Nicky Nicolson nickynicolson

This graph gist demonstrates use of Jaccard calculation to determine similarity between two sets of nodes

Automating reading list management

What I'm using:

Zotero: In browser integration mean that its a single click to save citation information (and a full-text PDF if available)

...but I'm using Trello to stay on top of tasks, and my "to-read" list needs to have a presence in my "to-do" list, as Trello gives a better single view of my work than zotero.

What I'd like:

Cards for new "to-read" articles in Trello, automatically - without cut + paste.

	Peter investigates the taxonomy of Diplazium tomentosum.

	//console

	He starts with one of the types from the Leiden herbarium (http://plants.jstor.org/specimen/l0051073):

	[source,cypher]
	----
	CREATE (s:Specimen{id:"L0051073"
	,jstorUrl:'http://plants.jstor.org/specimen/l0051073'

	= The taxonomic mind-mapper
	Nicky Nicolson, Peter Hovenkamp
	:neo4j-version: 2.1.0
	:author: Nicky Nicolson
	:twitter: nickynicolson

	== Domain
	We investigate the use of the scheme in: https://docs.google.com/document/d/1FIxNrrGrIZs0l4QJEdGfctXbYVL4cQhKspmMEHmKAGg/edit with a use case in the taxonomy of Diplazium tomentosum (https://docs.google.com/document/d/1vni44RBwGNZ7iRCFf243NtcD-7xwjHrDeAPehato7KY/edit?usp=sharing)

	== Data model

	import urllib
	import urllib2

	try:
	# Base url for the TNRS match_names API
	url = 'http://api.opentreeoflife.org/v2/tnrs/match_names'
	# Encode data value to be looked up as an array of names:
	data = '{"names": ["'+value+'"]}'
	print "Looking up: " + data
	# Set HTTP headers:

	// Create meta-graph with orphan counts
	MATCH (n)
	/* ugly string replace to convert collection of labels to colon delimited string */
	WITH replace(replace(replace(replace(str(labels(n)),'[',''),']',''),'"',''),',',':') AS l, count(distinct n) AS c
	MERGE (mn:Meta_Node {name:l,count:c})
	WITH mn
	MATCH (a)-[r]->(b)
	WITH replace(replace(replace(replace(str(labels(a)),'[',''),']',''),'"',''),',',':') AS a_labels
	, count(distinct a) AS from_count
	, type(r) AS rel_type

	<?xml version="1.0" encoding="UTF-8"?>
	<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
	<xsl:output method="text" indent="yes" encoding="UTF-8"/>
	<xsl:variable name="delimiter" select="' '"/>
	<xsl:variable name="newline" select="' '"/>

	<xsl:template match="/">
	<!-- header line -->
	<xsl:value-of select="concat('primary_accession',$delimiter,'date_created',$delimiter,'date_last_amended', $delimiter, 'definition', $delimiter, 'title', $delimiter, 'reference', $delimiter, 'doi', $delimiter, 'specimen_voucher', $delimiter, 'bio_material', $delimiter, 'organism', $newline)"/>
	<!-- data -->

	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console

	from sklearn.metrics import confusion_matrix
	import pandas as pd

	def cm2df(cm, labels):
	df = pd.DataFrame()
	# rows
	for i, row_label in enumerate(labels):
	rowdata={}
	# columns
	for j, col_label in enumerate(labels):