{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After reading the paper [Motion Direction Biases and Decoding in Human Visual Cortex](http://www.jneurosci.org/content/34/37/12601.full) by Helena X. Wang, Elisha P. Merriam, Jeremy Freeman, and David J. Heeger (The Journal of Neuroscience, 10 September 2014, 34(37): 12601-12615; doi: 10.1523/JNEUROSCI.1034-14.2014), I was interested to test the hypothesis they raise in the discussion section :\n",
"\n",
"> The aperture-inward bias in V1–V3 may reflect spatial interactions between visual motion signals along the path of motion (Raemaekers et al., 2009; Schellekens et al., 2013). Neural responses might have been suppressed when the stimulus could be predicted from the responses of neighboring neurons nearer the location of motion origin, a form of predictive coding (Rao and Ballard, 1999; Lee and Mumford, 2003). Under this hypothesis, spatial interactions between neurons depend on both stimulus motion direction and the neuron's relative RF locations, but the neurons themselves need not be direction selective. Perhaps consistent with this hypothesis, psychophysical sensitivity is enhanced at locations further along the path of motion than at motion origin (van Doorn and Koenderink, 1984; Verghese et al., 1999). \n",
"\n",
"Concerning the origins of aperture-inward bias, I want to test an alternative possibility. In some recent modeling work:\n",
"\n",
"> Laurent Perrinet, Guillaume S. Masson. Motion-based prediction is sufficient to solve the aperture problem. Neural Computation, 24(10):2726--50, 2012 https://laurentperrinet.github.io/publication/perrinet-12-pred\n",
"\n",
"\n",
"I was surprised to observe a similar behavior: the trailing edge was exhibiting a stronger activation (i. e. higher precision revealed by a lower variance in this probabilistic model) while I would have thought intuitively the leading edge would be more informative. In retrospect, it made sense in a motion-based prediction algorithm as information from the leading edge may propagate in more directions (135° for a 45° bar) than in the trailing edge (45°, that is a factor of 3 here). While we made this prediction we did not have any evidence for it.\n",
"\n",
"In this script the predictive coding is done using the ``MotionParticles`` package and for a [motion texture](https://neuralensemble.github.io/MotionClouds/) within a disk aperture.\n",
"\n",
"\n",
"\n",
"\n",
"Let's first initialize the notebook:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import division, print_function\n",
"import numpy as np\n",
"import os\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format='retina'\n",
"#%config InlineBackend.figure_format = 'svg'\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"np.set_printoptions(precision=6, suppress=True)\n",
"from IPython.display import display, HTML\n",
"def show_video(filename): \n",
" return HTML(data=''.format(filename))\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## creating the stimulus\n",
"\n",
"\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"!rm -fr ../files/2016-07-16-Heeger16JNeuro /tmp/Heeger16JNeuro*"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/matplotlib/__init__.py:1398: UserWarning: This call to matplotlib.use() has no effect\n",
"because the backend has already been chosen;\n",
"matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
"or matplotlib.backends is imported for the first time.\n",
"\n",
" warnings.warn(_use_error_msg)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sequence ../files/2016-07-16-Heeger16JNeuro/Heeger16JNeuro-anim.mp4 already exists (remove to recompute)\n"
]
}
],
"source": [
"import MotionParticles as mp\n",
"\n",
"figpath = mp.figpath = '../files/2016-07-16-Heeger16JNeuro/'\n",
"matpath = mp.matpath = '/tmp/'\n",
"try: \n",
" os.mkdir(figpath)\n",
"except:\n",
" pass\n",
"N_X, N_Y, N_frame = mp.N_X, mp.N_Y, mp.N_frame\n",
"V_X = 1.\n",
"\n",
"stim_size = 0.5\n",
"stim_start = .2\n",
"stim_stop = .8\n",
"stim_noise = .05\n",
"\n",
"image = {}\n",
"experiment = 'Heeger16JNeuro'\n",
"\n",
"def generate_one(N_X, N_Y, N_frame,\n",
" X_0=0, Y_0=0, V_X=V_X, V_Y=0, stim_size=stim_size,\n",
" stim_duration=stim_stop-stim_start, stim_start=stim_start,\n",
" width=mp.width, stim_noise=stim_noise, im_contrast=1.,\n",
" hard=True, texture=True, sf_0=0.2, B_V=.7, B_sf=.05, B_theta=np.inf,\n",
" pink_noise=True,\n",
" **kwargs):\n",
"\n",
" r_x = width / 2.\n",
" r_y = r_x * N_Y / N_X\n",
" x, y, t = np.mgrid[r_x*(-1+1./(N_X)):r_x*(1-1./(N_X)):1j*N_X,\n",
" r_y*(-1+1./(N_Y)):r_y*(1-1./(N_Y)):1j*N_Y,\n",
" 0:(1-1./(N_frame+1)):1j*N_frame]\n",
"\n",
" tube = np.exp(- (x**2 + y**2) /2. / stim_size**2) # size is relative to the width of the torus\n",
"\n",
" if hard : tube = (tube > np.exp(-1./2)) * 1.\n",
"\n",
" if texture:\n",
" from MotionClouds import get_grids, envelope_gabor, random_cloud, rectif\n",
" fx, fy, ft = get_grids(N_X, N_Y, N_frame)\n",
" envelope = envelope_gabor(fx, fy, ft, V_X=V_X*N_Y/N_frame, V_Y=V_Y*N_X/N_frame, sf_0=sf_0, B_V=B_V, B_sf=B_sf, B_theta=B_theta)\n",
" texture = 2*rectif(random_cloud(envelope))-1\n",
" texture /= np.abs(texture).max()\n",
" tube *= texture\n",
"\n",
" tube /= tube.max()\n",
" tube *= im_contrast\n",
"\n",
" if stim_duration>0.:\n",
" N_start = int(stim_start * N_frame)\n",
" N_flash = int(stim_duration * N_frame)\n",
" tube[:, :, :N_start] = 0.\n",
" tube[:, :, (N_start + N_flash):] = 0.\n",
"\n",
" # now adding noise\n",
" if pink_noise:\n",
" from MotionClouds import get_grids, envelope_color, random_cloud, rectif\n",
" fx, fy, ft = get_grids(N_X, N_Y, N_frame)\n",
" envelope = envelope_color(fx, fy, ft, alpha=1)\n",
" noise_image = 2*rectif(random_cloud(envelope))-1\n",
" tube += stim_noise * noise_image\n",
" else:\n",
" tube += stim_noise * np.random.randn(N_X, N_Y, N_frame)\n",
"\n",
" return tube\n",
"\n",
"def generate(N_X, N_Y, N_frame, **kwargs):\n",
" tube = generate_one(N_X=N_X, N_Y=N_Y, N_frame=N_frame//2, V_X=.5)\n",
" tube = np.concatenate((tube, generate_one(N_X=N_X, N_Y=N_Y, N_frame=N_frame//2, V_X=-.5)), axis=-1)\n",
" return tube\n",
"\n",
"image['im'] = generate(N_X=N_X, N_Y=N_Y, N_frame=N_frame)\n",
"mp.anim_save(image['im'], os.path.join(figpath, experiment + '-anim'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!ls -lR ../files/2016-07-16-Heeger16JNeuro /tmp/Heeger16JNeuro*"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"show_video(os.path.join(figpath, experiment + '-anim' + mp.vext))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## predictive coding\n",
"\n",
"We now use this image and look at what happens when running the algorithm decribed in:\n",
"\n",
"\n",
"> Laurent Perrinet, Guillaume S. Masson. Motion-based prediction is sufficient to solve the aperture problem. Neural Computation, 24(10):2726--50, 2012 https://laurentperrinet.github.io/publication/perrinet-12-pred\n",
"\n",
"\n",
"This uses the ``MotionParticles`` package available upon request (meaning, there are bits of code that need some clean-up...). Note here that we also look at what happens when modifying latencies - but we will see this does not change results."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Studying CONDENSATION by changing image along variable(s) : {'latency': [0, 10]}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:01 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:01 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:01\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Condensation\n",
"0% 100%\n",
"[##############################] | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n",
"Saving movie\n",
"0% 100%\n",
"[ ]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving sequence ../files/2016-07-16-Heeger16JNeuro/Heeger16JNeuro-latency_0_0000_spatial_readout.mp4\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[##############################] | ETA: 00:00:17 | ETA: 00:00:18 | ETA: 00:00:19 | ETA: 00:00:18 | ETA: 00:00:18 | ETA: 00:00:17 | ETA: 00:00:16 | ETA: 00:00:15 | ETA: 00:00:14 | ETA: 00:00:14 | ETA: 00:00:13 | ETA: 00:00:12 | ETA: 00:00:12 | ETA: 00:00:11 | ETA: 00:00:10 | ETA: 00:00:09 | ETA: 00:00:09 | ETA: 00:00:08 | ETA: 00:00:07 | ETA: 00:00:07 | ETA: 00:00:06 | ETA: 00:00:05 | ETA: 00:00:04 | ETA: 00:00:04 | ETA: 00:00:03 | ETA: 00:00:02 | ETA: 00:00:02 | ETA: 00:00:01 | ETA: 00:00:00 | ETA: 00:00:00 | ETA: 00:00:00\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving sequence ../files/2016-07-16-Heeger16JNeuro/Heeger16JNeuro-latency_0_0000_particles.mp4\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The axisbg attribute was deprecated in version 2.0. Use facecolor instead.\n",
" warnings.warn(message, mplDeprecation, stacklevel=1)\n",
"[##############################] | ETA: 00:00:55 | ETA: 00:01:04 | ETA: 00:01:08 | ETA: 00:01:00 | ETA: 00:00:55 | ETA: 00:00:54 | ETA: 00:00:50 | ETA: 00:00:48 | ETA: 00:00:45 | ETA: 00:00:42 | ETA: 00:00:40 | ETA: 00:00:38 | ETA: 00:00:35 | ETA: 00:00:33 | ETA: 00:00:32 | ETA: 00:00:29 | ETA: 00:00:27 | ETA: 00:00:25 | ETA: 00:00:23 | ETA: 00:00:21 | ETA: 00:00:19 | ETA: 00:00:16 | ETA: 00:00:14 | ETA: 00:00:12 | ETA: 00:00:10 | ETA: 00:00:08 | ETA: 00:00:06 | ETA: 00:00:03 | ETA: 00:00:01 | ETA: 00:00:00 | ETA: 00:00:00\n",
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"Saving sequence ../files/2016-07-16-Heeger16JNeuro/Heeger16JNeuro-latency_10_0000_spatial_readout.mp4\n"
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"name": "stdout",
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"Saving sequence ../files/2016-07-16-Heeger16JNeuro/Heeger16JNeuro-latency_10_0000_particles.mp4\n"
]
},
{
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"[##############################] | ETA: 00:00:46 | ETA: 00:01:08 | ETA: 00:01:02 | ETA: 00:00:57 | ETA: 00:00:54 | ETA: 00:00:52 | ETA: 00:00:50 | ETA: 00:00:47 | ETA: 00:00:44 | ETA: 00:00:41 | ETA: 00:00:39 | ETA: 00:00:37 | ETA: 00:00:37 | ETA: 00:00:35 | ETA: 00:00:32 | ETA: 00:00:29 | ETA: 00:00:27 | ETA: 00:00:25 | ETA: 00:00:22 | ETA: 00:00:20 | ETA: 00:00:18 | ETA: 00:00:16 | ETA: 00:00:14 | ETA: 00:00:12 | ETA: 00:00:09 | ETA: 00:00:07 | ETA: 00:00:06 | ETA: 00:00:03 | ETA: 00:00:01 | ETA: 00:00:00 | ETA: 00:00:00\n",
"Total time elapsed: 00:01:00\n"
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}
],
"source": [
"image['result'] = {}\n",
"latencies = [0, 10]\n",
"figname = os.path.join(mp.figpath, experiment)\n",
"\n",
"_ = mp.figure_image_variable(\n",
" figname, \n",
" N_X, N_Y, N_frame, generate, order=None, \n",
" do_figure=True, do_video=True, N_quant_X=50, N_quant_Y=50,\n",
" latency=latencies)\n",
"try:\n",
" for latency in latencies:\n",
" matname = mp.make_figname(figname, {'latency': latency}).replace(mp.figpath, mp.matpath) + '.npy'\n",
" image['result'][latency] = np.load(matname)\n",
"except:\n",
" print('no result yet for ', matname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## analyzing results:\n",
"\n",
"Averaged over the 20 trials of the simulations. we show here the histogram of the particles representing motion: it represents the posterior distribution of the model. At each pixel, value gives the strength of the probability and hue gives the direction (in short, as in the above paper but with a black background for zero probability)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"show_video(os.path.join(figpath, experiment + '-latency_0_0000_spatial_readout' + mp.vext))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The posterior thus follows the motion and quickly converges to the physical motion (balancing from $V_X=1$ to $V_X=-1$, respectively from green to red). Note that in this motion-based predictive scheme, motion is better tracked along coherent trajectories and that it is \"dragged\" in its direction : the bias is aperture-outward.\n",
"\n",
"## x-t histograms of the results\n",
"\n",
"This is confirmed by showing the histogram of detected positions for the stimulus:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"v_hist = {}\n",
"N_quant = 50\n",
"time = np.linspace(0, 1, N_frame)\n",
"particles = image['result'][0].copy()\n",
"v_hist, x_edges, y_edges = mp.spatial_readout(particles, N_quant_X=N_quant, N_quant_Y=1, display=False)\n",
"v_hist /= np.sum(v_hist, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Position of the middle of the bins = [-0.98 -0.94 -0.9 -0.86 -0.82 -0.78 -0.74 -0.7 -0.66 -0.62 -0.58 -0.54\n",
" -0.5 -0.46 -0.42 -0.38 -0.34 -0.3 -0.26 -0.22 -0.18 -0.14 -0.1 -0.06\n",
" -0.02 0.02 0.06 0.1 0.14 0.18 0.22 0.26 0.3 0.34 0.38 0.42\n",
" 0.46 0.5 0.54 0.58 0.62 0.66 0.7 0.74 0.78 0.82 0.86 0.9\n",
" 0.94 0.98]\n"
]
}
],
"source": [
"_, x_edges, y_edges = mp.spatial_readout(particles, N_quant_X=N_quant, N_quant_Y=N_quant, display=False)\n",
"x_middle = .5*(x_edges[1:] + x_edges[:-1])\n",
"print('Position of the middle of the bins =', x_middle)\n",
"y_middle = .5*(y_edges[1:] + y_edges[:-1])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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OiD+IiPy3eQEAAAA8Y6xO1vIcSa+R9ClJZhmML/FHkm6V9EZJTUlvlfSnkr7ucjcQAAAA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\nJKlTbGIkSZIkdYpNjCRJkqRO+QcvAJnP0DZYgwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 511,
"width": 408
}
},
"output_type": "display_data"
}
],
"source": [
"particles = image['result'][0].copy() \n",
"fig, axs = mp.spatial_readout(particles, N_quant_X=N_quant, N_quant_Y=1, ruler=False)\n",
"average_pos = np.sum(v_hist*x_middle[:, np.newaxis], axis=0)\n",
"_ = axs[0].plot(time, average_pos, lw=4, ls='--')\n",
"_ = axs[0].plot([0, 1], [stim_size, stim_size], c='orange', lw=4, ls='--')\n",
"_ = axs[0].plot([0, 1], [-stim_size, -stim_size], c='orange', lw=4, ls='--')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a conclusion, we observe a different behaviour in our predictive coding model than that observed in V1 and V3. It was to be expected mainly because we do not represent a neural activity but a probability (the a posteriori estimate) and that ---apart from the aperture which \"explains away\" motion--- our system is made to track motion and does it well in that case.\n",
"\n",
"In contrast, what is observed in V1 (and following many of the predictive brain theories such as the free-energy account from Karl Friston), is reminiscent of some representation of the \"surprise\" between on one side the incoming stream of sensory input (and that is comprised in the above plot between the 2 orange horizontal lines) and on the other side the feedback stream from an area representing motion such as area MT.\n",
"\n",
"Our posterior representation is certainly closer to the latter (MT). Even if we postulate that this area manages to fill-out the representation of motion within some of the disk aperture, we could thus think that the aperture inward bias is perhaps linked to the prediction error represented in V1 (that is to input confronted to this representation with an aperture-outward bias). This obviously needs more thinking to be made a scientific evidence :-)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}