ptweir

import sys, os
import numpy as np
import roslib
roslib.load_manifest('rosbag')
import rosbag

def loadBag(inFileName):
    """
    Script to import strokelitude bag file
    usage: outDict =  loadBag(inFileName)

ptweir

function [data, h] = import_edr(file)
% function [data, h] = import_edr(file)
%
% Imports .edr file to matrix
% for use with files output by WinEDR,
%   http://spider.science.strath.ac.uk/sipbs/page.php?show=software_ses
% but created independently
%
% ---------------inputs------------------------------
% file      .edr file name ( e.g. 'File_001.EDR')

ptweir

// control intensity of LED using serial communication

int incomingByte = 0;	// for incoming serial data
int outVal = 0; // for signal out
int pwmPin = 6; // white wire (pin 5 and 6 have pwm frequency ~1000 Hz, faster than pin 9)
int statusLED = 13;
int delaymsec = 100;

void setup() {
  Serial.begin(9600);	// opens serial port, sets data rate to 9600 bps

ptweir

// polarization rotator experiment

int incomingByte = 1;	// for incoming serial data
int pwmPin = 9; // white wire
int dirPin = 11; // green wire
int statusLED = 13;
int delaymsec = 100;

void setup() {
  Serial.begin(9600);	// opens serial port, sets data rate to 9600 bps

ptweir

def findClosestSampleTimes(denseSamples,sparseSamples,debug=False):
    """inds, samples = findClosestSampleTimes(denseSamples,sparseSamples)
    NOTE: SHOULD CLEAN ADC SIGNAL BEFORE DOWNSAMPLING"""
    inds = np.empty(sparseSamples.shape)
    for sparseInd, sparseSample in enumerate(sparseSamples):
        inds[sparseInd] = np.argmin(abs(denseSamples - sparseSample))
    if debug:
        f = pylab.figure()
        ax = f.add_subplot(111)
        ax.plot(denseSamples,denseSamples*0,'.b')

ptweir

def selectRectangle(fig):
    pts = fig.ginput(2)
    pts = np.array(pts)
    pts.sort(axis=0)
    pts = np.round(pts).astype(int)
    return pts

"""
#example usage:
axROI.imshow(frames.mean(2),cmap='gray',origin='lower')

ptweir

def binMeanPlot(X,Y,ax=None,numBins=8,xmin=None,xmax=None):
    if xmin is None:
        xmin = X.min()
    if xmax is None:
        xmax = X.max()
    if ax is None:
        fig = pylab.figure()
        ax = fig.add_subplot(111)
    bins = np.linspace(xmin,xmax,numBins+1)
    XX = np.array([np.mean((bins[binInd], bins[binInd+1])) for binInd in range(numBins)])

ptweir

import numpy as np

def circmean(alpha,axis=None):
    mean_angle = np.arctan2(np.mean(np.sin(alpha),axis),np.mean(np.cos(alpha),axis))
    return mean_angle
    
def circvar(alpha,axis=None):
    if np.ma.isMaskedArray(alpha) and alpha.mask.shape!=():
        N = np.sum(~alpha.mask,axis)
    else: