUsa')\n", - "\n", - "# airport positions on background\n", - "points = alt.Chart(airports).transform_aggregate(\n", - " latitude='mean(latitude)',\n", - " longitude='mean(longitude)',\n", - " count='count()',\n", - " groupby=['state']\n", - ").mark_circle().encode(\n", - " longitude='longitude:Q',\n", - " latitude='latitude:Q',\n", - " size=alt.Size('count:Q', title='Number of Airports'),\n", - " color=alt.value('steelblue'),\n", - " tooltip=['state:N','count:Q']\n", - ").properties(\n", - " title='Number of airports in US'\n", - ")\n", - "\n", - "background + points\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (Pyodide)", - "language": "python", - "name": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/content/pyodide/folium.ipynb b/content/pyodide/folium.ipynb deleted file mode 100644 index 2828de0..0000000 --- a/content/pyodide/folium.ipynb +++ /dev/null @@ -1,154 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# `folium` Interactive Map Demo\n", - "\n", - "Simple demonstration of rendering a map in a `jupyterlite` notebook.\n", - "\n", - "Note that the `folium` package has several dependencies which themselves may have dependencies.\n", - "\n", - "The following code fragement, run in a fresh Python enviroment into which `folium` has already been installed, identifies the packages that are loaded in when `folium` is loaded:\n", - "\n", - "```python\n", - "#https://stackoverflow.com/a/40381601/454773\n", - "import sys\n", - "before = [str(m) for m in sys.modules]\n", - "import folium\n", - "after = [str(m) for m in sys.modules]\n", - "set([m.split('.')[0] for m in after if not m in before and not m.startswith('_')])\n", - "```\n", - "\n", - "The loaded packages are:\n", - "\n", - "```\n", - "{'branca',\n", - " 'certifi',\n", - " 'chardet',\n", - " 'cmath',\n", - " 'csv',\n", - " 'dateutil',\n", - " 'encodings',\n", - " 'folium',\n", - " 'gzip',\n", - " 'http',\n", - " 'idna',\n", - " 'importlib',\n", - " 'jinja2',\n", - " 'markupsafe',\n", - " 'mmap',\n", - " 'numpy',\n", - " 'pandas',\n", - " 'pkg_resources',\n", - " 'pytz',\n", - " 'requests',\n", - " 'secrets',\n", - " 'stringprep',\n", - " 'urllib3',\n", - " 'zipfile'}\n", - " ```\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following packages seem to need installing in order load `folium`, along with folium itself:\n", - "\n", - "```\n", - "chardet, certifi, idna, branca, urllib3, Jinja2, requests, Markupsafe\n", - "```\n", - "\n", - "Universal wheels, with filenames of the form `PACKAGE-VERSION-py2.py3-none-any.whl` appearing in the *Download files* area of a PyPi package page ([example](https://pypi.org/project/requests/#files)] are required in order to install the package.\n", - "\n", - "One required package, [`Markupsafe`](https://pypi.org/project/Markupsafe/#files)) *did not* have a universal wheel available, so a wheel was manually built elsewhere (by hacking the [`setup.py` file](https://github.com/pallets/markupsafe/blob/main/setup.py) to force it to build the wheel in a platform and speedup free way) and pushed to a downloadable location in an [*ad hoc* wheelhouse](https://opencomputinglab.github.io/vce-wheelhouse/)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "# Install folium requirements\n", - "%pip install -q folium" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Demo of `folium` Map\n", - "\n", - "Load in the `folium` package:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import folium" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And render a demo map:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "m = folium.Map(location=[50.693848, -1.304734], zoom_start=11)\n", - "m" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (Pyodide)", - "language": "python", - "name": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8" - }, - "orig_nbformat": 4, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/content/pyodide/interactive-widgets.ipynb b/content/pyodide/interactive-widgets.ipynb deleted file mode 100644 index 267273d..0000000 --- a/content/pyodide/interactive-widgets.ipynb +++ /dev/null @@ -1,268 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9ca234f7-84b7-4107-9bcd-74f5a4ffd07d", - "metadata": {}, - "source": [ - "# `ipywidgets` Interactive Demo\n", - "\n", - "Simple demonstration of rendering Interactive widgets in a `jupyterlite` notebook.\n", - "\n", - "`ipywidgets` can be installed in this deployment (it provides the @jupyter-widgets/jupyterlab-manager federated extension), but you will need to make your own deployment to have access to other interactive widgets libraries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d62fba6e", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q ipywidgets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bab23f8-de91-43c9-9cec-84f4924425fc", - "metadata": {}, - "outputs": [], - "source": [ - "from ipywidgets import IntSlider" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a15c5acb-ee72-4005-8761-5693db853f22", - "metadata": {}, - "outputs": [], - "source": [ - "slider = IntSlider()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ba89682-e0d7-4bd2-961a-f9956850fd5a", - "metadata": {}, - "outputs": [], - "source": [ - "slider" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50510ade-668f-4477-8cb2-41574609ac73", - "metadata": {}, - "outputs": [], - "source": [ - "slider" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7bac1ed8-8c77-426b-a781-1c1a6cfad829", - "metadata": {}, - "outputs": [], - "source": [ - "slider.value" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "976a70a0-e99d-4c20-b005-f59bbba10f85", - "metadata": {}, - "outputs": [], - "source": [ - "slider.value = 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3134c76e-cffb-4701-8230-e6c4bfbbfdb9", - "metadata": {}, - "outputs": [], - "source": [ - "from ipywidgets import IntText, link" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7b3fe0a-5695-4ef2-a573-40785e68fbae", - "metadata": {}, - "outputs": [], - "source": [ - "text = IntText()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e2fd50e-19e0-4e20-a1f7-ad65400ec636", - "metadata": {}, - "outputs": [], - "source": [ - "text" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb3bedce-7311-48c0-aeab-8fe3aa554b92", - "metadata": {}, - "outputs": [], - "source": [ - "link((slider, 'value'), (text, 'value'));" - ] - }, - { - "cell_type": "markdown", - "id": "71b68c3e-184e-4320-9513-d0bc72800a85", - "metadata": {}, - "source": [ - "# `bqplot` Interactive Demo\n", - "\n", - "Plotting in JupyterLite\n", - "\n", - "`bqplot` can be installed in this deployment (it provides the bqplot federated extension), but you will need to make your own deployment to have access to other interactive widgets libraries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "119eb9a3-ac98-42c3-98d4-1ac460eb75d3", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q bqplot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "23b32857-2958-4083-b16a-ac26cd2408d4", - "metadata": {}, - "outputs": [], - "source": [ - "from bqplot import *\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "np.random.seed(0)\n", - "\n", - "n = 100\n", - "\n", - "x = list(range(n))\n", - "y = np.cumsum(np.random.randn(n)) + 100.\n", - "\n", - "sc_x = LinearScale()\n", - "sc_y = LinearScale()\n", - "\n", - "lines = Lines(\n", - " x=x, y=y,\n", - " scales={'x': sc_x, 'y': sc_y}\n", - ")\n", - "ax_x = Axis(scale=sc_x, label='Index')\n", - "ax_y = Axis(scale=sc_y, orientation='vertical', label='lines')\n", - "\n", - "Figure(marks=[lines], axes=[ax_x, ax_y], title='Lines')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ddb6b44e-06a0-4049-a79d-33ffc90d5a03", - "metadata": {}, - "outputs": [], - "source": [ - "lines.colors = ['green']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e367e7fb-b403-41aa-9629-224827ec3005", - "metadata": {}, - "outputs": [], - "source": [ - "lines.fill = 'bottom'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4a167f3-07c4-4880-92f5-7fcdea0c61c6", - "metadata": {}, - "outputs": [], - "source": [ - "lines.marker = 'circle'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d1342f7-ec08-4f53-84dc-d712226d9e46", - "metadata": {}, - "outputs": [], - "source": [ - "n = 100\n", - "\n", - "x = list(range(n))\n", - "y = np.cumsum(np.random.randn(n))\n", - "\n", - "sc_x = LinearScale()\n", - "sc_y = LinearScale()\n", - "\n", - "bars = Bars(\n", - " x=x, y=y,\n", - " scales={'x': sc_x, 'y': sc_y}\n", - ")\n", - "ax_x = Axis(scale=sc_x, label='Index')\n", - "ax_y = Axis(scale=sc_y, orientation='vertical', label='bars')\n", - "\n", - "Figure(marks=[bars], axes=[ax_x, ax_y], title='Bars', animation_duration=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f86bbcfb-5b02-4700-b8d6-f90068893b55", - "metadata": {}, - "outputs": [], - "source": [ - "bars.y = np.cumsum(np.random.randn(n))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (Pyodide)", - "language": "python", - "name": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8" - }, - "orig_nbformat": 4, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/content/pyodide/ipycanvas.ipynb b/content/pyodide/ipycanvas.ipynb deleted file mode 100644 index 33e1b9a..0000000 --- a/content/pyodide/ipycanvas.ipynb +++ /dev/null @@ -1,178 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ipycanvas: John Conway's Game Of Life" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some of the following code is adapted from https://jakevdp.github.io/blog/2013/08/07/conways-game-of-life/" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%pip install -q ipycanvas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import asyncio\n", - "\n", - "import numpy as np\n", - "\n", - "from ipycanvas import RoughCanvas, hold_canvas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "def life_step(x):\n", - " \"\"\"Game of life step\"\"\"\n", - " nbrs_count = sum(np.roll(np.roll(x, i, 0), j, 1)\n", - " for i in (-1, 0, 1) for j in (-1, 0, 1)\n", - " if (i != 0 or j != 0))\n", - " return (nbrs_count == 3) | (x & (nbrs_count == 2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "def draw(x, canvas, color='black'):\n", - " with hold_canvas(canvas):\n", - " canvas.clear()\n", - " canvas.fill_style = '#FFF0C9'\n", - " canvas.rough_fill_style = 'solid'\n", - " canvas.fill_rect(-10, -10, canvas.width + 10, canvas.height + 10)\n", - " canvas.rough_fill_style = 'cross-hatch'\n", - "\n", - " canvas.fill_style = color\n", - " canvas.stroke_style = color\n", - "\n", - " living_cells = np.where(x)\n", - " \n", - " rects_x = living_cells[1] * n_pixels\n", - " rects_y = living_cells[0] * n_pixels\n", - "\n", - " canvas.fill_rects(rects_x, rects_y, n_pixels)\n", - " canvas.stroke_rects(rects_x, rects_y, n_pixels)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "glider_gun =\\\n", - "[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],\n", - " [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0],\n", - " [0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1],\n", - " [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1],\n", - " [1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\n", - " [1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0],\n", - " [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],\n", - " [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\n", - " [0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]\n", - "\n", - "x = np.zeros((50, 70), dtype=bool)\n", - "x[1:10,1:37] = glider_gun" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "n_pixels = 15\n", - "\n", - "canvas = RoughCanvas(width=x.shape[1]*n_pixels, height=x.shape[0]*n_pixels)\n", - "canvas.fill_style = '#FFF0C9'\n", - "canvas.rough_fill_style = 'solid'\n", - "canvas.fill_rect(0, 0, canvas.width, canvas.height)\n", - "\n", - "canvas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "draw(x, canvas, '#5770B3')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "for _ in range(300):\n", - " x = life_step(x)\n", - " draw(x, canvas, '#5770B3')\n", - "\n", - " await asyncio.sleep(0.1)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (Pyodide)", - "language": "python", - "name": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8" - }, - "orig_nbformat": 4, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/content/pyodide/ipyleaflet.ipynb b/content/pyodide/ipyleaflet.ipynb deleted file mode 100644 index 64dcff2..0000000 --- a/content/pyodide/ipyleaflet.ipynb +++ /dev/null @@ -1,259 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q bqplot ipyleaflet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from urllib.request import urlopen\n", - "import json\n", - "from datetime import datetime\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from js import fetch\n", - "\n", - "from ipywidgets import Dropdown\n", - "\n", - "from bqplot import Lines, Figure, LinearScale, DateScale, Axis\n", - "\n", - "from ipyleaflet import Map, GeoJSON, WidgetControl" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "URL = \"https://raw.githubusercontent.com/jupyter-widgets/ipyleaflet/master/examples/nations.json\"\n", - "\n", - "res = await fetch(URL)\n", - "text = await res.text()\n", - "\n", - "data = pd.read_json(text)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def clean_data(data):\n", - " for column in ['income', 'lifeExpectancy', 'population']:\n", - " data = data.drop(data[data[column].apply(len) <= 4].index)\n", - " return data\n", - "\n", - "def extrap_interp(data):\n", - " data = np.array(data)\n", - " x_range = np.arange(1800, 2009, 1.)\n", - " y_range = np.interp(x_range, data[:, 0], data[:, 1])\n", - " return y_range\n", - "\n", - "def extrap_data(data):\n", - " for column in ['income', 'lifeExpectancy', 'population']:\n", - " data[column] = data[column].apply(extrap_interp)\n", - " return data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = clean_data(data)\n", - "data = extrap_data(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "date_start = datetime(1800, 12, 31)\n", - "date_end = datetime(2009, 12, 31)\n", - "\n", - "date_scale = DateScale(min=date_start, max=date_end)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "date_data = pd.date_range(start=date_start, end=date_end, freq='A', normalize=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "country_name = 'Angola'\n", - "data_name = 'income'\n", - "\n", - "x_data = data[data.name == country_name][data_name].values[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_scale = LinearScale()\n", - "\n", - "lines = Lines(x=date_data, y=x_data, scales={'x': date_scale, 'y': x_scale})\n", - "\n", - "ax_x = Axis(label='Year', scale=date_scale, num_ticks=10, tick_format='%Y')\n", - "ax_y = Axis(label=data_name.capitalize(), scale=x_scale, orientation='vertical', side='left')\n", - "\n", - "figure = Figure(axes=[ax_x, ax_y], title=country_name, marks=[lines], animation_duration=500,\n", - " layout={'max_height': '250px', 'max_width': '400px'})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def update_figure(country_name, data_name):\n", - " try:\n", - " lines.y = data[data.name == country_name][data_name].values[0]\n", - " ax_y.label = data_name.capitalize()\n", - " figure.title = country_name\n", - " except IndexError:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "URL = \"https://raw.githubusercontent.com/jupyter-widgets/ipyleaflet/master/examples/countries.geo.json\"\n", - "\n", - "res = await fetch(URL)\n", - "text = await res.text()\n", - "\n", - "countries = json.loads(text)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m = Map(zoom=3)\n", - "\n", - "geo = GeoJSON(data=countries, style={'fillColor': 'white', 'weight': 0.5}, hover_style={'fillColor': '#1f77b4'}, name='Countries')\n", - "m.add_layer(geo)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "widget_control1 = WidgetControl(widget=figure, position='bottomright')\n", - "\n", - "m.add_control(widget_control1)\n", - "\n", - "def on_hover(event, feature, **kwargs):\n", - " global country_name\n", - "\n", - " country_name = feature['properties']['name']\n", - " update_figure(country_name, data_name)\n", - "\n", - "geo.on_hover(on_hover)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dropdown = Dropdown(\n", - " options=['income', 'population', 'lifeExpectancy'],\n", - " value=data_name,\n", - " description='Plotting:'\n", - ")\n", - "\n", - "def on_click(change):\n", - " global data_name\n", - "\n", - " data_name = change['new']\n", - " update_figure(country_name, data_name)\n", - "\n", - "dropdown.observe(on_click, 'value')\n", - "\n", - "widget_control2 = WidgetControl(widget=dropdown, position='bottomleft')\n", - "\n", - "m.add_control(widget_control2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (Pyodide)", - "language": "python", - "name": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8" - }, - "orig_nbformat": 4, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/content/pyodide/matplotlib.ipynb b/content/pyodide/matplotlib.ipynb deleted file mode 100644 index c2ec2be..0000000 --- a/content/pyodide/matplotlib.ipynb +++ /dev/null @@ -1,113 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.linspace(0, 10, 1000)\n", - "plt.plot(x, np.sin(x));" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Matplotlib: support for widgets backend" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q ipympl" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib widget" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.linspace(0, 10, 1000)\n", - "plt.plot(x, np.sin(x))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (Pyodide)", - "language": "python", - "name": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8" - }, - "orig_nbformat": 4, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/content/pyodide/plotly.ipynb b/content/pyodide/plotly.ipynb deleted file mode 100644 index 9716bee..0000000 --- a/content/pyodide/plotly.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plotly in JupyterLite\n", - "\n", - "`plotly.py` is an interactive, open-source, and browser-based graphing library for Python: https://plotly.com/python/" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q nbformat plotly" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Basic Figure" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.graph_objects as go\n", - "fig = go.Figure()\n", - "fig.add_trace(go.Scatter(y=[2, 1, 4, 3]))\n", - "fig.add_trace(go.Bar(y=[1, 4, 3, 2]))\n", - "fig.update_layout(title = 'Hello Figure')\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Basic Table with a Pandas DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.graph_objects as go\n", - "import pandas as pd\n", - "\n", - "from js import fetch\n", - "\n", - "URL = \"https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv\"\n", - "\n", - "res = await fetch(URL)\n", - "text = await res.text()\n", - "\n", - "filename = 'data.csv'\n", - "\n", - "with open(filename, 'w') as f:\n", - " f.write(text)\n", - "\n", - "df = pd.read_csv(filename)\n", - "\n", - "fig = go.Figure(data=[go.Table(\n", - " header=dict(values=list(df.columns),\n", - " fill_color='paleturquoise',\n", - " align='left'),\n", - " cells=dict(values=[df.Rank, df.State, df.Postal, df.Population],\n", - " fill_color='lavender',\n", - " align='left'))\n", - "])\n", - "\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quiver Plot with Points" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.figure_factory as ff\n", - "import plotly.graph_objects as go\n", - "\n", - "import numpy as np\n", - "\n", - "x,y = np.meshgrid(np.arange(-2, 2, .2),\n", - " np.arange(-2, 2, .25))\n", - "z = x*np.exp(-x**2 - y**2)\n", - "v, u = np.gradient(z, .2, .2)\n", - "\n", - "# Create quiver figure\n", - "fig = ff.create_quiver(x, y, u, v,\n", - " scale=.25,\n", - " arrow_scale=.4,\n", - " name='quiver',\n", - " line_width=1)\n", - "\n", - "# Add points to figure\n", - "fig.add_trace(go.Scatter(x=[-.7, .75], y=[0,0],\n", - " mode='markers',\n", - " marker_size=12,\n", - " name='points'))\n", - "\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (Pyodide)", - "language": "python", - "name": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8" - }, - "orig_nbformat": 4, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/content/pyodide/pyb2d/0_tutorial.ipynb b/content/pyodide/pyb2d/0_tutorial.ipynb deleted file mode 100644 index 3772314..0000000 --- a/content/pyodide/pyb2d/0_tutorial.ipynb +++ /dev/null @@ -1,649 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "b07a3b47-2262-4135-a1d2-52e8392b44eb", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "if \"pyodide\" in sys.modules:\n", - " import piplite\n", - " await piplite.install('pyb2d-jupyterlite-backend>=0.4.2')\n" - ] - }, - { - "cell_type": "markdown", - "id": "49c3f9ea-23ce-4c5c-b3fe-44f1cecadf20", - "metadata": {}, - "source": [ - "pyb2d is imported as b2d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dff93359-2c68-467a-9239-478a0e550a4b", - "metadata": {}, - "outputs": [], - "source": [ - "import b2d\n", - "# import pyb2d_jupyterlite_backend\n", - "from pyb2d_jupyterlite_backend.async_jupyter_gui import JupyterAsyncGui\n", - "import numpy as np\n", - "import matplotlib.pylab as plt" - ] - }, - { - "cell_type": "markdown", - "id": "bc977c4e-75ee-4349-9408-650c3dcd01e0", - "metadata": {}, - "source": [ - "# Tutorial 0: A free falling body\n", - "The first step with Box2D is the creation of the world. The world is parametrized by a gravity vector." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ff914a6-eb18-45a1-b1ed-e8ad7ab0d298", - "metadata": {}, - "outputs": [], - "source": [ - "# the world\n", - "gravity = (0, -10)\n", - "world = b2d.World(gravity)" - ] - }, - { - "cell_type": "markdown", - "id": "3afdbb2a-e694-4779-b95e-73a5b38d34b6", - "metadata": {}, - "source": [ - "Create a circle-shaped body" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99837a63-4628-483c-8f2d-cc4aec9cb1d5", - "metadata": {}, - "outputs": [], - "source": [ - "# the body def\n", - "body_def = b2d.BodyDef()\n", - "body_def.type = b2d.BodyType.dynamic\n", - "body_def.position = (0, 0)\n", - "\n", - "# the body\n", - "body = world.create_body(body_def)\n", - "\n", - "# shape\n", - "circle_shape = b2d.CircleShape()\n", - "circle_shape.radius = 1.0\n", - "\n", - "# the fixture\n", - "fixture_def = b2d.FixtureDef()\n", - "fixture_def.shape = circle_shape\n", - "fixture_def.density = 1.0\n", - "\n", - "# create and add the fixture to the body\n", - "fixture = body.create_fixture(fixture_def)" - ] - }, - { - "cell_type": "markdown", - "id": "bf9758a6-fb6e-4f9c-b15f-783f9488cf7e", - "metadata": {}, - "source": [ - "We can now have a look at the world: We render the world st. each meter in the Box2D world will be 100 pixels in the image:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b433892-3c82-43be-a085-eda3e4279b2c", - "metadata": {}, - "outputs": [], - "source": [ - "# from b2d.plot import render_world\n", - "b2d.plot.plot_world(world, ppm=100)" - ] - }, - { - "cell_type": "markdown", - "id": "5e1db1f1-6e47-454c-9ea9-86262d7da309", - "metadata": {}, - "source": [ - "Lets run the world for a total of 5 seconds. \n", - "Usually one wants to run the world at a certain frame rate.\n", - "With the frame rate and the total time we can compute the delta for each iteration and how many steps we need" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41a232a9-a3c5-425d-9aed-d3adb90d6314", - "metadata": {}, - "outputs": [], - "source": [ - "t = 5\n", - "fps = 40\n", - "dt = 1.0 / fps\n", - "n_steps = int(t / dt + 0.5)\n", - "print(f\"t={t} fps={fps} dt={dt} n_steps={n_steps}\")" - ] - }, - { - "cell_type": "markdown", - "id": "4d458acb-6d5c-47ba-bcbf-d15ea2cf2537", - "metadata": {}, - "source": [ - "in each step we query the bodies position and velocity and store then for later plotting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e042c7b-07a7-445f-ba04-e38173b46c0f", - "metadata": {}, - "outputs": [], - "source": [ - "positions = np.zeros([n_steps, 2])\n", - "velocites = np.zeros([n_steps, 2])\n", - "timepoints = np.zeros([n_steps])\n", - "\n", - "t_elapsed = 0.0\n", - "for i in range(n_steps):\n", - "\n", - " # get the bodies center of mass\n", - " positions[i, :] = body.world_center\n", - "\n", - " # get the bodies velocity\n", - " velocites[i, :] = body.linear_velocity\n", - "\n", - " timepoints[i] = t_elapsed\n", - "\n", - " world.step(time_step=dt, velocity_iterations=1, position_iterations=1)\n", - " t_elapsed += dt" - ] - }, - { - "cell_type": "markdown", - "id": "0ec7d66c-c979-40fa-8af3-9e99873ec105", - "metadata": {}, - "source": [ - "plot the y-position against the time. We can see that the body is falling down in an accelerating way:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "434cb907-1b76-414e-bb5e-6ea32dd1f829", - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(timepoints, positions[:, 1])\n", - "plt.ylabel('y-poistion [meter]')\n", - "plt.xlabel('t [sec]')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b7f58954-4ea1-49f9-b0b0-7df38336860d", - "metadata": {}, - "source": [ - "as expected the x position is not changing since the gravity vector is non-zero only in the x direction" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "39573eed-e6c8-45bf-8e35-4251b660ce3f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "plt.plot(timepoints, positions[:, 0])\n", - "plt.ylabel('x-poistion [meter]')\n", - "plt.xlabel('t [sec]')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cdb98dc5-3bc8-4933-91a0-a1db3afb9c34", - "metadata": {}, - "source": [ - "# Tutorial 1: A falling body in a box, more pythonic\n", - "Create a world, but in a more pythonic way, and animate the world" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d58c2639-21da-490b-8dcd-205962f63dfc", - "metadata": {}, - "outputs": [], - "source": [ - "# the world\n", - "world = b2d.world(gravity=(0, -10))\n", - "\n", - "# create the dynamic body\n", - "body = world.create_dynamic_body(\n", - " position=(5, 5),\n", - " fixtures=b2d.fixture_def(shape=b2d.circle_shape(radius=1), density=1, restitution=0.75),\n", - ")\n", - "\n", - "# create a box\n", - "box_shape = b2d.ChainShape()\n", - "box_shape.create_loop([(0, 0), (0, 10),(10,10),(10, 0)])\n", - "box = world.create_static_body(\n", - " position=(0, 0), fixtures=b2d.fixture_def(shape=box_shape, friction=0)\n", - ")\n", - "b2d.plot.animate_world(world, ppm=20, t=10)" - ] - }, - { - "cell_type": "markdown", - "id": "10dbb85a-84b0-4820-8fb3-6108d9c0fe00", - "metadata": {}, - "source": [ - "note that when we animate that world again, the body has already been fallen" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c919702c-7c62-4d87-bf1f-df5027d72a83", - "metadata": {}, - "outputs": [], - "source": [ - "b2d.plot.animate_world(world, ppm=20, t=2)" - ] - }, - { - "cell_type": "markdown", - "id": "7322e9c5-8608-4375-81ed-766cbb2af927", - "metadata": {}, - "source": [ - "# Tutorial 2: Interactive worlds\n", - "While animating the world already is already nice, interacting with the world is even better.\n", - "pyb2d has a framwork to interact with the world for multiple backends.\n", - "This framework is called `TestbedBase` since you can \"test\" your world in an interactive way" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97cddd47-5a88-4cae-8543-cfcdf658255a", - "metadata": {}, - "outputs": [], - "source": [ - "from b2d.testbed import TestbedBase\n", - "\n", - "class InteractiveExample(TestbedBase):\n", - " def __init__(self, settings=None):\n", - " super(InteractiveExample, self).__init__(settings=settings)\n", - " # create two balls\n", - " body = self.world.create_dynamic_body(position=(5, 5),\n", - " fixtures=b2d.fixture_def(shape=b2d.circle_shape(radius=1), density=1, restitution=0.5),\n", - " )\n", - " body = self.world.create_dynamic_body(position=(8, 5),\n", - " fixtures=b2d.fixture_def(shape=b2d.circle_shape(radius=1), density=1, restitution=0.8),\n", - " )\n", - " # create a box\n", - " box_shape = b2d.ChainShape()\n", - " box_shape.create_loop([(0, 0), (0, 10),(10,10),(10, 0)])\n", - " box = self.world.create_static_body(\n", - " position=(0, 0), fixtures=b2d.fixture_def(shape=box_shape, friction=0)\n", - " )\n", - " \n", - "s = JupyterAsyncGui.Settings()\n", - "s.resolution = [300,300]\n", - "b2d.testbed.run(InteractiveExample, backend=JupyterAsyncGui, gui_settings=s);" - ] - }, - { - "cell_type": "markdown", - "id": "64bf55d1-4117-4af0-8f1f-65de33751743", - "metadata": { - "tags": [] - }, - "source": [ - "# Tutorial 3: Joints" - ] - }, - { - "cell_type": "markdown", - "id": "07147cab-23be-4406-85f7-4b3d174e3954", - "metadata": { - "tags": [] - }, - "source": [ - "## Tutorial 3.1: Prismatic Joint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9a2d178d-33d7-4c51-b0ff-f66c98cac673", - "metadata": {}, - "outputs": [], - "source": [ - "world = b2d.world(gravity=(0, -10))\n", - "anchor_body = world.create_static_body(position=(0, 0))\n", - "b = world.create_dynamic_body(\n", - " position=(10, 10),\n", - " fixtures=b2d.fixture_def(shape=b2d.polygon_shape(box=[2, 0.5]), density=1),\n", - " linear_damping=0.0,\n", - " angular_damping=0.0,\n", - ")\n", - "world.create_prismatic_joint(anchor_body, b, local_axis_a=(1, 1))\n", - "b2d.plot.animate_world(world, ppm=20, t=3, bounding_box=((0,0),(10,10)))" - ] - }, - { - "cell_type": "markdown", - "id": "07971d69-b2ef-4d74-8c1c-48f38dcc708c", - "metadata": { - "tags": [] - }, - "source": [ - "## Tutorial 3.2: Pully Joint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "39b7fef2-4b4b-4904-9899-1a34f1039693", - "metadata": {}, - "outputs": [], - "source": [ - "world = b2d.world(gravity=(0, -10))\n", - "\n", - "\n", - "a = world.create_dynamic_body(\n", - " position=(-5, 0),\n", - " fixtures=b2d.fixture_def(shape=b2d.polygon_shape(box=[2, 0.8]), density=1),\n", - " linear_damping=0.0,\n", - " angular_damping=0.0,\n", - ")\n", - "b = world.create_dynamic_body(\n", - " position=(5, 0),\n", - " fixtures=b2d.fixture_def(shape=b2d.polygon_shape(box=[2, 0.5]), density=1),\n", - " linear_damping=0.0,\n", - " angular_damping=0.0,\n", - ")\n", - "world.create_pully_joint(\n", - " a,\n", - " b,\n", - " length_a=10,\n", - " length_b=10,\n", - " ground_anchor_a=(-5, 10),\n", - " ground_anchor_b=(5, 10),\n", - " local_anchor_a=(0, 0),\n", - " local_anchor_b=(0, 0),\n", - ")\n", - "b2d.plot.animate_world(world, ppm=20, t=5, bounding_box=((-10,-12),(10,12)))" - ] - }, - { - "cell_type": "markdown", - "id": "7e8fb17d-1dda-45cb-98df-6b98be5b4e6c", - "metadata": {}, - "source": [ - "## Tutorial 3.3: Revolute Joint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a2686e5f-3fa2-412d-8a40-2e9a67123d43", - "metadata": {}, - "outputs": [], - "source": [ - "world = b2d.world(gravity=(0, -10))\n", - "bodies = []\n", - "b = world.create_static_body(position=(0, 15))\n", - "bodies.append(b)\n", - "for i in range(5):\n", - " b = world.create_dynamic_body(\n", - " position=(i * 4 + 2, 15),\n", - " fixtures=b2d.fixture_def(shape=b2d.polygon_shape(box=[2, 0.5]), density=1),\n", - " linear_damping=0.0,\n", - " angular_damping=0.0,\n", - " )\n", - " bodies.append(b)\n", - "world.create_revolute_joint(\n", - " bodies[0], bodies[1], local_anchor_a=(0, 0), local_anchor_b=(-2, 0.0)\n", - ")\n", - "for i in range(1, len(bodies) - 1):\n", - " a = bodies[i]\n", - " b = bodies[i + 1]\n", - " world.create_revolute_joint(a, b, local_anchor_a=(2, 0.0), local_anchor_b=(-2, 0.0))\n", - "b2d.plot.animate_world(world, ppm=20, t=5, bounding_box=((-20,-10),(20,20)))" - ] - }, - { - "cell_type": "markdown", - "id": "9caa18f0-4eb7-4e72-8445-8f6d096d9465", - "metadata": { - "tags": [] - }, - "source": [ - "## Tutorial 3.4: Weld Joint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d74fb2f-7da5-4ad7-8f21-fba1f97acee2", - "metadata": {}, - "outputs": [], - "source": [ - "# the world\n", - "world = b2d.world(gravity=(0, -10))\n", - "\n", - "\n", - "bodies = []\n", - "\n", - "# create a static body as anchor\n", - "b = world.create_static_body(\n", - " position=(0, 4), fixtures=b2d.fixture_def(shape=b2d.polygon_shape(box=[0.3, 0.5]))\n", - ")\n", - "bodies.append(b)\n", - "\n", - "for i in range(4):\n", - " b = world.create_dynamic_body(\n", - " position=(i + 1.0, 4),\n", - " fixtures=b2d.fixture_def(shape=b2d.polygon_shape(box=[0.3, 0.5]), density=0.1),\n", - " linear_damping=2.5,\n", - " angular_damping=2.5,\n", - " )\n", - " bodies.append(b)\n", - "\n", - "for i in range(len(bodies) - 1):\n", - " a = bodies[i]\n", - " b = bodies[i + 1]\n", - " world.create_weld_joint(\n", - " a,\n", - " b,\n", - " local_anchor_a=(0.5, 0.5),\n", - " local_anchor_b=(-0.5, 0.5),\n", - " damping=0.1,\n", - " reference_angle=0,\n", - " stiffness=20,\n", - " )\n", - " world.create_weld_joint(\n", - " a,\n", - " b,\n", - " local_anchor_a=(0.5, -0.5),\n", - " local_anchor_b=(-0.5, -0.5),\n", - " damping=0.1,\n", - " reference_angle=0,\n", - " stiffness=20,\n", - " )\n", - "b2d.plot.animate_world(world, ppm=20, t=5, bounding_box=((0,-5),(5,5)))" - ] - }, - { - "cell_type": "markdown", - "id": "7373461e-d1fa-4ad9-aeaa-048287839fd9", - "metadata": {}, - "source": [ - "## Tutorial 3.5: Wheel Joint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1eb711e0-ae53-43ed-b0c1-c0a2fe42b407", - "metadata": {}, - "outputs": [], - "source": [ - "world = b2d.world(gravity=(0, -10))\n", - "edge = world.create_static_body(\n", - " position=(0, 0), fixtures=b2d.fixture_def(shape=b2d.edge_shape([(-20, 0), (5, 0)]))\n", - ")\n", - "\n", - "# random slope\n", - "x = np.linspace(5, 50, 10)\n", - "y = np.random.rand(10) * 4 - 2\n", - "y[0] = 0\n", - "xy = np.stack([x, y]).T\n", - "xy = np.flip(xy, axis=0)\n", - "edge = world.create_static_body(\n", - " position=(0, 0),\n", - " fixtures=b2d.fixture_def(shape=b2d.chain_shape(xy, prev_vertex=(10, 0))),\n", - ")\n", - "# create car\n", - "left_wheel = world.create_dynamic_body(\n", - " position=(-3, 2),\n", - " fixtures=b2d.fixture_def(shape=b2d.circle_shape(radius=2), density=1),\n", - ")\n", - "right_wheel = world.create_dynamic_body(\n", - " position=(3, 2),\n", - " fixtures=b2d.fixture_def(shape=b2d.circle_shape(radius=2), density=1),\n", - ")\n", - "\n", - "chasis = world.create_dynamic_body(\n", - " position=(0, 2),\n", - " fixtures=b2d.fixture_def(shape=b2d.polygon_shape(box=[3, 0.5]), density=1),\n", - ")\n", - "\n", - "wheel_joint_def = dict(\n", - " stiffness=10,\n", - " enable_motor=True,\n", - " motor_speed=-100,\n", - " max_motor_torque=100,\n", - " collide_connected=False,\n", - " enable_limit=True,\n", - " lower_translation=-0.4,\n", - " upper_translation=0.4,\n", - " local_axis_a=(0, 1),\n", - ")\n", - "world.create_wheel_joint(chasis, left_wheel, local_anchor_a=(-3, 0), **wheel_joint_def)\n", - "world.create_wheel_joint(chasis, right_wheel, local_anchor_a=(3, 0), **wheel_joint_def)\n", - "\n", - "\n", - "b2d.plot.animate_world(world, ppm=20, t=15, bounding_box=((-10,-5),(20,5)))" - ] - }, - { - "cell_type": "markdown", - "id": "d9a0fffc-ae40-47ec-b185-2a6fe0dde496", - "metadata": {}, - "source": [ - "## Tutorial 3.6: Distance Joint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c121398-f08f-4ea0-a875-de141ba53508", - "metadata": {}, - "outputs": [], - "source": [ - "world = b2d.world(gravity=(0, -10))\n", - "\n", - "for i in range(10):\n", - "\n", - " # create static anchor (does not need shape/fixture)\n", - " anchor = world.create_static_body(position=(i, 0))\n", - "\n", - " # 5 below the anchor\n", - " body = world.create_dynamic_body(\n", - " position=(i, -10),\n", - " fixtures=b2d.fixture_def(shape=b2d.circle_shape(radius=0.4), density=0.5),\n", - " )\n", - "\n", - " # distance joints of various stiffness-es\n", - " world.create_distance_joint(anchor, body, length=10, stiffness=0.5 * (i + 1))\n", - "\n", - "b2d.plot.animate_world(world, ppm=20, t=10, bounding_box=((-2,-20),(10,0)))" - ] - }, - { - "cell_type": "markdown", - "id": "fb6afaff-8236-4206-85c7-3ba2de466ba9", - "metadata": {}, - "source": [ - "# Tutorial 4: Particles" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f5c3b83-51d9-47cb-9b73-d5f8b0e03a76", - "metadata": {}, - "outputs": [], - "source": [ - "world = b2d.world(gravity=(0, -10))\n", - "pdef = b2d.particle_system_def(radius=0.1)\n", - "psystem = world.create_particle_system(pdef)\n", - "\n", - "emitter_pos = (0, 0)\n", - "emitter_def = b2d.RandomizedLinearEmitterDef()\n", - "emitter_def.emite_rate = 400\n", - "emitter_def.lifetime = 5.1\n", - "emitter_def.size = (2, 1)\n", - "emitter_def.velocity = (6, 20)\n", - "emitter = b2d.RandomizedLinearEmitter(psystem, emitter_def)\n", - "b2d.plot.animate_world(world, ppm=20, t=10, bounding_box=((-10,-20),(20,5)), pre_step=emitter.step)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ea9d7882-d3a4-45bb-b59b-cb1c9cd33990", - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/content/pyodide/pyb2d/color_mixing.ipynb b/content/pyodide/pyb2d/color_mixing.ipynb deleted file mode 100644 index 9dc274c..0000000 --- a/content/pyodide/pyb2d/color_mixing.ipynb +++ /dev/null @@ -1,123 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "if \"pyodide\" in sys.modules:\n", - " import piplite\n", - " await piplite.install('pyb2d-jupyterlite-backend>=0.4.2')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from b2d.testbed import TestbedBase\n", - "import random\n", - "import numpy\n", - "import b2d\n", - "\n", - "class ColorMixing(TestbedBase):\n", - "\n", - " name = \"ColorMixing\"\n", - "\n", - " def __init__(self, settings=None):\n", - " super(ColorMixing, self).__init__(settings=settings)\n", - " dimensions = [30, 30]\n", - "\n", - " # the outer box\n", - " box_shape = b2d.ChainShape()\n", - " box_shape.create_loop(\n", - " [\n", - " (0, 0),\n", - " (0, dimensions[1]),\n", - " (dimensions[0], dimensions[1]),\n", - " (dimensions[0], 0),\n", - " ]\n", - " )\n", - " box = self.world.create_static_body(position=(0, 0), shape=box_shape)\n", - "\n", - " fixtureA = b2d.fixture_def(\n", - " shape=b2d.circle_shape(1), density=2.2, friction=0.2, restitution=0.5\n", - " )\n", - " body = self.world.create_dynamic_body(position=(13, 10), fixtures=fixtureA)\n", - "\n", - " pdef = b2d.particle_system_def(\n", - " viscous_strength=0.9,\n", - " spring_strength=0.0,\n", - " damping_strength=0.5,\n", - " pressure_strength=0.5,\n", - " color_mixing_strength=0.008,\n", - " density=2,\n", - " )\n", - " psystem = self.world.create_particle_system(pdef)\n", - " psystem.radius = 0.3\n", - " psystem.damping = 1.0\n", - "\n", - " colors = [\n", - " (255, 0, 0, 255),\n", - " (0, 255, 0, 255),\n", - " (0, 0, 255, 255),\n", - " (255, 255, 0, 255),\n", - " ]\n", - " posiitons = [(6, 10), (20, 10), (20, 20), (6, 20)]\n", - " for color, pos in zip(colors, posiitons):\n", - "\n", - " shape = b2d.polygon_shape(box=(5, 5), center=pos, angle=0)\n", - " pgDef = b2d.particle_group_def(\n", - " flags=b2d.ParticleFlag.waterParticle\n", - " | b2d.ParticleFlag.colorMixingParticle,\n", - " # group_flags=b2d.ParticleGroupFlag.solidParticleGroup,\n", - " shape=shape,\n", - " strength=1.0,\n", - " color=color,\n", - " )\n", - " group = psystem.create_particle_group(pgDef)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyb2d_jupyterlite_backend.async_jupyter_gui import JupyterAsyncGui\n", - "\n", - "s = JupyterAsyncGui.Settings()\n", - "s.resolution = [1000,500]\n", - "s.scale = 8\n", - "s.fps = 40\n", - "\n", - "tb = b2d.testbed.run(ColorMixing, backend=JupyterAsyncGui, gui_settings=s)" - ] - } - ], - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/content/pyodide/pyb2d/games/angry_shapes.ipynb b/content/pyodide/pyb2d/games/angry_shapes.ipynb deleted file mode 100644 index cb199ef..0000000 --- a/content/pyodide/pyb2d/games/angry_shapes.ipynb +++ /dev/null @@ -1,419 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "2859de40-f927-4790-b192-c5b0531058f7", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "if \"pyodide\" in sys.modules:\n", - " import piplite\n", - " await piplite.install('pyb2d-jupyterlite-backend>=0.4.2')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ac3e93b-3e9e-4cd7-a183-0214b0dcb513", - "metadata": {}, - "outputs": [], - "source": [ - "from b2d.testbed import TestbedBase\n", - "import math\n", - "import numpy\n", - "import b2d\n", - "\n", - "class AngryShapes(TestbedBase):\n", - "\n", - " name = \"AngryShapes\"\n", - "\n", - " class Settings(TestbedBase.Settings):\n", - " substeps: int = 2\n", - "\n", - " def draw_segment(self, p1, p2, color, line_width=1):\n", - " screen_p1 = self._point(self.world_to_screen(p1))\n", - " screen_p2 = self._point(self.world_to_screen(p2))\n", - " screen_color = self._uint8_color(color)\n", - " screen_line_width = self._line_width(line_width)\n", - "\n", - " cv.line(self._image, screen_p1, screen_p2, screen_color, screen_line_width)\n", - "\n", - " def draw_polygon(self, vertices, color, line_width=1):\n", - " # todo add C++ function for this\n", - " screen_vertices = numpy.array(\n", - " [self._point(self.world_to_screen(v)) for v in vertices], dtype=\"int32\"\n", - " )\n", - " screen_color = self._uint8_color(color)\n", - " screen_line_width = self._line_width(line_width)\n", - "\n", - " cv.polylines(\n", - " self._image, [screen_vertices], True, screen_color, screen_line_width, 8\n", - " )\n", - "\n", - " def draw_solid_polygon(self, vertices, color):\n", - " # todo add C++ function for this\n", - " screen_vertices = numpy.array(\n", - " [self._point(self.world_to_screen(v)) for v in vertices], dtype=\"int32\"\n", - " )\n", - " screen_color = self._uint8_color(color)\n", - "\n", - " cv.fillPoly(self._image, [screen_vertices], screen_color, 8)\n", - "\n", - " def __init__(self, settings=None):\n", - " super(AngryShapes, self).__init__(settings=settings)\n", - "\n", - " self.targets = []\n", - " self.projectiles = []\n", - " self.marked_for_destruction = []\n", - " self.emitter = None\n", - "\n", - " # particle system\n", - " pdef = b2d.particle_system_def(\n", - " viscous_strength=0.9,\n", - " spring_strength=0.0,\n", - " damping_strength=100.5,\n", - " pressure_strength=1.0,\n", - " color_mixing_strength=0.05,\n", - " density=0.1,\n", - " )\n", - "\n", - " self.psystem = self.world.create_particle_system(pdef)\n", - " self.psystem.radius = 1\n", - " self.psystem.damping = 0.5\n", - "\n", - " self.build_outer_box()\n", - " self.build_castle()\n", - " self.build_launcher()\n", - " self.arm_launcher()\n", - " self.build_explosives()\n", - "\n", - " def build_outer_box(self):\n", - " # the outer box\n", - "\n", - " shape = b2d.edge_shape([(100, 0), (600, 0)])\n", - " box = self.world.create_static_body(\n", - " position=(0, 0), fixtures=b2d.fixture_def(shape=shape, friction=1)\n", - " )\n", - "\n", - " def build_target(self, pos):\n", - " t = self.world.create_dynamic_body(\n", - " position=pos,\n", - " fixtures=[\n", - " b2d.fixture_def(shape=b2d.circle_shape(radius=4), density=1.0),\n", - " b2d.fixture_def(\n", - " shape=b2d.circle_shape(radius=2, pos=(3, 3)), density=1.0\n", - " ),\n", - " b2d.fixture_def(\n", - " shape=b2d.circle_shape(radius=2, pos=(-3, 3)), density=1.0\n", - " ),\n", - " ],\n", - " linear_damping=0,\n", - " angular_damping=0,\n", - " user_data=\"target\",\n", - " )\n", - " self.targets.append(t)\n", - "\n", - " def build_castle(self):\n", - " def build_pyramid(offset, bar_shape, n):\n", - " def build_brick(pos, size):\n", - " hsize = [s / 2 for s in size]\n", - " self.world.create_dynamic_body(\n", - " position=(\n", - " pos[0] + hsize[0] + offset[0],\n", - " pos[1] + hsize[1] + offset[1],\n", - " ),\n", - " fixtures=b2d.fixture_def(\n", - " shape=b2d.polygon_shape(box=hsize), density=8\n", - " ),\n", - " user_data=\"brick\",\n", - " )\n", - "\n", - " bar_length = bar_shape[0]\n", - " bar_width = bar_shape[1]\n", - "\n", - " nxm = n\n", - " for y in range(nxm):\n", - " py = y * (bar_length + bar_width)\n", - " nx = nxm - y\n", - " for x in range(nx):\n", - " px = x * bar_length + y * (bar_length) / 2.0\n", - " if y + 1 < nxm - 1:\n", - " if x == 0:\n", - " px += bar_width / 2\n", - " if x + 1 == nx:\n", - " px -= bar_width / 2\n", - "\n", - " build_brick((px, py), (bar_width, bar_length))\n", - " if x < nx - 1:\n", - " self.build_target(\n", - " pos=(\n", - " px + offset[0] + bar_length / 2,\n", - " py + offset[1] + bar_width,\n", - " )\n", - " )\n", - " build_brick(\n", - " (px + bar_width / 2, py + bar_length),\n", - " (bar_length, bar_width),\n", - " )\n", - "\n", - " build_pyramid(offset=(100, 0), bar_shape=[40, 4], n=4)\n", - " build_pyramid(offset=(400, 0), bar_shape=[30, 3], n=4)\n", - "\n", - " def build_launcher(self):\n", - "\n", - " self.launcher_anchor_pos = (30, 0)\n", - " self.launcher_anchor = self.world.create_static_body(\n", - " position=self.launcher_anchor_pos\n", - " )\n", - "\n", - " def arm_launcher(self):\n", - " self.reload_time = None\n", - " self.is_armed = True\n", - " self.projectile_radius = 3\n", - " projectile_pos = (self.launcher_anchor_pos[0], self.launcher_anchor_pos[1] / 2)\n", - "\n", - " self.projectile = self.world.create_dynamic_body(\n", - " position=projectile_pos,\n", - " fixtures=b2d.fixture_def(\n", - " shape=b2d.circle_shape(radius=self.projectile_radius), density=100.0\n", - " ),\n", - " linear_damping=0,\n", - " angular_damping=0,\n", - " user_data=\"projectile\",\n", - " )\n", - " self.projectiles.append(self.projectile)\n", - " self.projectile_joint = self.world.create_distance_joint(\n", - " self.launcher_anchor, self.projectile, length=1, stiffness=10000\n", - " )\n", - " self.mouse_joint = None\n", - "\n", - " def build_explosives(self):\n", - " self.explosives = []\n", - "\n", - " def on_mouse_down(self, p):\n", - " if self.is_armed:\n", - " body = self.world.find_body(pos=p)\n", - " if body is not None and body.user_data is not None:\n", - " print(\"got body\")\n", - " if body.user_data == \"projectile\":\n", - " print(\"got projectile\")\n", - " kwargs = dict(\n", - " body_a=self.groundbody,\n", - " body_b=body,\n", - " target=p,\n", - " max_force=50000.0 * body.mass,\n", - " stiffness=10000.0,\n", - " )\n", - "\n", - " self.mouse_joint = self.world.create_mouse_joint(**kwargs)\n", - " body.awake = True\n", - " return True\n", - "\n", - " return False\n", - "\n", - " def on_mouse_move(self, p):\n", - " if self.is_armed:\n", - " if self.mouse_joint is not None:\n", - " self.mouse_joint.target = p\n", - " return True\n", - " return False\n", - "\n", - " def on_mouse_up(self, p):\n", - " if self.is_armed:\n", - " if self.mouse_joint is not None:\n", - " self.world.destroy_joint(self.mouse_joint)\n", - " if self.projectile_joint is not None:\n", - " self.world.destroy_joint(self.projectile_joint)\n", - " self.projectile_joint = None\n", - " self.mouse_joint = None\n", - " delta = self.launcher_anchor.position - b2d.vec2(p)\n", - " scaled_delta = delta * 50000.0\n", - " print(scaled_delta)\n", - "\n", - " self.projectile.apply_linear_impulse_to_center(scaled_delta, True)\n", - " self.reload_time = self.elapsed_time + 1.0\n", - " self.is_armed = False\n", - " return False\n", - "\n", - " def begin_contact(self, contact):\n", - " body_a = contact.body_a\n", - " body_b = contact.body_b\n", - " ud_a = body_a.user_data\n", - " ud_b = body_b.user_data\n", - " if ud_b == \"projectile\":\n", - " body_a, body_b = body_b, body_a\n", - " ud_a, ud_b = ud_b, ud_a\n", - " if ud_a == \"projectile\":\n", - "\n", - " if ud_b == \"target\" or ud_b == \"brick\":\n", - " self.marked_for_destruction.append(body_a)\n", - " emitter_def = b2d.RandomizedRadialEmitterDef()\n", - " emitter_def.emite_rate = 20000\n", - " emitter_def.lifetime = 0.7\n", - " emitter_def.enabled = True\n", - " emitter_def.inner_radius = 0.0\n", - " emitter_def.outer_radius = 1.0\n", - " emitter_def.velocity_magnitude = 1000.0\n", - " emitter_def.start_angle = 0\n", - " emitter_def.stop_angle = math.pi\n", - " emitter_def.transform = b2d.Transform(body_a.position, b2d.Rot(0))\n", - " self.emitter = b2d.RandomizedRadialEmitter(self.psystem, emitter_def)\n", - " self.emitter_die_time = self.elapsed_time + 0.02\n", - "\n", - " def pre_step(self, dt):\n", - "\n", - " if self.reload_time is not None:\n", - " if self.elapsed_time >= self.reload_time:\n", - " self.arm_launcher()\n", - "\n", - " # delete contact bodies\n", - " for body in self.marked_for_destruction:\n", - " if body in self.projectiles:\n", - " self.projectiles.remove(body)\n", - " self.world.destroy_body(body)\n", - " if body == self.projectile:\n", - " self.reload_time = self.elapsed_time + 1.0\n", - " self.marked_for_destruction = []\n", - "\n", - " # delete bodies which have fallen down\n", - " for body in self.world.bodies:\n", - " if body.position.y < -100:\n", - " if body.user_data == \"projectile\":\n", - " self.projectiles.remove(body)\n", - " if body.user_data == \"target\":\n", - " self.targets.remove(body)\n", - " self.world.destroy_body(body)\n", - "\n", - " # emmiter\n", - " if self.emitter is not None:\n", - " self.emitter.step(dt)\n", - " if self.elapsed_time >= self.emitter_die_time:\n", - " self.emitter = None\n", - "\n", - " def draw_target(self, target):\n", - " center = target.position\n", - " center_l = target.get_world_point((-3, 3))\n", - " center_r = target.get_world_point((3, 3))\n", - " eye_left = target.get_world_point((-1, 1))\n", - " eye_right = target.get_world_point((1, 1))\n", - " pink = [c / 255 for c in (248, 24, 148)]\n", - "\n", - " self.debug_draw.draw_solid_circle(\n", - " center=center, radius=4, axis=None, color=pink\n", - " )\n", - " self.debug_draw.draw_solid_circle(\n", - " center=center_l, radius=2, axis=None, color=pink\n", - " )\n", - " self.debug_draw.draw_solid_circle(\n", - " center=center_r, radius=2, axis=None, color=pink\n", - " )\n", - "\n", - " # schnautze\n", - " nose_center = target.get_world_point((0, -1))\n", - " nose_center_l = target.get_world_point((-0.3, -1))\n", - " nose_center_r = target.get_world_point((0.3, -1))\n", - "\n", - " self.debug_draw.draw_circle(\n", - " center=nose_center,\n", - " radius=2,\n", - " # axis=None,\n", - " color=(1, 1, 1),\n", - " line_width=0.2,\n", - " )\n", - " # eyes\n", - " for nose_center in [nose_center_l, nose_center_r]:\n", - " self.debug_draw.draw_solid_circle(\n", - " center=nose_center, radius=0.6, axis=None, color=(1, 1, 1)\n", - " )\n", - " # eyes\n", - " for eye_center in [eye_left, eye_right]:\n", - " self.debug_draw.draw_solid_circle(\n", - " center=eye_center, radius=1, axis=None, color=(1, 1, 1)\n", - " )\n", - " self.debug_draw.draw_solid_circle(\n", - " center=eye_center, radius=0.7, axis=None, color=(0, 0, 0)\n", - " )\n", - "\n", - " def draw_projectile(self, projectile):\n", - "\n", - " center = projectile.position\n", - " # center_l = target.get_world_point((-3,3))\n", - " # center_r = target.get_world_point(( 3,3))\n", - " eye_left = projectile.get_world_point((-1, 1))\n", - " eye_right = projectile.get_world_point((1, 1))\n", - "\n", - " self.debug_draw.draw_solid_circle(\n", - " center=center,\n", - " radius=self.projectile_radius * 1.1,\n", - " axis=None,\n", - " color=(1, 0, 0),\n", - " )\n", - "\n", - " # eyes\n", - " for eye_center in [eye_left, eye_right]:\n", - " self.debug_draw.draw_solid_circle(\n", - " center=eye_center, radius=1, axis=None, color=(1, 1, 1)\n", - " )\n", - " self.debug_draw.draw_solid_circle(\n", - " center=eye_center, radius=0.7, axis=None, color=(0, 0, 0)\n", - " )\n", - "\n", - " def post_debug_draw(self):\n", - " for target in self.targets:\n", - " self.draw_target(target)\n", - "\n", - " for projectile in self.projectiles:\n", - " self.draw_projectile(projectile)" - ] - }, - { - "cell_type": "markdown", - "id": "6df7c8b9-216b-4fd2-8ee8-aeec294e149d", - "metadata": {}, - "source": [ - "# Controlls\n", - "* To play this game, click and drag the red ball and release it to shot it.\n", - "* Use the mouse-wheel to zoom in/out, a\n", - "* Click and drag in the empty space to translate the view." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df412e76-7a9a-4e1d-8bc7-c02e222e10dc", - "metadata": {}, - "outputs": [], - "source": [ - "from pyb2d_jupyterlite_backend.async_jupyter_gui import JupyterAsyncGui\n", - "s = JupyterAsyncGui.Settings()\n", - "s.resolution = [1000,500]\n", - "s.scale = 2\n", - "s.translate = [100,100]\n", - "tb = b2d.testbed.run(AngryShapes, backend=JupyterAsyncGui, gui_settings=s);" - ] - } - ], - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/content/pyodide/pyb2d/games/billiard.ipynb b/content/pyodide/pyb2d/games/billiard.ipynb deleted file mode 100644 index 13fff3a..0000000 --- a/content/pyodide/pyb2d/games/billiard.ipynb +++ /dev/null @@ -1,299 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "if \"pyodide\" in sys.modules:\n", - " import piplite\n", - " await piplite.install('pyb2d-jupyterlite-backend>=0.4.2')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy\n", - "import b2d\n", - "import math\n", - "import random\n", - "\n", - "from b2d.testbed import TestbedBase\n", - "\n", - "class Billiard(TestbedBase):\n", - "\n", - " name = \"Billiard\"\n", - "\n", - " def __init__(self, settings=None):\n", - " super(Billiard, self).__init__(gravity=(0, 0), settings=settings)\n", - " dimensions = [30, 50]\n", - " self.dimensions = dimensions\n", - "\n", - " # the outer box\n", - " box_shape = b2d.ChainShape()\n", - " box_shape.create_loop(\n", - " [\n", - " (0, 0),\n", - " (0, dimensions[1]),\n", - " (dimensions[0], dimensions[1]),\n", - " (dimensions[0], 0),\n", - " ]\n", - " )\n", - " self.ball_radius = 1\n", - " box = self.world.create_static_body(\n", - " position=(0, 0), fixtures=b2d.fixture_def(shape=box_shape, friction=0)\n", - " )\n", - "\n", - " self.place_balls()\n", - " self.place_pockets()\n", - "\n", - " # mouse interaction\n", - " self._selected_ball = None\n", - " self._selected_ball_pos = None\n", - " self._last_pos = None\n", - "\n", - " # balls to be destroyed in the next step\n", - " # since they are in the pocket\n", - " self._to_be_destroyed = []\n", - "\n", - " def place_pockets(self):\n", - " pocket_radius = 1\n", - " self.pockets = []\n", - "\n", - " def place_pocket(position):\n", - " pocket_shape = b2d.circle_shape(radius=pocket_radius / 3)\n", - " pocket = self.world.create_static_body(\n", - " position=position,\n", - " fixtures=b2d.fixture_def(shape=pocket_shape, is_sensor=True),\n", - " user_data=(\"pocket\", None),\n", - " )\n", - " self.pockets.append(pocket)\n", - "\n", - " d = pocket_radius / 2\n", - "\n", - " place_pocket(position=(0 + d, 0 + d))\n", - " place_pocket(position=(self.dimensions[0] - d, 0 + d))\n", - "\n", - " place_pocket(position=(0 + d, self.dimensions[1] / 2))\n", - " place_pocket(position=(self.dimensions[0] - d, self.dimensions[1] / 2))\n", - "\n", - " place_pocket(position=(0 + d, self.dimensions[1] - d))\n", - " place_pocket(position=(self.dimensions[0] - d, self.dimensions[1] - d))\n", - "\n", - " def place_balls(self):\n", - " self.balls = []\n", - "\n", - " base_colors = [\n", - " (1, 1, 0),\n", - " (0, 0, 1),\n", - " (1, 0, 0),\n", - " (1, 0, 1),\n", - " (1, 0.6, 0),\n", - " (0, 1, 0),\n", - " (0.7, 0.4, 0.4),\n", - " ]\n", - " colors = []\n", - " for color in base_colors:\n", - " # ``full`` ball\n", - " colors.append((color, color))\n", - " # ``half`` ball (half white)\n", - " colors.append((color, (1, 1, 1)))\n", - "\n", - " random.shuffle(colors)\n", - " colors.insert(4, ((0, 0, 0), (0, 0, 0))) # black\n", - "\n", - " n_y = 5\n", - " c_x = self.dimensions[0] / 2\n", - " diameter = (self.ball_radius * 2) * 1.01\n", - "\n", - " bi = 0\n", - " for y in range(n_y):\n", - "\n", - " py = y * diameter * 0.5 * math.sqrt(3)\n", - " n_x = y + 1\n", - " ox = diameter * (n_y - y) / 2\n", - " for x in range(y + 1):\n", - " position = (x * diameter + 10 + ox, py + 30)\n", - " self.create_billard_ball(position=position, color=colors[bi])\n", - " bi += 1\n", - "\n", - " self.create_billard_ball(position=(c_x, 10), color=((1, 1, 1), (1, 1, 1)))\n", - "\n", - " def create_billard_ball(self, position, color):\n", - "\n", - " ball = self.world.create_dynamic_body(\n", - " position=position,\n", - " fixtures=b2d.fixture_def(\n", - " shape=b2d.circle_shape(radius=self.ball_radius),\n", - " density=1.0,\n", - " restitution=0.8,\n", - " ),\n", - " linear_damping=0.8,\n", - " user_data=(\"ball\", color),\n", - " fixed_rotation=True,\n", - " )\n", - " self.balls.append(ball)\n", - "\n", - " def begin_contact(self, contact):\n", - " body_a = contact.body_a\n", - " body_b = contact.body_b\n", - "\n", - " ud_a = body_a.user_data\n", - " ud_b = body_b.user_data\n", - " if ud_a is None or ud_b is None:\n", - " return\n", - "\n", - " if ud_b[0] == \"ball\":\n", - " body_a, body_b = body_b, body_a\n", - " ud_a, ud_b = ud_b, ud_a\n", - "\n", - " if ud_a[0] == \"ball\" and ud_b[0] == \"pocket\":\n", - " self._to_be_destroyed.append(body_a)\n", - "\n", - " def pre_step(self, dt):\n", - " for b in self._to_be_destroyed:\n", - " self.balls.remove(b)\n", - " self.world.destroy_body(b)\n", - " self._to_be_destroyed = []\n", - "\n", - " def ball_at_position(self, pos):\n", - " body = self.world.find_body(pos)\n", - " if body is not None:\n", - " user_data = body.user_data\n", - " if user_data is not None and user_data[0] == \"ball\":\n", - " return body\n", - " return None\n", - "\n", - " def on_mouse_down(self, pos):\n", - " body = self.ball_at_position(pos)\n", - " if body is not None:\n", - " self._selected_ball = body\n", - " self._selected_ball_pos = pos\n", - " return True\n", - "\n", - " return False\n", - "\n", - " def on_mouse_move(self, pos):\n", - " if self._selected_ball is not None:\n", - " self._last_pos = pos\n", - " return True\n", - " return False\n", - "\n", - " def on_mouse_up(self, pos):\n", - " if self._selected_ball is not None:\n", - " self._last_pos = pos\n", - " # if the mouse is in the starting ball itself we do nothing\n", - " if self.ball_at_position(pos) != self._selected_ball:\n", - " delta = b2d.vec2(self._selected_ball_pos) - b2d.vec2(self._last_pos)\n", - " delta *= 100.0\n", - " self._selected_ball.apply_linear_impulse(\n", - " delta, self._selected_ball_pos, True\n", - " )\n", - " self._selected_ball = None\n", - " self._selected_ball_pos = None\n", - " self._last_pos = None\n", - " return False\n", - "\n", - " def post_debug_draw(self):\n", - "\n", - " for pocket in self.pockets:\n", - " self.debug_draw.draw_solid_circle(\n", - " pocket.position, self.ball_radius, (1, 0), (1, 1, 1)\n", - " )\n", - "\n", - " for ball in self.balls:\n", - " _, (color0, color1) = ball.user_data\n", - "\n", - " self.debug_draw.draw_solid_circle(\n", - " ball.position, self.ball_radius, (1, 0), color0\n", - " )\n", - " self.debug_draw.draw_solid_circle(\n", - " ball.position, self.ball_radius / 2, (1, 0), color1\n", - " )\n", - " self.debug_draw.draw_circle(\n", - " ball.position, self.ball_radius, (1, 1, 1), line_width=0.1\n", - " )\n", - "\n", - " if self._selected_ball is not None:\n", - "\n", - " # draw circle around selected ball\n", - " self.debug_draw.draw_circle(\n", - " self._selected_ball.position,\n", - " self.ball_radius * 2,\n", - " (1, 1, 1),\n", - " line_width=0.2,\n", - " )\n", - "\n", - " # mark position on selected ball with red dot\n", - " self.debug_draw.draw_solid_circle(\n", - " self._selected_ball_pos, self.ball_radius * 0.2, (1, 0), (1, 0, 0)\n", - " )\n", - "\n", - " # draw the line between marked pos on ball and last pos\n", - " if self._last_pos is not None:\n", - " self.debug_draw.draw_segment(\n", - " self._selected_ball_pos, self._last_pos, (1, 1, 1), line_width=0.2\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Controlls\n", - "* To play this game, click and hold inside a billiard ball, move and release the mouse to shoot the ball.\n", - "* Use the mouse-wheel to zoom in/out, a\n", - "* Click and drag in the empty space to translate the view." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyb2d_jupyterlite_backend.async_jupyter_gui import JupyterAsyncGui\n", - "backend = JupyterAsyncGui\n", - "s = backend.Settings()\n", - "s.resolution = [500,600]\n", - "s.scale = 8\n", - "s.fps = 40\n", - "s.translate = [125,100]\n", - "b2d.testbed.run(Billiard, backend=backend, gui_settings=s);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/content/pyodide/pyb2d/games/goo.ipynb b/content/pyodide/pyb2d/games/goo.ipynb deleted file mode 100644 index cbe018c..0000000 --- a/content/pyodide/pyb2d/games/goo.ipynb +++ /dev/null @@ -1,575 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "if \"pyodide\" in sys.modules:\n", - " import piplite\n", - " await piplite.install('networkx')\n", - " await piplite.install('pyb2d-jupyterlite-backend>=0.4.2')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import b2d\n", - "from b2d.testbed import TestbedBase\n", - "import math\n", - "import random\n", - "import numpy\n", - "from functools import partial\n", - "import networkx\n", - "from pyb2d_jupyterlite_backend.async_jupyter_gui import JupyterAsyncGui\n", - "\n", - "def best_pairwise_distance(data, f, distance):\n", - " n = len(data)\n", - " best = (None, None, float(\"inf\"))\n", - " for i in range(n - 1):\n", - " da = f(data[i])\n", - " for j in range(i + 1, n):\n", - " db = f(data[j])\n", - "\n", - " d = distance(da, db)\n", - " if d < best[2]:\n", - " best = (i, j, d)\n", - " return best\n", - "\n", - "class Level(object):\n", - " def __init__(self, testbed):\n", - " self.testbed = testbed\n", - " self.world = testbed.world\n", - "\n", - " self.gap_size = 15\n", - " self.kill_sensors_height = 0.5\n", - " self.usable_size = 20\n", - " self.h = 10\n", - " self.end_zone_height = 3\n", - "\n", - " self.outline_verts = [\n", - " (0, self.h),\n", - " (0, 2 * self.h),\n", - " (0, self.h),\n", - " (self.usable_size, self.h),\n", - " (self.usable_size, 0),\n", - " (self.usable_size + self.gap_size, 0),\n", - " (self.usable_size + self.gap_size, self.h),\n", - " (2 * self.usable_size + self.gap_size, self.h),\n", - " (2 * self.usable_size + self.gap_size, 2 * self.h),\n", - " ]\n", - "\n", - " # outline of the level\n", - " shape = b2d.chain_shape(vertices=numpy.flip(self.outline_verts, axis=0))\n", - " self.outline = self.world.create_static_body(position=(0, 0), shape=shape)\n", - "\n", - " # kill sensors\n", - " self.kill_sensor_pos = (\n", - " self.usable_size + self.gap_size / 2,\n", - " self.kill_sensors_height / 2,\n", - " )\n", - "\n", - " shape = b2d.polygon_shape(box=(self.gap_size / 2, self.kill_sensors_height / 2))\n", - " self._kill_sensor = self.world.create_static_body(\n", - " position=self.kill_sensor_pos,\n", - " fixtures=b2d.fixture_def(shape=shape, is_sensor=True),\n", - " )\n", - " self._kill_sensor.user_data = \"destroyer\"\n", - "\n", - " # end sensor\n", - " shape = b2d.polygon_shape(box=(self.usable_size / 2, self.end_zone_height / 2))\n", - " self._end_sensor = self.world.create_static_body(\n", - " position=(\n", - " 1.5 * self.usable_size + self.gap_size,\n", - " self.h + self.end_zone_height / 2,\n", - " ),\n", - " fixtures=b2d.fixture_def(shape=shape, is_sensor=True),\n", - " )\n", - " self._end_sensor.user_data = \"goal\"\n", - "\n", - " goo_radius = 1\n", - " a = self.testbed.insert_goo(\n", - " pos=(self.usable_size / 3, self.h + goo_radius), static=True\n", - " )\n", - " b = self.testbed.insert_goo(\n", - " pos=(self.usable_size * 2 / 3, self.h + goo_radius), static=True\n", - " )\n", - " c = self.testbed.insert_goo(\n", - " pos=(self.usable_size * 1 / 2, self.h + goo_radius + 4), static=False\n", - " )\n", - "\n", - " self.testbed.connect_goos(a, b)\n", - " self.testbed.connect_goos(a, c)\n", - " self.testbed.connect_goos(b, c)\n", - "\n", - " def draw(self, debug_draw):\n", - "\n", - " # draw outline\n", - " for i in range(len(self.outline_verts) - 1):\n", - " debug_draw.draw_segment(\n", - " self.outline_verts[i],\n", - " self.outline_verts[i + 1],\n", - " color=(1, 1, 0),\n", - " line_width=0.3,\n", - " )\n", - "\n", - " left = list(self.kill_sensor_pos)\n", - " left[0] -= self.gap_size / 2\n", - " left[1] += self.kill_sensors_height / 2\n", - "\n", - " right = list(self.kill_sensor_pos)\n", - " right[0] += self.gap_size / 2\n", - " right[1] += self.kill_sensors_height / 2\n", - " debug_draw.draw_segment(left, right, (1, 0, 0), line_width=0.4)\n", - "\n", - "\n", - "class FindGoos(b2d.QueryCallback):\n", - " def __init__(self):\n", - " super(FindGoos, self).__init__()\n", - " self.goos = []\n", - "\n", - " def report_fixture(self, fixture):\n", - " body = fixture.body\n", - " if body.user_data == \"goo\":\n", - " self.goos.append(body)\n", - " return True\n", - "\n", - "\n", - "class Goo(TestbedBase):\n", - "\n", - " name = \"Goo\"\n", - "\n", - " def __init__(self, settings=None):\n", - " super(Goo, self).__init__(settings=settings)\n", - "\n", - " self.goo_graph = networkx.Graph()\n", - " self.level = Level(testbed=self)\n", - "\n", - " # mouse related\n", - " self.last_mouse_pos = None\n", - " self.is_mouse_down = False\n", - " self.could_place_goo_when_mouse_was_down = False\n", - "\n", - " # callback to draw tentative placement\n", - " self.draw_callback = None\n", - "\n", - " # goos marked for destruction\n", - " self.goo_to_destroy = []\n", - "\n", - " # joints marked for destruction\n", - " self.joints_to_destroy = []\n", - " self.gamma = 0.003\n", - " self.break_threshold = 0.5\n", - "\n", - " # time point when goo can be inserted\n", - " self.insert_time_point = 0\n", - " self.insert_delay = 1.0\n", - "\n", - " # handle finishing of level\n", - " self.with_goal_contact = dict()\n", - "\n", - " # amount of seconds one has to be in the finishing zone\n", - " self.win_delay = 3.0\n", - "\n", - " # particle system will be defined an used on win!\n", - " # this is then used for some kind of fireworks\n", - " self.psystem = None\n", - " self.emitter = None\n", - " self.emitter_stop_time = None\n", - " self.emitter_start_time = None\n", - "\n", - " # trigger some fireworks on win\n", - " def on_win(self, win_body):\n", - "\n", - " if self.psystem is None:\n", - " # particle system\n", - " pdef = b2d.particle_system_def(\n", - " viscous_strength=0.9,\n", - " spring_strength=0.0,\n", - " damping_strength=100.5,\n", - " pressure_strength=1.0,\n", - " color_mixing_strength=0.05,\n", - " density=0.1,\n", - " )\n", - "\n", - " self.psystem = self.world.create_particle_system(pdef)\n", - " self.psystem.radius = 0.1\n", - " self.psystem.damping = 0.5\n", - "\n", - " emitter_def = b2d.RandomizedRadialEmitterDef()\n", - " emitter_def.emite_rate = 2000\n", - " emitter_def.lifetime = 0.9\n", - " emitter_def.enabled = True\n", - " emitter_def.inner_radius = 0.0\n", - " emitter_def.outer_radius = 0.1\n", - " emitter_def.velocity_magnitude = 1000.0\n", - " emitter_def.start_angle = 0\n", - " emitter_def.stop_angle = 2 * math.pi\n", - " emitter_def.transform = b2d.Transform(\n", - " win_body.position + b2d.vec2(0, 20), b2d.Rot(0)\n", - " )\n", - " self.emitter = b2d.RandomizedRadialEmitter(self.psystem, emitter_def)\n", - " self.emitter_stop_time = self.elapsed_time + 0.2\n", - "\n", - " def draw_goo(self, pos, angle, body=None):\n", - " self.debug_draw.draw_solid_circle(pos, 1, axis=None, color=(1, 0, 1))\n", - " self.debug_draw.draw_circle(pos, 1.1, (1, 1, 1), line_width=0.1)\n", - "\n", - " if body is not None:\n", - " centers = [\n", - " body.get_world_point((-0.3, 0.2)),\n", - " body.get_world_point((0.3, 0.2)),\n", - " ]\n", - " for center in centers:\n", - " self.debug_draw.draw_solid_circle(\n", - " center, 0.4, axis=None, color=(1, 1, 1)\n", - " )\n", - " self.debug_draw.draw_solid_circle(\n", - " center, 0.2, axis=None, color=(0, 0, 0)\n", - " )\n", - "\n", - " def draw_edge(self, pos_a, pos_b, stress):\n", - " no_stress = numpy.array([1, 1, 1])\n", - " has_stress = numpy.array([1, 0, 0])\n", - " color = (1.0 - stress) * no_stress + stress * has_stress\n", - " color = tuple([float(c) for c in color])\n", - " self.debug_draw.draw_segment(pos_a, pos_b, color=color, line_width=0.4)\n", - "\n", - " def insert_goo(self, pos, static=False):\n", - " if static:\n", - " f = self.world.create_static_body\n", - " else:\n", - " f = self.world.create_dynamic_body\n", - "\n", - " goo = f(\n", - " position=pos,\n", - " fixtures=b2d.fixture_def(shape=b2d.circle_shape(radius=1), density=1),\n", - " user_data=\"goo\",\n", - " )\n", - " self.goo_graph.add_node(goo)\n", - " return goo\n", - "\n", - " def connect_goos(self, goo_a, goo_b):\n", - " length = (goo_a.position - goo_b.position).length\n", - " joint = self.world.create_distance_joint(\n", - " goo_a,\n", - " goo_b,\n", - " stiffness=500,\n", - " damping=0.1,\n", - " length=length,\n", - " user_data=dict(length=length, stress=0),\n", - " )\n", - " self.goo_graph.add_edge(goo_a, goo_b, joint=joint)\n", - "\n", - " def query_placement(self, pos):\n", - "\n", - " radius = 8\n", - "\n", - " # find all goos in around pos\n", - " pos = b2d.vec2(pos)\n", - " box = b2d.aabb(\n", - " lower_bound=pos - b2d.vec2(radius, radius),\n", - " upper_bound=pos + b2d.vec2(radius, radius),\n", - " )\n", - " query = FindGoos()\n", - " self.world.query_aabb(query, box)\n", - " goos = query.goos\n", - " n_goos = len(goos)\n", - "\n", - " if n_goos >= 2:\n", - "\n", - " # try to insert to goo as edge between\n", - " # 2 existing goos\n", - " def distance(a, b, p):\n", - " if self.goo_graph.has_edge(a[0], b[0]):\n", - " return float(\"inf\")\n", - " return numpy.linalg.norm((a[1] + b[1]) / 2 - p)\n", - "\n", - " i, j, best_dist = best_pairwise_distance(\n", - " goos,\n", - " f=lambda goo: (goo, numpy.array(goo.position)),\n", - " distance=partial(distance, p=pos),\n", - " )\n", - "\n", - " if best_dist < 0.8:\n", - "\n", - " def draw_callback():\n", - " self.draw_edge(goos[i].position, goos[j].position, stress=0)\n", - "\n", - " def insert_callack():\n", - " self.connect_goos(goos[i], goos[j])\n", - "\n", - " return True, draw_callback, insert_callack\n", - "\n", - " # try to insert the goo as brand new\n", - " # goo and connect it with 2 existing goos\n", - " f = lambda goo: (goo, (goo.position - b2d.vec2(pos)).length)\n", - "\n", - " def distance(a, b):\n", - " if not self.goo_graph.has_edge(a[0], b[0]):\n", - " return float(\"inf\")\n", - " return a[1] + b[1]\n", - "\n", - " i, j, best_dist = best_pairwise_distance(goos, f=f, distance=distance)\n", - " if best_dist < float(\"inf\"):\n", - "\n", - " def draw_callback():\n", - "\n", - " self.draw_edge(pos, goos[i].position, stress=0)\n", - " self.draw_edge(pos, goos[j].position, stress=0)\n", - " self.draw_goo(pos, angle=None)\n", - "\n", - " def insert_callack():\n", - " goo = self.insert_goo(pos=pos)\n", - " self.connect_goos(goo, goos[i])\n", - " self.connect_goos(goo, goos[j])\n", - "\n", - " return True, draw_callback, insert_callack\n", - "\n", - " return False, None, None\n", - "\n", - " def on_mouse_down(self, pos):\n", - " self.last_mouse_pos = pos\n", - " self.is_mouse_down = True\n", - " can_be_placed, draw_callback, insert_callback = self.query_placement(pos)\n", - " self.could_place_goo_when_mouse_was_down = can_be_placed\n", - " if can_be_placed:\n", - " if self.elapsed_time < self.insert_time_point:\n", - " return True\n", - " self.draw_callback = draw_callback\n", - " return True\n", - " return False\n", - "\n", - " def on_mouse_move(self, pos):\n", - " self.last_mouse_pos = pos\n", - " if self.is_mouse_down:\n", - " can_be_placed, draw_callback, insert_callback = self.query_placement(pos)\n", - " if can_be_placed:\n", - " if self.elapsed_time < self.insert_time_point:\n", - " return True\n", - " self.draw_callback = draw_callback\n", - " return True\n", - " else:\n", - " self.draw_callback = None\n", - " return self.could_place_goo_when_mouse_was_down\n", - "\n", - " def on_mouse_up(self, pos):\n", - " self.last_mouse_pos = pos\n", - " self.is_mouse_down = False\n", - " self.draw_callback = None\n", - " can_be_placed, draw_callback, insert_callback = self.query_placement(pos)\n", - " if can_be_placed:\n", - " if self.elapsed_time < self.insert_time_point:\n", - " return True\n", - " # self.draw_callback = draw_callback\n", - " insert_callback()\n", - " self.insert_time_point = self.elapsed_time + self.insert_delay\n", - " return True\n", - " return False\n", - "\n", - " def begin_contact(self, contact):\n", - " body_a = contact.body_a\n", - " body_b = contact.body_b\n", - " if body_b.user_data == \"goo\":\n", - " body_a, body_b = body_b, body_a\n", - "\n", - " user_data_a = body_a.user_data\n", - " user_data_b = body_b.user_data\n", - " if body_a.user_data == \"goo\":\n", - " if user_data_b == \"destroyer\":\n", - " self.goo_to_destroy.append(body_a)\n", - " elif user_data_b == \"goal\":\n", - " self.with_goal_contact[body_a] = self.elapsed_time + self.win_delay\n", - "\n", - " def end_contact(self, contact):\n", - " body_a = contact.body_a\n", - " body_b = contact.body_b\n", - " if body_b.user_data == \"goo\":\n", - " body_a, body_b = body_b, body_a\n", - "\n", - " user_data_a = body_a.user_data\n", - " user_data_b = body_b.user_data\n", - " if body_a.user_data == \"goo\":\n", - " if user_data_b == \"goal\":\n", - " if body_a in self.with_goal_contact:\n", - " del self.with_goal_contact[body_a]\n", - "\n", - " def pre_step(self, dt):\n", - "\n", - " # query if goo can be inserted\n", - " if (\n", - " self.is_mouse_down\n", - " and self.last_mouse_pos is not None\n", - " and self.draw_callback is None\n", - " ):\n", - " can_be_placed, draw_callback, insert_callback = self.query_placement(\n", - " self.last_mouse_pos\n", - " )\n", - " if can_be_placed and self.elapsed_time >= self.insert_time_point:\n", - " self.draw_callback = draw_callback\n", - "\n", - " # compute joint stress\n", - " for goo_a, goo_b, joint in self.goo_graph.edges(data=\"joint\"):\n", - " jd = joint.user_data\n", - "\n", - " # distance based stress\n", - " insert_length = jd[\"length\"]\n", - " length = (goo_a.position - goo_b.position).length\n", - "\n", - " d = length - insert_length\n", - " if d > 0:\n", - "\n", - " # reaction force based stress\n", - " rf = joint.get_reaction_force(30).length\n", - "\n", - " normalized_rf = 1.0 - math.exp(-rf * self.gamma)\n", - "\n", - " jd[\"stress\"] = normalized_rf / self.break_threshold\n", - " if normalized_rf > self.break_threshold:\n", - " self.joints_to_destroy.append((goo_a, goo_b, joint))\n", - "\n", - " else:\n", - " jd[\"stress\"] = 0\n", - "\n", - " for goo_a, goo_b, joint in self.joints_to_destroy:\n", - " self.goo_graph.remove_edge(u=goo_a, v=goo_b)\n", - " self.world.destroy_joint(joint)\n", - " self.joints_to_destroy = []\n", - "\n", - " # destroy goos\n", - " for goo in self.goo_to_destroy:\n", - " self.goo_graph.remove_node(goo)\n", - " self.world.destroy_body(goo)\n", - "\n", - " # destroy all with wrong degree\n", - " while True:\n", - " destroyed_any = False\n", - " to_remove = []\n", - " for goo in self.goo_graph.nodes:\n", - " if self.goo_graph.degree(goo) < 2:\n", - " destroyed_any = True\n", - " to_remove.append(goo)\n", - " if not destroyed_any:\n", - " break\n", - " for goo in to_remove:\n", - " self.goo_graph.remove_node(goo)\n", - " self.world.destroy_body(goo)\n", - " self.goo_to_destroy = []\n", - "\n", - " # check if we are done\n", - " for goo, finish_time in self.with_goal_contact.items():\n", - " if finish_time <= self.elapsed_time:\n", - " self.on_win(goo)\n", - "\n", - " if self.emitter is not None:\n", - " if self.emitter_stop_time is not None:\n", - " if self.elapsed_time > self.emitter_stop_time:\n", - " self.emitter.enabled = False\n", - " self.emitter_start_time = self.elapsed_time + 0.4\n", - " self.emitter_stop_time = None\n", - " p = list(self.emitter.position)\n", - " p[0] += (random.random() - 0.5) * 10.0\n", - " p[1] += (random.random() - 0.5) * 2.0\n", - " self.emitter.position = p\n", - " if self.emitter_start_time is not None:\n", - " if self.elapsed_time > self.emitter_start_time:\n", - " self.emitter.enabled = True\n", - " self.emitter_start_time = None\n", - " self.emitter_stop_time = self.elapsed_time + 0.2\n", - " self.emitter.step(dt)\n", - "\n", - " def post_debug_draw(self):\n", - "\n", - " self.level.draw(self.debug_draw)\n", - "\n", - " # draw mouse when mouse is down\n", - " if (\n", - " self.is_mouse_down\n", - " and self.last_mouse_pos is not None\n", - " and self.draw_callback is None\n", - " ):\n", - " d = (self.insert_time_point - self.elapsed_time) / self.insert_delay\n", - " if d > 0:\n", - " d = d * math.pi * 2\n", - " x = math.sin(d)\n", - " y = math.cos(d)\n", - " p = self.last_mouse_pos[0] + x, self.last_mouse_pos[1] + y\n", - " self.debug_draw.draw_segment(\n", - " p, self.last_mouse_pos, color=(1, 0, 0), line_width=0.2\n", - " )\n", - " self.debug_draw.draw_circle(\n", - " self.last_mouse_pos, 1, (1, 0, 0), line_width=0.2\n", - " )\n", - "\n", - " # draw the tentative placement\n", - " if self.draw_callback is not None:\n", - " self.draw_callback()\n", - "\n", - " for goo_a, goo_b, joint in self.goo_graph.edges(data=\"joint\"):\n", - " self.draw_edge(\n", - " goo_a.position, goo_b.position, stress=joint.user_data[\"stress\"]\n", - " )\n", - "\n", - " for goo in self.goo_graph:\n", - " self.draw_goo(goo.position, goo.angle, body=goo)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Controlls\n", - "* To play this game, click and drag next to the existing \"goos\"\n", - "* try to bridge the tiny gap\n", - "* Use the mouse-wheel to zoom in/out, a\n", - "* Click and drag in the empty space to translate the view." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "s = JupyterAsyncGui.Settings()\n", - "s.resolution = [1000,500]\n", - "s.scale = 8\n", - "tb = b2d.testbed.run(Goo, backend=JupyterAsyncGui, gui_settings=s);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/content/pyodide/pyb2d/games/rocket.ipynb b/content/pyodide/pyb2d/games/rocket.ipynb deleted file mode 100644 index 316e9ce..0000000 --- a/content/pyodide/pyb2d/games/rocket.ipynb +++ /dev/null @@ -1,282 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "f7b8452d-61fe-4356-8084-cac603096fef", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "if \"pyodide\" in sys.modules:\n", - " import piplite\n", - " await piplite.install('pyb2d-jupyterlite-backend>=0.4.2')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0bfa61e4-9817-4bea-aa66-6a660a423ae6", - "metadata": {}, - "outputs": [], - "source": [ - "from b2d.testbed import TestbedBase\n", - "import random\n", - "import numpy\n", - "import b2d\n", - "import math\n", - "\n", - "class Rocket(TestbedBase):\n", - "\n", - " name = \"Rocket\"\n", - "\n", - " def __init__(self, settings=None):\n", - " super(Rocket, self).__init__(gravity=(0, 0), settings=settings)\n", - "\n", - " # gravitational constant\n", - " self.gravitational_constant = 6.0\n", - "\n", - " self.planets = {}\n", - "\n", - " # home planet\n", - " home_planet = self.world.create_kinematic_body(\n", - " position=(10, 0),\n", - " fixtures=b2d.fixture_def(shape=b2d.circle_shape(radius=20)),\n", - " user_data=\"home_planet\",\n", - " )\n", - "\n", - " # target planet\n", - " target_planet = self.world.create_kinematic_body(\n", - " position=(100, 100),\n", - " fixtures=b2d.fixture_def(shape=b2d.circle_shape(radius=10)),\n", - " user_data=\"target_planet\",\n", - " )\n", - "\n", - " # black hole\n", - " black_hole = self.world.create_kinematic_body(\n", - " position=(0, 400),\n", - " fixtures=b2d.fixture_def(shape=b2d.circle_shape(radius=1)),\n", - " user_data=\"black_hole\",\n", - " )\n", - "\n", - " self.planets = {\n", - " home_planet: dict(radius=20, density=1, color=(0, 0.2, 1)),\n", - " target_planet: dict(radius=10, density=1, color=(0.7, 0.7, 0.7)),\n", - " black_hole: dict(radius=1, density=10000, color=(0.1, 0.1, 0.1)),\n", - " }\n", - "\n", - " # a tiny rocket\n", - " self.rocket = self.world.create_dynamic_body(\n", - " position=(10, 10),\n", - " fixtures=[\n", - " b2d.fixture_def(shape=b2d.polygon_shape(box=[1, 1]), density=1),\n", - " b2d.fixture_def(\n", - " shape=b2d.polygon_shape(vertices=[(-1, 1), (0, 4), (1, 1)]),\n", - " density=1,\n", - " ),\n", - " ],\n", - " angular_damping=0.5,\n", - " linear_damping=0.2,\n", - " user_data=\"rocket\",\n", - " )\n", - " # check if the rocket is gone\n", - " self.touched_black_hole = False\n", - "\n", - " # particle system\n", - " pdef = b2d.particle_system_def(\n", - " viscous_strength=0.9,\n", - " spring_strength=0.0,\n", - " damping_strength=100.5,\n", - " pressure_strength=1.0,\n", - " color_mixing_strength=0.05,\n", - " density=0.1,\n", - " )\n", - "\n", - " psystem = self.world.create_particle_system(pdef)\n", - " psystem.radius = 0.1\n", - " psystem.damping = 0.5\n", - "\n", - " self.emitters = []\n", - " self.key_map = {\"w\": 0, \"a\": 1, \"d\": 2}\n", - "\n", - " angle_width = (math.pi * 2) / 16\n", - " emitter_def = b2d.RandomizedRadialEmitterDef()\n", - " emitter_def.emite_rate = 2000\n", - " emitter_def.lifetime = 1.0\n", - " emitter_def.enabled = False\n", - " emitter_def.inner_radius = 1\n", - " emitter_def.outer_radius = 1\n", - " emitter_def.velocity_magnitude = 10.0\n", - " emitter_def.start_angle = math.pi / 2 - angle_width / 2.0\n", - " emitter_def.stop_angle = math.pi / 2 + angle_width / 2.0\n", - " emitter_def.body = self.rocket\n", - "\n", - " delta = 0.2\n", - " self.emitter_local_anchors = [\n", - " (0, -delta), # main\n", - " (-delta, -0.5), # left,\n", - " (delta, -0.5), # right\n", - " ]\n", - " self.emitter_local_rot = [math.pi, math.pi / 2, -math.pi / 2] # main\n", - "\n", - " # main trust\n", - " emitter_def.emite_rate = 2000\n", - " world_anchor = self.rocket.get_world_point(self.emitter_local_anchors[0])\n", - " emitter_def.transform = b2d.Transform(\n", - " world_anchor, b2d.Rot(self.emitter_local_rot[0])\n", - " )\n", - " emitter = b2d.RandomizedRadialEmitter(psystem, emitter_def)\n", - " self.emitters.append(emitter)\n", - "\n", - " # left\n", - " emitter_def.emite_rate = 200\n", - " world_anchor = self.rocket.get_world_point(self.emitter_local_anchors[1])\n", - " emitter_def.transform = b2d.Transform(\n", - " world_anchor, b2d.Rot(self.emitter_local_rot[1])\n", - " )\n", - " emitter = b2d.RandomizedRadialEmitter(psystem, emitter_def)\n", - " self.emitters.append(emitter)\n", - "\n", - " # right\n", - " emitter_def.emite_rate = 200\n", - " world_anchor = self.rocket.get_world_point(self.emitter_local_anchors[1])\n", - " emitter_def.transform = b2d.Transform(\n", - " world_anchor, b2d.Rot(self.emitter_local_rot[1])\n", - " )\n", - " emitter = b2d.RandomizedRadialEmitter(psystem, emitter_def)\n", - " self.emitters.append(emitter)\n", - "\n", - " def pre_step(self, dt):\n", - "\n", - " # check if the rocket has died\n", - " if self.touched_black_hole:\n", - " if self.rocket is not None:\n", - " self.world.destroy_body(self.rocket)\n", - " self.rocket = None\n", - " else:\n", - " rocket_center = self.rocket.world_center\n", - " rocket_mass = self.rocket.mass\n", - " # compute gravitational forces\n", - " net_force = numpy.zeros([2])\n", - " for planet, planet_def in self.planets.items():\n", - " radius = planet_def[\"radius\"]\n", - " planet_center = planet.position\n", - " planet_mass = planet_def[\"density\"] * radius ** 2 * math.pi\n", - " delta = rocket_center - planet_center\n", - " distance = delta.normalize()\n", - " f = (\n", - " -self.gravitational_constant\n", - " * rocket_mass\n", - " * planet_mass\n", - " / (distance * distance)\n", - " )\n", - " net_force += delta * f\n", - " f = float(net_force[0]), float(net_force[1])\n", - " self.rocket.apply_force_to_center(f)\n", - "\n", - " # run the rockets engines\n", - " for emitter, local_anchor, local_rotation in zip(\n", - " self.emitters, self.emitter_local_anchors, self.emitter_local_rot\n", - " ):\n", - " world_anchor = self.rocket.get_world_point(local_anchor)\n", - " emitter.position = world_anchor\n", - " emitter.angle = self.rocket.angle + local_rotation\n", - " emitter.step(dt)\n", - "\n", - " def begin_contact(self, contact):\n", - " body_a = contact.body_a\n", - " body_b = contact.body_b\n", - " if body_b.user_data == \"rocket\":\n", - " body_a, body_b = body_b, body_a\n", - "\n", - " user_data_a = body_a.user_data\n", - " user_data_b = body_b.user_data\n", - " if body_a.user_data == \"rocket\":\n", - " if user_data_b == \"black_hole\":\n", - " self.touched_black_hole = True\n", - "\n", - " def on_keyboard_down(self, key):\n", - " if key in self.key_map:\n", - " self.emitters[self.key_map[key]].enabled = True\n", - " return True\n", - " return False\n", - "\n", - " def on_keyboard_up(self, key):\n", - " if key in self.key_map:\n", - " self.emitters[self.key_map[key]].enabled = False\n", - " return False\n", - " return False\n", - "\n", - " def pre_debug_draw(self):\n", - " pass\n", - "\n", - " def post_debug_draw(self):\n", - " for planet, planet_def in self.planets.items():\n", - " pos = planet.position\n", - " self.debug_draw.draw_solid_circle(\n", - " pos, planet_def[\"radius\"] + 0.1, axis=None, color=planet_def[\"color\"]\n", - " )\n", - " if planet.user_data == \"black_hole\":\n", - " self.debug_draw.draw_circle(\n", - " pos, planet_def[\"radius\"] * 5, color=(1, 1, 1), line_width=0.1\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "357866b3-876e-421f-8d2a-77d6697551d3", - "metadata": {}, - "source": [ - "# Controlls\n", - "* To play this game, use 'w','a','s','d' on your keyboard to steer the rocket\n", - "* try to land on the other planet\n", - "* avoid the black hole\n", - "* Use the mouse-wheel to zoom in/out, a\n", - "* Click and drag in the empty space to translate the view." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7bab75b7-cec1-4348-b95d-9ffd282ded5c", - "metadata": {}, - "outputs": [], - "source": [ - "from pyb2d_jupyterlite_backend.async_jupyter_gui import JupyterAsyncGui\n", - "s = JupyterAsyncGui.Settings()\n", - "s.resolution = [1000,1000]\n", - "s.scale = 3\n", - "tb = b2d.testbed.run(Rocket, backend=JupyterAsyncGui, gui_settings=s);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "674c57c8-b5b1-45a9-b75e-5ddc487f7d9b", - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/content/pyodide/pyb2d/gauss_machine.ipynb b/content/pyodide/pyb2d/gauss_machine.ipynb deleted file mode 100644 index b7bb72c..0000000 --- a/content/pyodide/pyb2d/gauss_machine.ipynb +++ /dev/null @@ -1,128 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "39a80aae-a990-4ed8-b880-3db2e7f70f16", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "if \"pyodide\" in sys.modules:\n", - " import piplite\n", - " await piplite.install('pyb2d-jupyterlite-backend>=0.4.2')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d9f8d68-0f3b-49c1-9512-e3e8344e7342", - "metadata": {}, - "outputs": [], - "source": [ - "from b2d.testbed import TestbedBase\n", - "import random\n", - "import numpy\n", - "import b2d\n", - "\n", - "\n", - "class GaussMachine(TestbedBase):\n", - "\n", - " name = \"Gauss Machine\"\n", - "\n", - " def __init__(self, settings=None):\n", - " super(GaussMachine, self).__init__(settings=settings)\n", - "\n", - " self.box_shape = 30, 20\n", - " box_shape = self.box_shape\n", - "\n", - " # outer box\n", - " verts = numpy.array(\n", - " [(0, box_shape[1]), (0, 0), (box_shape[0], 0), (box_shape[0], box_shape[1])]\n", - " )\n", - " shape = b2d.chain_shape(vertices=numpy.flip(verts, axis=0))\n", - " box = self.world.create_static_body(position=(0, 0), shape=shape)\n", - "\n", - " # \"bins\"\n", - " bin_height = box_shape[1] / 3\n", - " bin_width = 1\n", - " for x in range(0, box_shape[0], bin_width):\n", - " box = self.world.create_static_body(\n", - " position=(0, 0), shape=b2d.two_sided_edge_shape((x, 0), (x, bin_height))\n", - " )\n", - "\n", - " # reflectors\n", - " ref_start_y = int(bin_height + box_shape[1] / 10.0)\n", - " ref_stop_y = int(box_shape[1] * 0.9)\n", - " for x in range(0, box_shape[0] + 1):\n", - "\n", - " for y in range(ref_start_y, ref_stop_y):\n", - " s = [0.5, 0][y % 2 == 0]\n", - " shape = b2d.circle_shape(radius=0.3)\n", - " box = self.world.create_static_body(position=(x + s, y), shape=shape)\n", - "\n", - " # particle system\n", - " pdef = b2d.particle_system_def(\n", - " viscous_strength=0.9,\n", - " spring_strength=0.0,\n", - " damping_strength=100.5,\n", - " pressure_strength=1.0,\n", - " color_mixing_strength=0.05,\n", - " density=2,\n", - " )\n", - "\n", - " psystem = self.world.create_particle_system(pdef)\n", - " psystem.radius = 0.1\n", - " psystem.damping = 0.5\n", - "\n", - " # linear emitter\n", - " emitter_pos = (self.box_shape[0] / 2, self.box_shape[1] + 10)\n", - " emitter_def = b2d.RandomizedLinearEmitterDef()\n", - " emitter_def.emite_rate = 400\n", - " emitter_def.lifetime = 25\n", - " emitter_def.size = (10, 1)\n", - " emitter_def.transform = b2d.Transform(emitter_pos, b2d.Rot(0))\n", - "\n", - " self.emitter = b2d.RandomizedLinearEmitter(psystem, emitter_def)\n", - "\n", - " def pre_step(self, dt):\n", - " self.emitter.step(dt)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51e5df1b-f5e6-486a-a6c0-173597198e5d", - "metadata": {}, - "outputs": [], - "source": [ - "from pyb2d_jupyterlite_backend.async_jupyter_gui import JupyterAsyncGui\n", - "s = JupyterAsyncGui.Settings()\n", - "s.resolution = [350,400]\n", - "s.scale = 11\n", - "tb = b2d.testbed.run(GaussMachine, backend=JupyterAsyncGui, gui_settings=s);" - ] - } - ], - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/content/pyodide/pyb2d/newtons_cradle.ipynb b/content/pyodide/pyb2d/newtons_cradle.ipynb deleted file mode 100644 index 9ef3ff3..0000000 --- a/content/pyodide/pyb2d/newtons_cradle.ipynb +++ /dev/null @@ -1,130 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "82ae535a-a041-40f0-8c40-e689b894b0bc", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "if \"pyodide\" in sys.modules:\n", - " import piplite\n", - " await piplite.install('pyb2d-jupyterlite-backend>=0.4.0')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7381ff66-7924-4216-b141-8f7b15ed038a", - "metadata": {}, - "outputs": [], - "source": [ - "from b2d.testbed import TestbedBase\n", - "import b2d\n", - "\n", - "\n", - "class NewtonsCradle(TestbedBase):\n", - "\n", - " name = \"newton's cradle\"\n", - "\n", - " def __init__(self, settings=None):\n", - " super(NewtonsCradle, self).__init__(settings=settings)\n", - "\n", - " # radius of the circles\n", - " r = 1.0\n", - " # length of the rope\n", - " l = 10.0\n", - " # how many balls\n", - " n = 10\n", - "\n", - " offset = (l + r, 2 * r)\n", - " dynamic_circles = []\n", - " static_bodies = []\n", - " for i in range(n):\n", - " if i + 1 == n:\n", - " position = (offset[0] + i * 2 * r + l, offset[1] + l)\n", - " else:\n", - " position = (offset[0] + i * 2 * r, offset[1])\n", - "\n", - " circle = self.world.create_dynamic_body(\n", - " position=position,\n", - " fixtures=b2d.fixture_def(\n", - " shape=b2d.circle_shape(radius=r * 0.90),\n", - " density=1.0,\n", - " restitution=1.0,\n", - " friction=0.0,\n", - " ),\n", - " linear_damping=0.01,\n", - " angular_damping=1.0,\n", - " fixed_rotation=True,\n", - " )\n", - " dynamic_circles.append(circle)\n", - "\n", - " static_body = self.world.create_static_body(\n", - " position=(offset[0] + i * 2 * r, offset[1] + l)\n", - " )\n", - "\n", - " self.world.create_distance_joint(\n", - " static_body,\n", - " circle,\n", - " local_anchor_a=(0, 0),\n", - " local_anchor_b=(0, 0),\n", - " max_length=l,\n", - " stiffness=0,\n", - " )\n", - "\n", - " static_bodies.append(static_body)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6f694d32-8b23-40cf-91c3-0edd6abb658d", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b24ad88f6cfd4db19fc3145000e79635", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pyb2d_jupyterlite_backend.async_jupyter_gui import JupyterAsyncGui\n", - "s = JupyterAsyncGui.Settings()\n", - "s.resolution = [1000,300]\n", - "b2d.testbed.run(NewtonsCradle, backend=JupyterAsyncGui, gui_settings=s);" - ] - } - ], - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/content/pyodide/renderers.ipynb b/content/pyodide/renderers.ipynb deleted file mode 100644 index 286490f..0000000 --- a/content/pyodide/renderers.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"metadata":{"language_info":{"codemirror_mode":{"name":"python","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8"},"kernelspec":{"name":"python","display_name":"Pyolite","language":"python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# JupyterLab Renderers","metadata":{}},{"cell_type":"markdown","source":"## FASTA","metadata":{}},{"cell_type":"code","source":"def Fasta(data=''):\n bundle = {}\n bundle['application/vnd.fasta.fasta'] = data\n bundle['text/plain'] = data\n display(bundle, raw=True)\n\nFasta(\"\"\">SEQUENCE_1\nMTEITAAMVKELRESTGAGMMDCKNALSETNGDFDKAVQLLREKGLGKAAKKADRLAAEG\nLVSVKVSDDFTIAAMRPSYLSYEDLDMTFVENEYKALVAELEKENEERRRLKDPNKPEHK\nIPQFASRKQLSDAILKEAEEKIKEELKAQGKPEKIWDNIIPGKMNSFIADNSQLDSKLTL\nMGQFYVMDDKKTVEQVIAEKEKEFGGKIKIVEFICFEVGEGLEKKTEDFAAEVAAQL\n>SEQUENCE_2\nSATVSEINSETDFVAKNDQFIALTKDTTAHIQSNSLQSVEELHSSTINGVKFEEYLKSQI\nATIGENLVVRRFATLKAGANGVVNGYIHTNGRVGVVIAAACDSAEVASKSRDLLRQICMH\"\"\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## GeoJSON","metadata":{}},{"cell_type":"code","source":"def GeoJSON(data):\n bundle = {}\n bundle['application/geo+json'] = data\n bundle['text/plain'] = data\n display(bundle, raw=True)\n \nGeoJSON({\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"Point\",\n \"coordinates\": [125.6, 10.1]\n },\n \"properties\": {\n \"name\": \"Dinagat Islands\"\n }\n})","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/content/python.ipynb b/content/python.ipynb deleted file mode 100644 index 689e883..0000000 --- a/content/python.ipynb +++ /dev/null @@ -1,721 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# A Python kernel backed by Pyodide\n", - "\n", - "![](https://raw.githubusercontent.com/pyodide/pyodide/master/docs/_static/img/pyodide-logo.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import pyodide_kernel\n", - "pyodide_kernel.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simple code execution" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "a = 3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "a" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "b = 89\n", - "\n", - "def sq(x):\n", - " return x * x\n", - "\n", - "sq(b)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "print" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Redirected streams" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "print(\"Error !!\", file=sys.stderr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Error handling" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true, - "trusted": true - }, - "outputs": [], - "source": [ - "\"Hello\"\n", - "\n", - "def dummy_function():\n", - " import missing_module" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "dummy_function()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Code completion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### press `tab` to see what is available in `sys` module" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from sys import " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Code inspection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### using the question mark" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "?print" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### by pressing `shift+tab`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "print(" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Input support" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "name = await input('Enter your name: ')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "'Hello, ' + name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Rich representation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import display, Markdown, HTML, JSON, Latex" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## HTML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "print('Before display')\n", - "\n", - "s = '

HTML Title

'\n", - "display(HTML(s))\n", - "\n", - "print('After display')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Markdown" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "Markdown('''\n", - "# Title\n", - "\n", - "**in bold**\n", - "\n", - "~~Strikthrough~~\n", - "''')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pandas DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from string import ascii_uppercase as letters\n", - "from IPython.display import display\n", - "\n", - "df = pd.DataFrame(np.random.randint(0, 100, size=(100, len(letters))), columns=list(letters))\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Show the same DataFrame " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IPython.display module" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import clear_output, display, update_display\n", - "from asyncio import sleep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Update display" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "class Square:\n", - " color = 'PeachPuff'\n", - " def _repr_html_(self):\n", - " return '''\n", - "
\n", - "
''' % self.color\n", - "square = Square()\n", - "\n", - "display(square, display_id='some-square')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "square.color = 'OliveDrab'\n", - "update_display(square, display_id='some-square')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Clear output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "print(\"hello\")\n", - "await sleep(3)\n", - "clear_output() # will flicker when replacing \"hello\" with \"goodbye\"\n", - "print(\"goodbye\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "print(\"hello\")\n", - "await sleep(3)\n", - "clear_output(wait=True) # prevents flickering\n", - "print(\"goodbye\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Display classes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import HTML\n", - "HTML('''\n", - "
\n", - "
''')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import Math\n", - "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import Latex\n", - "Latex(r\"\"\"\\begin{eqnarray}\n", - "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", - "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", - "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", - "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n", - "\\end{eqnarray}\"\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import ProgressBar\n", - "\n", - "for i in ProgressBar(10):\n", - " await sleep(0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import JSON\n", - "JSON(['foo', {'bar': ('baz', None, 1.0, 2)}], metadata={}, expanded=True, root='test')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import GeoJSON\n", - "GeoJSON(\n", - " data={\n", - " \"type\": \"Feature\",\n", - " \"geometry\": {\n", - " \"type\": \"Point\",\n", - " \"coordinates\": [11.8, -45.04]\n", - " }\n", - " }, url_template=\"http://s3-eu-west-1.amazonaws.com/whereonmars.cartodb.net/{basemap_id}/{z}/{x}/{y}.png\",\n", - " layer_options={\n", - " \"basemap_id\": \"celestia_mars-shaded-16k_global\",\n", - " \"attribution\" : \"Celestia/praesepe\",\n", - " \"tms\": True,\n", - " \"minZoom\" : 0,\n", - " \"maxZoom\" : 5\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Network requests and JSON" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import json\n", - "from js import fetch" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "res = await fetch('https://httpbin.org/get')\n", - "text = await res.text()\n", - "obj = json.loads(text) \n", - "JSON(obj)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sympy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from sympy import Integral, sqrt, symbols, init_printing\n", - "\n", - "init_printing()\n", - "\n", - "x = symbols('x')\n", - "\n", - "Integral(sqrt(1 / x), x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Magics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import os\n", - "os.listdir()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%cd /home" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%pwd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "current_path = %pwd\n", - "print(current_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%%writefile test.txt\n", - "\n", - "This will create a new file. \n", - "With the text that you see here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%history" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%%timeit \n", - "\n", - "time.sleep(0.1)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (Pyodide)", - "language": "python", - "name": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 4 -}