{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Oregon Curriculum Network](http://4dsolutions.net/ocn/)\n", "\n", "[School of Tomorrow (Home)](School_of_Tomorrow.ipynb)\n", "\n", "# Delta Calc (Δ calc)\n", "\n", "Δ calc (delta calc) is traditional calculus. In the School of Tomorrow, we abet Δ calc with λ calc (lambda calc), which we've expanded to mean \"computer science\" (roughly) or \"discrete mathematics\". \n", "\n", "Discrete math approaches calculus as we boost the frequency i.e. as the frame rate increases to appear \"smoothly continuous\"." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from typing import Callable\n", "import numpy" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def D(f: Callable) -> Callable:\n", " \"\"\"\n", " return a deriviative function\n", " \"\"\"\n", " def Df(x: float, h=1e-8) -> float:\n", " return (f(x + h) - f(x))/h\n", " return Df" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def parabola(x: float) -> float:\n", " \"The classic calculus function, no?\"\n", " return x * x # or x**2 -- don't have to say 'squared' in SoT" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt # we're gonna plot" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from numpy import linspace, vectorize" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```linspace``` and ```arange``` will be commonly used, if the curriculum is at all numpy-enabled." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "domain = linspace(-10, 10, 100) # nice domain of 100 points exactly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well want to disambiguate the \"vector\" as: \n", "\n", "1. a kind of storage unit or data recepticle, with \"vectorized\" operations happening in parallel or independently to each cell or element.\n", "\n", "2. the other kind of \"vector\" lives in a mathematical \"vector space\" as an element define to add with other vectors.\n", "\n", "Below, we \"vectorize\" the parabola function (per definition 1) so as to apply it to our domain without any need for explicit for-loop syntax." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "y = vectorize(parabola)(domain)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'y')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "plt.plot(domain,y)\n", "fig.suptitle('Parabola', fontsize=20)\n", "plt.xlabel('x', fontsize=18)\n", "plt.ylabel('y', fontsize=16)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "deriv = D(parabola)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "dy = vectorize(deriv)(domain)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.suptitle('Parabola', fontsize=20)\n", "plt.xlabel('x', fontsize=18)\n", "plt.ylabel('y', fontsize=16)\n", "plt.plot(domain,y, label=\"y\")\n", "plt.plot(domain,dy, label=\"dy/dx\")\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Still using a graphing calculator?\n", "\n", "Switch today!\n", "\n", "Use the Calculator of Tomorrow.\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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"text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo(\"eTDH7m4vEiM\") " ] } ], "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": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 }