darinwilson

# Ambient experiment for Sonic Pi (http://sonic-pi.net/)
#
# The piece consists of three long loops, each of which plays one of
# two randomly selected pitches. Each note has different attack,
# release and sleep values, so that they move in and out of phase
# with each other. This can play for quite awhile without
# repeating itself :)

live_loop :note1 do
  use_synth :hollow

findsimilar.py

from whoosh.index import open_dir
from whoosh.index import create_in
from whoosh.fields import *
from whoosh.qparser import QueryParser
import glob
import os


# USER SET PARAMETERS ############

soodoku

"
Basic Text Classifier 		 
	- Takes a csv with a text column, and column of labels
	- Splits into train and test
	- Preprocesses text using tm/bag-of-words, 1/2-order Markov
	- Uses SVM and Lasso

@author: Gaurav Sood								 
 	
"

drjwbaker

#I have a list of strings in a text file (`mylist.txt`). I want to search for these strings in a tsv file (`somestuff.tsv`) and make a new file that contains only the lines in which the strings appear. Some strings in the text file will not appear in the tsv file.

#see https://gist.github.com/MartinPaulEve/c0610fa89da4df4d546a

#!/usr/bin/env python
output = []

# use a "with" block to automatically close I/O streams
with open('mylist.txt') as word_list:

jennybc

There are packages for this now!

2017-08-03: Since I wrote this in 2014, the universe, specifically Kirill Müller (https://github.com/krlmlr), has provided better solutions to this problem. I now recommend that you use one of these two packages:

• rprojroot: This is the main package with functions to help you express paths in a way that will "just work" when developing interactively in an RStudio Project and when you render your file.
• here: A lightweight wrapper around rprojroot that anticipates the most likely scenario: you want to write paths relative to the top-level directory, defined as an RStudio project or Git repo. TRY THIS FIRST.

I love these packages so much I wrote an ode to here.

I use these packages now instead of what I describe below. I'll leave this gist up for historical interest. 😆

brianckeegan

# Serrano, Boguna, Vespigani backbone extractor
# from http://www.pnas.org/content/106/16/6483.abstract
# Thanks to Michael Conover and Qian Zhang at Indiana with help on earlier versions
# Thanks to Clay Davis for pointing out an error

import networkx as nx
import numpy as np

def extract_backbone(g, weight='weight', alpha=.05):
    backbone_graph = nx.Graph()

benmarwick

# sources:
# http://www.jgoodwin.net/?p=1223
# http://orgtheory.wordpress.com/2012/05/16/the-fragile-network-of-econ-soc-readings/
# http://nealcaren.web.unc.edu/a-sociology-citation-network/
# http://kieranhealy.org/blog/archives/2014/11/15/top-ten-by-decade/
# http://www.jgoodwin.net/lit-cites.png


###########################################################################
# This first section scrapes content from the Web of Science webpage. It takes

shlomibabluki

# coding=UTF-8
import nltk
from nltk.corpus import brown

# This is a fast and simple noun phrase extractor (based on NLTK)
# Feel free to use it, just keep a link back to this post
# http://thetokenizer.com/2013/05/09/efficient-way-to-extract-the-main-topics-of-a-sentence/
# Create by Shlomi Babluki
# May, 2013

benmarwick

# Set working directory
dir <- "C:\\" # adjust to suit
setwd(dir)

# configure variables and filenames for MALLET
## here using MALLET's built-in example data and
## variables from http://programminghistorian.org/lessons/topic-modeling-and-mallet

# folder containing txt files for MALLET to work on
importdir <- "C:\\mallet-2.0.7\\sample-data\\web\\en"

gupul2k

#Script tags POS and NER[Named Entity Recognition] for a supplied text file.
#Date: Nov 2 2012
#Author: Hota Sobhan

import nltk

f = open('C:\Python27\Test_File.txt')
data = f.readlines()

#Parse the text file for NER with POS Tagging