mattdennewitz

  background: -webkit-linear-gradient(90deg, #1D976C 10%, #93F9B9 90%); /* Chrome 10+, Saf5.1+ */
  background:    -moz-linear-gradient(90deg, #1D976C 10%, #93F9B9 90%); /* FF3.6+ */
  background:     -ms-linear-gradient(90deg, #1D976C 10%, #93F9B9 90%); /* IE10 */
  background:      -o-linear-gradient(90deg, #1D976C 10%, #93F9B9 90%); /* Opera 11.10+ */
  background:         linear-gradient(90deg, #1D976C 10%, #93F9B9 90%); /* W3C */
        

mattdennewitz

(defun cubshat ()
  (interactive)
  (color-theme-install
   '(cubshat
      ((background-color . "#222222")
      (background-mode . light)
      (border-color . "#222222")
      (cursor-color . "#999999")
      (foreground-color . "#e5dcdc")
      (mouse-color . "black"))

mattdennewitz

Keybase proof

I hereby claim:

• I am mattdennewitz on github.
• I am mattdennewitz (https://keybase.io/mattdennewitz) on keybase.
• I have a public key whose fingerprint is FCA5 4A5B 36C3 51D8 86B8 0B9E CC56 E923 11EB F00F

To claim this, I am signing this object:

mattdennewitz

1. don't drop it - dj rashad
2. generator 1 - kfw
3. wide awake - kaitlin aurelia smith
4. zebra - onp
5. clearing - grouper
6. brazil - luke abbott
7. reservoir - metronomy
8. black draft - nathan fake
9. rough 2 - tessela
10. the red wing - fuck buttons

mattdennewitz

-- create marcel projection data source using 3 previous years
-- of data, excluding pitchers.
-- 
-- note: this exclusion method risks removing batters who have pitched.
--       instead, we should look for players with more
--       PA than batters faced.
drop table if exists marcel_batting;
create table
    marcel_batting
as (

mattdennewitz

var React = require('react');
var _ = require('underscore');
 
var SigForm = React.createClass({
    onChange: function() {
        var node = React.findDOMNode(this.refs.form);

        var serialized = {
            sigName: node['wholename'].value,
            email: node['email'].value,

mattdennewitz

# models.py:
# ----------

from django.contrib.gis.db import models as geomodels

class Place(geomodels.Model):
    woe_id = geomodels.IntegerField() 
    place_id = geomodels.CharField(max_length=255)
    place_type = geomodels.CharField(max_length=255)
    place_type_id = geomodels.IntegerField()

mattdennewitz

# -*- coding: utf8 -*-

"""
Download and import US census data from 
`http://www.census.gov/geo/www/cob/z52000.html`

    # shapefile mapping:
    zipcode_mapping = {
        'area' : 'AREA',
        'perimeter' : 'PERIMETER',

mattdennewitz

from django.contrib.gis.db import models as geomodels
 
class Place(geomodels.Model):
    woe_id = geomodels.IntegerField() 
    place_id = geomodels.CharField(max_length=255)
    place_type = geomodels.CharField(max_length=255)
    place_type_id = geomodels.IntegerField()
    label = geomodels.CharField(max_length=255)
    created = geomodels.DateTimeField()
    geometry = geomodels.PolygonField(srid=4326)

mattdennewitz

import sys
from math import log, sqrt

def pnormaldist(qn):
    b = [1.570796288, 0.03706987906, -0.8364353589e-3,
         -0.2250947176e-3, 0.6841218299e-5, 0.5824238515e-5,
         -0.104527497e-5, 0.8360937017e-7, -0.3231081277e-8,
         0.3657763036e-10, 0.6936233982e-12]

    if qn < 0.0 or 1.0 < qn: