carsonfarmer carsonfarmer

Python Software Foundation 2016 Google Summer of Code Application

PySAL: Python Spatial Analysis Library

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	import Neuralyzer

	struct IrisData: Codable {
	let row: [Double]
	}

	var parsedData: [IrisData] = []
	if let file = Bundle.main.path(forResource: "iris", ofType: "json") {
	do {
	let jsonDecoder = JSONDecoder()

	func circularMean() {
	let formatter = DateFormatter()
	formatter.dateFormat = "yyyy/MM/dd HH:mm:ss zzz"
	let times = [Date](arrayLiteral: formatter.date(from: "2017/10/17 23:00:17 UTC")!, formatter.date(from: "2017/10/17 23:40:20 UTC")!, formatter.date(from: "2017/10/17 00:12:45 UTC")!, formatter.date(from: "2017/10/17 00:17:19 UTC")!)
	// 1 second of time = 360/(24 * 3600) = 1/240th degree
	let angles = times.map { secondOfDay(fromDate: $0) / 240 }
	print(degreeToTime(degree: meanAngle(angles: angles), forDate: Date())!)
	}

	func meanAngle(angles: [Double]) -> Double {

	from shapely import wkt
	from shapely import ops
	import matplotlib.pyplot as plt
	%matplotlib inline

	a = wkt.loads("LINESTRING (-180 68.96363636363637, -177.55 68.2, -174.92825 67.20589, -175.01425 66.58435, -174.33983 66.33556, -174.57182 67.06219, -171.85731 66.91307999999999, -169.89958 65.97723999999999, -170.89107 65.54139000000001, -172.53025 65.43791, -172.555 64.46079, -172.95533 64.25269, -173.89184 64.2826, -174.65392 64.63124999999999, -175.98353 64.92288000000001, -176.20716 65.35666999999999, -177.22266 65.52024, -178.35993 65.39052, -178.90332 65.74044000000001, -178.68611 66.11211, -179.88377 65.87456, -179.43268 65.40411, -180 64.97970870219837)")
	b = wkt.loads("LINESTRING (180 64.97432999999999, 178.7072000000003 64.53493, 177.4112800000002 64.60821, 178.3130000000002 64.07593, 178.9082500000002 63.25197000000014, 179.37034 62.98262000000011, 179.48636 62.56894, 179.2282500000001 62.30410000000015, 177.3643 62.5219, 174.5692900000002 61.76915, 173.68013 61.65261, 172.15 60.95, 170.698500000000

	Total CO2 emissions from fossil-fuels (1000 metric tons) for the United States

	Data source:
	Source organization(s): CDIAC (Carbon Dioxide Information Analysis Center)
	Link to source organization: http://cdiac.ornl.gov/
	File name: nation.1751_2009.csv
	Link to complete reference: http://cdiac.ornl.gov/ftp/ndp030/CSV-FILES/

	Data attributes:
	year:

	def run_remote_code(url):
	"""Fetch and run remote Python code.

	This is a custom function that fetches
	and runs remote Python code inside of an
	IPython Notebook.

	Careful, this could be super bad stuff!
	"""
	from IPython.core.magics.code import CodeMagics