{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "# Week 02 - List And Loops\n", "\n", "### LS30A LAB 1B/1D\n", "#### TA: Hao Lee\n", "#### LA: Evelyn Malamut" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "source": [ "# Review\n", "### Continuous Time System\n", "\n", "$$R' = aR+bJ+c\\\\J'=dR+eJ+f$$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "source": [ "- What is the difference between R' and R? \n", "- What is a, b, c, d, e, f?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "source": [ "Ans: \n", "\n", "- R is a state variable, R' is the rate of change\n", "- a, e can be per-captia rate or proportionality, b and d are mostly proportionality, and c and f can be constant growth rate. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Nomenclature\n", "- Per-captia rate: increase/ decrease rate per unit of itself.\n", "- Proportionality: increase/ decrease rate related to anything.\n", "\n", "## e.g., \n", "- R's growth rate is proportional to its own population, with proportionality a\n", "- R's per-captia growth rate is a\n", "- R's growth rate is proportional to J, with proportionality b\n", "- R has a constant growth rate c" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "source": [ "# Programming\n", "## Type of Data\n", "- Variables have data types\n", "```\n", "var1 = 1\n", "var2 = 'some string'\n", "print(var1 + var2)\n", "```\n", "\n", "### This makes no sense\n", "\n", "### Reason: They have different data types" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Data Types of Variables" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] } ], "source": [ "a = 1\n", "print(type(a))\n", "b = 'I am a string'\n", "print(type(b))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "source": [ "## List\n", "#### Put multiple variables together" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ " $\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[1, 2, 3\\right]$ " ], "text/plain": [ "[1, 2, 3]" ] }, "execution_count": 30, "metadata": { }, "output_type": "execute_result" } ], "source": [ "var1 = 1\n", "var2 = 2\n", "var3 = 3\n", "my_array = [var1, var2, var3]\n", "show(my_array)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "source": [ "# It does not need to be the same data type" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 'String', Graphics object consisting of 1 graphics primitive]\n" ] } ], "source": [ "var1 = 1\n", "var2 = 'String'\n", "var3 = plot(x^2,(x,-2,2))\n", "\n", "my_list= [var1,var2,var3]\n", "print(my_list)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "source": [ "# How can we retrieve variables from a list?\n", "#### => Indexing" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 'String', Graphics object consisting of 1 graphics primitive]\n" ] }, { "data": { "image/png": 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", "text/plain": [ "Graphics object consisting of 1 graphics primitive" ] }, "execution_count": 32, "metadata": { }, "output_type": "execute_result" } ], "source": [ "print(my_list)\n", "show(my_list[2])\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "source": [ "# How Indexing Works? " ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "source": [ "![](indexing.png)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "source": [ "- If starts from the beginning, index starts from 0\n", "- If starts from the end, index starts from -1" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Example" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 4, 5]\n", "1\n", "4\n" ] } ], "source": [ "my_list = [1,2,3,4,5]\n", "print(my_list)\n", "print(my_list[0])\n", "print(my_list[-2])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "source": [ "## We can retrieve more than one element!" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "[2, 3, 4]" ] }, "execution_count": 34, "metadata": { }, "output_type": "execute_result" } ], "source": [ "my_list[1:4]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "source": [ " Be careful, for index range 1:4, only elements at position 1,2,3 are retrieved. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Add components into a list\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 4, 5]\n", "[1, 2, 3, 4, 5, 9]\n" ] } ], "source": [ "print(my_list)\n", "my_list.append(9)\n", "print(my_list)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "source": [ "## Plot of List\n", "### Currently we have learned plot\n", "```\n", "plot(function,(x,x_min,x_max),options)\n", "```\n", "
\n", "\n", "##### For most data set, we do not have the function!\n", "Example: For a data set (x1,x2,x3,x4.......,xn),(y1,y2,y3,y4,........yn), please find their relationship!\n", "\n", "X= (1,2,3,4,5)\n", "\n", "Y= (1,4,9,16,25)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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QTAAACYIJACBBMAEAJAgmAIAEwQQAkJCZYHLzXQAgq9xLDgAgITNnmAAAskowAQAkCCYAgATBBACQIJgAABIEEwBAgmACAEgQTAAACYIJACBBMAEAJAgmAICEzASTm+8CAFnl5rsAAAmZOcMEAJBVggkAIEEwAQAkCCYAgATBBACQIJgAABIEEwBAgmACAEgQTAAACYIJACBBMAEAJGQmmNx8FwDIKjffBQBIyMwZJgCArBJMAAAJggkAIEEwAQAkCCYAgATBBACQIJgAABIEEwBAQtmDafHixZHL5fo8Ghoayr0bAIABM7wSbzphwoT49a9/3ft82LBhldgNAMCAqEgwDR8+3FklAGDQqMh3mHbs2BFNTU3R2toa11xzTTz//PPvum2xWIxCodDnAQCQJWUPpvPOOy9WrFgRv/zlL+PHP/5xdHV1xdSpU+PVV1897PYdHR2Rz+d7H83NzeUeCQDgmORKpVKpkjvo6emJD33oQ3HTTTfFggUL3vHzYrEYxWKx93mhUIjm5ubo7u6OUaNGVXI0AIAjUpHvML3dyJEjY9KkSbFjx47D/rympiZqamoqPQYAwFGr+HWYisViPPvss9HY2FjpXQEAVETZg2nhwoXR2dkZO3fujCeeeCI+97nPRaFQiDlz5pR7VwAAA6LsH8m9+OKLce2118Yrr7wSp512WkyZMiU2btwYLS0t5d4VAMCAqPiXvvurUChEPp/3pW8AIDPcSw4AIEEwAQAkCCYAgISKX4cJsuA//zNi+/aIyy6LmDix2tMAcLxxholB7/vfj5g1K2LhwoiPfjRi69ZqTwTA8UYwMej99Kf/+Pcbb0T8/OfVmwWA41Nmgmnp0qUxfvz4aGtrq/YoDDKtrX2ff/CDVRkDgOOY6zAx6L38csRXvhLxxz9GfPrTEf/2bxG5XLWnAuB4IpgAABIy85EcAEBWCSYAgATBBACQIJgAABIEEwBAgmACAEgQTAAACYIJACBBMAEAJAgmAICEzASTm+8CAFnlXnIAAAmZOcMEAJBVggkAIEEwAQAkCCYAgATBBACQIJgAABIEEwBAgmACAEgQTAAACYIJACBBMAEAJGQmmNx8FwDIKjffBQBIyMwZJgCArBJMAAAJggkAIEEwAQAkCCYAgATBBACQIJgAABIEEwBAwpALpr/9LeLVVyOydblOACDLhlQwPfdcxIc/HDF6dMQ55xwKJwCAlCEVTLfcErFz56F/P/lkxL/+a3XnAQCOD5kJpoG4+e7//u97PwcAOJwhdfPdxx6LuPzyiH37IurrI37724gzzijrLgCAQWh4tQcYSBdeGLF9e8SOHRGTJkXU1VV7IgDgeDCkgikioqnp0AMA4Ehl5jtMAABZJZgAABIEEwBAQub+Sq5UKsXevXujtrY2crlctccBAMheMAEAZI2P5AAAEgQTAECCYAIASBBMAAAJggkAIEEwAQAkCCYAgATBBACQIJgAABIEEwBAwvBqD1Auf78HHQBASn/vWTtogmnv3r2Rz+erPQYAcBzo7u6OUaNGHfH2g+bmu0d6hqlQKERzc3Ps2rWrX/+h2traYtOmTf2aqb+/U+l9WLu1V3LtA/Hfqr+/Y+3Wbu2VmWswrH3InmHK5XL9+p9j1KhR/dp+2LBh/dr+aH5nIPYRYe1Zm2uwrH2g1mHt1n6krN3ay8mXvo9Qe3t7xX9nIPZxNKy9sr8zWNY+UOuw9sqy9sptP5C/U+l9DKa1H6lB85HckSoUCpHP5/v92eVgYO3Wbu1Dh7Vbu7WX17DFixcvLvu7ZtywYcNi2rRpMXz4oPlE8ohZu7UPNdZu7UONtVdm7UPuDBMAQH/5DhMAQIJgAgBIEEwAAAmCCQAgQTABACQMqWDasGFDzJo1K5qamiKXy8WaNWuqPVJZ9Xd969evj1wu947HH//4xwGauPI6Ojqira0tamtro76+PmbPnh3bt2+v9lhlczTrW758+WGP+5tvvjlAU1fe3XffHWeddVbvFX/PP//8+MUvflHtscqmv+sbCsf8cDo6OiKXy8X8+fOrPUpFHMn6hsKxX7x48TvW19DQUPb9DKlg6unpicmTJ8eSJUuqPUpFHO36tm/fHrt37+59jBs3rkITDrzOzs5ob2+PjRs3xtq1a+PAgQMxc+bM6OnpqfZoZXG06xs1alSfY7579+54//vfP0BTV97YsWPjO9/5TmzevDk2b94cH//4x+NTn/pUbNu2rdqjlcXRrG+wH/P/b9OmTXHPPffEWWedVe1RKqI/6xsKx37ChAl91vf000+XfyelISoiSqtXr672GBVzJOtbt25dKSJKr7322gBNVX179uwpRUSps7Oz2qNUxJGsb9myZaV8Pj+AU2XDKaecUvrJT35S7TEq5r3WN9SO+d69e0vjxo0rrV27tnTxxReXvvGNb1R7pLLqz/qGwrG/9dZbS5MnT674fobUGSYO7+yzz47GxsaYMWNGrFu3rtrjVFR3d3dERNTV1VV5kso40vXt27cvWlpaYuzYsXHVVVfFk08+ORDjVcXBgwdj1apV0dPTE+eff361xym7I13fUDrm7e3tceWVV8Yll1xS7VEqor/rGwrHfseOHdHU1BStra1xzTXXxPPPP1/2fQy966bTq7GxMe65554455xzolgsxr333hszZsyI9evXx0UXXVTt8cquVCrFggUL4sILL4yJEydWe5yyO9L1nXnmmbF8+fKYNGlSFAqFuPPOO+OCCy6Ip556alB9HPv000/H+eefH2+++WacfPLJsXr16hg/fny1xyqb/qxvqBzziIhVq1bF7373u9i0aVO1R6mI/q5vKBz78847L1asWBFnnHFGvPzyy3H77bfH1KlTY9u2bXHqqaeWb0cVP4eVUeEjucO66qqrSrNmzarARNV3/fXXl1paWkq7du2q9igVcbTrO3jwYGny5MmlefPmVWiy6igWi6UdO3aUNm3aVPrWt75VGj16dGnbtm3VHqtsjmV9g/WYv/DCC6X6+vrS1q1be18bTB/JlWN9g/XYv92+fftKY8aMKf37v/97Wd/XR3L0MWXKlNixY0e1xyi7efPmxUMPPRTr1q2LsWPHVnucsjuW9Z1wwgnR1tY26I77iSeeGB/+8Ifj3HPPjY6Ojpg8eXLceeed1R6rbI5lfYP1mG/ZsiX27NkT55xzTgwfPjyGDx8enZ2dcdddd8Xw4cPj4MGD1R7xmJRjfYP12L/dyJEjY9KkSWVfo4/k6OPJJ5+MxsbGao9RNqVSKebNmxerV6+O9evXR2tra7VHKqtyrK9UKsXWrVtj0qRJFZgwO0qlUhSLxWqPUTH9Wd9gPeYzZsx4x19HfeUrX4kzzzwzbr755hg2bFiVJiuPcqxvsB77tysWi/Hss8/Gxz72sbK+75AKpn379sVzzz3X+3znzp2xdevWqKuri9NPP72Kk5VHan2LFi2Kl156KVasWBERET/4wQ/igx/8YEyYMCH2798fP/3pT+OBBx6IBx54oFpLKLv29vZYuXJlPPjgg1FbWxtdXV0REZHP52PEiBFVnu7YHcn6vvzlL8cHPvCB6OjoiIiI2267LaZMmRLjxo2LQqEQd911V2zdujWWLl1atXWU2y233BKXX355NDc3x969e2PVqlWxfv36ePTRR6s9Wlmk1jcUj3lERG1t7Tu+vzdy5Mg49dRTB8X3Fo9kfUPx2C9cuDBmzZoVp59+euzZsyduv/32KBQKMWfOnLLuZ0gF0+bNm2P69Om9zxcsWBAREXPmzInly5dXaarySa1v9+7d8cILL/T+fP/+/bFw4cJ46aWXYsSIETFhwoR4+OGH44orrhjw2Svl7rvvjoiIadOm9Xl92bJlMXfu3IEfqMyOZH0vvPBCnHDCPz59f/311+NrX/tadHV1RT6fj7PPPjs2bNgQH/3oRwdq7Ip7+eWX40tf+lLs3r078vl8nHXWWfHoo4/GpZdeWu3RyiK1vqF4zDlkKB77F198Ma699tp45ZVX4rTTTospU6bExo0bo6Wlpaz7yZVKpVJZ3xEAYJDxpW8AgATBBACQIJgAABIEEwBAgmACAEgQTAAACYIJACBBMAEAJAgmAIAEwQQAkCCYAAAS/g/intjkEhiBcwAAAABJRU5ErkJggg==", "text/plain": [ "Graphics object consisting of 1 graphics primitive" ] }, "execution_count": 36, "metadata": { }, "output_type": "execute_result" } ], "source": [ "listToPlot=[(1,1),(2,4),(3,9),(4,16),(5,25)]\n", "fig = list_plot(listToPlot)\n", "show(fig)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "source": [ "## (*,*) IS ANNONYING" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Graphics object consisting of 1 graphics primitive" ] }, "execution_count": 37, "metadata": { }, "output_type": "execute_result" } ], "source": [ "X = (1,2,3,4,5)\n", "Y=(1,4,9,16,25)\n", "listToPlot=zip(X,Y)\n", "listToPlot #just wrote the variable's name can also print it!\n", "list_plot(listToPlot,axes_labels=['Index','Value'],title='Test Data')\n", "# take a look at the new options we set!!!!!" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Connect all the points\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "Graphics object consisting of 1 graphics primitive" ] }, "execution_count": 38, "metadata": { }, "output_type": "execute_result" } ], "source": [ "X = (1,2,3,4,5)\n", "Y=(1,4,9,16,25)\n", "listToPlot=zip(X,Y)\n", "list_plot(listToPlot,axes_labels=['Index','Value'],title='Test Data',plotjoined=True)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "source": [ "## Loops\n", "### Whatis loops for?\n", "=> Doing Repeated Task for Different Things\n", "\n", "```\n", "for var in list:\n", " do something to var\n", "```" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "3\n", "6\n", "10\n", "15\n", "21\n" ] } ], "source": [ "\n", "listVar=[1,2,3,4,5,6]\n", "\n", "curSum = 0\n", "for var in listVar:\n", " curSum = curSum+var\n", " print(curSum)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Create list in a Loop\n", "This is very useful when we are trying to do the following operation\n", "\n", "```\n", "for testObj in list_of_testObj:\n", " result = testObj_do_some_task\n", "```\n", "\n", " We want to record the results" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "source": [ "## e.g., Do square root to [1,2,3,4,5,6,7]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.00000000000000, 1.41421356237310, 1.73205080756888, 2.00000000000000, 2.23606797749979, 2.44948974278318, 2.64575131106459]\n" ] } ], "source": [ "allResult=[] #this is call an empty list\n", "in_list = [1.,2.,3.,4.,5.,6.,7.]\n", "for element in in_list:\n", " allResult.append(sqrt(element))\n", "print(allResult)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "source": [ "## Animation\n", "\n", "### What animation does?\n", "Create a list of plots and plot them!!!!!" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false, "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "

" ], "text/plain": [ "Animation with 15 frames" ] }, "execution_count": 42, "metadata": { }, "output_type": "execute_result" } ], "source": [ "plots=[]\n", "power =range(15)\n", "for z in power:\n", " p=plot(x^z,(x,-10,10),axes_labels=['x','y'],ymax=5000,ymin=-5000)\n", " plots.append(p)\n", "a=animate(plots)\n", "show(a)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n" ] } ], "source": [ "listVar = [1,2,3]\n", "for var in listVar:\n", " print(var)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ ] } ], "metadata": { "kernelspec": { "display_name": "SageMath (stable)", "language": "sagemath", "metadata": { "cocalc": { "description": "Open-source mathematical software system", "priority": 10, "url": "https://www.sagemath.org/" } }, "name": "sagemath" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 0 }