nathanntg

% Example code for analysis of neural correlate with spectral differences.
% This looks at differences in spectral power (but will not uncover
% relationships between neural activity and timing differences, as all that
% is masked by the warping process; a similar approach could look at
% correlates between neural activity and amount of warping).

%% Step 1: warp spectrograms
% How the spectrogram is warped is important, as you want to avoid spectral
% artifacts of the warping process. The exact code will vary a bit
% depending on what spectral routine you are using.

nathanntg

import ORSSerial

///  An example of an extension of the ORSSerialPacketDescriptor that enables identifying delimited packets.
class DelimitedSerialPacketDescriptor: ORSSerialPacketDescriptor {
    var delimiter: NSData?
    
    convenience init(delimiter: NSData, maximumPacketLength maxPacketLength: UInt, userInfo: AnyObject?, responseEvaluator: ORSSerialPacketEvaluator) {
        self.init(maximumPacketLength: maxPacketLength, userInfo: userInfo, responseEvaluator: responseEvaluator)
        
        // set delimiter

nathanntg

function [Area, Dsq, Time, AreaCoef, DCoef, angle, axisRatio, xyCorrelation, Mean, std_th] = SimpleCalculate2DArea(fix_x, fix_y, varargin)
%SIMPLECALCULATE2DAREA Summary of this function goes here
%   This function calculates the are covered by the 2D displacement distribution
%   at each delta t.
%
% function [Area] = DiffusionCoefficient2D (Fix,varargin)
%
% INPUT:
% Fix   : matrix with the eye movements data. This matrix should be
%         organized in the the following way:

nathanntg

import numpy as np

""" 
This is NOT the polynomial time solution (should build lookup table instead).
"""


def knapsack(c, w, remaining_capacity, i=None):
    """
    Recursively solve the knapsack problem to maximize utility given the remaining capacity and items with weight w

nathanntg

import re

# match all script blocks
r = re.compile('<script[^>]*?>.*?</script>', re.IGNORECASE | re.DOTALL)
# can be used to easily remove script tags
html_without_scripts = r.sub('', html)

# match all style blocks
r = re.compile('<style[^>]*?>.*?</style>', re.IGNORECASE | re.DOTALL)
# can be used to easily remove script tags

nathanntg

% number of random points to generate
number = 500;
x_dim = 100;
y_dim = 100;

% generate points
points = rand(number,2) * [x_dim 0; 0 y_dim];

% list of nearest neighbors
nearest_neighbors = nan(1, number);