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Diffstat (limited to 'notes/jupyter_python_example.ipynb')
-rw-r--r-- | notes/jupyter_python_example.ipynb | 166 |
1 files changed, 166 insertions, 0 deletions
diff --git a/notes/jupyter_python_example.ipynb b/notes/jupyter_python_example.ipynb new file mode 100644 index 0000000..b6fdb16 --- /dev/null +++ b/notes/jupyter_python_example.ipynb @@ -0,0 +1,166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# modelthing + Jupyter Demo\n", + "\n", + "First pull down the compiled python model from the server and `exec()` it to bring the function into our namespace." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import requests\n", + "server = \"http://localhost:5000\"\n", + "model_path = \"examples/newtonian_gravity\"\n", + "req = requests.get(\"%s/m/%s/repr/?format=python\" % (server, model_path))\n", + "exec(req.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that `exec` doesn't return anything; it has already inserted a function named \"NewtonianGravitation\" into our current namespace:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.483111111111111e-11" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "NewtonianGravitation(1,2,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function NewtonianGravitation in module __main__:\n", + "\n", + "NewtonianGravitation(m_1, m_2, r)\n", + " Simple/classical Inverse-square law force\n", + " \n", + " Args:\n", + " m_1\n", + " m_2\n", + " r\n", + " \n", + " Returns array of:\n", + " F\n", + "\n" + ] + } + ], + "source": [ + "help(NewtonianGravitation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's use it!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from pylab import *" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.text.Text at 0x7f11f5293a20>" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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j060bvP46dOoEgweH2yIikl9S6aUy3t2XZyIYabzat4eXX4ZNN4Uddgg9WURE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\nDLwFDK1y3jnAeYRqwp+AT4FjquyzDlB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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x7f11f52bb128>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m_pound = 2.2e3 # One pound (lb) in grams\n", + "m_earth = 5.972e24 # Mass of Earth\n", + "r_earth = 6.371e6 # Radius of Earth\n", + "r = frange(r_earth, r_earth*3, r_earth/1000)\n", + "plot(r - r_earth, [NewtonianGravitation(m_pound, m_earth, x) for x in r])\n", + "xlabel(\"Distance from Surface of Earth (meters)\")\n", + "ylabel(\"Gravitational Force\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} |