Skip to content

Instantly share code, notes, and snippets.

@reivan
Last active August 29, 2015 14:20
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save reivan/4a0fa0d2802b5fc550d5 to your computer and use it in GitHub Desktop.
Save reivan/4a0fa0d2802b5fc550d5 to your computer and use it in GitHub Desktop.
Income distribution with basic income http://nbviewer.ipython.org/gist/reivan/4a0fa0d2802b5fc550d5
{
"metadata": {
"name": "",
"signature": "sha256:423a57c01560403da1b88b4ca32e7dc217d077da37bef3f7cce8621859a3973b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# http://www.basicincome.org/bien/pdf/FiguresFAQ.pdf\n",
"\n",
"max_income = 10\n",
"ubi_value = 2\n",
"gross_income = linspace(0, max_income, 11)\n",
"net_income_no_tax = gross_income\n",
"net_income_ubi_no_tax = ubi_value + gross_income # halper data\n",
"net_income_ubi = ubi_value + gross_income * 0.5 # 50% flat tax model\n",
"\n",
"fig, ax = plt.subplots()\n",
"\n",
"fill_between(gross_income, [ubi_value] * len(gross_income), color='green', alpha=0.2)\n",
"fill_between(gross_income, net_income_ubi_no_tax, net_income_ubi, color='yellow', alpha=0.2)\n",
"plot(gross_income, [ubi_value] * len(gross_income), ls = '--', color='black')\n",
"plot(gross_income, net_income_no_tax, color='black', alpha=0.5, label='no tax')\n",
"plot(gross_income, net_income_ubi_no_tax, ls = '--', color='black')\n",
"plot(gross_income, net_income_ubi, linewidth=2, color='black', label='after tax')\n",
"\n",
"legend(loc='upper left')\n",
"\n",
"xlim((0, max_income))\n",
"ax.spines['right'].set_visible(False)\n",
"ax.spines['top'].set_visible(False)\n",
"ax.xaxis.set_ticks_position('bottom')\n",
"ax.yaxis.set_ticks_position('left')\n",
"title('Income distribution with basic income')\n",
"ylabel('Net Income')\n",
"xlabel('Gross Income')\n",
"text(8, 7, 'TAX', fontsize=12)\n",
"text(max_income / 2, 0.8, 'BASIC INCOME', fontsize=12, horizontalalignment='center')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 64,
"text": [
"<matplotlib.text.Text at 0x10e3b1d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEZCAYAAACervI0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRMRTCgAREU8pAEREPKUAEBHx1ActkpF6fwNn8AAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10edc710>"
]
}
],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment