Christopher Groskopf onyxfish

## README.md

      
              2 files
            
          
              6 forks
            
          
              14 comments
            
          
              43 stars
            
          
                onyxfish
                / README.md
            
            
              Last active
              January 2, 2023 14:37
            
              
                Google Spreadsheets script to generate slugs from a range of cells
              
          
    This script for Google Spreadsheets allows you to generate slugs for your data such as might be used for creating unique urls.
Use it like this!


#
A
B
C


1
a
b
slug


2
foo
baz bing
=slugify(A2:B4)


3
bar
BAZ


4
FOO
baz-bing


## example1.py
import nltk

with open('sample.txt', 'r') as f:
    sample = f.read()

sentences = nltk.sent_tokenize(sample)
tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True)

## csvkit_tutorial.sh
#!/bin/bash

###################
# Getting started #
###################

# Setup a workspace
mkdir va_benefits
cd va_benefits

## fabfile.py
from fabric.api import *

"""
Base configuration
"""
env.project_name = '$(project)'
env.database_password = '$(db_password)'
env.site_media_prefix = "site_media"
env.admin_media_prefix = "admin_media"
env.newsapps_media_prefix = "na_media"

## wordpress.vcl
backend app1 {
    .host = "127.0.0.1";
    .port = "8000";
}

acl purge {
	"127.0.0.1";
    "::1";
}

## README.md

      
              5 files
            
          
              1 fork
            
          
              0 comments
            
          
              5 stars
            
          
                onyxfish
                / README.md
            
            
              Created
              March 30, 2017 14:06
            
              
                Import the entire Bureau of Labor Statistics (BLS) Quarterly Census of Wages (QCEW) dataset into a PostgreSQL database
              
          
    QCEW Data Loader

These scripts import the entire Bureau of Labor Statistics Quarterly Census of Employement and Wages (from 1990 to latest) into one giant PostgreSQL database.
The database created by this process will use about 100GB of disk space. Make sure you have enough space available before you start!
Configuration

Database name, table name, and more can be configured via config.sh.

  
## newsapps-varnish-admin.php
<?php
	if($_POST['varnish_hidden'] == 'Y') {
		//Process form data
		$varnish_server1 = (!isset($_POST['varnish_server1'])? '': $_POST['varnish_server1']);
		update_site_option('varnish_server1', $varnish_server1);

		$varnish_server2 = (!isset($_POST['varnish_server2'])? '': $_POST['varnish_server2']);
		update_site_option('varnish_server2', $varnish_server2);

		$varnish_server3 = (!isset($_POST['varnish_server3'])? '': $_POST['varnish_server3']);

## map.py
from colorsys import hsv_to_rgb
import json
import logging
log = logging.getLogger('electioncenter.lib.kml')
import struct

from django.contrib.gis.geos import GEOSGeometry

def hsv_to_kml_hex(h, s, v):
    """

## ordered_json.py
#!/usr/bin/env python

from collections import OrderedDict
import json

write_data = OrderedDict([
    ('a', '1'),
    ('b', '2'),
    ('c', '3')
])

## linestring.py
import agate
import geojson

class LineString(agate.Aggregation):
    def __init__(self, lat_column, lng_column):
        self._lat_column_name = lat_column
        self._lng_column_name = lng_column

    def get_aggregate_data_type(self, table):
        return agate.Text()
	import nltk

	with open('sample.txt', 'r') as f:
	sample = f.read()

	sentences = nltk.sent_tokenize(sample)
	tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
	tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
	chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True)
	#!/bin/bash

	###################
	# Getting started #
	###################

	# Setup a workspace
	mkdir va_benefits
	cd va_benefits
	from fabric.api import *

	"""
	Base configuration
	"""
	env.project_name = '$(project)'
	env.database_password = '$(db_password)'
	env.site_media_prefix = "site_media"
	env.admin_media_prefix = "admin_media"
	env.newsapps_media_prefix = "na_media"
	backend app1 {
	.host = "127.0.0.1";
	.port = "8000";
	}

	acl purge {
	"127.0.0.1";
	"::1";
	}
	<?php
	if($_POST['varnish_hidden'] == 'Y') {
	//Process form data
	$varnish_server1 = (!isset($_POST['varnish_server1'])? '': $_POST['varnish_server1']);
	update_site_option('varnish_server1', $varnish_server1);

	$varnish_server2 = (!isset($_POST['varnish_server2'])? '': $_POST['varnish_server2']);
	update_site_option('varnish_server2', $varnish_server2);

	$varnish_server3 = (!isset($_POST['varnish_server3'])? '': $_POST['varnish_server3']);
	from colorsys import hsv_to_rgb
	import json
	import logging
	log = logging.getLogger('electioncenter.lib.kml')
	import struct

	from django.contrib.gis.geos import GEOSGeometry

	def hsv_to_kml_hex(h, s, v):
	"""
	#!/usr/bin/env python

	from collections import OrderedDict
	import json

	write_data = OrderedDict([
	('a', '1'),
	('b', '2'),
	('c', '3')
	])
	import agate
	import geojson

	class LineString(agate.Aggregation):
	def __init__(self, lat_column, lng_column):
	self._lat_column_name = lat_column
	self._lng_column_name = lng_column

	def get_aggregate_data_type(self, table):
	return agate.Text()