Finn Årup Nielsen fnielsen

## Nielsen2014Data_femalehurricanes.ipynb

      
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                fnielsen
                / Nielsen2014Data_femalehurricanes.ipynb
            
            
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              August 29, 2015 14:02
            
              
                Modeling of Female Hurricanes dataset.
              
          
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## typetesting.py
from numpy import max, min


def mean_diff(a):
    """
    Parameters
    ----------
    a : array_like
    """
    return float((max(a) - min(a)) / (len(a) - 1))

## gist:7988477
import gdata.youtube.service
from time import sleep


class YtsClassic(object):
    """
    http://finnaarupnielsen.wordpress.com/2009/09/21/getting-comments-from-youtube-via-pythons-gda/
    """
    def __init__(self):
        self.yts = gdata.youtube.service.YouTubeService()

## gist:7873982
# This example is close to the example provide by the emokit module.

from emokit import emotiv
import gevent

headset = emotiv.Emotiv()
gevent.spawn(headset.setup)
gevent.sleep(1.5)

try:

## instance_class_variable.py
class MyClass:
    my_static_variable = "Static"    # not self.
    def __init__(self):
        self.my_instance_variable = "Instance"
    def change_static(self):
        MyClass.my_static_variable = "Changed"   # not self.
    def change_instance(self):
        self.my_instance_variable = "Also changed"


## pimaclassifier.py
def accuracy(truth, predicted):
    if len(truth) != len(predicted):
        raise Exception("Wrong sizes ...")
    total = len(truth)
    if total == 0:
        return 0
    hits = len(filter(lambda (x, y): x == y, zip(truth, predicted)))
    return float(hits)/total


## brownzipf.py
from __future__ import division
from itertools import *
from pylab import *
from nltk.corpus import brown
from string import lower
from collections import Counter


# The data: token counts from the Brown corpus
tokens_with_count = Counter(imap(lower, brown.words()))

## gist:6761078
from Tkinter import *

root = Tk()

canvas = Canvas(root, width=400, height=200)
canvas.pack()
canvas.create_oval(10, 10, 110, 60, fill="grey")
canvas.create_text(60, 35, text="Oval")
canvas.create_rectangle(10, 100, 110, 150, outline="blue")
canvas.create_text(60, 125, text="Rectangle")

## gist:6761017
from Tkinter import *

def hello():
    """Callback function for button press."""
    print "Hello World"

# Main root window
root = Tk()

# A button with a callback function

## gist:6599783
import gdata.youtube.service
yts = gdata.youtube.service.YouTubeService()
urlpattern = "http://gdata.youtube.com/feeds/api/videos/" + \
             "9ar0TF7J5f0/comments?start-index=%d&max-results=25"
index = 1
url = urlpattern % index
comments = []
while True:
    ytfeed = yts.GetYouTubeVideoCommentFeed(uri=url)
    comments.extend([comment.content.text for comment in ytfeed.entry])
	from numpy import max, min


	def mean_diff(a):
	"""
	Parameters
	----------
	a : array_like
	"""
	return float((max(a) - min(a)) / (len(a) - 1))
	import gdata.youtube.service
	from time import sleep


	class YtsClassic(object):
	"""
	http://finnaarupnielsen.wordpress.com/2009/09/21/getting-comments-from-youtube-via-pythons-gda/
	"""
	def __init__(self):
	self.yts = gdata.youtube.service.YouTubeService()
	# This example is close to the example provide by the emokit module.

	from emokit import emotiv
	import gevent

	headset = emotiv.Emotiv()
	gevent.spawn(headset.setup)
	gevent.sleep(1.5)

	try:
	class MyClass:
	my_static_variable = "Static" # not self.
	def __init__(self):
	self.my_instance_variable = "Instance"
	def change_static(self):
	MyClass.my_static_variable = "Changed" # not self.
	def change_instance(self):
	self.my_instance_variable = "Also changed"
	def accuracy(truth, predicted):
	if len(truth) != len(predicted):
	raise Exception("Wrong sizes ...")
	total = len(truth)
	if total == 0:
	return 0
	hits = len(filter(lambda (x, y): x == y, zip(truth, predicted)))
	return float(hits)/total
	from __future__ import division
	from itertools import *
	from pylab import *
	from nltk.corpus import brown
	from string import lower
	from collections import Counter


	# The data: token counts from the Brown corpus
	tokens_with_count = Counter(imap(lower, brown.words()))
	from Tkinter import *

	root = Tk()

	canvas = Canvas(root, width=400, height=200)
	canvas.pack()
	canvas.create_oval(10, 10, 110, 60, fill="grey")
	canvas.create_text(60, 35, text="Oval")
	canvas.create_rectangle(10, 100, 110, 150, outline="blue")
	canvas.create_text(60, 125, text="Rectangle")
	from Tkinter import *

	def hello():
	"""Callback function for button press."""
	print "Hello World"

	# Main root window
	root = Tk()

	# A button with a callback function
	import gdata.youtube.service
	yts = gdata.youtube.service.YouTubeService()
	urlpattern = "http://gdata.youtube.com/feeds/api/videos/" + \
	"9ar0TF7J5f0/comments?start-index=%d&max-results=25"
	index = 1
	url = urlpattern % index
	comments = []
	while True:
	ytfeed = yts.GetYouTubeVideoCommentFeed(uri=url)
	comments.extend([comment.content.text for comment in ytfeed.entry])