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david  committed 629fccf

upgraded to 0.13

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  • Parent commits 5abd3a7

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Files changed (2)

File lessons/01 - Lesson.ipynb

 {
  "metadata": {
-  "name": "01 - Lesson"
+  "name": ""
  },
  "nbformat": 3,
  "nbformat_minor": 0,
       "# General syntax to import a library but no functions: \n",
       "##import (library) as (give the library a nickname/alias)\n",
       "import matplotlib.pyplot as plt\n",
-      "import pandas as pd #only needed to determine version number"
+      "import pandas as pd #only needed to determine version number\n",
+      "\n",
+      "# Enable inline plotting\n",
+      "%matplotlib inline"
      ],
      "language": "python",
      "metadata": {},
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Pandas version 0.11.0\n"
+        "Pandas version 0.13.0\n"
        ]
       }
      ],
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 6,
        "text": [
         "1  Jessica     155\n",
         "2     Mary      77\n",
         "3     John     578\n",
-        "4      Mel     973"
+        "4      Mel     973\n",
+        "\n",
+        "[5 rows x 2 columns]"
        ]
       }
      ],
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "Location = r'C:\\Users\\hdrojas\\.xy\\startups\\births1880.csv'\n",
+      "Location = r'C:\\Users\\david\\births1880.csv'\n",
       "df = read_csv(Location)"
      ],
      "language": "python",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>4 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 11,
        "text": [
         "0  Jessica  155\n",
         "1     Mary   77\n",
         "2     John  578\n",
-        "3      Mel  973"
+        "3      Mel  973\n",
+        "\n",
+        "[4 rows x 2 columns]"
        ]
       }
      ],
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 12,
        "text": [
         "1  Jessica  155\n",
         "2     Mary   77\n",
         "3     John  578\n",
-        "4      Mel  973"
+        "4      Mel  973\n",
+        "\n",
+        "[5 rows x 2 columns]"
        ]
       }
      ],
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 13,
        "text": [
         "1  Jessica     155\n",
         "2     Mary      77\n",
         "3     John     578\n",
-        "4      Mel     973"
+        "4      Mel     973\n",
+        "\n",
+        "[5 rows x 2 columns]"
        ]
       }
      ],
      "metadata": {},
      "outputs": [
       {
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 16,
        "text": [
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>1 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 17,
        "text": [
         "  Names  Births\n",
-        "4   Mel     973"
+        "4   Mel     973\n",
+        "\n",
+        "[1 rows x 2 columns]"
        ]
       }
      ],
      "metadata": {},
      "outputs": [
       {
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 18,
        "text": [
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>1 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 19,
        "text": [
         "  Names  Births\n",
-        "4   Mel     973"
+        "4   Mel     973\n",
+        "\n",
+        "[1 rows x 2 columns]"
        ]
       },
       {
+       "metadata": {},
        "output_type": "display_data",
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O02DYMLj+enjtNb3TiPrwhL6zLuw+Ynv88ceZO3cu3pV2+C0sLMTPzw8APz8/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\nXb5zStu2Vd+vtFSdr/bcc+o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lNTWVqKgo4uPjSUhIYO3atQGI7y1Dh5pr5Tm9qLRIkUOHTN9u0iSzfJhIKPO7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\nFTwREQkL/x/b5cyB2kBB+wAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0xaa97fd0>"
+       ]
       }
      ],
      "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Author:** [David Rojas LLC](http://hdrojas.pythonanywhere.com/)  "
+     ]
     }
    ],
    "metadata": {}

File lessons/02 - Lesson.ipynb

 {
  "metadata": {
-  "name": "02 - Lesson"
+  "name": ""
  },
  "nbformat": 3,
  "nbformat_minor": 0,
       "# General syntax to import a library but no functions: \n",
       "##import (library) as (give the library a nickname/alias)\n",
       "import matplotlib.pyplot as plt\n",
-      "import pandas as pd"
+      "import pandas as pd\n",
+      "\n",
+      "# Enable inline plotting\n",
+      "%matplotlib inline"
      ],
      "language": "python",
      "metadata": {},
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Pandas version 0.11.0\n"
+        "Pandas version 0.13.0\n"
        ]
       }
      ],
       "Return random integers from `low` (inclusive) to `high` (exclusive).\n",
       "'''\n",
       "\n",
-      "randint?"
+      "random.randint?"
      ],
      "language": "python",
      "metadata": {},
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "seed(500)\n",
-      "random_names = [names[randint(low=0,high=len(names))] for i in range(1000)]\n",
+      "random.seed(500)\n",
+      "random_names = [names[random.randint(low=0,high=len(names))] for i in range(1000)]\n",
       "\n",
       "# Print first 10 records\n",
       "print random_names[:10]"
      "collapsed": false,
      "input": [
       "# The number of births per name for the year 1880\n",
-      "births = [randint(low=0,high=1000) for i in range(1000)]\n",
+      "births = [random.randint(low=0,high=1000) for i in range(1000)]\n",
       "print births[:10]"
      ],
      "language": "python",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>10 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 12,
        "text": [
         "6  Jessica     155\n",
         "7     Mary     403\n",
         "8     Mary     199\n",
-        "9     Mary     191"
+        "9     Mary     191\n",
+        "\n",
+        "[10 rows x 2 columns]"
        ]
       }
      ],
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "Location = r'C:\\Users\\hdrojas\\.xy\\startups\\births1880.txt'\n",
+      "Location = r'C:\\Users\\david\\births1880.txt'\n",
       "df = read_csv(Location)"
      ],
      "language": "python",
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "df"
+      "df.info()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": [
-        "<pre>\n",
-        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
-        "Int64Index: 999 entries, 0 to 998\n",
-        "Data columns (total 2 columns):\n",
-        "Mary    999  non-null values\n",
-        "968     999  non-null values\n",
-        "dtypes: int64(1), object(1)\n",
-        "</pre>"
-       ],
-       "output_type": "pyout",
-       "prompt_number": 17,
+       "output_type": "stream",
+       "stream": "stdout",
        "text": [
         "<class 'pandas.core.frame.DataFrame'>\n",
         "Int64Index: 999 entries, 0 to 998\n",
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "When the dataframe is large, pandas will print out a summary of the data.  \n",
-      "\n",
       "Summary says:  \n",
+      "\n",
       "* There are ***999*** records in the data set  \n",
       "* There is a column named ***Mary*** with 999 values  \n",
-      "* There is a column named ***539*** with 999 values  \n",
+      "* There is a column named ***968*** with 999 values  \n",
       "* Out of the ***two*** columns, one is ***numeric***, the other is ***non numeric***  "
      ]
     },
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 18,
        "text": [
         "1  Jessica   77\n",
         "2      Bob  578\n",
         "3  Jessica  973\n",
-        "4  Jessica  124"
+        "4  Jessica  124\n",
+        "\n",
+        "[5 rows x 2 columns]"
        ]
       }
      ],
      "collapsed": false,
      "input": [
       "df = read_csv(Location, header=None)\n",
-      "df"
+      "df.info()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": [
-        "<pre>\n",
-        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
-        "Int64Index: 1000 entries, 0 to 999\n",
-        "Data columns (total 2 columns):\n",
-        "0    1000  non-null values\n",
-        "1    1000  non-null values\n",
-        "dtypes: int64(1), object(1)\n",
-        "</pre>"
-       ],
-       "output_type": "pyout",
-       "prompt_number": 19,
+       "output_type": "stream",
+       "stream": "stdout",
        "text": [
         "<class 'pandas.core.frame.DataFrame'>\n",
         "Int64Index: 1000 entries, 0 to 999\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 20,
        "text": [
         "996  Jessica  511\n",
         "997     John  756\n",
         "998  Jessica  294\n",
-        "999     John  152"
+        "999     John  152\n",
+        "\n",
+        "[5 rows x 2 columns]"
        ]
       }
      ],
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 21,
        "text": [
         "1  Jessica     155\n",
         "2  Jessica      77\n",
         "3      Bob     578\n",
-        "4  Jessica     973"
+        "4  Jessica     973\n",
+        "\n",
+        "[5 rows x 2 columns]"
        ]
       }
      ],
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The data we have consists of baby names and the number of births in the year 1880. We already know that we have 999 records and none of the records are missing (non-null values). We can verify the \"Names\" column still only has five unique names.  \n",
+      "The data we have consists of baby names and the number of births in the year 1880. We already know that we have 1,000 records and none of the records are missing (non-null values). We can verify the \"Names\" column still only has five unique names.  \n",
       "\n",
       "We can use the ***unique*** property of the dataframe to find all the unique records of the \"Names\" column."
      ]
      "metadata": {},
      "outputs": [
       {
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 23,
        "text": [
-        "array([Mary, Jessica, Bob, John, Mel], dtype=object)"
+        "array(['Mary', 'Jessica', 'Bob', 'John', 'Mel'], dtype=object)"
        ]
       }
      ],
         "unique       5\n",
         "top        Bob\n",
         "freq       206\n",
-        "dtype: object\n"
+        "Name: Names, dtype: object\n"
        ]
       }
      ],
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Create a groupby onject\n",
-      "Name = df.groupby(df['Names'])\n",
+      "# Create a groupby object\n",
+      "Name = df.groupby('Names')\n",
       "\n",
       "# Apply the sum function to the groupby object\n",
       "df = Name.sum()\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>5 rows \u00d7 1 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 27,
        "text": [
         "Jessica   97826\n",
         "John      90705\n",
         "Mary      99438\n",
-        "Mel      102319"
+        "Mel      102319\n",
+        "\n",
+        "[5 rows x 1 columns]"
        ]
       }
      ],
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>1 rows \u00d7 1 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 28,
        "text": [
         "       Births\n",
         "Names        \n",
-        "Bob    106817"
+        "Bob    106817\n",
+        "\n",
+        "[1 rows x 1 columns]"
        ]
       }
      ],
      "metadata": {},
      "outputs": [
       {
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 29,
        "text": [
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Here we can plot the ***Births*** column and label the graph to show the end user the highest point on the graph. In conjunction with the table, the end user has a clear picture that **Bob** is the most popular baby name in the data set. \n",
-      "\n",
-      "***plot()*** is a convinient attribute where pandas lets you painlessly plot the data in your dataframe. We learned how to find the maximum value of the Births column in the previous section. Now to find the actual baby name of the 998 value looks a bit tricky, so lets go over it.  \n",
-      "\n",
-      "**Explain the pieces:**  \n",
-      "*df['Names']* - This is the entire list of baby names, the entire Names column  \n",
-      "*df['Births']* - This is the entire list of Births in the year 1880, the entire Births column  \n",
-      "*df['Births'].max()* - This is the maximum value found in the Births column  \n",
-      "\n",
-      "[df['Births'] == df['Births'].max()] **IS EQUAL TO** [Find all of the records in the Births column where it is equal to 998]  \n",
-      "df['Names'][df['Births'] == df['Births'].max()] **IS EQUAL TO** Select all of the records in the Names column **WHERE** [The Births column is equal to 998]  \n",
-      "\n",
-      "An alternative way could have been to use the ***Sorted*** dataframe:  \n",
-      "Sorted['Names'].head(1).value  \n",
-      "\n",
-      "The ***str()*** function simply converts an object into a string.  "
+      "Here we can plot the ***Births*** column and label the graph to show the end user the highest point on the graph. In conjunction with the table, the end user has a clear picture that **Bob** is the most popular baby name in the data set. "
      ]
     },
     {
      "collapsed": false,
      "input": [
       "# Create graph\n",
-      "df['Births'].plot()\n",
-      "\n",
-      "# Maximum value in the data set\n",
-      "MaxValue = df['Births'].max()\n",
-      "\n",
-      "# Name associated with the maximum value\n",
-      "MaxName = df[df['Births'] == df['Births'].max()].index[0]\n",
-      "\n",
-      "# Text to display on graph\n",
-      "Text = str(MaxValue) + \" - \" + MaxName\n",
-      "\n",
-      "# Add text to graph\n",
-      "plt.annotate(Text, xy=(1, MaxValue), xytext=(8, 0), \n",
-      "                 xycoords=('axes fraction', 'data'), textcoords='offset points')\n",
+      "df['Births'].plot(kind='bar')\n",
       "\n",
       "print \"The most popular name\"\n",
-      "df[df['Births'] == df['Births'].max()]\n",
-      "#Sorted.head(1) can also be used"
+      "df.sort(columns = 'Births', ascending = False)"
      ],
      "language": "python",
      "metadata": {},
         "      <th>Bob</th>\n",
         "      <td> 106817</td>\n",
         "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Mel</th>\n",
+        "      <td> 102319</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Mary</th>\n",
+        "      <td>  99438</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Jessica</th>\n",
+        "      <td>  97826</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>John</th>\n",
+        "      <td>  90705</td>\n",
+        "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
+        "<p>5 rows \u00d7 1 columns</p>\n",
         "</div>"
        ],
+       "metadata": {},
        "output_type": "pyout",
        "prompt_number": 30,
        "text": [
-        "       Births\n",
-        "Names        \n",
-        "Bob    106817"
+        "         Births\n",
+        "Names          \n",
+        "Bob      106817\n",
+        "Mel      102319\n",
+        "Mary      99438\n",
+        "Jessica   97826\n",
+        "John      90705\n",
+        "\n",
+        "[5 rows x 1 columns]"
        ]
       },
       {
+       "metadata": {},
        "output_type": "display_data",
-       "png": 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uqNo10EOHDuHj4wOAj48Phw4dAuDgwYNYLBb7+ywWCzab7az5vr6+2Gw2AGw2\nGx06dACgWbNmeHp6UlBQcN51uSqzayIvvGB0Pb36qqmrdZnajeY0V0PPuW8fTJ1qdNOuWAHPPw+7\ndxsPbmjd2tyM4Dr7U9Vere4DdXNzw80JhsiJj4/Hz88PAC8vL8LCwoiKigL+98fsyOmvvvrK9PV/\n8EEUkZFw0UXpXH21Y7evvqfrYn825umGuD97944iPR3++c90vv4abr89ii+/hLw84/UmTeru851x\nf1b8nJubizLRhfp4c3JyzqiBdurUSfLz80VE5ODBg9KpUycREZkxY4bMmDHD/r6+ffvKpk2bJD8/\nX4KDg+3z33//fbnrrrvs7/nyyy9FROTXX3+VSy65REREFi9eLHfeead9mTvuuENSUlLOma8Km9Bg\naT1UqTP9/LPIW2+JWK0iV18tMneuyIkTjk7lfBrzcdNM1e7CHTRoEMnJyYBxpeyQIUPs81NSUigt\nLSUnJ4fs7GzCw8Np164drVu3JjMzExFh4cKFDB48+Kx1LV26lOjoaABiY2NJS0vj6NGjFBUVsXbt\nWvr27WvC14WGpaIeOn681kNV45aTAw8+CFdeaTwF5ZVXYNcuSEiAVq0cnU41WJW1rrfccou0b99e\n3N3dxWKxyLx586SgoECio6MlMDBQYmJizrg69tlnnxV/f3/p1KmTpKam2udv2bJFrFar+Pv7y6RJ\nk+zzT506JSNGjJCAgACJiIiQnJwc+2vz5s2TgIAACQgIkPnz55834wU2wSmsX7++ztZdUiLSo4fI\nyy/Xfl11mdNMmtNcrpqzvFxk7VqRQYNEvL1FHnxQ5IcfHJPtdK6wP13huOkKKq2BLl68+JzzP/30\n03POf/TRR3n00UfPmt+9e3d27Nhx1vzmzZuzZMmSc65r/PjxjB8/vrJ4CrjoIuP+tYgI+POf4feL\nnZVqsH7+GRYuNAYVadrUGGLv/fehZUtHJ1ONjY6F20CsWAH/+IcxXq4Ocq0aou++M0YGWrDAuAXl\n7383/u8E1zG6HD1umkOH8msgbr4ZBg/WeqhqeH77DUaPNobXa97c+JK4fDlERWnjqRxLG9B6cPql\n5HXphReMJ0i88krNlq+vnLWlOc3l7Dn/+U84dAgWLUonMdG4UMiZOfv+VObR54E2IH+sh0ZEODqR\nUrXz738b9c6tW42rapVyJloDbYBWroT77jO6utq2dXQapWrmhx+MbttVq4yHyivz6HHTHNqANlD3\n328cgFbBEq65AAAgAElEQVSu1DqRcj3FxUYvysSJxpB7ylx63DSH1kDrgSNqIs8/D/n51auHukrt\nRnOayxlz3nuv8RzOe+753zxnzHkurpJT1Z7WQBuoiy4ynh8aEWF0g2kXmHIVSUmwaRNkZmrviXJu\n2oXbwK1aZTwcWOuhyhVs2wZ9+8Lnn0NwsKPTNFx63DSHduE2cIMHG2Pmxsfr/aHKuRUVwfDhMHeu\nNp7KNWgDWg8cXRNJTDTuo3v55crf5+icVaU5zeUMOcvLYexYGDIERow493ucIWdVuEpOVXtaA20E\n/nh/qNZDlbNJTDTOQJ9/3tFJlKo6rYE2IloPVc7o00+Ns8/Nm8HX19FpGgc9bppDG9BG5oEHYO9e\nWL1ar3BUjpeXBz16GE9Tuf56R6dpPPS4aQ6tgdYDZ6qJzJgBhw/DSy+d/Zoz5ayM5jSXo3KWlhr1\nzvvuq1rjqftTORutgTYyFfXQ8HCjHnrddY5OpBqrBx8EHx946CFHJ1Gqhmr6JO5XXnlFrFardOnS\nRV555RUREcnMzJQePXpIWFiYXHvttZKVlWV//3PPPScBAQHSqVMnWbNmjX3+li1bxGq1SkBAgPz9\n73+3zz916pSMHDlSAgICJCIiQnJzc8+Zoxab0KitWiVyxRUiBQWOTqIao/ffF/H3FykqcnSSxkmP\nm+ao0V7csWOHWK1WKS4ulrKyMunTp49899130rt3b0lNTRURkY8//liioqJERGTXrl0SGhoqpaWl\nkpOTI/7+/lJeXi4iIj169JDMzEwREenfv7988sknIiIyZ84cSUhIEBGRlJQUiYuLO/cG6B9Cjf3j\nHyIDB4r89pujk6jGZNcukUsuEfnqK0cnabz0uGmOGtVA9+zZQ0REBB4eHjRt2pTevXuzfPlyLr/8\nco4dOwbA0aNH8f39krpVq1YxatQo3N3d8fPzIyAggMzMTPLz8zlx4gTh4eEAjB07lpUrVwKwevVq\nxo0bB8CwYcNYt25drc60HclZayJ/rIc6a84/0pzmqs+cJ07AsGEwcyaEhlZvWd2fytnUqAZqtVqZ\nNm0ahYWFeHh48NFHHxEeHk5iYiJ//vOfefDBBykvL+fLL78E4ODBg0SedvOhxWLBZrPh7u6OxWKx\nz/f19cVmswFgs9no0KGDEbJZMzw9PSksLKSt3n9hmj/WQ5WqSyJw++3Qs6cxMpZSrq5GDWhwcDBT\np04lNjaWli1b0q1bN5o0acLEiRN57bXXuPnmm/nwww+ZMGECa9euNTvzWeLj4/Hz8wPAy8uLsLAw\noqKigP99G3T0dAVnyVMxnZOTzuTJMGpUFNu2RTk8j6vvz9Ono6J0f54+/eqrsH17Oq+/DlD95XV/\n1i5Peno6ubm5KPOYch/otGnTsFgsTJ06lePHjwMgInh5eXHs2DESExMBePjhhwHo168fTz75JFde\neSXXX38933zzDQCLFy8mIyODN954g379+vHEE08QGRlJWVkZ7du35/Dhw2dvgN7PZIoHH4SdO+Hf\n/wZ3d0enUQ3N//2fMSbzpk3QsaOj0yg9bpqjxveB/vTTTwDs37+f5cuXc+uttxIQEMCGDRsA+Oyz\nzwgKCgJg0KBBpKSkUFpaSk5ODtnZ2YSHh9OuXTtat25NZmYmIsLChQsZPHiwfZnk5GQAli5dSnR0\ndK021JH++K3UGc2YASdOpDNmDPz2m6PTVM4V9idozgqHDsEtt8C779au8dT9qZxNje8DHT58OAUF\nBbi7uzN37lw8PT15++23ueeeeygpKaFFixa8/fbbAHTu3JmRI0fSuXNnmjVrxty5c3H7fRicuXPn\nEh8fT3FxMQMGDKBfv34ATJw4kTFjxhAYGIi3tzcpKSkmbK46H3d3mD4dXngB7rgD/vUvaKLDbKha\nKiuDUaNg/HgYMMDRaZQylw7lp87w88/G8xh79DCe3qLD/anaeOQR2LoVPvkEmjZ1dBpVQY+b5tBz\nDHWGVq3go49gwwb45z8dnUa5stWrjTFu339fG0/VMGkDWg9cpSZSkdPLC9LSYOlSo0vX2bja/nR2\ndZHz+++NW1aWLIFLLjFnnY15fyrnpGPhqnO69FLjMVM9e8Kf/gQJCY5OpFxFcbExWML06cYzaJVq\nqLQGqir1ww/Quzc89xyMGePoNMrZicCECcaTVhYt0hq6s9Ljpjn0DFRV6qqrjO7cG26Ali2Ne/mU\nOp+kJOPB2JmZ2niqhk9roPXAVWoi58t59dXw8cdGN+6aNfWb6VxcfX86G7Nybt0Kjz4Ky5YZX7bM\n1tj2p3J+2oCqKunWDVasgNGjISPD0WmUsykshOHDYe5c6NTJ0WmUqh9aA1XV8umncOutxhnptdc6\nOo1yBuXlcNNNEBwML77o6DSqKvS4aQ49A1XV0qcPvPMODBxojJ2r1HPPwfHj8PuQ10o1GtqA1gNX\nqYlUNeegQcYoRX37QnZ23WY6l4a2Px2tNjnXrjW6bT/4oO4fQtAY9qdyLXoVrqqRUaOMYf9iYuDz\nz+H3R7eqRuTAARg7FhYvhssvd3Qapeqf1kBVrbz8Mrz5pnFhkY+Po9Oo+lJaCr16Gbc1PfSQo9Oo\n6tLjpjm0AVW19tRTxrB/6enQtq2j06j6MGkS5OXB8uV6v6cr0uOmObQGWg9cpSZS05yPPw6xsdC/\nP5w4YW6mc2no+7O+VTfn++9DairMn1+/jWdD3Z/KdWkDqmrNzQ1mzjTuFR00yBgLVTVMu3bB5MnG\nYAmeno5Oo5RjaReuMk15uXFRSWEhrFwJF13k6ETKTMePG8+JnTbN+D0r16XHTXPU+Ax09uzZhISE\nYLVamT17tn3+a6+9xtVXX43VamXq1Kn2+TNmzCAwMJDg4GDS0tLs87du3UpISAiBgYFMnjzZPr+k\npIS4uDgCAwOJjIxk3759NY2q6kmTJvDuu0bDedttUFbm6ETKLCIwcSJERWnjqZSd1MCOHTvEarVK\ncXGxlJWVSZ8+feS7776Tzz77TPr06SOlpaUiIvLTTz+JiMiuXbskNDRUSktLJScnR/z9/aW8vFxE\nRHr06CGZmZkiItK/f3/55JNPRERkzpw5kpCQICIiKSkpEhcXd84sNdyEerV+/XpHR6gSs3KeOiUS\nEyMSHy/y22+mrPIMjW1/1rWq5HzpJZHu3UWKi+s+z/k0pP3paK5w3HQFNToD3bNnDxEREXh4eNC0\naVN69+7N8uXLefPNN3nkkUdw//2O6ksvvRSAVatWMWrUKNzd3fHz8yMgIIDMzEzy8/M5ceIE4eHh\nAIwdO5aVK1cCsHr1asaNGwfAsGHDWLduXS2/Kqj60ry5MW5udrZRL9OeIte2caMxytDSpeDh4eg0\nSjmPGg2kYLVamTZtGoWFhXh4ePDxxx9z7bXXsnfvXjIyMnj00Ufx8PBg1qxZXHvttRw8eJDIyEj7\n8haLBZvNhru7OxaLxT7f19cXm80GgM1mo8Pvd+c3a9YMT09PCgsLaXuO+yTi4+Px8/MDwMvLi7Cw\nMKKiooD/XRHn6OkKzpLnXNNRUVGmru+jj6BHj3TGjIFFi8zNW8GZ9t8fp83en3U5XeGPr69Ykc4d\nd8CCBVH4+en+dNW/z4qfc3NzUeap8UVE8+bNY+7cubRs2ZIuXbrQvHlzPv30U2644QZmz57N5s2b\niYuL44cffmDSpElERkZy2223AXD77bfTv39//Pz8ePjhh1m7di0An3/+OS+88AL//ve/CQkJYc2a\nNVz++xAnAQEBZGVlndWAajHcuR05YjyQe8wYePhhR6dR1VFWZow01asXPPmko9MoM+lx0xw1voho\nwoQJbNmyhQ0bNtCmTRuCgoKwWCwM/f2Jyz169KBJkyYcOXIEX19fDhw4YF82Ly8Pi8WCr68veXl5\nZ80H42x0//79AJSVlXHs2LFznn26gj9+K3VWdZHzkkuM8VLfeQdef92cdTbm/VkXzpfzsceMC8L+\n+c/6zXM+rr4/VcNT4wb0p59+AmD//v0sX76c2267jSFDhvDZZ58BsHfvXkpLS7nkkksYNGgQKSkp\nlJaWkpOTQ3Z2NuHh4bRr147WrVuTmZmJiLBw4UIGDx4MwKBBg0hOTgZg6dKlREdH13ZblYNcfrnx\nGLQXXoDff6XKya1caYxx+9570LSpo9Mo5aRqevVRz549pXPnzhIaGiqfffaZiIiUlpbK6NGjxWq1\nyjXXXHPG1WjPPvus+Pv7S6dOnSQ1NdU+f8uWLWK1WsXf318mTZpkn3/q1CkZMWKEBAQESEREhOTk\n5JwzRy02QdWzb74Rad9e5MMPHZ1EVSY7W+TSS0V+vzheNUB63DSHDqSg6tXXXxvD/r37LgwY4Og0\n6o9++QWuuw7uvBPuvtvRaVRd0eOmOXQov3rgKjWR+sgZGgqrVkF8PGzYULN16P40V0VOEaPRDAmB\nhATHZjoXV9ufquHTBlTVu8hISEmBESMgK8vRaVSFd96BLVvgrbf0CStKVYV24SqH+c9/jOHh1q6F\nrl0dnaZx27LF6FL//HPo1MnRaVRd0+OmOfQMVDnMwIHw6qvGY9D27nV0msarsNDoDXjjDW08laoO\nbUDrgavURByRMy4Onn7auGG/qs8L0P1pnvJy6N8/nWHDYNgwR6epnCvsT3CdnKr2ajSUn1JmmjDB\neBB3nz6QkQHt2zs6UePx7LPG81tnzHB0EqVcj9ZAldN45hn44ANITwdvb0enafjS0mD8eKP+qV9a\nGhc9bppDG1DlNESM8XI/+wzWrYPWrR2dqOHavx/Cw40vLL17OzqNqm963DSH1kDrgavURByd083N\neGxWjx7GBUa//HLu9zk6Z1U5a86SEuOioQceMBpPZ835R5pTORttQJVTcXMzBp3384OhQ42DvTLX\nAw8Y4xM/+KCjkyjl2rQLVzmlsjLjCl0wuhmb6eVupnjvPXjiCaPu6enp6DTKUfS4aQ5tQJXTKimB\nwYPhsstg/nxoov0ltbJzJ1x/vVFf1oErGjc9bppDD0n1wFVqIs6Ws3lzWL4ccnNh0iTjIiNwvpzn\n40w5jx837vN86aWzG09nylkZzamcjTagyqldfLEx5F9WFjzyyP8aUVV1Isa9tjfcAGPGODqNUg2H\nduEql1BQYFwxOmoUTJvm6DSu5aWXjIdjb9xonNUrpcdNc+ilGcoleHsbg8736gV/+hP8/e+OTuQa\nPv8cnn8eMjO18VTKbDXuwp09ezYhISFYrVZmz559xmsvvvgiTZo0obCw0D5vxowZBAYGEhwcTFpa\nmn3+1q1bCQkJITAwkMmTJ9vnl5SUEBcXR2BgIJGRkeyr6kCpTshVaiLOnrN9e/j0U3j22XTmzXN0\nmgtz9P788Ue45RZITjZuCzofR+esKs2pnE2NGtCdO3fyzjvvsHnzZr7++mv+85//8P333wNw4MAB\n1q5dy5VXXml//+7du/nggw/YvXs3qamp3H333fbug4SEBJKSksjOziY7O5vU1FQAkpKS8Pb2Jjs7\nm/vvv5+pU6fWdltVA3DllTBrFjz2GCxZ4ug0zquszGg8//Y36NfP0WmUaphq1IDu2bOHiIgIPDw8\naNq0Kb1792b58uUA/OMf/+CFF1444/2rVq1i1KhRuLu74+fnR0BAAJmZmeTn53PixAnCw8MBGDt2\nLCtXrgRg9erVjBs3DoBhw4axbt26Gm+ko0VFRTk6QpW4Ss4xY6JITTWuzP3Pfxyd5vwcuT+nTQMP\nD3j88Qu/11V+75pTOZsa1UCtVivTpk2jsLAQDw8PPv74Y6699lpWrVqFxWKh6x+ukz948CCRkZH2\naYvFgs1mw93dHYvFYp/v6+uLzWYDwGaz0aFDByNks2Z4enpSWFhI27Ztz8oTHx+P3+99VF5eXoSF\nhdn/iCu6U3S64U2vXg19+6YzfTrcf7/j8zjL9OefwwcfRLFlC3z+uePz6LTjpyt+zs3NRZlIaigp\nKUm6d+8uvXr1koSEBLnjjjskIiJCjh07JiIifn5+cuTIERERuffee2XRokX2ZSdOnChLly6VLVu2\nSJ8+fezzMzIyZODAgSIiYrVaxWaz2V/z9/eXgoKCs3LUYhPqzfr16x0doUpcMef69SKXXiry5ZcO\ni3Nejtife/ca+yMrq+rLuOLv3Zm5Qk5XOG66ghpfRDRhwgS2bNnChg0baNOmDV26dCEnJ4fQ0FA6\nduxIXl4e3bt359ChQ/j6+nLgwAH7snl5eVgsFnx9fcnLyztrPhhno/v37wegrKyMY8eOnfPsUzVu\nUVHGKEWDB8NXXzk6jWP98osxWMJTTxkD8iul6lhNW95Dhw6JiMi+ffskODjYfuZZwc/Pz37GuGvX\nLgkNDZWSkhL54Ycf5KqrrpLy8nIREQkPD5dNmzZJeXm59O/fXz755BMREZkzZ47cddddIiKyePFi\niYuLO2eOWmyCakCWLBFp315kzx5HJ3GM8nKRMWNERo82flaqMnrcNEeN7wMdPnw4BQUFuLu7M3fu\nXFr/4eGNbm5u9p87d+7MyJEj6dy5M82aNWPu3Ln21+fOnUt8fDzFxcUMGDCAfr9fMjhx4kTGjBlD\nYGAg3t7epKSk1DSqagRGjICTJyEmBjIyKr9toyF6+23Yvh02bTKeaKOUqgeObsFryxU2wRVqIiIN\nI+drr4n4+4ucVj53mPran5s3G3XPb7+t2fIN4ffuTFwhpyscN12BjkSkGpR774UTJ4wz0Q0b4JJL\nHJ2obhUUGGffb74JQUGOTqNU46Jj4aoG6ZFHIC0NPvus4T73srwcbrwRrFaYOdPRaZQr0eOmObQB\nVQ2SiDFe7vbtsGYNtGzp6ETme+op49me69bpA8dV9ehx0xz6OLN6cPrNzM6sIeV0c4PZsyEgAG6+\n2Xg4d32ry/25Zg289RakpNS+8WxIv3dn4Co5Ve1pA6oarCZN4J13oHVrY1zYX391dCJz7NsH48YZ\njyhr397RaZRqvLQLVzV4paUwZAi0bQsLFhgNq6sqKYGePWHkSHjwQUenUa5Kj5vm0AZUNQrFxdC/\nPwQHwxtvuO69knffDYcOwdKlrrsNyvH0uGkOF/4u7jpcpSbSkHO2aAGrV8O2bfDQQ8ZFRnXN7P25\naJHxPNR588xtPBvy790RXCWnqj29dk81Gq1bQ2qqMX7un/4E//ynoxNV3Y4dcP/9Dfu2HKVcjXbh\nqkbnxx+hVy9ISDAaJWd3/Dhce63R4I8e7eg0qiHQ46Y59AxUNTrt2hldob16GWeit9/u6ETnJwLj\nx0OfPtp4KuVstAZaD1ylJtKYcl5xBaxdC9OnG7eD1AUzcr70Ehw4AC+/XPs859OYfu/1wVVyqtrT\nM1DVaAUGGgMS9OljjFQ0aJCjE50pI8MYoi8zE5o3d3QapdQfaQ1UNXqbNxtjyi5eDNHRjk5jyM83\n6p5JSfD7E/6UMo0eN82hXbiq0evRw7iv8pZb4IsvHJ3GGDEpLg7uuEMbT6WcmTag9cBVaiKNOWev\nXrBwoTFi0fbt5qyzpjkffdToUn78cXNyXEhj/r3XBVfJqWqvxg3o7NmzCQkJwWq1Mnv2bACmTJnC\n1VdfTWhoKEOHDuXYsWP298+YMYPAwECCg4NJS0uzz9+6dSshISEEBgYyefJk+/ySkhLi4uIIDAwk\nMjKSffv21TSqUlXSr58xStGAAfDNN47JsHw5fPihMWiCKw85qFSjUJOncO/YsUOsVqsUFxdLWVmZ\n9OnTR7777jtJS0uT3377TUREpk6dKlOnThURkV27dkloaKiUlpZKTk6O+Pv7S3l5uYiI9OjRQzIz\nM0VEpH///vLJJ5+IiMicOXMkISFBRERSUlIkLi7unFlquAlKnVdysojFIvL99/X7ud9+K3LppSJZ\nWfX7uarx0eOmOWr0HXfPnj1ERETg4eFB06ZN6d27N8uXLycmJoYmv39tjoiIIC8vD4BVq1YxatQo\n3N3d8fPzIyAggMzMTPLz8zlx4gTh4eEAjB07lpUrVwKwevVqxo0bB8CwYcNYt25dLb8qKFU1Y8ca\nD+Tu0wdstvr5zJMnYdgwePppoyarlHJ+NbqNxWq1Mm3aNAoLC/Hw8OCjjz6yN4IV5s2bx6hRowA4\nePAgkZGR9tcsFgs2mw13d3csFot9vq+vL7bfj1g2m40OHToYIZs1w9PTk8LCQtq2bXtWnvj4ePz8\n/ADw8vIiLCyMqKgo4H/1CEdOf/XVV9x3331Ok+d806fXbpwhz/mm62N/3n13FCdOwJ//nM7s2TBk\nSPXXV9X9KQLz5kVxzTUQFJROenrD25/69+nYfBU/5+bmokxU01PXpKQk6d69u/Tq1UsSEhLkvvvu\ns7/2zDPPyNChQ+3T9957ryxatMg+PXHiRFm6dKls2bJF+vTpY5+fkZEhAwcOFBERq9UqNpvN/pq/\nv78UFBSclaMWm1Bv1q9f7+gIVaI5zzZtmkhYmEhRUfWXrWrON94QCQkROXmy+p9hBv29m8sVcrrC\ncdMV1PgyhQkTJrBlyxY2bNiAl5cXnTp1AmD+/Pl8/PHHvPfee/b3+vr6cuDAAft0Xl4eFosFX19f\nezfv6fMrltm/fz8AZWVlHDt27Jxnn66g4tugs9OcZ3v6aeMK3QED4Oefq7dsVXJmZRlj3C5bBhdf\nXLOMtaW/d3O5Sk5VezVuQH/66ScA9u/fz4oVK7j11ltJTU1l5syZrFq1Cg8PD/t7Bw0aREpKCqWl\npeTk5JCdnU14eDjt2rWjdevWZGZmIiIsXLiQwYMH25dJTk4GYOnSpUQ7yx3uqlFxczOG0QsONm5x\nOXXKvHUfOQIjRsBbbxmjIimlXExNT1179uwpnTt3ltDQUPnss89ERCQgIECuuOIKCQsLk7CwMPtV\ntCIizz77rPj7+0unTp0kNTXVPn/Lli1itVrF399fJk2aZJ9/6tQpGTFihAQEBEhERITk5OScM0ct\nNqHeuEKXjojmrExZmcjIkSI33SRSWlq1ZSrLWVYmEhsrMmWKOflqQ3/v5nKFnK5w3HQFNR4LNyMj\n46x52dnZ533/o48+yqOPPnrW/O7du7Njx46z5jdv3pwlS5bUNJ5Spmra1BhoYehQGDfO+Llp05qv\n7+mnjbPZ554zL6NSqn7pWLhKVUNxsTFurr8/vP220cVbXampxiPUtmwxHq2mVH3T46Y5dKwTpaqh\nRQtYtQp27IAHHjCe11kd+/ZBfLwxcL02nkq5Nm1A68Hp92I5M81ZNX/6E3zyCaxbB08+ef73/TFn\nSQkMHw4PPQQ9e9Ztxupw9P6sKs2pnI0+D1SpGmjTBtLSoHdvo0F94IELL3PffXDllXD//XWfTylV\n97QGqlQtHDhg3Cf68MNw553nf9+CBfDss8azR1u3rr98Sp2LHjfNoWegStVChw7w6afGmWirVnDb\nbWe/57//Nc5Q16/XxlOphkRroPXAVWoimrNm/P1hzRqjkfz9WQiAkfPYMWOQ+FdeAavVcRkr42z7\n83w0p3I2egaqlAm6dIGPPoL+/Y2HYcfEGFfoxsdDbOy5z0yVUq5Na6BKmWjjRrj5ZlixAjZtMh6O\nnZEBzZs7OplS/6PHTXNoA6qUydLSjDPOpk2NweKvuMLRiZQ6kx43zaE10HrgKjURzWmO2FhjoITH\nH093icbT2fdnBc2pnI3WQJWqA336QDP916VUg6ZduEop1cjocdMc2oWrlFJK1YA2oPXAVWoimtNc\nmtNcmlM5mxo3oLNnzyYkJASr1crs2bMBKCwsJCYmhqCgIGJjYzl69Kj9/TNmzCAwMJDg4GDS0tLs\n87du3UpISAiBgYFMnjzZPr+kpIS4uDgCAwOJjIxk3759NY3qcF999ZWjI1SJ5jSX5jSX5lTOpkYN\n6M6dO3nnnXfYvHkzX3/9Nf/5z3/4/vvvSUxMJCYmhr179xIdHU1iYiIAu3fv5oMPPmD37t2kpqZy\n99132/vfExISSEpKIjs7m+zsbFJTUwFISkrC29ub7Oxs7r//fqZOnWrSJte/079IODPNaS7NaS7N\nqZxNjRrQPXv2EBERgYeHB02bNqV3794sW7aM1atXM27cOADGjRvHyt/HNVu1ahWjRo3C3d0dPz8/\nAgICyMzMJD8/nxMnThAeHg7A2LFj7cucvq5hw4axbt26Wm+sUkopZZYaNaBWq5XPP/+cwsJCfvnl\nFz7++GPy8vI4dOgQPj4+APj4+HDo0CEADh48iMVisS9vsViw2Wxnzff19cVmswFgs9no0KEDAM2a\nNcPT05PCwsKabaWD5ebmOjpClWhOc2lOc2lO5WxqdKdacHAwU6dOJTY2lpYtWxIWFkbTpk3PeI+b\nmxtubm6mhLyQ+vqc2khOTnZ0hCrRnObSnObSnMqZ1PhW7wkTJjBhwgQApk2bhsViwcfHhx9//JF2\n7dqRn5/PZZddBhhnlgcOHLAvm5eXh8ViwdfXl7y8vLPmVyyzf/9+Lr/8csrKyjh27Bht27Y9K4fe\ny6SUUsoRanwV7k8//QTA/v37Wb58ObfeeiuDBg2yf/NKTk5myJAhAAwaNIiUlBRKS0vJyckhOzub\n8PBw2rVrR+vWrcnMzEREWLhwIYMHD7YvU7GupUuXEh0dXasNVUoppcxU45GIevXqRUFBAe7u7rz8\n8stcf/31FBYWMnLkSPbv34+fnx9LlizBy8sLgOeee4558+bRrFkzZs+eTd++fQHjNpb4+HiKi4sZ\nMGAAr776KmDcxjJmzBi2b9+Ot7c3KSkp+Pn5mbPVSimlVG2JE2vSpImEhYVJaGioXHPNNfLFF19U\n+v7169fLwIED6ymdc2rZsqUp63nzzTdlwYIFpqyrIapsP+vfYdW4ubnJ6NGj7dO//vqrXHLJJbrv\nTFLT/at/v1Xn1MNdX3zxxWzfvh2AtLQ0HnnkER3l4wLMuqDqzjvvNGU9DZUrXLjm7Fq2bMmuXbs4\ndeoUHh4erF27FovFUq19W1ZWRjMdtf+czNi/qnIuM5Tf6RcRiQhTpkwhJCSErl27smTJEvv7jh8/\nzvuZpcgAAAk2SURBVMCBAwkODiYhIaHRXmQ0c+ZMwsPDCQ0N5YknngDg5MmT3HjjjYSFhRESEsKH\nH34IwMMPP0yXLl0IDQ3loYceAuCJJ57gxRdfBOC7776jT58+hIWF0b17d3Jycjh58iR9+vShe/fu\ndO3aldWrVztkOx3tfH+HP//8MyNGjODqq69m9OjR9vl+fn488cQT9v327bffOiK20xgwYAAfffQR\nAIsXL2bUqFH2f7NZWVn8+c9/5pprruEvf/kLe/fuBWD+/PkMGjSI6Oho+vTpw7hx41i1apV9nbfd\ndluj/Xv8o8r278mTJ5kwYQIRERFcc801us9qwrEnwJVr2rSphIWFSXBwsHh6esq2bdtERGTp0qUS\nExMj5eXlcujQIbniiiskPz9f1q9fLx4eHpKTkyO//fabxMTEyNKlSx28FfWrVatWkpaWJnfccYeI\niPz2228ycOBAycjIkGXLlsnf/vY3+3uPHTsmR44ckU6dOp0xT0TkiSeekBdffFFERMLDw2XlypUi\nIlJSUiK//PKLlJWVyfHjx0VE5PDhwxIQEFAv2+csWrVqJcuWLTvv36Gnp6fYbDYpLy+X6667Tv7v\n//5PRET8/Pzk9ddfFxGRuXPnyu233+7IzXCoVq1ayX//+18ZPny4nDp1SsLCwiQ9Pd3efXj8+HEp\nKysTEZG1a9fKsGHDRETk3XffFYvFIkVFRSIismHDBhkyZIiIiBw9elQ6duwov/32mwO2yLlcaP8+\n8sgjsmjRIhERKSoqkqCgIDl58qR24VaDU5+BtmjRgu3bt/PNN9+QmprKmDFjANi4cSO33norbm5u\nXHbZZfTu3ZvNmzfj5uZGeHg4fn5+NGnShFGjRrFx40YHb0X9S0tLIy0tjW7dutG9e3e+/fZbvvvu\nO0JCQli7di0PP/wwGzdupHXr1nh6euLh4cHEiRNZsWIFLVq0OGNdP//8MwcPHrRfHX3RRRfRokUL\nysvLeeSRRwgNDSUmJoaDBw/ar8xuLC70d3j55Zfj5uZGWFjYGTfXDx06FIBrrrmm0d90HxISQm5u\nLosXL+bGG28847WjR48yfPhwQkJC+Mc//sHu3bvtr8XGxtovUOzVqxfZ2dkcOXKExYsXM3z4cJo0\ncepDW72pbP+mpaWRmJhIt27duP766ykpKTnjdkN1YS5TPIiMjOTIkSMcPnz4nM+yq+jXP71/X0Qa\nbX//I488wh133HHW/O3bt/PRRx/x2GOPER0dzeOPP05WVhbr1q1j6dKlvP7661UaNvG9997jyJEj\nbNu2jaZNm9KxY0dOnTpVF5vitCr7O2zevLl9XtOmTSkrK7NPV7z2x/mN1aBBg3jwwQfZsGEDhw8f\nts9//PHHiY6OZsWKFezbt4+oqCj7axdffPEZ6xg7diwLFy7kgw8+YP78+fWU3DWcb/8CLF++nMDA\nwDPm5efn12c8l+YyX9P27NlDeXk5l1xyCT179uSDDz6gvLycw4cPk5GRQXh4OCJCVlYWubm5lJeX\ns2TJEnr27Ono6PUuNjaWefPmcfLkScAYFvHw4cPk5+fj4eHBbbfdxoMPPsi2bds4efIkR48epX//\n/rz00kt8/fXXgPHlQ0Ro1aoVFovFXmMqKSmhuLiY48ePc9lll9G0aVPWr1/v0k/Lqam//vWv5/07\nVFU3YcIEnnjiCbp06XLG/OPHj3P55ZcD8O6771a6jvj4eF555RXc3NwIDg6us6yu6Hz7t2/fvvbb\nBgH7BZuq6pz6DLS4uJhu3boBxgE9OTkZNzc3br75Zr788ktCQ0Nxc3Nj5syZXHbZZXzzzTf06NGD\ne++9l++++44bbrjBPphDY1BWVkbz5v/f3v2DJLfGcQD/ZuilwaChlpZwiSwOJQY1FA5JSUQ1ZGR/\npCWh0oSshigMHCqCwEkHJ2nrDxhCQ9CYQ2kKFUQQ1CJUQwUVmXqHS+fme3u773suV33fvp/tnLM8\nz8OBL8/Dw+/3B/R6PU5PT9HY2AgAUCqV8Pv9OD8/x9TUFGQyGeRyOTweDx4eHtDZ2Ynn52ek02ms\nrq4CyCzF6Pf7YbFYMD8/D7lcjvX1dfT396OjowOCIECr1aKqqipn8862t3X+7D/8kZOPbJa7zEdv\ncy8vL8f4+Lj47u399PQ0zGYzXC4X2tvbM06Zvl23srIyqNVqdHd3Z3EG+e3f1ndubg52ux2CICCV\nSkGlUiEQCHz5//JnSC6kQPknGo3CYrEgFArleii/Na5z/nl8fIQgCIhEIlAqlbkeDn0Rv8wRLn3O\n4/HAZDLB5XLleii/Na5z/tnd3YVarYbNZmN4UlZxB0pERCQBd6BEREQSMECJiIgkYIASERFJwAAl\nIiKSgAFK9AGZTAaHwyE+r6ysYGFhIYcjIqJ8wwA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/+9u3P/cUiIgoCDgUAspoB1BmtAMoM9oBlBntAMqMdgCfcCgQEZGFewo2X1cM\n3e4A+7O/fftzT4GIiIKAQyGgjHYAZUY7gDKjHUCZ0Q6gzGgH8AmHAhERWbinYPN1xdDtDrA/+9u3\nP/cUiIgoCDgUAspoB1BmtAMoM9oBlBntAMqMdgCfcCgQEZGFewo2X1cM3e4A+7O/fftzT4GIiIJg\nyA+F8vJyJCUlweVyYf369dpxbpDRDqDMaAdQZrQDKDPaAZQZ7QA+GdJDobu7G9/61rdQXl6Ompoa\nlJSU4H//93+1Y92A97UDKGN/e2P/UDSkh8If/vAHxMfHIzY2FuHh4XjkkUewc+dO7Vg3oE07gDL2\ntzf2D0VDeih4vV5MmDDBOnY6nfB6vYqJiIhubkN6KPS+OiCU1WkHUFanHUBZnXYAZXXaAZTVaQfw\nSZh2gKuJiYlBfX29dVxfXw+n03nZfdLS0vwwPAI5fLYG8Ln9MTgDPXjZP5DY3779B9M9LS1t4Ocd\nyt+n0NXVhcTEROzZswfR0dHIyMhASUkJJk2apB2NiOimNKSvFMLCwvDzn/8cs2fPRnd3N5YvX86B\nQEQUQEP6SoGIiIJrSG80ExFRcA3p5aNQIyJ47bXX8N5778HhcGDWrFmYN2/eTfAqqhtz8uRJnD9/\n3jr+whe+oJiGgqWsrAxf+9rXMGyYvf6tOXfu3AFvczgcKCsrC2KawePykR+tXLkSH330EXJzcyEi\n2L59OyZOnIhNmzZpRwuKsrIyPPnkk2hsbMS4ceNw7NgxTJo0CdXV1drRguLDDz/Ej3/8Y9TV1aGr\nqwtA7xeF3/72t8rJgmPJkiXYt28fFixYgMceewxJSUnakYLCGDPgbQ6HA1/+8peDF8YPOBT8KCkp\nCTU1Nda/lHp6epCcnIw///nPysmCY8qUKfjtb38Lj8eDqqoq7N27Fy+99BJefPFF7WhBMWXKFKxc\nuRLTpk3DLbfcAqD3i8I999yjnCx42tvbUVJSgi1btsDhcGDZsmXIzc3FiBEjtKMFxdmzZ1FfX4/E\nxETtKD6z13VegMXHx+P48ePW8fHjxxEfH6+YKLjCw8MxduxY9PT0oLu7G/feey/279+vHStowsPD\nsXLlSmRmZmL69OmYPn26rQYCAERERGDBggXIyclBY2MjXn/9dUydOhUbNmzQjhZwZWVlmDp1KmbP\nng0AqKqqwoMPPqic6sZxT8EP+tYUOzo6MGnSJGRkZMDhcOAPf/gDZsyYoZwueEaNGoWOjg7MmjUL\nS5YswbhfUKn2AAAKZUlEQVRx43DnnXdqxwqauXPnYuPGjXj44YcxfPhw6/zo0aMVUwXPzp07sWXL\nFhw5cgRLly7FH//4R4wbNw5nz55FcnIyvv3tb2tHDKhnn30WlZWVuPfeewEAU6dOxdGjR5VT3Tgu\nH/nB/19T7NtYFpGQXFP01ZkzZ3Dbbbehp6cHL7/8Mk6fPo0lS5ZgzJgx2tGCIjY29oovKqitrVVI\nE3z5+flYvnw5vvSlL/W77Z133sFXvvIVhVTBk5mZicrKSkydOhVVVVUAepcUDx06pJzsxvBKwQ/c\nbrf1+YkTJ/DHP/4RDocDGRkZGDdunF6wIDt58iTGjx+P22+/HQUFBTh37hyam5ttMxTq6uq0I6jp\n6urCsWPHrjgQANz0AwEAJk+ejJdffhldXV04cuQINmzYgC9+8YvasW4Y9xT8aPv27cjMzMSrr76K\n7du3IyMjA6+++qp2rKBZsGCBtcEKAMOGDcOCBQsUEwXf73//e/ziF7/Atm3brA87CAsLwy233IK2\nttB8u2h/+NnPfobq6moMHz4cubm5GDlyJH7yk59ox7phXD7yoylTpuCdd96xrg4+/vhjZGVlhdzl\no6/S09Px/vuX/2CRtLQ0/OlPf1JKFFyPPvoojh49ivT09MuG489+9jPFVMHz4IMPoqqqCtnZ2fiL\nv/gLAL1LqXbYZL6ZcPnIj0QEd911l3U8ZsyYQf1w7VAzduxY7Ny5E1//+tcB9G48jh07VjlV8Bw4\ncAA1NTW2+2bFPg8//DAefvjhy87Z4f/F3Llz4XA4rvh3PRS/eY1DwY/uv/9+zJ49G4sXL4aI4JVX\nXsGcOXO0YwXNv/3bv2HJkiX41re+BaD3hyK99NJLyqmCJyUlBU1NTYiOjtaOoqKgoEA7goqKigo4\nnU7k5uYiMzMTAKwBEYpDkctHfnSlt7l46KGHtGMF3aeffgoAtnk5at9Lkj/99FNUVVUhIyPDeklq\nKP5L0VeHDx/G008/jZqaGpw7dw5Ab/9QfFnmjejq6sLbb7+NkpISfPDBB/jqV7+K3NxcTJ48WTua\nTzgUAuTjjz/G2LFjQ/JfCjfqpZdeQl5eHv75n//5sr59L8n97ne/q5gu8PiS5F5/8zd/g+eeew7f\n/e538etf/xr/+Z//ie7ubvzwhz/UjhY0nZ2dKCkpwVNPPYVnn33WumoOJVw+8oN9+/bh+9//PkaP\nHo0f/OAHyMvLwyeffIKenh5s3br1pl9COnv2LIDeb9670lC42fElyb3OnTuHr3zlKxAR/NVf/RWe\nffZZTJs2zRZD4fz583jjjTdQWlqKuro6rFmzJmRXCXil4Af33HMPioqK0N7ejm9+85soLy/HzJkz\n8ec//xmPPPJIv1fk0M1p+/btKCwstK4M3n33XTz//PNYuHChcrLg+OIXv4jf/e53WLBgAbKyshAd\nHY3vf//7+PDDD7WjBVReXh6qq6vxwAMPICcnB6mpqdqRBkdo0NLS0qzPk5KSLrstPT092HHUFBYW\nSnt7u1y4cEHuu+8+GTNmjGzbtk07VtCkpqZKc3OzdXzy5ElJTU1VTBRclZWVcvr0aTl+/Ljk5+fL\nQw89JPv27dOOFXAOh0PuvPPOK36MGDFCO94N4/KRH3x+ieS2225TTKLrrbfewo9+9CO8/vrriI2N\nxWuvvYZZs2YhLy9PO1pQiM1fkpyRkQEAGDFiBLZs2aIbJoh6enq0I/gVh4IfHDp0yHpr4HPnzl32\nNsF9r8Kwg76fIfCb3/wGCxYsQEREhC32FPrY9SXJN9vr9O2OQ8EPuru7tSMMCXPnzkVSUhJuu+02\n/Ou//itOnjxpqyun559//rKXJD/++OMhu9l4I2621+nbHTeaya9aWloQERGBsLAwnDlzBh0dHRg/\nfrx2rIC68847B/ziN3z4cMTHx+Mf//Efb9o3hbvZXqdvd3xDPPKbV199FeHh4QgLC8MPf/hDPPro\no2hsbNSOFXCffvopOjo6rvhx4sQJvPDCC1izZo12zIAJCwvDnDlzsG3bNlRUVCA+Ph5f/vKX8fOf\n/1w7GvmAQ4H85h/+4R8wcuRIvPfee9izZw+WL1+OFStWaMdSFRYWhrS0NKxevVo7SkCdP38ev/rV\nr/Doo49i48aNIf06fbvj8hH5Td+7pH7ve99DamoqlixZctkPHKGb0033On2b41Agv/nqV7+KmJgY\nvP3226iqqsJtt92GzMxM27x1tl0NGzYMd9xxxxVvczgcOH36dJAT0WBwKJDfnDlzBm+99RZSU1Ph\ncrnQ1NSEDz74ANnZ2drRiOg6cU+B/OaOO+7AXXfdhffeew9A73p6fHy8cioiuhG8UiC/efbZZ3Hg\nwAF8+OGHOHz4MLxeLxYtWoT/+Z//0Y5GRNeJVwrkN6+//jp27txprS/HxMSgo6NDORUR3QgOBfKb\n4cOHY9iwz/5InTlzRjENEfmCQ4H8ZuHChXj88cfR1taGf//3f0dWVha+8Y1vaMciohvAPQXyq7ff\nfhu7d+8GAGRnZ8Pj8SgnIqIbwaFAg2b39/4huplwKFBAdXV1obq6GosXL0Z1dbV2HCK6Bu4pUEDZ\n5b1/iG4WvFIgIiILrxSIiMjCoUBERBYOBSIisnAoEF3BsGHD8NRTT1nHP/7xj/Hcc88pJiIKDg4F\noiu49dZb8frrr6OlpQUAfwA92QeHAtEVhIeH42//9m/xL//yL/1u+/Wvf42ZM2di2rRp8Hg8OHny\nJIDed4nNz8/Hl770JcTGxuK1117DU089hSlTpmDOnDno6uoCABw4cAButxvTp0/H/fffjxMnTgAA\nNmzYgMmTJyMtLQ25ubnBK0v0ORwKRANYtWoVXn755X4/OWzWrFmoqKjAwYMHkZOTgx/96EfWbbW1\ntdi7dy/Kysrw6KOPwuPx4NChQ7j99tvxxhtv4OLFi1i9ejV+9atfYf/+/Vi2bBn+/u//HgCwfv16\nvP/++/jTn/6EF154IahdifqEaQcgGqpGjBiBpUuXYsOGDbj99tut8/X19Vi0aBFOnDiBCxcuYOLE\niQB6l5jmzJmDW265BSkpKejp6cHs2bMBAKmpqairq8Phw4dRXV1tveVHd3c3oqOjAQBTpkzB4sWL\nMW/ePMybNy/IbYl68UqB6Cq+853vYPPmzZe9Dfjq1avx7W9/G4cOHcILL7yAc+fOWbfdeuutAHo3\nqsPDw63zw4YNQ1dXF0QEkydPRlVVFaqqqnDo0CGUl5cDAN544w383d/9HQ4ePIgZM2agu7s7SC2J\nPsOhQHQVo0aNwqJFi7B582Zrs/n06dPWv+63bNli3fd63hwgMTERH3/8MSoqKgAAFy9eRE1NDUQE\nx48fh9vtRnFxMdrb2/nzKEgFhwLRFXz+1UZPPvkkPvnkE+v42WefxcKFCzF9+nTcdddd1n0dDsdl\nj/v/r1hyOBwIDw/HL3/5S6xduxbp6emYOnUq9u3bh+7ubuTl5WHKlCmYNm0a1qxZg5EjRwa4JVF/\nfO8jIiKy8EqBiIgsHApERGThUCAiIguHAhERWTgUiIjIwqFAREQWDgUiIrJwKBARkeX/ALHNfEqM\nXQGvAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0xab884a8>"
+       ]
       }
      ],
      "prompt_number": 30
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Author:** [David Rojas LLC](http://hdrojas.pythonanywhere.com/)  "
+     ]
     }
    ],
    "metadata": {}