abevieiramota/gist:9faf17d1e03b14caac1a

## gistfile1.txt
{
 "metadata": {
  "name": "",
  "signature": "sha256:83672b62a61a5fd0972e682a5f0abbdc50de88cb2c52348a4bae355fc1e4a49c"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      ">>> # coding: utf-8\n",
      ">>> import matplotlib.pyplot as plt\n",
      ">>> import matplotlib.tri as tri\n",
      ">>> import numpy as no\n",
      ">>> import math\n",
      "%matplotlib inline\n",
      ">>> \n",
      ">>> nome= ['Itaquera', 'Ibirapuera','Lapa','Pte. dos Rem\u00e9dios','Mo\u00f3ca','Osasco', 'P. D. Pedro II','Parelheiros', 'Pinheiros', 'S. Caetano do Sul','Sto. Amaro', 'Sto. Andr\u00e9- Centro']\n",
      ">>> CO = [0.52, 0.89, 1.97, 0.97, 0.65, 2.06, 1.61, 0.85, 1.14, 1.22, 0.90, 1.11]\n",
      ">>> lon =[-46.4665375, -46.6601486, -46.7011490, -46.7433205, -46.5983849, -46.7915649, -46.6275940, -46.6969566, -46.7015762, -46.5562553, -46.7094955, -46.5304451]\n",
      ">>> lat = [-23.5796871, -23.5911026, -23.5086594, -23.5184059, -23.5494080, -23.5260277, -23.5445442, -23.7759705, -23.5607567, -23.6181698, -23.6542473, -23.6562881]\n",
      ">>> \n",
      ">>> lev2 = [4.0]\n",
      ">>> levels = [-1.750, -1.500, -1.250, -1.000, -0.750, -0.500, -0.250, 0.0, 0.250, 0.5, 0.75, 1.0, 1.25, 1.50, 1.75]\n",
      ">>> cores = [(1, 0, 0), (1, 0.55, 0), (1, 0.84, 0), (1, 1, 0), (0.196, 0.8, 0.196),(0.13, 0.545, 0.13), (0, 0, 0.8), (0, 0, 1), (0.53, 0.81, 0.92), (1, 1, 1), (0.623, 0.372, 0.623)]\n",
      ">>> print nome, CO, lon, lat\n",
      ">>> triang = tri.Triangulation(lon, lat)\n",
      ">>> plt.hold=False\n",
      ">>> plt.xlim(xmin=-46.7915649, xmax=-46.4665375)\n",
      "(-46.7915649, -46.4665375)\n",
      ">>> plt.ylim(ymin=-23.7759705, ymax=-23.5086594)\n",
      "(-23.7759705, -23.5086594)\n",
      ">>> #plt.axvline(x=-41.996288, linewidth=4, color='k')\n",
      ">>> #plt.tricontourf(triang, w, levels=levels, colors = cores)\n",
      ">>> plt.tricontourf(triang, CO, cmap='autumn')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['Itaquera', 'Ibirapuera', 'Lapa', 'Pte. dos Rem\\xc3\\xa9dios', 'Mo\\xc3\\xb3ca', 'Osasco', 'P. D. Pedro II', 'Parelheiros', 'Pinheiros', 'S. Caetano do Sul', 'Sto. Amaro', 'Sto. Andr\\xc3\\xa9- Centro'] [0.52, 0.89, 1.97, 0.97, 0.65, 2.06, 1.61, 0.85, 1.14, 1.22, 0.9, 1.11] [-46.4665375, -46.6601486, -46.701149, -46.7433205, -46.5983849, -46.7915649, -46.627594, -46.6969566, -46.7015762, -46.5562553, -46.7094955, -46.5304451] [-23.5796871, -23.5911026, -23.5086594, -23.5184059, -23.549408, -23.5260277, -23.5445442, -23.7759705, -23.5607567, -23.6181698, -23.6542473, -23.6562881]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.tri.tricontour.TriContourSet instance at 0x7f96bb676c68>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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I2uZ/iDUDYQAwEChVDINoDAPSU4bCAGAgaJdKlaDn5SsyOSAzDKIxDMRlxWOXwTAAGAiU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\nPGHPMpIkCRcvXsx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       "text": [
        "<matplotlib.figure.Figure at 0x7f96bbf22450>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "",
	"signature": "sha256:83672b62a61a5fd0972e682a5f0abbdc50de88cb2c52348a4bae355fc1e4a49c"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	">>> # coding: utf-8\n",
	">>> import matplotlib.pyplot as plt\n",
	">>> import matplotlib.tri as tri\n",
	">>> import numpy as no\n",
	">>> import math\n",
	"%matplotlib inline\n",
	">>> \n",
	">>> nome= ['Itaquera', 'Ibirapuera','Lapa','Pte. dos Rem\u00e9dios','Mo\u00f3ca','Osasco', 'P. D. Pedro II','Parelheiros', 'Pinheiros', 'S. Caetano do Sul','Sto. Amaro', 'Sto. Andr\u00e9- Centro']\n",
	">>> CO = [0.52, 0.89, 1.97, 0.97, 0.65, 2.06, 1.61, 0.85, 1.14, 1.22, 0.90, 1.11]\n",
	">>> lon =[-46.4665375, -46.6601486, -46.7011490, -46.7433205, -46.5983849, -46.7915649, -46.6275940, -46.6969566, -46.7015762, -46.5562553, -46.7094955, -46.5304451]\n",
	">>> lat = [-23.5796871, -23.5911026, -23.5086594, -23.5184059, -23.5494080, -23.5260277, -23.5445442, -23.7759705, -23.5607567, -23.6181698, -23.6542473, -23.6562881]\n",
	">>> \n",
	">>> lev2 = [4.0]\n",
	">>> levels = [-1.750, -1.500, -1.250, -1.000, -0.750, -0.500, -0.250, 0.0, 0.250, 0.5, 0.75, 1.0, 1.25, 1.50, 1.75]\n",
	">>> cores = [(1, 0, 0), (1, 0.55, 0), (1, 0.84, 0), (1, 1, 0), (0.196, 0.8, 0.196),(0.13, 0.545, 0.13), (0, 0, 0.8), (0, 0, 1), (0.53, 0.81, 0.92), (1, 1, 1), (0.623, 0.372, 0.623)]\n",
	">>> print nome, CO, lon, lat\n",
	">>> triang = tri.Triangulation(lon, lat)\n",
	">>> plt.hold=False\n",
	">>> plt.xlim(xmin=-46.7915649, xmax=-46.4665375)\n",
	"(-46.7915649, -46.4665375)\n",
	">>> plt.ylim(ymin=-23.7759705, ymax=-23.5086594)\n",
	"(-23.7759705, -23.5086594)\n",
	">>> #plt.axvline(x=-41.996288, linewidth=4, color='k')\n",
	">>> #plt.tricontourf(triang, w, levels=levels, colors = cores)\n",
	">>> plt.tricontourf(triang, CO, cmap='autumn')"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"['Itaquera', 'Ibirapuera', 'Lapa', 'Pte. dos Rem\\xc3\\xa9dios', 'Mo\\xc3\\xb3ca', 'Osasco', 'P. D. Pedro II', 'Parelheiros', 'Pinheiros', 'S. Caetano do Sul', 'Sto. Amaro', 'Sto. Andr\\xc3\\xa9- Centro'] [0.52, 0.89, 1.97, 0.97, 0.65, 2.06, 1.61, 0.85, 1.14, 1.22, 0.9, 1.11] [-46.4665375, -46.6601486, -46.701149, -46.7433205, -46.5983849, -46.7915649, -46.627594, -46.6969566, -46.7015762, -46.5562553, -46.7094955, -46.5304451] [-23.5796871, -23.5911026, -23.5086594, -23.5184059, -23.549408, -23.5260277, -23.5445442, -23.7759705, -23.5607567, -23.6181698, -23.6542473, -23.6562881]\n"
	]
	},
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 5,
	"text": [
	"<matplotlib.tri.tricontour.TriContourSet instance at 0x7f96bb676c68>"
	]
	},
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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0M4iIhPLaa6/Fnb4SNhCIiEhfnDIiIiIADAQiInqOgaCDY8eOweVy4dGjR8q2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\nrkJggg==\n",
	"text": [
	"<matplotlib.figure.Figure at 0x7f96bbf22450>"
	]
	}
	],
	"prompt_number": 5
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [],
	"language": "python",
	"metadata": {},
	"outputs": []
	}
	],
	"metadata": {}
	}
	]
	}