{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", "

1  Analysis of dice rolls.
2  Playing with Widgets
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis of dice rolls." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-01-16T03:00:47.574671", "start_time": "2017-01-16T03:00:46.244791" }, "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "from ipywidgets import widgets, interact" ] }, { "cell_type": "raw", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Create a dice.\n", " - Default: 6 side, but can be n > 2 sides.\n", " - Output: An `Int`\n", "- Create a Trial\n", " - Default: 100 rolls, but can be n.\n", " - Output:\n", " - rollsList: Array with all of the roll results.\n", " - cumltList: Array with the cumulative probability." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-01-16T03:00:48.223997", "start_time": "2017-01-16T03:00:48.177967" }, "collapsed": false }, "outputs": [], "source": [ "def dice(num_of_sides = 6, loaded_num = 1, loaded_weight = 1):\n", " '''\n", " Virtual dice with output of a side. Default is an equally weighted dice. \n", " Input:\n", " num_of_sides: Number of sides for the dice. (Default = 6)\n", " loaded_num : The number that is biased or \"loaded\". (Default = 1)\n", " loaded_wght : Number used to get loaded weight. (Default = 1)\n", " For equal weight distribution, the loaded number has\n", " the same probability as any other dice number. (1/6, 1/3,..., 1/n) \n", " \n", " Output: Int value corresponding to a side of the dice.\n", " \n", " Example:\n", " [dice(10, 7, 9) for output in range(20)] -> [7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 1, 7, 7, 7]\n", " [dice(6, 5, 3) for output in range(20)] -> [5, 5, 6, 5, 6, 5, 2, 6, 5, 2, 4, 6, 6, 5, 2, 3, 5, 5, 6, 5]\n", " '''\n", " \n", " if loaded_num > num_of_sides:\n", " raise ValueError('The loaded number chosen is greater than the total number of sides on the dice!')\n", " elif loaded_weight > num_of_sides:\n", " raise ValueError('The loaded weight chosen is greater than the total number of sides on the dice!')\n", " elif num_of_sides < 1:\n", " raise ValueError('Total number of sides is less than one!')\n", " \n", " sides = [num for num in range(1, num_of_sides+1)]\n", " return np.random.choice(sides, p=wght(num_of_sides, loaded_num, loaded_weight))\n", "\n", "\n", "def wght(num_of_sides = 6, loaded_num = 1, loaded_weight = 1):\n", " '''\n", " Weight function for the probabilities of a side on a roll.\n", " Input:\n", " num_of_sides: Number of sides for the dice. (Default = 6)\n", " loaded_num : The number that is biased or \"loaded\". (Default = 1)\n", " loaded_wght : Number used to get loaded weight. (Default = 1)\n", " For equal weight distribution, the loaded number has\n", " the same probability as any other dice number. (1/6, 1/3,..., 1/n)\n", " \n", " Output: List of corresponding weights for each side.\n", " \n", " Example:\n", " Dice with 10 sides, loaded number to be 4, and the loaded weight to be \n", " the chance of 3/10(rather than 1/10): \n", " \n", " wght(10, 4, 3) -> [0.07777777777777778, 0.07777777777777778, 0.07777777777777778,\n", " 0.3,0.07777777777777778, 0.07777777777777778, 0.07777777777777778, 0.07777777777777778,\n", " 0.07777777777777778, 0.07777777777777778]\n", " '''\n", " \n", " if loaded_weight == 1:\n", " return [1/num_of_sides]*len(range(num_of_sides))\n", " \n", " ld_wght = loaded_weight/num_of_sides\n", " wght_list = [(1-ld_wght)/(num_of_sides-1)]*len(range(num_of_sides))\n", " wght_list[loaded_num-1] = ld_wght\n", " \n", " return wght_list\n", "\n", "\n", "def trials(num_of_trials=100, num_of_sides=6, loaded_num=1, loaded_weight=1):\n", " '''\n", " Trials consisting of a specified number of time the dice will be rolled. Output gives\n", " a list of all the rolled values and a list of cumulative proportion per roll.\n", " \n", " Input:\n", " num_of_trials: Number of rolls. (Default = 100)\n", " num_of_sides : Number of sides for the dice. (Default = 6)\n", " loaded_num : The number that is biased or \"loaded\". (Default = 1)\n", " loaded_wght : Number used to get loaded weight. (Default = 1)\n", " For equal weight distribution, the loaded number has\n", " the same probability as any other dice number. (1/6, 1/3,..., 1/n)\n", " Output: (In this order.)\n", " rollsList: List of rolled values. Length equal to the num_of_trials input.\n", " cumltList: List of cumulative proportion per roll. \n", " '''\n", "\n", " sides = list(range(1, num_of_sides+1))\n", " rollsList = []\n", " \n", " cumltList = [[] for side in range(num_of_sides)]\n", " cumlt = [0 for side in range(num_of_sides)]\n", " \n", " for trial in range(num_of_trials):\n", " roll = dice(num_of_sides, loaded_num, loaded_weight)\n", " rollsList.append(roll)\n", " \n", " for side in sides:\n", " if roll == side:\n", " cumlt[side-1] += 1\n", " \n", " cumltList[side-1].append(cumlt[side-1]/(trial+1))\n", " \n", " return rollsList, cumltList\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-01-16T03:00:48.925070", "start_time": "2017-01-16T03:00:48.897551" }, "code_folding": [], "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "def simu_dice(num_of_trials = 100, num_of_sides = 6, loaded_num = 1, loaded_wght = 1, \\\n", " plt_style = 'seaborn-ticks', cuml_tot = True, ret = False):\n", " rolls, cuml = trials(num_of_trials, num_of_sides, loaded_num, loaded_wght)\n", "\n", " plt.style.use(style=plt_style) \n", " \n", " if cuml_tot is True:\n", " for side in cuml:\n", " ax = plt.plot(np.array([num+1 for num in range(len(rolls))]), np.array(side), \\\n", " label = 'Side {}'.format(cuml.index(side) + 1)) \n", " else:\n", " ax = plt.plot(np.array([num+1 for num in range(len(rolls))]), np.array(cuml[loaded_num-1]), \\\n", " label = 'Side {}'.format(cuml.index(cuml[loaded_num-1]) + 1))\n", " \n", " \n", " ax = plt.plot([num+1 for num in range(len(rolls))], np.array([1/num_of_sides]*len(rolls)), \\\n", " color = 'k', linewidth = 2, label = 'Theoretical \\n Probability')\n", " ax = plt.title(plt_style)\n", " ax = plt.legend()\n", " ax = plt.ylim(0.0, 1.0)\n", " ax = plt.xlabel('# of Trials')\n", " ax = plt.ylabel('Cumulative Proportions')\n", " \n", " plt.show()\n", " \n", " if ret is True:\n", " return rolls, cuml" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-01-16T03:01:28.320852", "start_time": "2017-01-16T03:01:27.826025" }, "collapsed": false }, "outputs": [ { "data": { "image/png": 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FkiVLqKqqqrf/r776iqeeeooFCxa0/sVdpyTdQgghhBDXtHKdgZP5pe3aZzcfF5wdNY2u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s2N2G6OINgIsYuJUpgwhSm56JJLXHCJ+xV2KsMJ6S6WvfgopVSK6a07CB2XYipDyctQcDLkPUUx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\nGUilUnsFbfBxJpM5E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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_of_sides = widgets.IntSlider(6, 1, 25, 1)\n", "\n", "interact(simu_dice, \\\n", " num_of_sides = widgets.IntSlider(6, 1, 25, 1) ,\\\n", " num_of_trials = widgets.IntSlider(100, 0, 100000, 100) ,\\\n", " loaded_num = widgets.IntSlider(1, 1, num_of_sides.max, 1) ,\\\n", " loaded_wght = widgets.IntSlider(1, 1, num_of_sides.max, 1) ,\\\n", " plt_style = tuple(style for style in plt.style.available) ,\\\n", " cuml_tot = True ,\\\n", " ret = widgets.fixed(False)\n", " );" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Playing with Widgets" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T16:41:58.538197", "start_time": "2017-01-15T16:41:58.534694" }, "collapsed": false }, "outputs": [], "source": [ "from ipywidgets import widgets\n", "from ipywidgets import interact\n", "from IPython.display import display" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T16:32:59.274103", "start_time": "2017-01-15T16:32:59.261093" }, "collapsed": false }, "outputs": [], "source": [ "text = widgets.Text()\n", "display(text)\n", "\n", "def handle_submit(sender):\n", " print('Butters')\n", " \n", "text.on_submit(handle_submit)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T16:38:05.685877", "start_time": "2017-01-15T16:38:05.673369" }, "collapsed": false }, "outputs": [], "source": [ "button = widgets.Button(description = 'Click me!')\n", "display(button)\n", "\n", "def on_button_click(b):\n", " print('Button clicked!')\n", " \n", "button.on_click(on_button_click)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T17:57:19.287223", "start_time": "2017-01-15T17:57:19.163140" }, "collapsed": false }, "outputs": [], "source": [ "def f(x, y):\n", " print(x)\n", " print(y)\n", " \n", "sld = interact(f, x=100, y=50)\n", "chk = interact(f, x=True, y=False)\n", "txt = interact(f, x='Suren', y='Butters') " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T17:20:16.493538", "start_time": "2017-01-15T17:20:16.482031" }, "collapsed": true }, "outputs": [], "source": [ "outputText = widgets.Text()\n", "outputText" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T17:21:48.104109", "start_time": "2017-01-15T17:21:48.089600" }, "collapsed": true }, "outputs": [], "source": [ "inputText = widgets.Text()\n", "\n", "def makeUpperCase(sender):\n", " outputText.value = inputText.value.upper()\n", " \n", "inputText.on_submit(makeUpperCase)\n", "inputText" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T21:11:07.347061", "start_time": "2017-01-15T21:11:07.082386" }, "collapsed": false }, "outputs": [], "source": [ "t = np.arange(0.0, 1.1, .01)\n", "\n", "def pltSin(A, f):\n", " plt.plot(t, A*np.sin(2*np.pi*t*f))\n", " plt.axis([0, 1, -10, 10])\n", " plt.autoscale(False)\n", " plt.show()\n", "\n", "\n", "interact(pltSin, A = widgets.IntSlider(1, 0, 10, 1), f = widgets.FloatSlider(1, min=0, max=10, step=1))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "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" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "227px", "width": "237px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, 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