diff --git a/ThinkStats2/chap01ex.ipynb b/ThinkStats2/chap01ex.ipynb index e7fab98..88b6c3a 100644 --- a/ThinkStats2/chap01ex.ipynb +++ b/ThinkStats2/chap01ex.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 131, "metadata": { "collapsed": false }, @@ -18,7 +18,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -48,724 +48,724 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -793,723 +793,723 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", "
0 1 1011NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3410.389399 3869.349602 6448.271112 2 90003410.3893993869.3496026448.27111229NaN 8.81258.8125
1 1 2112NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3410.389399 3869.349602 6448.271112 2 90003410.3893993869.3496026448.27111229NaN 7.87507.8750
2 2 1221NaNNaNNaNNaN 55NaN 3 535... 0 0 0 7226.301740 8567.549110 12999.542264 2 120007226.3017408567.54911012999.542264212NaN 9.12509.1250
3 2 2322NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 7226.301740 8567.549110 12999.542264 2 120007226.3017408567.54911012999.542264212NaN 7.00007.0000
4 2 3423NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 7226.301740 8567.549110 12999.542264 2 120007226.3017408567.54911012999.542264212NaN 6.18756.1875
5 6 1561NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 4870.926435 5325.196999 8874.440799 1 230004870.9264355325.1969998874.440799123NaN 8.56258.5625
6 6 2662NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 4870.926435 5325.196999 8874.440799 1 230004870.9264355325.1969998874.440799123NaN 9.56259.5625
7 6 3763NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 4870.926435 5325.196999 8874.440799 1 230004870.9264355325.1969998874.440799123NaN 8.37508.3750
8 7 1871NaNNaNNaNNaN 55NaN 11NaN... 0 0 0 3409.579565 3787.539000 6911.879921 2 140003409.5795653787.5390006911.879921214NaN 7.56257.5625
9 7 2972NaNNaNNaNNaN 55NaN 11NaN... 0 0 0 3409.579565 3787.539000 6911.879921 2 140003409.5795653787.5390006911.879921214NaN 6.62506.6250
10 12 110121NaNNaNNaNNaN 55NaN 11NaN... 0 0 0 3612.781968 4146.013572 6909.331618 1 310003612.7819684146.0135726909.331618131NaN 7.81257.8125
11 14 111141NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 2418.069494 2810.302771 3039.904507 2 560002418.0694942810.3027713039.904507256NaN 7.00007.0000
12 14 212142NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 2418.069494 2810.302771 3039.904507 2 560002418.0694942810.3027713039.904507256NaN 4.00004.0000
13 14 313143NaNNaNNaNNaN 33NaNNaNNaN... 0 0 0 2418.069494 2810.302771 3039.904507 2 560002418.0694942810.3027713039.904507256NaNNaN NaN
14 15 114151NaNNaNNaNNaN 11NaNNaNNaN... 0 0 0 1667.816099 3200.862017 5553.495599 1 330001667.8160993200.8620175553.495599133NaNNaN NaN
15 15 215152NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 1667.816099 3200.862017 5553.495599 1 330001667.8160993200.8620175553.495599133NaN 7.68757.6875
16 15 316153NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 1667.816099 3200.862017 5553.495599 1 330001667.8160993200.8620175553.495599133NaN 7.50007.5000
17 18 117181NaNNaNNaNNaN 55NaN 11NaN... 0 0 0 2957.257457 3404.403067 4153.371741 2 140002957.2574573404.4030674153.371741214NaN 6.31256.3125
18 18 218182NaNNaNNaNNaN 11NaNNaNNaN... 0 0 0 2957.257457 3404.403067 4153.371741 2 140002957.2574573404.4030674153.371741214NaNNaN NaN
19 21 119211NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3408.342437 3965.763949 7237.122630 1 480003408.3424373965.7639497237.122630148NaN 8.75008.7500
20 21 220212NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3408.342437 3965.763949 7237.122630 1 480003408.3424373965.7639497237.122630148NaN 8.18758.1875
21 23 121231NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 6210.373020 8120.841310 13533.382043 2 640006210.3730208120.84131013533.382043264NaN 5.56255.5625
22 23 222232NaNNaNNaNNaN 11NaNNaNNaN... 0 0 0 6210.373020 8120.841310 13533.382043 2 640006210.3730208120.84131013533.382043264NaNNaN NaN
23 24 123241NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3409.573258 4068.628645 7424.840414 1 270003409.5732584068.6286457424.840414127NaN 6.75006.7500
24 24 224242NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3409.573258 4068.628645 7424.840414 1 270003409.5732584068.6286457424.840414127NaN 7.37507.3750
25 24 325243NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3409.573258 4068.628645 7424.840414 1 270003409.5732584068.6286457424.840414127NaN 6.81256.8125
26 28 126281NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3407.794208 3808.343516 6949.846082 2 570003407.7942083808.3435166949.846082257NaN 8.12508.1250
27 31 127311NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3405.679025 4272.084519 5211.943113 1 20003405.6790254272.0845195211.94311312NaN 7.12507.1250
28 31 228312NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3405.679025 4272.084519 5211.943113 1 20003405.6790254272.0845195211.94311312NaN 6.06256.0625
29 31 329313NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3405.679025 4272.084519 5211.943113 1 20003405.6790254272.0845195211.94311312NaN 7.43757.4375
...
13563 12547 2125472NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3453.545517 6628.022524 11499.619080 1 520003453.5455176628.02252411499.619080152NaN 7.68757.6875
13564 12547 3125473NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3453.545517 6628.022524 11499.619080 1 520003453.5455176628.02252411499.619080152NaN 7.62507.6250
13565 12550 1125501NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3080.452699 3745.326058 5268.550165 1 790003080.4526993745.3260585268.550165179NaN 8.12508.1250
13566 12551 1125511NaNNaNNaNNaN 55NaN 11NaN... 0 0 0 2418.538866 3653.453268 3951.940400 2 750002418.5388663653.4532683951.940400275NaN 7.50007.5000
13567 12554 1125541NaNNaNNaNNaN 33NaNNaNNaN... 0 0 0 1914.676604 2177.957240 2764.045534 2 750001914.6766042177.9572402764.045534275NaNNaN NaN
13568 12554 2125542NaNNaNNaNNaN 44NaNNaNNaN... 0 0 0 1914.676604 2177.957240 2764.045534 2 750001914.6766042177.9572402764.045534275NaNNaN NaN
13569 12556 1125561NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 2474.619764 3250.573384 3965.699528 1 440002474.6197643250.5733843965.699528144NaN 5.81255.8125
13570 12556 2125562NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 2474.619764 3250.573384 3965.699528 1 440002474.6197643250.5733843965.699528144NaN 6.68756.6875
13571 12556 3125563NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 2474.619764 3250.573384 3965.699528 1 440002474.6197643250.5733843965.699528144NaN 6.00006.0000
13572 12556 4125564NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 2474.619764 3250.573384 3965.699528 1 440002474.6197643250.5733843965.699528144NaN 5.81255.8125
13573 12561 1125611NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 2418.089703 2698.650781 4497.301527 1 100002418.0897032698.6507814497.301527110NaN 6.56256.5625
13574 12561 2125612NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 2418.089703 2698.650781 4497.301527 1 100002418.0897032698.6507814497.301527110NaN 6.12506.1250
13575 12564 1125641NaNNaNNaNNaN 33NaNNaNNaN... 0 0 0 1820.850938 2129.214067 2768.191208 2 440001820.8509382129.2140672768.191208244NaNNaN NaN
13576 12565 1125651NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 3195.641221 3834.241709 6652.409365 1 780003195.6412213834.2417096652.409365178NaN 6.43756.4375
13577 12565 2 35 1 81256523518NaNNaNNaNNaNNaN... 0 0 0 3195.641221 3834.241709 6652.409365 1 780003195.6412213834.2417096652.409365178NaNNaN NaN
13578 12566 1125661NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 2080.317155 2422.820274 2627.548587 2 20002080.3171552422.8202742627.54858722NaN 6.00006.0000
13579 12566 2125662NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 2080.317155 2422.820274 2627.548587 2 20002080.3171552422.8202742627.54858722NaN 7.00007.0000
13580 12568 1125681NaNNaNNaNNaN 11NaNNaNNaN... 0 0 0 2734.687353 4258.980140 7772.212858 2 280002734.6873534258.9801407772.212858228NaNNaN NaN
13581 12568 2125682NaNNaNNaNNaN 55NaN 11NaN... 0 0 0 2734.687353 4258.980140 7772.212858 2 280002734.6873534258.9801407772.212858228NaN 6.37506.3750
13582 12568 3125683NaNNaNNaNNaN 44NaNNaNNaN... 0 0 0 2734.687353 4258.980140 7772.212858 2 280002734.6873534258.9801407772.212858228NaNNaN NaN
13583 12569 1125691NaNNaNNaNNaN 33NaNNaNNaN... 0 0 0 2580.967613 2925.167116 5075.164946 2 610002580.9676132925.1671165075.164946261NaNNaN NaN
13584 12569 2125692NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 2580.967613 2925.167116 5075.164946 2 610002580.9676132925.1671165075.164946261NaN 6.37506.3750
13585 12570 1125701NaNNaNNaNNaN 33NaNNaNNaN... 0 0 0 5181.311509 6205.829154 11325.017623 2 400005181.3115096205.82915411325.017623240NaNNaN NaN
13586 12570 2125702NaNNaNNaNNaN 33NaNNaNNaN... 0 0 0 5181.311509 6205.829154 11325.017623 2 400005181.3115096205.82915411325.017623240NaNNaN NaN
13587 12570 3125703NaNNaNNaNNaN 33NaNNaNNaN... 0 0 0 5181.311509 6205.829154 11325.017623 2 400005181.3115096205.82915411325.017623240NaNNaN NaN
13588 12571 1125711NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 4670.540953 5795.692880 6269.200989 1 780004670.5409535795.6928806269.200989178NaN 6.18756.1875
13589 12571 2125712NaNNaNNaNNaN 33NaNNaNNaN... 0 0 0 4670.540953 5795.692880 6269.200989 1 780004670.5409535795.6928806269.200989178NaNNaN NaN
13590 12571 3125713NaNNaNNaNNaN 33NaNNaNNaN... 0 0 0 4670.540953 5795.692880 6269.200989 1 780004670.5409535795.6928806269.200989178NaNNaN NaN
13591 12571 4125714NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 4670.540953 5795.692880 6269.200989 1 780004670.5409535795.6928806269.200989178NaN 7.50007.5000
13592 12571 5125715NaNNaNNaNNaN 66NaN 11NaN... 0 0 0 4670.540953 5795.692880 6269.200989 1 780004670.5409535795.6928806269.200989178NaN 7.50007.5000
\n", @@ -1580,68 +1580,68 @@ "13591 12571 4 NaN NaN NaN NaN 6 \n", "13592 12571 5 NaN NaN NaN NaN 6 \n", "\n", - " pregend2 nbrnaliv multbrth ... laborfor_i religion_i \\\n", - "0 NaN 1 NaN ... 0 0 \n", - "1 NaN 1 NaN ... 0 0 \n", - "2 NaN 3 5 ... 0 0 \n", - "3 NaN 1 NaN ... 0 0 \n", - "4 NaN 1 NaN ... 0 0 \n", - "5 NaN 1 NaN ... 0 0 \n", - "6 NaN 1 NaN ... 0 0 \n", - "7 NaN 1 NaN ... 0 0 \n", - "8 NaN 1 NaN ... 0 0 \n", - "9 NaN 1 NaN ... 0 0 \n", - "10 NaN 1 NaN ... 0 0 \n", - "11 NaN 1 NaN ... 0 0 \n", - "12 NaN 1 NaN ... 0 0 \n", - "13 NaN NaN NaN ... 0 0 \n", - "14 NaN NaN NaN ... 0 0 \n", - "15 NaN 1 NaN ... 0 0 \n", - "16 NaN 1 NaN ... 0 0 \n", - "17 NaN 1 NaN ... 0 0 \n", - "18 NaN NaN NaN ... 0 0 \n", - "19 NaN 1 NaN ... 0 0 \n", - "20 NaN 1 NaN ... 0 0 \n", - "21 NaN 1 NaN ... 0 0 \n", - "22 NaN NaN NaN ... 0 0 \n", - "23 NaN 1 NaN ... 0 0 \n", - "24 NaN 1 NaN ... 0 0 \n", - "25 NaN 1 NaN ... 0 0 \n", - "26 NaN 1 NaN ... 0 0 \n", - "27 NaN 1 NaN ... 0 0 \n", - "28 NaN 1 NaN ... 0 0 \n", - "29 NaN 1 NaN ... 0 0 \n", - "... ... ... ... ... ... ... \n", - "13563 NaN 1 NaN ... 0 0 \n", - "13564 NaN 1 NaN ... 0 0 \n", - "13565 NaN 1 NaN ... 0 0 \n", - "13566 NaN 1 NaN ... 0 0 \n", - "13567 NaN NaN NaN ... 0 0 \n", - "13568 NaN NaN NaN ... 0 0 \n", - "13569 NaN 1 NaN ... 0 0 \n", - "13570 NaN 1 NaN ... 0 0 \n", - "13571 NaN 1 NaN ... 0 0 \n", - "13572 NaN 1 NaN ... 0 0 \n", - "13573 NaN 1 NaN ... 0 0 \n", - "13574 NaN 1 NaN ... 0 0 \n", - "13575 NaN NaN NaN ... 0 0 \n", - "13576 NaN 1 NaN ... 0 0 \n", - "13577 NaN NaN NaN ... 0 0 \n", - "13578 NaN 1 NaN ... 0 0 \n", - "13579 NaN 1 NaN ... 0 0 \n", - "13580 NaN NaN NaN ... 0 0 \n", - "13581 NaN 1 NaN ... 0 0 \n", - "13582 NaN NaN NaN ... 0 0 \n", - "13583 NaN NaN NaN ... 0 0 \n", - "13584 NaN 1 NaN ... 0 0 \n", - "13585 NaN NaN NaN ... 0 0 \n", - "13586 NaN NaN NaN ... 0 0 \n", - "13587 NaN NaN NaN ... 0 0 \n", - "13588 NaN 1 NaN ... 0 0 \n", - "13589 NaN NaN NaN ... 0 0 \n", - "13590 NaN NaN NaN ... 0 0 \n", - "13591 NaN 1 NaN ... 0 0 \n", - "13592 NaN 1 NaN ... 0 0 \n", + " pregend2 nbrnaliv multbrth ... laborfor_i religion_i \\\n", + "0 NaN 1 NaN ... 0 0 \n", + "1 NaN 1 NaN ... 0 0 \n", + "2 NaN 3 5 ... 0 0 \n", + "3 NaN 1 NaN ... 0 0 \n", + "4 NaN 1 NaN ... 0 0 \n", + "5 NaN 1 NaN ... 0 0 \n", + "6 NaN 1 NaN ... 0 0 \n", + "7 NaN 1 NaN ... 0 0 \n", + "8 NaN 1 NaN ... 0 0 \n", + "9 NaN 1 NaN ... 0 0 \n", + "10 NaN 1 NaN ... 0 0 \n", + "11 NaN 1 NaN ... 0 0 \n", + "12 NaN 1 NaN ... 0 0 \n", + "13 NaN NaN NaN ... 0 0 \n", + "14 NaN NaN NaN ... 0 0 \n", + "15 NaN 1 NaN ... 0 0 \n", + "16 NaN 1 NaN ... 0 0 \n", + "17 NaN 1 NaN ... 0 0 \n", + "18 NaN NaN NaN ... 0 0 \n", + "19 NaN 1 NaN ... 0 0 \n", + "20 NaN 1 NaN ... 0 0 \n", + "21 NaN 1 NaN ... 0 0 \n", + "22 NaN NaN NaN ... 0 0 \n", + "23 NaN 1 NaN ... 0 0 \n", + "24 NaN 1 NaN ... 0 0 \n", + "25 NaN 1 NaN ... 0 0 \n", + "26 NaN 1 NaN ... 0 0 \n", + "27 NaN 1 NaN ... 0 0 \n", + "28 NaN 1 NaN ... 0 0 \n", + "29 NaN 1 NaN ... 0 0 \n", + "... ... ... ... ... ... ... \n", + "13563 NaN 1 NaN ... 0 0 \n", + "13564 NaN 1 NaN ... 0 0 \n", + "13565 NaN 1 NaN ... 0 0 \n", + "13566 NaN 1 NaN ... 0 0 \n", + "13567 NaN NaN NaN ... 0 0 \n", + "13568 NaN NaN NaN ... 0 0 \n", + "13569 NaN 1 NaN ... 0 0 \n", + "13570 NaN 1 NaN ... 0 0 \n", + "13571 NaN 1 NaN ... 0 0 \n", + "13572 NaN 1 NaN ... 0 0 \n", + "13573 NaN 1 NaN ... 0 0 \n", + "13574 NaN 1 NaN ... 0 0 \n", + "13575 NaN NaN NaN ... 0 0 \n", + "13576 NaN 1 NaN ... 0 0 \n", + "13577 NaN NaN NaN ... 0 0 \n", + "13578 NaN 1 NaN ... 0 0 \n", + "13579 NaN 1 NaN ... 0 0 \n", + "13580 NaN NaN NaN ... 0 0 \n", + "13581 NaN 1 NaN ... 0 0 \n", + "13582 NaN NaN NaN ... 0 0 \n", + "13583 NaN NaN NaN ... 0 0 \n", + "13584 NaN 1 NaN ... 0 0 \n", + "13585 NaN NaN NaN ... 0 0 \n", + "13586 NaN NaN NaN ... 0 0 \n", + "13587 NaN NaN NaN ... 0 0 \n", + "13588 NaN 1 NaN ... 0 0 \n", + "13589 NaN NaN NaN ... 0 0 \n", + "13590 NaN NaN NaN ... 0 0 \n", + "13591 NaN 1 NaN ... 0 0 \n", + "13592 NaN 1 NaN ... 0 0 \n", "\n", " metro_i basewgt adj_mod_basewgt finalwgt secu_p sest \\\n", "0 0 3410.389399 3869.349602 6448.271112 2 9 \n", @@ -1772,13 +1772,14 @@ "[13593 rows x 244 columns]" ] }, - "execution_count": 1, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import nsfg\n", + "import numpy as np\n", "df = nsfg.ReadFemPreg()\n", "df" ] @@ -1792,7 +1793,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 132, "metadata": { "collapsed": false }, @@ -1810,10 +1811,10 @@ "8 7\n", "9 2\n", "10 1\n", - "dtype: int64" + "Name: birthord, dtype: int64" ] }, - "execution_count": 2, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -1831,12 +1832,42 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 133, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13 weeks or less 3522\n", + "14 to 26 weeks 793\n", + "27 weeks or more 9278\n", + "TOTAL 13593\n" + ] + } + ], + "source": [ + "cdbk = df.prglngth.value_counts().sort_index()\n", + "thirteenorless = 0\n", + "fourteento26 = 0\n", + "twentysevenormore = 0\n", + "for i in range(0, 49):\n", + " if i <= 13:\n", + " thirteenorless += cdbk[i]\n", + " elif 13 < i < 27:\n", + " fourteento26 += cdbk[i]\n", + " elif 27 <= i:\n", + " twentysevenormore += cdbk[i]\n", + " \n", + "twentysevenormore += cdbk[50]\n", + "\n", + "print '13 weeks or less ', thirteenorless\n", + "print '14 to 26 weeks ', fourteento26\n", + "print '27 weeks or more ', twentysevenormore\n", + "print 'TOTAL ',thirteenorless + fourteento26 + twentysevenormore\n" + ] }, { "cell_type": "markdown", @@ -1849,12 +1880,38 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 134, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inapplicable 352\n", + "Under 20 years 3182\n", + "20 to 24 years 4246\n", + "25 to 29 years 3178\n", + "30 and up 2635\n", + "TOTAL 13593\n" + ] + } + ], + "source": [ + "inapplicable = len(df[np.isnan(df.agepreg)])\n", + "under20 = len(df[df.agepreg < 20])\n", + "twentyto24 = len(df[(df.agepreg >= 20) & (df.agepreg < 25)])\n", + "twentyfiveto29 = len(df[(25 <= df.agepreg) & (df.agepreg < 30)])\n", + "thirtyandup = len(df[df.agepreg >= 30])\n", + "\n", + "print 'Inapplicable ', inapplicable\n", + "print 'Under 20 years ', under20 \n", + "print '20 to 24 years ', twentyto24\n", + "print '25 to 29 years ', twentyfiveto29\n", + "print '30 and up ', thirtyandup\n", + "print 'TOTAL ',inapplicable + under20+twentyto24 + twentyfiveto29 + thirtyandup #doesn't include inapplicable" + ] }, { "cell_type": "markdown", @@ -1865,7 +1922,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 135, "metadata": { "collapsed": false }, @@ -1873,10 +1930,10 @@ { "data": { "text/plain": [ - "7.2656284576233681" + "7.265628457623368" ] }, - "execution_count": 3, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -1894,12 +1951,26 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 136, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "3.2956309433503437" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['totalwgt_kg'] = df.totalwgt_lb * 0.453592\n", + "df.totalwgt_kg.mean()" + ] }, { "cell_type": "markdown", @@ -1910,12 +1981,49 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 137, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": true }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inapplicable 353\n", + "Married 6409\n", + "Divorced 735\n", + "Widowed 31\n", + "Separated 219\n", + "Never married 5846\n", + "TOTAL 13593\n" + ] + } + ], + "source": [ + "inapplicable = len(df[np.isnan(df.fmarout5)])\n", + "married = len(df[df.fmarout5 == 1])\n", + "divorced = len(df[df.fmarout5 == 2])\n", + "widowed = len(df[df.fmarout5 == 3])\n", + "separated = len(df[df.fmarout5 == 4])\n", + "nevermarried = len(df[df.fmarout5 == 5])\n", + "\n", + "print 'Inapplicable ', inapplicable\n", + "print 'Married ', married\n", + "print 'Divorced ', divorced\n", + "print 'Widowed ', widowed\n", + "print 'Separated ', separated\n", + "print 'Never married ', nevermarried\n", + "print 'TOTAL ', inapplicable + married + divorced + widowed + separated + nevermarried" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The values calculated above match the codebook results [here](http://www.icpsr.umich.edu/nsfg6/Controller?displayPage=labelDetails&fileCode=PREG§ion=&subSec=8016&srtLabel=611938) and we see that overwhelmingly married women and women that were never married are the majority of completed pregnancies." + ] }, { "cell_type": "markdown", @@ -1926,7 +2034,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 138, "metadata": { "collapsed": false }, @@ -1934,22 +2042,52 @@ { "data": { "text/plain": [ - "0 True\n", - "1 True\n", - "2 True\n", - "3 True\n", - "4 True\n", - "5 True\n", - "6 True\n", - "7 True\n", - "8 True\n", - "9 True\n", - "10 True\n", - "11 True\n", - "12 True\n", - "13 False\n", - "14 False\n", - "...\n", + "0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + "5 True\n", + "6 True\n", + "7 True\n", + "8 True\n", + "9 True\n", + "10 True\n", + "11 True\n", + "12 True\n", + "13 False\n", + "14 False\n", + "15 True\n", + "16 True\n", + "17 True\n", + "18 False\n", + "19 True\n", + "20 True\n", + "21 True\n", + "22 False\n", + "23 True\n", + "24 True\n", + "25 True\n", + "26 True\n", + "27 True\n", + "28 True\n", + "29 True\n", + " ... \n", + "13563 True\n", + "13564 True\n", + "13565 True\n", + "13566 True\n", + "13567 False\n", + "13568 False\n", + "13569 True\n", + "13570 True\n", + "13571 True\n", + "13572 True\n", + "13573 True\n", + "13574 True\n", + "13575 False\n", + "13576 True\n", + "13577 False\n", "13578 True\n", "13579 True\n", "13580 False\n", @@ -1965,10 +2103,10 @@ "13590 False\n", "13591 True\n", "13592 True\n", - "Name: outcome, Length: 13593, dtype: bool" + "Name: outcome, dtype: bool" ] }, - "execution_count": 4, + "execution_count": 138, "metadata": {}, "output_type": "execute_result" } @@ -1986,7 +2124,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 139, "metadata": { "collapsed": false }, @@ -1997,7 +2135,7 @@ "9148" ] }, - "execution_count": 5, + "execution_count": 139, "metadata": {}, "output_type": "execute_result" } @@ -2016,7 +2154,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 140, "metadata": { "collapsed": false }, @@ -2027,7 +2165,7 @@ "1125" ] }, - "execution_count": 6, + "execution_count": 140, "metadata": {}, "output_type": "execute_result" } @@ -2045,12 +2183,25 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 141, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "798" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(live[(live.birthwgt_lb >= 9) & (live.birthwgt_lb <= 95)])" + ] }, { "cell_type": "markdown", @@ -2061,7 +2212,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 142, "metadata": { "collapsed": false }, @@ -2072,7 +2223,7 @@ "(4413, 4735)" ] }, - "execution_count": 7, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -2092,7 +2243,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 143, "metadata": { "collapsed": false }, @@ -2103,7 +2254,7 @@ "7.201094430437772" ] }, - "execution_count": 8, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -2114,7 +2265,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 144, "metadata": { "collapsed": false }, @@ -2122,10 +2273,10 @@ { "data": { "text/plain": [ - "7.3258556149732623" + "7.325855614973262" ] }, - "execution_count": 9, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -2143,12 +2294,63 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 145, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "38.6009517335\n" + ] + } + ], + "source": [ + "firsts_prglngth = firsts.prglngth.mean()\n", + "print firsts_prglngth" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "38.5229144667\n" + ] + } + ], + "source": [ + "others_prglngth = others.prglngth.mean()\n", + "print others_prglngth" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13.1102608186\n" + ] + } + ], + "source": [ + "diff = abs(firsts_prglngth - others_prglngth) #in weeks\n", + "print diff * 168 #168 hours / week so now in hours" + ] }, { "cell_type": "markdown", @@ -2176,7 +2378,9 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "The work on cleaning data at least as far as was done in the first chapter primarily relied on understanding how the survey results and the recodes were reported. For example, Allen takes codes used to report certain conditions for birth weight and changes them to NaNs. What would you do if where you are getting your data from is not well-documented enough for you to know whether what you interpret are logical values or codes? Do you take a guess at what seems reasonable and what isn't and how do you report this in regards to the validity of your conclusions?" + ] }, { "cell_type": "markdown", @@ -2209,7 +2413,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.6" + "version": "2.7.11" } }, "nbformat": 4, diff --git a/ThinkStats2/chap02ex.ipynb b/ThinkStats2/chap02ex.ipynb index 0cc3234..d44250e 100644 --- a/ThinkStats2/chap02ex.ipynb +++ b/ThinkStats2/chap02ex.ipynb @@ -12,11 +12,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'caseid', u'rscrinf', u'rdormres', u'rostscrn', u'rscreenhisp',\n", + " u'rscreenrace', u'age_a', u'age_r', u'cmbirth', u'agescrn',\n", + " ...\n", + " u'pubassis_i', u'basewgt', u'adj_mod_basewgt', u'finalwgt', u'secu_r',\n", + " u'sest', u'cmintvw', u'cmlstyr', u'screentime', u'intvlngth'],\n", + " dtype='object', length=3087)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "%matplotlib inline\n", "\n", @@ -34,11 +50,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "import thinkstats2\n", "hist = thinkstats2.Hist(resp.totincr)\n", @@ -54,11 +78,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhhkCktQwQ0CSGmYISFLD/j+Ujva7El4sqAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import thinkplot\n", "thinkplot.Hist(hist, label='totincr')\n", @@ -74,11 +118,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hist = thinkstats2.Hist(resp.age_r)\n", "thinkplot.Hist(hist, label='age_r')\n", @@ -94,12 +158,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nypCQJLUyJCRJrQwJSVKr/w9hCRiMPaZgpgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(resp.numfmhh)\n", + "thinkplot.Hist(hist, label='numfmhh')\n", + "thinkplot.Show(xlabel='No. of people in household', ylabel='Frequency')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The mode is 2 and the distribution of this histogram looks kind of like a Gaussian distribution with a very flat area in the middle and a shorter tail on the left than on the right." + ] }, { "cell_type": "markdown", @@ -110,12 +205,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEPCAYAAABsj5JaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZma5OEDMzCyX/w+IFb3MFJuN4QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(resp.parity)\n", + "thinkplot.Hist(hist, label='parity')\n", + "thinkplot.Show(xlabel='No. of Children', ylabel='Frequency')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The mode is 0 and the distribution looks like half of a Gaussian distribution with the top of the peak occuring at the 0. As a result, there is no tail on the left and a tail on the right that continues until about 10. I looked on Wikipedia for a list of probability distributions and it kind of looks like a Poisson distribution with a $\\lambda$ of 1." + ] }, { "cell_type": "markdown", @@ -126,12 +252,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Children , Frequency\n", + "22 , 1\n", + "16 , 1\n", + "10 , 3\n", + "9 , 2\n", + "8 , 8\n" + ] + } + ], + "source": [ + "print 'Children , Frequency'\n", + "for parity, freq in hist.Largest(5):\n", + " print parity, ',', freq" + ] }, { "cell_type": "markdown", @@ -142,12 +285,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEZCAYAAAB1mUk3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(resp[resp.totincr == 14].parity)\n", + "thinkplot.Hist(hist, label='parity')\n", + "thinkplot.Show(xlabel='No. of Children', ylabel='Frequency', title='No of Children for highest income respondents')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The mode is 0 and the distribution for the parity of the highest income respondents also again looks like half of a Gaussian." + ] }, { "cell_type": "markdown", @@ -158,12 +332,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Children , Frequency\n", + "8 , 1\n", + "7 , 1\n", + "5 , 5\n", + "4 , 19\n", + "3 , 123\n" + ] + } + ], + "source": [ + "print 'Children , Frequency'\n", + "for parity, freq in hist.Largest(5):\n", + " print parity, ',', freq" + ] }, { "cell_type": "markdown", @@ -174,12 +365,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.07586206897\n", + "1.24957581367\n" + ] + } + ], + "source": [ + "highest_inc = resp[resp.totincr == 14]\n", + "others = resp[resp.totincr != 14]\n", + "\n", + "print highest_inc.parity.mean()\n", + "print others.parity.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The mean parity for the highest income respondents was around 0.174 lower than all of the other respondents. This means that respondents earning less than \\$75,000 have born around 0.174 more children than respondents making over \\$75,000 a year." + ] }, { "cell_type": "markdown", @@ -190,12 +403,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEPCAYAAABRHfM8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA8PMzLI4MMzMLIsDw8zMsvx/zm82jNePPsAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(resp.rscreenrace)\n", + "thinkplot.Hist(hist, label='rscreenrace')\n", + "thinkplot.Show(xlabel='No. of Children', ylabel='Frequency')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above is a histogram of the respondent's race as reported in screening. The first three codes (1, 2, 3) are other race groups. 4 codes a black respondent and 5 codes a white respondent. We know that the NSFG intentionally oversampled minorities but what we see here is that predominantly the respondents are white and the sum of the respondents of all other races is still less than the total number of white respondents. This could indicate that even with the oversampling, there is still a bias in the data towards representing white people more than other races. Or these percentages could work out to be an accurate model of America's population." + ] }, { "cell_type": "markdown", @@ -210,12 +454,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import nsfg\n", + "df = nsfg.ReadFemPreg()\n", + "\n", + "live = df[df.outcome == 1]\n", + "firsts = live[live.birthord == 1]\n", + "others = live[live.birthord != 1]\n", + "\n", + "firsts_hist = thinkstats2.Hist(firsts.totalwgt_lb)\n", + "others_hist = thinkstats2.Hist(others.totalwgt_lb)\n", + "\n", + "width = 0.05\n", + "thinkplot.PrePlot(2)\n", + "thinkplot.Hist(firsts_hist, align='right', width=width, label='firsts')\n", + "thinkplot.Hist(others_hist, align='left', width=width, label='others')\n", + "thinkplot.Show(xlabel='birth weight (lb)', ylabel='frequency')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above is a histogram of the birth weight in pounds of first babies vs other babies. We see that both first babies and other babies have a Gaussian distribution with the mode of other babies being aorund 7 lb and the mode for first babies being around 7.5 lbs." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean totalwgt_lb of first babies 7.20109443044\n", + "Mean totalwgt_lb of other babies 7.32585561497\n", + "d -0.0886729270726\n" + ] + } + ], + "source": [ + "import math\n", + "\n", + "print 'Mean totalwgt_lb of first babies ', firsts.totalwgt_lb.mean()\n", + "print 'Mean totalwgt_lb of other babies ', others.totalwgt_lb.mean()\n", + "\n", + "def CohenEffectSize(group1, group2):\n", + " diff = group1.mean() - group2.mean()\n", + " var1 = group1.var()\n", + " var2 = group2.var()\n", + " n1, n2 = len(group1), len(group2)\n", + " pooled_var = (n1 * var1 + n2 * var2) / (n1 + n2)\n", + " d = diff / math.sqrt(pooled_var)\n", + " return d\n", + "\n", + "print 'd ', CohenEffectSize(firsts.totalwgt_lb, others.totalwgt_lb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The mean weight of first babies in pounds is 7.2 and the mean weight of other babies is 7.33 pounds. This would indicate that first babies are lighter than other babies. However the difference in means is only 0.088 standard deviations so this data isn't really telling about whether or not first babies are lighter than other babies." + ] } ], "metadata": { diff --git a/ThinkStats2/chap03ex.ipynb b/ThinkStats2/chap03ex.ipynb index dae099d..5545828 100644 --- a/ThinkStats2/chap03ex.ipynb +++ b/ThinkStats2/chap03ex.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -33,12 +33,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "Pmf({0: 0.46617820227659301, 1: 0.21405207379301322, 2: 0.19625801386889966, 3: 0.087138558157791451, 4: 0.025644380478869556, 5: 0.010728771424833181})" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import thinkstats2\n", + "import thinkplot\n", + "\n", + "pmf = thinkstats2.Pmf(resp.numkdhh)\n", + "pmf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above result tells us that the majority of the respondents in this dataset, about 46.6% of them, do not have any children under 18 in their household. This probably means that these respondents are having their first baby as it is unlikely that a respondent would have a second baby after their previous one has reached an age above 18." + ] }, { "cell_type": "markdown", @@ -49,12 +73,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEZCAYAAAB1mUk3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nflBXAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "thinkplot.Pmf(pmf)\n", + "thinkplot.Config(xlabel='Number of Children under 18', ylabel='Probability', \n", + " title='PMF of Number of Children Under 18 in Household')\n", + "thinkplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The PMF shows that again, most of the respondents have 0 children under 18 in their household. It actually kind of looks like the probability exponentially decays as the number of children under 18 increases." + ] }, { "cell_type": "markdown", @@ -65,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "metadata": { "collapsed": false }, @@ -105,12 +161,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 85, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEZCAYAAACEkhK6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XuYHFWd//H3JyIQQK4iYBDkJgjK7dEIhssIKgGUsAhK\ngqsCIiqo+7Du4rK6JIqgrq6gqBDM8lMEghfCRRCDwigXkYABQRIILJcQIndEJEJIvr8/zulQ0+mZ\nPj2Tnp6On9fzzDPdVedUfau7ur51zqmuVkRgZmbWzKhOB2BmZt3BCcPMzIo4YZiZWREnDDMzK+KE\nYWZmRZwwzMysyEqVMCR9T9J/DvM6r5V01HCuc7AkfULSnyU9K2m9ERDP/ZL26dC6XyPpt5L+Ium/\nB1F/b0nzB5i/bF8sKHuupC+2GsNI0cn3cSCSXpf3dXU6lhVlKPvKQMcqSZtLWippwJzQVQlD0gOS\nns87wZOSLpc0pjY/Ij4REV/uZIxVkk6W9GKO9ylJ10vaLc/7cH6DvlFXZ0Ke/r/5ee2NfDb//VXS\n7EHEsgrwDeCdEbF2RDxdN7+2np/XTT9P0n+1ur4u8DHgsYhYJyL+rVEBSWMlXSHpaUlPSLpJ0kcq\nRfr9ElODfXFEfOEp75PnNZi+VNKWnYipLo5XSvpJTkJLJe1VN39VSWflE58nJF0qaZNGy4qI+Xlf\nb/m17y/Jd9MJ4iA0fZ26KmGQNujAiFgb2AR4DPh2Z0NqanqOd0PgBuBnlXn3Ae+vy+ofAu6uW0YA\n6+Sd/1URscsg4tgYWA2Y06Tc22pJrVtIesUgqm0O3DXAMncHfg1cC2wVEa8GPgHsN6ggB2mQ29ZM\nowPDsCe0AbbtOuAIYGGDef8CvA14E/Ba4BnadwwYEUl+JOm2hAEggIh4EfgpsP2yGZXmmqR1cwvk\nsUatEUkfkXRfPmu/T9LEyryjJN2V6/1C0maVee+SNCefdX67Fk8zEbEE+AGwsaT18+Q/A3eQD0K5\nm+jtwGX9bfeAL0w6+zpd0gJJD0v6Zj5j2waYm4s9LelXAyzma8Cp/Sz/w5Kuq5u27Mw0v/7fkXRl\nbgldJ2mjHMdT+TXdqW6xYyX9Kb/W0yStWln2eyTNzq/19ZLeXJl3v6R/l3Q78FyjprSkt0u6Odf/\nfU4CSDoX+DBwYn7/G3WnfA04NyK+HhFPAUTE7IiYWCkjSSdIejS/5h+pzOi360DSLpJuVeoOmw6s\nXpm3t6T5edsWArWWZrPX4l8l3Z7nX1h9HQst27/yWfQX83qelXRVZZ9F0j8rtfYfl3RS3bZJ0uck\n3ZvnT5e0bp5Xa8UeJelBUkLuIyIWR8S3IuJGYGmDOF8P/DIinsjHgIuAHRpuUF03S7PtGgxJx0ia\np9TauUS5tVO/7sr6j8qPt5LUK+kZpWPUhZVy20mamT8TcyQdVrfa9SX9PG/D7yRtUanbcJ9vEPco\nSV/P79G9wIEl29uNCQMASWsAHwB+10+RUaQP2+uAzYDngTMrdc8A9stn/28HbsvzJgCfAw4mtQqu\nAy7M815NaiGcBLya1EIYVxjvasCRwPzaAYh0BvND0sEL4HDgEuDFRosoWM3ngbHAjsBO+fHnI2Ie\nL3+o1omId/ZTP4DvAm/o5yBaKzPQ88NIr88GpO34HXBLfv4z4Jt15ScB7wK2ArbN24CkXYBpwDHA\n+sDZwGWSXlmpeziwP7BuRPQ5uCgl358Dp+d1fxO4QtJ6EXEkcD7w1dxqu6au7mhgd/q2BhvZGHgV\n6Uz3o8B3JK0zUIUc/wzSycP6wE+A9zVY7rqk/fZjha/FYcC7gS1I7/1HmsTezETSfrkhqWX62Rz/\n9qR95AjSdm8AjKnU+zRwELBnnv90Ll+1F7Adg2utTQP2kLRJ/hwfAVw5QPn6/bPhdg1G/oycChxK\n6vF4CJg+wLqrvkRKfOsCm5JbSXmbZgI/Ih1jDge+K2m7St0PACeT9pH7gC/nuv3u8w3W/zHgANK+\n8pa8DU11Y8K4RNJTpKboO4GvNyoUEU9FxIyIeCEi/gacRtpRa5YAb5a0ekQ8GhG1rppjgdMi4p58\nEPoKsLOk15EOTnfm5S6JiNNJrYSBfCDH+yCwCykR9dkeYG9Ja5O6o37YYBkCHs9nDU9JOqGfdU0C\npkTEkxHxJDAlL7O2jOr//iwi7YCnNClXja1qRkTcls/+ZgCLIuL83I98EbBzXflvR8QjEfFMXm/t\nDP4Y4KyIuCWS84AXgGp32Rm57gsN4joQuCciLoiIpRExndTKem/BNq1H+mw06hKpehH4Ut4XfgE8\nR0p6A9kdWCWfRS+JiJ8Bs+rKLAFOzmfbL1D+WjyaX8fLWf51btW5EXFfXv+PK8t7H3B5RNwQEYuB\nL9D3wHgs8J8RsTDP/yJwaOVMO/K2LernfWtmHjAfWEA6BmxHOvgOdbsaGZM/b7W/p+l7gjgJmBYR\nt+dt/Q9gd1V6JAawGNhc0piIeDG3qADeA9wfET/M7/XtpBOXaitjRkTcmo9P51e2oZV9/jDg9Mpn\n77SCmLsyYUyIiPVJZwefAn4r6TX1hSSNlnR2bjo/A/wGWFeSIuJ5Upb+BLBQqbvqDbnq5sAZtZ0E\neJK0k48hnTHVD4T1e/VLdlFErB8RG0fEOyPiturMiPg7cAXpzHr9iGjUYgpgg4hYLy/rf/pZ12tJ\nZzk1D5LOfGrLKPV9YCNJ72mhTs2jlceLGjxfq678w5XHD5K2AdL78K91H9ZNK/Pr69Z7bV5e1YP0\nPRvuz9Ok7pCGg6kVT9a1bJ5n+e2rtwnpYFcfV9Xj+QBUU/JaVF/ngeJ4Cai2TGoXREA6iNVUT4Sq\ny+vzGcifpSfrYp1R+fzclZe7UaXMQO9bM98lffbXA9YknZRc1UL9/rarkQX581b7W480DlnTZx/L\nJ6ZPUraP/Rvp+HuzpDskHZmnbw7sVvdeT6Lv6zfQe1O6z9cfy+rrNdSNCaM2hhERMYN0NrZHg3Kf\nBbYB3pqbfXvV1b86It5Nav7fDZyT588Hjq3uJBGxVkTcRDrjrD97eN0K2KbzgBPy//6UdEktIO1w\nNZsDj7QaTD5YTWH5M7e/AWssC0jauNVlN1B9/arxzge+3OB9uKga6gDLfYTU3121GcsfrJcTEYtI\nXWn1XUUrwkKW/wDX71P121XyWpR6iOVfly1JB/Wmrw0p/mXvWe5C2aBu+fvXxbpmRFRba0MZTN6J\n1Er4S95Pv00aBxvSWMQgPULl8yZpTdJr8TDpswKVzwvpWANARDwWER+LiDHAx0ndTluS3uveutdv\n7Yg4vjCe19dN62+f7/M+0ve40a9uTBjL5PGGdWl8tctapDPaZ/PONLlS7zWSDso7+2JSV0LtTPEs\n4KTcV4ukdSTV+veuALaXdLCkV0j6DH0z/6BExG9I/fhn9lOk9Dry6cDnJb06j7d8gb5JqNlyqvN/\nRBqM3b8y7XZgB0k75jGZk2n9w18fw3GSxuT36CRe7gM+B/i4pLGQPoySDsgfyhJXAttIOjy/Vx8A\n3kjq4y3x78BHlAaT188x7FQdnByk3wEvSfqUpFUkHUIaaxrIUF+LqquA7SQdkde/Pqkr8Kd1raX+\n/BR4Tx5cfSWpy6n6np4NnFrrlpG0oaSDKvNLL96oXQiwWt7XamYBH5K0dl7/caSWwFPLLahwfUNw\nIXBk5fNwKnBTpMt5nyAdqD+YB5iPIo3TpaCkQ/XyRTjPkI4/S0n75xskfTC/P6+U9BZJzbo6of99\n/vIGZX8MfDp/9tYDTizZ4G5MGJcrXR3wF9IZ8IciYm6DcqeTsvsTwI30HRgbRTqjX5Dn70XqniIi\nLiGNW0zPXVl/BMbneU+S+v6+muttRd8m6qBFxLW5L7Hh7MLFnEIaYP4j6eB+C3lArHA5y+bng8d/\nkZr+kafNIx0gfg3cQ7ogoFVR9/gC0iDfvaT+6S/ndd1K6rs/M3dt3MPLFwc03ZZ8AHkPqaX5RP5/\nYPS94GCg+r8D9gH2Be6T9ATpZOKKwm3rb7mLgUNIF0DU9qcBB9eH+lrULetx0knAx0mXpf8ReAr4\nZMnyIuIu0kH6QtIZ7ZP07WI6A7gUmJk/ozfSNyGWxHo36Qz9taQE93xlXOCzpPGbeaRuuPHAPw2w\nrPr9baiqn5Ffk07KLiYdS7YgDVLXHEM68XiCdOCuHiveCvxe0rOkccxPR8QDEfEc6eKFw0mv7yOk\n41E1aTYOrP99vvadq+r2nwP8kpePE80u8ABA0eYfUJI0nnTwHkUaIPpqP+XeStq5PhARF7dS18zM\n2q+tCSNfGXEP6SztEVJz8vD6FkEudzWpC+l/I+Li0rpmZjY82t0lNRaYFxEP5qb4dGBCg3KfIvWN\nPjaIumZmNgzanTDG0PfSrYepu0JE0muBgyPie/QdoGpa18zMhs9IGPQ+ncIRejMz65xVmhcZkgX0\nvcZ8U5a/JvgtpCuSRPoq/P6SXiqsC4Ak3yTMzKxFEdHSZcftbmHMArZWuhHXqqRLxfrcWC8itsx/\nW5DGMT4ZEZeV1K1bTlf+nXzyyR2PwfF3Pg7H351/3Rz/YLS1hRERSyQdT7rOvnZp7BxJx6bZMbW+\nSrO67YzXzMz61+4uKSLiKupuyBYRZ/dT9qi658vVNTOzzhgJg97/0Hp6ejodwpA4/s5y/J3V7fG3\nqu3f9B4O6Qa03b8dZmbDRRIxwga9zcxsJeGEYWZmRdo+6G02WBdefB3TLriGRYsG88NsI8Po0atx\n9KR9mHjInp0OxWzI3MKwEavbkwXAokUvMO2Ca5oXNOsCThg2YnV7sqhZWbbDzF1S1hVuuOLUTofQ\nsnEHntTpEMxWKLcwzMysiBOGmZkVccIwM7MiThhmZlbECcPMzIo4YZiZWREnDDMzK+KEYWZmRZww\nzMysiBOGmZkVccIwM7MibU8YksZLmivpHkknNph/kKTbJc2WdLOkcZV5D1TntTtWMzPrX1tvPihp\nFHAmsC/wCDBL0qURMbdS7FcRcVku/2bgx8Ab87ylQE9EPN3OOM3MrLl2tzDGAvMi4sGIWAxMByZU\nC0TE85Wna5GSRI2GIUYzMyvQ7oPxGGB+5fnDeVofkg6WNAe4HDiqMiuAqyXNknRMWyM1M7MBjYiz\n94i4JCLeCBwMnFKZNS4idgUOAI6TtEdHAjQzs7b/gNICYLPK803ztIYi4npJW0paPyKeioiFefrj\nkmaQuriub1R38uTJyx739PTQ09Mz9OjNzFYSvb299Pb2DmkZ7U4Ys4CtJW0OLAQOByZWC0jaKiLu\ny493BVaNiKckrQGMiojnJK0JvBuY0t+KqgnDzMz6qj+RnjKl38Npv9qaMCJiiaTjgZmk7q9pETFH\n0rFpdkwF3ifpQ8CLwCLg/bn6RsAMSZHjPD8iZrYzXjMz61/bf9M7Iq4Ctq2bdnbl8deArzWodz+w\nc7vjMzOzMiNi0NvMzEY+JwwzMyvihGFmZkWcMMzMrIgThpmZFXHCMDOzIk4YZmZWxAnDzMyKOGGY\nmVkRJwwzMyvihGFmZkWcMMzMrIgThpmZFXHCMDOzIk4YZmZWxAnDzMyKOGGYmVkRJwwzMyvihGFm\nZkXanjAkjZc0V9I9kk5sMP8gSbdLmi3pZknjSuuamdnwaWvCkDQKOBPYD9gBmChpu7piv4qInSJi\nF+Bo4Pst1DUzs2HS7hbGWGBeRDwYEYuB6cCEaoGIeL7ydC1gaWldMzMbPu1OGGOA+ZXnD+dpfUg6\nWNIc4HLgqFbqmpnZ8Fil0wEARMQlwCWS9gBOAd7V6jImT5687HFPTw89PT0rKjwzs67X29tLb2/v\nkJbR7oSxANis8nzTPK2hiLhe0paS1m+1bjVhmJlZX/Un0lOmTGl5Ge3ukpoFbC1pc0mrAocDl1UL\nSNqq8nhXYNWIeKqkrpmZDZ+2tjAiYomk44GZpOQ0LSLmSDo2zY6pwPskfQh4EVgEvH+guu2M18zM\n+tf2MYyIuArYtm7a2ZXHXwO+VlrXzMw6w9/0NjOzIk4YZmZWxAnDzMyKOGGYmVkRJwwzMyvihGFm\nZkWcMMzMrIgThpmZFXHCMDOzIk4YZmZWxAnDzMyKOGGYmVkRJwwzMyvihGFmZkWcMMzMrIgThpmZ\nFXHCMDOzIk4YZmZWxAnDzMyKtP03vSWNB04nJadpEfHVuvmTgBPz078Cn4yIP+Z5DwB/AZYCiyNi\nbLvjNWuHcQee1OkQBmX06NU4etI+TDxkz06HYiNAW1sYkkYBZwL7ATsAEyVtV1fs/4C9ImIn4BRg\namXeUqAnInZxsrBuM3r0ap0OYcgWLXqBaRdc0+kwbIRod5fUWGBeRDwYEYuB6cCEaoGIuCki/pKf\n3gSMqczWMMRo1hZHT9pnpUkaZtD+LqkxwPzK84dJSaQ/HwV+UXkewNWSlgBTI+KcFR+iWXtMPGTP\nru7K6dZuNGufto9hlJL0DuBIYI/K5HERsVDShqTEMScirm9Uf/Lkycse9/T00NPT08Zozcy6S29v\nL729vUNaRrsTxgJgs8rzTfO0PiTtSBq7GB8RT9emR8TC/P9xSTNIrZOmCcPMzPqqP5GeMmVKy8to\n9/jALGBrSZtLWhU4HLisWkDSZsDPgH+OiPsq09eQtFZ+vCbwbuDONsdrZmb9aGsLIyKWSDoemMnL\nl9XOkXRsmh1TgS8A6wPflSRevnx2I2CGpMhxnh8RM9sZr5mZ9a8oYUi6GJgG/CIilraygoi4Cti2\nbtrZlcfHAMc0qHc/sHMr6zIzs/Yp7ZL6LjAJmCfpK5K2bVbBzMxWLkUJIyJ+FRFHALsCDwC/knSj\npCMlvbKdAZqZ2chQPOgtaQPgI6TvSswGziAlkKvbEpmZmY0opWMYM0jjEOcB761d7gpcJOmWdgVn\nZmYjR+lVUudExJXVCZJWi4gXIuItbYjLzMxGmNIuqVMaTPvdigzEzMxGtgFbGJI2Jt0ParSkXUg3\nAwRYG1ijzbGZmdkI0qxLaj/SQPemwP9Upv8V8J3JzMz+gQyYMCLiB8APJL0vIn42TDGZmdkI1KxL\n6oMR8SPg9ZJOqJ8fEf/ToJqZma2EmnVJrZn/r9XuQMzMbGRr1iV1dv7f+n1wzcxspdKsS+pbA82P\niE+v2HDMzGykatYldeuwRGFmZiNeyVVSZmZmTbukTo+If5F0ORD18yPioLZFZmZmI0qzLqnz8v+v\ntzsQMzMb2Zp1Sd2a//8m/yb3dqSWxt0R8eIwxGdDcOHF1zHtgmtYtOiFTodiZiuB0tubHwicBdxH\nup/UFpKOjYhftDM4G5qVJVmMHr1ap0MwM8rvVvsN4B0R0RMRewPvAL5ZUlHSeElzJd0j6cQG8ydJ\nuj3/XS9px9K6NrCVJVkcPWmfTodhZpT/HsZfI+LeyvP/I92AcECSRgFnAvsCjwCzJF0aEXPrlrVX\nRPxF0nhgKrBbYV0rdMMVp3Y6BDPrcs2ukjokP7xF0pXAj0ljGIcBswqWPxaYFxEP5uVNByYAyw76\nEXFTpfxNpNupF9U1M7Ph06yF8d7K40eBvfPjx4HRBcsfA8yvPH+YlAj681GgNi7Sal0zM2ujZldJ\nHTlcgUh6B3AksMdg6k+ePHnZ456eHnp6elZIXGZmK4Pe3l56e3uHtIzSq6RWB44GdgBWr02PiKOa\nVF0AbFZ5vmmeVr/8HUljF+Mj4ulW6tZUE4aZmfVVfyI9ZUrr95QtvUrqPGBj0i/w/YZ08G466E0a\n59ha0ub5exyHA5dVC0jaDPgZ8M8RcV8rdc3MbPiUXiW1dUQcJmlCRPxA0gXAdc0qRcQSSccDM0nJ\naVpEzJF0bJodU4EvAOsD35UkYHFEjO2v7iC20czMVoDShLE4/39G0puAPwOvKakYEVcB29ZNO7vy\n+BjgmNK6ZmbWGaUJY6qk9UitgctIv8D3hbZFZWZmI05RwoiI7+eHvwG2bF84ZmY2UhUNekvaQNK3\nJf1B0q2STpe0QbuDMzOzkaP0KqnpwGPA+4BDgSeAi9oVlJmZjTylYxibRMSXKs9PkfSBdgRkZmYj\nU2kLY6akwyWNyn/vB37ZzsDMzGxkaXbzwb+SbjYo4F+AH+VZo4DngM+2NTozMxsxmt1L6lXDFYiZ\nmY1spWMYSDoI2Cs/7Y2In7cnJDMzG4lKL6v9CvAZ4K789xlJp7UzMDMzG1lKWxgHADtHxFIAST8A\nZgP/0a7AzMxsZCm9Sgpg3crjdVZ0IGZmNrKVtjBOA2ZLupZ0xdRewOfaFpWZmY04TRNGvuX49cBu\nwFvz5BMj4s/tDMzMzEaWpgkjIkLSlRHxZvwDRmZm/7BKxzD+IOmtzYuZmdnKqnQM423AByU9APyN\nNI4REbFjuwIzM7ORpTRh7NfWKMzMbMRrdi+p1YGPA1sDd5B+V/ul4QjMzMxGlmZjGD8A3kJKFvsD\n32h1BZLGS5or6R5JJzaYv62kGyX9XdIJdfMekHS7pNmSbm513WZmtuI065LaPl8dhaRpQEsHbUmj\ngDOBfYFHgFmSLo2IuZViTwKfAg5usIilQE9EPN3Kes3MbMVr1sJYXHswyK6oscC8iHgwIhaTfrlv\nQrVARDwREbcCjZavghjNzGwYNGth7CTp2fxYwOj8vHaV1NpN6o8B5leeP0xKIqUCuFrSEmBqRJzT\nQl0zM1uBmv0exiuGK5B+jIuIhZI2JCWOORFxfaOCkydPXva4p6eHnp6e4YnQzKwL9Pb20tvbO6Rl\nFP8exiAtADarPN80TysSEQvz/8clzSC1TpomDDMz66v+RHrKlCktL6Pd4wOzgK0lbS5pVeBwBr69\niJY9kNaQtFZ+vCbwbuDOdgZrZmb9a2sLIyKWSDoemElKTtMiYo6kY9PsmCppI+AW4FXAUkmfAbYH\nNgRmSIoc5/kRMbOd8ZqZWf/a3SVFRFwFbFs37ezK40eB1zWo+hywc3ujMzOzUr5k1czMijhhmJlZ\nEScMMzMr4oRhZmZFnDDMzKyIE4aZmRVxwjAzsyJOGGZmVsQJw8zMijhhmJlZEScMMzMr4oRhZmZF\nnDDMzKyIE4aZmRVxwjAzsyJOGGZmVsQJw8zMijhhmJlZEScMMzMr0vaEIWm8pLmS7pF0YoP520q6\nUdLfJZ3QSl0zMxs+bU0YkkYBZwL7ATsAEyVtV1fsSeBTwH8Poq6ZmQ2TdrcwxgLzIuLBiFgMTAcm\nVAtExBMRcSvwUqt1zcxs+KzS5uWPAeZXnj9MSgTtrmtmK9C4A0/qdAiDMnr0ahw9aR8mHrJnp0NZ\nKbQ7YQybyZMnL3vc09NDT09Px2IxWxmMHr0aixa90OkwhmTRoheYdsE1ThhAb28vvb29Q1pGuxPG\nAmCzyvNN87QVXreaMMxs6I6etA/TLrhmpUgatvyJ9JQpU1peRrsTxixga0mbAwuBw4GJA5TXEOqa\n2Qo08ZA9u/rMvFu70UaytiaMiFgi6XhgJmmAfVpEzJF0bJodUyVtBNwCvApYKukzwPYR8Vyjuu2M\n18zM+tf2MYyIuArYtm7a2ZXHjwKvK61rZmad4W96m5lZEScMMzMr4oRhZmZFnDDMzKyIE4aZmRVx\nwjAzsyJOGGZmVsQJw8zMijhhmJlZEScMMzMr4oRhZmZFnDDMzKyIE4aZmRVxwjAzsyJOGGZmVsQJ\nw8zMijhhmJlZEScMMzMr0vaEIWm8pLmS7pF0Yj9lviVpnqTbJO1Smf6ApNslzZZ0c7tjNTOz/rX1\nN70ljQLOBPYFHgFmSbo0IuZWyuwPbBUR20h6G/A9YLc8eynQExFPtzNOMzNrrt0tjLHAvIh4MCIW\nA9OBCXVlJgA/BIiI3wPrSNooz9MwxGhmZgXa2sIAxgDzK88fJiWRgcosyNMeBQK4WtISYGpEnNPG\nWM1sJTXuwJM6HULLRo9ejaMn7cPEQ/bsdCjLjPSz93ERsStwAHCcpD06HZCZdYfRo1frdAhDsmjR\nC0y74JpOh9FHu1sYC4DNKs83zdPqy7yuUZmIWJj/Py5pBql1cn2jFU2ePHnZ456eHnp6eoYWuZl1\ntaMn7cO0C65h0aIXOh3KoK3I2Ht7e+nt7R3SMhQRKyaaRguXXgHcTRr0XgjcDEyMiDmVMgcAx0XE\ngZJ2A06PiN0krQGMiojnJK0JzASmRMTMBuuJdm5Ht6o2w2+44tQORmJmrRiOz64kIkKt1GlrCyMi\nlkg6nnSwHwVMi4g5ko5Ns2NqRFwp6QBJ9wJ/A47M1TcCZkiKHOf5jZKFmZkNj3Z3SRERVwHb1k07\nu+758Q3q3Q/s3N7ozMys1Egf9DYzsxHCCcPMzIo4YZiZWREnDDMzK+KEYWZmRZwwzMysiBOGmZkV\nccIwM7MiThhmZlbECcPMzIo4YZiZWREnDDMzK+KEYWZmRZwwzMysiBOGmZkVccIwM7MiThhmZlbE\nCcPMzIo4YZiZWZG2JwxJ4yXNlXSPpBP7KfMtSfMk3SZp51bqmpnZ8GhrwpA0CjgT2A/YAZgoabu6\nMvsDW0XENsCxwFmldVcGvb29nQ5hSBx/Zzn+zur2+FvV7hbGWGBeRDwYEYuB6cCEujITgB8CRMTv\ngXUkbVRYt+t1+w7n+DvL8XdWt8ffqnYnjDHA/Mrzh/O0kjIldc3MbJis0ukAGlCnA6gZd+BJbV/H\nQ/dcx9WzXmz7eszMhkoR0b6FS7sBkyNifH7+OSAi4quVMmcB10bERfn5XGBvYItmdSvLaN9GmJmt\npCKipRP0drcwZgFbS9ocWAgcDkysK3MZcBxwUU4wz0TEo5KeKKgLtL7RZmbWurYmjIhYIul4YCZp\nvGRaRMyRdGyaHVMj4kpJB0i6F/gbcORAddsZr5mZ9a+tXVJmZrbyWCm+6S3pUEl3SloiaddOx1Oq\nm7+YKGmapEcl/bHTsQyGpE0lXSPpT5LukPTpTsdUStJqkn4vaXaO/eROxzQYkkZJ+oOkyzodS6sk\nPSDp9vwe3NzpeFolaR1JP5E0J38G3lZSb6VIGMAdwD8Bv+l0IKVWgi8mnkuKvVu9BJwQETsAuwPH\ndcvrHxEvAO+IiF2AnYH9JY3tcFiD8Rngrk4HMUhLgZ6I2CUiuvG1PwO4MiLeCOwEFHX3rxQJIyLu\njoh5jKCL73RSAAAHcklEQVRLcgt09RcTI+J64OlOxzFYEfHniLgtP36O9IHpmu/5RMTz+eFqpLHI\nrupblrQpcADw/U7HMkiiS4+fktYG9oyIcwEi4qWIeLakbldu8ErCX0wcISS9nnSm/vvORlIud+fM\nBv4MXB0RszodU4u+CfwbXZboKgK4WtIsScd0OpgWbQE8Ienc3CU4VdLokopdkzAkXS3pj5W/O/L/\n93Y6NutektYCfgp8Jrc0ukJELM1dUpsCb5O0fadjKiXpQODR3MIT3dUzUDMuInYltZKOk7RHpwNq\nwSrArsB38jY8D3yutGJXiIh3dTqGFWwBsFnl+aZ5mg0TSauQksV5EXFpp+MZjIh4VtK1wHi6Zzxg\nHHCQpAOA0cCrJP0wIj7U4biKRcTC/P9xSTNIXczXdzaqYg8D8yPilvz8p0DRRTdd08JoQbecrSz7\nUqOkVUlfTOy2q0W69eyw5n+BuyLijE4H0gpJr5a0Tn48GngXMLezUZWLiJMiYrOI2JK031/TTclC\n0hq5ZYqkNYF3A3d2NqpyEfEoMF/SG/KkfSk82VgpEoakgyXNB3YDfi7pF52OqZmIWALUvpj4J2B6\nN30xUdIFwI3AGyQ9JOnITsfUCknjgCOAffKlkX+QNL7TcRXaBLhW0m2kcZdfRsSVHY7pH8lGwPV5\nDOkm4PKImNnhmFr1aeD8vA/tBJxaUslf3DMzsyIrRQvDzMzazwnDzMyKOGGYmVkRJwwzMyvihGFm\nZkWcMMzMrIgThg2JpKWS/rvy/F8l/dcKWva5kg5ZEctqsp5DJd0l6dcN5m0j6QpJd0u6RdJ0SRtK\n+rCkb/ezvJ/nG7wh6a/9lBmWbRsMSdcO5WcCJO0p6VZJi+u3UdJX808R/EnS6UOP1oaTE4YN1QvA\nIZLW73QgVZJe0ULxo4GPRsS+dctYDbiCdM+dbSPiLcB3gQ1zkYZfYoqI91Tu/tnSF53ybe+7SoOY\nHwQ+DJxfV2534O0R8SbgTcBYSXsNT5S2InTdzmkjzkvAVOCE+hn1Z9G1s21Je0vqlXSJpHslnSZp\nUv5RoNslbVFZzLvyHUHn5pvW1e7U+rVc/rba3ULzcn8r6VLSt+fr45lYuXnlaXnaF4A9gGmSvlpX\nZRJwY/Vb1BHx24io3UZhjKRf5NbHsrqS7m+UQCWdqfSDNTOB19SV/4qkW4BDJW2ZlztL0m9qt3DI\nr+cZkm7Ir9tyLZR8q5k7Ks+Xtfhyy+Er+XWbm7/tjqTVJV2Yz/ovBlav1H+XpBtz6+oiSWs0irka\nQ0Q8FBF3snyyDGB1SauT7iG1CvBo/TbYyNU1Nx+0ESuA7wB3NDjgNipbsyOwHfAM8H/AORHxNqVf\nvvsULyegzSPirZK2Jt0OYyvS2eszufyqwA35IAywC7BDRDxUXbGkTYCv5PnPkG5NfVBEfEnSPqQf\nU5pdF++bgFsH2J6dSLdFXwzcLelbEbGABq2KfHDfJiLemGO5C5hWKfJEbsEg6VfAsRFxn9IPI32P\ndL8fgI0jYpykN5LuPXZxg7gGatW8Ir9u+wOTSfeh+gTwt4jYQdKbgT/kODYAPg/sGxGLJP076X05\npT7mEhFxk6ReYGGedGZE3F1a3zrPCcOGLCKek/QD0i+oLSqsNisiHgOQdB/pnlqQfj2xp1Lux3kd\n9+Zy25Fu9vZmSYflMmsD25AO3DfXJ4vsrcC1EfFUXuf5wF68fMPHwdxE8de1W6JLugvYnHTH4UbL\n2hO4MG/LQknX1M2/KC9nTeDtwE8k1Zbzykq5S/Iy5kh6Da2rJZhbc7yQXocz8nLvkHR7nr4bsD0p\nISvHcWN9zKVyst8OeC3pNfqVpKsi4oZBbId1gBOGrShnkM5Mz61Me4nc7ZkPOKtW5r1Qeby08nwp\nfffL6tmy8nMBn4qIq6sBSNob+NsAMbaaFP4E7D3A/Oo2LGFon6da3KOAp/PvFDRbZ6PteQmojt+s\nXje/Vn+geFX5PzMijuin3ECvdSP/BNwUEYsAlG4SujvghNElPIZhQyWAiHia1Bo4ujLvAaDWZTGB\nvmfKpQ5TshXpl8LuBn4JfFLp9yxqVzKt0WQ5NwN7SVo/D4hPBHqb1LkA2D1335DXtaekHVqIv3bw\n/S3wgTz+sgnwjkaFI+KvwP2Slo0LSNqxybKrHgU2lLSe0qD9ewpi/C3pzr1IehOpuxDSnVjH5de+\ndlvvbQqW11+MDwF7S3qFpFeSknHX3KHZnDBs6KotgG8AG1SmnUM6QMwmdW/0d0Y6UJ/7Q6SD/RWk\nfv0XSb8DfRfwhzzAexZ9z6qXX0HEn0m/KtYLzCZ1if18oPVHxN9JB9xP54HtO0n9/Y812YblHkfE\nDOBeUqvl/9G3a6d+/UcAR+cB/TuBg/opt1zcEfES8EXS7638kr4H5P5e5+8Ba0n6E2lc45a8rCeA\njwAX5m6qG4FtmywLSW9R+rmBQ4GzKoPwPyWNV91Beg9mR8QV/S3HRh7f3tzMzIq4hWFmZkWcMMzM\nrIgThpmZFXHCMDOzIk4YZmZWxAnDzMyKOGGYmVkRJwwzMyvy/wGSuK1lc0sHdAAAAABJRU5ErkJg\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "biasedPmf = BiasPmf(pmf)\n", + "thinkplot.Pmf(biasedPmf)\n", + "thinkplot.Config(xlabel='Number of Children under 18', ylabel='Probability', \n", + " title='Biased PMF of Number of Children Under 18 in Household')\n", + "thinkplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So if you are a child and you are being asked how many children under 18 live in this household, the highest percentage of respondent's children would say around 2, the next highest being 3 and then 1." + ] }, { "cell_type": "markdown", @@ -121,12 +210,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEZCAYAAAB1mUk3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "thinkplot.Pmfs([pmf, biasedPmf])\n", + "thinkplot.Config(xlabel='Number of Children under 18', ylabel='Probability', \n", + " title='PMF of Number of Children Under 18 in Household')\n", + "thinkplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I am having some trouble figuring out how to do legends in thinkplot but the darker blue line is the actual PMF and the lighter blue purple line is the biased, observed PMF." + ] }, { "cell_type": "markdown", @@ -137,12 +258,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of actual Pmf 1.02420515504\n", + "Mean of biased Pmf 2.40367910066\n" + ] + } + ], + "source": [ + "print \"Mean of actual Pmf\", pmf.Mean()\n", + "print \"Mean of biased Pmf\", biasedPmf.Mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a pretty large and significant difference in the mean between the biased and the actual pmfs. I guess this means that if you ask the respondents with children under 18 in their household, the children will on average observe 2.4 children (including themselves) in the household. But the actual number of children under 18 in the household is only about 1." + ] }, { "cell_type": "markdown", @@ -161,12 +301,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], - "source": [] + "source": [ + "import nsfg\n", + "\n", + "df = nsfg.ReadFemPreg()\n", + "pregMap = nsfg.MakePregMap(df)\n", + "res = []\n", + "for key in pregMap:\n", + " babies = pregMap[key]\n", + " if len(babies) >= 2:\n", + " res.append(df.iloc[babies[0]].prglngth - df.iloc[babies[1]].prglngth)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So nsfg.MakePregMap returns a dictionary where the keys are all respondents and the values are lists where each element is an index to a recorded pregnancy in the dataset. I loop through all of the respondents and I check if they have had 2 or more children. If they have, I find their first pregnancy (which is the first index in the list) and their second pregnancy (the second index in the list) and I subtract those. That means that a positive value in res, which stores all of the differences, means that the first baby came later than the second baby and a negative value means the first baby came before the second baby." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEZCAYAAACEkhK6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pmf = thinkstats2.Pmf(res)\n", + "thinkplot.Pmf(pmf)\n", + "thinkplot.Config(xlabel='Difference in weeks between first child and second child', ylabel='Probability', \n", + " title='PMF of Difference in weeks between 1st and 2nd child',\n", + " axis=[-10, 10, 0, 0.35])\n", + "thinkplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If I don't restrict the axes in the plot above, the plot fans out to values of +/- 40 which doesn't make sense to me so I restrict the x-axis which is the difference in weeks to +/- 10 weeks since I think it is a reasonable assumption that babies come within 10 weeks of each other, regardless of birth order. We see that primarily, there is no real difference between the first and second baby pregnancy lengths but there is a slightly higher probability that a first baby will be born around 1-3 weeks later than the second baby.\n", + "\n", + "My approach here could definitely be improved. One reason that I think the plot fans out to +/- 40 would be if prglngth is 0 which probably encodes something or should be cleaned to represent the median." + ] }, { "cell_type": "markdown", diff --git a/ThinkStats2/chap04ex.ipynb b/ThinkStats2/chap04ex.ipynb index 7ddb94b..12787bf 100644 --- a/ThinkStats2/chap04ex.ipynb +++ b/ThinkStats2/chap04ex.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": { "collapsed": false }, @@ -33,12 +33,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], - "source": [] + "source": [ + "import thinkstats2\n", + "live = preg[preg.outcome == 1]\n", + "cdf = thinkstats2.Cdf(live.totalwgt_lb)" + ] }, { "cell_type": "markdown", @@ -49,12 +53,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0gCDQZI0wGCQJA0wGCRJAwwGSdKA/w/OfL7gQxQHCwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import thinkplot\n", + "thinkplot.Cdf(cdf)\n", + "thinkplot.show(xlabel='weight (lbs)', ylabel='CDF')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This CDF shows that about 10% of live babies are born at 6 or under pounds and 80% of babies are born at 8 or under pounds. The mode appears to be around 7 pounds or so as that is where the CDF grows the fastest." + ] }, { "cell_type": "markdown", @@ -65,12 +100,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The probability of my birth weight at 8.5 pounds is 0.840672715202\n" + ] + } + ], + "source": [ + "#weight: 8.5 pounds at birth\n", + "print \"The probability of my birth weight at 8.5 pounds is\", cdf[8.5]" + ] }, { "cell_type": "markdown", @@ -81,12 +127,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The probability of my birght weight at 8.5 pounds among children not born first is 0.823529411765\n" + ] + } + ], + "source": [ + "#am a second child\n", + "others = live[live.birthord != 1]\n", + "others_cdf = thinkstats2.Cdf(others.totalwgt_lb)\n", + "print \"The probability of my birght weight at 8.5 pounds among children not born first is\", others_cdf[8.5]" + ] }, { "cell_type": "markdown", @@ -97,12 +156,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The percentile rank of my birth weight among live babies is 84.0672715202\n" + ] + } + ], + "source": [ + "print \"The percentile rank of my birth weight among live babies is\", cdf.PercentileRank(8.5)" + ] }, { "cell_type": "markdown", @@ -113,12 +182,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The median birth weight is 7.375 lbs\n" + ] + } + ], + "source": [ + "print \"The median birth weight is\", cdf.Value(0.5), \"lbs\"" + ] }, { "cell_type": "markdown", @@ -129,12 +208,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The interquartile range (IQR) is between 6.5 lbs and 8.125 lbs\n" + ] + } + ], + "source": [ + "print \"The interquartile range (IQR) is between\", cdf.Percentile(25), \"lbs\", \"and\", cdf.Percentile(75), \"lbs\"" + ] }, { "cell_type": "markdown", @@ -145,12 +234,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "7.0625" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cdf.Random()" + ] }, { "cell_type": "markdown", @@ -161,12 +263,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], - "source": [] + "source": [ + "samples = cdf.Sample(100)" + ] }, { "cell_type": "markdown", @@ -177,12 +281,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZJCQJI1kkJAkjWSQkCSN9P8BVFc/YumCOHkAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ranks = [cdf.PercentileRank(x) for x in samples]\n", + "rank_cdf = thinkstats2.Cdf(ranks)\n", + "thinkplot.Cdf(rank_cdf)\n", + "thinkplot.Show(xlabel='percentile rank', ylabel='CDF')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the CDF is a straight line, this means that the distribution is uniform. This line means that roughly 20% of the samples are below the 20th percentile, 40% of the sample is below the 40th percentile, etc. This means that the distribution of the percentile ranks is uniform even though the cdf has a distinctive shape." + ] }, { "cell_type": "markdown", @@ -193,12 +329,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAEPCAYAAACUb2mtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXgMRXtmLrtIAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import random\n", + "\n", + "rands = [random.random() for x in range(0,1000)]\n", + "pmf = thinkstats2.Pmf(rands)\n", + "thinkplot.Pmf(pmf)\n", + "thinkplot.Config(xlabel='Random Number', ylabel='Probability', \n", + " title='')\n", + "thinkplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I had to look at the solutions for these exercises for help in interpreting the above graph which doesn't really show much. Manipulating the line width shows a square wave which basically means for a random number, the probability is that it either is in rands or it isn't and since each number can only appear at most once, the probability is 1/1000 which is 0.0010 if it is in rands and 0 if it isn't." + ] }, { "cell_type": "markdown", @@ -209,12 +381,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9P+DJZ+nliAGSAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cdf = thinkstats2.Cdf(rands)\n", + "thinkplot.Cdf(cdf)\n", + "thinkplot.show(xlabel='Random Number', ylabel='CDF')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again we see that since the CDF is a straight line, the distribution is uniform and that 20% of the random numbers are 0.2 or below in value and so on. This intuitively makes sense given that we are generating random numbers between 0 and 1." + ] }, { "cell_type": "markdown", diff --git a/ThinkStats2/chap07ex.ipynb b/ThinkStats2/chap07ex.ipynb index afad1c2..15f48de 100644 --- a/ThinkStats2/chap07ex.ipynb +++ b/ThinkStats2/chap07ex.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -26,14 +26,201 @@ "Using data from the NSFG, make a scatter plot of birth weight versus mother’s age. Plot percentiles of birth weight versus mother’s age. Compute Pearson’s and Spearman’s correlations. How would you characterize the relationship between these variables? " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is the scatter plot of birth weight vs mother's age." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEPCAYAAACgFqixAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import nsfg\n", + "import thinkplot\n", + "df = nsfg.ReadFemPreg()\n", + "\n", + "weights = df.totalwgt_lb\n", + "agepreg = df.agepreg\n", + "\n", + "thinkplot.Scatter(weights, agepreg)\n", + "thinkplot.Show(xlabel='Birth weight (lb)', ylabel='Age (years)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There does not appear to be a clear relationship between birth weight and mother's age from this scatter plot." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is a plot of the percentiles of birth weight vs. mother's age." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "//anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", + " warnings.warn(\"No labelled objects found. \"\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import thinkstats2\n", + "\n", + "df = df.dropna(subset=['agepreg', 'totalwgt_lb'])\n", + "bins = np.arange(5, 50, 5)\n", + "indices = np.digitize(df.agepreg, bins)\n", + "groups = df.groupby(indices)\n", + "\n", + "for i, group in groups:\n", + " ages = [group.agepreg.mean() for i, group in groups]\n", + " cdfs = [thinkstats2.Cdf(group.totalwgt_lb) for i, group in groups]\n", + " \n", + " for percent in [75, 50, 25]:\n", + " weights = [cdf.Percentile(percent) for cdf in cdfs]\n", + " label = '%dth' % percent\n", + " thinkplot.Plot(ages, weights)\n", + "\n", + "thinkplot.Show(xlabel=\"Mother's Age\", ylabel='Birth weights (lb)')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the plot above, the top line is the 75th percentile, the middle line is the 50th percentile, and the last line is the 25th percentile for birth weight. What we see here is that in the 10 bins we created for the mother's age, the birth weight of the baby by percentile can occur at all ages. That is to say, other than the dip after 40 that occurs in all percentiles, the age of the mother does not contribute to the birth weight of the baby as we see a flat line for all three percentiles along the age axis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is the calculated Pearson's correlation." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0688339703541\n" + ] + } + ], + "source": [ + "print thinkstats2.Corr(df.totalwgt_lb, df.agepreg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Pearson's correlation is very close to 0 which implies that there is no relationship between these variables. However, Pearson's correlation only measures linear relationships so it could be the case that there is a nonlinear relationship. In addition, Pearson's correlation is not robust to the presence of outliers and skewed distributions so this could also be the cause of the calculated correlation being close to 0." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is the calculated Spearman's correlation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0946100410966\n" + ] + } + ], + "source": [ + "print thinkstats2.SpearmanCorr(df.totalwgt_lb, df.agepreg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Spearman's correlation is a value that is also very close to 0 but is 0.03 higher than the Pearson's correlation. Since Spearman's correlation is robust to the presence of outliers and skewed distributions, we could attribute the difference between the correlations to these factors. This value is also close to 0 so it would appear that these variables are not correlated or are only very slightly positively correlated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the calculation of the Pearson's and Spearman's correlations, the percentile plot, and the scatter plot, I do not think there is a relationship between the mother's age and the baby's birth weight." + ] } ], "metadata": { diff --git a/ThinkStats2/chap09_companion_part1.ipynb b/ThinkStats2/chap09_companion_part1.ipynb index 172825e..b74951c 100644 --- a/ThinkStats2/chap09_companion_part1.ipynb +++ b/ThinkStats2/chap09_companion_part1.ipynb @@ -19,18 +19,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "137\n" + ] + } + ], "source": [ "from random import choice\n", "\n", "def simulate_fair_coin_flips(n):\n", " \"\"\" Return the number of heads that occur in n flips of a\n", " fair coin p(heads) = 0.5 \"\"\"\n", - " pass\n", + " count = 0\n", + " for _ in range(n):\n", + " if choice('HT') == 'H':\n", + " count += 1\n", + " return count\n", "\n", "print simulate_fair_coin_flips(250)" ] @@ -44,18 +56,45 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 17, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "import thinkstats2\n", "import thinkplot\n", "import matplotlib.pyplot as plt\n", "\n", - "# your implementation here (imports included for convenience)" + "# your implementation here (imports included for convenience)\n", + "res = []\n", + "for _ in range(1000):\n", + " res.append(simulate_fair_coin_flips(240))\n", + "\n", + "cdf = thinkstats2.Cdf(res)\n", + "thinkplot.Cdf(cdf)\n", + "thinkplot.show(xlabel='No of Heads in 240 coin flips', ylabel='CDF')" ] }, { @@ -71,12 +110,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p-value: 0.4\n" + ] + } + ], + "source": [ + "print \"p-value:\", float(format(100 - cdf.PercentileRank(139), '.2f'))" + ] }, { "cell_type": "markdown", @@ -89,16 +138,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "125\n" + ] + } + ], "source": [ "def simulate_fair_coin_flips_two_sided(n):\n", " \"\"\" Return the number of heads or tails, whichever is larger,\n", " that occur in n flips of a fair coin p(heads) = 0.5 \"\"\"\n", - " pass\n", + " count_heads = 0\n", + " count_tails = 0\n", + " for _ in range(n):\n", + " if choice('HT') == 'H':\n", + " count_heads += 1\n", + " else:\n", + " count_tails += 1\n", + " return max(count_heads, count_tails)\n", "\n", "print simulate_fair_coin_flips_two_sided(250)" ] @@ -112,12 +176,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": true }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZJKQJHUySUiSOpkkJEmd/j8eW7ouORRd8QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "res = []\n", + "for _ in range(1000):\n", + " res.append(simulate_fair_coin_flips_two_sided(250))\n", + "\n", + "cdf = thinkstats2.Cdf(res)\n", + "thinkplot.Cdf(cdf)\n", + "thinkplot.show(xlabel='No of Most Common Outcome in 250 coin flips', ylabel='CDF')" + ] }, { "cell_type": "markdown", @@ -128,12 +221,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p-value: 5.8\n" + ] + } + ], + "source": [ + "print \"p-value:\", float(format(100 - cdf.PercentileRank(139), '.2f'))" + ] }, { "cell_type": "markdown", @@ -145,7 +248,9 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "Using the same cdf, if we compute the percentile rank of 180, we get 100% which means that our calculation of the p-value would be 0. The reason is because in our 1000 trials, we don't have any trials where the most common outcome exceeds 155. You would need to increase the number of trials in order to get a percentile rank of 180 that might not be 100% because you need trials where you actually flipped heads 180 times out of the 250." + ] }, { "cell_type": "markdown", @@ -160,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -290,7 +395,7 @@ "4 0 373450 8.0500 NaN S " ] }, - "execution_count": 85, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -312,16 +417,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observed age difference 2.81093577935\n" + ] + } + ], "source": [ "def compute_age_diff(data):\n", " \"\"\" Compute the absolute value of the difference in mean age\n", " between men and women on the titanic \"\"\"\n", - " pass\n", + " men_only = data[data.Sex == 'male']\n", + " women_only = data[data.Sex == 'female']\n", + " return abs(men_only.Age.mean() - women_only.Age.mean())\n", "\n", "observed_age_diff = compute_age_diff(data)\n", "print \"observed age difference\", observed_age_diff" @@ -338,18 +453,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2.649336479663038" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from numpy.random import permutation\n", "\n", "def shuffle_ages(data):\n", " \"\"\" Return a new dataframe (don't modify the original) where\n", " the values in the Age column have been randomly permuted. \"\"\"\n", - " pass\n", + " res = data\n", + " res.Age = permutation(res.Age).astype(int)\n", + " return res\n", "\n", "compute_age_diff(shuffle_ages(data))" ] @@ -363,12 +491,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p-value: 0.6\n" + ] + } + ], + "source": [ + "res = []\n", + "for _ in range(1000):\n", + " res.append(compute_age_diff(shuffle_ages(data)))\n", + "\n", + "cdf = thinkstats2.Cdf(res)\n", + "print \"p-value:\", float(format(100 - cdf.PercentileRank(observed_age_diff), '.2f'))" + ] }, { "cell_type": "markdown", @@ -385,7 +528,9 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "The average age of male versus female passengers on the Titanic was different. Since the p-value we calculated was around 1%, we know that the difference in average age between genders is statistically significant because we tested our null hypothesis by shuffling the ages and the percentage of the shuffled data that is equal to or greater than our observed value is under 1%, which means that this effect is not due to chance. So it follows that the average ages of men vs women on the titanic was actually different, not just due to chance." + ] } ], "metadata": { diff --git a/ThinkStats2/chap10ex.ipynb b/ThinkStats2/chap10ex.ipynb index a530c90..d0c0ee2 100644 --- a/ThinkStats2/chap10ex.ipynb +++ b/ThinkStats2/chap10ex.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -26,14 +26,228 @@ "Using the data from the BRFSS, compute the linear least squares fit for log(weight) versus height. How would you best present the estimated parameters for a model like this where one of the variables is log-transformed? If you were trying to guess someone’s weight, how much would it help to know their height? " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code below just imports the dataframe and drops any rows that have NaN in weight or height since we cannot use those records." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], - "source": [] + "source": [ + "import brfss\n", + "df = brfss.ReadBrfss()\n", + "df = df.dropna(subset=['wtkg2', 'htm3'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that our data is cleaned, the cell below takes the log of the weight and computes the linear least squares fit for height as a function of the log of the weight." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intercept 68.0232589936\n", + "Slope 23.2493214367\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import thinkstats2\n", + "\n", + "xs = np.log(df.wtkg2)\n", + "ys = df.htm3\n", + "inter, slope = thinkstats2.LeastSquares(xs, ys)\n", + "print \"Intercept\", inter\n", + "print \"Slope\", slope" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our results can be interpreted as:\n", + "\\begin{align}\n", + " height &= 23.249 \\times log(weight) + 68.0232\n", + "\\end{align}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the reading, Allen makes the point that we don't really know what the intercept means. This intercept means that at a weight of 0, the height is 68.0232 units but log(0) is undefined. So instead, below we take the mean of log(weight) and apply our estimated parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of log(weight) 4.34132797232\n", + "Intercept at mean of log(weight) 168.956188484\n", + "Mean of height 168.956188484\n" + ] + } + ], + "source": [ + "print 'Mean of log(weight)', np.mean(xs)\n", + "print 'Intercept at mean of log(weight)', slope*np.mean(xs) + inter\n", + "print 'Mean of height', np.mean(ys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What we see above is that the intercept at the mean of log(weight) perfectly predicts the height in this dataset. This means our model is accurate close to the mean." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now given that one of our variables is log-transformed, we have another way to represent the equation shown above which is:\n", + "\\begin{align}\n", + "e^{height} &= e^{23.249} \\times weight + e^{68.0232}\n", + "\\end{align}\n", + "\n", + "We can then use weight instead of the log of weight to calculate $e^{height}$. Below we represent the slope and the intercept when weight is 0 in this way." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slope expressed as exponential of height 12504087610.8\n", + "Intercept expressed as exponential of height 3.48438408945e+29\n" + ] + } + ], + "source": [ + "#another way to present the parameters is to exponentiate, gives you: e^height = e^slope * weight + e^b\n", + "print 'Slope expressed as exponential of height', np.exp(slope)\n", + "print 'Intercept expressed as exponential of height', np.exp(inter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the size of these values and the fact that we have probably lost some accuracy in our estimations, I think it is better to stick with the equation in its original form and use the log of weight." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to determine how much it would help to know a person's height in guessing their weight, I wanted to know how sampling error affects the calculated fit. I used the SamplingDistributions function given in the book and produced the 90% confidence interval for each height. To answer this question, I used height as the explanatory variable and log(weight) as the dependent variable." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def SamplingDistributions(df, iters=101):\n", + " t = []\n", + " for _ in range(iters):\n", + " sample = thinkstats2.ResampleRows(df)\n", + " weights = np.log(sample.wtkg2)\n", + " heights = sample.htm3\n", + " estimates = thinkstats2.LeastSquares(heights, weights)\n", + " t.append(estimates)\n", + " inters, slopes = zip(*t)\n", + " return inters, slopes\n", + "\n", + "inters, slopes = SamplingDistributions(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I then take the calculated intercepts and slopes and use the PlotConfidenceIntervals function. The plot is below and the x-axis is height and the y-axis is log(weight)." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import thinkplot\n", + "\n", + "def PlotConfidenceIntervals(xs, inters, slopes,\n", + " percent=90, **options):\n", + " fys_seq = []\n", + " for inter, slope in zip(inters, slopes):\n", + " fxs, fys = thinkstats2.FitLine(xs, inter, slope)\n", + " fys_seq.append(fys)\n", + " p = (100 - percent) / 2\n", + " percents = p, 100 - p\n", + " low, high = thinkstats2.PercentileRows(fys_seq, percents)\n", + " thinkplot.FillBetween(fxs, low, high, **options)\n", + " thinkplot.label()\n", + "\n", + "\n", + "PlotConfidenceIntervals(df.htm3, inters, slopes)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What this plot shows us is that there isn't a lot of variability due to the sampling error. We see that closest to the mean height of 168, there is a lot less variability as the filled polygon is very faint. So for heights close to the mean, we should be accurately able to guess the log(weight). As we move away from the mean, there still isn't a large difference between the 5th and 95th percentiles so we should still be able to reasonably approximate log(weight). We are more likely to be wrong or more inaccurate in our guess the further we get from the mean but having height should give us a small range of weights to guess within." + ] }, { "cell_type": "markdown", @@ -47,7 +261,9 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "This half chapter took me a long time to get through because I feel I do not have a very strong or intuitive knowledge of statistics. I do not really know how to interpret percentile graphs like the one in the reading and although I've used the code in the book, I am not 100% confident on how the code works and how to interpret the result." + ] }, { "cell_type": "markdown", diff --git a/ThinkStats2/chap11ex.ipynb b/ThinkStats2/chap11ex.ipynb index c8cdff9..d0531ac 100644 --- a/ThinkStats2/chap11ex.ipynb +++ b/ThinkStats2/chap11ex.ipynb @@ -27,13 +27,23 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "source": [ + "Your co-worker's age\n", + "\n", + "Whether or not it is your co-worker's first baby\n", + "\n", + "Your co-worker's race\n", + "\n", + "The sex of your co-worker's baby\n", + "\n", + "The season your co-worker's baby was concieved in\n", + "\n", + "How many babies your co-worker is having" + ] }, { "cell_type": "markdown", @@ -47,7 +57,11 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "I am not entirely clear about the part where Allen joins the two NSFG files. I understand why he did it but I do not understand why he can't concatenate the race field into one, if you had just cleaned up the data in one of the tables to match the other one before joining. Is this something that is not possible?\n", + "\n", + "I am not also not sure at what point an estimated slope or intercept becomes statistically significant." + ] }, { "cell_type": "markdown", diff --git a/datasets/titanic_train.csv b/datasets/titanic_train.csv new file mode 100644 index 0000000..63b68ab --- /dev/null +++ b/datasets/titanic_train.csv @@ -0,0 +1,892 @@ +PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S +2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C +3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S +4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S +5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S +6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q +7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S +8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S +9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S +10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C +11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S +12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S +13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S +14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S +15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S +16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S +17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q +18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S +19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S +20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C +21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S +22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S +23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q +24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S +25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S +26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S +27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C +28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S +29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q +30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S +31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C +32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C +33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q +34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S +35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C +36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S +37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C +38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S +39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S +40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C +41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S +42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S +43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C +44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C +45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q +46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S +47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q +48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q +49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C +50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S +51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S +52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S +53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C +54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S +55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C +56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S +57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S +58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C +59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S +60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S +61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C +62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28, +63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S +64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S +65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C +66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C +67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S +68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S +69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S +70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S +71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S +72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S +73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S +74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C +75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S +76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S +77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S +78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S +79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S +80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S +81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S +82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S +83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q +84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S +85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S +86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S +87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S +88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S +89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S +90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S +91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S +92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S +93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S +94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S +95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S +96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S +97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C +98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C +99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S +100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S +101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S +102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S +103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S +104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S +105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S +106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S +107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S +108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S +109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S +110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q +111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S +112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C +113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S +114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S +115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C +116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S +117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q +118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S +119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C +120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S +121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S +122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S +123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C +124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S +125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S +126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C +127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q +128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S +129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C +130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S +131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C +132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S +133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S +134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S +135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S +136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C +137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S +138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S +139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S +140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C +141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C +142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S +143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S +144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q +145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S +146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S +147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S +148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S +149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S +150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S +151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S +152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S +153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S +154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S +155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S +156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C +157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q +158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S +159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S +160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S +161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S +162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S +163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S +164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S +165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S +166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S +167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S +168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S +169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S +170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S +171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S +172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q +173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S +174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S +175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C +176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S +177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S +178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C +179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S +180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S +181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S +182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C +183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S +184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S +185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S +186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S +187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q +188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S +189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q +190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S +191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S +192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S +193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S +194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S +195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C +196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C +197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q +198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S +199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q +200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S +201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S +202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S +203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S +204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C +205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S +206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S +207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S +208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C +209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q +210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C +211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S +212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S +213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S +214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S +215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q +216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C +217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S +218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S +219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C +220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S +221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S +222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S +223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S +224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S +225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S +226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S +227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S +228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S +229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S +230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S +231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S +232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S +233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S +234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S +235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S +236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S +237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S +238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S +239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S +240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S +241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C +242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q +243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S +244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S +245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C +246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q +247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S +248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S +249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S +250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S +251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S +252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S +253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S +254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S +255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S +256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C +257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C +258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S +259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C +260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S +261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q +262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S +263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S +264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S +265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q +266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S +267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S +268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S +269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S +270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S +271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S +272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S +273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S +274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C +275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q +276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S +277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S +278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S +279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q +280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S +281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q +282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S +283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S +284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S +285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S +286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C +287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S +288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S +289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S +290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q +291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S +292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C +293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C +294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S +295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S +296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C +297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C +298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S +299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S +300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C +301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q +302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q +303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S +304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q +305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S +306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S +307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C +308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C +309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C +310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C +311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C +312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C +313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S +314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S +315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S +316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S +317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S +318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S +319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S +320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C +321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S +322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S +323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q +324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S +325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S +326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C +327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S +328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S +329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S +330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C +331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q +332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S +333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S +334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S +335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S +336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S +337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S +338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C +339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S +340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S +341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S +342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S +343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S +344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S +345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S +346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S +347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S +348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S +349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S +350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S +351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S +352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S +353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C +354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S +355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C +356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S +357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S +358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S +359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q +360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q +361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S +362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C +363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C +364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S +365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q +366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S +367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C +368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C +369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q +370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C +371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C +372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S +373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S +374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C +375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S +376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C +377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S +378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C +379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C +380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S +381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C +382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C +383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S +384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S +385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S +386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S +387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S +388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S +389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q +390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C +391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S +392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S +393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S +394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C +395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S +396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S +397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S +398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S +399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S +400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S +401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S +402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S +403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S +404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S +405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S +406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S +407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S +408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S +409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S +410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S +411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S +412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q +413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q +414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S +415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S +416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S +417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S +418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S +419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S +420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S +421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C +422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q +423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S +424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S +425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S +426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S +427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S +428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S +429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q +430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S +431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S +432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S +433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S +434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S +435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S +436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S +437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S +438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S +439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S +440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S +441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S +442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S +443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S +444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S +445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S +446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S +447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S +448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S +449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C +450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S +451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S +452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S +453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C +454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C +455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S +456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C +457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S +458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S +459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S +460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q +461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S +462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S +463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S +464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S +465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S +466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S +467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S +468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S +469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q +470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C +471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S +472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S +473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S +474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C +475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S +476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S +477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S +478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S +479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S +480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S +481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S +482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S +483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S +484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S +485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C +486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S +487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S +488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C +489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S +490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S +491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S +492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S +493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S +494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C +495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S +496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C +497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C +498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S +499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S +500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S +501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S +502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q +503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q +504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S +505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S +506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C +507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S +508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S +509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S +510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S +511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q +512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S +513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S +514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C +515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S +516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S +517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S +518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q +519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S +520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S +521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S +522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S +523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C +524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C +525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C +526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q +527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S +528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S +529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S +530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S +531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S +532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C +533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C +534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C +535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S +536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S +537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S +538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C +539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S +540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C +541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S +542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S +543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S +544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S +545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C +546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S +547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S +548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C +549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S +550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S +551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C +552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S +553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q +554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C +555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S +556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S +557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C +558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C +559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S +560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S +561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q +562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S +563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S +564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S +565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S +566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S +567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S +568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S +569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C +570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S +571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S +572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S +573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S +574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q +575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S +576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S +577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S +578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S +579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C +580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S +581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S +582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C +583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S +584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C +585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C +586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S +587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S +588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C +589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S +590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S +591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S +592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C +593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S +594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q +595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S +596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S +597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S +598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S +599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C +600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C +601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S +602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S +603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S +604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S +605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C +606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S +607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S +608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S +609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C +610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S +611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S +612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S +613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q +614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q +615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S +616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S +617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S +618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S +619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S +620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S +621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C +622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S +623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C +624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S +625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S +626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S +627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q +628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S +629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S +630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q +631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S +632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S +633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C +634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S +635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S +636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S +637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S +638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S +639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S +640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S +641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S +642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C +643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S +644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S +645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C +646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C +647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S +648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C +649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S +650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S +651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S +652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S +653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S +654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q +655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q +656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S +657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S +658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q +659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S +660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C +661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S +662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C +663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S +664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S +665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S +666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S +667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S +668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S +669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S +670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S +671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S +672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S +673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S +674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S +675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S +676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S +677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S +678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S +679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S +680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C +681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q +682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C +683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S +684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S +685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S +686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C +687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S +688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S +689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S +690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S +691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S +692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C +693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S +694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C +695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S +696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S +697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S +698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q +699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C +700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S +701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C +702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S +703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C +704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q +705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S +706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S +707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S +708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S +709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S +710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C +711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C +712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S +713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S +714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S +715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S +716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S +717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C +718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S +719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q +720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S +721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S +722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S +723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S +724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S +725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S +726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S +727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S +728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q +729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S +730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S +731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S +732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C +733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S +734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S +735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S +736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S +737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S +738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C +739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S +740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S +741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S +742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S +743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C +744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S +745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S +746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S +747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S +748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S +749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S +750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q +751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S +752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S +753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S +754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S +755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S +756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S +757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S +758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S +759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S +760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S +761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S +762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S +763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C +764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S +765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S +766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S +767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C +768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q +769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q +770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S +771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S +772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S +773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S +774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C +775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S +776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S +777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q +778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S +779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q +780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S +781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C +782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S +783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S +784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S +785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S +786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S +787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S +788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q +789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S +790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C +791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q +792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S +793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S +794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C +795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S +796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S +797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S +798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S +799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C +800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S +801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S +802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S +803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S +804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C +805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S +806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S +807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S +808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S +809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S +810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S +811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S +812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S +813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S +814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S +815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S +816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S +817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S +818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C +819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S +820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S +821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S +822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S +823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S +824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S +825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S +826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q +827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S +828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C +829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q +830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28, +831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C +832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S +833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C +834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S +835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S +836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C +837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S +838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S +839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S +840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C +841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S +842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S +843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C +844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C +845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S +846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S +847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S +848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C +849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S +850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C +851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S +852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S +853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C +854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S +855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S +856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S +857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S +858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S +859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C +860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C +861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S +862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S +863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S +864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S +865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S +866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S +867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C +868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S +869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S +870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S +871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S +872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S +873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S +874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S +875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C +876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C +877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S +878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S +879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S +880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C +881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S +882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S +883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S +884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S +885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S +886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q +887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S +888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S +889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/inclass/day02/Bikeshare Basic Explorations.ipynb b/inclass/day02/Bikeshare Basic Explorations.ipynb index c12af34..b638194 100644 --- a/inclass/day02/Bikeshare Basic Explorations.ipynb +++ b/inclass/day02/Bikeshare Basic Explorations.ipynb @@ -18,11 +18,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " datetime season holiday workingday weather temp atemp \\\n", + "0 2011-01-01 00:00:00 1 0 0 1 9.84 14.395 \n", + "1 2011-01-01 01:00:00 1 0 0 1 9.02 13.635 \n", + "2 2011-01-01 02:00:00 1 0 0 1 9.02 13.635 \n", + "3 2011-01-01 03:00:00 1 0 0 1 9.84 14.395 \n", + "4 2011-01-01 04:00:00 1 0 0 1 9.84 14.395 \n", + "\n", + " humidity windspeed casual registered count \n", + "0 81 0 3 13 16 \n", + "1 80 0 8 32 40 \n", + "2 80 0 5 27 32 \n", + "3 75 0 3 10 13 \n", + "4 75 0 0 1 1 \n", + " season holiday workingday weather temp \\\n", + "count 10886.000000 10886.000000 10886.000000 10886.000000 10886.00000 \n", + "mean 2.506614 0.028569 0.680875 1.418427 20.23086 \n", + "std 1.116174 0.166599 0.466159 0.633839 7.79159 \n", + "min 1.000000 0.000000 0.000000 1.000000 0.82000 \n", + "25% 2.000000 0.000000 0.000000 1.000000 13.94000 \n", + "50% 3.000000 0.000000 1.000000 1.000000 20.50000 \n", + "75% 4.000000 0.000000 1.000000 2.000000 26.24000 \n", + "max 4.000000 1.000000 1.000000 4.000000 41.00000 \n", + "\n", + " atemp humidity windspeed casual registered \\\n", + "count 10886.000000 10886.000000 10886.000000 10886.000000 10886.000000 \n", + "mean 23.655084 61.886460 12.799395 36.021955 155.552177 \n", + "std 8.474601 19.245033 8.164537 49.960477 151.039033 \n", + "min 0.760000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 16.665000 47.000000 7.001500 4.000000 36.000000 \n", + "50% 24.240000 62.000000 12.998000 17.000000 118.000000 \n", + "75% 31.060000 77.000000 16.997900 49.000000 222.000000 \n", + "max 45.455000 100.000000 56.996900 367.000000 886.000000 \n", + "\n", + " count \n", + "count 10886.000000 \n", + "mean 191.574132 \n", + "std 181.144454 \n", + "min 1.000000 \n", + "25% 42.000000 \n", + "50% 145.000000 \n", + "75% 284.000000 \n", + "max 977.000000 \n" + ] + } + ], "source": [ "import pandas as pd\n", "\n", @@ -43,11 +92,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "datetime 0\n", + "season 0\n", + "holiday 0\n", + "workingday 0\n", + "weather 0\n", + "temp 0\n", + "atemp 0\n", + "humidity 0\n", + "windspeed 0\n", + "casual 0\n", + "registered 0\n", + "count 0\n", + "dtype: int64" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data.isnull().sum()" ] @@ -63,12 +135,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "((data.registered + data.casual) == data['count']).mean()" + ] }, { "cell_type": "markdown", @@ -79,12 +164,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + } + ], + "source": [ + "print type(data.count)\n", + "print type(data['count'])" + ] }, { "cell_type": "markdown", @@ -97,11 +194,830 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 986\n", + "1 667\n", + "2 487\n", + "3 438\n", + "4 354\n", + "5 332\n", + "6 269\n", + "7 250\n", + "8 250\n", + "9 230\n", + "10 213\n", + "11 193\n", + "12 190\n", + "13 146\n", + "14 137\n", + "15 146\n", + "16 141\n", + "17 134\n", + "18 128\n", + "19 141\n", + "20 120\n", + "21 116\n", + "22 104\n", + "23 87\n", + "24 95\n", + "25 103\n", + "26 91\n", + "27 124\n", + "28 105\n", + "29 99\n", + "30 87\n", + "31 105\n", + "32 64\n", + "33 100\n", + "34 70\n", + "35 67\n", + "36 78\n", + "37 61\n", + "38 65\n", + "39 69\n", + "40 76\n", + "41 56\n", + "42 71\n", + "43 47\n", + "44 60\n", + "45 47\n", + "46 47\n", + "47 51\n", + "48 59\n", + "49 54\n", + "50 52\n", + "51 57\n", + "52 41\n", + "53 54\n", + "54 56\n", + "55 56\n", + "56 51\n", + "57 45\n", + "58 46\n", + "59 50\n", + "60 37\n", + "61 53\n", + "62 33\n", + "63 45\n", + "64 33\n", + "65 35\n", + "66 42\n", + "67 31\n", + "68 39\n", + "69 45\n", + "70 40\n", + "71 37\n", + "72 35\n", + "73 31\n", + "74 35\n", + "75 38\n", + "76 29\n", + "77 27\n", + "78 38\n", + "79 32\n", + "80 28\n", + "81 22\n", + "82 29\n", + "83 26\n", + "84 22\n", + "85 30\n", + "86 28\n", + "87 16\n", + "88 32\n", + "89 25\n", + "90 21\n", + "91 28\n", + "92 13\n", + "93 12\n", + "94 18\n", + "95 19\n", + "96 18\n", + "97 19\n", + "98 19\n", + "99 15\n", + "100 18\n", + "101 18\n", + "102 21\n", + "103 14\n", + "104 15\n", + "105 12\n", + "106 8\n", + "107 13\n", + "108 22\n", + "109 14\n", + "110 11\n", + "111 17\n", + "112 15\n", + "113 16\n", + "114 9\n", + "115 11\n", + "116 10\n", + "117 10\n", + "118 7\n", + "119 11\n", + "120 17\n", + "121 11\n", + "122 9\n", + "123 9\n", + "124 7\n", + "125 9\n", + "126 4\n", + "127 7\n", + "128 16\n", + "129 10\n", + "130 6\n", + "131 8\n", + "132 9\n", + "133 8\n", + "134 8\n", + "135 5\n", + "136 2\n", + "137 4\n", + "138 8\n", + "139 10\n", + "140 4\n", + "141 7\n", + "142 10\n", + "143 7\n", + "144 8\n", + "145 8\n", + "146 5\n", + "147 7\n", + "148 12\n", + "149 5\n", + "150 8\n", + "151 5\n", + "152 3\n", + "153 7\n", + "154 3\n", + "155 6\n", + "156 7\n", + "157 2\n", + "158 4\n", + "159 5\n", + "160 3\n", + "161 8\n", + "162 3\n", + "163 4\n", + "164 10\n", + "165 4\n", + "166 6\n", + "167 6\n", + "168 7\n", + "169 4\n", + "170 7\n", + "171 5\n", + "172 2\n", + "173 4\n", + "174 5\n", + "175 7\n", + "176 5\n", + "177 9\n", + "178 3\n", + "179 7\n", + "180 5\n", + "181 5\n", + "182 3\n", + "183 7\n", + "184 6\n", + "185 4\n", + "186 6\n", + "187 8\n", + "188 2\n", + "189 2\n", + "190 3\n", + "191 4\n", + "192 2\n", + "193 5\n", + "194 3\n", + "195 9\n", + "196 4\n", + "197 3\n", + "198 5\n", + "199 3\n", + "200 2\n", + "201 2\n", + "202 1\n", + "203 5\n", + "204 5\n", + "205 4\n", + "206 3\n", + "207 2\n", + "208 3\n", + "209 2\n", + "210 1\n", + "212 2\n", + "213 5\n", + "214 2\n", + "215 2\n", + "216 1\n", + "217 2\n", + "218 6\n", + "219 5\n", + "220 3\n", + "221 4\n", + "222 5\n", + "223 1\n", + "224 1\n", + "225 5\n", + "226 5\n", + "227 5\n", + "228 4\n", + "229 4\n", + "230 1\n", + "232 5\n", + "233 3\n", + "234 1\n", + "235 4\n", + "236 4\n", + "237 3\n", + "238 3\n", + "239 1\n", + "240 7\n", + "241 1\n", + "242 3\n", + "243 2\n", + "244 1\n", + "245 1\n", + "246 1\n", + "247 1\n", + "248 4\n", + "249 2\n", + "250 2\n", + "251 4\n", + "253 1\n", + "254 4\n", + "255 2\n", + "256 3\n", + "257 2\n", + "258 2\n", + "259 1\n", + "260 4\n", + "262 3\n", + "263 1\n", + "264 1\n", + "265 1\n", + "266 2\n", + "267 2\n", + "268 2\n", + "269 3\n", + "272 1\n", + "274 1\n", + "275 2\n", + "276 2\n", + "279 3\n", + "280 1\n", + "282 1\n", + "283 1\n", + "284 1\n", + "286 3\n", + "287 2\n", + "288 1\n", + "289 1\n", + "291 1\n", + "292 1\n", + "293 2\n", + "294 1\n", + "295 3\n", + "297 1\n", + "298 1\n", + "299 1\n", + "304 1\n", + "308 1\n", + "310 1\n", + "311 1\n", + "312 1\n", + "317 1\n", + "320 1\n", + "321 1\n", + "325 1\n", + "326 1\n", + "327 1\n", + "331 1\n", + "332 1\n", + "350 1\n", + "352 1\n", + "354 1\n", + "355 1\n", + "356 1\n", + "357 1\n", + "361 1\n", + "362 1\n", + "367 1\n", + "Name: casual, dtype: int64\n", + "0 15\n", + "1 135\n", + "2 150\n", + "3 195\n", + "4 190\n", + "5 177\n", + "6 155\n", + "7 126\n", + "8 114\n", + "9 114\n", + "10 72\n", + "11 87\n", + "12 57\n", + "13 52\n", + "14 67\n", + "15 54\n", + "16 56\n", + "17 46\n", + "18 51\n", + "19 66\n", + "20 53\n", + "21 52\n", + "22 59\n", + "23 61\n", + "24 51\n", + "25 40\n", + "26 47\n", + "27 43\n", + "28 46\n", + "29 39\n", + "30 49\n", + "31 44\n", + "32 32\n", + "33 38\n", + "34 43\n", + "35 31\n", + "36 28\n", + "37 30\n", + "38 36\n", + "39 39\n", + "40 31\n", + "41 38\n", + "42 26\n", + "43 44\n", + "44 31\n", + "45 28\n", + "46 33\n", + "47 34\n", + "48 45\n", + "49 42\n", + "50 37\n", + "51 30\n", + "52 27\n", + "53 39\n", + "54 33\n", + "55 40\n", + "56 28\n", + "57 33\n", + "58 35\n", + "59 29\n", + "60 28\n", + "61 34\n", + "62 34\n", + "63 32\n", + "64 42\n", + "65 30\n", + "66 33\n", + "67 29\n", + "68 35\n", + "69 26\n", + "70 33\n", + "71 30\n", + "72 36\n", + "73 30\n", + "74 34\n", + "75 23\n", + "76 33\n", + "77 28\n", + "78 32\n", + "79 30\n", + "80 34\n", + "81 35\n", + "82 29\n", + "83 34\n", + "84 33\n", + "85 30\n", + "86 40\n", + "87 29\n", + "88 40\n", + "89 34\n", + "90 26\n", + "91 31\n", + "92 34\n", + "93 29\n", + "94 34\n", + "95 48\n", + "96 35\n", + "97 30\n", + "98 32\n", + "99 37\n", + "100 31\n", + "101 27\n", + "102 34\n", + "103 33\n", + "104 41\n", + "105 30\n", + "106 31\n", + "107 33\n", + "108 41\n", + "109 27\n", + "110 35\n", + "111 34\n", + "112 37\n", + "113 20\n", + "114 33\n", + "115 42\n", + "116 37\n", + "117 23\n", + "118 32\n", + "119 35\n", + "120 37\n", + "121 32\n", + "122 24\n", + "123 28\n", + "124 26\n", + "125 22\n", + "126 32\n", + "127 36\n", + "128 32\n", + "129 23\n", + "130 41\n", + "131 22\n", + "132 28\n", + "133 32\n", + "134 35\n", + "135 27\n", + "136 27\n", + "137 36\n", + "138 25\n", + "139 32\n", + "140 17\n", + "141 38\n", + "142 41\n", + "143 23\n", + "144 38\n", + "145 23\n", + "146 24\n", + "147 27\n", + "148 34\n", + "149 24\n", + "150 35\n", + "151 25\n", + "152 27\n", + "153 29\n", + "154 26\n", + "155 32\n", + "156 37\n", + "157 26\n", + "158 31\n", + "159 23\n", + "160 20\n", + "161 34\n", + "162 33\n", + "163 32\n", + "164 22\n", + "165 22\n", + "166 25\n", + "167 22\n", + "168 37\n", + "169 30\n", + "170 29\n", + "171 24\n", + "172 19\n", + "173 22\n", + "174 26\n", + "175 29\n", + "176 39\n", + "177 26\n", + "178 34\n", + "179 22\n", + "180 23\n", + "181 20\n", + "182 19\n", + "183 30\n", + "184 30\n", + "185 20\n", + "186 29\n", + "187 26\n", + "188 27\n", + "189 29\n", + "190 24\n", + "191 22\n", + "192 27\n", + "193 19\n", + "194 20\n", + "195 16\n", + "196 21\n", + "197 23\n", + "198 23\n", + "199 18\n", + "200 20\n", + "201 17\n", + "202 27\n", + "203 28\n", + "204 24\n", + "205 13\n", + "206 23\n", + "207 29\n", + "208 19\n", + "209 26\n", + "210 20\n", + "211 24\n", + "212 21\n", + "213 17\n", + "214 25\n", + "215 15\n", + "216 18\n", + "217 20\n", + "218 28\n", + "219 16\n", + "220 24\n", + "221 17\n", + "222 20\n", + "223 20\n", + "224 20\n", + "225 23\n", + "226 19\n", + "227 15\n", + "228 19\n", + "229 15\n", + "230 26\n", + "231 13\n", + "232 22\n", + "233 15\n", + "234 18\n", + "235 20\n", + "236 14\n", + "237 16\n", + "238 17\n", + "239 16\n", + "240 22\n", + "241 23\n", + "242 12\n", + "243 19\n", + "244 26\n", + "245 17\n", + "246 15\n", + "247 15\n", + "248 21\n", + "249 11\n", + " ... \n", + "484 5\n", + "485 2\n", + "486 5\n", + "487 3\n", + "488 4\n", + "489 7\n", + "490 2\n", + "491 4\n", + "492 1\n", + "493 3\n", + "494 1\n", + "495 2\n", + "496 1\n", + "497 2\n", + "498 4\n", + "499 1\n", + "500 2\n", + "501 1\n", + "502 1\n", + "503 2\n", + "504 4\n", + "505 3\n", + "506 3\n", + "507 3\n", + "508 3\n", + "509 2\n", + "510 4\n", + "511 1\n", + "512 3\n", + "513 1\n", + "514 4\n", + "515 3\n", + "516 4\n", + "517 2\n", + "518 1\n", + "521 1\n", + "522 1\n", + "523 4\n", + "525 3\n", + "527 2\n", + "529 2\n", + "530 2\n", + "531 2\n", + "532 2\n", + "533 5\n", + "534 3\n", + "536 2\n", + "537 1\n", + "538 1\n", + "539 5\n", + "540 6\n", + "541 1\n", + "542 2\n", + "543 3\n", + "544 2\n", + "545 2\n", + "546 2\n", + "547 3\n", + "548 2\n", + "549 2\n", + "551 2\n", + "552 1\n", + "553 2\n", + "554 3\n", + "555 1\n", + "556 1\n", + "557 3\n", + "558 1\n", + "559 1\n", + "560 1\n", + "561 1\n", + "562 1\n", + "563 2\n", + "564 4\n", + "565 1\n", + "566 1\n", + "567 2\n", + "568 1\n", + "570 1\n", + "571 2\n", + "572 2\n", + "573 2\n", + "575 3\n", + "576 2\n", + "577 1\n", + "578 2\n", + "579 2\n", + "580 3\n", + "581 2\n", + "582 2\n", + "583 1\n", + "584 1\n", + "586 3\n", + "589 1\n", + "591 1\n", + "593 1\n", + "594 2\n", + "595 2\n", + "596 2\n", + "597 1\n", + "598 2\n", + "601 2\n", + "602 2\n", + "603 2\n", + "604 3\n", + "605 2\n", + "608 2\n", + "609 1\n", + "610 1\n", + "613 1\n", + "614 1\n", + "615 2\n", + "616 1\n", + "617 3\n", + "618 2\n", + "619 2\n", + "620 1\n", + "621 1\n", + "622 1\n", + "623 2\n", + "624 1\n", + "625 4\n", + "626 1\n", + "628 3\n", + "629 2\n", + "631 1\n", + "633 1\n", + "634 2\n", + "636 1\n", + "637 1\n", + "638 1\n", + "639 1\n", + "640 4\n", + "641 1\n", + "642 3\n", + "643 1\n", + "644 1\n", + "645 1\n", + "646 3\n", + "647 3\n", + "648 3\n", + "649 2\n", + "650 1\n", + "651 1\n", + "652 4\n", + "653 2\n", + "655 2\n", + "656 2\n", + "658 1\n", + "659 1\n", + "660 1\n", + "661 3\n", + "662 2\n", + "663 2\n", + "664 2\n", + "665 4\n", + "666 1\n", + "667 1\n", + "668 2\n", + "669 2\n", + "670 5\n", + "672 1\n", + "673 1\n", + "675 2\n", + "677 4\n", + "678 1\n", + "679 2\n", + "680 1\n", + "681 2\n", + "682 1\n", + "684 2\n", + "688 2\n", + "689 1\n", + "690 1\n", + "692 2\n", + "693 1\n", + "694 1\n", + "696 1\n", + "697 4\n", + "698 2\n", + "699 1\n", + "700 2\n", + "702 1\n", + "703 1\n", + "704 2\n", + "706 1\n", + "708 2\n", + "709 1\n", + "711 3\n", + "712 2\n", + "713 1\n", + "715 2\n", + "716 2\n", + "718 1\n", + "719 2\n", + "720 1\n", + "723 1\n", + "725 2\n", + "726 1\n", + "727 1\n", + "733 3\n", + "734 3\n", + "735 1\n", + "737 2\n", + "739 1\n", + "740 2\n", + "741 2\n", + "742 1\n", + "743 2\n", + "744 3\n", + "745 2\n", + "746 2\n", + "749 4\n", + "750 1\n", + "751 1\n", + "756 1\n", + "757 3\n", + "758 2\n", + "761 1\n", + "764 1\n", + "766 1\n", + "767 3\n", + "768 1\n", + "769 2\n", + "770 1\n", + "773 1\n", + "775 1\n", + "778 1\n", + "779 1\n", + "780 1\n", + "781 1\n", + "782 1\n", + "786 1\n", + "787 2\n", + "788 1\n", + "789 1\n", + "790 1\n", + "791 1\n", + "794 1\n", + "800 1\n", + "802 1\n", + "803 1\n", + "806 1\n", + "807 1\n", + "811 1\n", + "812 2\n", + "833 1\n", + "839 1\n", + "857 2\n", + "886 1\n", + "Name: registered, dtype: int64\n" + ] + } + ], "source": [ "pd.options.display.max_rows = 500\n", "print data.casual.value_counts().sort_index()\n", @@ -117,11 +1033,3046 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
casualregistered
098615
1667135
2487150
3438195
4354190
5332177
6269155
7250126
8250114
9230114
1021372
1119387
1219057
1314652
1413767
1514654
1614156
1713446
1812851
1914166
2012053
2111652
2210459
238761
249551
2510340
269147
2712443
2810546
299939
308749
3110544
326432
3310038
347043
356731
367828
376130
386536
396939
407631
415638
427126
434744
446031
454728
464733
475134
485945
495442
505237
515730
524127
535439
545633
555640
565128
574533
584635
595029
603728
615334
623334
634532
643342
653530
664233
673129
683935
694526
704033
713730
723536
733130
743534
753823
762933
772728
783832
793230
802834
812235
822929
832634
842233
853030
862840
871629
883240
892534
902126
912831
921334
931229
941834
951948
961835
971930
981932
991537
1001831
1011827
1022134
1031433
1041541
1051230
106831
1071333
1082241
1091427
1101135
1111734
1121537
1131620
114933
1151142
1161037
1171023
118732
1191135
1201737
1211132
122924
123928
124726
125922
126432
127736
1281632
1291023
130641
131822
132928
133832
134835
135527
136227
137436
138825
1391032
140417
141738
1421041
143723
144838
145823
146524
147727
1481234
149524
150835
151525
152327
153729
154326
155632
156737
157226
158431
159523
160320
161834
162333
163432
1641022
165422
166625
167622
168737
169430
170729
171524
172219
173422
174526
175729
176539
177926
178334
179722
180523
181520
182319
183730
184630
185420
186629
187826
188227
189229
190324
191422
192227
193519
194320
195916
196421
197323
198523
199318
200220
201217
202127
203528
204524
205413
206323
207229
208319
209226
210120
211NaN24
212221
213517
214225
215215
216118
217220
218628
219516
220324
221417
222520
223120
224120
225523
226519
227515
228419
229415
230126
231NaN13
232522
233315
234118
235420
236414
237316
238317
239116
240722
241123
242312
243219
244126
245117
246115
247115
248421
249211
.........
484NaN5
485NaN2
486NaN5
487NaN3
488NaN4
489NaN7
490NaN2
491NaN4
492NaN1
493NaN3
494NaN1
495NaN2
496NaN1
497NaN2
498NaN4
499NaN1
500NaN2
501NaN1
502NaN1
503NaN2
504NaN4
505NaN3
506NaN3
507NaN3
508NaN3
509NaN2
510NaN4
511NaN1
512NaN3
513NaN1
514NaN4
515NaN3
516NaN4
517NaN2
518NaN1
521NaN1
522NaN1
523NaN4
525NaN3
527NaN2
529NaN2
530NaN2
531NaN2
532NaN2
533NaN5
534NaN3
536NaN2
537NaN1
538NaN1
539NaN5
540NaN6
541NaN1
542NaN2
543NaN3
544NaN2
545NaN2
546NaN2
547NaN3
548NaN2
549NaN2
551NaN2
552NaN1
553NaN2
554NaN3
555NaN1
556NaN1
557NaN3
558NaN1
559NaN1
560NaN1
561NaN1
562NaN1
563NaN2
564NaN4
565NaN1
566NaN1
567NaN2
568NaN1
570NaN1
571NaN2
572NaN2
573NaN2
575NaN3
576NaN2
577NaN1
578NaN2
579NaN2
580NaN3
581NaN2
582NaN2
583NaN1
584NaN1
586NaN3
589NaN1
591NaN1
593NaN1
594NaN2
595NaN2
596NaN2
597NaN1
598NaN2
601NaN2
602NaN2
603NaN2
604NaN3
605NaN2
608NaN2
609NaN1
610NaN1
613NaN1
614NaN1
615NaN2
616NaN1
617NaN3
618NaN2
619NaN2
620NaN1
621NaN1
622NaN1
623NaN2
624NaN1
625NaN4
626NaN1
628NaN3
629NaN2
631NaN1
633NaN1
634NaN2
636NaN1
637NaN1
638NaN1
639NaN1
640NaN4
641NaN1
642NaN3
643NaN1
644NaN1
645NaN1
646NaN3
647NaN3
648NaN3
649NaN2
650NaN1
651NaN1
652NaN4
653NaN2
655NaN2
656NaN2
658NaN1
659NaN1
660NaN1
661NaN3
662NaN2
663NaN2
664NaN2
665NaN4
666NaN1
667NaN1
668NaN2
669NaN2
670NaN5
672NaN1
673NaN1
675NaN2
677NaN4
678NaN1
679NaN2
680NaN1
681NaN2
682NaN1
684NaN2
688NaN2
689NaN1
690NaN1
692NaN2
693NaN1
694NaN1
696NaN1
697NaN4
698NaN2
699NaN1
700NaN2
702NaN1
703NaN1
704NaN2
706NaN1
708NaN2
709NaN1
711NaN3
712NaN2
713NaN1
715NaN2
716NaN2
718NaN1
719NaN2
720NaN1
723NaN1
725NaN2
726NaN1
727NaN1
733NaN3
734NaN3
735NaN1
737NaN2
739NaN1
740NaN2
741NaN2
742NaN1
743NaN2
744NaN3
745NaN2
746NaN2
749NaN4
750NaN1
751NaN1
756NaN1
757NaN3
758NaN2
761NaN1
764NaN1
766NaN1
767NaN3
768NaN1
769NaN2
770NaN1
773NaN1
775NaN1
778NaN1
779NaN1
780NaN1
781NaN1
782NaN1
786NaN1
787NaN2
788NaN1
789NaN1
790NaN1
791NaN1
794NaN1
800NaN1
802NaN1
803NaN1
806NaN1
807NaN1
811NaN1
812NaN2
833NaN1
839NaN1
857NaN2
886NaN1
\n", + "

731 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " casual registered\n", + "0 986 15\n", + "1 667 135\n", + "2 487 150\n", + "3 438 195\n", + "4 354 190\n", + "5 332 177\n", + "6 269 155\n", + "7 250 126\n", + "8 250 114\n", + "9 230 114\n", + "10 213 72\n", + "11 193 87\n", + "12 190 57\n", + "13 146 52\n", + "14 137 67\n", + "15 146 54\n", + "16 141 56\n", + "17 134 46\n", + "18 128 51\n", + "19 141 66\n", + "20 120 53\n", + "21 116 52\n", + "22 104 59\n", + "23 87 61\n", + "24 95 51\n", + "25 103 40\n", + "26 91 47\n", + "27 124 43\n", + "28 105 46\n", + "29 99 39\n", + "30 87 49\n", + "31 105 44\n", + "32 64 32\n", + "33 100 38\n", + "34 70 43\n", + "35 67 31\n", + "36 78 28\n", + "37 61 30\n", + "38 65 36\n", + "39 69 39\n", + "40 76 31\n", + "41 56 38\n", + "42 71 26\n", + "43 47 44\n", + "44 60 31\n", + "45 47 28\n", + "46 47 33\n", + "47 51 34\n", + "48 59 45\n", + "49 54 42\n", + "50 52 37\n", + "51 57 30\n", + "52 41 27\n", + "53 54 39\n", + "54 56 33\n", + "55 56 40\n", + "56 51 28\n", + "57 45 33\n", + "58 46 35\n", + "59 50 29\n", + "60 37 28\n", + "61 53 34\n", + "62 33 34\n", + "63 45 32\n", + "64 33 42\n", + "65 35 30\n", + "66 42 33\n", + "67 31 29\n", + "68 39 35\n", + "69 45 26\n", + "70 40 33\n", + "71 37 30\n", + "72 35 36\n", + "73 31 30\n", + "74 35 34\n", + "75 38 23\n", + "76 29 33\n", + "77 27 28\n", + "78 38 32\n", + "79 32 30\n", + "80 28 34\n", + "81 22 35\n", + "82 29 29\n", + "83 26 34\n", + "84 22 33\n", + "85 30 30\n", + "86 28 40\n", + "87 16 29\n", + "88 32 40\n", + "89 25 34\n", + "90 21 26\n", + "91 28 31\n", + "92 13 34\n", + "93 12 29\n", + "94 18 34\n", + "95 19 48\n", + "96 18 35\n", + "97 19 30\n", + "98 19 32\n", + "99 15 37\n", + "100 18 31\n", + "101 18 27\n", + "102 21 34\n", + "103 14 33\n", + "104 15 41\n", + "105 12 30\n", + "106 8 31\n", + "107 13 33\n", + "108 22 41\n", + "109 14 27\n", + "110 11 35\n", + "111 17 34\n", + "112 15 37\n", + "113 16 20\n", + "114 9 33\n", + "115 11 42\n", + "116 10 37\n", + "117 10 23\n", + "118 7 32\n", + "119 11 35\n", + "120 17 37\n", + "121 11 32\n", + "122 9 24\n", + "123 9 28\n", + "124 7 26\n", + "125 9 22\n", + "126 4 32\n", + "127 7 36\n", + "128 16 32\n", + "129 10 23\n", + "130 6 41\n", + "131 8 22\n", + "132 9 28\n", + "133 8 32\n", + "134 8 35\n", + "135 5 27\n", + "136 2 27\n", + "137 4 36\n", + "138 8 25\n", + "139 10 32\n", + "140 4 17\n", + "141 7 38\n", + "142 10 41\n", + "143 7 23\n", + "144 8 38\n", + "145 8 23\n", + "146 5 24\n", + "147 7 27\n", + "148 12 34\n", + "149 5 24\n", + "150 8 35\n", + "151 5 25\n", + "152 3 27\n", + "153 7 29\n", + "154 3 26\n", + "155 6 32\n", + "156 7 37\n", + "157 2 26\n", + "158 4 31\n", + "159 5 23\n", + "160 3 20\n", + "161 8 34\n", + "162 3 33\n", + "163 4 32\n", + "164 10 22\n", + "165 4 22\n", + "166 6 25\n", + "167 6 22\n", + "168 7 37\n", + "169 4 30\n", + "170 7 29\n", + "171 5 24\n", + "172 2 19\n", + "173 4 22\n", + "174 5 26\n", + "175 7 29\n", + "176 5 39\n", + "177 9 26\n", + "178 3 34\n", + "179 7 22\n", + "180 5 23\n", + "181 5 20\n", + "182 3 19\n", + "183 7 30\n", + "184 6 30\n", + "185 4 20\n", + "186 6 29\n", + "187 8 26\n", + "188 2 27\n", + "189 2 29\n", + "190 3 24\n", + "191 4 22\n", + "192 2 27\n", + "193 5 19\n", + "194 3 20\n", + "195 9 16\n", + "196 4 21\n", + "197 3 23\n", + "198 5 23\n", + "199 3 18\n", + "200 2 20\n", + "201 2 17\n", + "202 1 27\n", + "203 5 28\n", + "204 5 24\n", + "205 4 13\n", + "206 3 23\n", + "207 2 29\n", + "208 3 19\n", + "209 2 26\n", + "210 1 20\n", + "211 NaN 24\n", + "212 2 21\n", + "213 5 17\n", + "214 2 25\n", + "215 2 15\n", + "216 1 18\n", + "217 2 20\n", + "218 6 28\n", + "219 5 16\n", + "220 3 24\n", + "221 4 17\n", + "222 5 20\n", + "223 1 20\n", + "224 1 20\n", + "225 5 23\n", + "226 5 19\n", + "227 5 15\n", + "228 4 19\n", + "229 4 15\n", + "230 1 26\n", + "231 NaN 13\n", + "232 5 22\n", + "233 3 15\n", + "234 1 18\n", + "235 4 20\n", + "236 4 14\n", + "237 3 16\n", + "238 3 17\n", + "239 1 16\n", + "240 7 22\n", + "241 1 23\n", + "242 3 12\n", + "243 2 19\n", + "244 1 26\n", + "245 1 17\n", + "246 1 15\n", + "247 1 15\n", + "248 4 21\n", + "249 2 11\n", + ".. ... ...\n", + "484 NaN 5\n", + "485 NaN 2\n", + "486 NaN 5\n", + "487 NaN 3\n", + "488 NaN 4\n", + "489 NaN 7\n", + "490 NaN 2\n", + "491 NaN 4\n", + "492 NaN 1\n", + "493 NaN 3\n", + "494 NaN 1\n", + "495 NaN 2\n", + "496 NaN 1\n", + "497 NaN 2\n", + "498 NaN 4\n", + "499 NaN 1\n", + "500 NaN 2\n", + "501 NaN 1\n", + "502 NaN 1\n", + "503 NaN 2\n", + "504 NaN 4\n", + "505 NaN 3\n", + "506 NaN 3\n", + "507 NaN 3\n", + "508 NaN 3\n", + "509 NaN 2\n", + "510 NaN 4\n", + "511 NaN 1\n", + "512 NaN 3\n", + "513 NaN 1\n", + "514 NaN 4\n", + "515 NaN 3\n", + "516 NaN 4\n", + "517 NaN 2\n", + "518 NaN 1\n", + "521 NaN 1\n", + "522 NaN 1\n", + "523 NaN 4\n", + "525 NaN 3\n", + "527 NaN 2\n", + "529 NaN 2\n", + "530 NaN 2\n", + "531 NaN 2\n", + "532 NaN 2\n", + "533 NaN 5\n", + "534 NaN 3\n", + "536 NaN 2\n", + "537 NaN 1\n", + "538 NaN 1\n", + "539 NaN 5\n", + "540 NaN 6\n", + "541 NaN 1\n", + "542 NaN 2\n", + "543 NaN 3\n", + "544 NaN 2\n", + "545 NaN 2\n", + "546 NaN 2\n", + "547 NaN 3\n", + "548 NaN 2\n", + "549 NaN 2\n", + "551 NaN 2\n", + "552 NaN 1\n", + "553 NaN 2\n", + "554 NaN 3\n", + "555 NaN 1\n", + "556 NaN 1\n", + "557 NaN 3\n", + "558 NaN 1\n", + "559 NaN 1\n", + "560 NaN 1\n", + "561 NaN 1\n", + "562 NaN 1\n", + "563 NaN 2\n", + "564 NaN 4\n", + "565 NaN 1\n", + "566 NaN 1\n", + "567 NaN 2\n", + "568 NaN 1\n", + "570 NaN 1\n", + "571 NaN 2\n", + "572 NaN 2\n", + "573 NaN 2\n", + "575 NaN 3\n", + "576 NaN 2\n", + "577 NaN 1\n", + "578 NaN 2\n", + "579 NaN 2\n", + "580 NaN 3\n", + "581 NaN 2\n", + "582 NaN 2\n", + "583 NaN 1\n", + "584 NaN 1\n", + "586 NaN 3\n", + "589 NaN 1\n", + "591 NaN 1\n", + "593 NaN 1\n", + "594 NaN 2\n", + "595 NaN 2\n", + "596 NaN 2\n", + "597 NaN 1\n", + "598 NaN 2\n", + "601 NaN 2\n", + "602 NaN 2\n", + "603 NaN 2\n", + "604 NaN 3\n", + "605 NaN 2\n", + "608 NaN 2\n", + "609 NaN 1\n", + "610 NaN 1\n", + "613 NaN 1\n", + "614 NaN 1\n", + "615 NaN 2\n", + "616 NaN 1\n", + "617 NaN 3\n", + "618 NaN 2\n", + "619 NaN 2\n", + "620 NaN 1\n", + "621 NaN 1\n", + "622 NaN 1\n", + "623 NaN 2\n", + "624 NaN 1\n", + "625 NaN 4\n", + "626 NaN 1\n", + "628 NaN 3\n", + "629 NaN 2\n", + "631 NaN 1\n", + "633 NaN 1\n", + "634 NaN 2\n", + "636 NaN 1\n", + "637 NaN 1\n", + "638 NaN 1\n", + "639 NaN 1\n", + "640 NaN 4\n", + "641 NaN 1\n", + "642 NaN 3\n", + "643 NaN 1\n", + "644 NaN 1\n", + "645 NaN 1\n", + "646 NaN 3\n", + "647 NaN 3\n", + "648 NaN 3\n", + "649 NaN 2\n", + "650 NaN 1\n", + "651 NaN 1\n", + "652 NaN 4\n", + "653 NaN 2\n", + "655 NaN 2\n", + "656 NaN 2\n", + "658 NaN 1\n", + "659 NaN 1\n", + "660 NaN 1\n", + "661 NaN 3\n", + "662 NaN 2\n", + "663 NaN 2\n", + "664 NaN 2\n", + "665 NaN 4\n", + "666 NaN 1\n", + "667 NaN 1\n", + "668 NaN 2\n", + "669 NaN 2\n", + "670 NaN 5\n", + "672 NaN 1\n", + "673 NaN 1\n", + "675 NaN 2\n", + "677 NaN 4\n", + "678 NaN 1\n", + "679 NaN 2\n", + "680 NaN 1\n", + "681 NaN 2\n", + "682 NaN 1\n", + "684 NaN 2\n", + "688 NaN 2\n", + "689 NaN 1\n", + "690 NaN 1\n", + "692 NaN 2\n", + "693 NaN 1\n", + "694 NaN 1\n", + "696 NaN 1\n", + "697 NaN 4\n", + "698 NaN 2\n", + "699 NaN 1\n", + "700 NaN 2\n", + "702 NaN 1\n", + "703 NaN 1\n", + "704 NaN 2\n", + "706 NaN 1\n", + "708 NaN 2\n", + "709 NaN 1\n", + "711 NaN 3\n", + "712 NaN 2\n", + "713 NaN 1\n", + "715 NaN 2\n", + "716 NaN 2\n", + "718 NaN 1\n", + "719 NaN 2\n", + "720 NaN 1\n", + "723 NaN 1\n", + "725 NaN 2\n", + "726 NaN 1\n", + "727 NaN 1\n", + "733 NaN 3\n", + "734 NaN 3\n", + "735 NaN 1\n", + "737 NaN 2\n", + "739 NaN 1\n", + "740 NaN 2\n", + "741 NaN 2\n", + "742 NaN 1\n", + "743 NaN 2\n", + "744 NaN 3\n", + "745 NaN 2\n", + "746 NaN 2\n", + "749 NaN 4\n", + "750 NaN 1\n", + "751 NaN 1\n", + "756 NaN 1\n", + "757 NaN 3\n", + "758 NaN 2\n", + "761 NaN 1\n", + "764 NaN 1\n", + "766 NaN 1\n", + "767 NaN 3\n", + "768 NaN 1\n", + "769 NaN 2\n", + "770 NaN 1\n", + "773 NaN 1\n", + "775 NaN 1\n", + "778 NaN 1\n", + "779 NaN 1\n", + "780 NaN 1\n", + "781 NaN 1\n", + "782 NaN 1\n", + "786 NaN 1\n", + "787 NaN 2\n", + "788 NaN 1\n", + "789 NaN 1\n", + "790 NaN 1\n", + "791 NaN 1\n", + "794 NaN 1\n", + "800 NaN 1\n", + "802 NaN 1\n", + "803 NaN 1\n", + "806 NaN 1\n", + "807 NaN 1\n", + "811 NaN 1\n", + "812 NaN 2\n", + "833 NaN 1\n", + "839 NaN 1\n", + "857 NaN 2\n", + "886 NaN 1\n", + "\n", + "[731 rows x 2 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.concat([data.casual.value_counts(), data.registered.value_counts()], axis=1)" ] @@ -137,16 +4088,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEACAYAAABYq7oeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIn4UDiIi4kfhICIifhQOIiLi5/8DLVB936G6+owAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "%matplotlib inline" + "%matplotlib inline\n", + "plt.plot(data.casual.value_counts().sort_index())\n", + "plt.plot(data.registered.value_counts().sort_index())\n", + "plt.xlim([0, 1000])\n", + "plt.legend(['Casual Users', 'Registered Users'])" ] }, { @@ -160,12 +4136,95 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
seasonholidayweathertempatemphumiditywindspeedcasualregisteredcount
workingday
02.5198620.0895221.38716219.88983923.34983762.19228612.63991659.308290129.198330188.506621
12.5004050.0000001.43308120.39069623.79815361.74311912.87414325.107663167.904209193.011873
\n", + "
" + ], + "text/plain": [ + " season holiday weather temp atemp humidity \\\n", + "workingday \n", + "0 2.519862 0.089522 1.387162 19.889839 23.349837 62.192286 \n", + "1 2.500405 0.000000 1.433081 20.390696 23.798153 61.743119 \n", + "\n", + " windspeed casual registered count \n", + "workingday \n", + "0 12.639916 59.308290 129.198330 188.506621 \n", + "1 12.874143 25.107663 167.904209 193.011873 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby('workingday').mean()" + ] }, { "cell_type": "markdown", @@ -176,12 +4235,126 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": true }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
seasonholidayworkingdaytempatemphumiditywindspeedcasualregisteredcount
weather
12.4970800.0283650.67283120.55712223.99412656.71676912.89254240.308676164.928115205.236791
22.5303460.0324630.68348619.61460823.07241469.10056512.17990530.785462148.170078178.955540
32.5098950.0174620.73923219.54635622.75309181.34109414.07124817.442375101.403958118.846333
41.0000000.0000001.0000008.20000011.36500086.0000006.0032006.000000158.000000164.000000
\n", + "
" + ], + "text/plain": [ + " season holiday workingday temp atemp humidity \\\n", + "weather \n", + "1 2.497080 0.028365 0.672831 20.557122 23.994126 56.716769 \n", + "2 2.530346 0.032463 0.683486 19.614608 23.072414 69.100565 \n", + "3 2.509895 0.017462 0.739232 19.546356 22.753091 81.341094 \n", + "4 1.000000 0.000000 1.000000 8.200000 11.365000 86.000000 \n", + "\n", + " windspeed casual registered count \n", + "weather \n", + "1 12.892542 40.308676 164.928115 205.236791 \n", + "2 12.179905 30.785462 148.170078 178.955540 \n", + "3 14.071248 17.442375 101.403958 118.846333 \n", + "4 6.003200 6.000000 158.000000 164.000000 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby('weather').mean()" + ] }, { "cell_type": "markdown", @@ -194,13 +4367,182 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "collapsed": false, "scrolled": true }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
workingdayweathertempatemphumiditywindspeedcasualregisteredcount
seasonholiday
100.6990441.42256212.63458515.36287056.51816414.57854915.689101101.805736117.494837
10.0000001.4929588.69662010.29676148.18309916.7889738.14084565.78873273.929577
200.7050281.42793322.82440226.65114260.83873413.33910947.609683167.953073215.562756
10.0000001.14583322.77208326.42093761.64583317.12534038.333333159.500000197.833333
300.6996591.36177528.75628432.50357463.94804711.57401850.773606183.519530234.293136
10.0000001.50000029.69083333.56286568.9479179.71911891.958333145.864583237.822917
400.6997731.46436716.59738420.01080766.18802111.66414428.133055170.427218198.560273
10.0000001.33333318.07416721.40916765.78125012.06293140.885417169.864583210.750000
\n", + "
" + ], + "text/plain": [ + " workingday weather temp atemp humidity \\\n", + "season holiday \n", + "1 0 0.699044 1.422562 12.634585 15.362870 56.518164 \n", + " 1 0.000000 1.492958 8.696620 10.296761 48.183099 \n", + "2 0 0.705028 1.427933 22.824402 26.651142 60.838734 \n", + " 1 0.000000 1.145833 22.772083 26.420937 61.645833 \n", + "3 0 0.699659 1.361775 28.756284 32.503574 63.948047 \n", + " 1 0.000000 1.500000 29.690833 33.562865 68.947917 \n", + "4 0 0.699773 1.464367 16.597384 20.010807 66.188021 \n", + " 1 0.000000 1.333333 18.074167 21.409167 65.781250 \n", + "\n", + " windspeed casual registered count \n", + "season holiday \n", + "1 0 14.578549 15.689101 101.805736 117.494837 \n", + " 1 16.788973 8.140845 65.788732 73.929577 \n", + "2 0 13.339109 47.609683 167.953073 215.562756 \n", + " 1 17.125340 38.333333 159.500000 197.833333 \n", + "3 0 11.574018 50.773606 183.519530 234.293136 \n", + " 1 9.719118 91.958333 145.864583 237.822917 \n", + "4 0 11.664144 28.133055 170.427218 198.560273 \n", + " 1 12.062931 40.885417 169.864583 210.750000 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(['season', 'holiday']).mean()" + ] }, { "cell_type": "markdown", @@ -213,12 +4555,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2011, 1, 1, 2)\n" + ] + } + ], + "source": [ + "def parse_date_time(datetime):\n", + " \"\"\"Converts data time from YYYY-MM-DD HH:MM:SS to a tuple\n", + " of year, month, day, hour\"\"\"\n", + " return int(datetime[0:4]), int(datetime[5:7]), int(datetime[8:10]), int (datetime[11:13])\n", + "\n", + "print parse_date_time('2011-01-01 02:00:00')" + ] }, { "cell_type": "markdown", diff --git a/inclass/day03/machine_learning_basics.ipynb b/inclass/day03/machine_learning_basics.ipynb index c9f6417..0d1466e 100644 --- a/inclass/day03/machine_learning_basics.ipynb +++ b/inclass/day03/machine_learning_basics.ipynb @@ -25,11 +25,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.17\n" + ] + } + ], "source": [ "import sklearn\n", "import matplotlib.pyplot as plt\n", @@ -63,11 +71,71 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Boston House Prices dataset\n", + "\n", + "Notes\n", + "------\n", + "Data Set Characteristics: \n", + "\n", + " :Number of Instances: 506 \n", + "\n", + " :Number of Attributes: 13 numeric/categorical predictive\n", + " \n", + " :Median Value (attribute 14) is usually the target\n", + "\n", + " :Attribute Information (in order):\n", + " - CRIM per capita crime rate by town\n", + " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", + " - INDUS proportion of non-retail business acres per town\n", + " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", + " - NOX nitric oxides concentration (parts per 10 million)\n", + " - RM average number of rooms per dwelling\n", + " - AGE proportion of owner-occupied units built prior to 1940\n", + " - DIS weighted distances to five Boston employment centres\n", + " - RAD index of accessibility to radial highways\n", + " - TAX full-value property-tax rate per $10,000\n", + " - PTRATIO pupil-teacher ratio by town\n", + " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", + " - LSTAT % lower status of the population\n", + " - MEDV Median value of owner-occupied homes in $1000's\n", + "\n", + " :Missing Attribute Values: None\n", + "\n", + " :Creator: Harrison, D. and Rubinfeld, D.L.\n", + "\n", + "This is a copy of UCI ML housing dataset.\n", + "http://archive.ics.uci.edu/ml/datasets/Housing\n", + "\n", + "\n", + "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", + "\n", + "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", + "prices and the demand for clean air', J. Environ. Economics & Management,\n", + "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", + "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", + "pages 244-261 of the latter.\n", + "\n", + "The Boston house-price data has been used in many machine learning papers that address regression\n", + "problems. \n", + " \n", + "**References**\n", + "\n", + " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", + " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", + " - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n", + "\n" + ] + } + ], "source": [ "from sklearn.datasets import *\n", "\n", @@ -84,11 +152,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.740607742865\n" + ] + } + ], "source": [ "from sklearn.datasets import *\n", "from sklearn.linear_model import LinearRegression\n", @@ -109,11 +185,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Quality 0.774838\n", + "Test Quality 0.691532\n" + ] + } + ], "source": [ "from sklearn.datasets import *\n", "from sklearn.cross_validation import train_test_split\n", @@ -147,15 +232,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEPCAYAAAC5sYRSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "\n", - "n_trials = 100\n", + "n_trials = 1000\n", "\n", "X = data.data\n", "y = data.target\n", @@ -166,8 +262,8 @@ "avg_test_quality = np.zeros(d)\n", "\n", "for n_features in range(1,d+1):\n", - " quality_train = numpy.zeros(n_trials)\n", - " quality_test = numpy.zeros(n_trials)\n", + " quality_train = np.zeros(n_trials)\n", + " quality_test = np.zeros(n_trials)\n", "\n", " for i in range(n_trials):\n", " # permute the columns of X\n", @@ -206,11 +302,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optical Recognition of Handwritten Digits Data Set\n", + "===================================================\n", + "\n", + "Notes\n", + "-----\n", + "Data Set Characteristics:\n", + " :Number of Instances: 5620\n", + " :Number of Attributes: 64\n", + " :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n", + " :Missing Attribute Values: None\n", + " :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n", + " :Date: July; 1998\n", + "\n", + "This is a copy of the test set of the UCI ML hand-written digits datasets\n", + "http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n", + "\n", + "The data set contains images of hand-written digits: 10 classes where\n", + "each class refers to a digit.\n", + "\n", + "Preprocessing programs made available by NIST were used to extract\n", + "normalized bitmaps of handwritten digits from a preprinted form. From a\n", + "total of 43 people, 30 contributed to the training set and different 13\n", + "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n", + "4x4 and the number of on pixels are counted in each block. This generates\n", + "an input matrix of 8x8 where each element is an integer in the range\n", + "0..16. This reduces dimensionality and gives invariance to small\n", + "distortions.\n", + "\n", + "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n", + "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n", + "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n", + "1994.\n", + "\n", + "References\n", + "----------\n", + " - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n", + " Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n", + " Graduate Studies in Science and Engineering, Bogazici University.\n", + " - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n", + " - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n", + " Linear dimensionalityreduction using relevance weighted LDA. School of\n", + " Electrical and Electronic Engineering Nanyang Technological University.\n", + " 2005.\n", + " - Claudio Gentile. A New Approximate Maximal Margin Classification\n", + " Algorithm. NIPS. 2000.\n", + "\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAD+CAYAAAAJQfilAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from sklearn.datasets import *\n", "import matplotlib.pyplot as plt\n", @@ -235,11 +394,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train accuracy 0.998886\n", + "Test accuracy 0.942158\n" + ] + } + ], "source": [ "from sklearn.datasets import *\n", "from sklearn.cross_validation import train_test_split\n", @@ -265,11 +433,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "ename": "IndexError", + "evalue": "index 52 is out of bounds for axis 0 with size 52", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m visualize_predictions(X_test[mask, :],\n\u001b[1;32m 16\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m model.predict(X_test)[mask])\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mvisualize_predictions\u001b[0;34m(images, targets, predictions)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdigits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'binary'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m ax.text(0.05, 0.05, str(predictions[i]),\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransAxes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m color='green' if (targets[i] == predictions[i]) else 'red')\n", + "\u001b[0;31mIndexError\u001b[0m: index 52 is out of bounds for axis 0 with size 52" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAegAAAHaCAYAAADL4tqqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsfWmMbGl53lN717519XaX2VlmcOQJHkhCwBAGEuMohIEY\ngTEOliCRLOHYjpIoxCHKDytSFBPsmERWAoml2CGWExmBDQbEkoAZyUksSwbLNjBz7+3bXV1d+77n\nR9/nu8/56lTf6qpT3ffifqWj6jvTyznf+b73eZfnfV/fdDrFpVzKpVzKpVzKpdxf4r/oG7iUS7mU\nS7mUS7mUWbkE6Eu5lEu5lEu5lPtQLgH6Ui7lUi7lUi7lPpRLgL6US7mUS7mUS7kP5RKgL+VSLuVS\nLuVS7kMJnucf8/l8DwRlfDqd+vj15T2vTy7v+Xzk8p7PR/SegQfzvi/veX1i749F5FwBGgBOK+tq\nt9uo1Wqo1WqoVquo1Wo4Pj7G7du3HVepVEIqlUIymXRcu7u72N3dxd7enrkSiQQCgYC5gsEg/P75\ngQOfb3YNz1qKVq/X8Wd/9mf40z/9U/P5wgsvYDwez1xbW1vY2dnBzs4Odnd3sbOzg1wuh0wmg3Q6\nba5YLLbWex4Oh2i1Wmg2m2i1Wmi1WqjX6/jOd76D7373u47PUCiEaDTquK5cuYInnngCjz/+uPnc\n3t727J7r9TqKxSKOjo5QLBZRLBZxeHho9sTBwQFu376No6MjRCIRhMNhhMNh8/Xjjz+Ol7zkJXjp\nS1+Kl73sZXjpS1+KTCZz7ntjMpnM7IF+v48bN27gxo0bePHFF/Hiiy/ixo0buHLlCh555BE88sgj\nePTRR/Hoo48ikUic6e+d9Z4PDw/NnuX+PT4+NuvJKxaL4aGHHsL169dx/fp183UwuLpKOe8z+Nhj\nj5l9wSuVSq18z/e670ajgVKphKOjI5RKJZRKJdTr9Zn7Gw6HqNfr5qrVaqjX6+h0Ouj1euh2u+aa\nTCaOPR0IBJDL5fDUU0/hqaeewpNPPomnnnoKTzzxBAAgm82e6Z4rlQr29/dx69Yt3Lp1C/v7+zg6\nOjL3xKvdbiOfzyOXy5krn8+jUCiYa2trC4VCAdFoFMFg0HHPXp7Do6Mjo7uox46OjmbWORAIGN1F\nPfbEE0+sbU8vIucO0KfJZDJBv9834FAul1EqlVCpVMxL7/V6GA6H5hqNRmaBu90u6vU6gsEgxuMx\nOp0OEokENjY2HFc4HIbf73dshkAg4Pnz+Hw++Hw++P1++P1+jMdjTCYTjEYjc/96uDqdDjqdDqLR\nKDY2NhCLxTAej8+snO4l0+kUk8nE8dnr9czhp3FULpdxeHiIcrmMVquFfr+P6XRqfm48HjueZTQa\nYTKZmN+5rPB3jkYjc9FYOz4+RrlcRrVaRbPZxHA4RCAQQCwWQy6XM++RB8Ln82E4HGIwGGAwGKDf\n76PX65krFAohFArB5/N5vs5uMp1OMRqNzP0MBgN0u1202230+32jYGlYhEKhexoOXov9bgeDgdm3\ng8EAwWAQg8EA0WgUgUAA0+kUg8EA7XbbKDPufZ/Ph1AohEgk4rhCoZDn96ufzWbTYWy22210Oh34\n/X74fD4Eg0Hz3uPxOKLRKMLhMAKBwNLK1AvRdfP7/ZhOpwgGg4hEIojH4wCAYDCIjY0NtNttc4XD\nYQSDQUwmE/OM1DvUedxL9wLA02Q6nTr2Bs/TYDAwungymQA4Ocf9fh+dTsesLc8n91O/30cikTA6\nj5/hcHjp+7OvwWBgDJlOp2P2BHGD+yYQCKBSqeDw8BCRSAR+vx+j0QjBYNCsJz9DoZDDAYhEIp4A\nuS33FUCPRiP0ej00m01UKhXjKalC7nQ6RrGpEicgA0C/3zceVyKRQCqVcnjc8XjcbFh6BF4CtH3I\nuDFHo5FDQff7fbNpeNBarZbZqNz03PBeiZsXx+gFrfmjoyMcHR3h+PjYWPa9Xg8ATgVoLwwK23Dp\ndruo1WoolUrmfo6Pj1Gr1dDv9+H3+02kJJFIOMDPDZi55hsbG2Zt9T2tU6bTKYbDIXq9HjqdjgFn\nGkAEaFVUBOjzAA63d9vv9zEcDo3CJ+jS+Ol0Oub98F71isViJhKUyWSMgvNK1HjgXmw0GuYiULfb\nbaNQg8GgUazJZBLRaNQo2YsEaABGb6hsbGzA7/cjHA4jHo8jnU4bI6TZbBqjZzQaOYxTn89nHBN9\n9mX3OQGahhvPFNee5596jgDt9/uN8c491el00Gw2HZFCGiSrADRBlxf1bLvdRrPZRKPRQL1ed3wP\nDZtyuWwMnW63i2q16jAueMViMYMrqVQKfr//ex+gx+PxDEAfHBwYz67RaKDb7ToOogIDPex6vW4W\nMplMYnNzE/l8Hvl83oAeFSAABAIBTxUGAIcFqx66KhP1mqmk4/E4YrGY2fTr8qB5yLh+BOhSqYT9\n/X3s7+/j4ODAoeRsD3o0GhmFbVvQq3rQ3W7XoWQrlQrK5TLK5bLxotvtNgAYYE4kEphOpw7FRUXS\n7/cNSCvwAzCH6zw8aCooW2FwfSeTifGQqHSpUC8SoPn/+On3+zEcDh2GXSqVcoQq+XUmk8H29jYG\ngwH8fj+i0ain96xKv9vtotfrGX2he6HdbhvjIhgMIhaLmX0Ti8XuK4BWkObX9KIJKI1GA7VazeyT\nQCCAwWAAwBnypfFhe9HLiHrQep5oxNkedK/Xg8/nw3g8NsZyr9dDu91Go9FAtVpFLpfD1tYWJpMJ\nQqHQqem8Re5PHRDep3rPBGg7kkg9PRqN0Ol0UK1WcXBwYNZNr3Q6ja2tLcee9npfA/chQHe7XTSb\nTZTLZQPQBC+Gqfr9/owHzRehYbnhcIhUKoXd3V20Wi0MBgOzccfjMYAT5b6stXaauHnQPp/PEdoh\nSPC5FKS56el1eyl6yAhe7XYb1WoVR0dH2N/fxwsvvICbN2+atSbAEaDH47EJAQEwYO8FQBPAmOYo\nl8uoVCozV7/fRywWQywWQzQaRSwWQzgcxvHxsQEQvnc7tN3pdBCLxcz7CYfD5xri7vV6aLVaJnfH\nEPd0Op3rQZ+H2ADNtbPDgdPp1OwZTR8RAPgZDAaxtbVlIh1MRXgpBGi+13a7bXKhtgfN/HkwGEQ0\nGkU6nUYymTR7534AaAAmvM17cUvJxeNxBzgDMEaego/uJa8A2vagCdDUV+pBTyYT45BQ3zUaDeOM\ntNtt4znH43FkMpml143GC+9RdS31qwI0n4lrreBMncK0jIa0C4WCAedYLOaay/dC7juAtj3ow8ND\nh2LlRlAvWkPcajE3m02kUim0220DzlQewF1w9jqETLFDfcC9PWha9ZrX8fr+eHj1kBEsFKBfeOGF\nU3+eRg7DtnYOalnhIWk0Go48eLVaNVetVjN5I3pBm5ubSCQSxnBotVomDDvPg+YeWEekwk00xE2u\nRaVSMXtZPWgF6PPMjaqC0xC3HbXS6BC/ZtpIP2u1Gvx+P+LxOHK5HIbDoaf3q2tK5U9wVi+axu90\nOjWeGlNf95sHrZ8ATEQlEomYr6PRqAOcGZa18/Ea4vYCoNXJ4HnSELd60PSc6bDYzxCJRNDtdg04\n53I5o1dWvT/qWd6jHeK2f87n86Hdbs8YQmqA0thpNBoOcPZ6T1MuDKDVwuPFF07A4qECYOL+0WgU\nfr/fkbfgFQgETE6MYB8MBh1kChIEwuEwNjY2jFJc5Tnsi0rMzoO6heXdSFVuB9RL0Ty4WpYapWB+\nVD0hNW70d7k906pgp3l8n89ngJS5ef6deDyOjY0Nk/+x19Mm6vHQ8Xl4ENex1roGGrkZDAYm3Fav\n11GtVmfevypTelP0ak9bL/trL55BlZ4ax/q3KfQy9KJhrQYnz5z+7LL3rGuq6RDlrWg0SklX6XTa\nkJTOG6B1X29sbBjjwe371ODRM2kzn/l9SmhKJpPIZrMm1x4KhTyJyOh54plSTgfvTz8pNCa63S4i\nkQjq9bpJQzB6qMQst7z8POH+0uiqcmW4f+f9fuoPGvdqsFJnxuNxtFotdLvdlTHkNLkwgHYjKvGF\nqbdMC1BZftFo1FFeRSu4XC5jY2PDMEtHo5GDtc2Slnk5k2WEilNDgOoVq2FARcG/C8DBKKVVTOty\nXZ6ThgRpCDWbTXS7XUMyoYehCpdf24qaX3sF0GQxU4EOh0OEQiEkEgmk02nk83m0223DsGT+mAZd\nrVYzURMARgEyZBWPx5FIJBzs3XWFkW3jjeFtRntqtRoqlcpMWJhfk11OBeF2j25GyCp7xi09M+/3\n2oYU97GeVXqnBAaefftnlxUa461Wy6RpisUiKpWK4a1wX5OAFI1GzX66KA9a9znvzy3/6uYADAYD\no1OoM8m2pyPDK51OY3d3F7lcDolEYqWUHo0KGgJ8z9yz4XDYGND2fZBbw3tWR8AmysbjcYdjsCjz\nXA1KG2P4//gcajjo39C9rhEifioRTX/nOuRCAdq2bFjXR3Bm7isSiSCTySCbzSKXyyGbzSIejzsU\nLT+prKkIuWlIVFAiiZYGLSt22QEJTnbomtYWLXluGL54VWyaL1o3QNPraDQa6PV6xkNzq3XmRWBn\nzkaf3T4Iywg9HIYgaaTZ+XDNK7M8j7nrdrttvDxdW6YQyOan8bFugOZhthmsBGhdX/WSVCEwVGgL\nlaZdYrasKNi7AbR66rYHQgDkWvOcMsqhBocqwVXum+e62WyiWq2aKoRKpeIwPAE4IjEE6IsiiSlA\nAyf7lIQ8FZZB6p4nb0QBut/vm/VPpVIOnUmibCKRWIkQqwCtZ0qjaEwbZjIZcx+McrJ88/j42Bj4\ntkPTarWMIUFuyFneiV0KqkxtBWjuBV7Ut/Zln2Gb/b1OkL5wD5oKV0kENkCHw2FkMhns7u7iypUr\n2N3dNRaZfkYiEQB32YNk+dKDVso9/+aquUebNDGvdIokIHrQGuJTi9QmB60ToHnIbQ8auAvQNHzU\nCGo2mw5ChQ3Qq4Z7VHHRiEkkEq4RF9ZGK3eB+0g9aEYn1INOJBIOK93rdbatec3d2QCdSqXg8/mM\nMWl79dxj8wBa649XMTTsEkHbo7BD0vb3qWfFtbY9aJ4ZEuLOqoBtmedBM8R9L4C+KA+azGwAhrTm\nlsIgY7vRaBhQ03QU9Vm/3zfPl0wmTROkzc1NE2n0yoNWQ4xGpYaTASCfz2Nzc9PRnOTw8BChUAiD\nwQC1Ws2kJnhpqo1RxrOUMM3zoO10qm1oED/slF4wGHSU8LlFCr8nPWh6uVrsboe4GbKJRCImTPPY\nY4/h4YcfNmFg/eRiKgGHFp3tQSvrcBVA0fyE5tBtgG61WjN128DdzecW4qZF57VnR6BQUo0dCmSI\nm96m1vyxEUy32zWELC9z0PSgWfoQCoUwmUxmcqytVguBQMAAdbPZxNHRkbkPKgq3EDc9aLupg9di\nKws3gK5Wq441V4C2Q9xu62qX43iR/1fv+bQwtw3StgdN75TnU0Pc+vOr3LMN0KVSCcVi0Zw7RoZo\nyPCcXTRAE0xJCtTcvP18ADAYDEzpoA3Q1JcEmlQqhUKhgGvXrmFra8uzJjHzQtzc2wREn8+HfD5v\nnCpe9LZrtZoBar/f76ovaZxrbnsRsUut3Lg+dv6f1SCMXmm+X3snkMdg/87vOQ/aJp6od6lEEhJm\nkskk8vk8dnZ2cO3atZnWg+FwGJ1Ox7BiaS0OBgNTaK5GwTJgYpcvkNimxKpOp4NKpWLqMDVPZLMr\ngbvlE6FQyJG3W2eIm+tvh254GDTsRg9oXi2lelZekZMIyrwnfm2zK6PRKCqVigFwlkfYXqCGsmwC\n0zrFZpRq+oOKiNGLWCzmiF4QMLRTV6fTcQ1jBwKBmf20rLgBrtu71nuwyXx2+NP2oPXevVBuWltO\nw5yNdZhSAjDjMdFQowFxXufNLu8B7hKuTjPAaNypUU39Yj8ry8mSySTS6fRMPndZ0fPENdSyV63D\nVmMoHo8jlUqZtBI9Yq0WcKsUWNXgn/cuaRCTz0Qek01q41qNRiNHqex5gDNwwQBtWzp26IGEL4KW\nkpU0R8eXoJuHG8Pv93sGdgzJK0FKQ8S8yuUybt265ejANY+xTQDSjbzu1oM2CYvehQ1izWbTEeah\nN9JsNtHr9TCdTk04Tb2QVVmims9UZcb/BzjbUTIExUsBhV/r+1diH3+nV8aFCus/NU9erVZRr9cd\nilVLAKPRKFKpFLLZrFlHNpLpdrsz90pFwwYWAFbuaKRGI72J4XA4c970DOu/lYxld7GyjTu30Pky\n4pZ35LPQwAPgICzxa+V8rKveXPernarRphpuyn40GuHw8NB0VmSXP7KetT+EW9csreWNRCKOlMhZ\nhfqUwJbJZDAej9FqtYzzw6541N/AXZ7AzZs3cXR0hHq9bvLtStTS7o4aQVx0f6juCIVCJkVqG5nE\nlXQ6bcLw2WzWlfltl/SeVoHjtVy4B63hP3rMGnqg0prXrk5DwNx46o3ygHoB0AwL0wtitysO+OBV\nqVRMy8x6vW6a2LtZXeq1MuxGi35d7GIeMvXaeA8akSBjmxuVoUICzmQyMQBN65ieyKoATc+R/7Zz\nSG4kQ158Rg252j2tlUm8KklpntCgo2fXbrdNX3kqV5IVCdAbGxtIJpPI5XLGc1ajkACooXnWcXP/\ns0PeMqJRB107N2VpAzT/m+1luTXK8Ipxbt+HXgAcOW6//27HJ720wcq6PGh6+arw3T7daoDH4/EM\nOJdKJUd4W/PQdlMOGiTUr6sYcKo7EomEo4kNiZn8uxq1pOfPZyApFYADnNUo5H9fBqBpYE4mk5n9\ny+cgQG9tbeHKlSvY2toy55Rh9nltpd3y2uuQC21UYufn9FARoMfjsSMvy0tZprYHrR6p0um9AGiy\nhdkAoVqtmm5XvOglaahN2c22B22Hguzyn3V60ACMIlZg5idLl5jja7Vajo1JxWbn8bzyoPm1nU/i\n1wrMPEwMFaonN8+D1gO7KlnJFnrQds0zAXqeB02AphGk3Zd4z3ptbGwYTzoSiazU6MEGaCpLEpDc\nQtR8VruEZZ4HrWfRq+jFvTxoKns3gGYqYV1MfgAO/oFGVLQJk1ZRqIxGIwPMCtJ2aFnbndKDrtfr\niEajjnTRKvtDDa9kMmnWu91uw+8/6d7HKCLvnZwLtvY8Pj42HrSSztzyv8t40FqXrTrKzYPOZDLY\n2trC1atXsbe3Z3Q4U2anhd296vlwmlx4iFuVpVp49IJZYmMfdLfD7RbitnNqXrFFmes+Pj42B4aW\nbbVadVi2BGg+t34q81UBmh7HOix6WsHA3ck4/HsaXmJokIqj1Wrh+Ph4pjyBXrSGuFe5Z9tDtElS\n3CtuoSiGuDWn7gbQNlFpHYdMc6NKCHPzoAHMeNCMvjDEXalUMBgMZuoySW5hs4t1ALSbkat7Wf+b\nTRabF+Lm3/Nif7sZ+7qPmMJxA2jqE6/C7W6ilRNahmlfzOGqEKB1iE2pVHI00eCntrKlI8GWtjTg\nViHF8veQGObznTTzqFQq5utWq4VKpWLC2iREVqtVR7dHhrg1P64hbhtUFxE17iluv0s96EKhgKtX\nr+L69eumRIx8DzV+TvOg1yX3lQetOWgepul06vCg+fLchOCuysHLxdOQJRUuR2KyLSnLOzQ/elrp\nkRvzVUliq1r180gnLD2jYteohB4Itr4jQJfLZWM90xAiEYUdmVYNcbv9rCojPpddl6g5UDtMazOi\nbTDXmmNbFlEO+nP82s17YIhb6+L5TPTwEokEMpkMhsPhzNrTk1Uiy2g0MiFHVj4sK25EL9ag0+Ob\nRzSat/ZuHsyi67qI2CFuKlC+UxoWaizYfBa33+m2NqvcI41MBVCdwU5uhy2j0Whmkhu9PJuop6Wr\nSkikw7Jq1YrqDBpj/X7f6A/ycqrV6kxLW5ZKarnSvOjdvD1zL9H9x39rBEU96EgkgkQigWw2i62t\nLezu7pp6+nK5DJ/P53ACbP6ArXe83jPAfeBB2yQx3QAMwZ6FfKRKWyn/XigFHvJEImG8ObUo2VKv\nWq066vlICJq3DnbuzOv8hs0aJXDoRQa8Dl5nTt2tRjoejyObzZopYTs7OygUCkin06azkJdi55YA\nmBDVzs6OyfPHYjGHhxkIBExJWL1eNyMRJ5MJ0um0o1aXndOW2S/06PUAM5ynBpw9upNRHho3jJoA\nzogN52EPh0NHVz2uxzL5OjcJBoOmvzBb5m5sbDh6W9PAsPftZDJBKpUyYfZms4nDw0OHcuMepHHE\ne+fnsqLROL0fevzcD/TquKalUsmEuDU0alcnrBr6tvevbTRqAxvbK2ZZEs8hdY9bVUIul8POzg52\nd3exvb2Nzc1NZLNZpFIpR2XAquts61g3wpSWn/JvMjKn3cc2NzdNDphn0gbps+xpjbwBcHjmNFQY\nsq7Varh9+7aZr60EPEZvuXf5LBQl/ml6zcvUzX3lQU8mkxmA9vl8jj65px2UeZa0vWjLCnPFHGuo\nuXJ6PZubm0Yx82K3nHvdt73Zvchv6IHi2uigDjJAdbKSArQ2e1DFQIDe3t7Gzs4Otra2DEATJL0U\nVXD01Agku7u7JteUTqcd3cbUm6jX6wace70estksstms6bikIdyzenrqGfBvMsJydHSEw8ND3L59\n23jBBGh6dArQBCol1xCgx+OxaWpCZeuW211WgsG7E4Xo4SQSCcfoT5b3aL05LypgAjRzrQRnCtvw\n0thYlbzkdu5tgpjf7zf7X2umue56qfemv2dZcQNopoJo3NGz1nwn95RbM6FwOGzm3ZOomcvlUCgU\nzHkkQNMIVQPQy7V2I8AyrE+dPR6PTSpMS5s2Nzexu7uLQqGATCbjMFSX8aDVqNKUKQF6Y2PDNDKq\n1WrY39/HeDxGpVJxOFUEaL4PGhp2mkxz1Jp68oLPcOEsbn1Q2yNlIwn1oE/bXPMsO+Bu2HQVwGOO\nkPepxC7S9Zl/4b2TNLHIPdv37VV+w15rzYuyFINsdBug6S3ZbUAJjiRYEJzPw4PWHFI2mzVMzXg8\njs3NzRlWPRvgcEh7v993NGdhOJSGoBLHFlUMGr6k8cOQNj3og4MDk6vj72Xu3/agGV7TEKGGNZlL\nVA/6rMrMTdSDDgROBtSk02nH3Gqdte3GRiaw8Hu63a6DQEZQIsmI+eFlRSNEauzz7Ove4fvnSMrj\n42PD+WBOWg1aJbOtIpob1YgBjQb1oO3olnJZlPUfCp30p9dWnrxyuZz5TKVSjtC+F8azm3Ol+kpD\n+sDdCBP5EmRP08vn/dLAV4LYWfe0grOeEQXoYDBoPOjJZIJWq4WDg4MZZ4YAzTaq/L3qVKkhpSkz\nL1I494UHbddB04NOJBJGSSwa4nYDOyVkrSIMcTN/MRqNkEwmZxiZ5XLZEAxarRZKpdKZ79nNIl1G\nbDIelYGGTjnC0Q5x1+t142XTgwacIe7t7W1cu3YN+XzeUV+6LoDWQwfA1Atz3GSj0cDBwQEODg7g\n8/kMADME3uv10Gg0DENdwTmdTjsIVmchj2lkgukNMvxLpZLxoGu12kyJTzKZnBvi1nDs8fExABiP\nm+swry/AMkKA5rljqFtHNvJT+0Lz0soF8jSazaYDoNWjoocTja427N5OFdkeu3rQOv88GAyi1+uZ\ngRU0vvXda05zWbE9aO47/h3lK2gZJz+1nND2oHO5HHZ3d7G3t2dALpPJmB7YboMnVllnNyfIDaSp\na/gZCATMeU2lUtjZ2cHDDz9sxsRqP4VliXsa9SKeqAetc8vpQTebTZPacKuTZ7Mre+3cQty6Zx54\nD9qNJKYh7slk4qiDPkuIW3OBgDdsUb6gSCRiXgRfkH4eHx9jNBqh2WyaHNe97tktxO21B60hGQIJ\nFT+7nyk4NxoNR6iYCowAnclkDEDT4/JCCcwTvnv+bhJMEomEo1MXlX232zVeJz0QPcAkwBGclbB1\nVma3etAsiXILcTcaDWSzWZO64d92A2htx0pvz+/3G2IL0zfLMl7dhCHuWCxm9h8NTQKzDn/Rq9fr\nIRAIoN/vm0lSh4eHJs+u5DF6psoIXlZOy4vaIW41emhE6t6m/tEoitbkLyskJrkRF9VotvtR85PP\nqZ/c+2yp+dBDD82AXSKRMCkE3seqci+HgsL/p6VjbL3M1s2PPvoo8vn8QsS9RcV+VrccdCgUMoQw\nGprj8dhEIjY3Nw2gK0Cr8aYATeNJ95oXOvBCPWi3fAFwNxxCRqq2/7TDKPqpoQb1aBk+0/DoMnlp\nt++1CQFnVZgaCtKuU2ppr6Ic3AhhJMewXKNUKpnJP+olMW/OvCI5AQxLZTIZR0mY5l+8YuhS3H4f\nlR5wlw3v8/lMLk7ntWobWR4mer06R5g8A71W9Tj4SSWm5TZc20ql4mjyMplMcPPmTVMVwF7uyr4n\n45t9xb1qEuO2x7W+2i5f0xC7bWTwed3OpT14YNV7VgPAzTsFMKNkCdg0gqrVqmM4jE7LY/pD9dWi\na60hbu4JJZZS1wWDQUfJEgCT22cUketO7gdDxJwepZUgqxjLmhbjJ/kQGnmjgd/v940Bubm5OcN/\nmU6npokO00ylUgnj8disOcdt2nr1rB60Co1ORhp6vR4ikciMgTkcDk2KQFMGjAjyXU8mJ3PoGSVt\nNps4Pj52GLdMca6SugEuEKDtQ2U3kSBY0SIfDp2zm91ePkFdh1awrpCsPjuJ7zWQnFXUq+Uz9/t9\ncxBXJYppHlO7WdkAzRpFvdiuj0DF8NDe3h42Nzcd+SLb6DkPsQ0j4O5hzGazptFHMBg0THodZMKc\nJMOxLK2gQlYi4KL3otaz20WPaTAYGM+I9wg4w9o3b940eWuye20SJQeYeAXQ84TroJwOW3lSAdvA\n4OZteDk/3AZnbdtJXcIzpvfK++l0OjOMaNai89KctEYBlrlHBWimVchpYXiX+5K8CfI+aDQkEgns\n7u6aaVUKzl51IaRO1WikTgyj/mDjEQ7PIYnRzlFPJhMT3ep0OgbU2u22Iyyvxo8XRr+mwDiyNZ1O\nuwK0kkZ5qY7jupC0zEjp0dERptOpeYZVeRWUC/eg7ZfATcGQD+vs6P2oR2LnnTQXQGIFmXVsukGv\n6yIAxU2aRRmtAAAgAElEQVTUg6ZX1e12EQ6HHaUpywrDvtqSlGFXgnOpVEKtVpvxtMm05TQxHiJl\nXMbjcUcTkItYUx4cGnixWAyZTMah2Bi+5xowf8tSpkajgXA4bEK6SgRcVPjc88CZa0QQVqMScBpT\ntVrNQSxjyZMCtLJhVTGvg0FP9jvXmx6qvmcboO2omObrNJ/qJUBru0jVDzpL2yb3qD7g1yzfY895\n7nH+bv7dRdda75Ffx2IxBzgzEjKdTs2ZpRGphicJVdvb29je3jZ9pPnzdhnXsuLGUuZI12KxiP39\nfezv75umJBwoQYNR0378miS8TqeDUqlkGPWFQgGj0cgRgbQb2iwroVAIyWTSGGixWAyFQmEGoAeD\nAdLpNFKplNF3qVTKlDMCd41NngmuCSMenGvAiOOqcl950ARo9aB9Pp9jdrOGwmxSiNLhSbAgGYN5\nY9tjX4e3cRahYcH7ZviP7EGvAJoHS0FZQbrRaMx05gLuWovpdNooBIbV6EHbtYrnCc78W+qZMH+q\nZDY+J4GGbQaVOAbArLcSARe9j0U8aM2DammNDc40msrlsitAs1UrPWgdJej1niYQEfzppapQcWkO\nUT2O0zzoVfc4cDdXrAxpuwxMdYXehxIo+clWq9xHNICUBHQWIqQar0py9Pv9hhDb7/cRj8fR7XbN\nHiBA617e2dkxfQc2NzdNKRUJVvZ+W1ZUr1IHs0qlWCzi1q1beOGFF1CtVh3zEuLxuHEwNK1EAAdO\nGiANBgNTJUJw5r4mkHPtVtkf9KBZ+ZHL5WZIeARo5u2ZOmJqQ/POfB4CNPuKE69Yhrqo7jhNLhSg\nNZShCl4BGoDr7GY3Uogd4iaD186f3U8etH0IdLarFx40CWHMsdoN9xWg7ZQBDxPny25tbeH69evI\n5XImFKThn4tYRzvEyhC1loINBgNzn9Pp1IS0ub48WKpEqCjOQl46S4ib0SC+W2U9M4dFwhBr1Rni\nZrkfxyVyRrfdRtPLNeaZobGr66Keqh3itgk1dmnWOkPc6j1rmkwNYq2V16tQKMwYeclk0vw9jSic\nZQ15rqbTu8NR1NFIJBKo1Wo4OjoyYEsPmvt5e3sb169fRz6fN+FYhrjntUFeRmx+DA39arVqAPq7\n3/0uarWaMRRisRhSqZTphGcPe+En670J+uQ5kADJqBLf7aoATeOF5Er2prA9aDYs0ot6QzGm3W47\n9DbD/yTu8flXlQsPcdtWL+BkxE4mE5M35ELy5dqlB6TMt9ttcyCVuEVSzXnk7M4i92JFrqq85h1Y\n3XQKGPo33Qg+nU7HvC8qYLtVqH0peC0jNjNfy/Jswo5WBNDz01aeagXbrMxQKGRqTc/SFtEGCAJo\nOp02hLV+v49wOOzK/Of9sVUi74GRI5b4UWnYE97UY1o1rKn70d5/GrJWg5gDGti+VKM/arQom9ar\n5ipqOLAvQSKRMOkEJZuGQqGZ9o1ag0z9Eg6HTa08yZPkDChJblFxiy7pGruxz9WDVFJgJpMx4Exd\npu2B1ymaluE7Z3RHS/MKhYJjBCwvElC1JJHEzkwmY6pHdH8TJ5Z9Nu4//fnRaGT2i5IWNUUCwLW+\n3407ofrNy0jihQG0HZJya33H5hg6Oq1erztqj/U6PDxEuVw2HYyocOkNsdMX86fJZNKUb32vCgGK\n+cpsNuvw1jlPlgpVQ33T6dSwLUnoGI/Hpr5S89I6bEOZptrKcRVjiJ6uXgQ2emv8VBDnYbJndqsH\nDdw1Fpc1jjT0zBxVJpMxuWYOFKnVao6cnhqZ9sVQKMPGzK3bBqYC86oAzbOn90YjRY286XTqyOnz\nYmieIUwtXbK9fu3dvorXrwY493in03FwWhiNU46AEjEJ3gxdKrizDpxRJj7PKsxzwDm+lnu6VCqh\nXC6bEkfuAd4ra3R1LK1yQLwUt8iE1tzzvGnfChqkW1tbDmBmpEJTTNQvJG2SJ1OpVIwnqsRIr5/N\nTteweoLRW+oBnbPAPuisqNCLuWuezQe6zMomdmjeSusCyWjlAanVaib8p1Nhut2u2dwK0FRw3DyF\nQgGFQsFY2X8eAJrs0FQqZZQu106b9KuFSIAiQBO8Op3ODDhzU84b5afd15Zda+bSeb/sUuXWZH8R\ngG40Go46WS1rWybsql6chjMBGHDO5/PGe7CVl3Z0Y4mb3heVoeYa3QB6VSWtZWA6AtE2AAjQ7JbG\nXPnx8bEBaAKMGi/ch15Gsfj7aYRmMhl0Oh0HOCvZiO/KBmgSxvi9NkDX63VjDHAU7ioyHo9N2Jjv\nnqxoW4dx/RhFYfnXuoiBgHOteB7Uw1TDmE4QAXqeB80UE40dts/kueaeUkOXBsC6nk3/zVC3hr7t\naYUcpckObdFoFKlUyqQavMSVCwdoWmLzCvdVKdODjsVijoYJvMjUZfG5ArTtQXtV53q/Cz1o7ZTE\nHIqOo9OSNA19MzdKZVKr1RwAzYuNEUisIGuSdY2rWsHKtq5UKqhWq+j1eq4GwaIetIIzQX4VD5oH\nUkFV2zHSkLSjP91u13ADyHClJ0cwoHJWD1rDxF7lHfXsMXzJIQDK3VCA1ilLCtAMLc/Lm3sFMLYH\nnclkTAUHn0NJWXbkbjwez3iFyrYnQJP8xGELq3rQuqc5h/jo6Mh40FomqCF89aC1D/u6PWhGghSc\n+f80pZPP5x0eNL1nNUaZ16UnTV1EB4wpMy8a2cwTe08EAgET6iYfhFEhjhUmQPNckhVObCFAe4Ur\nFwrQ0f/wH5D81V/FBED/iSdQ/qmfmvGgGf5QgI5GoyZXoV2vqFA6nY6rB60ArTm7My3kRz4C/Kf/\nBPj9wPd938nX9zHABwIBZD7xCWz/1/+K8XSK9iOP4Ovvfz9amQwajYY56Oz2RHDm1yRAdDod1Go1\nBINBRxmCetGsAWQYXXOQZw4JfvSjwH/8jydfv//9GP/4jxtjoVKp4OjoCJ1Ox3gSjIjwMLsBtN1L\nmuuj3olbDnAR4TMGfD4EXvtaTK9cQe+Tn3R0OWPuzg5rtlotU2XQ6XRMCI17k/uX4Vs3D5qylJKe\nTIAf+AHg6lVMPvlJB7ucoVb7vBCgdSZ6sVg051FD3OoFeVK7/fDDQDp9cgZDIQQ+//kZgObatttt\nR39zt8gdPWUFHg1x08vlOYnH44YbsLBY+xkf/KCjBLJcLuPw8BCHh4c4Pj52ALTeK9eQe52GxjoA\n2v/Rj2LjE5/Ahs+H0ZNPovnRj86krngxxK0DO9w86Ha7jXK57ADo0WhkAJr6iMYnf+/Ca23r5098\nAnDhCmg0SMl7JIu2Wi0cHx87wtrlctl8TWOEzPBsNmsqW7ivH2wP+uAAoV/5FdR+7/cwAJB8//ux\n+5Wv4LuPP+56ULQncTAYdIy+46fWSrPjEq11+1pKbt8GfumXgD/+45OX/s53Av/tvwE/+qMzHoxa\nnjxENgnLruVWRe4FuxUAAsUiNj7+cRx95SvojsfI/+RP4iX/9/+i8eSThvhChrBb60beg+Yjlb1I\nRU5PS2vVNcdzJo/jj/7o5JD9/u8DwSDwQz8EvPa1GIbDjvnK7XbbUS4zGAxmAJqXPYOZYVveJ0Ga\noXK7dOxeYn7XL/4i8NRTQKPhWgdJpawXu7dVq1VjlQ+HQ2OF67Q0RiuoxFZtRgHgBDyefBK4E/ZX\n0hTXzCbjTSYTx8Q2KjA2hCE4U3nT86MHbXc/O5OR7PcDX/4ykM2e/POOR6YkKr1/ko7mvVv7vNr6\nR0uz3Doa3lPc9vPf/JuY3LlPdufSNWQ0i7lYGqLaI9oek+kpQN++Df/HPobhH/4hxoEAgu95D2Kf\n+hRCr32tg2fCs+3WBnPeOmnVha0DdZ3PrAPn6OfJe97j+Bt6X/zd1HGMljAXTk6FPUmMEQ1Nq9B7\n9mpqGHDBHjTGYwT7fUw3NhDo9+G7csU87ObmJprNpjl0fv9JwxIOAtccNJUBF0wJQ5w1ms/nkUgk\nVicbjMdAu32iJDodYG9vphSAoERrm0aCXZfJw85WhMqKZC7kLEzieevsm0wQHg4xCYUQHAwQuDPc\ngiC1sbGBfD4/Axy8Bw1RKdOR+ST+HU1XKFv3zCHBb30LePWrATYJed3rEP70p+F7+9tnyvI0r8Wm\n93rY+cmw63g8NoSOcDhsykPY3k9JhCyxWFhu3QJ++7eBD30I+IVfOP2d+GZ7AADO8kEAxoug90wP\nmuSqlcHZumc3lm6r1ZppOjEcDlEul81AELaK5ftgeVI4HDa5OTZ+0OEgS9VuT6cnXv8dYYibypJn\ni6kEzX8SpNVT4nOxhhWAiVjYTSuYXzyTUeGyn/E//gcmf/fvOqIqZMFPp3dbYpK/sbu7i83NTdM4\nw6ve66fKeAxfpwN/PA5/twvs7RkDhkYXSxHH47EZVBMIBExazGbMHx4eolKpmK5jDAdrAxaew6XC\nxS76mQ6eXky/qJ4YDoeO0lPuabZa5drncjmk02lcvXoVOzs7ZkDJSnt6jlwcQF+5gsk/+AdIvuIV\nmEajGL7+9Ri/4Q1IvPiiCRd0u11zoPx+P3q9nmnCbxfBAzC5Vr04EpEAvVLYYW8P+NmfBa5fB2Ix\n4M1vBp59Fj6r3IfWlYb0OIyBgEdFqB4L8+1UDgyjreJF+69exeiDH0Tu6acxjUbRe93rMH3jG5Gv\n1Qw4p1IpM3rRvsjy1gu4y3jkM1EpK6uRhBZ6rAsD9CteAfyzfwZUqydK7bd/G/6/8Bdm6owBmLVz\nKw9Si1kb1/CwxWIxbG5umuYr3CcEFNZTLyw//dPAv/7XQL1+6rfpc2huV0uYCBjBYNDBTrat9JWV\ngHXP/NsEDUYrbAY9c6dKumu1Wub5uBf8fr+pm1eAZp5xqZaUPh/wpjedpJY+8AH43/MeYxDTw6HR\npgCtbHTgbgkh9woBmnWzvDTypiVNC9+zy37GM8+YdJJdpsaaYEZTQqGQAwi0N7Rd2uOZ7O1h+jM/\ng+BjjwGxGCZvfCPw7LMIHhw4zraG+0mi6/V6ODo6cpw/7ivmmdklkpEBAjQNZBojZwoXz9HPY+Ey\nkc9Eb9hOP1WrVUev8Waz6eA40DDioCBt2rTSnp4jFwfQ9Tp8n/40Bn/yJxgnEgi/+93IffazqLzq\nVYaFSZautv6kNQ/M1hayoQbnoGoDeVq/K3nQtRrwW78FvPjiSQ7sHe+A79d/HXjXu8whIanD9qDT\n6bTxjAnOAFw9aB5ULzxof6MB32c+g+63voVxIoHIe96Dwuc/D/9f/+umvWA+n58p2ieg2S0yQ6GQ\nuS/eN5WcgjMVTCKRMHWECz/Hy14G/ON/fKKEEwng6acxxWwrTZ/P5xh5yE8qXLcQGkvv+E4KhQK2\nt7dNdyY2W2A4cWGD7jOfAba3ge///pPw6xyjyi2sqvlR3i8BmqFbBWjPPGiXe3bzoAnE9kQrTYVo\ni1qG3klu433zLFIpax7zTM/xta8Bu7tAqQS86U0IPPYYwn/xL5ruUzTeCM5MgahBqZEhDRPTM2Y+\n1faglwrLu+xn3In0uHnQrAFmKJt9t9WDtveN5wBdq8H3qU9h8p3vYJpKwf8jP4LQb/wGgq9/vSMy\nxs5no9HIGHLHx8cOPasOhq693+835N1sNmuGUxQKBeTzeeNkLVyy5KKf8Wu/htHf+lszBFO7moKG\nvpKOGT1iWkGNtkwm4+jVToBeek/PkYsD6C9+EXj0UQQKBfinU+C55xD/3/8bib/210wOiXWuzDdT\nWbTb7ZlaW26aVCqFQqGAvb097O7umlAm84sredBf+ALw6KNALnfy7+eeA77+dfje/e6TZ7pzUJR9\nq/2StS5TW02qQmQOT7uJrdSo5ItfBB57DKHtbQQmk5N1fv554N3vNkxruw+4es/MMeqMZ2X3asc2\nXWc+u+Z8z2RovO99JxcAfOhDmG5uznjQDFNyX9CL07wSP23vPhwOG4t9a2sLu7u7uHLlCnK5nKOU\nZGGD7mtfAz71qRPvqNsFmk3gve8FfvVXZ9/JHA+aoh60G0DraMqVlIDLPW/8vb+H0T//58bg4dpy\ncpHODtcoFi+GtbXsSTtdqQdtM8MXlt3dk89CAXjb2xD4P/8HoVe/2gAXGcAMZ2pdOQ0M/jeSyAi6\n/Nr2nm2APvPaW/sZ1645jCFGK7rdrqP/vXqW+XzeEeL2uimGQ77wBeCRR+C/M5UKzz0H//PPI/js\nszMeNHPmjUbDUUOvqRw1rpUBTs6AW4ib3ujCenuOfh7/8A/PdFOsVqszxr1bbw1OQ2TUjV4zDWXu\nZxrNS+/pOXJxjUoefhh4/nn4R6OTsM9XvoLR930fEomEqb0kA5Rt1DgqsVKpOFqxaUiNHuHe3h4e\nfvhhJBIJ7276+nXgG98Aer2Te/7iF+F75hnAsmBZtqObOJlMOsLZtAhtK1onSXkS4uY6j8cn9/zV\nrwLPPIPwHYINxSYvkT2vJQ8s4ucnS7aYx2PnH7X81dA4E0CXSicK+MYN4H/+Tww+9zn47uStNKxH\nJcu51rVazfXX8R1o6ROtYHrRu7u7yPFwn1V+/udPLgD4yleAf/NvXMEZmM1B6/PYOWi7hMXOg66k\nBFzuuf2xj2G8v++oomB7Rzs/5ybkJjA0zw5Rdv556UECnc5J/jmROMk1/u7vwvdzP2cAk/nRQCDg\nyDfSSwLgyD23221jwKlBpOMmVREzx3jmlqrWfsY3voHJnWigrjU5Hdped3t723jxfPe2Uee5XL8O\n3/PPA/0+fJEI8KUvAc88Y5wPNcIjkYjhgDCE3Wq1Zpj/BGS9tDUozyMjoBrZWGitXfQznnnGQTJm\nBcjx8fGMvmPzI/vKZDIGoAuFAq5fv244Klri6cX0KlsurkPHq151EoJ4+mkgFAKefhrD970PuDO0\n4L4Ul3vGBz5w0Xd1ujyI9wwAb387UKmc3PPHPgakUide3qX8+ZZiEXjb207y0KMR8KM/epJrXEOd\nrKfitp/v5OzvS5mnN6rVi76z+TLvnu+UVD6IcrEttD784ZOL8iAspH3PD4I8iPf81a86/30P4tV9\nJT/4gyfXgyS85zme8X0jjzwC/MEfOP/big1DzkXs/fwgyIOoNx7Eez5FfKvW2Z7pj/l85/fHVpDp\ndGri1Zf3vD65vOfzkct7Ph/RewYezPu+vOf1ib0/FpFzBehLuZRLuZRLuZRLWUwuds7ipVzKpVzK\npVzKpbjKJUBfyqVcyqVcyqXch3KuJLEHMVdwec/rk8t7Ph+5vOfzkcsc9PnJg37Pi8q5s7hPy3lr\ns3TWoLXbbRweHuLg4MBMe2E/V51DWy6XzRg2vfb29vDoo4/ikUceMZ+FQmHuPbgV/HuRp+/3+/jm\nN7+Jb37zm/ijP/ojc3FajV67u7t44okn8Pjjj5vPnZ2dme/TGbdnuedOp2O6g3H6EBvDc6RaqVRC\no9GYWbtHH33UNILX66w1mafds9sUHI590/tjPa7uhXq97rpObDbBmlw2gdCuc+wIpLWNnBq1zDrP\nW3t7Px8cHDieic/4+OOP48knn8RTTz1lrkwm42j0cK+1P+2eOWRG90G5XJ5Z53q9PlMb6jb0ozun\nDO7KlSuO53j5y1+Oa9euzTSOYR3p98o63+u+3fRdp9OZWX97z/PfrI3XPb27u4uHHnoI169fN5/5\nfP5M933aPR8fH+PGjRt48cUX8eKLL+LGjRtmwqCemVAotLCO3trawtWrV3HlyhXzmbX6NKxyz6x/\n5n1Q19n7o1qtmjXT9WP9uV5n7Ui5bCOZiy2zsoQN7u1+v8ViEcVi0czlrFarZpJRMBhELBYzzQbY\nRIBNNLThPweCuwGMlzOh2apRG7FrlzB219LpUFrQrx2Q9PBqg4tVjAYdFM9xgdy8bDKg83Q5FYjr\nx05KvLxumMBGHVRebNXIzj52YwHtKa6NP/Ta2NiA3+83g1a4fvz/OraP/z0UCq00h9ato5nd1pUg\n2W63Tcc1NsyYNxBBe0fz33ZHqUUUgvbcbrVaBqQ5U53tDjntzL7Yu9rv95uRmHaXNL/fj93dXWxv\nbyOXyznalK5z4APPoHbJ457hKFTAOQtY94620fRCtD88P3m+tKNVq9UyU614VatVNBoN072PM7bZ\nidDuBKjPuC4SsN1oh8/I/c29ZU+34xno9Xrm/tk1zdaN9oTAZd8F10i7NXKSn85nt0cc82zyb7MJ\nznkSq+8rgCZwsG2jWwcjDsym8g4Gg+bQa1s5zhpVcKnVakilUmaYBTvieDUajMKNyk1BUKDS46Zg\npzBboWmTf4I0leKq4Azc7RpWr9dRKpVw+/ZtlEolR79ldjPTblLs4sY+1byflSeEWaIAPQ+caezQ\nuGGbPXaWcxv/6ff7zexZGiAAHP29dUYs+6mv+ix6KUBzTdkbmAYHlYENYvbvZLcxArTu/0WESlS7\nhXFYAMe4UpG5jUnlEAEOdaChoyMbg8Egtra2cOXKFRQKBROhOG+AptLVcwfA1VC3u7t5cW989xqF\n4B7gxdGY7HuvF98DB8MoKBJY/H6/4xlX7ePvJvZ50paW1HsATHdE3gPP1HQ6NeeOQB0KhZDJZBzj\nYAeDwdxhMmcV3hdb1+pseIK0tvbk99TrddOSlh396Ayel9xXAM15nPV63Xh19me5XDZDG6gIeOC1\nJzCBTWfC1ut1VKtV43XHYjEA8BScAaenRFBRq017bVMhqzLQTapetHrPq2wSrkutVsPR0RH29/dR\nLBZnohdUIrphK5WK49DRqvRSCD46ZUbXUgeKADCK3gZTXSPuCe0VzH7oCiz8XQTnVRScPahD94W9\nplxzetDs/6tzid1agRKcqczUu7qXsAUiJ1ZVq1UTRbEB2v59Pp/PRFLYO5r/1rnakUjEtN4lQOu8\n3HUCtD2IggCwCEBzzb0CaTU4eTGCpRfB2AZu9fSYFgPuGln0Rm2AXgeYuLWq5b3wk4Ns1Ejg92mv\nbuCkPWw2mzWOAe9f26musv40CnQ6mw3Q/Jsa2WJ6i+1NY7HY2tZ0ntx3AN3tdk2+8eDgwDQ25xQS\nzujUBuXsl6uWMpU6DyYt03g8jmAw6DigEc5p9UhUEauBwMPGTchQkO3t2R40LW4q4FU3CIGXHvT+\n/j4ODw8d86p5XzQuGo2GGRRAUFhXyMf2oOlFu3nQGgXh5RZOZJhWUx6cSUtDTwdqxGIxRyh0lWdR\nkNZ9oR60rr3tQduN97nearRRzpJuUICmB20DNNfZ9tADgYABZJ6/ZDI50584Go2a+e6c8UuA9hIA\n5z2fnaZRgGYUQEFZ885eDqLQ/cyzzdwoU3jFYhGVSsUY8bw4LYr7gwCtIW7qHC9nybuJ7UHbIW41\nHHWKHfeMrWM4a1n3mk7I499cRcdoGkANY3WY+E7UIVGDkz3D9b7OQ+4rgCZJol6vG8/u9u3bMyMP\nqTDoOSeTSRQKBTOPloMcOJSCAMMpQJyMQnBeVQnb4gbQVHi2xaZ/m4rALcTNUK6GGJcVjVQQoG/f\nvj2jhIPBoGPDsiE8FRmHOKwDoJWMNM+D7nQ6Rply4hMHyFMh8uvpdGrC2szzdTqdGXDmGD2OyfQC\noHUerhtAVyqVmbWnoWB70Pyd85TXWZQZ19aeWGV70N1udyY/y+ESzD1zyAEHYfCT89B1IhTPrZcA\naAv3kA6h4cCdRUPcXt6XhtwJBPSgi8Uibt68iVu3buH4+NiRk+b92vtDvVbqG5/PNxPGX6cHrXvC\nbfaz7UEHAgFjcGvuPRQKoV6vz0zy499aNa2naQDNLbt50Po9PH8EZ+b2vTZ6TpMLA2hVMvxak/hk\nlJbLZaOQCbwEVi5cLpfD1taWyYVpbk1DQUoAYD5hHQvOaU/tdtuEMI+Pj1GpVAxIk+zhlj+clw/z\narycklR0Lq56ogxZJpNJhMNhTKdT9Ho91Ot1A2JLjZFcQGwLneDJKUPpdBrdbheRSGQGDKLRqHku\nwMmUVW+cc4A1XO82XWqVdeY+0IujGpWE1ev1HCFhXolEAsFg0IRDi8UiWq3WjKJmSF5/x6JpGz2D\nGoq352kDdwlVNGp0yhbPoL4L/ZpT3c48S3nBZ7AvNep45tvttnlGGnWMAOiUKq9GBdr3aBucmvfU\ncYecKU/ynf1s9jPPW4N1gbPuN7K21VumIUIDgT+jaUjb6HTjjawrsuL2TPw7ih2NRgM+n89MvqOn\nzWdwu3+v5UIB2ra6lN3KRD5nt9JapzXFwd5bW1vY2dnB7u6uYeoCd4kB9PToffJvMIxCD8tL4bO0\nWi1Uq1UUi0UcHh6iVCoZBjqNDVV69JY4ho2D26l43cJvy4gqdlW2OtuUlxJ+OJSdc365Wb1WBCSe\nRSIRA56MIlBpJZNJdDqdmdnNCrrK3m21Wg4LWP8GgZ9e3tIzfy0hMYVAzPnaug9UIXMkJt8DCY3d\nbheHh4fodDpmL+t+2djYcIxGJHlrkXXm++U60PDa2Ngws3Ank4mD88HvZ4kP5xUXCgVjJHH/Mo9H\nMAwGg2sjhGnEhOFhrjsvrhvvJRgMIpvNGs9+HVUJvEc3Y5H7m8RVlvDoFQwGXWcVa3SIOnQesdAr\n0XNDB4kps263a/K9Cs7cZ4zuqEMSDAbPRd/xzJAzwX1OAykSiRijnue22WxiPB4jlUoZLCJ2cJ+o\nMfc9CdB6sDSEqXlbvkz1EqgQOC91Z2fH5LWURQ3AeBT0aBjOWFcpwmQyMS+YIaz9/X0zr5gADdzd\n8JpDVW9QN6yX4TcqBfVO0+m0GQzPfKHtAXY6HeM1rROgqbB4uPjfNaRKhaDe3mg0gt/vN5Yww1XK\n3LaVIkPaDMXSo1rV09OqBOaaWYnAcjYqWgJ0Nps1+VoefILN4eGhUTL2fsnlcubZF+VUqGfDiEk0\nGjUKi/tuMpnM/E2d9cx68kKhYACZv09L8tRD9VLUO2VKiIYZQZpGEvPf9P5jsRhyuRxSqZSp311X\nyN2u7uDZUdCz6225F6kLleCkrHB+rcC2DvId75Xrl0wmjTfJqBwAh14lwCrhS4mZ8wDaK3236D4n\nfjlru44AACAASURBVHD/0MjPZDIm8kkvmoYyn8lrojHlwgFamde2x8ONqAedG8MG6N3dXTMsnoeA\nAMLFJECfpwetAM3D1W63DdmAG5VKTxWHbthQKOQa6l5G3DxoAnShUMDe3h729vawublp8v7VatWA\nTTKZNGu4ToAGYA6WKrBsNmuMOTtE2O12jbIgKaRerxsLXyMXduicXqt60KsCNA21crmMo6Mj02RC\nAZoedCwWQzabxc7ODvb29kyemvuGITcqGJ6LdDrtAOdkMrnwOtsphGg0im636wDW6XTqCL2r96Qe\nNNNMCuTqBWo+3Uux87vM8asHTYCmEmbEKJvNOuqz1+VBqzOiJUiqA6in8vm840qlUjg+PjYXoyP8\nHTRKSLiiYXkeAK25buZ5+byAk1TGf9u6x80h8VrfuXnQ3Ofc06zN1gic3+9HNpud8aDJMtdnWodc\nKEC7EYHcQtx+v98sZDKZRCaTMV6eetCBQGCmOQGJYgxx04PWRiDr8KD7/b7Dg759+7ZhWNpAoZ4c\nO+2wDEwtSq/yHfNC3Ol02nT1efjhh7Gzs4P9/X34fD50Oh0T4mZTgXURUagE+M4mk4nxcnXP9Pt9\nQ2bipR60AjTft4a453nQmitdNcRNo+b4+NjRBa9arToAmooqm81ie3sb169fx9HRkTFYDw8PcevW\nLYzHY6PQaMjl83mTAkomk8aLWWSd1bNQ0FcwJkDz/2tKgABND5pesl225kZw8kpshrSy/W2QTiQS\nRp+kUikUCoVz9aDVkLDLFbneLEvjlc/nsb+/j0Qi4eCDcE8zGjgYDNZeX67nhmVHfCaSCQHM9HdQ\nnWPno+d50F7qOzVEuYcJ0vx7bExk16vn83kHwZfrzvVYlcR2mlw4SUxz0HbdK9l18Xjc5NqSySRy\nuRyy2ay52OqOpQvNZtMQVGxloAQGDY0ues+22MQa1iKSyk+WbrlcdhwkFvSrdaeHlFakeh/LrrN9\n73ZuiN5TIpFAOp1GPp/H9vY29vb2TDkWc7t2HTefw0vhO7uX99rr9Rxd49hQQ8OIVNZk7XItaZDw\nYs5dCUNnAWg3cpU2I6lUKsaDJltVvV4aZgS8QqGAbreLYDCIwWCAer2Og4MDjEYjV55ALpdzrQw4\nTZSQqEx2W8nf67LJMrqv1uWRqmg0Tt+5dtbi2pAgRoBme1dNa6wDoN3WSL1RElb9fv9MVGJzc9Ow\ni0nS5HujcUdR7xlwlj7xb+vnMs+hhi3Xnfl9AMZ45llTHcd9ofo/k8nMGMbL6js3sY2KRCLhaMSk\nIM094raftCMlgZ661Mv7VbkwgNbQh1pWbgeem1gVGMk8tNhta0tZ0LaswtS1GZK02vUql8s4PDw0\nvaHpbdpdwc5DbEOIAKZhXjYa0fASc4UkduiG1edYF1t00Wezy5ZYrmE3pLBDtBsbG9jc3MS1a9ew\ns7ODfD7vCHPaiu5ewpy3ejRsTcuL+4HArB7b5uYmstmsqSVWA0HfgdaUak5tmXuet6Y2manf7zvW\nm++fJYtqIKmxo61X1yl2iFsZ0XYaiwxkNUi55usisQF3o0LRaNTsSTfgAmCIYqzb1xas2jedxjFB\nQsPbABxnVnXtKh6pvffG47Eh79rVK3relPNiEw5pkGYyGUcZrFfCqh9yZ6j/7PKrdrvtiCyORiNz\nrkg8rFarODg4wHA4NPt8Op2a5/FaLrQO2rbEbeDk93Bj80AxJKUADcwHfVvcDIFFRMGZ4MRcMy2r\nVqtlwpnlctnRQ1fDJucFalRemgNTohQPGgAHWYIH/TSAXkf+/qzPpq0c5wE0Q7RkafNipED7RNOC\n1+dfRMj8ZGi12+06AJq9zuv1ulHIrN2ORCIoFAqGTaxgofegZXkEGobr1LBaBWBsgCY50AZBlqlR\nKbOuvlAoGH5IIBAwRsg6Re9NvSLdqzxvbhEjNYrWBdB8X3b9tYLzxsaGSWEw3MoGO24gzd/DfcJ9\nS4NI35em+VbhVdj53Mlk4uD4aFqJ7YAJztPp1KRyWHYXj8dNIxt2mfOacEWCMUPyFFt3aI8HDXUr\nQFcqFUQiEXM2gJOoBbtSei33tQfNy63mUstheKjOCtDLgrSG5RlW125n7IJ2fHzs8KD1584ToG0y\nnhs5RfP8bqUaqrAVoO8XD5rhZAVoMqQBmLIgcha2traQz+cdqRJ60HYN+iLC+1Byo/aO58X0ixqd\nqVQKm5ubxkggQKtHrEahejGaM16nB82vNSROz1nb87IsMhAIIB6Pn4sBZ9cYz/Og1bBhqJPh7fMA\naBoqvAe+Y+07MBqNzH3Qg+73+4asqSDNswvcTd0oS54eIg1yenqr7A/de3y3dpUMKycUnBkdIECT\n/c/0JPkf6/agAZh3rE1JeIa491VX01jqdruoVqsAYHQowdnrZleUC/WgNRxNwNC6Mn6Phrg5JtDN\ngwbgICQQpG1Zlh1oh4u1lKdarZocY7FYxNHRkSPETdb2OpsIzLtnW+HaAM0DpmQJ9d7cPGitOb0o\n4T3ZLVVJ5tAQdzgcRiqVMiS469evI5fLzTTU0MEPZ9kfZGyzyQ5H2tkedLfbNXnZaDSKbDZrvE7m\n4hQs1IPWyIt6XXaIe1WAcQNoN7IPh8A0Gg0zSlDBOZ/Pr01xqczzoBWgteRHiYEEaCrodYa46aER\n3PjOCM6xWMz0hwdgAHo0Gjk8aF4KzgBmPGg9s0ylrEpo0hAw/60ArR605qv1HLJaYWtrC1tbW8Yw\n5l5elwcNwOGQEJzJP9DcuF6MFHQ6HQAwxE4+Szqd/t4DaDtnbHu0GuLWtpJkjmpYSuvrbLbgPA+a\nimxZD5oAxQ1Zq9VwfHxs5ovSm2arxHsxa+2oga7RKuIG0BpmY7hvPB6bUCnDqzzobuHNRYh261B0\nttisfQVn9fK1Xpgzc7PZrKP7FhXmMkJDgcpJvWbuhWq1isFgYMqgGHbP5/NmT2upD9dPjUJgNg+o\nIe5lPGg9N7r/7GiRLTRO7UYxiUQCm5ubppZ03WLXQSspTPcA4M5c13w/f5eujX6e9vVpopEHijZL\nYQ6Te1cvzidQL7rRaJj2v2Taq3ND75kGiz7/qh6qOlX0ygFnF65Wq2UMSHUI2Lktk8mYks5EIuHQ\n114DNNdIjYVAIGD6E7BF8LzJdTwXNPjYUpqRAA65WYcOvDCA1pID7c+6zgYiq4pdGqaApZfevzJa\nbZav/n+71MUtB7mMKKuW66nRCM0vah6Wh84tJ6l1l7Yn7aVxcS9hWJ7Mfo6fpBIjR4CNY5iKKJVK\nxlthMwiye5cFaLuMRtn6tiGjxBSSaVimwtK8VCqFF154AQcHB6hUKubZNN+o+2WZHDQNXypMKhmb\nid1oNFx/3q5gsPfFeaVyNLJlhyft88Yw5cHBgfFAmVKwm1bYHb1ogGjedxUwobJXA67ZbDomrnGv\nsBMha/oJhqxk8Pl8JqSsaSkaUewyxzKzRTrNuQn7+OtVKpVQLBbRaDQwGAxMdEh7C7DePJPJzKQU\nvCCv3UvUCKXBzoZHvV7P5NLnPbO+D0ZDbWIih+8sG6F1k/sCoFn4/SAAtJsSdrtohauiU+WhAG4z\ncu2cohc1gFRQPp/PHB7mxcnAJPlO80C2d6KXDdJ6CM5DGF5LJpPIZrMmSkGQZU5YIx3VatUQlwaD\nAXK5nAHnVWY/n2bIqAFDIGPPeYb0FJxZA3/79m3cvn3bALTmUe2mCxoSX3T9WW6USCQcZX/2nmw2\nm67Pq+VMdm+B8z6/NkjrWeN9KEDfvn0bwEmpHtdP67/Zk4AX62btjmqrAPRoNEKz2XRE3jjZjGvI\nvcRxlARoBW+Cs05aUoZyp9NBPp83Hb9WGQ/Lkio2L+LkwaOjI9NvgBUKNAq0JaxNytOojxeANk9s\nvRQKhZBIJJDP500uOZVKuf6s1tG3Wi2HDtcoRa/XcxD/gLNNl3OTC29UotNdeMi1hOd+EptwNQ+c\n7V609JD9fr9RhG7hSu0m5rUHzd9Dr5NeI1nyrEc8zYO2Q9yqjLUxAd/bur1oAnQikTDgrGU/zWbT\nkRtrtVqo1WqmLITviOC8Sh7JNmTcANr2oFutljEmyCJV743dx9i3m0aQ7pdVctAM+Sl5RsOtBCqG\n9FTILK7VagDgaASjpYTneYZVcWq1hJsHzXuu1WqOKATXk+Q9JTAxBaHkp1WEHnS5XMb+/j5eeOEF\nlEolRySAe0orRdSD5nNzTymway8Jnk/Wfy8r9KA5Y0C74zWbTYcHzZ792tBGUzk2z2KZlOMiQh6N\npnHoQU8md/vg5/N5158nAViJezYvh8YqI6XA6uAMXLAHrSQUzRvdrx60bTnZwMw6aM3zqgdNEKHn\nzEOjCtFWuKtalbop6SlyczHURaVDwpR60HxmNUpsD5oKWf/meYa4E4mEg0xIb7lSqSAQCBgPmnkx\nn+/u8A2CM5v+Lytuuf55IW6WZHEyFT0bm4jFbnokGtoetPYVXiYHbbOAdd8pcYnkGJXxeIxisQjg\nJBfdaDRmjLbzJEK6ETjdPOhOp2O8f4KzEpT4Ncvw2LFQa2eVDb6KEKCPj4+xv7+Pb3/72zg4OHA8\nCz8ZZaSOsWt5CXQ8n1o+xPa3Z+005yY2QN+8edNMWbMBmvpEPWibP6T9FtapM/j7ubb0oAnOuVzO\nGDy2HB4eOurSlatg194rOHvBv7gvPGi19NyIHfeLzFPCPDz3ykHbvwNwJ/woo9QLD5o/r2Q6u76c\neRmG+OiJat7d9qDtEDd/93m9N3rQwF12aCKRMLlmGhrqQRMQeZgIzgwBLitukQYClu5lDXHz3zqZ\nSD9VIfN7NPysHvQyOWiGuAnOShQkOHNqmC1cK4Kz3++/UIa/Dc52Llo9aIIzz4a2MOWVy+XQbDaN\n9wk4O9yR2buKKEDfunUL3/72t3Hz5s2Z57I/9VzanA8+n47t5XAH7vNVAZqposPDQ9y4cQPFYtGh\nD07LQTMSYVcqrFvsdeIZ4jz7eURXAIaER7IenRyNVHDNgbtOmRd68EJZ3Oo9urUYVGVvM0o1jMXD\nqHlSLhqbmutFxt5Z2zlq6RZr40izZ3jJ5/MhHo87BpLzk20y2ZKS9YlKGmLIVmePrvKi3ZS1PgO/\nR3PRGq7kmEbNV8+7zstz1mfTw8D9Qu9nZ2fHMTmMe2symZgSCzaX4TAFtoe1r3uJ3a1Iw41q0GkH\nJTUW7P3Mn2F0QutJCZ7M8bE0i01WFlV4bukPtkPUcDojLLbxoKQpLQ3SaAv3v0ZyvM41MkLCeuxy\nueyYHKf18NwvSvSy8898N8Ph0ICRz+cz74Qh0rM0p3ADWuAu2NMYSqfTMyQ3m+imBFOb/czmN0oK\n017jWvWyjNi5fu5PRuKou8PhMLLZrGFo93o9VKtVR9ovGo2af69T5v3+Rf8uHScaHOl0GgBMtzE2\nMJlMJkgmk+bseNGg50IBWkO7ZNG55dLcrON5Ck1bb1JJcvPS2yCt/qwDEdTb5QFLJBKGCR0MBs34\nOu0FTLajdgDii1XlwnvR30Fg9Hrtebj5HOolUxFzEhPruAnQtlJYpfHLqmI/Bw9JPp83uTq38hV6\nGPYVj8cdedhFGztoZzDuB92/3J+2hzuZTGa6zNkeIPcdw5QK0NrgYZn97Eae2djYcPzdjY0N16iA\nG7HnNPKMXd7oleheJUv/6OgIlUrFhHgJrBq+t9ukKgeAoX8OZOHzA3ejNVTUZ7lP21vTxh2FQmEm\ndWTn9PXis+g4T4KIXgzTp9PptTQC0Zyzkup4XwDMBD/bwDnPKMuyYhtR2WwWo9HIOGkkjukQDeqD\nVeXCANr2RKfT6dzQrlt+6TSA1qT9dDo1ViPBmU1Ozjrz1/bW9Of0oNEb00vznwxx0ipn7SD/HYvF\nZuYXeykK0HwuKl01cNj4g53Q5lntqnjPG6T1Ofg3k8kkNjc3HbkmMk5rtZqDRa0tWgnQ9KT4+xcR\npgu0Ny9zVepVkihoX277WZ+Ja2xb8wRoLRE6i4ekIK15fBrQkUjEMc6T4wTZPtKtdtotdaVRAxp6\nXolyDLRhEOuFaajre2JzGuYh1SDTsGuv1zORJA4v4ajNs6RE3PLkvJ9YLGYA2i3frAac7hUaanqp\n0cZwMmeMs5Wm161XA4GAaT7CDmGpVMrRm54d5ngmuYb3WxrTTXjueM8cFkQeESOjTAVxj62SMqPc\nFwBNxT+PHHUWD5obW+vSqDCVncnuQWeZtqOgzHvnJw+Ztp3Ui7Wko9HINMDnvxkCpDeVSCSM5a8l\nW16vPz9p2dMCZKMBKjj15OeBsxpU5wXOBAY1Dvx+vzn4NJpyuRwODg4QCoUwHA5Rr9cNScu+yOzk\n2iyqzDRkx68ZTtfUCwATWeGenudB28YQQ9A2ASeVSrm2elxk/ficqig1nB6NRo2C5d4fj8dGQdkA\nrc+r5BldU69zjrwfrXMvFouO8a703PhuWPudTqcdaTXen6ae6Pm1Wi1zzs+ay7XBmREKNtDhLHYA\njugbDRy7Fnc0GhmDkIaaGmzKPs9kMuZZY7HYWlppEqA5+jeXy5na7kqlYkib9LbZg+FB8aAJ0Mlk\n0pBQ7SFJzWbTEUl7oAFawU5DaW7lRfbmdvM4NCSkIM3DxkVmDmGZnB2VkdYVM6ykB49hMQJcvV5H\ntVo1nlu9XjfeNK1/KupgMIhkMunoQLYOD9r2erQEiO0bK5WK8aBp/Z4W4j7P0DaF4EJQYohbwZnl\nD6PRyBCaSOhQ75mRD/5eepCLCA1A5gGZb7YjO2SOKzDz/dt7muFwrq8SCW1vSd/pWbxTex/w/Wrb\nQ3rLakTOq12lB80zSJDRNfXaayJAM8TNdrs2oAF3Q/j0LPP5/Ixx6fP5DDCrIUd2N0eBnlUB244G\n74eRN86u1wgc9aF6o1x3NTTs0bv2KFUadOvyoKPRKDKZDHZ2dvDQQw9ha2sLN2/eNDXcrVYLxWLR\n1BsT4B4EgKaxyt4RNJ7JZaEzoxUhy+wP17/twf0vJbYioTWpTGIlcVExaCN2BWV6RvYkI1VuGhJS\nY+CsObt7CVl/algMh8OZ4nzgLjjywBIk18mEZamPggHrQzm7ulKpOEYkEqSppLUJQqPRmFkbBYuz\nEq5UgdskQbsExe37NDSoeVO7JM7v9zv4ChqOZUvARdef4KPEx8FggHQ6PbMfdZpPLBYzbGElFNI7\nU2BWlr8Otl+13MdeP3vvEnCZFmg0GqhWq4ZAqKQY9eTduAnrSH9oOF6jC9wDarxprpk5aOoYGid+\nv9/sd+ZO+clZ3svOQ9dnp1OSSCSQzWYdkT6mKqgb7IgiU4L0vvP5vBnZaHvU5Nuc1SGZd76Y4uN6\n2U1zuP7ab4AGlFuf/HXLac9hX27Ce7X5IIx+sr+C3+9HLpdzYNCqIH3hAK3eD1+0HjRa6yRV1Wo1\nhMNhtNtth5KLx+OoVCqGuUkwUZa41o0qa3wdnp8ClBuRyv6+Rb7XKyFzWIfZN5tNR/9oMmHZwajV\napk8NL3ser1uJnexhEkvLQfSz0XEPjh2pESb2diAQu+DjPl2u41bt245mn4Mh0PjVWt3NC0hs42A\n08RtP7MZQjabNVGceDxuPCR+cgqX9lumAaENbLjPuX/PYlyuss6dTscx9INfa/coADOlSlpjbM+3\n9lLIM2CfdUZMNDLSbDbR7/cdLTp1n9o92bnf2+22I+Lhdi0i8/YHB4wQnGk80ClhLt8tzadVAxyd\nyil/qhdX0Xdu54vPw/NN8qMaQvYeOq15z3mIGxi7kTLdhLpEm2lRf/IcMzKnRggNxFXkwqdZqXXt\nZokxTE0vj52LePi1DR8VnXp7bgBNS/KsrRHPKuo1nsZ0tsHZjXzjpXAtmRtn03hVvjoekUqOipgA\nzQEh7Gdtl9GQLKHXIgfS9pIZLXHr2GZ/33g8NuVTfLZms4nDw0MUi0VjwNnKwwsFYu9nlswRnBki\ntXOMrVYLh4eHODw8hM/nMykSDbPzPNAbIkCvsj8WXWcab/a0Nq4tORRuAK2pJC9a17qJdoIiMTQa\njZpoEJtM0IPW5hj8eYIdgY1OAD0j5bgsA9DA7P4gQE+nU7M/IpHITJSKZZkK0KPRyJG/VoC2u6K5\nlbAuIm77Q7kE1K1Mx/D3atWCgrN9vs6TIGY/C4mMmgKZ5+3aAK3pG52i5/P5HADN711FLnyaFa1K\nBWgm5OPxOLrdrgFoMp2Hw6HZfHrRqpkH0KpAFBDPy4N2A147FKxAvi6QZsiJYwJ56XhEgrTNJmV4\nXD1oKgo7lB0Ohw2JhZv/LMpB8/o2K1hrs7UkaTQaORjbSlThUAJ60Da50G40c1YPms/H/cx1ofJN\npVLGAter0+kgFouZkDtH9dkAzRyietCr7o1F1pnG29HREW7fvo2DgwMUi0VHGoEArQazetBuZEKv\nJBgMGg+aZXVMZTHnzXW3Q+/8eR0gQ9JdrVYz925zCZbxoAHn/uB90oMncVXBmdUf6jlr/bYbQGut\nvRoky6y/vT/cPGhNJdK40JajXhnAy4obQU9LcvXcuwkZ2pqG0ul5dAZ8Pp+JimkaZBW5WA/63/5b\n+D7+ccDvB17xCoR+/udnPOh2u23INiRNtVotR56aoK55aebFNMyqVuVSpJp+H3jd64DBABiNgHe8\nA/jwh2efy/rdbsBrf79+n6ce9J/8CfDOdwI+HzCdAt/5Dvz/9J+i+9a3GoCmd1kqlQxIl0olVCoV\n1xyNhrhp8RNg9P7ZdpPWsjaRP02mvR78r389/IMBMBxi8Na3YvTTP+04FDwAPOhax60hehoajUbD\nhDoVoN1C3HYIfSHp9+H7wR+E787emL797Qh/6EOGOMIGBnZ9q5bcEZyLxaLZE0oMsz3oVaM/xqMY\njxH+K38Fk709dP7Lf5lZZ0ZKisUiDg4OcOvWLRwcHMx4hG5RLQK0nYdeSR5+GEinT/RGKITQV75i\niJqhUMgYhawQIcOWvc9tEqrmgtmS0u/3o1QqOfKobt4zo0r3lI98BL6Pfxy+O7pu+vGPw3fnHSrf\ngQRDRlZojKnnrCV3rMcmQKdSqRlD2U49LbxnPvIR+D/+8ZNw+5NPovPLvzzDxmd0Yp4HbfM/lkkf\nLSy3bgHvfS9QLJ7sjfe/H/jgB8092REIJRMTeN1Eo11aAmeHuAHMhLgX3h9z5OI86IMD4N/9O+CP\n/xgIh4F3vhOJT38akVe+0sx95oQiXVztzqV5TSV68KJi0zwYAX0piUSAL30JiMWA8Rh4zWuAH/oh\n4FWvmn2+O8YBN7FasyRWLFI7fBqBYSFF95KXAP/v/518PZkAV69i8Ja3ODo9MVer+TqGhzVnx/um\nwup2uyb3wjIcvdhpiT/Hd7DIOg8/9zmMIxGMBwNE3/Qm4DWvQe+xxxwhJTYi0RzSYDBwgDMvAjqZ\no3Zkg2B3WiriNPFtbDj2hu81r4H/LW9ByNob9I706nQ6hsyjnaw05cO6Zx3Vd5amJG5iCD+/9EsY\nv/SlQKPh6OHMfWCvZ6lUQrlcdvSu1h7WOl/XKxKbQ/x+4MtfBrJZAEBgMHD0FCdTmeHhZrOJWq1m\nDHsaiQRdvgcNveqZ0zNqr91CIHP7NnyWrvP99/8O/3vfO2Ow+v1+1Gq1mSoTniF6rJPJxEQstMPV\nKoMwZu75l38Zwz/8Q4wDAQTf8x6EfvM3gTe/2bEmyu5Xw4JEO2U92x6955HLYBD4hV8Avv/7gVYL\neOUrgTe/GdMnnnA4byR3Ue/RQ2a1gS0K4rxUn3APkTi2SF574Uda6adXlfEYaLdPDlynA+ztIR6P\nI5vNYmdnB6PRyMzt1UuZmTxQo9HI4SHzstvcrZxvZnu/fv/Ei3bZZARnKlg+h5uHwfykbnI3paGN\nK5b2Qr7wBeCxx+C7fh3hYtF4dplMxuE58h5oGWsTB+aVGZpjuHs4HM4AnioR3cz3kul0imk0islo\nhEm3CwyHGEmtK0PXnPBkh2gbjYYp42CrVJYO8d2Ew2FTH8oSlVwuh2w2a3oFn3mc4AJ7YzKZmAPO\nq9FoOGb9sqEJKw9osOr9scnOqgA9fvFFhD77WbQ++EFs/Pt/b9aX6QCy+YvFounMRSOH5CwOQ0in\n07h69SoKhYLpWuV1zfOdGz8xNu+IRsqoELV1ZjabNYMcCBKTycTsW5Im+eyczHR4eIhms2magrBM\nk3nqMxn7LrrO/dFmGfR8Jt3DNEQUxD0HvPEYvk4H/ngc/m4X2NubYZMzatVqtRyRzng8juPjY7Tb\nbfj9J70Jrly5gu3tbTN33hN9rLKzc3IBQCIBvPzlwP4+Jo895qjJZx2zzVVxm9oGwDXqxcoLngN2\nIOSwIbtaZ1m5OIDe2wN+9meB69dPFNub3ww8+yxiR0fGc/b7/aZphzIyAWd5kiphhlZZYsAZpPF4\nfOX5rQBOFMMrXwl8+9vAT/4k8Mwzrt/G/CHDU2wsYF+8dz2QCs52yF439JkP5Cc/CbzrXSY/zFpa\nApmGohiattsgahRAAdqtNpohO1r5Z2E1TsdjRF/zGvi/8x103vc+tF7+cnSPj403RAIbgVlJTgwz\naQcpvncquclkYoCF4EwAZP7yzH2LF9gbjAIRDOr1OiqVigOgmdOj90wQZJ0rveizNNlxv90JQv/o\nH6H5cz+HUbmM8Hhs7ossbaY8WHqnioksdYZXt7a2sLu7i62tLWQyGRPa9lx8PuBNbwICAeADH4Dv\nfe8zBjHzu9zbXDf2ZFfQ44QwG5y5V6vVqinpJBBqdICphnvKHF03T+x8qabrNLKi5aKeA/TeHqY/\n8zMIPvYYEIth8sY3As8+i0C57ABoRq3Y7pJEXnagYzVCKpVCOBzG9va26dHtOUCrvPAC8Ad/ALz6\n1TNlgoyq0AjlxeZRtrgxwHV6FQ1Vnge3ctpl5eIAulYDfuu3gBdfPMknveMdiPzmbyL+N/6G6fpD\nD8+ex8mwiYLXaDQyCiESiZjJLQrQnmwIv/8kZNxoAH/7bwPf/Cbw5JOOb7E9aODE03cDaAKhkC4O\nuQAAIABJREFUPsc8D5r3vlR4aDgEPvUp4F/9K6P8ub7qfdoArbl+XnZ5AkHXDodPJhOjKM/sQft8\naH31qxhVKkj82I/B961voXtnUhUBpF6vu5aB0DNVD5rhOL4fn88314PWcpszHbAF9gYbk2jfaF6s\nQFAPmrlnpnx0nu6qHjQ+8xmMCwX0XvYyTL/0JYwtgD48PMTBwYHxKGko2x50Pp/H3t4erl27hs3N\nTWSzWeNBr0UBf+1rwO4uUCoBb3oTfC95CQJ/+S8DuJsbHY1GBqCZE6QxaYc2tbyJF/UMuQraHEaH\n7SwE0C66Dr/2a8C73+367bbBToBmOJxnf5lmSwtLrQbfpz6FyXe+g2kqBf+P/AhCv/Eb8D/7rPn7\n1BvUxyT0amqPETg6S1tbW8jlcoYQt5b90WqdrPFHPwokEpjcCbVrh0Q9e8q5cRNNffGTek89aO4P\nL9/LxQH0F74APPookMud/Pu55xD8+tcRe+45U6LCnIq2TWTugCFZbpB+v286KtGDzufzq4UsT5NU\nCnjDG4DPftYVoOlB89/0Ju0LgAFDes9KprBD3Px9ZyZY/M7vnHh3hQL8d5pxRKNRc/in06mDsU3v\n040tz3dAIgTbVtoAPZ1OHfmdMwE0FVQ8jt5f+kuIfvWr6L7hDTMhWM0D8msl8agHrbn/YDBoPGj1\nojOZjOP7ltovp+wN9aArlYohXtHgoAfNKIeGuNWDZn53FQXn/73fg/93fgeFz30O6Hbhb7fx+L/8\nl/jW3/k7KJfLKBaLuHXrFkql0sx0NlVM+Xweu7u7eOSRR0wb3bUq4N3dk89CAXjb2+D//d8H/upf\ndRjGk8lJRzkl7IzHY1QqFdOdj3ON6XXbJZ8Mh/M9aH5dvdd7iouuw9e/fipA28xpmwdCjgeNBM/7\nOXzhC8Ajj8B/p6c9nnsO/uefh//Nb3YNcev0Np45nqtMJmNajtKAo8fp+f4geffHfgx461sBOEe8\nagSuWCya8sbDw0OUSiXXX+k2TMUu07OjKw++B339OvCNbwC93gn56otfhO8HfsAAaSKRwHg8NqDL\nPFGz2TTF8QBmNgg9aJZdZDIZR2nKShvi+BgIhU6s4G4X+PzngX/yT1y/lQDNg8/aTO0EtbGxYTY1\nyXDKfFRwnkwmRnksxX789V8H3vUuc29UOvQuGbZRxuJwOHSta202m4YdS8uZzQpUkQB359MqmWKR\ndTZ56HYbkf/1v3D07nc7ckcsm7LFrQEBn9k+ZOxZrECdyWTOvrZ37nmRvcF1sz1Ve7CD5qCZT6Wi\nI/it6kEP/sW/QOsf/sOT9/nlLyP3n/8zvv73/z4a3/0uKpUKjo6OcHBwgKOjI4c3x1If1h+zB/O1\na9fM+VVF5ql0OiephETiJKf7u78L34c/PGNMTadTQ57Srl8ajmUJmZbc8WK/aF4c36jgvLAH7aLr\n5qXG1Nh0K63StbVH5noK0Nevw/f88yfVCSTHPvOMowqF90jw0+l9o9EIV69eRSQSMR7zzs6O4Sow\nxO153vwnfuLEKP6pnzL/ya4pp/7g2dvf38f+/j6KxaLrr6SuVj2ohGNednRFh64sKxcH0K961Yml\n8/TTJ4rt6adPaPH3sxwcAD/+4ycKYjI5KV96y1vW/mdXLkfodE4s4l/5FW9uaM3iOzxE9Cd+Ahvj\nMabjMdo//MOov+Y1wI0bF31r8+WC9safO/n/7L15jKxZeR7+1NbdVV17dfVyl557L3MDM8MED7uC\n8c8ONg4ohLAFJwQMMaAoEZiESE5kRyiJhOI/EgdbQYliQhTFxInlECQHbAskm8UCY1kEZcYzzNWM\n79bd1V370t3Vtf3+6Puc+3ynTvWttbsvrlf6VH1nejnf+c73Pu/6vLkc8I53HOeh223gfe8zlcXn\nVly67qMfPetVnSyD1lwqnfXKBsu3vgX8xm8ATz55vF6fD/j0p4G/8lfOemVjy9lWcX/qU94+4m73\nuNrxvMqTTwJ/+qen/mcntjIjkeN83UMivZe/HAff/KaXDGJ396yXdbKc0dmYljRe/WrkXvYyYGvr\nrJdysly9elz887CJreseBnnY1vyGN7jx416P8sMovtOkW/P5fOd/+CeAXq9nEHG+5tnJfM2nI/M1\nn47omoGHc93zNc9O7PMxjJwqQM9lLnOZy1zmMpfhZEZNaHOZy1zmMpe5zGUSmQP0XOYyl7nMZS7n\nUE61SOxhzBXM1zw7ma/5dGS+5tOReQ769ORhX/OwcupV3CflvEnHRmKLg4MDlEol3Lx5Ezdv3sSt\nW7dw69YtbG1t9fWgsQ/Nvshek81mzXUSofyggRWjiGvyTb1exzPPPINnnnkGTz/9tPl0zSC9ePEi\nHn/8cTzxxBN44okn8Nhjj+Hy5ct998sezFHXXC6XDUsUm/T5NUcJbm9vo1wu49q1a7h69SquXr1q\nviZTFHsaE4nEyAMRTlqzMqvxKpfLZg5xLpdDLpczdJ/2pWeIRCmu4Rirq6vm3q5cuYIrV67gwoUL\nZhoSe1+j0ehY+zysHB0d4caNG7hx4waef/55c62vr2NzcxOPPPKI+YzH431n/6QJYaOueWdnx7OO\nGzduoFgsmn3gtby87Px51z4nEomZvoM6oUjPC++Dn3/+53/e1+/c6XSwubnpOePXrl0zlK/a+3rS\nGR/UaTHq+djf3+97N3d2dkzfPylXC4WC83evra3h+vXrePTRR83nxsbG1HRHPp/HrVu3PDo5n8/3\n7anP5/M8bz5/JbLhFQqFRtqjUddcLBZx9+5d3LlzB3fu3MHdu3ext7dnBgORQvrw8NCwT/LKZDLO\n++BwlknWPIycbZuVJaRB1Gk6hULB8KTW63VD4agzPUmCT8IPHQXGJn/OTm02m2byiH1NUwZxa+tw\nh9MQJT7gpz3Jivy0nPfMEYkkH2m326jX68jn8wiFQoaghCPvYrHY1NesStemIFXSe6VBJPEKmX04\nPCUSiRi2KMA7m1enchUKBQ8JDgcSTFNswg+eeZL183zbY/ns6UHTOLPKYc5PJatRWkxljeI76qJA\nJL0jcJ/ydtZCg1hHAXImOPeVbGJ6vwA80+/sqWaz0g0PEn3WOs9ep/MtLy979Ak/OR1NqWQDgYAh\nWOHc9mlPGFM9R9Ile5BHt9tFvV43XPf20BHb6LTf13Gfgz3+0jWdihen8unwIsA7M/zo6KjvfMzq\nnJw7gOYm6SSdfD5viOtJ3cfpSWQ0CgaDHmAm0w9BRue9chqWKpZpbu4ggJnpLNQThH+PF8etkTye\nU12U/D0SiRjg4/SWXC7noetk1GLaxoZt3NjATBAh1zYAY4XTGCPPOYFdGdn4tQ77qFQq5neQWnFp\naWk45rMR7437z/vi39dZsmS+4rQ2Km0dhTmttaixQGBuNBpmBGmtVvMAM5mVdIwq6RA5YEUZ9GYt\nSgFMjm16mva+6kxkBULX6NFxxo5OKvxbLnAmm9Xy8rJ5HgRE5cXmecrn88ZoJUuez+ebOjjbxhvZ\n8GicKZOXzaRIKltGq3gp6E163nV9dExohKo+OTw8NBTFZK5sNBpmzzi74PDw0IzMnPY7acu5A2h7\nkEAul8Pe3l6fF+16oQ4ODjzKYmFhwYyKi8ViSKVSRvkpDdssNtYGaF5UhKcF0PYgCb4saswQoBmZ\noOdIBcERjs1mE+Vy2TPBJZ1OTx2guU6X96yUgjQofD6fZ4ygPR6OEQMaSfza7/cbgC6VSh7eY54Z\nVwpiEun1ekZZ0UMlP7AaoFS+elZs8JgUOOwID70v9Zw5+Yd7r3SptqINh8PmeRBgTkOoNxqNhqFM\n5fCDcrnsAWh7Hrvr4v87bXCm2N6zgrMa+oFAwEwWo8d3dHRk9mFpaQmBQMDoIb4ng9IT4woBUKNy\nR0dHHi+UYKdT8ZRyd2VlBSv3eL/5/3nOOb99Gh40DQiNEqlO0feT+8iIYiwWQzqdRrPZNF7+LDEE\nOIcArR40840Mc2u4Si1gfu168brdrvGcV1ZWjPdNpTc2t/UJQkB8kAd9GiBtgzMBzOVBc106a5bf\nX61WjQfKkFkmkzEKYtprtvdOPWh9sTSCwmdvh/14rvRF5LnpdDrY3983HNmtVsuAczqdnjpA24Pt\nCc4KJModbXvQPNfTCnGr56M1ILYHbb9XVPS8ODUMuA8woVDo1DxoBSZG3gjQNHwODw89yl/BehBg\nz2wc4gB5UHhbU2W8d6bsdB/K5bLRf0wDLSwsmGc1TbGHZvDd1DGPdATsfQ4Gg0in08bY1pGNlEmf\nwSAPWutUqBfa7bYxRHkxjZdKpUyumpEJYLapnHMN0PSgmU+qVCqeUBVFcxSunADBmWFchmhnGYZz\nhbh1OtVp5aAB73QcO4+oHrQNdn6/3+SnqayZPkilUtjY2DAv4izWq+M3dcqWetAaamVag79DjSCu\nn8qZf4MeNPPAzWbTgDNJ/6d9b1RkOpnLDsWqB00PadqhV1tx2Xln7hlnsOt7FQgEEI/H+0Z6EvgY\nzTgNgLY96EKhYDxoe19p7NC7f1CI+yzy0K4QN9MteibU2FaA5ghNTnZrt9sG+JLJ5EzSNrYHXa/X\n+wZUcNyvraOz2ayZL84phBNP77PWp3ulRoQ95EPXx68jkQiSySSy2SwajYYnzcfzM6tzfmYArV4k\nv+bG1Wo1YwmXSiVTwATAjEl0vVBUNhpWVs9V85B2XnYY0e/Toiv74kui4McIAMM/PICuXIuGgWzr\nflzF7DJo9D40z8IrFAqhWCzC5/OZ/SdQalEO93paRRO6H+qNMV8Vi8WMlcuclk6Z0WfKr5kXpRJj\n6GyQ2MV144hdgNXr9YznzJF3rLEoFotoNBomdMniGb78PFN2CkenC427RjvC4no3CBYKGrFYDPF4\nHPF43HytXrUWJnE0Iw2/SderX9O4sGdsF4tF886xoJAFUpwQRtBidTHPPYubJt3fUYXGA99FerwE\nbP73SCSCUCgEn89nHBsanmrYMuqks60nOdP6bg5KEWgEwNYpdkql1WohFAqZaur9/X2zXuo+/r1x\nxT7jigOq/9Rw1/OvaR9eHBU863TOmQK0zg9tt9umcrtarZpcUr1eN+P3lpaWEAwGzbg95mb4qTkz\nWv5UKgwLTio2GNtVuayErVQqJnRJL6lQKBiFwTC7HmaukyEego/OfNUQ5zCiB4+HnIDHfdOZw1S2\nsVjMKAKCG4uICAxaTEEFoS/qJACtXhgAo3AYgl9eXsbBwYFn7Bs/bbChB8q1EpxZsa7VoxyFx7aa\nSUJXrjPeaDRQKpXMqDu2jjEiQIvd7/cjHo+b+2k0GigUCmi326YKluufRGxDSCMoBOJwOOxsYWRx\nj36qwcTPaDSKRCKB5eVl40WNKy5jgtEIGvVMjdEoZuiSRmg0GvW001y4cAHZbNbMKWZUxs5Fn4bo\nmNFoNOpJOdEwZRRJc8w0UHgmTooITGONCsDUI5qW8fv9SCQSRqfwa+3QYfGhXWDG+1tYWPCkd6Yp\nCqysVbL1uX5N/UMDmyFu1VWzkDMHaLtXuFqtmovhKR4yHkiCtPapRqNRlEolj1fS6XSm+qLZHr+G\njLUoaX9/34Cy9i8WCgVUKhXjRQPuOcX0PmyAtr3oYUW9cz1Qdm4rmUx6egB1qLrO4+ZaNFzLykYe\n1EleKPWaARgQ0grzRCJh2uv0YluYGk38N9fPtVOZLS4uGoNomgCtoT9GG6rVqvHyOIs2l8t5og4E\nPAI08+TFYtGcN137uGKHrDUvqHsaDodNmiiTyWBlZQXpdNrUKWi/MM+wts7QE1xeXp7KntpGsR2V\n2N3dxe7urjHUFaCZT0yn01hfX8f6+rrpbbUBmntxmgDNQi6d1b6wsNDHq0APU9sEg8GgqRFxAfQ0\nwvWuKvPFxUUcHR0ZD5lrYB8xC8BWVlY8rV/sDrFrTewCUKZNpin6e2kEAzBr8fl8JhWghbWMjIbD\nYc/P/1CGuO2qOrWsCNDsm9Ph9RwSn0qlkEwmzde5XA537941B7fRaBilPS0L0lVwpS9Ns9n0WPP5\nfB57e3vI5/N9HjQAz0MmIDM0qACtVY2j3Ae/jx6vC6CXlpbQ6XSQSCSwsrKCtbU1rK2tIZFIGM+T\nz6dYLHryuARuerP8m5O8UNwTKgJa5PQkEomEp5XDVkR2FTdf/lqtZsLGtgdND2UWHrR6BgzD5vN5\nc163t7edYWECWrfbxf7+PgCYfC/BedKK3GE8aNYcrK+v4+LFi7h48SI2NjY8kQtedsEez7Z+3zQ8\naD5jnj++cwRotgSyX14Bmt0HJILJZDJGj8RiMROOn2X7zCAhQDONx7Op9Sv8mmlBfS/ZVjorD1rD\n23ZFv4aNQ6GQ2eONjQ1z7ezseMAZQB9AU5+oITCLmh3qFC1uZPU5AE8hnhbWNhoN0+WysLDgKZCc\ntpwpQGtxCkPTBGcFaIY0acmnUikPMxG/TiaTpheTIcFpe9Au71lzPXxh6EHn83lj0VcqFeNBa4hb\ne3cjkYgJcdNDoSK0i+GGFf05AH3Kl/kUFkJsbGzg4sWLyGQyHs+5WCyakDcBWj1ooP/QjyNaEMXf\nRUVlpxPs6ACAvnaqVqtlQssECL7wCnTxeBzJZNKTi5wUTDQ0tr+/3+dB37lzB1tbW8hms/D7/SZU\nnM1mjWHJEDc9Qa45Go1OVKDnyvWf5EGvr6/jypUreMlLXoLNzU1PTpqfriIbzUvyc5I9tcOOLoDe\n3d3tI4ThvrEQaWNjA5ubm8Yo40WDWM/UaYa4+dztdI2tfwhyhULBODGDQtyT1K+oKECrB23ndBcX\nF5FOp7G2tobLly9jc3MTm5ubWFpaMunM3Xsz3hWg7Z7kWXmoPJf6nJmW4xnju2+3ptJppFM1y6Lf\nMwNoPWSuNhqt1AVgQr+pVMp4eGtra1hdXTVfsx1ob2/PWKDT7GXUAgy13kmOwE+SBOhVKBRMFTS9\nP6C/YlO9EQ3bjuuRuhQMLXPNEYZCIUPfmU6nTThzb28PsVjMFC2pJe7K2UyjhWxYUHQVcnW7XU9P\nI9vaXAVQmu9bXl42ZA56v5N60HZLlXYksICw0WgYEonFxUXTF8rwOAGexoZdMLe0tNRXnDfMHg4q\ndAT6Q4CRSATxeBypVMoYcXabkrI/zUq4Rn0P7ephGvi2QUvCIhpjNPQZ1tbiyGmHVF1n1VWQ5CqC\nA+5HwPS52vU19vPns5k2QPNMcC+bzabnfghuqsu0LkGjmq57d+3LJGKH5en1cx/0b7o6JOiI8T1m\njzkBmkQxs5Bz1WblEvUWaJFdvHgR6XTahKTC4bCnOlRJLrSCe9KHbVdGagsBL/WeeZXLZQ/d4DSq\nKScRzbsmEgmj/BOJhKc6VEPFNsUmDQzNodvK4jREPQoqh2q1akCQRXq5XA47OzsolUrG6NNinEQi\ngVQqhXQ6jXg8PhWA1tQAaxD29vZQqVTQarUQDAYRj8fR6XT6okGrq6umbUgVg1Js0qhjIZEadMMC\ntN1m5erB1kiAAiEVnQ0cs5RBBZq28WXn1VnDwsgUwUK7JWZ5du1KYu7pICIjPc+20cuvb968ia2t\nLeTzedNCqt6cGljjFJi6hPsYi8VMHY3WnrCQs9lsolKpYHd313SBVKtVbG9v486dOygUCiZto22S\nrFdgFEvTe+MKoxEkG0kmkx6iID4D6kF22WheWlN8BHfuxaSRrJPk3AM0LTEWdhCgtdqYxWO2V+fi\nwJ4EFNlvSW+ZSrNUKhlqUnpIDNUzbF+v102kYJYW1zDCA7u8vIxEImEUNCtttWpbc+yMbvAAqxd6\nFlWvrtaJo6MjVCoV7O3tmVzk7u6uYaIj4GkxDj0qFsexXehBgygeJFrAUygUsLOzg729PVSrVQPQ\nsVgMwWAQq6urHnDOZrMIBAJGgdRqNUPwb9cQ+Hw+D6MX7+1BooasFudoSw7fGe1z5dlX5rDTYg3j\nul3dE3YEQPOkLC50FV/adSqzEO6hfVY1pKvGr325ClS3trawvb1talvYBUCwsO9/Gu+mOkw0DJmG\nYv0Nc+OVSgU+n89wDPA9ZETx4OAAADzV4Fp/od0r0wZo9o4TfBkNVcywAbparTrBOZVK/cX1oAnQ\ntgdtt3PoQ1SLX6t6J91EvlTMJbIQjH2XLAYrl8t9YXo+fC32OCvRvCst3larZTxocvWqkaPFG/Sw\nmO/SfCWVwGmJeoEs5GCa486dO7h16xZu375tohdKMsD8qu1B08ua1INm5bgN0NzTQCBgjEydmMOr\n1WqZ1qp6vY7d3V1TiKUFfoHAMbMbQ3TDtnzo3tkREhqRGlJWpigqYoKzMp7NUmxP9KT0iqZyqEPs\nAszT9KAHFbfxYorMRUtrF6d2u11D5JTP5w1Aa0GVbTxPoyKdwERwZsiaXTgK1pVKxfM+Li0teUhw\n6EFryFwr/u11T7JmLQQlQNNrZsU2SZfomLg8aBahspMolUqZyMUs5KEAaOXSXltbw4ULFzwPz+5z\ndoW49QUeV9SD1mpcHQu3s7ODcrnsAWNe9gt2VqIeNJV6u902yksJENSDppdF0gf+LjvEfdoetDK2\nERDz+Tzu3LmDF154ATdu3DD3qUVRmkdTD1rbhKYR4laAzufzntwtwcMF0LVazVTm1mo187PKg82c\nqYLzsKPwNNKkAO0KcbuYolxtKqchdojbBc7qQdMzG8QvYEd/ZrVm+6xq6oIXqSRpKPFT75Wf5XLZ\nRO8I0KTnBeDJuU7r3dR2V3qQoVAI9XrdFOUSoNk5AdzPA9vPDBjsQWth4aQetBrjBGg6WsD9d9WO\nUvD/kT+BRvfCwgLS6TTq9bpHH05bzhyg7eIG24L1+e5zyLIXc3193fm71IPWl0E9aPtlHgWwWfTD\nMJ/mm7WvtVQqmZ8ZVBxykkyjMOIkoQfNKmhWvqu1Ta9ai/h4aQRArfRx+7QnEVdVL5XFzs4Obt26\nheeff96EODUHqSQcrOBOJpOeauZhAdr1nBWgi8Wi8XbYu0+vjsaBtg4mk0lEIhEEAgHjQefzeQQC\nAQM2/Fm2fFDBDQuWWmylfNyuug2bqKFarXrabdhmo170LCqgXfnnQcxQCtDskLBbF23GsFmdW91r\nLfyjDuH0LRKr2BE4+17ZqaIeeLPZNB40DVFXdGvYe3TpIP2dmu9nSJptkTpJTi+7ENYuIlMPelpi\nh7i17bJUKpmIk4Iszw/3gVEMcp0vLS1hbW3NU/Tr2q9Jz9OZAbS2F/HFtikBXVV2fDFdLUfqzfFB\n89BQWVYqFeMpkpB92PyZWo1sk9JwGsPGpVKprw9XPWm7l1E9fltBTur1u8SuaCQY8wBybu7R0RFK\npRIODg7Q7XYRCoU8XoidI5pmxfyw96H30uv1+ljFtP/WHmlHcgoWGk5CBmNX93c6Hc80JaUx5BnV\nc8Hqf6YXjo6OcOfOHezu7qJcLpuhJHq+Ga5LpVJjFbbZIKZjJLWmg0ZPqVQyPeSHh4ceY4K1Fq7Z\nvtMGaA0VDzIogH4+fOVz1zoS6pxutztx1GSQaHqMV7lcNvlYfjIsTM+Zl100Rk+OqTMtOrUruScx\nQOzCNO65PXv77t272N3dRbFYRK1WM0OJ7Dog5RvgtbGxgUuXLmFlZQXRaHTq9Qw2QOsZ8vv9CIfD\niMfjWFtbc1aR2zrc1XnE9J9yVUzD4DtTgKbnxmIjFnC4CgPsnJPdSqJtIbSYl5eXjRfU6XSMEmTv\nGnAfWIcR/m4WSKinpb20VKhKy6dhK7YlKEDb1vW08uYuUcWse6cKgUqsXC5jf38fnU7H9KPzOdEL\nmTYZwihCJURLvt1uO0Faw1u8FKAZ2rfvY9h70RAw94+FaaSsJUE/maFsgC6XywBgAJEAzcI2DWPb\nAE2jcxSA4fuioK+hfYYktY+82+2aYqByuYx0Om3ur9FoGAOOlbiT5g9tcRWB2u+LRscGFToqQGu4\nlWdp2sL6CIZVWRFMIiPWr7DCX3PQNJ7tFJkCpUYvNI2jYWJXhPJBe21XzfPZs22Q0Soy4ilAqzNC\nvc3cbTab9bTLrq+vY2VlBbFYbOq0mZqDJtZwX8geuLKygnK53JdG6HQ65l7ZqWPrSVfvNo2MSc/+\nufCg+bV60JpTdnnQvHEFBBdA87BqwQ4BmeA8bP6AOUM+ZAUAWoapVMpQlPIA81MLI8hspCFaVvae\nhgdNRa+GDl92hjBZoc7iCT4j9aBP6sU8DeGLxvuwqSb5qcVg7PNeWVnxEJPouRvHg6YCpvInQNse\n9NLSkilSIUCzorXVahlvdXt727RlEaCpbNTgSKVSJmw7igetIWq+B+pBA/AANKtzdfiL3tvBwYEx\nfnq9njEkpimulIb9vlCoL2yvR6cXcVIX94Oh22mLetDcP3rO7Djgs7ajbC7Dw66zmaUHrTlZ6gfS\nF5N0Z3t721RpE6BdeXNG4VZWVnDp0iU88sgjWF1dNeeGOe1pihq1TOmxjoOkNTQ61KDg/pP1j05M\nuVz2pP2Uu0NJVaZhmJ45QNtkCPbUm0FVmyp6IO0QNz1dDSPSwmHObliA1gKJxcVFtNtt4y2Q+ICW\nFos3WMihyp/WNDA4ZGe3uUxT6EFr1KHX66FWq5ncDJUHPWi+XARoe4jHaYe3eR9qqPn9x6xbNjjz\nOTO0lslkkM1msbKy4glxa2iXv39UD1or/Pn8bQ+a+TYFFlbvshI2GAwa5V2pVEwe0tUykkql+gom\nh90/BeherzcwxE3viWNJFxYWjBFKw5MFhN1uF8FgEOFweOoRIFdKaBgP2g5LqgfNs8OCvFkBtHrQ\nLoDO5XKeueuq9zR07yqSswFavcRxiUrsfdQzwBqPXC5nais4QYw92bpufs3WwpWVFVy+fBnXr1/H\nysqKJwc9qxC3AjV1tgKtGnsanr99+zYCgYAJ5/Ndd3nQCs7TOPtnCtBa1AWgL8R9kgdNoRLh71QP\nmrlietDMQdP7jUQiJjw0jFD5UZkBx14nwZlKim0Fe3t7nqpowPui8t40ZMem/mmRq7iCALFtAAAg\nAElEQVRESRx0DQA8AJ3L5Yzy1fDUoBD3WYgWc1DsHKjtcWYyGayurvYB9CT5RzuESQONIW7uI0Pg\n2krDn7VDmCRacYW4tWUklUqNVZClIW6esUEhbnt9Pp+vz3umggoGg4Z5bFYArcA7yKDV76P3Y4e4\ndQ661sNMW/jek9iIHiiBjUBXqVTMfbrufdCeUDSCpZ6zdjCMIrbeJUAXi0Xs7Ozg9u3b2N7e9hAD\n0YN2rZkedDabNQCdSqXGCsEPKwRoeua2oaPXoMI21uOwuHZQmJt/b1K6Y8qZAbRdqQ3Ac5g0dGkz\nueTz+b4qQFWsuuH6N+yczKghH9fhYR6dSos5LCoBzg7Vnktt57DXpV7QLL1StdAZumT4TS18vpgE\n53A47Mnb0jI9Tc+Z4vqbfAYMYbHimYQeGkLW+oFoNGqe4Thit/S0Wi3s7+8jnU6becT82yxSU/5k\njaJonyyNNbtC2Q5hjrt/tjJht8Ta2pohSYnH4335xF6v5ym0bDQaAGDuPx6Pm5555TueVAkP+77Y\nQM5z0Wg0UC6XTWqt0+mgXC6bZ8JCQkYS9FI2Lr2GES0ipeembUYMt7ItyRY7J82fty+NDDKlowNg\nNH0xjtidLIwOscAumUwiHA4jk8k4K89ZF8Qq9GKxaNIh2vI2TS96WOOVqUs9V6FQyJwNUiGnUikP\ni97BwQHK5bKJatGQZsHnJHLmbVYusa0+rcAuFArmRWIhim6GAo/mpDTfNm7bgUs0TEgl1263TUWs\n5qmVsUgVlV2sM+ueYleLig4sUXY07pWun1XDfOHPApxPEhssY7GY52WqVCrmvqk4Y7HYRL2MWohC\noGfKQgsSlSKTEQigv+XJxYI3bVGQ59mNRCLIZDImBcN2Ett4sMPJ9CSUs5uGCY24aVS36vui9KYu\nkhw1fPiz3H/VK9rVwXYse6qY3bkwap88Q/48Z4ziafsoqVxt6Xa7fS1V9Xq9rzuEPO167nVK17T4\n5XmuCdIsjqLxwein8ifwMxqNesLFW1tb5hnonp8mM52KvhPUbezc4bleWVmB3+839UiHh4col8tY\nWFgwBczTqr84VwBtg5Z60Awd5vN5M/5Oc3LMN9shGSo2BdJpAqC2+QAwVjlfZl6D+HD1XpnbmzVx\nAsFAQYAFMzZAx2Ixs280jNLpdJ8HfZ5EPWgOlABgXiZWZrIoKhqNTswGpL3lPJMMEft8PuPZuDwk\n+8wOarebttjvGTsp0uk0gGNwZguV3TJIbmWdQFer1RCJRMzPEKDJfkZgmPR9UwV6kkGr6TDqBhaF\nkSiEgw/siBxZorSVjK1sjBqMAnTqJROcmXIhs1aj0TCGkQpb9vQKBoMmwkIAZHrBHgjCOdejVvm7\nhLqDxXZM22j0gSkjpj60tYwAzS6HQOCYzjaVSpkUH5/BWYimzHjWmLMm2+DKygq63a4hBCJA8+wR\nnCfRJ5RzBdCAN4RFxa8vUz6fN8xJPp+vD5zVe9Z8ku2l2t7sJOvlAyXI8uE9yIPW+7W9AS3UmbaH\narefKL+4FjjxBeKLzeIO8lWfRw9aDTGGuEnsr5ECgh4r76cB0ARl5rx5LjTsyDYN27NwFTSdRi0C\nzyIrxNn5QHBeW1vz8HPzOjw8NFzQzD/n83lToasAzTYh/s1JAMIF0LZByxA395TtR91u13ie1Cfa\niaBXIpEw0/JWV1fN+RmnPRO4TyvLn4tGo55aBH5Nb1+l3W5je3sbOzs7fSFiMlwBx0DOc0/DVKv8\nqY+m7UEfHBwY/nq2T7H9To23arWKcDhs6gFKpZIBeqYhaMSclaizoa1YSihUrVZxdHRkAFoZyLQF\n0vUsR5VzCdCDPGgSOfB7CM6tVsv8vA3S+ns1xO3yZsddL40JNRL0hVBKQa2o1J9XD3rWIW7uj85g\ntT1oVp+zyp0eNAur6E2cVw9aFVU0GvW0OdEY6Xa7SKfTWF1dnQpA03Nm6JfPUUOO2oLHIisCtKua\nXylipy1aM6GfS0tLSCQSxojRfmNe+/v7CAQCODg4wO7uLprNpmFJY6EQ95vc53Y4fdw1u0LcrogT\njXjuHY0fTgSzQ+56pdNplEolNBoNo1/4u0dtzwRgDHOGtTVq4qrUVmm1WqbXneCspBg0RJjvp2Gq\nHrQaIZN60Moqp0YCi78eeeQRXLhwwRj5vBgS9vl8psCKbZzcm2kB27iixXW8X+oRetDUJRQljCE4\nT4v+81wBtLZc6Wg4eqXsY+bDVD5ieie06GjxathBuV4HEaKMKi4Pl2Cr+dtBHrRa+nYO0s776d+a\nxLNmT7j2aO/t7aFQKHiUq2s0Jl9urUan92hXi+rnpMVBrp5KKl6tymy326b1h20/avHTEOHQdbY3\nEQjHFdcZIrhpmJtn2zbc9vf3PUacFjpxsMa0xVU8w2fId46GRrPZ9JxbdkfwPrm/NiEPDQ2C86Rt\ng1SgdohbgZogNKg1aRjRIji2/3AoD0F2FAWsEcGTxFUfwvNjF79p6oTGVSKR8NTosCXSbrUaRVzv\nr51OBLyGMUPcoVDIc040GqTODA25WVTRu6q1tUDWbgdTg6nb7ZpxwdQV9jNilMvu6NGxvOPKuQNo\nu8ez1WqZA8Z+UYZCtB2r1+shl8uZ0Wv7+/totVom56JkIgzHTKOq0SXqVWtYXZWH5snondAa1r5S\nnS5kh//HBTtGJFipXSgUsLu7i+3tbRSLRTMaU5WBkjxQ+Wlrka0k7SjFpBXHfAFsTnD7pWu1WtjZ\n2cHu7q6hTyyVSuYF035dNYJm0W/OligqdMCbH2fYTI0GvdgSSAA8jUgFvXglamBIWy+S2dBL1mfh\naoPS9M8kMijiZF9Mg6nSHeX5cs/JPc4qXT7LWQ1IYAuPsg/W63Vsb2+bfmNSyPLva852dXXVM9Pc\nLoYdRW/w+1ndDMC8w/a7Ddynu+X6WXTK+eyFQsHz/Pg5a1EHiAaPiwzGdo74NdvhSCZDr992oJrN\npuHCsKNH48q5A2jt8dTCARZfEXjV06BCIECzb5TfR+C3AZqtFLNQfKpEXJWmalXy4fIg2b2l9EQU\n4CYJe7fbbcN0tbOzY/JbBDTm6XR97IXleDYF51qtZrxCO/euoD3JPmsfKVnZ2JurSrjdbhuAJnFC\nqVQyL4x6eLOmVOV5Zq7KBmeug73zSifIXB0NOBqmsxYNl2qO3B7gwLXyOWgxll3sdnR0ZEKz0/Cg\nXR6+iwNcc8aj5vAVoHm+NXQ8a4CmAcRhGmp0sr+e3rNGZLLZrKdGhA6KRrJGETsK5mozU11mjyTV\notNisWjegVnwtA8Su75DqV/ty54ixgIwJZ3iOGEXPzcnkv3QArTtQdvFKQQXDQNyg+gNqgfN0A9/\nbzweRyKRMAd7FuT4epgfVJhGUNHKapuZSZUcf/8kYKcedC6Xw61bt7C9vW2ULj1oXZ8qaqYbGMLS\nfK+2qGj4a9LiIFVczJGT4UxD3q1WC7lczuNBkxtdQUeJQqYBHC5hagWA5wza57nZbJpxgxqWZETA\nDhXOUtRgpDGjVbhK8FGpVAxAM5Q3KJfOaNa0AJrvFgHa5UFrOHZUA0wNI479XFxcNBGPUQiORv27\nSqdK7832oHlWeL/Ua6urq54aEbvYdBSQ5vfRi1ad5gJo6i/bg+b7WiwWzTsQiUSMATtrUd3qouhU\nohHqXp5zPn+lbSZTmr7HCtBMsT38AP2ZzwC//uvHX3/kIwi87319rE+kP1RQaDabBpxp7TM/rSPb\nCHyuEPfYnp21Znz8485vsz1ou9qULwoVAQ+Q3+93hrj5oDXsNLT8yq8An/sc4PcDTz6J9qc/bTzo\nXC6H27dv4+7du57wscuD5iHmOjXUzr1ln6eub6zioJ/7OeB3fgdYWwO+/32PB80pQLVarS9f12q1\nzOABKrNSqWS8ZbWip855fuUKkEgc73MoBP+3v22eN8HJleNqt9soFosm7ULDkdXG1Wp1dt6Gtc92\nEZBSYuonPX173J7Lg57mPvt8Piz8g3+A8Je/jG42i/LXvz4wzK0521H3zvagWZSq+cWRANo6G/jj\nPx74d2mIckQpKTVpdDLE7ff7EYvFsLi4aMCZIe5B7Hgj7UOlAt9HPgLf//t/gN+P3q//OgIXLzq9\naK7dBdAkPioWi4b+1Y4uTU0sXYfPfx5deI0HG4y1zU2rzvm1jv3k17aRz08CtLIGTiJnB9BPP328\nkX/yJ0AwCLzlLQi+8Y0GSJlsJ1CQVIAFTvv7+yb/TCXHzWZYnGBPi00vzYEMDdCONeOv/3Xg2jXP\nt9mtUxqGsgvG6Hmo4rJzrXz4GuofupF/awv4tV8Dnn0WWFgA3vteLP7v/432E094QtQEOypSAJ4U\nAg83K2BtoVfhKm7TIqmhLeYPfQj42MeAD3wAAJwAzTycXWBHZWCHYJlL4/7bdKUTA6DfD/zBHwCp\n1PE/MRxhPtdm77POymXhjc1VP/GarX2216ADMfRST0KJN4bpQJi0a6L7sz+L5t//+wh9+MOe94vs\nWfF43FTHUyHbxU38+qS/c5KxMbKhYZ2NQdLr9TxDNThrnl5zvV43AMG+Z0YG7QEwLGobN/Li+8Qn\ngLe+Ffit3wLabfj29xGo1TwFenzmWivRaDQQCoU8oV7uGz1x1g+4WBbHPh8OXYff/E303vtep3dv\nT+XSFBPbTdkWqekaPQs2u9u061rODqD/7M+A170OYC/hj/0YFv/P/8Hiz/yMabJnjlFBmFaJ5udY\npk9mKFI7cjACwz7875oHHikv41gz/tf/Av7JP/F826Be3Gg02ndpiJXKQEv8VTnYubehpdMBGo1j\nJbG/D2xsmGIX7pFahDQKaAj4fD5P5bfrhddwLT0spimSyaTpsx2a/u5HfxS4eVNuoWM8C52j6/Kg\nWcHN80ACfq0FWFhYwMbGBi5fvoxsNjudMXe9HjBGLpteK/tKK5UKisWi6ddmi5sO+KACnjjsPWCf\nuQ7m3NR75tdMd7BHvtfrmT75VCplKooHcexPsmbfiy8ag5V/P5PJYGNjw7CX2Wtma52rE8DukNCJ\neAT9eDxu9n3k9sIhz4ZGVXQgg3YD2HolHo8jmUwinU4jmUx6vOexz0e1CnzjG8B/+S/H/w4GgXgc\n/kbDk4ZMpVLodruGpOfg4AClUslENNvtNkKhEOLxOHq9nkcn6NjXTCaDeDxuCoLHFlvXXbjgiQrZ\n+XH70u4POip27z3/mzpgpMTleSdr26R0n2cH0C9/OfBLvwSUSseA9+Uvw/8jP2I86EQi4QkD0vrh\nBjLMy9ArrUkWTvBrBWjmbMauhHasGa95Td+32S01tLRcAM38nbZS2W0JtMw0ZD40QF+4AHzyk8Dm\nJhCJAG9+M9o//uNYePFFD0BrCFtD2TyQCr6uPWMep9FoGM+P/YAE50nYdZjiUIDe3d3tA2i7EpN/\nV2cU83NtbW26AO3zAT/1U0AgAHz0o8cpkCHFNm5sgI7H4/D7/chms8ZDmpR0wiXqBZFal2uxQ4Js\nOaEBsbCwYACazFtUWNrfP6kHre+IEn+k02mjC5aWljzDRvh37egOI1h2lTMBWkFwou6PIc+GDdD6\nLmpo2DYeFKB1fOrYAP3ii8DKynGE5f/+X+DVrwY+8xlPLQXBlme01+sZ/vZqtWreQ55fevokTyFL\nWzqdNmH5iQDaoevwkz+J7r1hP9q7bTPg8ZN6TFu+NCJ6/Ch9xlHSS8+7RgYmkbMD6Je9DPiFXzg+\ntNEo8NRT8AfuE8prD7NWBtZqNXPwCND8XF5eNj+vtJScN0pFMW7RhGvNGHCY1IMm0NqE/BxoT8+V\nYTW7gIwe9FiVsOUy8KUvHXtJiQTw7ncj/MUvYvF1r/MANJmJqIRJQqGK7fDwEIA7RKk5d35yAAXJ\nL+Lx+NgA7Qpx53K5PoDWc6ORDCoFenecCb26ujo9gP7Wt4CNDWBv7/iMPPbYsYf6ANGWNfWgqSQI\ngJFIpM+DnjZA2x40i5S0eIb1EdxjRiToxRIo6EGzWGka9LXmffX54MN9gObfpsEQiUQMLTCfK40P\n1/tmE5fYHnQikZis+2OEs0FDc1iA1iEOyWTS9G5P1ELabgN/+qfAv//3x+D8iU8A//pfw/+xj3nq\neZLJpEmLkUCFNUPKzMZwu4Iz30MCGz3/sXm4HboOX/gCem9/ex/7mXrQDGeT0pb6lrpKa36oU1T/\n8mtGWHTa38ML0MCxdfahDx1//Yu/iN7GhmEH44vv8/k8Vo/9wtG7BmA8img0ikwm4wm3Ualp0cRY\nxBnWmnH5ct+38CGyMIhrVc9ZB30QUGg9Dwpxs3VkJID+6lePc+T3+JXxzndi4etfx8Ib32hy/VRs\nmo+lKDEJi4GG3TMaILScJ2lN0RA3KV9dHjTvQ1tPyIyVzWaxvr6O9fV1rK2tmWpXKoiJAXpj4/gz\nmwXe8Y7jQqAhABqAMYCY9y0Wi+YdIOCEQiFPjnGWHjTXUSgUsLe318er3Gw2jZegn7YHzbM+jf59\nit/vR8/vB+69ZzqNLBAImGiJ7o+y5mkHCAk/7JoU9aA1xE2AHrn7Y8izcVKIm8YEvVVdm3rQWig3\ntjF06dKxbnv1q4///e53A7/8yx4DgYW8TCsxusnCQYIu6TtjsZjHQObFZ8Vai7HPtEPX4Y/+CN23\nva2P/cyVc2YdjhKRAPf1uRboaiSHZ1pTOuzEeLgBem/v+MDeugV88YvwffObpuGeL57f7zdKmTcd\nCoWcnlM8HjcvaCqVwsbGhpnkMi2yeHvN+Pa3+75FPTc+zF6vZya26KUFK7Sc+cBtwoexKmE3N4/X\neHh4HJb/2tfQffJJz1hADUOr4lIw5b8HtQ3oWvm93PdkMmmKdkZqd+n1ji94gYMe5t7eXt8ZYJ6L\nKQ/13pmjvHz5Mi5fvoxEIuEpdJkIoPf3j3OM0ehxDuz3fx/41KeGvM373QiMEpXLZaOsQqGQMewy\nmYyJBk0NoGWfdR06dlR7oNnCCMCErenJ0YtTRisOl5iGaJ4Y0qFBY5feNI18bZfS9IwWfymJDi9l\nM6QxzWjAyB70CGdDAVqrjvneEyD9fr8BaAVpTpOyW6BGlrW1Y4D+wQ+Av/SXgK99DXj8cZPzZy9/\nIpEwBkS9Xjd9w/V6HT6fz3jErKFghCWdTpuv6V3bVeEji0PX4TWv6evtdxWJ8XKJnVbhf7PXbIPz\nw+9Bv+tdQLF43Hbw2c8C8TgwYd/YzMW15vMsr33tsfX71FPHa37qKbQ++MHjcNB5lr/zd46rXgsF\nYHMTyz//88cphvMqudyxZ+TzHYcH3/e+4xzYeRdrn5c++Ung9a8/61WdLNaa/f/8nwN/62+d9aoG\ny8N6Nn71V4/X2mode6af/7wx5M6lOHQdPvpR4ODgrFc2tpwtQH/9695/nyFJ+tBir/lhkE99ymux\nV6tnt5Zh5Qtf8Pyzcfs28P3vn9FihpCrV4Hvfe+sVzG6WPt8uLt7XCB0nsVac/fo6Hwr4Yf1bLzi\nFcB3v+v9b+fdsLd1HXC+z8YDxDdt9qQT/5jPd47Nr/vS6/VMkmy+5tnJfM2nI/M1n47omoGHc93z\nNc9O7PMxjJwqQM9lLnOZy1zmMpfh5HwN8p3LXOYyl7nMZS4A5gA9l7nMZS5zmcu5lDlAz2Uuc5nL\nXOZyDuVUq7gfxmT+fM2zk/maT0fmaz4dmReJnZ487GseVk69zeqkorR8Po9bt27h5s2buHnzJm7d\nuoVcLueZOEKSfk6p0mlVa2trhilqY2MD6+vrZi6qfu9JzeMulqOT1qyMP6QQLJVKePHFF/HCCy+Y\nz62tLSQSCXORzIHr1TWHw+GR9vSkNbuGku/s7Jh1cY07OzseMgoSxg8r4XDYc3+JRAJra2u4evUq\nrl27Zj4v32NeO2nNnEZVLBYNF3Q+n8fe3h52d3ext7eHvb09tNtts2fcv0wm03cf+/v7zjXHYjFD\n9ckrkUgMHKc36tlwydHRkeGH5lUqlXDnzh3cvXvX8/nII4/g0UcfxaOPPorr16/j+vXrSCaTnqlo\nJAoZJCet2d6ng4MD3L17Fzdu3MDzzz+P559/Hjdu3EC1WkU2m/XsVSaTcf490n7qRZIYZY+KRqNj\nrdklHM3Is0ISm52dHWxvb2NnZwc7OzvI5/POoTWud5BMbZPs84PWvbW1heeeew7PPvssnn32WTz3\n3HPY29vr02vLy8u4dOkSLl686Pkk659rNvOwMupeV6vVvvfQRfDR6XT63uFCoWCmW+mZW1lZMWec\n5/3ixYt9z2p5efmBa3YRWJVKpb6zsLOzY9bPq1Qq9eFKOBz2MJ/xHCtNMK/4CZwY47LnnVkftE00\n3u12+0jMOe6LYEFie7IrKV/u8vIyQqEQut0u9vf3USgUzO8kTR/ZsnSkmf05zn3YrD/KV6wDwXX0\nG5mxyFVLRisO15j0xaMoQxmpDl0XBx+QeUs/lQJx0N6RzlMvjr8jUf6wHLs65k+5yGkEKf1htVo1\njFq9Xs+z3/w+cojb0mw2DW84919H6ZFqdlxmI3us4SDO7Xw+j0qlYiZw+f1+DxORMp1RKU9l1CTu\nj1/UCU82daqumUx8gxjhCGa6dk5F45CFZrOJxcXFPj78ce9HJ62VSiUDIIVCAdVq1TO/2T5PwWDQ\nw+/P30VwUNY/MhvqUI1h30vdW37S4SBFJs+qvQ8+n89DTUnAIpXkxLSeIwiZ2chpTT3t+j5OOwuF\nQoY1cXFxEfv7+57RkmRmI/Mbn83i4qKZ9jesIUzGML1oCCut5/7+vlkPjXLS/XIgkX7yd/OchcNh\nc644RnMWcqYAbc8OJjhzCk2pVEKpVPJ8X0/o/XTkGgeXc+gDf18ikcDKyoqh0OShVvq2SRSdPcrM\nHnGnHMZ63/xeHWOm069sJTfuy6eUhjqsXC+CGcGI5PakFVTAIs2grVg53UavVCrlmSQ2LJWm0h1y\nb3nZhgVfbI4dLZfLzqiBSzgVjS8iwYcDToD7xuC4QgDkxXWSzpNgUqlUzCxznlNe5BTXOeLTAGkF\nZxuYFUjIE16r1YwSHWT08LwqHzpHDur7yvGpavxNCtCcn7yzs4NcLodyuWxoJwnQOniGxm+1WjVz\nmAnypC3lRXpd/gx/flgh8OgwBp21rbrCfia9Xs+AM6kk6ZhEIhEPBeishfehZ7hUKvV9H/WiDnsh\nX3u9Xjfvmt/vN4M9fPfG2vI9pwPGsziMKFUt9W6xWDRYwugVR4/SONDpiWpQ93q9Ph3DaWec1sfZ\nz7OQMwVoWua0mAjOCtDlctmzaYB3UpROdOHvrdfrqFar6Ha7SCQSHnDmxBQqBx7uSTxoHWXGWaMc\nVq7etA3O9CJ0rujS0hI6nY558fj/x53wogM3yEPrujhDlyDArzkIgZfunZLFk5tXB4HE43HDzTwO\nQHPd6vGokbG/v28mbVFhhMNhA+xq2LmEUQ01jjgxDIDhHR5XbAC0AZreXi6XM2eEZ5XhNRdITwPU\ndI2DvGedsU3FpENLXMJ95Jpp7JD7PRaL4fDw0ERUTkofDCt8/tVqFfl83oQzdQ60y4PmHvZ6PXNP\nxWIRS0tLSKVSWFtbw+HhoZnMRL2jM4GHBWnuo4505QQlG6AVnKn8FZg5KYn/jwM0lpaWxt7DYcUF\n0IVCoe/7uDc6npKjX5ku4PnljATqY+opOiujeKg8n+S0p9FmgzT1L9fDQUE8G3pR9/KctdttEwnQ\nWd2zkDMDaA27EiRoJRKcualqsdreJl96krarB1uv15FMJj3gnE6nPZs5aVhoWA+aI+MICrxo+fJ+\nmH/mlB3Otx5XdDKOjq/TdRGguceaNtCL4EvFpkChgwWoTPRnxgVozfHbEQDOnqVyplHhCie6pNls\n9p0lRleoWMadvqX3oiBNb1TDsTs7O561cj9VGasHbYdZJ12b7rfr4h7rKMpBz1KNOV4APOMJdToT\nADOgZVzhmENO39re3sbW1lZfWkTPk06QazabfeMmM5mMAWdOE1tcXDTr1PnAw65RpykxxVGtVj1h\nbu4z10oQpvGpc4Z1utXS0tLMwqyD7oNnuFgs9n0fh3nwvdc0gZ3q0xA3zxjTbgxxj+JBE6CJJTY4\nl8tl8+4vLS2ZEZixWMxj1PHivvJ50Lun4fdDCdD0JmktaY7FDnGrNe6aNkOA7vV6qFaraDQaJvFf\nrVYNOGcyGc+cT2By5aAAqFNSFKB52WF1BQIq5EgkYg4vwXmSh2+HuE/yoOnVcPoTi73s3LI9xYVT\nhCKRiLHydeqPhsiHEc2bq0WrOWUaQ/Ts7FGGdqjKJa1Wy5wrKhOeMT6TSV88O1w5yIO2Z2mrJ6q5\nRioyYDKvc9D6BnnQVHwPGhmpkRZefr/fgDNHETLtoCP8xhWGuAnQOzs72Nra8twbnyNTCDrVStMp\nvLLZrAecE4mEp0gpGAyOdDbs6BnB2fag9/f3PQBND1KjKeqB8n3VyXizFJ0ORg96EEAz9cUQdzqd\n9kwT5HtOHaEhbuqkUXPQ1MVcnxacEpwrlQo6nQ6SySSWlpaQyWRw4cIFZLNZ8z2lUgnBYNCkPuyo\nnHrQP5Q5aMCriHnztkJutVrmUGrodGVlBZlMxlTVpVIpk3dmEYt6jDZ48lBMWgjE+1Alp596EZT1\na/W8GeKnt8Rw7SQg4Sq44iHTEDDXxDGfmlNWsE4kEp4IAEFF57mqIlEQHyVa4Soesr29drvtCfXa\nc1pdRUgKbhysrsPVmeOdxuxi29CgJ6ppEBatRCIRYxgwAkGrnt7bqHnPSUU9bAAeQ8guHOSnAjQ/\nXXOUxy0Mc+UI7QJNgp2ulUVJui5eWthJb9weRcj8r6adRkl/2OdW9Z5eTIVwr2ncMgfKcasE58XF\nRSwvL8/Ui3Pdi33ZEQiuje+UHXXT1BLTe/al9RajnBNXgakdhaOhwbPEyKx69jxXdOr4jPQZ2nnr\nacvZTrMaQvhSJRIJZDIZA8wMSzDHSYCm8tP5zwA8h9tWIpMWWKjicrXo2CBlH6YjeAgAACAASURB\nVGTAm0ejBzqt8MmgMKa+YARnO9zOPbJbTrTKUXPW/D67kGkUsOP363rsF10VmF3trIChe28DTDKZ\nxMbGhmmtyWazSKVS5uxMOm9ZoytUDnZ9AvORzIWxuC6dTmNtbc20JE1SKHiS6F7b55TPa9A+uz5d\nIW7ey9raGpLJpHkvNRIz7NmwvWK7+EqBzgUCNDj5GY/HTSiUnhO7GZgPbTQaZj43a1bouY66zy7D\nxjYm7foaGpKBQACtVgu1Ws0U3nGmu4LNLMWlJ/g8XfUrXHe73TYtskw3ENj1flm3kkqlTIuset3D\nrtHWx2rku6KetVoNi4uLnsgtvW/qXzXubadjGtEsl5x7gGbBTDKZxOrqKi5cuGD6m/Uli8fjODo6\nMptdLpeN0rOtTw545wGZpBBIX6wHAbR9UYEA/QAdjUb7cnXjyCBw1sIl3WsNrw4qUrLBWiu8FbQV\nGEc5wNxLbUGzq5e53oWFBRNapyJztUq49j+RSHj6e1dWVpBKpTz98pNGVhgaZhRH0x/09A4PD9Hr\n9QxAr6ysGIMhnU6bCthpA7Se25MAw7XPgz41HcWvaXTQmFalO2qxm+ssKzir8cl18ywvLy97erEZ\nfSsUCtjd3TVtmoeHh6ZtjzqFkS0F0FHrEx601/wegj+LX2OxmPlehlsbjYaJTuzv75s2yVmLC6A1\nEqVGvd4f18y1MizsAmg6XOMYyva5daW+BgF0KBTyGGrs3eYaqY8m0W2jyrkHaPWgV1dXcenSJVy+\nfNlTvETlwFaLcrlsHi6tJwVo5o+48aMSg9jiCv25Ln3I+rC1hL9WqyESiZgeu2l50LbXYYdmtMXK\n5UHbfbl2K42dl7YP8CgH2eVBu9qLfD6fsdTVULO9ef15vZgX04u9kPy5aXnQDKO6CghdAH3x4kXj\nRdCDnoUicBmXtlJz7bPWJ+jXaszx0pYgfjKcOGoqwW7B4/7aIG2/3zTmaZDRo19bW8POzg4WFhYM\nOFerVfj9fuOd04Om4cn7GQWgBxnyg6IVNNJJaORqHVxeXkYqlTIFqKfpQTOEbbe6RqNRk5ZxrZkt\nnYMAWo25cQ3lQXsMeMPfDHeznzsQCDg9aNWHrijoX2iAVg86m83i0qVLuHr1qjOUdnBwgEqlYg5K\nJBLxVOBp/obWNfvsxhV98VwA5fKY7b5iru/g3mDxcDhsimmmkYN2edFaOKM5LyXt0PC27UHbecYH\nAfKoHrS9Z4PC5gzzEWxTqZTzbGj4jZ+02AkyDKnZxtYke+8qILSLggjQLKYhQLMlkDnyWXjQLsPS\n5UHb+6z1H/w6k8k4Iy0u40jDjqMYb4xKaEU2AVpzhFrhrGC3srKCCxcueFi5qCcYwdrb2zPvCAGa\n66ZnyzDtOPv9ICOWgEUPOpVKoVqtmpwqc+LxeByrq6vGKz2rELd6vryi0ahZZ7fbNbUB+qxsgGa4\nnh60vq+jetDjhLh9Pl+fB10sFhEOh43Bp10e9rObhZwZQNuFVRqeUg9Pw2t8wdbW1pw5D60iVpIE\nWsMskdcKwWk0mRPYtNhBPVB6DgQcVVIAjDVHhb68vOxhT5tFkZgLpF1AbrMu8bDzJdW2E1eB1jhi\nGzzqndsWq6voxGVA2KFXtfxp9ZOdaVqiRWI2yQqLT/g8SILBc0xjgedp1EKZYcXlbYyyz3q+aVDY\nxhHrLOxQ7jhit60NKsjU/SRAJxIJY1Bks1msra1hY2MDzWbTkJMw/M5qb7uOQHXGKIBoe89asW97\neq7oG3UDW/S0CpzGPM/UtN7DQfdhG/LshrAjVjQotQec+kaLS22CI76L9nkcdn12JNC1NnYRaNV4\nIBAwES2G4e0zpZHH0zCIzgVRiebmDg4OTNWcena2ctC8om2J296Py3Mdp0DFJWoB0ipstVqmEIgv\njqvKmx5yKBTyVBzqPkxawu8iKuGatIWBBTEslvD7/aa9w77U42R4k4pYq+Mn8T6HvTe2VJDooNPp\neF5KgoRy+lJ5u0L9016fq43JrormGbQtezWGxiWqOUlOCruqYnftM6tc+e5WKhUUCoW+lrx4PG7O\n0yDwH2fNJ3n9/D6GYbUVMxqNesKmLt1BQFTPjuk0m2hjlHVrXYUW3elFohyG2gOBAJrNpgFjUh/b\naRO2O/V6vZm+h7Y+5j6SdpcMaaFQqI+tkGkcO5KVTCaxtraGTCZj0jmDcvQPEoJ+JBIxEQ6eU71Y\nh8S/xWiJz3fMEhaPx+Hz+Txtl6xVmqRPe1Q5N33QzMup9aLemeZx7XCnvijDALSr9H9cIUCTxIAP\nm/lFAiFDaEpacnBwgGAwaLworpPAPg2AptLXVhT93QrQzOET6A4ODvpClouLi0ilUia8yXuh90nC\nhEnDw8PeG2sLNGeoCo9rZ9iMFbqkhT0LkLYL87hPttGqva4LCwszWZ8rzG0rRtc+0yNia2ChUDBc\nA+y26HQ6BuyUDW9Sj87OAQ7Km58E0Fp4NMiwtwvM9GfVQxx2n20d5rpYH8O97XQ6fayEDGkToPkM\nisWiAZVZvYcuh4kATXDWM69nHzgepmIPK1Fa1VgsZozAcXK8GoXiv5k+VGOB6QoajzzT3D+/32/4\nIGzP2QXQP3RtVjaTGD1oO7yg4U6bM9n1UtqWMF9UG6SVyWsSIUATnJlTdjXaM//NisbDw0OPVc3L\n9qAnDXG7PGgeLn2J2AakCkL3jl9ns1nUajXs7+8b44MgraHFWQsB2S7oUe+BxhijGIFAwBDX2O1m\nsxBXj7zd66oFLPqceE4XFhZmogRcwOTyWlz7TGBQYppwOIz19XXDhhUIHBd4sshM2/mmueZBudwH\nedB26N3OWdLAsz1omw1r2HXruz4IoGngHx4eGiM5EAj0saJpgRMBulQqwe/3z/Q91H1SD91FfKTv\nob6P0WjUU78Qi8X6hpLwGYya41XmRSV6stkT9bxzzwnQjLxRlMGQulOL3UZNd4wi58qD5gYOE+J2\nPbxBL9qg4qxJw23A/fyzetIMS9n5KgVnvoSuXKsaKpMqZgIDLVzNgWrFKwGa61LCDlXcfr/fhNla\nrRYAeMKvmvebtdAwUNCwjR1eBAyCM39ulh70oLy+hrgVsOwQN73/WZHx6/tjFy7pezFon+lJaqjW\nBudEImGMVg0fT2Pd/F16NvV7uEbmoEmY4gpx8371vNse9DRC3AS2TqfzQA+aYWG7MI5fa4ibAM13\ncZbvoR3i9vv9xntmgVWtVuvj5uf3R6NRpNNpw0EQjUb7QNyObgwrjI5pqLvX6/VNGATgeff5fmrU\njZ80gPhc/kJ40Dx0GqrR1iIqJX1xFKAHiQ3cdnh8UEXwuOIKkQeDwb4WKZ/P15db4oPtdI75l1mY\n4ipUcB2AYdathUoEaTtvwj3gy+/6Hfo1gZxKUENSrJqdFFBsT8n2mKgg+ZLRWOD92EYFwTkajSKV\nSvXtwzRa2Vz/zVWg52KMAvr5mjVPx59zGaWD9m8YcXmj9sWzy/UpqNmFPN3uMUHI8vIyEokE6vW6\n8Wj43CZRZnou6CW6PGjqCyppBWhlNFOxw6o0PpiHJn0t9ccoesOVu9WCRhoCi4uLfa1JerZ17zT6\nyBA33wuC86S6Y5DomfH5jhkRa7WaGVZSLBaNlwzA5HJpIJO85tKlS4ZCdRrC86AY0e12+9oaAfTt\nc7fb9VT98yKVKWsBiFl2mnDcfT5Jzn2b1Sji8soBmFYAAv64BQjDCl8QVuMq6LG9h8QrjBgwbMWx\njwA8BgzDMqPmZtSSJKmB5rXpidhVk3zxlKZSeWh7vR4ODw9RKpUMwQNwnGMatUfUtWbun4bQ+RJR\neabTac+Lwa9t3lxeygoViUTMs+dLOcma+fd1HfQ2y+Uy8vk8crmcGRTPiTqMZOiwB3qcbKXRPmOl\nJVXWtmFA2xYFMq6dPcOpVAorKyvY2NjoSyPx084xMtcMwNM/TGOUBt00jDcCNACn1w+4K881MqTf\na0c51Cixr3FCmq69Zh/z2tqaMXwSiURfKJuRNNuLZs8/a14KhYIHoPj+8H5GPR92tTz/DilqdRAF\nU15sP2OomGC8vr5uGPs4I34W5DsuCQaDpriVleThcLiP9rjT6fSlbOwWPIbDbaObIO9KtUy09int\nwbkQV2W4z+czXLV23m9S73mQKECrYmJ4hz3dSt6un1Q+2p5AdiMtthnmcCtAc36pevX8nRyCYVd/\n0uLUT+ZnDg6O5y9TaWk7XLvtHvE4jKjno4V3upfhcBiVSqWvgKPT6Xj4mBuNhnmZ2KJSLpeNB0VP\nIxaLTZzrt0PaykKVz+exvb1tvItSqYR6vW6scCoAgnOr1UKlUumriNaWMH5tKwU+2weJhon5bihA\nZ7NZ1Ot1Q6ZjX+rh8WvWYxwd3Z+tzHPK5zot3gGNrrmMbQVFLQ61AdpVyDcIoMdts3HtNfnWm80m\ngGPjPZ1Oe4x1Dae6iEpYn8C5x7ZBrkbtqOdDgYgXe/k51IiEHtVq1RSvcf8037y+vo7NzU3jVZ8m\nQDPdEo/HTYQnFov1caJ3u92+DhBGNGq1mokCqBNg9+LbDuCk8kMH0FQW9KD9fr+H8EPzbdOycmyh\nV0YvTZlyCM4sisvlcsazYq7E5/M5i7u0knfYh6/WtHrzqryCwaAZoaYHNBgMmoEBOjiABgILWTiS\njffHPOS4wjUzv8gQOsGZe1mr1fqs/Ha7bcbKlUolQ1DD9AKHxfPeCUhULJOIXblqAzTnFHMfdU4x\nARq4zxtfLpc9dLacCEXWMwCenlMFrWFEw678mmkAnlPmwm3DFoAJGWp+jx6ietA0/DSSNYnofbLw\nzwZpV3rLbs/Ud8iuFXABtF2zMM6ada9pzLL9KBaLoVqteupE+LVrAh33lSFcAoTO3aZnOM75IBCp\nl6geNAG6XC6jVqt56ofoQTPfvL6+jsuXL5tCvVnR17qEofV4PA6//3jyXiKR8BRuMkztKmxrNBoo\nFosmt22nDdVA5fkfNQUySH6oAJqbph605oO1itRVWDItIaho+FQfol63bt1COBw24FwsFj0etBZ2\nKTgPq+TUGyUA8b419NdsNvvGRYZCIQ/1HQ8oLU9WkfJwU6kTuMcVLfRgkRQ9H2VY4t9Wz6fVaiES\niSAYDBpwZqieVercPzJ30YuZlgfNNdkhbs4p1oiEhrgB71AXKjK9ODIVuJ8u4flRD20YcQGdetAs\nBCRlruYdAXh6c2m0MQKgDFzq1dGjG1fUA7TfZ9vYHjbEbXvQXJ9dlDVJW55rr/k7CM6ZTMbTCqSf\nZKDTS9fFqnqfz9f3/Ph+jno+9J3SaWE01lU3aA3RoBD35uam8fqZnjnNELcCNfHAjsCp8cavq9Uq\ndnd3DdeDHVlQkFYdPfegLbFD3FQQWg5/GjlohneorFx5Us1DAcfhYvK+atEQQ9xU4HYe60FCkFte\nXjYeqLZOsVKVbVKsuGQT/97enqf1odvtmr5M7V8HgGw2azyASQFaCz16vZ5hqGLxEXuwbS+n2Wya\nnCfBkQDNtjaCJ0OKGpqbROwwqSvEvbW15Xm5uW4qU6WV1GfBsDb3muHDVCrliYqMWrjE7yfoKkBT\nicVisb7CMQCGCpHg3G63+zxoehOkg5wUoLlu/dR3Wv+fpnDs4lCNoOmz4/MAMDAHPU7Fv2uvecYZ\nbtUKfr1osNFr5WUPXanX6wCAVCplJkcRoO01DCMnAbRWbJdKJU/e3A5xK0BrB44+w1kKgZm94cM8\nP11XoVAwPA92iFvBmW1atiE7iTyUAK1gp588TEpPSY9G88BaiamtFtOSUQ4fKwYZvsxkMsYDZ1EG\nlSB7d+mZDyPqKTN3zAPKqmtSoro8aL6QykhkA9E0QoAqrv3T/CU9J3rXetFb0/V2Oh1Pz6tW9A/y\nvkYVVx7T3gstbtI2Enqu9qxiux7AZu4iT3Cv1+sbUDGMcnDdL/ePhlC32+3jJ+c96IAXAGb/+Q66\nBliMA26D9lv33d5jPlMFXoaA+XMERC1woiFPYNdhHzpdaZw2K1vskLfqI7vam2Cu6+EAj16v56EK\nVjCt1WpYXl72RBD4+x8kTBkyUsa0HD1FRgdJLkKjlwVv4XDYY7wxCmhX/89aJn23+RxsCmGCNd8D\njSaOoqNP/NsT/4YzErsox07cUzlQmRCgNc+qCvqshGti2DaTyRiFqwDNg8CfGba/UQFJFaMaLJFI\nxFRnK3MYlQKVkR0GtA2kWYoW+2iLl23BsghEFbG2NKmRpkM0pmGoaYjbbmPTPKjtkQEw1do6cIAR\nIbsghaDICvpOp2OiHwR/tjaNKgrQNAa14p1Xp9MxLVSqqKiYdM0uY2USsQu1qBQBL0DzmWhtitZ2\nHBwcIBQKoVKpoF6ve8a7qr5gTlfbtEZlEhskmi8H7oMB/x+Bmu+iDqegN2qzvBEMSWLC9jCeR57F\nB4mm2DjxqdFomPkGS0tLSCQS6PV6Jl3DC7jfWqU1Ftw7u3biPAt1jxpHGiFVgKYBMo1uBeAhBmig\nvyjHlbin8qC3qKMTp6WYJxGGX3TaDsnnWfyhfb60WoetktaQuBbIqfJRli0NgQPoI2Vw5eJOQ7h2\nKi8qGYbWaGjRW9bnSgWuIK8APY1IyiAPWtc+CKB9Pp8ZhahjEAnEWqBHZawV3wRuPsNxwRmAKaJh\nSiQUuj9YgPfJv8nZ6po28Pl8pprbHoAzDe9ZgdluAQLu77V60Bqm5V6pF6cA7ZqypFPPtBd6Uu/P\nBmf+W99VBTOuhSDI865T+tTrpQets6sZnRtG+LsIPtVqFfV63Tg+WnhlF7axIloBulKpoN1um1Dz\ntLzMWQvPAjs+2JKq7x/3SJ2Av9AA7XpJ1Xu22z5sK8gOcZ6VaFVyMpk0NJ+8R7tCliGlYQFaw1ka\n2mVYW1sMbC+JXrV60JMUykwiPPi24lLCGeB+HtRW0LoXCtDTNNQGgYZdkGcDtN/vNwB96dIlbG5u\nYnNzE+VyGXt7e9jd3UW73UatVjOFZwxV8vmp5xyNRse+B+6PFjjy99sV6gz30ggmQKsHrVWy0zgz\ng8B5kAethrsaGvqs2ZOuc5UVoNWDZoRpmh40ALOHmjtnhXan0zHPwX5nCXzklGYkgz3/1WrVVOHz\n2Q5ba6EeNPPOrBb3+XwGaMkgp9Pa2u22KcxiQSnZuDQCdlr6YxJxedBcP3C/sLNer5t3UEmqJpGH\nGqBt79lVWUflQsWhI/B4naUHrSHuZDJpJkrZhSI8HPF4HAcHByN50Hw5XWkBl8Ljv5vN5oke9GkC\ntOs+mPPUMFmr1fIoTz0n9os2bQ/aPo/DetA2QL/kJS/B9evXkcvlDNgxz0tlwDAzw7IKzpP0oNOD\nVkY4u96g2z2mPGSoTz03v99v3j27PmGa+Wd7TTZA83mqB81CQBvc7RA3i0lpyKoHTUOLZ2ZSsQvV\nCLJ0LHhfdlifeedqtYp8Pt839EE9aO2I0FD3g8TlQbNlkR40U2S2vqKnTC+fhgTvme/hwwLQPAv0\noPmMAG+Im172tOh5zwygtTBC+XwHcWTbQKKFYPSWy+Wyp3rRVpBa0emq5JyWDAJB/j/9PtI3asEF\nrX3N3QSDQaTTaXN/JLgYZp/t+7PBRHN5SjzBKmh77qyumcVkynM8qXdh7xEAj0Lm11yj9uNyrXYF\nKwHSngM9btGPLXb0QY0BKnhWRitDFIt7+Du0OMg2HBgl0vSN3+/3DGZRlrhJRffeJiRhWJXnkX+f\n7xbBgFXoymE9jfdNf8eg8605ewKMTU7RbreN3uCzsKk47eI7l34aR2ydZr+Petnny9afXK+CN9tM\n+b0EmFENONff1f9HsFJDia2RNCTp0XNPZ8VhbTsdeha0bY57qoWPrj32+XzmfSWmuJyaSdvwBsmZ\nAbRtlZCMXr0a9YJ0QxjzZ1UhP7e3t1EoFEwoELjfsmNffCCzKFKwvXrbg9BLC1PoBdEa5sGu1WoI\nBAIGJKkQNWQ3itgHloqM7Rpk4KrVatja2sLW1hZ2d3cNpR9wvK+s7g0EAlhfXzeTabR6c5I91IvV\nqQpsLF7Rq1arIZfLoVQqecgblGOXbVocbxeJRCbuySS4drtdo5i0ZYmtXHyOvMgSRQNDRweSAEIr\njGedYmCIVCkQm81mHztbrVbD7du3sb29jWKxaCpYmeNke83Kygqy2SxSqZSnVWVc0Up4Pi+7p5nn\nm+tmzy45ze2L98X3SQtJtXd62nrDtdd23p6fdm9uIBDwrJkUuww/c3gFf5/WuowSfbN1NAAP7a7S\nZPL5aCoK8HLMh8PhB84YmERssKQOtadZKbcC0376jHWf1UHhWWFxnta32NgyDVw5U4CmpUrFaU+L\nsV86zSfRW1LWKAI0KRTVcx4E0LMKb2vBGj9dnqs9FYoGCgGa+R8AHoCmYh9H1Fsm4DEERY+ZNKR7\ne3smD0qAZiUmX9pIJGIAmhR+kyhhlwWs3rL2fdpXtVr1ADQrTu0K2FQqhWQyaby7Sb1+9Wh4rhSg\ntYiLng6rXe1K2XK5jEKhcCJAzyq9wOiUsljRWx50NgjQ3GuG2skzTYCOxWJDt4CdJAqQg6q2XQAd\nCAScZEEK1j6fzwDSSQA9Db3h2muuxwZsDa3zkwYoABPN0jPFArKjoyNDGjJqeszW0dRZ1B8a7dM0\nEvUs4M1lU99MKwTs2lOmNLiH5CLQq9lsGiNM61Fc+1ypVEyxJnUP2wnVG+f5mKbTd6YAnfqv/xUX\nvvAFdDodvPimN6Hyylf2edB2SIHAp8MFdnd3sbu7i1wuh2KxOBRA22GMoeTOHeADHwByOcDvBz7y\nEeDjH+/7Nq7VZg+zc1/M8zIsqQCtOUbOaNYDMhJA/8qvAJ/73PGan3wS3f/4H9G+97epGGq1GgqF\nAvL5PPL5vPlaDSCGAv1+vwkPp1IpQ0RAgGZxyCTif8lL4IvHAZ8PvVAIta98xRhlStpAT1RZrbhe\nDgZheFs9aAI0Pf6JPei7dxH82Z9F8N7ZaH/wgzj6wAcMjSjzmsyhc8+B+1OJ6JlyCAHvTwFajdWp\nyJUrQCJxfDZCIXS/8Q1P7yv3nOdCz0a1WjXPgXvtAmiei2l50PjVX0Xwc58Dej10f+7nEPgbf+OB\nHjQr21081wr4XL/dhqfphpH1BtD3DuLzn0f3ngete63Dc9SA0JGe/Jrgy0ItFi/x3lVXMqIxCkAv\nf/zjiP/u7+JCKoU//k//ydC00mAG4HE+APQZTsB9gGa1NwF6FiFu37/7d1j4/OcR8vnQeulLUfvl\nX0bt3iARGpR7e3s4PDw0c77J/8B0l73P1C3Uv2zPU3Ij7QoY63wMkDMD6OBzzyHyW7+FF377t1Fp\nNPCSj30May99KbalhYH5DTvErblZMjTdvXvXhAXZCgAMDnFTRtrEYBD4t/8W+JEfAep14FWvAt78\nZuBlL/N8Gz1UWpe0HDXsYn/NtdoeNHNJrVarL8RNa/lE2doCfu3XgGefBRYWgPe+F77/8T/Qfsc7\nPKFVGju5XM4Mdcjlck4vNRwOw+8/5vzlXFcqYnLsTupB+3w+HP3e76Fzjz+8dS/XWa1WDYNVsVjs\nA2wWsvAa5EGn0+k+D3oi4AiF0Ps3/wa9V7wCqNcReu1rEfnxH0dzbc2AM881i3vUk9Y8KTm4bYCm\nh2enSyYSvx/4gz8AUikAQPeeAqfnzCjV7u6uORc7OzvY3d31tNXQgKAHSkBYXV1FJpMx5CsTe9BP\nPw3ff/7P6P3xHwPBIPxvfSsWXvnKEwGaRW+selZ+68PDQ3M2WDjo8qAn0huOdxC/+Zvovuc9fXvN\nOg+9jo6OjMGgl8uD7na7hkxEI02pVMrMcR8WoNvvfz/K738/4h/7mMldUyfR0CRA26F/AhZwH6Bp\nmM4sxL21Bf9nP4vqd76Dw24XsQ9/GKHf/m3UfvRHUSgUDJPf3bt30Wg0TGW+Uum69tkGaJ4pV4j7\nh8aDDvzgB2i/8pVYiMUQ9vmw/6pX4dHvfx8/ePxxT4UthSFOVdCFQsFY83t7e+Zws2iBm27PcR3b\nU1pfP74AIBoFHnsMvTt3gJe+1HPQNOfCHPn+/n5foZO2O+jPK/EGrWmCvYbjhmmX6PV6QKeDXq0G\nxGLwNRpor656QqoaieDwju3tbeRyOU/O11XQlkqlsLq6imw2awBvUiXc6/WAXg8dCfFxvczP7u7u\nolAoGFBm7pYcxVRCDFtxZKNe9J6pwCd5qXwbG/BtbBz/I5EAHnsMC3t7iFy9atpKWJlL0CsUCsZq\n9/v9xtupVCoIh8Oee2IqRNtx7HazUavRuc/odI4/gb69LpVKyOfznijV9vY2dnd3Pb+LXqZNuJJO\np5FIJDzTpCaKVDz7LPC618FHop7/7/9D9Pd/H8HXv97sBYGV3iRbFJVlS6NH0WjUGMWcVxyLxfpS\nbhO1VXU6QKNxbBDt7wMXLhhPlP3uOnTC5munTtSL6R5tD3NFD1j5r1Xqw0jvDW+A/9lnTUsV9Vqj\n0fCALwFaL4KWHXGYNYeC794+94JB4OAArWzWE6Vg+oj1HzbnOQ1JZfWzi4/tIjPNY9sFyJPK2bVZ\nvfzlWPgX/wKL+/voAkh/5zvoXrniGa2XSCSwv7+PXq+Her2OnZ0dLCwsmMHgBOh6vW76XzX02u12\nsbq6igsXLiCdTmN5eXni4iUjf/7nwPe+B7zudc72JBoSDLdyNCLQT09o/7tUKnkKb6j8CCSjKIve\nxgbwj/4RfFeuAJEIum96E+qvfz3Ke3t9IUteWmDl893nFWeIi0Qa2WwWKysryGQyUw0X93o99Hw+\nLL7tbVjw+1H/238bjbe8xQAbZysXCgVP4QZfIFZr60tz8eJFXLx40RgSWpA4dcIaORvBe4V/NKaa\nzSbS6bSn0jwUCpnwb6fTQbVaNflDtdybzaYBc4Y0k8kkUqmUIdEYmUDD5zuOAvn96H74w2i+5z1o\nNBrGa97d3cXevbNiF97ZObylpSVcunQJly9fxtraGpLJZF8r28TexctfeqEEWQAAIABJREFUDvzS\nLwGlErC4CHz5ywg99pghzeBoQwAewh2d7EQFS14BtjimUilzZbPZqdVU4MIF4JOfBDY3gUjkeL9/\n8ifRucdbz7GcnBNuF0MSYPU8s/dYp4lpEZR6yuN6dtquxjOsLIOMcKo3rOkXDR2rgTa1s+DY584n\nPoHEX/7LiIfDOHzjG9H5iZ9AZG/PrIOUrczrM4JVrVZNrl5z0ktLS6ZPnudef4+OfmU0bhpdIZSz\na7N67DEc/eN/jOz73oduOIzmk08i1GoZ65UADRwr7Fqthp2dHRwdHWF3d9coZiovAjQBgoeYQ+fT\n6TSi0eh0mGvqdeDd7wY+8xn0lpfRtULW9ILoeeRyOeTz+b7iFh5wu3CsXq97Cm+oaMYBk16pBN+X\nvoTmc8+hE41i4X3vA/77f0flNa/B3t4etra2jEekeVweSP27tBTX1taM16wATR7vSb2kbreLw9/7\nPRymUmhtbSHzMz8DfyKBaiZjJkPt7u4in897PCJ6+Qxla+hqfX0d6+vrWF1d9QC0GjxTURZyNnyx\nGAL3esnpUbTbbZMLpNejzF+dTscUstijBg8PD00RGgGa+XRGikaJXvR6PfS+/nX01tfRzeUQfOtb\n0VtfR/3iRQPQd+/eRS6X81Sy0nALh8MeatJkMmmMN+6zpg+mQgr0spcBv/ALwE/91HEU66mn4LtH\nisGJUARobaViYZMCFc82ATqTyXjOdDqdnkrKBuUy8KUvATdvHkdX3v1u4AtfQOctbzFdGtzvYrHo\n5DLnuhVseeY1bK8/Mwighz3nqq/Ys8z8K/eDtTb21e12jf5mJFSrpWfRPYNyGf7f+R0c/NmfoRWJ\nYOn970fqK19B48d+zAOq0WjUky5i2L3RaDhz0IyEsp5F2zT1YsTlhwKgA4EAuh/8IA7/7t89LoL4\nl/8SPQFnXrTO2Dql/MPaktDpdDyKmbmFTCaD1dXV6XnQ7fbxC/b+9wNvfzt6Asx8KQ4PD01+fGtr\nC7dv38bOzo6zhN+2PHlg6BXS0hubP/yrX0X36lW0uZdveQsC3/oWytevGwV88+ZN5HK5vtwXvTuC\nAQ85gU6VGSt0ebgnBeh2NovWwQEOolGU/+pfReh730P1Na8xoWEaPcpYpQppeXnZtPhwnel0GplM\nBqlUygC0Pd1oIrHOhr9zf0gHQ2EAPOAcCBwPyrBbBll3YCtq5v/ZXz2pB91bXz8+d5kM2m97G/x/\n8ieoJxIolUrI5XK4c+dO39lQgE6lUtjY2MD6+jo2NjZM8V0ymUQikTBG0FTzcx/60PEFAL/4i+il\nUh4Pml0PWjdBQ0eVL6NSsVjM8OCvrq5ifX3dGD1TaRv86leBa9eAezO88c53An/0R+i8+c0eD5pG\np5IuaY2KHXVzVaTb4WstlLV7mB8k2pVAI1KfJwGOOk/DxZobZ3uWy4Oeqtzb50A2C3Q66L797Qh/\n97sI//RPG91FkKYxr8YMo5UapVCsUYBW71lTIhqVe6gB2u/3I1Quw59OI7S9jYXf/V3s/c//idje\nnifEzYfNQ8yNUmtMv15eXvZYwlQYiURiOgD99/4e8PjjwM//PABvzzMfOAGahQk3b97E7du3+8r3\nlcrP/rQPzbjh2N7ly/B/5zvoNBpo+/1Y+MM/ROnKFVQqFezt7eHu3bt48cUXsbOz05cjB2DYouhl\n0EtSgKbxM6gQb1TpNRpo12po+f04KpUQ+eY38cI73+kJcTP0an7mnuJitIHn4OLFi9jc3EQikTDn\nyu59tlt1xhbrbNBL03YVv9/fx5sdiUSQy+UMq1WlUkEul+tTzCw+pAcdiUSMB63htaE96EYDvXYb\n3XAY3VoNoa9+Ffsf/ajJ1fF86Nng+aCiIkBfuXIFV69eNYac5vK09Wwq1a17e0A2C9y6BXzxi2h9\n5SsI1+sGoFk8SQOOAF2tVk2lM8PuyoOfyWSwtraGCxcuIJFIeMKdE3nQm5vAt78NHB4eh+W/9jXg\nNa/xdGkwxL27u9sXUXMRbWh0QFuKXDlRFxnHMOLz+RDw++EDTGplUIibRCQ0iMhCGIlEjEHnytFO\n1Yve3ITvO99BsN2GPxSC/xvfQPdVrzLT+dSLZmqAaQTm6JVhUus69KI+dF12lGBSOVOA9r/3vQgW\ni0AoBPyH/4DI+jqi91oCaJVooQwrXBuNhnNzVGmsra3h0qVLSCQSHoUxEUB/61vAb/zGcZvEU08B\nPh98/+pfofcTP9EH0Gxb2tnZwZ07d/Diiy96PExeNqEJlbIeDG2/GtX67L761ej8zb+J6BvfiG4w\niObjj+P2W96C6g9+YAwIEk7Y3j1DU3zRksmkxyNlGDCVSg09XWsoyeWQfO97Ee920W21kHvTm3Dz\npS9F/cYNT+FSPp/vWzOV6fLysgc8CGA2cExNHGfD/+lPw//X/prn24LBoAFZGl7hcBitVstwFZNs\nhR62XgzTsmqXhqyOqBwaTHI5BN71LgQA9NpttN7zHpRf+1rsP/OMSdHs7Oxge3u7bx08kzTYHnnk\nEVy/ft3DtjW1sLYt73oXQL3x2c8ikEphqd02rV2MurHzAYAJJTN/z2dhV/avrKxgfX0d0Wi0TzmP\nLa997XFk5amnjtf81FPARz+Kzt6eZ2Y4W4FsUW9Z269svUFjnmeBZ2zc6EXgAx9A8A//ECgUkHzF\nK3D4T/8paj/9030hbgVoFrux+C6ZTJrIlg1eUz8Xr30tfO9+NwKvfjUC9/Y58A//IcL3JnrpFQ6H\nzZAgdsqQEMq+VGfwnaU3rlckEpn6uT9bLu6vf937b6sy9NzJG95wXI0p0mu3gTEJQ05LWv/sn6Hx\nyU+aFxu3b5/1kk6U3pUrKH7ta6ZAql6vH/egn2dxnI1zL1evov3d73qNw3N+NgD0641q9WzWMYp8\n6lPH10Mkvf/239C2nQeHAXGu5CHc55Pk7MY4zeXMZBbtDXOZy1zmMpfpiu80lbXP53sokKHX65k4\n0HzNs5P5mk9H5ms+HdE1Aw/nuudrnp3Y52MYOVWAnstc5jKXucxlLsPJPMQ9l7nMZS5zmcs5lDlA\nz2Uuc5nLXOZyDuVUq7gfxlzBfM2zk/maT0fmaz4dmeegT08e9jUPK6feZnVSznt3dxcvvvgiXnjh\nBbzwwgt48cUXUSgUnH2Vrr5AsgCR1Wh9fd1wRGu/9NLS0sA1uPoER83TNxoN3LlzB3fv3sXdu3dx\n584dbG1teYZ88PP69et4/PHH8cQTT5grmUz29WCe1FM3jTW75OjoCDdu3MCNGzfw/PPPm0v7oUlY\nooQwvCKRyEzXPMo+c0KRXhcvXsT169fx6KOPms+1tbWprblYLJp1cZ3b29uesXecpzysZLNZXL16\nFdeuXcPVq1dx9epVXLhwoW/v4/H4TNdcKpX69jMcDnvY23g2MpmM6ZnnFY1GB67hpDW7en9v376N\nZ555Bk8//TSefvppPPPMM6jVap59unbtGjY3N50UjdPoVx3UXzzqmXbxIjSbTdy6dQu3bt3CzZs3\ncfPmTdy6dcv0dOu1srKCS5cu4eLFi+YzdW9S2bDrPmnNHNqjerdQKODZZ5/Fs88+i+eeew7PPfcc\nXnjhBSfvw6VLl7C5uYlHHnnEfCpNMC8dlDTpmpX8iXtaLBY95+Xpp5/GjRs3nGu+du0aHn30UaMn\nrl+/jng8PrGOHkbOtg/aIa7RksrWxWZ7sl2RtIH/ncTngUDAMJCl0+m+wQ/cMPtzVHHNeCajjs4m\nLhaLZoIK1036TpsCbyYsOw8QZavipcPklZhfiVOU2YzkJhx7N01x7TNn53JOMv8+iQT430mKQGYg\njvIkA9M0pusoBSM/ddwox/7ZIwKXl5cNcYlr8o8yRuk8aJLi6GCFaczY1Qk9pMQk9zAJJ8jUpgxV\nAAxZhdKNct0kzYhEIkNNYTtpn2394AI1DpggIxq5pG3CCt4H750XqR71cjGiTfsd1XPDi/ehk9tI\nurK4uGhGH5JznOyLPNvTFBoMOqCDU+U40IXPW8dOkt2MIycrlQp2d3fh9/vRaDSMYclJhCcB9KhC\n0ho1LEh+pbPWXefInkzIIUjdbtfobn5OnXgF5xSg9eXTh+16KaggCLrNZhOVSgXNZhPlchnVahXN\nZtM8eI6e1AM0yUvGF0ovsp8RnDktimBHqkSdmmKzhI1KyzcNIZWkDmVXgKbhQQpLtRr530idOG3F\n4NpnKgSdp60GAs9FOBz2GHoKcNMcHG/PaSbDEveOxCukoiRloDIr6fNX/msOe+AzUuOJw0J0FN64\nQkOLAL20tGT2k2c2Go2iXq97QEQZ9MjRTHDhmEzyZU8C0MB9kHYpUwXoRqOBYrGIYDBoDGfXvF/d\nc14cvsGLTHT2c5oFQHMvOZ2KVKClUgnlctmANaliFaQjkYgZ/jCtc62i9KQ0GDjkgxPaaMgpQBN0\nOXCjVCoZI7tarWJ1ddXMHSB397REzyIdjVKp5Jm/PYiZTQGaQ03i8Ti63a4x8Kj7ZiHnCqBd1vGg\nQ6bekI6Vo0fBn+HovmAwaOgqdaoNFfkkHjQfIq0zToRScM7n8x7vjxRyOgTD5qk9TZC2wYuWMsGB\ns2fr9bpn1isVpc7S5WGfprj2mS+XDslYXFw0z5QAE41GPROv+LV6ndPwoO3zSxBV67tWq5m9+f/Z\n+/LwuMp6/8+ZyT6ZLDOTpGlC9xaoFFqlUNlKVYpWLVBQKHJZ5FouWhW8+ADX/qC4UHC5gCDX4oLK\nQynq5bqxaSkFuRapKKAFbilgF7I0mWQyW5JJZs7vj/Tz9nvenEknmclMUs73ec6TpDmdfOad97yf\n7/7lwAa9jzmVH5I69zD3NRVXWuf0JJCgs1GOSNDFxcWqFzXxlZaWqvaNtIKIj4cY8dGa7unpURPG\nqqqqEAgE1OeVzTrrZ4S8eCBHo1HVHpWHshyCYOe54vderxeBQACBQED1uJa/51rlWrhvpFIXCoXQ\n1dVlIemenh417KOsrEx9/h6PRxEOPS65FH62JCvOkZceQknQXDMqfCRlKkz8f5Kcs9kf6TBLkiV2\nErSc85yOoDn6NRQKKYVVepRGCptmIxOKoIHhD6B06/ErMDRVhRYS3Qw8DEko/ED4wdfW1qr+q5Rs\n3RK6K5N/kxZ0V1eXImg+3HLDpnNzZ+t6H4vogzp0C5puWrpk+XlwLCUPb2nV5krs1lm61KSLm6EM\nzvplqEOSCS1oSWq5IGiphEkLmiEP9vvl5y/3gz5MpaenR7njqZDwM6LLbrwsaD5XJGjOqqZCw7Gv\nbrdbuQzpWpWubh7KJGd6ArIRfZ11cub6cK9y/CutZZ2M9fUvKiqC3++3jBikMs215f7PtXCf00pN\nR86hUEj1hU4kEigrK8Pg4KA697jHx8PFrU/gam9vR1dXF8LhsMWClqElaUHTNc5nJhqNqjOa4chc\nChU2ns30bNItn4kFTYLu7u62uN854jjX5516/XF51THK4bRj/hsAdYhINyYPLG7qrq4uRKNRVFRU\nwOfzYcqUKcPII9sHjQexjFPoBM0m+LJRO5OW0rm480nMgNVtSPIiQesWNAmR93JyTU1NDWKxmO3Y\nu2zFbp0lQQOHLGiSgtxP3d3dAKCITndx5wKvvndJVlRsSNAVFRWKoLkfpFXH7zntjIQnPRZ6fgAJ\nOttDWY9B8/DR55jz3xKJBMIHe2HL/Ao5JcwwDAQCAUydOhXxeDxrC1oq6yO5uEnOuktaeqVkvFl+\nnTJlimW8IJNN5RqNF0HTxU03Ms8ynaSLi4uV5czPXiec8XRxcwJXe3s7uru7bV3cVIa4ttKIksYU\nyZnPZi5FeiWo9Ogu7pEsaGIOh8OWSV7jGdKjTCiClocDXWyMrQCHYnySsHkIA1APJucpRyIRlJeX\nqwNMd/nkggS5YamZhUIhHDhwAMFgUE3eotuHlp3H47Fk3DKBhcoFDwaZHajHpbNxfcs1kGuneyB0\nVxDfBw8EfRZtrtzFdiLfs66Z070kJxXZ/X8+pCQUechLpSSbdZb3ynGSnJYEwDL9xm4KDq94PG6Z\nAZ4u8UafsZvNvqbCy9gz10MXzgpnmIlxZ76GtFB1BTTbnA+poFBJ4f6TSX8U/mz3uab7jGVsXc4M\n5meTiz2uKxs8A6SSTw+c7pJNJBKWZFn5XOQqLKaHbUzTVAonz7tgMIju7m7L7HqO9dXnJXu9XhXq\noeIpvTK5ODt0Yy6ZTCIcDiuDScbwqUwYhqHCNzSYZH5QZWWlmofNHCcq116v17L/dOUv289hQhG0\nPBw4Ao5ZiX19feoQkIlM8mDVk36kO0oebPIgy/ZAo1uJ7p4DBw6gra0N7e3tCIVCKi5XWloKj8ej\n5s7KcY2lpaUYHBxUVp7H41GbhEoKNTdiz3YEnl3GNh8ebuju7m60tbUpT4SM7csDQVck+PtcCjVW\nWscALFYerf107jGp+XPoPC1SOSa0r69vTOssrSquQVlZGaqrq5U1VlRUhHA4rDwnHDXJuLnc18xT\n0Mfy6c+I1+tVs86ZWZ9N2IZKTnl5ueXwkmRFT5XuRqWlJEtmKioq0NTUhIaGBlVOk01CjR46oGeH\nSrgkLLuwgd1nazcDuKamRh3MRUVFFsNAPjfZiMz3kImPfPaYv9LR0YFQKKQsVO5xKqn0xlVWVsLj\n8SiPDLPOs8GnE570BhFnT0+P2vdUOAGguroaXq9XjUStqqpS/4cYXS6Xms/O5zKb/cvwgFTewuEw\ngsGgKrsMhUKIRqOqsofPqdvtHqY4y/wQ5ixFo1GUlJQohUN6sOS5mAtP6IQmaK/Xi97eXqV101Ux\nMDBg645N57JMRya50HD4UJHM9u/fj7a2NrVx+/r6YJqmytitrq5GXV2dqtemhTQwMIDu7m5Eo1H1\nsMmLs0hzNQxc14xlWYo8GEjQkUgEiUQCpmkOs2RzvaZ2wr9DC1nuFTmwPp17jK7tUCiEsrIyyxoy\nzkR38VjXWU/s47xkWl4ejwfxeNxSukMC4KEiv9pZRHzf0jKvqqqy7JFsFDeSLOd7UwmKxWIqI5vl\nPDyUJEEz+1nWZU+dOhUNDQ2ora3NOUETl54YJRU6qejqhC0TM+VFgmbcmn87FyV5FJ5hdgmmev4K\nXcj0YpEQuRfKy8uVZ0bOjs/mnJChGj5fdiWkjO9zjal88vOXe6G7uxsVFRWKiE3ThNfrVUZJtphl\n/J5VE1QKiJdrSe9QeXm5KsGjpc/nyuv1WsJJ9CAUFRUhEolYCDqRSFgU+lycgxOSoOXhIy1nWjnA\ncPekzGKVrhI716h0F2ZLKNKCbm9vx969e9Ha2qq0e+KVFjTjcUcddZRF44tGo+jr60NZWRlqa2vV\n8HkAFhciYztjFbuEJhJ0T08POjo60NraqhpUSAuaonslcqk12oksZZBZxnZJQnbCRCF5EOgxXVrQ\nY1lnSaDcd1IzZx5EIpFQVhqtN5fLpbJLeaiQ1HW3LK0mqcRWVVVZDuZs9oYME9A648/c6wwVSAua\n2Jit7ff7VbOShoaGnBG0TJCTuQi6RS+tS1n3LBUjfq+XWLlcLrWmpaWlcLvdw0oQc2lBy3I5urbZ\nO4GKMvfFSBY0rb5cWdDy2ZA12SQ/aUG7XC5UVFSoRFF5sYlRbW0tOjo61JrTMKiqqsqZBS3DWCTj\nnp6eYRdd8nxOucclVn7la3V1dak953K5VB21JGjubT4P2cqEI2j98OGBSQuHD4seO3S5XJYkGT3W\nrFt78nfZiG5B7927Fy0tLcNiS5Kg6+rq0NjYiGnTpqGnpwfBYBDRaFQllJWUlFiSLWQZGd9DruJf\nUkumhdnR0YGWlhbs379fxWxY96pb0NJdaJeEkyvh3pDxP3lQHs7tGIlEEAwGlVUkNXhpxfT29qq/\nN9p11jPvaX3J7FRJZtItzm5nzNqOx+O2Sg+TmmQYSBJ0tgccCbqoqEgREkMw8XjcEsuXDR4AWGqd\nqYQ2NTWhvr4efr8fNTU1Kj45VrHLjpdxWd2CJnkxHmpXBy3DMrw8Ho+6X7q4c21By8RHxnb1CpCO\njg4V6uO5IC1oWbGgK2rZWtCyIY5suCMt6HA4rBq+eDweBAIBTJkyRRkZ7B5HBY3PMJViupLp4cjW\ngma2dldXFw4cOGBp8MKviURCecqYGFhaWmoJP/LrgQMHYBgG4vG4cnGbpqksaCpXiUQCQG6TCCcc\nQeuHDy3j3t5e9WCTmHX3nyQ0EiJdfzKOO9YNYEcEeo2cLKmSZRvUcGVMpra2Vrm2mXzQ1taG4uJi\ntR500/GQAA5ZkJlill+5TjL5ZWBgYNihwPIJHoC0nCgjxaCzJWe7dbYjYj0ePtL7Tpdwx3vsLPHR\nHsT63ySZUulM956SyaRFYZAeIT3xbqQkp1woRXrMm+9DPzx1K1KGIGRSXH19PQKBAKqrq1VMN5sD\n2C60lS5BURIYk95oYUqr2i55rLy83GJF23URG41IbxUvEol0xzLpik2WZLcrPebPNZc163Yd/rJZ\naz3Px643As8Gw7AmwZKc2erV7/erXBCWWsViMaVEsdMYjTFdkc1k30jil30EeI7RW5lMJpUHhfug\nsrJS1b7zqqurU6TMOnp9DzLeTQ8jPW+5aF4y4QhaZscxcYIuC6m926XD01JlTW4ymVR9uX0+X060\ndz3rkG4nPQYmlQ1mAno8HqUkyI3IjcrNRAVDJrbQHWSapsqMzUR0d7bMxJQPWHd3t4qfd3Z2KqtZ\nxhq5xrrLVSfnbInCbp1lIo3M1gUw7NDUs2NTqRRaW1vR2dmJcDisvDISv6zVHEtGtF6XK9+DvHQF\ngN4L2TdcNrdhJQC9F7JqgBZCcXExqqurlWs3072RqfDAkWSXrkRQZpbbNQPJVoHLJP9hpNeXSi+J\nwS5JTMZ09eQrkt9oFA1JbrxkUiYtO+aAhEIh9Pf3qxgpYFVO5HqMZy6IPD/slFepbNrlBOnVNjzb\n6ApnjhHDVWwdOjAwMCwUkWn7T753mQRIJYbPIWPf8qqqqlKubbrdpXeCv49Go6pNL7vU8fOUylOm\nRtRIMiEJmhmkAFSaPN8sCduOoOUHygOioaEBjY2N8Pv98Hg8WS2aTFLjBtSTBGQMjO+FlnNFRYWy\nIOi2l8kuTGjgYSgTlbjBuZkzrRWUrjmuE2PNLDegm72trQ1tbW2qty7d2noyEHB4Czrb7FF9nbm+\nUlvVvSj83q52nu8rEomoZjWSoCWp6B3dRrPOPACIX+KVe0ReiURCxctkvSstKVlbrteh8pBgXTIt\nmFyKjH1LgpYVBno3Lr2XdS5r/PV9NxrvDd+HTK6S7TLlRSUkXce/0bwPvUpCVkvwIklTeZYELa1Z\nu7yE8cgF0ZX7dOWt8h7u53RJu7J2uKamRhkdJOhYLAbTHGoHSgWJ7vNMCNpuTeR+lIo9kxnlJQmb\n5zUJurq6Gj6fTykV5BN6Q6LRKIBDHs50+TCjkQlL0CSjwcFBdHZ2qg5RI1nQ1M6Y0l9VVaXivbmw\noHkAS004XRaptKD54epJJ7Lnrqx95CEiMxplTI0DFjIR+WBLj0RPT4+y0jo6OtT3nZ2dlhpuWQPK\nv2lHzpLQsj0Y7NZZdofjAcbcA/1Q0glwcHBQue6lBQ1Ya3Ylmcj63kxEdwVK7wgv1pjLA4xKh+wQ\nxa90y7EhC5UA2WqR+4mW83i0SgRgWSNJXlIZliVLugWt13KPVeyIKVPFUCpkUtGwc32TjPUGJrpS\nkKmQoLu7u9UzJntr82KckwRIdzs9Lfrf1T1Z+nOYrYs70/CPnhMkCVreyzwFdvjjOcgkWNl/fKxe\nIbvzqbi42KJcuN1uZRHTDe/z+Sz7gB4TGllVVVWIx+PKm2VnQesJrNnKhCJoWeIhLUW6hnn4piNo\nxriqq6tV5ihjH7W1tTlxccv+ynoNptyQJGjpHqHLJJ0FTRc3gGFNHeQBLPvdHk7sEuoYMggGgypb\n+8CBA5YiflrQetyMks7FnQvN3W6do9GoitXJoRN6kprL5VLWq7yk1TIeLm6Z8SoTaiReWkeyPSe/\nl25OiVNa3roFHQqFLORMqyTXBG1nQctGDtKFLd3E0iMh1zRXeMbivbGzoFnKKBtryHwV3XU8ln0u\nkzAPHDigQi7SW9Ld3Y2+vj7bHggkPZmZPVKyZi6eQ+BQroEM3UjClXkVOkHLkA7vl0YGiZIhQnbM\n43OdSqVG7TG0U+D4eUvcbKRSXV0Nv9+vEhntxk1KI4sd2gYHB9VZQYLmmc/mWkccQTOphvGDkpIS\nVXbEJClanXZJF6ZpqtKWhoYGTJ8+Xc2erayszHkNpswg1dvr6QlvXq9XxbuAQ/V6MhbMQ9w0zWGN\nFah4sMwi0w9fT/Rg/JkE3d7ejv3796O9vV1h0K02vo5MfpIxHlkulAsXt8yslpmjeicg2VREWg56\nhzNZ48zPSnogJEmPNY7HA0wqXTLblUoPm03orQ657lREWGolD0auDZUsjhxkDFqf7pUrsYtB08JI\nN+zlcMRpl3yXyVrrpCRL1kZSFO0S2mQnOlpIdHXahcKy2dMyl6arqwvt7e04cOCACmvwGhgYUB5A\nvjePx6OeXb5XiUkSUS6rKewsaLtkPMMw0lrbetMoGW/WyZPPQ09PDwBYPJCj2dN2+0N3y8u+FLW1\ntSohTN+/tPiZF8WzlwlhfM/sd8GyPulKz0YmFEEnk0nLwdXX16fcr7I1G2Dv2pElB8zK0zvrZKPB\n0+qlBUOLR/aE1jVOSYyxWAyAtZiek2BoEaZSKUucpra2VtWT+nw+eL1epbBkilnv20wrTo+XA0MP\nRWVl5TCCkzFgmTWabp1zob1LoWuXcbxgMKhcSjohSBebjGHr06ykYsR4IN8DNWmSUyb45N6QDRJk\nXJkxcGkdE4seJpH7muvJoS/y4t6oqqqyKIG5Ehle4SQfZrbKDORoNIrS0lIkk0lEIhG0tbXBNE30\n9PSoZhU1NTXqdXUiyWTPUHGXAwpisZjl9ekalWvBOn+uJZ9l2YEdXF7qAAAgAElEQVRMeqn4mUtr\nNRthUhEH9tBF6vf7Vfw5HA5jYGBg2MxqPbGU5W5SSR4Pb4WdQUIXL9eeXhV55srmORI/RbfI9bg2\nXftjEZlBLb1i/Du64iYVunTKue51YWthGfrjWZqrqXKUCUnQ0h154MABdHR0qOJyxiv0GJSdhs8E\nA7qKsiUO2dBCJnzwcJWWrZ3LB7CSc2lpqdKiZfIStUwSNJs9jOUQlokb+pAJxoDoueAhwjWyO4QZ\n65frrNdf5qLTmd3a83BiUhsnQ9kRtF5GphM2X0t6Lnp6epRlKJNaMl1nWrbcG2zXKDO0eQjb4aEC\nxMNPWkW8vF6vpXTF5/Ohrq5uTMpbpiIJmqEBwzAs1j6vkpISldhpmqb6vOrq6tQzwL2mu2QzEemZ\noqcqFotZettXV1dbXJDS6+ByuSx7KRKJYHDQ2id/YGBAETnPmWxFEjSzmen14MXzTcbtpUIhlQyu\nhcxX0TPmc5WsqXdt4x6V+0KeBSwjZYmrHDAhSThdZng2oid+lpSUWBIzJUHbGXl2HhjpQWJYhDj5\nulyTXE2Vo0wogiZ5sakEXbDMKtYn4dhl6+mbhW3luHFzQdD6fF9JeHp5j7Sg+f7kZpBTt6hZy0QK\naUEzy3CsFrTUgqUFTYLWYy8SHw82tsKUSTbsYCQbJOTagqZbib2K2VPXjqBlJrVdmZNMlpOdkdhw\ngWvC7OhMRFrQ3Bts5MBpZkxS03Ho5WT8m/KQ4cVMUnbqYr0mCXq8LWjicrvdFsuZ37OOm/F2er9k\nnSwPbpLnaJ5LWtAyjBSPx4cRNIdJ8HVlfoj0dJSVlVnImTF8Wlsu16GGE9nsaRI0PWRyiI/MR6Cy\nILHryqmuPOj7JFcZ87oFTYLWLWiGFmkUyf7wkqApema4XdnWWEV3b1Np0RPs7GLVPLfsrGzdAJTl\nblwjnrNyr2UrE4qgpQUty350CxqwLrDu4tYtaF07GqvIh5tEIS1ouiZ5Lz9EWkayPpff03KTLm5a\n0F6v12JBy8YrmR7CMiNa14KlBc0DitYwE+6YsMc4C+u07SzoXIUS0r0P3cXd1dVlG4OWsTI9UUUe\nCLqLmwStW4yZCBUhOSZQ7wZ14MABhMNhi0vPLumGwj1NRai8vBw1NTXw+XwIBAKor69XyS0cTDCe\nFjSfNbr/ZdxctqKky57u+56eHgs519bWorKy0hITHq0FzbAMlV6doKkQS2VNejn0umeSs12OR7aW\nKHCIoOXfslMg7ZRJqfQFg0FlQdslOcpcgFy5uOl9k01TpBLpcrmGubh1C5p7cqTSLT17fSyiJ4aR\nE+il0i1j3b2tW9f8XlrQ0moGDp1NhmEc2RY0M1QlQbe0tCgLOl0M2k7Dka3veH8u8PEBt4tBSwva\nzsVt59aUZUOSoHULuqGhQcVG6a7PREZycesZ5zK+yZIDt9utPhdZo22nCEkX93hY0NKK6OrqQkdH\nh22mrV2GP0U+NLK8jYoSD1EqJ6O1oO1c3LIzGxNg9Ic3XSauLPPgfiBBc1/4/X5LqdB4ETStV2Co\njaldDDqZTKo1lb2QqQDW1tYqEtSf30yEn7NUaPr7+4cNZdBbY9K64fuRX5lYGggElBEgSwpzcdDS\nkquoqBhWQyy/kpAlftnAhB23iE2SUa6bwmTq4na5XLYubiqVulct09KtsYidVyHdmti5uO1Ej0Hz\nWef9JGvmCByxMWi5IRgfkoMDmKDCnsN6YgTda9LCpQtNz9Idi+hdcEio+oaLx+PqoJQZfjI2qsce\nZcxTNvSXjRLG0iRBan4VFRWWbmSyS04qlVKHGx+wsrIydHV1qUPB5XJZ3qd8L+naLY5FZBZ/WVkZ\nUqmUGsDAph3AUGN7SWZcF1kvzYRDXaMuKSmB3+9Xk5bYK5phkdH2BaZlKVvUylgyLWF2iJJJd+wL\nrDfK0CfrsG6Trm3mJMjwwnh4L+wSpfQ8Ce5h/n0ZkmDYQfasNwxjzI0odCy0zGtqalBfX6/iuLIG\nXdbO64qy9AIwWZB4eGUbix5NEpz+jElykQlM0hDQvUW5IDsSk94qNRaLWfYZLV7pVtYb2OjvXcZ6\ndStWxs9H672QzxqJkhYulTk2FGEOgD4uUr/sjBzZApnXEW9By4Ugyfb29iKVSqmYKP3/wPCHlSRI\nK6uzsxODg4OWPrVs7DAWKSoqQkVFBaqrq1WMyi4hIxwOW6w5asPp3Fh6aZZdPHismjGJjsoNXY0s\nG6iurkY8HodpmhZrmJakHkOScXV+TrJZPN9TNpuT61BaWqoe/pqaGjUZrKSkBFVVVQiHw5ZDi/FC\nOVauu7sbyWTS4lFh2Z3f71f18g0NDQgEAsr9apd9OpJwrbxer9obsoSH9ZZ203VIKFx/4pRWobQO\nZecj6UYcS4ersYoet6Xnh/tTuvwZKujt7VXNVZLJpPLWAMi4jaOd8Llkb3uXa2hog142qJeycR/L\nUj56ZwzDUG5b/o2JJtIwGA9FWffgcLogkwH5vPHv6CSdLqNcPq96iEoS9Fi8ALpibxhDza0ikYgl\nTJdMJlWJlUzy1ZUGhsz0/BJ6T2WprcvlOrJj0LJTkrSApLXHD9AufkMXA63nzs5O1cBEumTG2u6T\nLqpkMqnc0NyoUklgUoKeQSzLCvQaQVq1uuYuL70MIBOhpSPLqSoqKoa1oDRN0+JCp9WgE7Tutudn\nRYLOhfYoNXc+qHy90tJS1SGOE5+kqyqZTKrcBXYPisfjqKysVFN1ZOcg2cjf5/MpzwFbQI6GoMvK\nyuD1euFyuZTHgpZdIBBQZVdtbW1ob2+HYRiKHLi3JAHLcAO/5+xckjhDC9zX42FB24kkaJn4xENb\nEh+tvd7eXnR1dSlLRmZ1V1ZWjhmLJGjGQ6urqy3ud4YxmPA4OHhodKZsKsPPiKEdnhm5OGxzLbr1\nLCsXckHQMgeisrJSJVYyZMHnUidovfNaOrfy4Qha9xhkIlIx5mfH0lDTNFWobmBgALW1tZbmQXRb\nU8ng37Sr0JAkTUva7Xbn7AykTCiCTmdBs5MYLYyysjJLjIaXrBPs6emxzAtmpjIbz49FeIjSkvd6\nvSgtLbV8kCRCaulyMIWeFMR79SsdQcsMw9Fa0DxQy8vL0yo3drHckSxoWVvNTFQZhx+r8MHnA0ZX\nmSRntszUkzkGBwfh9Xot5Mye1T6fTw1PYftX3a3P/rpS+89E9A54jMXV1NRYSmmCwaCqo+zv70d3\nd7f6/yRoOU1HVyL4LFCJkq74XMQdRyMkaJJzVVUVgENxVMaiGVbhM0DlW5I8kz/HIky84nPJSVpy\nvKB0XdPdbhiG5awhQTP5kK9Ni3+iid6ESOa55IqgaY1SEYvFYupMoDImPZrSgpZNZEaK++qlhHo2\n9VgsaEnUVIClBd3b2wu/328ZGclObcRJRVfvccC9JC1o/g3p4j4iLWjd1894bnFxsXrw6N6MRCKW\nOkedoOlqk+SczYNGjZDuE8bDAVgsY34wjFHL7jh6UoiMjXJT2ZGzbFYx2g1L0tebAaT7mV/7+vqG\nJXnIzHR9Qk+uXdz8bE3TVAQg11n/Gzxw+TDG43F0dXWpGKXP50NjYyOmT5+uusyxREzWzI/lcKDl\nKtsYyvWhp6KzsxMAlCbPv0cFlLH2KVOmqFGNjDnX1dWhvLzcYl1InGPZH2MVYiY583mQsWcmN8kB\nCJy3TTcpY9jZEjTj8NwjzB6XgyioCFBpo/vTzoLm/uPnOVEtaL1aRLegsxE+h/x8DWOo9l1vnpLO\ngib56ln6diSdawtanh0ctkSC7unpUV5WWXbKHtv821w/Owua/1cSdElJyZETg9bLjaTbixuNb5QE\nS6u1pqZGaW48kGWsgO4GWoV8yGQy1lhE32jA0Bg5xnH1Vp/UbklgOrkkk0mlZTJGTvecTFYajSVn\nhzkTkQ+6TFDRHxC7Q4Gfm4y9ZJsklskDqYcLiElq5rK7HONobO1XW1trWXcm4o1F7PaGHg9m+MOu\nJaPsHkeXON3ujD9XVVXlfJSkLnaK2kjf65dMuOHzSw+N9B653W6luOQiXicPff7MZ4wkLKfOyb1q\nd9llWudL9KRTSbh2SrV0b+fSxS1DTSRou4REeRZT0QmFQpawC7/KShJesjc98zekZT0ar5DdeScb\nv/C8pXIWj8dVf3S+L/1iPw42zero6FCNpUjqVOZkmCkXinJBCVpv2ECXNi0xbgq5UVj+Qo1Fxgq4\nSaixy+xvZtbmWhOmRV1VVaWsAOniodVN94e0ppLJpNJQZcZufX29SlbKJqlttCIf+nRxct7Hz09a\n07k6GDIVWkp0sdMF1dHRgXA4bImty6x4uxGC4+EaZmkYy/I4IpJxL1lPKq01eorocqfikA/L2O65\nlN/r9eX6XmltbUVLS4vqkEd34nha+FxnPTNbTmjj1d7erhrGcH/oZTlyb6TLQh4v0RVgvie7Z8tO\nWda9WLkgaMZvAajcDJkERs8VG0y1traq/SzzWkpLSy09Bxh6kN4TWrC5bLgik91o4AFDZ3dvby+C\nwSDcbrfiFHmVlpaqLoBy2h/rnom1pKREnd80rHJxbhecoGW/ZxkLoNatu03kJB09CUGm0/N7ttQb\n7ZCJTEWPvzH7WJZ2caPLlogkF1pOPJTZupEEXVZWVhCC5oOvWxW8T88ezaVrLVPhGsqa9FAohM7O\nTkQiEVXeJkuYZNOPsZaujQYftXTpPmVzG3pcpAXN7G0SNLO080XQ0hqSHi27S3Zr49cDBw6oq6ur\nC5FIxJJ8k02ZYzqRLkgmlobDYbS3tyvLRw6nCIVCygLSzxipxOU7M57Cz0BPMtXDRzJJbDyeQ7ku\nNJTklC2doKPRKLq6ulQIkHX58mJ/AFlpYZqmZYKXvva5IGjmeLD5E6sO+vr6EAwG0dfXp8YaS3Iu\nKSlRTYdkZQiAYe+NnR5HWwEykhSWoINBlK9di8rXXkMKwIFrr0VvSYmFoIHh9XhSi5NZqzxYWDjO\nzcUh2zmxoO+4A/jRjwCXC1iwAO6NG1Ucms0+ODVLJlzR1S5rRAFreY7P50NDQ0NuLehdu4ALLwQM\nAzBN4K23gK99DfjCFyy32ZFzugYC6bJHc5WcAmDYOuP++wGtFIcxJTbFYHtY3UKS1rNeW67HvXKJ\nOXX33cqCpvIwGgua5DzuFnQqBZx4ItDcjNQvf6meHzmLW36VLkmZi5BIJFRrWHZ6Y0mLzOzN+Tr/\n138p9yoztru7u9VYR1r1nNgmGwMB9iModTduVpj7+4EzzgASCWBwELjgAuDmm21vlc+gHUHbKcr6\nM5izJLHvfhfuH/8YpS4XkvPnI37PPcPmgDOpigQdDAZVPbCsNOD3VKClRep2uy1d4KiQ2iWZjWWd\n7SxoWu3cy8FgEABsx03KGnleJSUlqK2tVetA4j+iLOiy669H9IwzcOD22xHr6UHn3r3oO3BAWRYj\nubh1fz9wqARIumRLSkrUiL+sLeiWFuDuu4HXXx8iiwsvRNEvf4nyiy9WST7JZBJer3cYQVMblVYf\nLSda4D6fT7VvlBZ0VmUz8+YBf/sbFx1obgbOOy/t7XYubruYnF32aM5c3DbrjM2bgUsvtdwm15Ij\n/Nrb21VJDQma5GBnQdslWeUKs+vnP0figx9ULm4SdKYWtN5HftzKp+66C5g/Hzg44EImarLbGolN\nfi/JmhcTsmRyFgClxPK9jlls1tl4+GEkVqywKEJ0Z7e2tmL//v3Yt28fWltbbasX0lnQPGOyVt5K\nS4GnnwYqKoBkEjj1VOAjHwFOOsn2djsXt2y6IZOXRnJxZ/UctrTA+N73kNq5E2ZxMdyrV8Pz29+i\nbNkyi4uba0MXNzDU9zwajaKqqkq5fRn/DwaDyqNBr0bJQaOMJXIk1FFb0GnW2T17tiJo2WRKxsGj\n0Sj6+vpsY9Aybs5noLKyUvVjoHubZZBHhAVt9vSg+PnnEb71VgxGoxgE0CcyjQFr+0PAunHTxWT0\nBAq7hJasJJkEYrEh7T0eh6u5eViThZKSEov1Idt5RiIRy4dHy4klNn6/f1w+aADAli3A7NnAUUfZ\n/lqume7iHmntxiXerK0zpk61/bt0bUormtYf69UlIeuJIOOJOdnQYGkNS9LS27pKzxAtDpbw5dTC\nt5P9+4HHHgO+8hXgP/9TuYtZHigbMshLJvdI65oHGBUQxnipYMuezVLRHpXyYbPOtKA5b1m30mjR\n6+WMTDxl8xquv978JWvl6GDDE/T3D1l3h/ks0yWo5TOD30gm4e7rA4qLgb4+uKZNsyi5/Bzj8bil\nayK/2o1VpXeFDXvC4TAqKiqUd1M+D2NSkGzWmZ81lV/Wv5umqRRNhp/4mcuv9BDJcbVyyArP7iPK\ngnbv3YuU34+GG27A1J07ET36aEQvv1wdpmwrR9JgFyJmAspYEg8C6RbhxWECVVVV2cdzp04F/v3f\ngWnThjbC8uXAhz5ke6udu9jOGpVds+QBbdfDNmt5+GFg9eq0v06XJKZr4npegO4SzBpzhutsl1BD\nLwlDCkzio8KTi7GjmWIeWLoUA/v2WYZ80LIHDrWoLC4utsSbJTGPe13ztdcC3/oWcLAMkLkbLEPh\nc6Z35YrFYsPa1tIbQMWH3iHZtpSX3+9HU1MTAoEAvF5v5sqSzTonzjgDfa2tKgmPg0m6u7stI2r1\nMAev5uZmNDc3W0a65ny+eSoFvO99wJtvAp/7HLB4se1tdjk36RRMnhk8K1n5kZOhNWmeQXd7u6o0\nqaurUwYHsfNrMplUnf/Y4IQJg319fWC3NwCorKxUJYVsyqMnSGZ8btusc1EkoryUMjGQ4UYqd2xh\nyr/FUB7PEsaxU6mU8nbKiXJHlAVtJJMoeuUV9HzjG4gecwyqbroJ8x55BG0f/KAl8C41HXb3YR0b\nF5UHHjOiuWnLy8tRV1cHv9+fm1F8oRDw618De/YA1dVDMY5Nm4CLL7bcpruCpTWqW6K0Lviw8SHL\neW/lgQHgN78Bbrst7S3pksRkaY3EnC6pJmtSyXCdAXt3oGyYwLIk2b5zXAjaBnPxL36Bgfe9z1Jn\nS4USgHKNUbPXp//ollLO5dFHgYYGYOFCYNs24ODnLLvx0fLUXXzxeNw2kZAEzWfRNIdayMr2pHIi\n16gJ2mad3Q8/jP6TT7bEntvb25ViMTh4aN4zG8jIHudsXsOe7PxMSIQ52S8u11CoKRwGzj0XePXV\nobCC7a3Dc26k211mR0vPi91c9jHjTvMMFp19tvo86+vr0d/fj9LSUtsEQoYVmSfCs5dhBT6TJHsS\ndE1NjTKoZFOUsa6ze8YMSx8FGnKSnBlXls8bCZrNh6Q3i22CqVjQ80lladJb0MZRR8FsboaxeDFK\nEwn0f/zjqP7ud1H+sY9Zsv/oMuFUmlgsBrf7UKtPavG0oFlSxYQDdmDKiQW9ZQswaxbg8w39vGoV\n8Kc/2RLHSAlXdmSXzoLOGZk8/viQZllXZ/vrTJLEiFmP2eW8LCXDdSYm3YK2q7+kgjZuFrQNZve2\nbRg4/nhLD99IJKISw+R62Q245zpmHR9PJ//7v0NK22OPAb29QCSC0jVr0P///p+FoDs7O4cNBmDp\nFGCtW+e+kHWstJjlRWuDV8YEnWad+044QVnQzCKXlj4taCZj8lDVG8HIASTpeklnJVVVwLJlwBNP\n2BK0nnMzODhosaAzIWi5h8aMO80zWPTRjypS5dnMyWYMhdB6lom+wNDzKj0CNEQ4UlcnaL1l6FjX\n2f25z6nMcja1KS4uttRtU3nnZ0C8MsGRn0F5ebka91pXV6eMQCaN8fOZ3AQ9ZQrM5maU7tkDY8YM\nlLzwAnrnzh2Wuh6LxVRMg99TU9cPLznMQrodeBhkTdDTpgHPPw/09Q0lJDz1VFpX1eHcxbqLWzbT\nkFpwzlzcDz00ontbx2xnRVNk3Wi6spSsDrRRrLPESQuaf18eYHrjl5wTng3mwaOPVhnR7KwVjUYV\n+fLzZZmdTtB6D+Ocy623Dl0A8MwzwHe+g+j3vofE3r3KqggGg+jo6LBMCGPcTpYS8iuVIulC5gHM\nq66uDjU1NZbDLGOCtlnngXnzVA28dHHLrGYSNDvKNTQ0YOrUqWhsbFRnBK17NoTJmQejs3Mojltd\nPaQI/eEPwA032N4qvT9sEDI4ODiMnCVB02pjBUlOLOg0zyBzOqqrqxXhlZeXIxgMqvbA8XhcPY/6\nRe8JMVNhk415eF7rrYfHus7EKBNyS0tLLaNr+dzpHkMZLqusrFSel7q6OrWXZUMh+TxMaoJ2uVxI\n3XEHKj7zGZQPDCA1fToOfPWrKO/utjzczKJjIgoTbOzS4elWY7vEhoYGVZuWk5rik04acvUsWjS0\nERYtAtassb1VT1ZLV7Kku7OoAduVkY1Z4vEhjfi++w57q7RKD2dBS9yHGy83KslwndNZ0PycmcDB\nBKCceyUOgzl68cUYfOst1UyFCVZUPunxodtVV8zyVf8uRXaEkjOtZc/7dM8grT993QOBAKZMmaJc\nyVOmTEF1dfXYANqsc2T1aiTeeMNi9bNshsJuhGwtWl9fj6amJkybNk15L+Q11o5yttLaClx22VB8\nNJUaqkpYsSLt7TwT+NyRoOVzpivHdgSd1XOY5hksSibVGUviYrta1vy73W7LXGu5bxjyYFcyv98/\njOSorOZqnd041FCKUlJSopQ52WtANmfSwzZ07bNXBXHLQTu5loL24jaPPx79zz13iAw6OoCDReAT\nVm6+OW0N44SVigqgo6PQKEYnk3GddcwtLYXDMlpZunToOnCg0EgOL/o6799fOCyZyIIFwF//WmgU\noxe7ZzA58YaGKJms6zyCjP9cOkeOWNm5cyd+/vOf4/777097zxe+8AXMnTsXCxcuxEsvvZRHdOnl\ny1/+MqZPn47jjz8+7T0TDfeVV16JhoaGSYV5y5YtuPPOO/Gtb30r7T0TDfPnP/95NDc3T6p1fuaZ\nZ/Cd73wHX//619PeM9Ew/+lPf8Jdd92Fb3zjG2nvmWiYCyFGPnomqz9mGPntOj9GMU1T+YUczOMn\nDub8iIM5PyIxA5MTt4N5/ETfH5n+J+dyrjFfAKYDeCXN774P4ELx82sAGgqNebLidjA7mB3MhcWc\n78txcU9CMQzjw4ZhvG4Yxi7DMK4vNJ4RpAnAPvHzOwf/baLLZMTtYM6POJjzI5MRc87FIehJJoZh\nuADcA+BsAO8BsNowjGMKi8oRRxxxxJFcixODthFzksc3HMzjJw7m/MhkxwxMTtwO5vETfX9kInm3\noEfrg+/v70d7ezt27dqFHTt2YMuWLfjFL36BO+64A9dddx0uuuginH766Zg5cybmzJmD+fPnY+HC\nhTjppJNw2mmnYdWqVbj22mtxxx134JFHHsFf/vIX7NmzB62trQgGg5apR+mUldFiHhgYUPNo//nP\nf+LVV1/Fn//8Zzz44IO45ZZbcMkll+Dkk0+Gz+dDc3Mz3vOe9+DUU0/FihUrsHr1alxzzTX49re/\njYceegh//OMf8dZbb6m+45s2bcKnP/1p1aXngQceGBHzzTffPAzfunXrEAqF0NLSgjfffBP/+Mc/\ncMUVV2Dz5s34zne+gy996Uv45Cc/iWnTpmHRokWYO3cuGhsb4fV6036ufr8fixYtwrnnnosvfOEL\n+Pa3v41TTjkFixcvxgsvvIBf/OIXOPHEExWesayz3XsZ6+/ffvttHHfccba/f/TRR7FixQqYpont\n27fj5JNPdjA7mEeN+XC4C415LGv9bsc8lt+PtD8OJwWtg9ZFzhjm176+PjUkm+P6urq60N3djd7e\nXpjm0LBvFomz8w9fTzbzlxe71ORkdrEmsscr+y+HQiGEw2H09fXBNE3Vh5mdt9gej0PR2a40Foup\nRi0spM/kQ1+/fj0AYNu2bdi2bRvOPPPMYfg4BYqj1uRYTtM0Vfcl0zRVP1q29hscHFRt/QCoQe2J\nRAKdnZ3w+Xzw+XxIJpM477zz4HK5sGzZMmzbtk1hK5RcfPHF2LZtG4LBIKZNm4ZbbrkFiUQCL774\nIgBgxYoVeOyxxzBnzhx4PJ4Ry8jyJQ7m/IiDOT8yGTEXQiYUQXO+rxxTxiHgchB8d3e36g1smqZq\nJ8jOV/wqZxXrFzthjQdBm6ZpGThA3KFQSBF0aWkpqqqqVFcutshLJBIoLi5GVVUVampq1Pv0eDxI\npVJ44IEHsG3bNuzYsQN///vfsT9NkwaS4Pr16y3kTHxywhKn/sg5xaZpKmWBHdqoRNTW1qK3t1e1\ny4vFYmoAQX19PRobG5Vn4tJLL8WXvvQlNDQ0YMqUKfje976H9evX45Zbbsnpmo9GNm3aZPvvra2t\n6vt77rknX3AyEgdzfsTBnB+ZjJgLIRMqSUwfdSeHe7e0tKjB63v37kUwGLQQNJvcs2UbAMsQcznT\nk1Z01kPN0wgVjVgshlAohI6ODrS3t6Onp0eNYCNBs0dsMplEb28vQqEQurq61KzUaDSqeh8nEglc\nffXVCAQC6j1t3rx5RCw6OQPAqaeequb9cp0bGxvVrGJa0IFAQI1q5Hi1mTNnoqGhAbW1taisrITL\n5YJpmgiHw+jo6FCvG4lEMGXKFHR2duIrX/kKLrjgApxxxhnq/Y9F7N7LWH7/xBNP4JhjjsG8efNw\n++23D/t9OBzGypUrsXDhQixYsAA/+clPHMwOZgfzuxzzWH+fjeQ9SWykvxePxxEKhdRF65PTaXix\nuTn70PJ7WnNysHx1dTWmT5+O6dOnY9q0aZg2bRoaGhpU31p+rTg46PvgmDEjU8x2EovF8M4771iu\n1tbWYd4Bu161vb298Pl8mD17NmbNmoVZs2Zh5syZaGxsVDh//etf40tf+hKmTZuGK6+8EjfeeOOo\nMIdCIbS2tqqrra1NKTxyKEJ/f7+lzzm/p0ego6MDO3fuVP2XE4kE5s6di6amJtUL/Y033kBJSQmu\nv/56VFZWYunSpWhvb0dxcXHW6zwWSaVSmDdvHp566ilMncshjjgAACAASURBVDoVixcvxubNm3HM\nMYcS4Tds2IBwOIwNGzags7MTRx99tIPZwTwqzPnCnQ1m9qTP91pPRszZio45U5kUFnRbWxtaWlrw\nzjvvYN++fdi3b58agSct6Orq6hEtaHnlw4KORqMWCzoUCqm4eUlJicLL5vK0oBlv1y1oWv9LlizB\n3Llz8cYbb+CGNFNxRrvOnZ2dyoLmvOKioiKUlZWpCUAcMEALGoBqys/PobOzU7nLw+EwEokEotGo\nSpzz+/3ZzeTOUl544QXMnTsX06dPR3FxMS666CL8+te/ttxjGAYikQgAIBKJOJjHIA7m/IiD+ciW\nCfWOTfPQ5CdJrhz5xVgoJ85w4Hp1dTVqamoAQE0O0j9MTtqR48s4Ri7Xk41M01QJasQTj8ct4/Xk\nzFC6tw3DUFOZdLc8rdnBwUGkUqnDYpCJWGeeeabFDaPPc+YULbuxmLTa5Ui7gYEBxGIxlUTGxLWi\noiKFj5/h4sWL8atf/Qof+9jH0NfXhwsuuKCgSWLvvPMOjjrqKPVzc3MzXnjhBcs9a9euxcqVKzF1\n6lREo1E8/PDD+YZpEQdzfsTBnB+ZjJgLJROKoEmWJE+ODGR5Dweu9/b2KnJmclJVVZUiulgspsYK\ncj6tPuC8tLRUZVCPx6xdZlrLedCcR8q5ohxrJ2PWxcXFCo8+53hgYAD79+/Hl7/8ZezevRsLFizA\nZz7zGdu/PxIJyrnZtJa9Xq9lDjQJWioU/D4Wi6GystIyFpFJZSRqjp186623MH36dGzcuBEAcNll\nl2Hjxo22SWIjKRX5lCeffBKLFi3CTTfdhEceeQQXX3wxrr76att7HcxjlyMdMzAxcBPz1q1b8eCD\nDyrM6WZwO5izF1bPZCsTiqABK0lLgubXQCCAwcFBNVO3oqJCfU/LTs79lQQt49WSoLOet5xGdMLj\nsHPOFPX5fHC5XMoNHAqFUFxcrPBIgh4YGMDg4CAMw8DatWvxrW99C9u3b8f73ve+UeOiokCvQ0lJ\nCWpra5VCIb+SgOUQ8kgkoubqJpNJVFRUqKz5srIyy71/+9vfcOGFF8IwDMyYMQMzZ87E66+/bosr\nH5Z1U1MT9u7dq37ev38/mpqsHQTvv/9+3HjjjTj11FNx5plnYufOnVi1ahU2bNgw7PUczPbiYB6S\n8cY9GswA8KlPfQo//vGPsWrVKpx44omHVZQdzGMTXUkYa9XKhIpBA7BY0Ew+IjE3NTVh1qxZOPro\nozF79mxMmzYNjY2NCAQCqKmpgdfrVUPvS0tLJ5QFnUqlLJZrXV2diufKDPSioiJF0KZpWlzeAwMD\nuP3223H99ddj9+7dmD9/Pjwez6ixud1uhYNxZSbSzZgxQxHpzJkzMW3aNDQ1NaG+vh5+v98yUL25\nuRn9/f1qwHw0GlWxIhJ0bW0tdu7cCcMw0NHRgV27dmHWrFm5Xu6MZfHixdi9ezf27NmjsuBXrlxp\nuWf69OnYsmULAKgmOQ7m0YmDOT/iYD6yZUJZ0LSedQuaxMornZuDZUPSgmasla9DC5rELQkxVyI7\nyEgLmparJOhEIqESyZjgZmdBJxIJDA4O4utf/zp8Ph9qa2sRCoXG5MohjpKSElRWVlri2jJhjgqC\nfpGgKysrcdxxx6lZrX6/H1VVVWhpaUFZWRmmTJmCD37wg/jd736Hyy67DMXFxfjmN78Jn8+X/SKP\nUdxuN+655x4sX74cqVQKV155JY499lhs3LgRhmFgzZo1WLduHS6//HI1E9jB7GB2ML+7MRdKJpQF\nTWIuKiqyEKrX60VtbS0CgQAaGhowdepU1NXVobq6GhUVFSxxgGEY6v+WlZUNK6Oi5SwTtJgoNh7v\nQyoHtNwrKipUDJpEV1FRoRqC6Ilrdi3jTNNENBrFBRdcgLvuussWw/r167F+/XrcfPPN2Lp1q0ra\noqtcrpf0LHDNPB6PBZseEpAXsfL9zp07F/Pnz0dZWRkCgQBWrVqF/v5+dHR04D/+4z8mhDtKKoMA\ncNVVV2HNmjUAgMbGRtx4442q09x9991XSKhKHMz5EQdzfmQyYs63TCgLmi7tiooKDA4OAgASiYSl\n5rmkpAQulwsDAwOIx+OWmmeWCRmGAa/XiylTpqCqqgqBQECRuSTm8cridrlcKqHN5/MhGo3CMAz4\n/X7V3jOVSqnSKVrHtGIlwdPSp7XP1plvvfUWvF6vsl51IQnS6u3t7bVYwTKuPTAwoDLldQLW26Qm\nEgnVFS0cDuOVV15BQ0MDiouL0dbWhsHBQXi9Xvj9fgQCAZSUlODHP/4xfvSjH2HhwoVwuVwIBAIF\n6ySWSqWwdu1aSw3mOeecY6nB7Onpwec+9zn8/ve/R1NTEzo7OwuCleJgzo84mPMjkxFzoWRCEnR5\neblKUBocHFQExYYZLpcLg4ODiMViqjd3MBi09NmurKxUPa1J0B6PRxG8bv3l+n2wfri2thb9/f0o\nLi5GbW2tSngzTVP12CYJMjFLt8Dley8vL8eMGTNw+umn46677sKpp546IhZZ7iUbpejX4OCgitnL\nLGxJ5iR0EvSePXtUf+5UKoVAIICuri4sWLAAPp8PgUAAL774IpYtW4bp06ejqKio4G4qWYMJQNVg\nysNh06ZNOP/881XiSiAQKAhWioM5P+Jgzo9MRsyFkgnl4pYE7fV6UVNTg9raWuUKlha0JOi2tjbs\n27cPHR0dFgu6oaFBJZHZWdC6iyWX70Na0HTL+/1+VVplmuYwC5r1xHwNWtDs4FVUVIRXXnkFv/vd\n7/Dss8/ive99L1555ZURscjBGNFoVPXe7ujoQGtrK/bt24d//vOfePvtt7Fnzx7s27cP77zzDlpa\nWtDW1oa2tja0t7cP6+TGlqQulwv9/f1IJpMoLS3FwMCAet+BQADBYBB9fX24/PLL8aEPfSjt9K18\niV0N5jvvvGO5Z9euXejq6sKyZcuwePFiB/MYxMGcH3EwH9kyoSxoWoySqPVOX6ZpKmsuHo+ju7sb\n7e3t2Lt3r4pVs9bY5/MpkpcWtNvtBoBxIWfgEEFXVlaqumCPx2MpV0qlUsqCpmWaiYt78eLF2Lt3\nL1asWIG3334bX/ziF3HbbbelxUILuq+vT7VBDYfDqpUqr0QioWLR/MrGKPqEsWAwqPqKM55dWlqq\nvBIkaGZ079q1C4888ggMw8BZZ52F97///bZYJ0ot4+DgIP76179i/fr12LJlC6655hrs2LHD9l4H\n89jlSMcMTAzcxLx161Y8+eST+PSnP40dO3ak9WY5mLOXI7IO2jAMRUYUZhKTHEgInBbFDOiWlhbU\n19ejoqJCkUR9fb0a6lBZWamypEnQ4yUkaI/Ho5QNdurSR2n29vYqC5TNPiQx03qmi5u/+9Of/oRk\nMolzzz13RCy0oEnQtKA5IYxf+/r6VD05r7KyMkt3MV60oAcHB9Hf3w9gSBFIJBLqM2B3t6OOOgpN\nTU2qjOyMM87Ayy+/bIt1otS6Njc3IxAIYPny5Vi+fDm6urqwdOlS3H333cNez8FsLw7mIZkINcXE\nXFZWhnPOOQfnnXceli5divPPP3/C1kFPNMyjlSO2DlovT6KlLImY7uxIJIKBgQEUFRUpEtYvj8dj\nKbsar6Yk+nuQ7Ur7+vosk6Pokn/77bfR3t6OaDQKAKisrFTu8IaGBgQCAYuLn1nobrcbp556Ki65\n5BIsW7bMFgOzuL/xjW/gueeeUy1Ho9GomlHNftm0oknc7B3OQRrt7e0WN3dXVxcikYhqTUribm9v\nR0NDAyKRiJrhvWTJEjz//PPYsmULbrrpJvz2t7/F1q1bx/0zSCeZ1GCec845eO6559QI0D//+c84\n9thjC4TYwZwvcTDnRyYj5kLJhLKggeFlRewORjIhoQSDQUXQbrdblQXJNppsXMLSKk5CyYekUqlh\nBB2JRCwEGYlE1CQrYIig3W43qqqqUF9frxqwVFVVqfcRjUbxgx/8AMceeyzC4TD+8Ic/2P59apQc\nhNHZ2anaoOoEzTnQMhmNCo3sLMaLWfO9vb0oLy9XM7sbGxtRUlKC5557Dq+99ho++clPwu/34+ST\nT8a6detQXFyMdevW4fOf/zzuvffevHwOumRSg3nMMcfg7LPPxvHHHw+32401a9Zg/vz5BcHrYHYw\nO5gLj7lQMqEIWtb56hY0SZlXLBZDLBazJWh5sU6aMdx8ELRuQdN6pXuZmeddXV2WeLPH40FVVRVq\namrQ0NAAv9+vLGiPx4Pi4mK8+OKL+M///E/U19ejo6MD119/Pf74xz+mxSIVBRK0bkX39PQgFosN\ny+Bmj23p1WByG93zAwMD6m+xF/rs2bPR3NyMUCiEoqIifOITn8App5yC1atXD3NlFUrsajClXHfd\ndVi6dClOOeUUB3MW4mDOjziYj0yZUAQNWMmZcU3p4m5ra8OBAweUW3VwcBBut3tEF7c+wSpf74F1\nw7Sgw+GwGqFJlzFJmW7sqqoq+P1+1NXVDXNxu1wu/OxnP8PWrVsRiURw55134itf+QrWrVuXFgsJ\n+nAu7lgslrYBid4kRbYfZQy6srIS3d3d6OzsRE1NDUKhkAotlJWV4dvf/nZad3w+JZMaTN53ww03\n4Oyzzy4QUisWB/P4i4M5PzIZMRdKJkUMmnOL9Rh0OBy2WNBer9fi3iZBMyO5UBa0XQx6//79tjHo\nKVOmoLGxcVgM2uPxYOvWrWhsbMSJJ57IAeCHxcI1JEHrFnRPT4/K6u7q6kJnZ6eKQduVWXV0dKCr\nqwvhcFi55vmePR4PWltb1eCP7u5uRCIR/PznP8eyZcvg9/vHbc0zlUxm0QLA3XffjQsuuAD19fUF\nQGkVB3N+xMGcH5mMmAslBbOg7Xo8yxnI/BqJRBRRdHZ2oqenB729vaokiyRcWVmJuro61NTUoLKy\nUsWcc2kx69OppPtYdtyixc9SJn4fDAYRjUaVUsG2n3Rr19bWwu/3q+EZjDvzfWzfvh2//e1v8fjj\nj6O3txeRSASXXnqpLVbGoGOxGI499lhMmzYNVVVV6O3tRTKZtMyELisrQyQSUUM95Fc985xjM5lp\nn0qlVBkbrWvGz/1+P1KpFHbs2IEvfvGL+N3vfodUKnXY2u3xlExm0ba0tOBXv/oVnn766WG/K4Q4\nmPMjDub8yGTEXCgpKEGzDphf4/E4YrGY5SszgmmRkeAMw7A0A5GX1+tFaWlpzq1lWqNSqaDbWL9o\nocrv4/E44vE4TNNEWVkZamtrh2Fn7TazttmYBQBuvfVW3HrrrZgxYwY8Hg/i8fhhRzcy1h0MBpFI\nJGCapupIRq9DdXU1otGoRcmQyhK7j7EcrLi4WE3RGhwcRHNzM2pqahCNRhGPx1WJhN/vx7333otr\nr70Wp59+OrZs2YJVq1ZlVCpRyFrGa665Brfffju2bduGl156CbFYLK1S4WAeuxzpmIGJgZuYAaCt\nrQ2bN292MI+zTPo6aNYBh8PhYQlL8qvMdObFuchs5VlXV6f6bjP2XFZWlvN4s4znkrzC4bCFBLu6\nulRP8Hg8jt7eXvVVJlyxIYgkZlrP1dXVaniFnG5FcblcuPPOO3HffffhN7/5zYiKCMnY6/WqpimS\nnDmbOhqNWpQlfk+XuGEYyvImQRcXFyMYDKK5uRk+nw+7d+9GbW2tIuja2lq8+eabWLduHQzDQHd3\nN5566ilLnbuUiVLr+pe//AUXXXQRTNNEZ2cnWltbcckll9i+noPZXhzMQzIRaop1zB6PB5dccglW\nrlw5YeugJxrm0Uqu6qALRtADAwPKTUuCI8nJJhrRaNSSQZxKpdQMZ9lru6mpCRUVFaqkip2tciky\nrkxlIRQKWWqGW1tb0d3dbel93d/fj4GBATXwQw7/8Pv9ipilB4CZ53aNVUzTxPvf/3587GMfOyxm\nErIkZ8boa2pqlJUfjUZVZjyvaDSK4uJiRc5MCONAk/r6erS1taG6uhpTp07Fk08+iWuvvRZHHXWU\nGj25fft2pYysXbsW55577rCax3yKrMFsbGzE5s2b8dBDD1nueeutt9T3V1xxBT7+8Y87mEcpDub8\niIP5yJaCu7hpgTIRiU0x+HMsFrN00yI5sFsXLeimpqZhgzDGw4KWCVdMhmpvb8e+ffuwZ88e7Nmz\nB52dncPi66ZpKpc2W2naWc9+v1+9PzkbWwpbZrI+cCQpKipCWVkZ3G43ysvLVdtPGUaQfbp7enpU\n8lhZWRkMw0AqlUJ/fz9isZjFgvb5fPjABz6ARx99FC6XC2effTaWLFmCP/zhD6isrMRnP/tZy2cx\n3h3cMpFMajCl5KtufiRxMOdHHMz5kcmIuVBSMILWy6n0mmE21IhGo8oiZutJl8s1zBXL5CfZ75qW\ntqy1y6b/tnwtJlkB9gMp9MQq9qvmzGU56pHvm+9FVzJI1vz6xz/+EVOnTkVnZyfOOussW6x0+aRS\nKZx++uk4/fTTVXIXa53Z77yvr0+RODPQ+/v7EY/HbSdcsY1pVVWV6uTmcrng8XhQU1ODNWvWoKam\nBhUVFdi0aRNuv/12lTtQVlZW0CQxykg1mMQMAF6vF3Pnzi0IRl0czPkRB3N+ZDJizrcUjKBdLpel\n37Ts/0yXqBwvCUDNiHa5XKqm2Ov1oqysDEVFRaioqLC8Rnl5uSIPaVGPlaBdLheKi4vViEXDMFQz\nFP7d0tJS5RYGDmV+M8OZlijvsWuiUllZaenoJV3dbrcbJSUl+MQnPoGdO3ciGAzaYpUETSWI75/k\nzLUgMZOQ6dJOJBIqo5v3sa65oqICHo8HDz/8MDZs2IB58+bhqquuwqpVq1BfX6/+1qxZs/Dss8+i\nuroaTzzxBNavX4/nn39+Qs+D1jF/5jOfwfPPP18QvA5mB7ODufCYCyUFJWiW+dA6liTHixYlCY5k\nR4ImOZumierqatX0w+v1qkxvEhuArNysVCqYIc7664qKClRUVFgUCwCKGFmzzMQ4Kg2pVEpZpbwn\nlUqpwRLyIvEPDAzgi1/8Ij784Q/jpz/9Kc466yx0dHSMiFta31I5kaVWXNOioqEtQYKme94wDKVQ\nlZWVwePxIBKJoLGxEUcffTT8fj9WrlyJZ555BieddJIi6CVLlqi/t2TJkmFj5fItmcyidTBnLw7m\n/IiD+ciWghG0YRjwPfAAmh96CMlkErvOOAN/XLTI1oKWzUtYBsQYKa2/RCKBQCAAn8+n2k+WlJQo\nYuHfzCYu7XK54HnPe1BeVQUYBsyiIkR+9jM1kEPiJl66uQEoC5px3UQiYcHDLPFoNGppuiIT5Hbv\n3o3/+Z//weuvv4677roLn/rUp7B9+/YR15nYSbTSRZ9MJhVeKjvEOjAwoNzzdhb0wMAApkyZokrD\nZsyYgZdfftk28xwAfvjDH+IjH/nImNc/F5JJDaYUB/PYxMGcH3EwH9lSMIIu+r//g+eXv8Rrmzah\nOxLBsf/+76ibMwdBYUGToO0SrmjtcaAGk50SiQQMw1AjHkk4dHFn0n0rnbhcLsDtRuqpp2DW1CCV\nSsHT2mrr4iYx03KVFjRnNLMTF8mXcXj5XuheJtEODAxgwYIFmD9/Pv7xj39Ysh3TiYy/833IPttU\nAORAETsLWifoRCKBkpISVRrGkIKs3aY8/fTTuP/++/Hcc8+lxTnRahnvuOMOfPOb38QVV1yRtozD\nwZy9HKmYgYmF++mnn8Y999yDCy+80ME8zjLp66CL3ngDA4sWobiyEmWpFMILF+LY115Dy6JFquSH\nBMZSJSZTkTT6+/tVjS7JkCRCIkmlUsqFzqxknbAyTRxzuVyAaaLY7QYOurH1hh81NTWW+mcZQ5YJ\nX/QGMLGMxEiiTqVSAKCS3vj73t5evPzyy7jzzjtx2mmn4ZprrrHFOtKGlfXYfF8kVCoPTNaj9c8S\nLbfbraz7kpIS7Ny5Ex6PBx6PB+3t7Zg2bZpSiiivvPIK/uVf/gWrVq3CXXfdlXZ9J0qtKzCE+d57\n78X27dsxe/ZsAPa1jA5me3EwD8lEqCkGhjCvWbMGTz31lMIMFGatJyPm0cqkr4M23/MelH71q/Ak\nEki5XJjy4osonjULU6ZMUSVB1dXVCIVClmYfvb296O/vVxY2rb5UKqUanzAGnEgkUF1dPWwEJZuY\nyAEaGcemDQM46yzA7QbWrEHxqlWorKxEIBBAPB5HKpWC1+u1kLRsVKK3CmW3MOKNRCIq1s2Yr0wi\ni8fjMAwDa9euhWEYeOONN2xhjrRhac2zlWcymbQdohEOh9X6UAkxDAMNDQ3w+XyYOXMmfvrTn2L/\n/v0oLy+3rWfcu3cvzj//fPzyl7+0xJUKlSSWSQ0mMT/wwAOWg6FQ4mDOjziY8yOTEXOhpHBJYvPn\nI3HNNWi87DLUl5ej97jjUGYYmDJlimpA4vf7VakVG2ewdleXZDKpCBoYIrtoNIrq6mpVf0xrEICl\ndGhUWd3/+79AYyPQ0QGcdRbKpk2Dd/ZsBAIBpFIp5fIlTtYb02UtW2myJzeVA3oHAKgMbiohzOJ+\nz3veg/e+97249957sXDhQlRXV4967eWkMGKxm3AVDodVApy8GhoaVP/tdevW4bzzzoNpmrb1jF/7\n2tfQ1dWFz372s6rNaCF762ZSg+lgdjA7mB3ME0EKRtButxvJyy9H/FOfQiKRQPnXv47BQEC17JTk\nJi06tv9kr2g5XINWKsm5q6sLtbW1w1y1jJMytjqqxLHGxqGvdXXAeeeh+G9/Q+UJJyhyrqqqQjgc\nHtaVS3oAZA9yacnKmK9O0NJtf9NNN+Ff//Vf0dPTg9LSUjUNK1OhBc1JW319fbYzosPhsHLNszGJ\nz+dDfX09fD6fKgfTQwaynvEHP/gBysvL8fjjj8Pj8eC+++4bFdbxkpFqMB3MuRMHc37EwXxkSsHG\nTbpcLpQczMSu7OpCxe9/j+LLLkNDQwOam5sxa9YsHH300Tj22GMxb948zJkzB7NmzcKMGTNw1FFH\noaGhQTXD0F3cwWAQra2tqrNXa2srOjo6EAqFEIvF1OAHNgvJOHEsHgdIhrEY8Pvfw3X88aisrITf\n70dTU5PCffTRR2PevHmYO3cu5s6di9mzZyvsU6dORV1dnYXkAKiYOsmRyoiMyScSCcybNw9PPfUU\nzjzzTHz1q18dEbJdosLTTz89bBTmc889N8y93dXVhUQioSZv0TNAC7qiogK33HIL1q1bh507d+Kh\nhx6yDO/Ytm0bHn/8cbz55pt44403sHHjRlx88cWZrXWG72W0v2cN5pNPPjkMM/+/jvnf/u3fHMwO\nZgfzuxhzNr/PRgpG0G63G8UXXYSyE09ExerVcH//+6idPh319fVoamrC9OnTMWfOHMydO3cYOTc1\nNanRkswcZvcrWs7t7e3Yv38/3nnnHbS3tyMYDKKnp0cRNN27LCPKSNrbgdNOAxYtApYsAT7+cRSt\nWKEIurGxETNmzMCcOXMwe/ZszJo1C7NmzcLMmTMt5CxnPZOgpeUv+2PzisfjCjfLtX7zm9/gE5/4\nhC3U9evXq0vfQM8++6yy2EnQO3bsGDYrOhaLYWBgAC6XC+Xl5aitrUVraysCgQBqamrw5ptvYvbs\n2di1a5ftXNdt27bh17/+NS699FJF1nv27MF11103pj2TiwdppFm0/P/EDAAnn3wyenp60N7e7mB2\nMDuY36WYs/l9NlIwggYAPPss8I9/AH/7G1DgtPiMZOZM4KWXhvD+/e/ADTcUDMqWLVvwvve9D3V1\ndba/JzmPZ8lBe3u7Jfuyubl5WEMB1jyeeeaZWL9+PRoaGrKyorMVuxrMdJgpTU1NBW2U4GDOjziY\n8yOTEXOh5LAEbRjGjwzDaDcMI20DZcMwvmsYxhuGYbxkGMbC3EIcvbz11lt4+eWX085KBoCvfvWr\nOO2007B06VK8/PLLeURnL6+99hpefvllS/mBLj/60Y+wevVqnHfeefjxj3+M1atX5xHhcHnwwQfx\ni1/8YsT40D/+8Q9ceOGFWLhwIV566aU8oksvzzzzDBoaGnD88cenveeOO+7A3LlzsXDhQkQikTyi\nsxcHc37EwZwfmYyYCyHG4dy7hmGcBiAK4GemaQ5bTcMwPgJgrWmaHzUM42QAd5mmuUS/7+C9Y+8S\nkkcxTVOldTuYx08czPkRB3N+RGIGJiduB/P4ib4/Mv1Ph70ATAfwSprffR/AheLn1wA0ZPK643k5\nmPOC1w3gnwexlAB4CcCxGuYNAB49+PPbAF6cAJh3AzgFwN91zAfveQzAXw9+vwRAbyHX2sHsYHYw\nFxZzoa5clFk1Adgnfn7n4L+NLaKfH3Ew50BM00wahnETgPsA7ATwI9M0XzMM4yoA5kF8PwPgNQxj\nN4AAgK8UCi+gMK8F8ACAZgA3S8ymad4HIAngrYOYYwBeQQHX2sHsYHYwFxZzwSRDjWcky+63AE4R\nP28B8N5Cax4OZgfzkYbbwexgdjAXFnPe1ygHC6m7Xl9HGlcEhqyqCX85mPN2XT8ZMQP4LoA3JgCW\nIxrzwX3x3QmAZSyYJ81aO5gLgvklAAsPx72ZllkZBy87+Q2ASwHAMIwlAEKmaaZ1Q4wE5uabbz6s\nsnC4e/j7t99+G8cdd5zt7x999FGsWLECpmli+/btOPnkkyW5OZjzh3m1YRjHZIJ5NJgy+b0dbv4+\nHe6DsgbA8aZpznUwjyvm1YZhfAbA7MmIOZO1LjRm4nYwj+/vBeaPCMxXYci4HVEOG4M2DGMTgDMB\n+A3D2AvgZgwlBJmmad5nmuZjhmGsELGCKw73muMtF198MbZt24ZgMIhp06bhlltuUWMoAWDFihV4\n7LHHMGfOHHg8Htx///0FRvyuxbwZwDkY8roUHPeLL76YCe4ODMX/8yrvQsybAVwJ4A4AKyYh5rxJ\nlpjhYM6bnIOhnByYpvlnwzCqDcNoMEcwaA9L0KZpHrarhGmaa0cFc5xl06ZNaX/HKU/33HNPntBk\nJu9SzPsBnJRLTJlIOtytra3q+xFwlwAozjmow8i7HjKWQQAAIABJREFUEPN+APWwJkaOu7wLMQMO\n5nzJqBN9M3JxG4bxYcMwXjcMY5dhGNfb/L7KMIzfHGxU8nfDMC4fJXAAyKjj1eHu4e+feOIJHHPM\nMZg3bx5uv/32Yb8Ph8NYuXIlFi5ciAULFuAnP/nJWCA7mPOEeTSYHMwOZgezgznXmMf6+6wkA9+5\nC0M1a9MxpIW/BOAY7Z4bAWw4+H0AQBBAkc1rmfmQZDJpzp492/znP/9pJhIJ84QTTjBfe+01yz23\n3nqrecMNN5imaZodHR2mz+czBwYGVDDfwTz+mAHcgEOJNZMF8/MAHnQwjzvmGw7ivnAyYjbztNbZ\nYDaHQJoO5vGXg5gzTqjmlYkFfRKAN0zT3GOa5gAOxQ0tPA/Ae/B7L4CgaZqDKJCM1IydYhiGah8X\niUTg9/tRVFSw6ZvvVswXYSjJcDJhrsNQ7aaDeXwxXwTgfhxMQHUwjxtmOJjzJqNKqAYyc3HrfvP9\nB/9Nyj0A5huG0QLgZQBfzBTxeEgmzdjXrl2LV199FVOnTsUJJ5yAu+66K98wLfIuxbzZPNSgIC+S\nA8w/APB3wzAO5AUw3rWYN5umuRFARV4AI7eY87XWuTg3HMz5EdM0HwPw9sGE6o0APnu4/5Mr8+ts\nAH8zTfMDhmHMBvAHwzCON00zqt/I5CEA4zpp6XDy5JNPYtGiRbjpppvwyCOP4OKLL8bVV19te6+D\neewyEmbTNG87+HWjYRjfn0yYAaw1DMN0MI9dMtwbyyYjZmCoR/REwE3MW7duxYMPPqgwcw494GDO\ntWzbts12DKU52oTqkfzfB/3kSwA8IX5WcUPxb78DcKr4+SkAJ9q8Vo49+/ayfft28+yzz1Y/b9iw\nwbztttss93z0ox81n3vuOfXzBz7wAXPHjh0Fi+c6mB3MDuYjB7OZJ9zZYDaHQFpwO5jHR+z2RyZX\nJi7uHQDmGIYx3TCMEtjHDfcA+BAAGIbRAGAegLcyeO1xkcWLF2P37t3Ys2cPEokENm/ejJUrV1ru\nmT59OrZs2QJgaK7xrl27MGvWrELABeBgzpc4mPMjDub8iIP5CJdMWBzAhwH8H4ZalN1w8N+uArDm\n4PeNAJ7EUEPzVwCsTvM6+VBWTNM0zccff9ycN2+eOWfOHHPDhg2maZrm97//fXPjxo2maZpmS0uL\nuXz5cnPBggXmggULzE2bNpmmWVjtzMHsYHYwHxmY84l7rJjtcDuYx0fs9kcmV6atPgFrX1GYprnR\nHJo6AtM0WzE0VjCJocSzNaN43XETwzDUBQBXXXUV1qwZgtbY2Igbb7wRbrcbqVQK9913XyGhKnEw\n50cczPkRB3N+xMF8hMrhGByZ1UFXY2jcYNPBnwNpXmu8FRXTNDOrswuFQub8+fPN/fv3m6Y5VGtn\nmoXTzhzMDmYH85GDOV+4s8Fsh9vBPD5itz8yuXJVB30xgP82TfOdg6vVmcHrjptkUme3adMmnH/+\n+WhqGqoYCwQChYCqxMGcH3Ew50cczPkRB/ORLbmqg54HwGcYxtOGYewwDONfcgVwLJJJnd2uXbvQ\n1dWFZcuWYfHixXjggQfyDdMiDub8iIM5P+Jgzo84mI9syVUddBGA9wL4AAAPgO2GYWw3TXO3fuNE\nqVcbHBzEX//6V6xfvx5btmzBNddcgx07dtje62AeuziY8yMO5vzIaDADEwM3MW/duhVPPvkkPv3p\nT2PHjh3w+Xy29zuYs5d0ddCjlUwI+h0A08TPzRg+um4/gE7TNPsA9BmG8SyAEzAUu7aIXMjxkqam\nJuzdu/cQuP37lauE0tzcjEAggOXLl2P58uXo6urC0qVLcffddw97PQezvTiYHczpxME8JOONezSY\ny8rKcM455+C8887D0qVLcf755+OWW25xMI+D6EqCHeaM5HBBagBuHEoSK8FQktix2j3HAPjDwXsr\nAPwdwHyb1xrnUPyQDA4OqiSE/v5+84QTTjBfffVVyz2vvfaa+aEPfcgcHBw0Y7GYedxxx5k7d+4s\nWAKCg9nB7GA+cjDnC3c2mE2zMAlXkxFztmK3PzK5MpkHnTQMYy2A32MoZv0j81D/ZNM0zftM03zd\nMAzWQScB3Gea5quj1hZyJG63G/fccw+WL1+OVCqFK6+8Esceeyw2btwIwzCwZs0aHHPMMTj77LNx\n/PHHw+12Y82aNZg/f36hIDuYHcwOZgezg3mCYi6UjCYGPawO2vJL0/y2YRjPAPgThrvACyJ2dXZS\nrrvuOixduhSnnHLKMBdLocTBnB9xMOdHHMz5EQfzESqHM7GRQR20uO8pDPXlXpXmtcbRiXBIMqmz\n430f+MAHzI9+9KPmf//3f5umObFrMB3MDmYH8+TAnC/c2WC2w+1gHh+x2x+ZXLmqgwaAzwP4JYCC\nj//KpM4OAO6++25ccMEFqK+vLwBKqziY8yMO5vyIgzk/4mA+siUnddCGYUwFcK5pmv8FwMgdvLFJ\nJnV2LS0t+NWvfoWrr76aWlhBxcGcH3Ew50cczPkRB/ORLbmqg74TwPXi57QkPVHq1a655hrcfvvt\n2LZtG1566SXEYjG88sortvc6mMcuDub8iIM5PzIazMDEwE3MANDW1obNmzc7mMdZJlod9IkANhtD\n0f4AgI8YhjFgmqY+lnLC1DP+5S9/wUUXXQTTNNHZ2YnW1lZccskltq/nYLYXB7ODOZ04mIdkItQU\n65g9Hg8uueQSrFy5csLWQU80zKOVCVUHrd1/PwqcJJZJnZ2Uyy+/vOAJKg5mB3M6cTBPPsxmnnBn\ng9k0C7PWkxFztmK3PzK5clIHrf+XUegH4yKZ1NlJYZp/IcXBnB9xMOdHHMz5EQfzkS05qYM2DONi\nHIpBRwC8kSuA2chIdXabNm1SMQ6v14u5c+cWBKMuDub8iIM5P+Jgzo84mI9QOZyJjczmQS8BUH3w\n+w8DeD7Na42vH+GgZFJnt337djMUCpmmaZqPP/64efLJJ5umWTj3iYPZwexgPnIw5wt3NpjtcDuY\nx0fs9kcmV07qoE3TfN40zZ6DPz6P4eMo8yqZ1NktWbIE1dXV6ns9zT/f4mDOjziY8yMO5vyIg/nI\nllzNg5byrwAezwZUtpJJnZ2UH/7wh/jIRz6SD2hpxcGcH3Ew50cczPkRB/ORLbmqgwYAGIaxDMAV\nAE5Ld89Eq1e744478M1vfhNXXHFF2lR9B3P24mDOjziY8yOZYAYmFu6nn34a99xzDy688EIH8zhL\nruqgM4lBLwHwhPj5BgDX29x3PIaSw2aP8Frj6eZXsn37dvPss89WP2/YsMG87bbbht338ssvm3Pm\nzDF3796t/g0Fim84mB3M6cTBPPkwm3nCnQ1m0yzMWk9GzNmK3f7I5MqEoDOZBz3tIDkvOcxrjf9K\nmJnV2e3Zs8ecM2eOuX37dsu/F+rDdzA7mB3MRw7mfOHOBrMdbgfz+Mi4EfTQa+PDAP7vIAnfcPDf\nrgKw5uD3PwAQBPBXAH8D8EKa18nHWpimOZT5N2/ePHPOnDnmhg0bTNM0ze9///vmxo0b/397Zx8c\n1XUl+N+xHMoTYHHJBMYWgRlbCMyYz4oCcbIBUy7xNYVj49rBFMZgiDAzxEuVXQFndo2YmkL2VmYS\nDFNB2Fk2eC1rZscuO54gNINByTIWiWxC5OEjgB2jAQwYCJhlgoWks3+811Kr9Vr9Wt19+yPnV/WK\nfn3vu/3T40nv9b33nKuqqitWrNDi4mKdPHmyTpo0ScvLy1U1u//55mzO5lwYzi69++sc5G3OmaG/\nN+i0xEGr6jdF5HfAHOAaUBnYgmP6irN78cUX+YM/+APq6+sZOHAg27bF5lvJDubsBnN2gzm7wZwL\nlER3cMLFQc8BfuK/nko/46D37t2b8EkkUZ29e/f2GWcXOX7nzp06d+5cVVXdv39/v2MwzdmNc1gn\nczbnXHAO451t5yBvc05/uWr/v0Gnaz3oB4Ad/pn6OTBERIaHaLsHYWa9JarT2NjYZ5xd5Pg333yT\nJUuWADB16lSuXLnCuXPnklU2Z0fOYZ3M2ZzN2ZzT7ZxKeSqkKw46ts7pgDrOCBNnF1unpKQkq8Hw\n5uwGc3aDObvBnAubMDdowzAMwzAcI173eB8VRH4MzAJ+raoTRGQdXn/681F13sdbB/ocsBSvG3y6\nqp6LaSvrK12FQVW7lk8x58xhzm4wZzdEO0N+eptz5oi9PsIelGiS2NeBVuAIAXHQeBPEfgH8BG+C\n2L8RZ5KYq82czbkP5yK8YZo5wPuxzn6d/w6c918/Dlw1Z3M2599f52xtYdaD/pmI/DdgG3CImPWg\ngSnA3wD/GXgFGAH8aaJ2M4k5uyFPnTtEZAXwd77Peu29vnkJcEBETuCFDV4QkeEa0yNkzuZszr8f\nzlkj5BPPKKAlTtlbwL1R+7uBKdl+8jBncy40b3M2Z3POrrPzc5ShE/kXwFHgGFF5u+mZ7CSXt7XA\nC3iZ07LtYs65tZmzI2f/b8YLOeDSH+e8OdfmnBXng8AkFzforcCfRe0fBX5DQGITXzIu69ev77M8\nTJ1I+UcffaTjx48PLF+5cqXW1dV1vTdmzBg9e/asqiepwAdAoznnjnMyTmHKg7wj5fG8zdmp80Hg\nm3jzF/LOWdPwe5hpZ9Wu30NzzmC5apdzqIRe0VvYMCvxtyB+DCwBEJFpQDtwVPtObJJxtPuBoRfz\n589nx44dAOzfv59bb72V4cN75FX5BC+W2ynm7I4UvM05CVJwrgOW4ydAcsnvmTOYsyuSTuiVcJKY\niNQCM4DbRKQVWI83Y1dVdZuq7hSRuVGD+f8LKItq4hReNjJnLFq0iMbGRi5evMjIkSPZsGEDbW1t\nXTlf586dy86dOyktLWXgwIFs3749tokBeN/+zbnAnPvyfu+998J4m7Mb51PAMHomQDLn9Dtjzs6I\nl9Ar7sS3MLO4F4nIbOD7eIlNhmlUDLTPd/CWnBwJfAuviztpwiyynajOjBkzqKqqYteuXaxZs4bO\nzk7Onz/P2rVrge60bBs3bqS1tZXW1lYee+wxnnrqKZYuXWrOOeoc1ilM+ZIlSzhw4ACDBg1i+fLl\nLFu2DIAxY8YA8Omnn9La2sqgQYPo6OigpaWFKVOmmLM5m/PvsXN/y1MiUR844RbLeAao9l/PAtqA\nm/39dXRPnkjYV58O+krGHmHjxo26bt06VVX95JNPtLi4WG/cuBEZK9gPvGLO5mzOWXNe53v/WT46\nq6NznYqzqkZfH+acQXznoPlaw7WP+2+6FstQYLD/+oT/b4mIDAAW4o1TO6OvZOwRRISrV68CcPXq\nVW677TZuvrmrQ+ELePF55mzO5pwd54XAdvz5LeacMWfM2Rmx87Uua4K47nQtlrEFGCciZ4BfAn8F\n/DNe8oo67Q5Cd0KYZOyrV6/m8OHD3HHHHUycOJFNmzZFF78IvC8i550IY85OhDFnJ8KkxblOvTXn\nP+9EmPQ6uzrXaXDGnN2gqjuB3/jztWqAP090TMIx6JDMAn6pqjNF5C7gX4AJqvr/ouRqRGRrVVVV\n10EzZszIbP99HzQ0NDB58mSeffZZXn/9dRYtWsSqVasirs/51VaLiJpz/zFnNxSqs6rel4/O4OWI\nzgXviPOePXt45ZVXupwHDBjQVcec00tjY2PgMpSqujqphvrq//b7yacBu6L2u8aUo977J+CrUftv\nA18KaCu9HftxaGpq0lmzZnXtV1dX63PPPdejzrx583Tfvn1d+zNnztTm5uaugHJzNmdzNuf+Oqsj\n71Sc1ZPs4W3OmSHo+gizhenibgZKRWRUH2PKJ4H7Afy4rjLgwxBtZ4Ty8nJOnDjByZMnaWtro66u\njvnz5/eoM2rUKHbv3g3AuXPnOHbsGHfeeWc2dAFzdoU5u8Gc3WDOBU6YuzgwG/g1Xoqydf57K4FK\n//XtQAPQ4m+PxGnHxcOKqqrW19drWVmZlpaWanV1taqqbt26VWtqalRV9cyZM1pRUaHjx4/X8ePH\na21trapm9+nMnM3ZnAvD2aV3f52DvM05MwRdH2G2sJnEoGdeUVS1Rr1VR1DVj4FqoANv4lllEu1m\nDBHp2gBWrlxJZaWndvvtt/PMM89QVFREZ2cn27Zty6ZqF+bsBnN2gzm7wZwLlER3cMLFQQ/Bm7Fd\n4u8PjdNWph9UVDVcnN3ly5d13LhxeurUKVX1Yu1Us/d0Zs7mbM6F4+zKOxXnIG9zzgxB10eYLV1x\n0IuA11T1tH+2LoRoN2OEibOrra1lwYIFlJR4EWNDhw7NhmoX5uwGc3aDObvBnAubdMVBlwHFIrJX\nRJpF5NF0CfaHMHF2x44d49KlS9x3332Ul5fz8ssvu9bsgTm7wZzdYM5uMOfCJl1x0DcDU4CZwECg\nSUSaVPVEbMVciVdrb2/nwIEDVFVVsXv3btasWUNzc3NgXXPuP+bsBnN2QzLOkBveEec9e/bQ0NDA\n448/TnNzM8XFxYH1zTl14sVBJ0uYG/RpvEUwIoyg99J1p4ALqnoduC4iPwMm0p32s4voE5kpSkpK\naG1t7ZY7daqrqyTCiBEjGDp0KBUVFVRUVHDp0iWmT5/O5s2be7VnzsGYsznHw5w9Mu2djPMtt9zC\nAw88wIMPPsj06dNZsGABGzZsMOcMEPuQEOQcikSD1EAR3ZPEBuBNErs7ps5YvOxhRXip+d4HxgW0\nleGheI/29vauSQifffaZTpw4UQ8fPtyjzpEjR/T+++/X9vZ2vXbtmt5zzz166NChrE1AMGdzNufC\ncXblnYqzanYmXOWjc6oEXR9htjDLTXaIyGq83No3AT/U7tzaqt6a0EdFJBIH3QFsU9XDST8tpImi\noiK2bNlCRUUFnZ2dLF++nLvvvpuamhpEhMrKSsaOHcusWbOYMGECRUVFVFZWMm7cuGwpm7M5m7M5\nm3OOOmeLZMage8VB9yhU/a6I/BR4h95d4FkhKM4umqeffprp06dz77339upiyRbm7AZzdoM5u8Gc\nC5REX7EJEQcdVe9tvLzcD8VpK4OdCN2EibOL1Js5c6bOmzdPX3vtNVXN7RhMczZnc84PZ1feqTgH\neZtzZgi6PsJs6YqDBvgW8I9A1pf/ChNnB7B582Yefvhhhg0blgXLnpizG8zZDebsBnMubNISBy0i\ndwDfUNUfAJI+vf4RJs7uzJkzvPHGG6xatSryFJZVzNkN5uwGc3aDORc26YqD/j6wNmo/7k06V+LV\n1qxZw/PPP09jYyMHDx7k2rVrtLS0BNY15/5jzm4wZzck4wy54R1xBjh79ix1dXXmnGFyLQ76S0Cd\neKP9Q4E5InJDVWOXpcyZeMZ3332XhQsXoqpcuHCBjz/+mMWLFwe2Z87BmLM5x8OcPXIhpjjWeeDA\ngSxevJj58+fnbBx0rjknS07FQcfU306WJ4mFibOLZunSpVmfoGLO5hwPc84/Z3XknYqzanbOdT46\np0rQ9RFmS0scdOwhSTwfZIQwcXbRRKb5ZxNzdoM5u8Gc3WDOhU1a4qBFZBHdY9BXgePpEkyFvuLs\namtru8Y4Bg8ezOjRo7PiGIs5u8Gc3WDObjDnAiXRV2zCrQc9DRjiv54N7I/TVmb7EXzCxNk1NTXp\n5cuXVVW1vr5ep06dqqrZ6z4xZ3M258JxduWdinOQtzlnhqDrI8yWljhoVd2vqlf83f30Xo7SKWHi\n7KZNm8aQIUO6XsdO83eNObvBnN1gzm4w58ImXetBR7MCqE9FKlXCxNlF89JLLzFnzhwXanExZzeY\nsxvM2Q3mXNikKw4aABG5D1gGfC2d7WaSvXv3sn37dvbt25dtldCYsxvM2Q3m7AZzzj/SFQeNiEwA\ntgGzVfW38RpzEVAeJs4OoKWlhUcffZSHHnqITZs2xW3PnIMxZ3OOhzl7ZNo7GefKykqqqqrM2QHp\nSlQSZpJYmPWgR+LN3J6WoK2MDsRHCBNnd/LkSS0tLdWmpqYe75PDMZjmbM7mnB/OrrxTcQ7yNufM\nEHR9hNnCVfJmZv/avwmv899bCVT6r18ELgIHgF8Cv4jTjotzoarezL+ysjItLS3V6upqVVXdunWr\n1tTUqKrqihUrtLi4WCdPnqyTJk3S8vJyVc3uf745m7M5F4azS+/+Ogd5m3Nm6O8NWrxj3SAi6vLz\n+oOIoKoStW/OGcCc3WDOboh19t/LO29zzgxB10cYwsziRkRmi8hRETkmImvj1HlBRI6LyEERmZSs\nCBCqzz5RnUj5rl27GDt2LGVlZV0B77HHP/nkk4wePZpJkyZx8ODBfhibsyvnZJwSlZtz35izOafq\nlKg8H537W54KCW/QInITsAWYBfwJ8IiIjI2pMwe4S1VH43V9b+2PTLpuHJ2dnaxevZqGhgYOHTrE\nq6++ytGjR3scX19fzwcffMDx48epqanhiSee6I+yOTtyDutkzuZszuacbudUylMhLYlK/P0dAKr6\nc2CIiAxPq2kShAmEf/PNN1myZAkAU6dO5cqVK5w7dy4buoA5u8Kc3WDObjDnwiZdiUpi65wOqOOM\nMIHwsXVKSkqymq3GnN1gzm4wZzeYc2GTcJKYiLwNfAU4oaoTRGQx8GVVfTKqzod4ebovAkuB7wLf\nVtUDMW3l9ki+T+wEhGy6hMWc3WDObsh3Z8hPb3POHJmaJPb3eKFTEXokKvHHn4uAp+kefw5MZqKq\n4mLzPd4B3vf3n8ELD4uUzwVagYV4C338HC+M7A9jT6I5F5zzV3yPycD7sc5+nZ3AL/3X04Dr5mzO\nyTi78k7VORvnOh+d0/RzJ02YG/QP8bqrPyciA/D+2P44qvwBoBZYot7483Dgmqpmc8DAnN2Qj87N\nwFDg84DQ2xn//aDX2cKc3WDObshH56yQMNWnqnaIyLN4aTwPAT9U1SMishJvbegSvAlig0XkBN6J\n/8sMOifEnN2Qx86rgZfxenrWRzur6jagA/jQd74GtOD9LFl5sDBnczbn7DpnC9EQAd4iMgp4S1Un\nBJS9BVSr6jv+/m4Cxp/9srwbKzDnzGHObjBnN8R2Y+ajtzlnjv50c4dKVJKA08AXo/ZHAGXxEpto\nH2nN1q9fnzD1WaI6kfKPPvqI8ePHB5avXLmSurq6rvfGjBnD2bNnI2njEJG1kcQr5pwbzsk4hSkP\n8o6Ux/M2Z7fO/r8v5KNzOn4PM+2s2n1fM+fMlQc5h03oFfYGLcQfB/gxsMT/8GnAZeCv6SOxiQti\nT0w08+fPZ8eOHQDs37+fW2+9leHDe4RtVwIT1Eu84gxzdkcK3uacBCk4PyIi3wTuciIaRTqc8+n3\n0Jzd0J+EXgnHoEWkFpgB3CYircB6vFWtVFW3qepOEZkbNVbwt8BCVT3pHx9JbHK0fz9W8ixatIjG\nxkYuXrzIyJEj2bBhA21tbYh4zxhz585l586dlJaWMnDgQLZv3x7bxCcEzEI35/x37sv7vffeC+Nt\nzm6c64DlwPfwogHyzdkZKTpjzs7okdBLRIaIyHDtY9JsmElii0RkNvB9vG/cw1T1+Zhq38FbcnIk\n8FfAb6LKTuFlI0tImDU8E9WZMWMGVVVV7Nq1izVr1tDZ2cn58+dZu9braY+kZdu4cSOtra20trby\n2GOP8dRTT7F06dJIMwPw4rrNOUecwzqFKV+yZAkHDhxg0KBBLF++nGXLlgEwZswYAD799FNaW1sZ\nNGgQHR0dtLS0MGXKFHN263wKGEbPBEjmnH5nzDmz5VHES+gVf+JbiL7zm+heD/pzeOtBj42p8wze\nRDHwEpVcB2729xcDL/iv1QUdHR1d6422tbXpxIkT9ciRIz3qbNy4UdetW6eqqp988okWFxfrjRs3\nFG/28QHgH8zZnM05a86LgQ+Be/PRWR2d61Sc1ZNUc848vvNbEWfvLXYDU7SP+2+6cnErMNh/fQXo\nUNV2fz8waUkmCZPrVUS4evUqAFevXuW2227j5pu7OhTagBvmbM7mnDXnEcB5ek5ANef0O2POzgia\nUN3nvTFdubi3AONE5AzwI+CKiIyKk7wi44TJ9bp69WoOHz7MHXfcwcSJE9m0aVN08RfwTp4zzNkN\n5uyGNDgvBLbjT0B1we+pM5izK3pNqNYESZsSjkGHZBZeWraZInIXsA/4F7yZ39HJK6iqquo6aMaM\nGcn036eVhoYGJk+ezLPPPsvrr7/OokWLWLVqVaT4RWCEiJwHc04Fc3ZDATrXqWqNiCyE/HPOpXMd\ncd6zZw+vvPJKl/OAAQMAMOf009jY2GsZSu09oXpZwob66v/2+8mnAbui9tcBa2Pq/BPw1aj9t4Ev\nBbSVwV7+bpqamnTWrFld+9XV1frcc8/1qDNv3jzdt29f1/7MmTO1ublZfUdzNmdzNud+O6sj71Sc\n1ZPs4W3OmSHo+gizhenibgZKE3RZnwTuBxBvHegyvEH8rFBeXs6JEyc4efIkbW1t1NXVMX/+/B51\nRo0axe7duwE4d+4cx44d484778yGLmDOrjBnN5izG8y5wAlzFwdm461CdBxv1RHwAq0r/de3Aw14\n+VJbgEfitOPiYUVVVevr67WsrExLS0u1urpaVVW3bt2qNTU1qqp65swZraio0PHjx+v48eO1trZW\nVbP7dGbO5mzOheHs0ru/zkHe5pwZgq6PMFsyqT41akNVa9RLao6qfgxU4yU4vwkvc1HWEZGuDWDl\nypVUVnpqt99+O8888wxFRUV0dnaybdu2bKp2Yc5uMGc3mLMbzLlASXQHJ1wc9BC81YxK/P2hcdrK\n9IOKqoaLs7t8+bKOGzdOT506paperJ1q9p7OzNmczblwnF15p+Ic5G3OmSHo+gizpSsOehHwmqqe\n9s/WhRDtZowwcXa1tbUsWLCAkhIvYmzo0KHZUO3CnN1gzm4wZzeYc2GTrjjoMqBYRPaKSLOIPJou\nwf4QJs7u2LFjXLp0ifvuu4/y8nJefvll15o9MGc3mLMbzNkN5lzYpCsO+mZgCjATGAg0iUiTqp6I\nrZgr8Wrt7e0cOHCAqqoqdu/ezZo1a2hubg5cI+ZYAAAZHUlEQVSsa879x5zdYM5uSMYZcsM74rxn\nzx4aGhp4/PHHaW5upri4OLC+OadOUBx0fwhzgz6NtwhGhKD0ZKeAC6p6HbguIj8DJuKNXfcg+kRm\nipKSElpbW7vlTp3q6iqJMGLECIYOHUpFRQUVFRVcunSJ6dOns3nz5l7tmXMw5mzO8TBnj0x7J+N8\nyy238MADD/Dggw8yffp0FixYwIYNG8w5A8Q+JAQ5hyLRIDVQRPcksQF4k8TujqkzFi9zWBHweeB9\nYFxAWxkeivdob2/vmoTw2Wef6cSJE/Xw4cM96hw5ckTvv/9+bW9v12vXruk999yjhw4dytoEBHM2\nZ3MuHGdX3qk4q2ZnwlU+OqdK0PURZguz3GSHiKwG/hlvzDo6daeqtyb0URGJxEF3ANtU9XDSTwtp\noqioiC1btlBRUUFnZyfLly/n7rvvpqamBhGhsrKSsWPHMmvWLCZMmEBRURGVlZWMGzcuW8rmbM7m\nbM7mnKPO2SKZMehecdA9ClW/KyI/Bd4hC4vDBxEUZxfN008/zfTp07n33nt7dbFkC3N2gzm7wZzd\nYM4FSqKv2ISIg46q9zZeXu6H4rSVwU6EbsLE2UXqzZw5U+fNm6evvfaaquZ2DKY5m7M554ezK+9U\nnIO8zTkzBF0fYbZ0xUEDfAv4R7x1OrNKmDg7gM2bN/Pwww8zbNiwLFj2xJzdYM5uMGc3mHNhk5Y4\naBG5A/iGqv4Ab4nJrBImzu7MmTO88cYbrFq1KvIUllXM2Q3m7AZzdoM5FzbpioP+PrA2aj/uTTpX\n4tXWrFnD888/T2NjIwcPHuTatWu0tLQE1jXn/mPObjBnNyTjDLnhHXEGOHv2LHV1deacYXItDvpL\nQJ14o/1DgTkickNVY5elzJl4xnfffZeFCxeiqly4cIGPP/6YxYsXB7ZnzsGYsznHw5w9ciGmONZ5\n4MCBLF68mPnz5+dsHHSuOSdLTsVBx9TfTpYniYWJs4tm6dKlWZ+gYs7mHA9zzj9ndeSdirNqds51\nPjqnStD1EWZLSxx07CFJPB9khDBxdtFEpvlnE3N2gzm7wZzdYM6FTVrioEVkEd1j0FeB4+kSTIW+\n4uxqa2u7xjgGDx7M6NGjs+IYizm7wZzdYM5uMOcCJdFXbMKtBz0NGOK/ng3sj9NWZvsRfMLE2TU1\nNenly5dVVbW+vl6nTp2qqtnrPjFnczbnwnF25Z2Kc5C3OWeGoOsjzJaWOGhV3a+qV/zd/fRejtIp\nYeLspk2bxpAhQ7pex07zd405u8Gc3WDObjDnwiZd60FHswKoT0UqVcLE2UXz0ksvMWfOHBdqcTFn\nN5izG8zZDeZc2KQrDhoAEbkPWAZ8LZ3tZpK9e/eyfft29u3bl22V0JizG8zZDebsBnPOP9IVB42I\nTAC2AbNV9bfxGnMRUB4mzg6gpaWFRx99lIceeohNmzbFbc+cgzFnc46HOXtk2jsZ58rKSqqqqszZ\nAelKVBJmkliY9aBH4s3cnpagrYwOxEcIE2d38uRJLS0t1aamph7vk8MxmOZszuacH86uvFNxDvI2\n58wQdH2E2cJV8mZm/9q/Ca/z31sJVPqvXwQuAgeAXwK/iNOOi3Ohqt7Mv7KyMi0tLdXq6mpVVd26\ndavW1NSoquqKFSu0uLhYJ0+erJMmTdLy8nJVze5/vjmbszkXhrNL7/46B3mbc2bo7w1avGPdICLq\n8vP6g4igqhK1b84ZwJzdYM5uiHX238s7b3PODEHXRxjCzOJGRGaLyFEROSYia+PUeUFEjovIQRGZ\nlKwIEKrPPlGdSPmuXbsYO3YsZWVlXQHvscc/+eSTjB49mkmTJnHw4MF+GJuzK+dknBKVm3PfmLM5\np+qUqDwfnftbngoJb9AichOwBZgF/AnwiIiMjakzB7hLVUfjdX1v7Y9Mum4cnZ2drF69moaGBg4d\nOsSrr77K0aNHexxfX1/PBx98wPHjx6mpqeGJJ57oj7I5O3IO62TO5mzO5pxu51TKUyEtiUr8/R0A\nqvpzYIiIDE+raRKECYR/8803WbJkCQBTp07lypUrnDt3Lhu6gDm7wpzdYM5uMOfCJl2JSmLrnA6o\n44wwgfCxdUpKSrKarcac3WDObjBnN5hzYZNwkpiIvA18BTihqhNEZDHwZVV9MqrOh3h5ui8CS4Hv\nAt9W1QMxbeX2SL5P7ASEbLqExZzdYM5uyHdnyE9vc84cmZok9vd4oVMReiQq8cefi4Cn6R5/Dkxm\noqriYvM93gHe9/efwQsPi5TPBVqBhXgLffwcL4zsD2NPojkXnPNXfI/JwPuxzn6dncAv/dfTgOvm\nbM7JOLvyTtU5G+c6H53T9HMnTZgb9A/xuqs/JyID8P7Y/jiq/AGgFlii3vjzcOCaqmZzwMCc3ZCP\nzs3AUODzgNDbGf/9oNfZwpzdYM5uyEfnrJAw1aeqdojIs3hpPA8BP1TVIyKyEm9t6BK8CWKDReQE\n3on/yww6J8Sc3ZDHzquBl/F6etZHO6vqNqAD+NB3vga04P0sWXmwMGdzNufsOmcL0RAB3iIyCnhL\nVScElL0FVKvqO/7+bgLGn/2yvBsrMOfMYc5uMGc3xHZj5qO3OWeO/nRzh0pUkoDTwBej9kcAZfES\nm2gfac3Wr1+fMPVZojqR8o8++ojx48cHlq9cuZK6urqu98aMGcPZs2cjaeMQkbWRxCvmnBvOyTiF\nKQ/yjpTH8zZnt87+vy/ko3M6fg8z7azafV8z58yVBzmHTegV9gYtxB8H+DGwxP/wacBl4K/pI7GJ\nC2JPTDTz589nx44dAOzfv59bb72V4cN7hG1XAhPUS7ziDHN2Rwre5pwEKTg/IiLfBO5yIhpFOpzz\n6ffQnN3Qn4ReCcegRaQWmAHcJiKtwHq8Va1UVbep6k4RmRs1VvC3wEJVPekfH0lscrR/P1byLFq0\niMbGRi5evMjIkSPZsGEDbW1tiHjPGHPnzmXnzp2UlpYycOBAtm/fHtvEJwTMQjfn/Hfuy/u9994L\n423ObpzrgOXA9/CiAfLN2RkpOmPOzuiR0EtEhojIcO1j0myYSWKLRGQ28H28b9zDVPX5mGrfwVty\nciTwV8BvospO4WUjS0iYNTwT1ZkxYwZVVVXs2rWLNWvW0NnZyfnz51m71utpj6Rl27hxI62trbS2\ntvLYY4/x1FNPsXTp0kgzA/Dius05R5zDOoUpX7JkCQcOHGDQoEEsX76cZcuWATBmzBgAPv30U1pb\nWxk0aBAdHR20tLQwZcoUc3brfAoYRs8ESOacfmfMObPlUcRL6BV/4luIvvOb6F4P+nN460GPjanz\nDN5EMfASlVwHbvb3FwMv+K/VBR0dHV3rjba1tenEiRP1yJEjPeps3LhR161bp6qqn3zyiRYXF+uN\nGzcUb/bxAeAfzNmczTlrzouBD4F789FZHZ3rVJzVk1Rzzjy+81sRZ+8tdgNTtI/7b7pycSsw2H99\nBehQ1XZ/PzBpSSYJk+tVRLh69SoAV69e5bbbbuPmm7s6FNqAG+ZszuacNecRwHl6TkA15/Q7Y87O\nCJpQ3ee9MV25uLcA40TkDPAj4IqIjIqTvCLjhMn1unr1ag4fPswdd9zBxIkT2bRpU3TxF/BOnjPM\n2Q3m7IY0OC8EtuNPQHXB76kzmLMrek2o1gRJmxKOQYdkFl5atpkichewD/gXvJnf0ckrqKqq6jpo\nxowZyfTfp5WGhgYmT57Ms88+y+uvv86iRYtYtWpVpPhFYISInAdzTgVzdkMBOtepao2ILIT8c86l\ncx1x3rNnD6+88kqX84ABAwAw5/TT2NjYaxlK7T2helnChvrq//b7yacBu6L21wFrY+r8E/DVqP23\ngS8FtJXBXv5umpqadNasWV371dXV+txzz/WoM2/ePN23b1/X/syZM7W5uVl9R3M2Z3M25347qyPv\nVJzVk+zhbc6ZIej6CLOF6eJuBkoTdFmfBO4HEG8d6DK8QfysUF5ezokTJzh58iRtbW3U1dUxf/78\nHnVGjRrF7t27ATh37hzHjh3jzjvvzIYuYM6uMGc3mLMbzLnACXMXB2bjrUJ0HG/VEfACrSv917cD\nDXj5UluAR+K04+JhRVVV6+vrtaysTEtLS7W6ulpVVbdu3ao1NTWqqnrmzBmtqKjQ8ePH6/jx47W2\ntlZVs/t0Zs7mbM6F4ezSu7/OQd7mnBmCro8wWzKpPjVqQ1Vr1Etqjqp+DFTjJTi/CS9zUdYRka4N\nYOXKlVRWemq33347zzzzDEVFRXR2drJt27ZsqnZhzm4wZzeYsxvMuUBJdAcnXBz0ELzVjEr8/aFx\n2sr0g4qqhouzu3z5so4bN05PnTqlql6snWr2ns7M2ZzNuXCcXXmn4hzkbc6ZIej6CLOlKw56EfCa\nqp72z9aFEO1mjDBxdrW1tSxYsICSEi9ibOjQodlQ7cKc3WDObjBnN5hzYZOuOOgyoFhE9opIs4g8\nmi7B/hAmzu7YsWNcunSJ++67j/Lycl5++WXXmj0wZzeYsxvM2Q3mXNikKw76ZmAKMBMYCDSJSJOq\nnoitmCvxau3t7Rw4cICqqip2797NmjVraG5uDqxrzv3HnN1gzm5IxhlywzvivGfPHhoaGnj88cdp\nbm6muLg4sL45p05QHHR/CHODPo23CEaEoPRkp4ALqnoduC4iPwMm4o1d9yD6RGaKkpISWltbu+VO\nnerqKokwYsQIhg4dSkVFBRUVFVy6dInp06ezefPmXu2ZczDmbM7xMGePTHsn43zLLbfwwAMP8OCD\nDzJ9+nQWLFjAhg0bzDkDxD4kBDmHItEgNVBE9ySxAXiTxO6OqTMWL3NYEfB54H1gXEBbGR6K92hv\nb++ahPDZZ5/pxIkT9fDhwz3qHDlyRO+//35tb2/Xa9eu6T333KOHDh3K2gQEczZncy4cZ1feqTir\nZmfCVT46p0rQ9RFmC7PcZIeIrAb+GW/MOjp1p6q3JvRREYnEQXcA21T1cNJPC2miqKiILVu2UFFR\nQWdnJ8uXL+fuu++mpqYGEaGyspKxY8cya9YsJkyYQFFREZWVlYwbNy5byuZszuZszuaco87ZIpkx\n6F5x0D0KVb8rIj8F3iELi8MHERRnF83TTz/N9OnTuffee3t1sWQLc3aDObvBnN1gzgVKoq/YhIiD\njqr3Nl5e7ofitJXBToRuwsTZRerNnDlT582bp6+99pqq5nYMpjmbsznnh7Mr71Scg7zNOTMEXR9h\ntnTFQQN8C/hHvHU6s0qYODuAzZs38/DDDzNs2LAsWPbEnN1gzm4wZzeYc2GTljhoEbkD+Iaq/gBv\nicmsEibO7syZM7zxxhusWrUq8hSWVczZDebsBnN2gzkXNumKg/4+sDZqP+5NOlfi1dasWcPzzz9P\nY2MjBw8e5Nq1a7S0tATWNef+Y85uMGc3JOMMueEdcQY4e/YsdXV15pxhci0O+ktAnXij/UOBOSJy\nQ1Vjl6XMmXjGd999l4ULF6KqXLhwgY8//pjFixcHtmfOwZizOcfDnD1yIaY41nngwIEsXryY+fPn\n52wcdK45J0tOxUHH1N9OlieJhYmzi2bp0qVZn6BizuYcD3POP2d15J2Ks2p2znU+OqdK0PURZktL\nHHTsIUk8H2SEMHF20USm+WcTc3aDObvBnN1gzoVNWuKgRWQR3WPQV4Hj6RJMhb7i7Gpra7vGOAYP\nHszo0aOz4hiLObvBnN1gzm4w5wIl0Vdswq0HPQ0Y4r+eDeyP01Zm+xF8wsTZNTU16eXLl1VVtb6+\nXqdOnaqq2es+MWdzNufCcXblnYpzkLc5Z4ag6yPMlpY4aFXdr6pX/N399F6O0ilh4uymTZvGkCFD\nul7HTvN3jTm7wZzdYM5uMOfCJl3rQUezAqhPRSpVwsTZRfPSSy8xZ84cF2pxMWc3mLMbzNkN5lzY\npCsOGgARuQ9YBnwtne1mkr1797J9+3b27duXbZXQmLMbzNkN5uwGc84/0hUHjYhMALYBs1X1t/Ea\ncxFQHibODqClpYVHH32Uhx56iE2bNsVtz5yDMWdzjoc5e2TaOxnnyspKqqqqzNkB6UpUEmaSWJj1\noEfizdyelqCtjA7ERwgTZ3fy5EktLS3VpqamHu+TwzGY5mzO5pwfzq68U3EO8jbnzBB0fYTZwlXy\nZmb/2r8Jr/PfWwlU+q9fBC4CB4BfAr+I046Lc6Gq3sy/srIyLS0t1erqalVV3bp1q9bU1Kiq6ooV\nK7S4uFgnT56skyZN0vLyclXN7n++OZuzOReGs0vv/joHeZtzZujvDVq8Y90gIury8/qDiKCqErVv\nzhnAnN1gzm6Idfbfyztvc84MQddHGMLM4kZEZovIURE5JiJr49R5QUSOi8hBEZmUrAgQqs8+UZ1I\n+a5duxg7dixlZWVdAe+xxz/55JOMHj2aSZMmcfDgwX4Ym7Mr52ScEpWbc9+Yszmn6pSoPB+d+1ue\nCglv0CJyE7AFmAX8CfCIiIyNqTMHuEtVR+N1fW/tj0y6bhydnZ2sXr2ahoYGDh06xKuvvsrRo0d7\nHF9fX88HH3zA8ePHqamp4YknnuiPsjk7cg7rZM7mbM7mnG7nVMpTIS2JSvz9HQCq+nNgiIgMT6tp\nEoQJhH/zzTdZsmQJAFOnTuXKlSucO3cuG7qAObvCnN1gzm4w58ImXYlKYuucDqjjjDCB8LF1SkpK\nspqtxpzdYM5uMGc3mHNhk3CSmIgsAGapaqW/vxj4sqo+GVXnLaBaVd/x93cD31bVAzFt5fZIvk/s\nBIRsuoTFnN1gzm7Id2fIT29zzhyZmiQWJlHJaeCLCeqgquJiA74CNETtP4MXHhZdpwZYGLX/a+AP\nY0+iOZuzOZtzss6uvFN1zsa5zkfnNP3cyaOJY6DDJCqZC/zEfz2NOKtZudrM2ZzN2ZzN2ZzzfQt7\nQvtMVOLvb/FP+q+AKVn/wczZnM3ZnM3ZnPN4E/9EGIZhGIaRS2Tw6egocAxYG6f8EtAGnAEmxZQ/\nB1wHPgN+A4yP0/5JoAN4KOAzvh3VxgcxZT8EzgNX8LpX3gf+JtoZeAHv6e4gMMmc88M5yvsjQIEd\nccpb/XbOAnvN2a2zX77DL/tdkLNfJ9r7zxM4LwI+jGrze338bQq8ps3ZnNPovDTgM3r8HsaW96qf\nqEKyG97Es8j4wud8kbEx5WeAPX75MeBgTPm/A/f45R8ElJ8A/shv4wrwrRiHW/H+sHzZb+P9GIev\n+SfqvL//Bf8/5M6oz2z0y6YC+805951jvP8V+AnezSX2+vvQb3sU3vU5zZzdOvt1tuI9XLQEOB8E\nvknPcci2eM5+na/4nzkKmAdcC3CKe02bszmn0Xko3voUN0eVz4lynkqIcfVQqT6TJFFiky8D7UCN\nX/4/gTuiEpt8Gfg3Vf03v/xlvJPWo32/zf+Dd8LKYxy+A/y7qv7Cb+OVaAdV3Qf8B92z2L8GXFfV\nD/36n+DPQlcv8cpwoNOcc945UqcN+N/AOWAfva+//wDqVPUk3vU53ZzdOvt8RPeSt7HOdcBy/ARI\nPuK3F+QM3jf5X/uf96/AjQCnvq5pczbndDkPBi6qantUedIJvTJxg06U2KQE72T9e1T59ag6sceX\n+XWij78IfENVf4B30m6LcbgbuC4ie0WkGe+JJzZxyo+AW0TkDN6N5WdRZQPwnsQiXMWbeWjOue0M\n3re+W31n8X+G2OuvEygWkb1AJTDTnLPivAUoxbuOYp1PAcOiPErwHuzb4jjHeq/AW10vtryva9qc\nzTldzr8C/mtMedIJvTJxg04bInIf8HW8J55ovoI3rhaPIrwuhjl44wbfAIbE1Pk68DtVvQN4Gviq\niAwy57x3Xgb8PLpqwOE3AVN87/8BfFlESs3ZufMs4BBwJAPOy/C+5cSS6Jo2Z3NOh/Nk4O9Sdc7E\nDTpRYpPTeL/MX4wqvyWqzmlgpIhMALYBtXh9/9HH3w7Uichv8MYfvi4i86PqfAhcVdXrqnoR76kl\n9mf9L3jjDOA9Td0AIouAtPn7Ef4T3tOTOee2M3hdsbN854eBx/12o3+uz+ElSriON45+HJhozs6d\nlwG74jiPwJv4FnE8jdd9OCCOc6TOON95PlAcUN7XNW3O5pwWZ1X9AH9eRsxnJEzo1YNEg9TJbiQI\nQvfLIxNUBtB7gkoRXl/+b/DGHoKOj27/Ir0nL43D66L4Y7xvdL8D/jSmzg7gnP/6drwLYILf5gfA\nT7V7AkL0pBpzzlHnAO8f+XVjf66TwP/F+yX+lf8548zZufPfAd/Dm1wY63wQLy42MqnmXronAvVy\n9uv8kV/nG8T/2xP3mjZnc06j83C8LyzFUeVJJ19J+w3a//A+g9D98t/6J+xjvG6D6PJ6vFl71/Ge\nMH4RcHyk/WbgIXoHub+IN8P4M+DNaAe8bwRn8b5FtPkncpf/3nFgHd54wnm/bIo554dzjPcV4OU4\n19953/lj4Fvm7NbZL3/dbz/i/RPf+Rzdfzf2+m38CviLBM4vAp/6bV7Hm8AY5BT3mjZnc06j81Z6\n/84klXzFEpUYhmEYRg6S05PEDMMwDOP3FbtBG4ZhGEYOYjdowzAMw8hB7AZtGIZhGDmI3aANwzAM\nIwexG7RhGIZh5CB2gzYMwzCMHOT/A5AgJtddx3L5AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def visualize_predictions(images, targets, predictions):\n", " \"\"\" Adapted from Jake Vanderplas' scikit learn tutorials. \"\"\"\n", @@ -284,9 +475,13 @@ " ax.set_xticks([])\n", " ax.set_yticks([])\n", "\n", - "visualize_predictions(digits.data,\n", - " digits.target,\n", - " model.predict(digits.data))" + "mask = y_test != model.predict(X_test)\n", + "visualize_predictions(X_test[mask, :],\n", + " y_test[mask],\n", + " model.predict(X_test)[mask])\n", + "\n", + "\n", + "\n" ] }, { @@ -307,11 +502,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEKCAYAAAAB0GKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzHJxwjAzs1ycMMzMLBcnDDMzy+X/ALplLZJYW8wHAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy\n", @@ -320,7 +526,7 @@ "from sklearn.linear_model import LogisticRegression\n", "\n", "data = load_digits()\n", - "n_trials = 5\n", + "n_trials = 50\n", "train_percentages = range(5,95,5)\n", "test_accuracies = numpy.zeros(len(train_percentages))\n", "\n", diff --git a/preclass/Exploring Tf-IDF.ipynb b/preclass/Exploring Tf-IDF.ipynb index 3ddcbe0..9fd9c5f 100644 --- a/preclass/Exploring Tf-IDF.ipynb +++ b/preclass/Exploring Tf-IDF.ipynb @@ -15,11 +15,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the 20 newsgroups by date dataset\n", + "Number of posts 11314\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUqv/D6MOnIMuR98ZAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First post!\n", + "I was wondering if anyone out there could enlighten me on this car I saw\n", + "the other day. It was a 2-door sports car, looked to be from the late 60s/\n", + "early 70s. It was called a Bricklin. The doors were really small. In addition,\n", + "the front bumper was separate from the rest of the body. This is \n", + "all I know. If anyone can tellme a model name, engine specs, years\n", + "of production, where this car is made, history, or whatever info you\n", + "have on this funky looking car, please e-mail.\n" + ] + } + ], "source": [ "%matplotlib inline\n", "from sklearn.datasets import fetch_20newsgroups\n", @@ -51,20 +84,115 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{u'2door': 1,\n", + " u'60s': 1,\n", + " u'70s': 1,\n", + " u'a': 3,\n", + " u'addition': 1,\n", + " u'all': 1,\n", + " u'anyone': 2,\n", + " u'be': 1,\n", + " u'body': 1,\n", + " u'bricklin': 1,\n", + " u'bumper': 1,\n", + " u'called': 1,\n", + " u'can': 1,\n", + " u'car': 4,\n", + " u'could': 1,\n", + " u'day': 1,\n", + " u'doors': 1,\n", + " u'early': 1,\n", + " u'email': 1,\n", + " u'engine': 1,\n", + " u'enlighten': 1,\n", + " u'from': 2,\n", + " u'front': 1,\n", + " u'funky': 1,\n", + " u'have': 1,\n", + " u'history': 1,\n", + " u'i': 3,\n", + " u'if': 2,\n", + " u'in': 1,\n", + " u'info': 1,\n", + " u'is': 2,\n", + " u'it': 2,\n", + " u'know': 1,\n", + " u'late': 1,\n", + " u'looked': 1,\n", + " u'looking': 1,\n", + " u'made': 1,\n", + " u'me': 1,\n", + " u'model': 1,\n", + " u'name': 1,\n", + " u'of': 2,\n", + " u'on': 2,\n", + " u'or': 1,\n", + " u'other': 1,\n", + " u'out': 1,\n", + " u'please': 1,\n", + " u'production': 1,\n", + " u'really': 1,\n", + " u'rest': 1,\n", + " u'saw': 1,\n", + " u'separate': 1,\n", + " u'small': 1,\n", + " u'specs': 1,\n", + " u'sports': 1,\n", + " u'tellme': 1,\n", + " u'the': 6,\n", + " u'there': 1,\n", + " u'this': 4,\n", + " u'to': 1,\n", + " u'was': 4,\n", + " u'were': 1,\n", + " u'whatever': 1,\n", + " u'where': 1,\n", + " u'wondering': 1,\n", + " u'years': 1,\n", + " u'you': 1}" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "import re\n", + "\n", "def tf(text):\n", " \"\"\" Returns a dictionary where keys are words that occur in text\n", " and the value is the number of times that each word occurs. \"\"\"\n", - " pass\n", + " res = {}\n", + " regex = re.compile('[%s]' % re.escape(string.punctuation))\n", + " wordList = regex.sub('', text).lower().split() #remove punctuation, make lower case, and split into list of words\n", + " for i in range(0, len(wordList)):\n", + " if wordList[i] not in res:\n", + " #res hasn't seen this word so add it to the dictionary with a count of 1\n", + " res[wordList[i]] = 1\n", + " else:\n", + " #res has seen this word so find the value of this word and increment by 1\n", + " res[wordList[i]] += 1\n", + " return res\n", "\n", "tf(data.data[0])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I manually verified that my tf function is working as expected by choosing a word shown in the dictionary and counting how many times it appears in the first post." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -74,11 +202,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lowest IDF (most common)\n", + "(u'the', 0.17780639867699963)\n", + "(u'to', 0.2921111699570584)\n", + "(u'a', 0.3085812880536072)\n", + "(u'and', 0.37834805355570833)\n", + "(u'of', 0.3893763845971865)\n", + "(u'i', 0.44614348557751443)\n", + "(u'in', 0.4694734533916627)\n", + "(u'is', 0.4778476418510821)\n", + "(u'that', 0.5596378846971432)\n", + "(u'it', 0.5821634734308023)\n", + "\n", + "Highest IDF (least common)\n", + "(u'jawbone', 9.333796175903101)\n", + "(u'mi2ditm2wvbgt3tdi2gtrc8ws14sy0cstdi2di', 9.333796175903101)\n", + "(u'mx3734ubq34lgu7t72qt7utxuu2plqt', 9.333796175903101)\n", + "(u'axixuya1mhrkh3k10ugt50y3cb7tyv', 9.333796175903101)\n", + "(u'230650', 9.333796175903101)\n", + "(u'echte', 9.333796175903101)\n", + "(u'md2qvgqvf1q3j1afdod3iid3hz3hzyi3j1d88zaizhaiz', 9.333796175903101)\n", + "(u'geysers', 9.333796175903101)\n", + "(u'q700900', 9.333796175903101)\n", + "(u'135mb', 9.333796175903101)\n" + ] + } + ], "source": [ "from math import log\n", "import operator\n", @@ -87,7 +245,20 @@ " \"\"\" Returns a dictionary where the keys are words and the values are inverse\n", " document frequencies. For this function you should use the formula\n", " idf(w, data) = log(N / |text in data that contain the word w|) \"\"\"\n", - " return {}\n", + " regex = re.compile('[%s]' % re.escape(string.punctuation))\n", + " res = {}\n", + " for i in range(0, len(data)): #in this function we are looping through all the posts\n", + " wordList = list(set(regex.sub('', data[i]).lower().split())) #for each document, get the list of words with\n", + " #no repeats, this will let us count the number of documents that contain a given word\n", + " for i in range(0, len(wordList)):\n", + " if wordList[i] not in res:\n", + " #res hasn't seen the word before so it's in this document\n", + " res[wordList[i]] = 1\n", + " else:\n", + " #res has seen the word before in a previous document so increment a count of # of documents word appears in\n", + " res[wordList[i]] += 1\n", + " res = {k: log(float(len(data))/v) for k,v in res.items()} #actually calculate idf for each word\n", + " return res\n", "\n", "idf = idf(data.data)\n", "sorted_idf = sorted(idf.items(), key=operator.itemgetter(1))\n", @@ -95,7 +266,7 @@ "print \"Lowest IDF (most common)\"\n", "for d in sorted_idf[0:10]:\n", " print d\n", - "\n", + " \n", "print \"\"\n", "print \"Highest IDF (least common)\"\n", "rev_sorted_idf = sorted(idf.items(), key=operator.itemgetter(1))\n", @@ -103,6 +274,13 @@ " print d" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based off of these results, it appears that the idf function is working as expected. The least common words appear to be mispellings or contain strings of numbers. I am not sure why they are in the posts but it makes sense that they are the least common since they are the most unqiue." + ] + }, { "cell_type": "markdown", "metadata": {},