werediver

import base64

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werediver

"""
Simple demonstration of how to implement Server-sent events (SSE) in Python
using Bottle micro web-framework.

SSE require asynchronous request handling, but it's tricky with WSGI. One way
to achieve that is to use gevent library as shown here.

Usage: just start the script and open http://localhost:8080/ in your browser.

Based on:

werediver

import threading


# Based on tornado.ioloop.IOLoop.instance() approach.
# See https://github.com/facebook/tornado
class SingletonMixin(object):
	__singleton_lock = threading.Lock()
	__singleton_instance = None

	@classmethod

werediver

module WingAR where

import Data.Maybe
import Text.Printf

-- See http://en.wikipedia.org/wiki/Aspect_ratio_%28wing%29
wing_ar :: Float -> Float -> Float
wing_ar l s = l^2 / s

readMaybe s =  listToMaybe $ fmap fst $ reads s

werediver

//-->fill(3,5)
// ans  =
//    1.    3.    4.     9.     10.  
//    2.    5.    8.     11.    14.  
//    6.    7.    12.    13.    15.  

function [x] = fill(N, M)
	x = zeros(N, M)
	Z = N + M - 1
	i = 1

werediver

//-->adiag([1 2 3 4; 5 6 7 8; 9 10 11 12],3)'
// ans  =
//    9.    6.    3.  

function [v] = adiag(x, z)
	// 2D matrix antidiagonal

	[N, M] = size(x)

	// The total diagonals count

werediver

function [y_out, m, sig] = ssa_normalize(y)
	// http://en.wikipedia.org/wiki/Student%27s_t-statistic
	m = mean(y)
	sig = sqrt(variance(y))	// stdev(y)
	y_out = (y - m) / sig
endfunction
 
function [y_out] = ssa(y, L, I)
	[LAMBDA, U, V] = ssa_decompose(y, L)
	y_out = ssa_reconstruct(LAMBDA, U, V, I)

werediver

% Force GNU Octave to interpret this file as a script
1;

function [y_out, m, sig] = ssa_normalize(y)
	% http://en.wikipedia.org/wiki/Student%27s_t-statistic
	m = mean(y);
	sig = sqrt(variance(y))	% stdev(y);
	y_out = (y - m) / sig;
endfunction

werediver

//
//  CircularQueue.h
//
//  Created on 9/21/13.
//

#import <Foundation/Foundation.h>


@interface CircularQueue : NSObject <NSFastEnumeration>

werediver

from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as pp
%matplotlib inline

def xcorr(a, b, mode='same'):
    a = np.asarray(a)
    b = np.asarray(b)
    
    a = (a - a.mean()) / np.sqrt(np.correlate(a, a))