scripts/does_matter/brownian-motion.py
def dorun(SNR=20, ntimes=20, samples=10, noise_samples=10, sweeps=20, burn=10,
        correlated=False):
    """
    we want to display the errors introduced by pixelation so we plot:
        * CRB, sampled error vs exposure time

    a = dorun(ntimes=10, samples=5, noise_samples=5, sweeps=20, burn=8)
    """
    if not correlated:
        times = np.logspace(-3, 0, ntimes)
    else:
        times = np.logspace(np.log10(0.05), np.log10(30), ntimes)

    crbs, vals, errs, poss = [], [], [], []

    for i,t in enumerate(times):
        print '###### time', i, t

        for j in xrange(samples):
            print 'image', j, '|', 
            if not correlated:
                s,im,pos = diffusion(diffusion_constant=0.2, exposure_time=t)
            else:
                s,im,pos = diffusion_correlated(diffusion_constant=0.2, exposure_time=t)

            # typical image
            common.set_image(s, im, 1.0/SNR)
            crbs.append(common.crb(s))

            val, err = common.sample(s, im, 1.0/SNR, N=noise_samples, sweeps=sweeps, burn=burn)
            poss.append(pos)
            vals.append(val)
            errs.append(err)


    shape0 = (ntimes, samples, -1)
    shape1 = (ntimes, samples, noise_samples, -1)

    crbs = np.array(crbs).reshape(shape0)
    vals = np.array(vals).reshape(shape1)
    errs = np.array(errs).reshape(shape1)
    poss = np.array(poss).reshape(shape0)

    return  [crbs, vals, errs, poss, times]
def dorun(SNR=20, ntimes=20, samples=10, noise_samples=10, sweeps=20, burn=10,
        correlated=False):
    """
    we want to display the errors introduced by pixelation so we plot:
        * CRB, sampled error vs exposure time

    a = dorun(ntimes=10, samples=5, noise_samples=5, sweeps=20, burn=8)
    """
    if not correlated:
        times = np.logspace(-3, 0, ntimes)
    else:
        times = np.logspace(np.log10(0.05), np.log10(30), ntimes)

    crbs, vals, errs, poss = [], [], [], []

    for i,t in enumerate(times):
        print '###### time', i, t

        for j in xrange(samples):
            print 'image', j, '|', 
            if not correlated:
                s,im,pos = diffusion(diffusion_constant=0.2, exposure_time=t)
            else:
                s,im,pos = diffusion_correlated(diffusion_constant=0.2, exposure_time=t)

            # typical image
            common.set_image(s, im, 1.0/SNR)
            crbs.append(common.crb(s))

            val, err = common.sample(s, im, 1.0/SNR, N=noise_samples, sweeps=sweeps, burn=burn)
            poss.append(pos)
            vals.append(val)
            errs.append(err)


    shape0 = (ntimes, samples, -1)
    shape1 = (ntimes, samples, noise_samples, -1)

    crbs = np.array(crbs).reshape(shape0)
    vals = np.array(vals).reshape(shape1)
    errs = np.array(errs).reshape(shape1)
    poss = np.array(poss).reshape(shape0)

    return  [crbs, vals, errs, poss, times]
def dorun ( SNR = 20 , ntimes = 20 , samples = 10 , noise_samples = 10 , sweeps = 20 , burn = 10 , correlated = False ) : if not correlated : times = np . logspace ( - 3 , 0 , ntimes ) else : times = np . logspace ( np . log10 ( 0.05 ) , np . log10 ( 30 ) , ntimes ) crbs , vals , errs , poss = [ ] , [ ] , [ ] , [ ] for i , t in enumerate ( times ) : print '###### time' , i , t for j in xrange ( samples ) : print 'image' , j , '|' , if not correlated : s , im , pos = diffusion ( diffusion_constant = 0.2 , exposure_time = t ) else : s , im , pos = diffusion_correlated ( diffusion_constant = 0.2 , exposure_time = t ) # typical image common . set_image ( s , im , 1.0 / SNR ) crbs . append ( common . crb ( s ) ) val , err = common . sample ( s , im , 1.0 / SNR , N = noise_samples , sweeps = sweeps , burn = burn ) poss . append ( pos ) vals . append ( val ) errs . append ( err ) shape0 = ( ntimes , samples , - 1 ) shape1 = ( ntimes , samples , noise_samples , - 1 ) crbs = np . array ( crbs ) . reshape ( shape0 ) vals = np . array ( vals ) . reshape ( shape1 ) errs = np . array ( errs ) . reshape ( shape1 ) poss = np . array ( poss ) . reshape ( shape0 ) return [ crbs , vals , errs , poss , times ]
we want to display the errors introduced by pixelation so we plot:
        * CRB, sampled error vs exposure time

    a = dorun(ntimes=10, samples=5, noise_samples=5, sweeps=20, burn=8)
we want to display the errors introduced by pixelation so we plot : * CRB sampled error vs exposure time