This video shows the results of unsupervised learning with different type of kezrnel normalization. This is to illustrate the results obtained in this paper on the An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features which is now in press.
In this notebook, we will go from the learning numpy and matplotlib to our first neural simulator. As a case study, we will use the work from Mainen & Sejnowski (1995). The rest of this post is in french.
Le but ici de cette première tache est de créer un "raster plot" qui montre la reproducibilité d'un train de spike avec des répétitions du même stimulus. E, particulier, nous allons essayer de répliquer la figure 1 de Mainen & Sejnowski (1995). Travail fait en collaboration avec les étudiants du L3 Sciences et Humanités Vision Lumière Couleurs d'AMU (Benjamin Chassagne, Romane Renou, Hassina Bendrer, Giulia Danielou)
Ce notebook a été élaboré lors d'un TP dans le cadre de la licence Sciences & Humanités.
This video shows different MotionClouds with different complexities, from a crystal-like Grating to textures with an incresing span of spatial frequencies (resp "Mc Narrow" and "MC Broad"). This is to illustrate the different stimuli used in this paper on the chracterization of speed-selectivity in the retina available @ https://www.nature.com/articles/s41598-018-36861-8 .
The goal here is to check if the Von Mises distribution is the a priori choice to make when handling polar coordinates.
As we see a visual scene, there is contribution of the motion of each of the objects that constitute the visual scene into detecting its global motion. In particular, it is debatable to know which weight individual features, such as small objects in the foreground, have into this computation compared to a dense texture-like stimulus, as that of the background for instance.
Here, we design a a stimulus where we control independently these two aspects of motions to titrate their relative contribution to the detection of motion.
Can you spot the motion ? Is it more going to the upper left or to the upper right?
(For a more controlled test, imagine you fixate on the center of the movie.)
It can be useful to find a pattern such an ISO8601-formatted date in a set of files. I discovered it is possible to do that in the atom editor:
MotionClouds may be considered as a control stimulus - it seems more interesting to consider more complex trajectories. Following a previous post, we design a trajectory embedded in noise.
Can you spot the motion ? from left to right or reversed ?
(For a more controlled test, imagine you fixate on the top left corner of each movie.)
(Upper row is coherent, lower row uncoherent / Left is -> Right column is <-)
TODO: use a biomimetic camouflage such as in this video.
When I see a rainbow, I perceive the luminance inside the arc to be brighter than outside the arc. Is this effect percpetual (inside our head) or physical (inside each droplet in the sky). So, this is a simple notebook to show off how to synthesize the image of a rainbow on a realistic sky. TL;DR: there must be a physical reason for it.
Outline: The rainbow is a set of colors over a gradient of hues, masked for certain ones. The sky will be a gradient over blueish colors.
What does the input to a population of neurons in the primary visual cortex look like? In this post, we will try to have a feeling of the structure and statistics of the natural input to such a "ring" model.
This notebook explores this question using a retina-like temporal filtering and oriented Gabor-like filters. It produces this polar plot of the instantaneous energy in the different orientations for a natural movie :
One observes different striking features in the structure of this input to populations of V1 neurons:
This structure is specific to the structure of natural images and to the way they transform (translations, rotations, zooms due to the motion and deformation of visual objects). This is certainly incorporated as a "prior" information in the structure of the visual cortex. As to know how and where this is implemented is an open scientific question.
This is joint work with Hugo Ladret.
PyTorch is a great library for machine learning. You can in a few lines of codes retrieve a dataset, define your model, add a cost function and then train your model. It's quite magic to copy and paste code from the internet and get the LeNet network working in a few seconds to achieve more than 98% accuracy.
However, it can be tedious sometimes to extend existing objects and here, I will manipulate some ways to define the right dataset for your application. In particular I will modify the call to a standard dataset (MNIST) to place the characters at random places in a large image.
When creating large simulations, you may sometimes create unique identifiers for each of it. This is useful to cache intermediate results for instance. This is the main function of hashes. We will here create a simple one-liner function to generate one.
MotionClouds may be considered as a control stimulus - it seems more interesting to consider more complex trajectories.
In some recent modeling work:
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
we study the role of transport in modifying our perception of motion. Here, we test what happens when we change the amount of noise in the stimulus.
In this script the predictive coding is done using the MotionParticles package and for a http://motionclouds.invibe.net/ within a disk aperture.
I am experimenting with the pupil eyetracker and could set it up (almost) smoothly on a macOS. There is an excellent documentation, and my first goal was to just record raw data and extract eye position.
from IPython.display import HTML
HTML('<center><video controls autoplay loop src="https://laurentperrinet.github.io/sciblog/files/2017-12-13_pupil%20test_480.mp4" width=61.8%/></center>')
This video shows the world view (cranio-centric, from a head-mounted camera fixed on the frame) with overlaid the position of the (right) eye while I am configuring a text box. You see the eye fixating on the screen then jumping somewhere else on the screen (saccades) or on the keyboard / hands. Note that the screen itself shows the world view, such that this generates an self-reccurrent pattern.
For this, I could use the capture script and I will demonstrate here how to extract the raw data in a few lines of python code.
In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see https://laurentperrinet.github.io/publication/perrinet-10-shl ). Compared to other posts, such as this previous post, we improve the code to not depend on any parameter (namely the Cparameter of the rescaling function). For that, we will use a non-parametric approach based on the use of cumulative histograms.
This is joint work with Victor Boutin and Angelo Francisioni. See also the other posts on unsupervised learning.
This poster was presented in Lille at a vision workshop, check out https://laurentperrinet.github.io/publication/perrinet-17-gdr
Apart the content (which is in French) which recaps some previous work inbetween art and science, this post demonstrates how to generate a A0 poster programmatically. In particular, we will use matplotlib and some quickly forged functions to ease up the formatting.
To code image as edges, for instance in the SparseEdges sparse coding scheme, we use a model of edges in images. A good model for these edges are bidimensional Log Gabor filter. This is implemented for instance in the LogGabor library. The library was designed to be precise, but not particularly for efficiency. In order to improve its speed, we demonstrate here the use of a cache to avoid redundant computations.
Convolutions are essential components of any neural networks, image processing, computer vision ... but these are also a bottleneck in terms of computations... I will here benchmark different solutions using numpy, scipy or pytorch. This is work-in-progress, so that any suggestion is welcome, for instance on StackExchange or in the comments below this post.
Lors de la visite au laboratoire d'une brillante élève de seconde (salut Lena!), nous avons inventé ensemble un jeu: le jeu de l'urne. Le principe est simple: il faut deviner la couleur de la balle qu'on tire d'une urne contenant autant de balles rouges que noires - et ceci le plus tôt possible. Plus précisément, les règles sont:
Nous avons d'abord créé ce jeu grâce au language de programmation Scratch sur https://scratch.mit.edu/projects/165806365/:
Ici, nous allons essayer de l'analyser plus finement.
In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see https://laurentperrinet.github.io/publication/perrinet-10-shl ). Compared to the previous post, we integrated the faster code to https://github.com/bicv/SHL_scripts.
See also the other posts on unsupervised learning,
This is joint work with Victor Boutin.
In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see https://laurentperrinet.github.io/publication/perrinet-10-shl ). Compared to the previous post, we optimize the code to be faster.
See also the other posts on unsupervised learning,
This is joint work with Victor Boutin.
In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see https://laurentperrinet.github.io/publication/perrinet-10-shl ). In particular, we will show how one can build the non-linear functions based on the activity of each filter and which implement homeostasis.
See also the other posts on unsupervised learning,
This is joint work with Victor Boutin.
Since the beginning, we have used a definition of bandwidth in the spatial frequency domain which was quite standard (see supp material for instance):
$$ \mathcal{E}(f; sf_0, B_{sf}) \propto \frac {1}{f} \cdot \exp \left( -.5 \frac {\log( \frac{f}{sf_0} ) ^2} {\log( 1 + \frac {B_sf}{sf_0} )^2 } \right) $$This is implemented in the folowing code which reads:
env = 1./f_radius*np.exp(-.5*(np.log(f_radius/sf_0)**2)/(np.log((sf_0+B_sf)/sf_0)**2))
However the one implemented in the code looks different (thanks to Kiana for spotting this!), so that one can think that the code is using:
$$ \mathcal{E}(f; sf_0, B_{sf}) \propto \frac {1}{f} \cdot \exp \left( -.5 \frac {\log( \frac{f}{sf_0} ) ^2} {\log(( 1 + \frac {B_sf}{sf_0} )^2 ) } \right) $$The difference is minimal, yet very important for a correct definition of the bandwidth!
An essential dimension of motion is speed. However, this notion is prone to confusions as the speed that has to be measured can be relative to different object. Is it the speed of pixels? The speed of visual objects? We try to distinguish the latter two in this post.
%matplotlib inline
import numpy as np
np.set_printoptions(precision=3, suppress=True)
import pylab
import matplotlib.pyplot as plt
import os
name = '2017-04-10_physical_speed'
DOWNSCALE = 1
import MotionClouds as mc
N_X, N_Y, N_frame = mc.N_X/DOWNSCALE, mc.N_Y/DOWNSCALE, mc.N_frame/DOWNSCALE
fx, fy, ft = mc.get_grids(N_X, N_Y, N_frame)
mc.figpath = '../files/2017-04-10_physical_speed/'
if not(os.path.isdir(mc.figpath)): os.mkdir(mc.figpath)
In standard Motion Clouds, speed is that of the pixels of the textons that produce the texture. It is thus defined as a (normal) distribution of probability around the speed plane that defines the mean speed:
z = mc.envelope_gabor(fx, fy, ft)
name_ = name + '_vanilla'
mc.figures(z, name_, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)
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Another way to consider speed is that of visual objects. Consider looking through the window of a train a field of similarly sized (Gabor-shaped) bushes on an infinite plane. The objects which are closer appear bigger on the retina and their speed is perceived as higher than the smaller bushes near the horizon. This relation between size and speed is linear and is called motion parallax (credit: http://www.rhsmpsychology.com/Handouts/monocular_cues_IV.htm).
from IPython.display import Image
Image('http://www.rhsmpsychology.com/images/monocular_IV.jpg')
Such a pattern of motion distribution is highly prototypical in natural settings and it is easy to generate a texture having such a profile but with textons that are uniformly spaced in space, similar to the "Golconde" painting from Magritte (By The Shimon Yanowitz Website, Fair use, https://en.wikipedia.org/w/index.php?curid=3773027):
Image('https://upload.wikimedia.org/wikipedia/en/7/71/Golconde.jpg')
More specifically, one finds that if we place MotionClouds on different planes perpendicular to the axis of vision, their frequency will increase as they get further , but their temporal frequency keeps constant: $$ f_0 \propto \arctan(\frac{F_0}{D}) $$ and $$ f_t \propto F_T $$
where $F_0$ is the central scale (spatial frequency) of the visual object, $f_0$ its frequency in retinal space and $D$ the distance. Compared to the classical definition of MotionClouds, this affects only the way we define the enveloppe around the speed plane. For a parallax-compatible motion, this enveloppe is on a plane around a fixed temporal frequency:
mc.B_sf
def envelope_parallax_speed(fx, fy, ft, K=1., V_X=mc.V_X, V_Y=mc.V_Y,
B_V=mc.B_V, sf_0=mc.sf_0, B_sf=mc.B_sf, loggabor=mc.loggabor,
theta=mc.theta, B_theta=mc.B_theta, alpha=mc.alpha):
"""
parallax speed envelope:
selects a gaussian perpendicualr to the plane corresponding to the speed (V_X, V_Y) with some thickness B_V
"""
envelope = mc.envelope_color(fx, fy, ft, alpha=alpha)
envelope *= mc.envelope_orientation(fx, fy, ft, theta=theta, B_theta=B_theta)
envelope *= mc.envelope_radial(fx, fy, ft, sf_0=sf_0, B_sf=B_sf, loggabor=loggabor)
f_radius = mc.frequency_radius(fx, fy, ft, ft_0=mc.ft_0, clean_division=True)
V = np.sqrt(V_X**2 + V_Y**2)
envelope *= np.exp(-.5*((np.sign(fx) * ft + sf_0 * V)**2/(B_V*sf_0)**2))
# Hack to remove the other part of the envelope - use angle instead to allow for higher B_theta values
return envelope
name_ = name + '_parallax'
opts= dict(V_X=.5, sf_0=.2, B_V=.1, B_sf=.25)
z = envelope_parallax_speed(fx, fy, ft, **opts)
mc.figures(z, name_, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)
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Let's explore what happens for different values of B_V:
opts_= dict(V_X=.5, sf_0=.2)
for B_V_ in np.logspace(-1, 1, 5, base=2)*opts['B_V']:
name_ = name + '_parallax_B_V_' + str(B_V_).replace('.', '_') #os.path.join(mc.figpath, )
z = envelope_parallax_speed(fx, fy, ft, B_V=B_V_, **opts_)
mc.figures(z, name_, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)
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More generally, this manipulation ---by changing the shape of the enveloppe--- changes the group vs phase speeds, as we did before with simulation of (earthly) gravitational waves. This can be done explictly by changing the shape of the enveloppe:
def envelope_physical_speed(fx, fy, ft, K=1., V_X=mc.V_X, V_Y=mc.V_Y,
B_V=mc.B_V, sf_0=mc.sf_0, B_sf=mc.B_sf, loggabor=mc.loggabor,
theta=mc.theta, B_theta=mc.B_theta, alpha=mc.alpha):
"""
physical speed envelope:
selects a gaussian perpendicualr to the plane corresponding to the speed (V_X, V_Y) with some thickness B_V
"""
envelope = mc.envelope_color(fx, fy, ft, alpha=alpha)
envelope *= mc.envelope_orientation(fx, fy, ft, theta=theta, B_theta=B_theta)
envelope *= mc.envelope_radial(fx, fy, ft, sf_0=sf_0, B_sf=B_sf, loggabor=loggabor)
f_radius = mc.frequency_radius(fx, fy, ft, ft_0=mc.ft_0, clean_division=True)
envelope *= np.exp(-.5*((ft + sf_0 * V_X - (f_radius - sf_0) / K )**2/(B_V*sf_0)**2))
# Hack to remove the other part of the envelope - use angle instead to allow for higher B_theta values
envelope *= fx*ft > 0
return envelope
name_ = name + '_group_speed'
K = 1.
opts= dict(V_X=.5, sf_0=.2, B_V=.2)
z = envelope_physical_speed(fx, fy, ft, K=K, **opts)
mc.figures(z, name_)
mc.in_show_video(name_)
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Let's explore what happens for different values of K:
for K_ in 1./np.linspace(0.6, 1.4, 7)*K:
name_ = name + '_group_speed_K_' + str(K_).replace('.', '_')
z = envelope_physical_speed(fx, fy, ft, K=K_, **opts)
mc.figures(z, name_)
mc.in_show_video(name_)
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In a previous notebook, we tried to reproduce the learning strategy specified in the framework of the SparseNet algorithm from Bruno Olshausen. It allows to efficiently code natural image patches by constraining the code to be sparse. In particular, we saw that in order to optimize competition, it is important to control cooperation and we implemented a heuristic to just do this.
In this notebook, we provide an extension to the SparseNet algorithm. We will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see https://laurentperrinet.github.io/publication/perrinet-10-shl ):
@article{Perrinet10shl,
Title = {Role of homeostasis in learning sparse representations},
Author = {Perrinet, Laurent U.},
Journal = {Neural Computation},
Year = {2010},
Doi = {10.1162/neco.2010.05-08-795},
Keywords = {Neural population coding, Unsupervised learning, Statistics of natural images, Simple cell receptive fields, Sparse Hebbian Learning, Adaptive Matching Pursuit, Cooperative Homeostasis, Competition-Optimized Matching Pursuit},
Month = {July},
Number = {7},
Url = {https://laurentperrinet.github.io/publication/perrinet-10-shl},
Volume = {22},
}
This is joint work with Victor Boutin.
In this notebook, we test the convergence of SparseNet as a function of different learning parameters. This shows the relative robustness of this method according to the coding parameters, but also the importance of homeostasis to obtain an efficient set of filters:
alpha_homeo has to be properly set to achieve a good convergence.See also :
This is joint work with Victor Boutin.
In a previous notebook, we tried to reproduce the learning strategy specified in the framework of the SparseNet algorithm from Bruno Olshausen. It allows to efficiently code natural image patches by constraining the code to be sparse.
However, the dictionaries are qualitatively not the same as the one from the original paper, and this is certainly due to the lack of control in the competition during the learning phase.
This is joint work with Victor Boutin.
This notebook tries to reproduce the learning strategy specified in the framework of the SparseNet algorithm from Bruno Olshausen. It allows to efficiently code natural image patches by constraining the code to be sparse.
the underlying machinery uses a similar dictionary learning as used in the image denoising example from sklearn and our aim here is to show that a novel ingredient is necessary to reproduce Olshausen's results.
All these code bits is regrouped in the SHL scripts repository (where you will also find some older matlab code). You may install it using
pip install git+https://github.com/bicv/SHL_scriptsfollowing this failed PR to sklearn that was argued in this post (and following) the goal of this notebooks is to illustrate the simpler code implemented in the SHL scripts
This is joint work with Victor Boutin.
In the context of a course in Computational Neuroscience, I am teaching a basic introduction in Probabilities, Bayes and the Free-energy principle.
Let's learn to use probabilities in practice by generating some "synthetic data", that is by using the computer's number generator. 2018-03-26_cours-NeuroComp_FEP
I enjoyed reading "A tutorial on the free-energy framework for modelling perception and learning" by Rafal Bogacz, which is freely available here. In particular, the author encourages to replicate the results in the paper. He is himself giving solutions in matlab, so I had to do the same in python all within a notebook...
A set of bash code to resize images to a fixed size.
Problem statement: we have a set of images with heterogeneous sizes and we want to homogenize the database to avoid problems when classifying them. Solution: ImageMagick.
We first identify the size and type of images in the database. The database is a collection of folders containing each a collection of files. We thus do a nested recursive loop:
Let's explore generators and the yield statement in the python language...
Sometimes, you need to pick up the $N$-th extremal values in a mutli-dimensional matrix.
Let's suppose it is represented as a nd-array (here, I further suppose you are using the numpy library from the python language). Finding extremal values is easy with argmax, argmin or argsort but this function operated on 1d vectors... Juggling around indices is sometimes not such an easy task, but luckily, we have the unravel_index function.
For those in a hurry, one quick application is that given an np.ndarray, it's eeasy to get the index of the maximal value in that array :
import numpy as np
x = np.arange(2*3*4).reshape((4, 3, 2))
x = np.random.permutation(np.arange(2*3*4)).reshape((4, 3, 2))
print ('input ndarray', x)
idx = np.unravel_index(np.argmax(x.ravel()), x.shape)
print ('index of maximal value = ', idx, ' and we verify that ', x[idx], '=', x.max())
Let's unwrap how we found such an easy solution...
It is insanely useful to create movies to illustrate a talk, blog post or just to include in a notebook:
from IPython.display import HTML
HTML('<center><video controls autoplay loop src="../files/2016-11-15_noise.mp4" width=61.8%/></center>')
For years I have used a custom made solution made around saving single frames and then calling ffmpeg to save that files to a movie file. That function (called anim_save had to be maintained accross different libraries to reflect new needs (going to WEBM and MP4 formats for instance). That made the code longer than necessary and had not its place in a scientific library.
Here, I show how to use the animation library from matplotlib to replace that
A new version of https://nteract.io/ is out, I will try today to push that new infomation to http://caskroom.io/ by creating a new cask for this application. I will base things on this previous contribution where I was simply editing an existing cask.
getting the token
"$(brew --repository)/Library/Taps/caskroom/homebrew-cask/developer/bin/generate_cask_token" '/Applications/nteract.app'
set-up variables
cd "$(brew --prefix)"/Homebrew/Library/Taps/caskroom/homebrew-cask github_user='laurentperrinet' project='nteract' git remote -v
A lively community of people including students, researchers and tinkerers from Marseille (France) celebrate the so-called "π-day" on the 3rd month, 14th day of each year. A nice occasion for general talks on mathematics and society in a lively athmosphere and of course to ... a pie contest!
I participated last year (in 2016) with a pie called "Monte Carlo". Herein, I give the recipe by giving some clues on its design... This page is a notebook - meaning that you can download it and re-run the analysis I do here at home (and most importantly comment or modify it and correct potential bugs...).
Une active communauté d'étudiants, chercheurs et bidouilleurs célèbrent à Marseille la "journée π" le 3ème mois, 14ème jour de chaque année. Une occasion de rêve pour en apprendre plus sur les mathématiques et la science dans une ambiance conviviale... Mais c'est aussi l'occasion d'un concours de tartes!
J'ai eu l'opportunité d'y participer l'an dernier (soit pour l'édition 2016) avec une tarte appelée "Monte Carlo". Je vais donner ici la "recette" de ma tarte, le lien avec le nombre π et quelques digressions mathématiques (notament par rapport à la présence incongrue d'un éléphant mais aussi par rapport à la démarche scientifique)... Cette page est un "notebook" - vous pouvez donc la télécharger et relancer les analyses et figures (en utilisant python + jupyter). C'est aussi un travail non figé - prière de me suggérer des corrections!
After reading the paper 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 :
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).
Concerning the origins of aperture-inward bias, I want to test an alternative possibility. In some recent modeling work:
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
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.
In this script the predictive coding is done using the MotionParticles package and for a http://motionclouds.invibe.net/ within a disk aperture.
Motion Clouds were defined in the origin to define parameterized moving textures. The library in itself is also interesting in itself to generate a spatial texture. Herein I describe a solution to generate just one static frame.
For a master's project in computational neuroscience, we adopted a quite novel workflow to go all the steps from the learning of the small steps to the wrtiting of the final thesis. Though we were flexible in our method during the 6 months of this work, a simple workflow emerged that I describe here.
Motion Clouds were defined in the origin to provide a simple parameterization for textures. Thus we used a simple unimodal, normal distribution (on the log-radial frequency space to be more precise). But the larger set of Random Phase Textures may provide some interesting examples, some of them can even be fun! This is the case of this simulation of the waves you may observe on the surface on the ocean.
Main features of gravitational waves are:
More info about deep water waves : http://farside.ph.utexas.edu/teaching/336L/Fluidhtml/node122.html
A feature of MotionClouds is the ability to precisely tune the precision of information following the principal axes. One which is particularly relevant for the primary visual cortical area of primates (area V1) is to tune the otirentation mean and bandwidth.
An essential component of natural images is that they may contain order at large scales such as symmetries. Let's try to generate some textures with an axial (arbitrarily vertical) symmetry and take advantage of the different properties of random phase textures.
Scratch (see https://scratch.mit.edu/) is a programming language aimed at introducing coding litteracy to schools and education. Yet you can implement even complex algorithms and games. It is visual, multi-platform and critically, open-source. Also, the web-site educates to sharing code and it is very easy to "fork" an existing project to change details or improve it. Openness at its best!
During a visit of a 14-year schoolboy at the lab, we used that to make a simple psychopysics experiment available at https://scratch.mit.edu/projects/92044597/ :
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
On va maintenant utiliser des forces elastiques pour coordonner la dynamique des lames dans la trame.
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
.. media:: http://vimeo.com/150813922
Ce meta-post gère la publication sur https://laurentperrinet.github.io/sciblog.
This is an old blog post, see the newer version in this post
This is an old blog post, see the newer version in this post and following.
A new version of owncloud is out, I will try today to push that new infomation to http://caskroom.io/
I will base things on this previous contribution
set-up variables
cd $(brew --prefix)/Library/Taps/caskroom/homebrew-cask github_user='meduz' project='owncloud' git remote -v
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post produit le montage final des séquences.
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post crée des ondes se propageant sur la série de lames.
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post implémente une configuration favorisant des angles droits.
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post implémente une configuration implémentant une vague de propagation.
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post implémente une dynamique sur le point focal.
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post explore les paramètres de la structure et de l'extension des reflections.
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post étudie quelques principes fondamentaux relatifs à des reflections mutliples dans des mirroirs.
Testing some multi-threading libraries
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post étudie commment sampler des points sur la structure.
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post étudie une reaction de reaction diffusion sur la structure.
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post étudie la connection entre le raspberry π (qui fait tourner les simulations) et les arduino (qui commandent les moteurs).
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post simule une configuration de contrôle sur l'ensemble de la structure.
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post simule une configuration de type Fresnel sur l'ensemble de la structure.
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post simule un rendu de reflection utilisant povray.
A scene with mirrors rendered with vapory (see https://laurentperrinet.github.io/sciblog/posts/2015-01-16-rendering-3d-scenes-in-python.html )
A smooth transition of MotionClouds while smoothly changing their parameters.
A new version of (MacTex)[http://www.tug.org/mactex/mactex-download.html] is out, I will try today to push that new infomation to http://caskroom.io/
I will base the steps I use on this previous contribution
set-up variables
cd $(brew --prefix)/Library/Taps/caskroom/homebrew-cask github_user='meduz' project='mactex' git remote -v
My installation notes for mutt_+Homebrew_+gmail, based on this post by steve losh and this other post
The Matching Pursuit algorithm is popular in signal processing and applies well to digital images.
I have contributed a python implementation and we will show here how we may use that for extracting a sparse set of edges from an image.
@inbook{Perrinet15bicv,
title = {Sparse models},
author = {Perrinet, Laurent U.},
booktitle = {Biologically-inspired Computer Vision},
chapter = {13},
editor = {Keil, Matthias and Crist\'{o}bal, Gabriel and Perrinet, Laurent U.},
publisher = {Wiley, New-York},
year = {2015}
}
When processing images, it is useful to avoid artifacts, in particular when you try to understand biological processes. In the past, I have used natural images (found on internet, grabbed from holiday pictures, ...) without controlling for possible problems.
In particular, digital pictures are taken on pixels which are most often placed on a rectangular grid. It means that if you rotate that image, you may lose information and distort it and thus get wrong results (even for the right algorithm!). Moreover, pictures have a border while natural scenes do not, unless you are looking at it through an aperture. Intuitively, this means that large objects would not fit on the screen and are less informative.
In computer vision, it is easier to handle these problems in Fourier space. There, an image (that we suppose square for simplicity) is transformed in a matrix of coefficients of the same size as the image. If you rotate the image, the Fourier spectrum is also rotated. But as you rotate the image, the information that was in the corners of the original spectrum may span outside the spectrum of the rotated image. Also, the information in the center of the spectrum (around low frequencies) is less relevant than the rest.
Here, we will try to keep as much information about the image as possible, while removing the artifacts related to the process of digitalizing the picture.
This is an old blog post, see the newer version in this post
This is an old blog post, see the newer version in this post
This is an old blog post, see the newer version in this post
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
Ce post utilise une image naturelle comme entrée d'une "trame sensorielle".
A long standing dependency of MotionClouds is MayaVi. While powerful, it is tedious to compile and may discourage new users. We are trying here to show some attempts to do the same with the vispy library.
In another post, we tried to use matplotlib, but this had some limitations. Let's now try vispy, a scientific visualisation library in python using opengl.
I was recently trying to embed a clip in a jupyter notebook using https://github.com/Zulko/moviepy, somthing that was working smoothly with python 2.7. After switching to python 3, this was not working anymore and left me scratcthing my head for a solution.
We live in a open-sourced world, so I filled an issue:
L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.
On va maintenant utiliser des forces elastiques pour coordonner la dynamique des lames dans la trame.
A new version of psychopy is out, I will try today to push that new infomation to http://caskroom.io/
I will base things on this previous contribution
set-up variables
cd $(brew --prefix)/Library/Taps/caskroom/homebrew-cask github_user='meduz' project='psychopy' git remote -v
I have heard Vancouver can get foggy and cloudy in the winter. Here, I will provide some examples of realistic simulations of it...
This stimulation was used in the following poster presented at VSS:
@article{Kreyenmeier2016,
author = {Kreyenmeier, Philipp and Fooken, Jolande and Spering, Miriam},
doi = {10.1167/16.12.457},
issn = {1534-7362},
journal = {Journal of Vision},
month = {sep},
number = {12},
pages = {457},
publisher = {The Association for Research in Vision and Ophthalmology},
title = {{Similar effects of visual context dynamics on eye and hand movements}},
url = {http://jov.arvojournals.org/article.aspx?doi=10.1167/16.12.457},
volume = {16},
year = {2016}
}A new version of owncloud is out, I will try today to push that new infomation to http://caskroom.io/
I will base things on this previous contribution
set-up variables
cd $(brew --prefix)/Library/Taps/caskroom/homebrew-cask github_user='meduz' project='owncloud' git remote -v
Dans un notebook précédent, on a vu comment créer une grille hexagonale et comment l'animer.
On va maintenant utiliser MoviePy pour animer ces plots.
Script done in collaboration with Jean Spezia.
Script done in collaboration with Jean Spezia.
Gizeh (that is, Cairo for tourists) is a great interface to the Cairo drawing library.
I recently wished to make a small animation of a bar moving in the visual field and crossing a simple receptive field to illustrate some simple motions that could be captured in the primary visual cortex ansd experiments that could be done there.
A new version of owncloud is out, I will try today to push that new infomation to http://caskroom.io/
I will base things on my contribution
set-up variables
cd $(brew --prefix)/Library/Taps/caskroom/homebrew-cask github_user='meduz' project='owncloud' git remote -v
TIKZ is a great language for producing vector graphics. It is however a bit tedious to go over the whole $\LaTeX$-like compilation when you get used to an ipython notebooks work-flow.
I describe here how to use a cell magic implemented by http://www2.ipp.mpg.de/~mkraus/python/tikzmagic.py and a hack to use euclide within the graph (as implemented in https://github.com/laurentperrinet/ipython_magics).
The above snippet shows how you can create a 3D rendered scene in a few lines of codes (from http://zulko.github.io/blog/2014/11/13/things-you-can-do-with-python-and-pov-ray/):
import vapory
camera = vapory.Camera( 'location', [0, 2, -3], 'look_at', [0, 1, 2] )
light = vapory.LightSource( [2, 4, -3], 'color', [1, 1, 1] )
sphere = vapory.Sphere( [0, 1, 2], 2, vapory.Texture( vapory.Pigment( 'color', [1, 0, 1] )))
scene = vapory.Scene(camera = camera , # a Camera object
objects = [light, sphere], # POV-Ray objects (items, lights)
included = ["colors.inc"]) # headers that POV-Ray may need
# passing 'ipython' as argument at the end of an IPython Notebook cell
# will display the picture in the IPython notebook.
scene.render('ipython', width=900, height=500)
Here are more details...
Following this post http://carreau.github.io/posts/10-No-PyLab-Thanks.ipynb.html, here is ---all in one single cell--- the bits necessary to import most useful libraries in an ipython notebook:
# import numpy and set the printed precision to something humans can read
import numpy as np
np.set_printoptions(precision=2, suppress=True)
# set some prefs for matplotlib
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams.update({'text.usetex': True})
fig_width_pt = 700. # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inches
fig_width = fig_width_pt*inches_per_pt # width in inches
FORMATS = ['pdf', 'eps']
phi = .5*np.sqrt(5) + .5 # useful ratio for figures
# define plots to be inserted interactively
%matplotlib inline
#%config InlineBackend.figure_format='retina' # high-def PNGs, quite bad when using file versioning
%config InlineBackend.figure_format='svg'
Below, I detail some thoughts on why it is a perfect preamble for most ipython notebooks.
In a previous post, I described steps to follow to live with decentralized, open-source cloud services (see here), let's focus on setting up a todo list.
Everything revolves under the http://todotxt.com/ specifications -- all in one, simple todo.txt file
I always used a single babel.bib BibTex file to keep a record of all my readings. Great. Then I managed it using a VCS (first SVN, then git). Super great. But at the same time, resources like CiteUlike or Mendeley (but also other services like orcid) allow for cloud-like services of having this data everywhere, anytime. Super super great!
But this fails if you have no connection to internet (remote conference, ...) or more importantly if these services change their policy (mendeley to citeulike sync disappeared at a sudden). The work you provide is not yours. These are mostly commercial services while all the open-source tools are there. Most importantly, the multiplicity of tools makes it difficult to share bibliographic data easily, while if we would have a tool to translate between them this would make it possible (without changing your habits).
The tools offered by so-called "cloud services" are useful but most often rely on the capacity to lock you in. Without leaving them completely, is there an alternative?
In a previous post on how to live with open-source cloud services and to not depend on centralized private cloud services (see here), let's focus on an install on a empty android phone.
A feature of MotionClouds is the ability to precisely tune the precision of information following the principal axes. One which is particularly relevant for the primary visual cortical area of primates (area V1) is to tune the otirentation mean and bandwidth.
This is part of a larger study to tune orientation bandwidth.
I needed to show prior information for the orientation of contours in natural images showing a preference for cardinal axis. A polar plot showing seemed to be a natural choice for showing the probability distribution function. However, this seems visually flawed...
The tools offered by so-called "cloud services" are useful but most often there is a (natural) tendency for these services to make you use their tools (youtube for google, iphone for apple). Why do we need to be locked to these services when open-source alternatives abound?
It's easy to record tracks (for instance while running) using the "My tracks" app on android systems. What about being able to re-use them?
In :doc:2014-11-22-reading-kml-my-tracks-files-in-ipython we reviewed different approches. Let's now try to use this data.
It's easy to record tracks (for instance while running) using the "My tracks" app (see https://play.google.com/store/apps/details?id=com.google.android.maps.mytracks) on android systems. What about being able to re-use them?
Here, I am reviewing some methods to read a KML file, including fastkml, pykml, to finally opt for a custom method with the xmltodict package.
A new version of owncloud is out, I will try today to push that new infomation to http://caskroom.io/
System Message: ERROR/3 (<string>, line 4); backlink
"2013-11-08-homebrew-cask-level-0-contributing-a-new-cask" slug doesn't exist.
though now phinze-cask paths are now brew-cask.
set-up variables
cd $(brew --prefix)/Library/Taps/caskroom/homebrew-cask github_user='meduz' project='owncloud' git remote -v
We test Reverse-phi motion and the Asymmetry of ON and OFF responses using MotionClouds.
A feature of MotionClouds is the ability to precisely tune the precision of information following the principal axes. One which is particularly relevant for the primary visual cortical area of primates (area V1) is to tune the orientation mean and bandwidth.
A feature of MotionClouds is the ability to precisely tune the precision of information following the principal axes. One which is particularly relevant for the primary visual cortical area of primates (area V1) is to tune the otirentation mean and bandwidth.
To install the necessary libraries, check out the documentation.
For the biphoton experiment:
Motion Clouds were defined in the origin to provide a simple parameterization for textures. Thus we used a simple unimodal, normal distribution (on the log-radial frequency space to be more precise). But the larger set of Random Phase Textures may provide some interesting examples, some of them can even be fun! This is the case of this simulation of the waves you may observe on the surface on the ocean.
from IPython.display import HTML
HTML('<center><video controls autoplay loop src="../files/2014-10-24_waves/waves.mp4" width=61.8%/></center>')
Main features of gravitational waves are:
More info about deep water waves : http://farside.ph.utexas.edu/teaching/336L/Fluidhtml/node122.html
In this notebook, we are interested in replicating the results from Dong and Attick (1995).
import numpy as np
import MotionClouds as mc
downscale = 2
fx, fy, ft = mc.get_grids(mc.N_X/downscale, mc.N_Y/downscale, mc.N_frame/downscale)
name = 'MotionPlaid'
mc.figpath = '../files/2014-10-20_MotionPlaids'
import os
if not(os.path.isdir(mc.figpath)): os.mkdir(mc.figpath)
Plaids are usually created by adding two moving gratings (the components) to form a new simulus (the pattern). Such stimuli are crucial to understand how for instance information about motion is represented and processed and its neural mechanisms have been extensively studied notably by Anthony Movshon at the NYU. One shortcomming is the fact that these are created by components which created interference patterns when they are added (as in a Moiré pattern). The question remains to know if everything that we know about components vs pattern processing comes from these interference patterns or reaaly by the processing of the two componenents as a whole. As such, Motion Clouds are ideal candidates because they are generated with random phases: by construction, there should not be any localized interference between components. Let's verfy that:
Defintion of parameters:
theta1, theta2, B_theta = np.pi/4., -np.pi/4., np.pi/32
This figure shows how one can create MotionCloud stimuli that specifically target component and pattern cell. We show in the different lines of this table respectively: Top) one motion cloud component (with a strong selectivity toward the orientation perpendicular to direction) heading in the upper diagonal Middle) a similar motion cloud component following the lower diagonal Bottom) the addition of both components: perceptually, the horizontal direction is predominant.
print( mc.in_show_video.__doc__)
Component one:
diag1 = mc.envelope_gabor(fx, fy, ft, theta=theta1, V_X=np.cos(theta1), V_Y=np.sin(theta1), B_theta=B_theta)
name_ = name + '_comp1'
mc.figures(diag1, name_, seed=12234565, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)
Component two:
diag2 = mc.envelope_gabor(fx, fy, ft, theta=theta2, V_X=np.cos(theta2), V_Y=np.sin(theta2), B_theta=B_theta)
name_ = name + '_comp2'
mc.figures(diag2, name_, seed=12234565, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)
The pattern is the sum of the two components:
name_ = name
mc.figures(diag1 + diag2, name, seed=12234565, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)
Script done in collaboration with Jean Spezia.
import os
import numpy as np
import MotionClouds as mc
name = 'MotionPlaid_9x9'
if mc.check_if_anim_exist(name):
B_theta = np.pi/32
N_orient = 9
downscale = 4
fx, fy, ft = mc.get_grids(mc.N_X//downscale, mc.N_Y//downscale, mc.N_frame//downscale)
mov = mc.np.zeros(((N_orient*mc.N_X//downscale), (N_orient*mc.N_X//downscale), mc.N_frame//downscale))
i = 0
j = 0
for theta1 in np.linspace(0, np.pi/2, N_orient)[::-1]:
for theta2 in np.linspace(0, np.pi/2, N_orient):
diag1 = mc.envelope_gabor(fx, fy, ft, theta=theta1, V_X=np.cos(theta1), V_Y=np.sin(theta1), B_theta=B_theta)
diag2 = mc.envelope_gabor(fx, fy, ft, theta=theta2, V_X=np.cos(theta2), V_Y=np.sin(theta2), B_theta=B_theta)
mov[(i)*mc.N_X//downscale:(i+1)*mc.N_X//downscale, (j)*mc.N_Y//downscale : (j+1)*mc.N_Y//downscale, :] = mc.random_cloud(diag1 + diag2, seed=1234)
j += 1
j = 0
i += 1
mc.anim_save(mc.rectif(mov, contrast=.99), os.path.join(mc.figpath, name))
mc.in_show_video(name, figpath=mc.figpath)
As in (Rust, 06) we show in this table the concatenation of a table of 9x9 MotionPlaids where the angle of the components vary on respectively the horizontal and vertical axes.
The diagonal from the bottom left to the top right corners show the addition of two component MotionClouds of similar direction: They are therefore also intance of the same Motion Clouds and thus consist in a single component.
As one gets further away from this diagonal, the angle between both component increases, as can be seen in the figure below. Note that first and last column are different instance of similar MotionClouds, just as first and last lines in in the table.
N_orient = 8
downscale = 2
fx, fy, ft = mc.get_grids(mc.N_X/downscale, mc.N_Y/downscale, mc.N_frame)
theta = 0
for dtheta in np.linspace(0, np.pi/2, N_orient):
name_ = name + 'dtheta_' + str(dtheta).replace('.', '_')
diag1 = mc.envelope_gabor(fx, fy, ft, theta=theta + dtheta, V_X=np.cos(theta + dtheta), V_Y=np.sin(theta + dtheta), B_theta=B_theta)
diag2 = mc.envelope_gabor(fx, fy, ft, theta=theta - dtheta, V_X=np.cos(theta - dtheta), V_Y=np.sin(theta - dtheta), B_theta=B_theta)
mc.figures(diag1 + diag2, name_, seed=12234565, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)
For clarity, we display MotionPlaids as the angle between both component increases from 0 to pi/2.
Left column displays iso-surfaces of the spectral envelope by displaying enclosing volumes at 5 different energy values with respect to the peak amplitude of the Fourier spectrum.
Right column of the table displays the actual movie as an animation.
An easy way to include movie in a notebook using Holoviews.
When studying a multi-dimensional random variable, if these are guassian the norm if the vector follows a $\chi$ distribution (see http://en.m.wikipedia.org/wiki/Chi_distribution).
Trying to answer the question in http://stackoverflow.com/questions/12233105/how-can-i-display-an-image-in-the-terminal/22537549?noredirect=1#comment41404352_22537549 :
Is there any sort of utility I can use to convert an image to ASCII and then print it in my terminal? I looked for one but couldn't seem to find any.
Following http://nbviewer.ipython.org/github/ipython/ipython/blob/1.x/examples/notebooks/Part%205%20-%20Rich%20Display%20System.ipynb and http://nbviewer.ipython.org/gist/fonnesbeck/ad091b81bffda28fd657 , let's try to integrate an interactive D3 plot within a notebook.
A long standing dependency of MotionClouds is MayaVi. While powerful, it is tedious to compile and may discourage new users. We are trying here to show some attempts to do the same with matplotlib or any other library.
I have recently been asked how to transfer lots of file from a backup server to a local disk. The context is that
Dans le notebook précédent, on a vu comment créer
On va maintenant utiliser:
http://matplotlib.org/api/animation_api.html
http://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial/
... pour créer des animations de ces lames.
Nous allons simuler un ring avec en entrée une courbe représentant des stimuli "naturels"
La densité est définie sur $[0, 2\pi[$ par
$ f(x) = \frac {e^{\kappa cos(\theta - m)}}{2 \pi I_{0}(\kappa)} ~$
avec $~ \kappa = \frac {1}{\sigma^{2}} $
L'orientation est définie sur $[0, \pi[$ par un remplaçant de la variable $~ (\theta_d - m) ~$ par $~ 2(\theta_o - m)$
nous obtenons alors la densité pour $\theta_o$: $ f(x) = \frac{1}{2 \pi I_{0}(\kappa)} \cdot {e^{\frac{cos(2(\theta - m))}{\sigma^{2}}}}$ et $~ p = \frac {1} {2\pi \sigma_1 \sigma_2} \cdot e^{- 2 \frac{(m_2-m_1)^{2}}{\sigma_{1}^{2} + \sigma_{2}^{2}}}$
car avec le changement de variable : $ \varepsilon(x) = \frac {1} {\sigma_{1} \sqrt{2\pi}} \cdot e^{\frac {-(2(x-m_{1}))^{2}} {2\sigma_{1}^{2}}} $ et $\gamma (x) = \frac {1} {\sigma_2 \sqrt{2\pi}} \cdot e^{\frac {-(2(x-m_2))^{2}} {2\sigma_{2}^{2}}} $
donc $\varepsilon(x) \cdot \gamma(x) = \frac {1}{2\pi \sigma_{1} \sigma_{2}} \cdot e^{-2(\frac {(x-m_{1})^{2}}{\sigma_{1}^{2}} + \frac{(x-m_{2})^{2}}{\sigma_{2}^{2}})} = \frac {1} {2\pi \sigma_1 \sigma_2} \cdot e^{- 2 \frac{(m_2-m_1)^{2}}{\sigma_{1}^{2} + \sigma_{2}^{2}}}$
Travail sur les Motion Clouds et observation des différents changement de paramètre particulièrement B_sf
http://neuralensemble.github.io/MotionClouds/
Simulation d'un ring model grâce au programme brian et observation des conséquences du changement de différents paramêtres
Stage M1 de Chloé Pasturel, encadrée par Laurent Perrinet (INT, CNRS)
(importé depuis https://invibe.net/LaurentPerrinet/TagAnrBalaV1 )
wget -O- http://neuro.debian.net/lists/trusty.de-md.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
sudo apt-key adv --recv-keys --keyserver pgp.mit.edu 2649A5A9
sudo apt-get updatesudo apt-get install aptitude
sudo aptitude install ipython-notebook
sudo aptitude install python-matplotlib
sudo aptitude install python-brian
sudo aptitude install texlive-full
sudo aptitude install python-pynn
pip install --user neurotoolsSometimes, it's good to go back to the basics.
In command mode, typing :help usr_02.txt (or simplier something like :h usr_<TAB>02<TAB><ENTER>), you learn the letters for navigating a file:
I was still using the arrows keys, but taking this habit makes thinks easier, especially when switching often keyboards.
Simalarly, to scroll the text - you can use:
Here, the :h ctrl-u page will give you more info (or :help usr_03.txt).
Note that to follow a link (think "searching a tag"), you can press * (or # to go backwards).
In a previous post, I have shown how to convert MoinMoin pages to this blog (Nikola angine). Let's now tidy up the place and remove obsolete pages.
Pages that were succesfully converted:
nikolFrom a previous post, we have this function to import a page from moinmoin, convert it and publish:
I have another wiki to take notes. These are written using the MoinMoin syntax, which is nice, but I found no converter from MoinMoin to anything compatible with Nikola.
Following this post http://carreau.github.io/posts/06-NBconvert-Doc-Draft.html , I thought I might give it a try using ipython within nikola:
URL = 'https://invibe.net/cgi-bin/index.cgi/SciBlog/2005-10-29?action=print'
NOTE: this should not be tried with these URLS as they do not exist anymore...
While SSH is rock solid, we stumbled on a strange bug while trying to establish a connection:
$ ssh myname@myserver.fr -vvv
OpenSSH_6.2p2, OSSLShim 0.9.8r 8 Dec 2011
(...)
Read from socket failed: Operation timed out
Nothing worked. The usual check of keys, encodings, permissions gave nothing.
Folds are useful when having long files to have a good perspective on its structure. Especially useful in LaTeX mode.
To install, I recommend using the python-mode described in http://unlogic.co.uk/2013/02/08/vim-as-a-python-ide/
The magical shortcut all begin with z. Type :hep fold to learn more about them.
Here, we will need to install additions to the nikola publishing tool: a rendering machine + a theme. Using homebrew in mcosx, this looks like:
brew install npm
npm install -g less
and
nikola install_theme zen-ipython
Once this was done, one could tune conf.py and then issue:
nikola new_post -f ipynb
Finally, one needs to build (nikola build) and publish (nikola deploy)
plot(rand(50))
I have managed to install Nikola, and build a site using it using this code:
git clone git://github.com/getnikola/nikola.git cd nikola pip install -r requirements-full.txt pip install . nikola init --demo invibe nikola build nikola serve
Pretty simple, congratulations to the developpers!
More info:
I mistyped :doc:`my contribution <2013-11-08-homebrew-cask-(level-0)-contributing-a-new-cask>`, so I have to modify my pull request
System Message: ERROR/3 (<string>, line 2); backlink
"2013-11-08-homebrew-cask-level-0-contributing-a-new-cask" slug doesn't exist.
set-up variables
cd $(brew --prefix)/Library/Taps/phinze-cask github_user='meduz' project='texshop' git remote -v
SparkleShare is a great alternative to DropBox
Fetch the Dazzle script on https://github.com/hbons/Dazzle
curl https://raw.github.com/hbons/Dazzle/master/dazzle.sh --output /usr/bin/dazzle && chmod +x /usr/bin/dazzle
call vim to open more files
vim -p file1 file2 file3
from http://doc.owncloud.org/server/5.0/admin_manual/maintenance/update.html
backup
rsync -a owncloud/ owncloud_bkp`date +"%Y%m%d"`/
Starting in Leopard (I believe) when you open a file downloaded from the web, OS X asks if you really mean it. While it is intended to stop maliciousness, it is only a source of aggravation for me. While there are some hints here on working around it, it turns out that you can disable it completely using a Terminal command:
defaults write com.apple.LaunchServices LSQuarantine -bool NO
Everything (almost) can be done by the find command:
finding in the current directory (.) all files containing a lock pattern:
find . -name *lock*
During the first year of BrainScaleS, we have concentrated on disseminating our work on the role of motion-based prediction in motion detection. This led to a publication on the hypothesis that this prior expectation may explain some phenomena explained otherwise by complex arrangements of mechanisms, namely that motion-based prediction is sufficient to solve the aperture problem (Perrinet and Masson, 2012). During the second year, we extended this hypothesis to other types of problems linked to the detection of motion. In particular, we focused on the case were the stimulus is transiently and unexpectedly blanked, a physiologically very relevant constraint occurring for instances during blinks of the eye. For this, we have used the same theoretical framework based on a Bayesian formulation and implemented using a particle filtering scheme, but used a different experimental protocol inspired by behavioral experiments conducted in the laboratory by CNRS-INT (Bogadhi, 2012). This is an important aspect as it allows to better understand the dynamics of the neural representation without sensory input and more generally to understand the interaction of the sensory flow with an internal neural representation of the environment.
| tl;dr `` sudo /usr/libexec/PlistBuddy -c 'set :LaunchEvents:com.apple.time:"Backup Interval":Interval 86400' /System/Library/LaunchDaemons/com.apple.backupd-auto.plist `` |
I use ownCloud as a remplacement of dropbox, but I had unfortunately lots of conflicts files (on client and server)
these contain the _conflict- pattern, so a solution is to move all of them to a backup folder:
cd /share/DriveOne/Web/owncloud/data/admin/files
find . -name *_conflict-* -exec mv {} /share/Backups/backups/duplicate-photos/ \;
In the context of BrainScales, we have developed a library to synthesize stimuli targeted at the characterization of motion perception. This process took the following steps:
 |
Compte rendu annuel d'activité des chercheurs du CNRSAnnée 2011 - 2012 |
Easy transformations of videos:
turn to the right
ffmpeg -i 2012-04-29\ 19.31.32.mov -vf "transpose=1" -sameq -y 2012-04-29\ 19.31.32_right.mov
recently, a message popped-up :
Package movie15 Warning: @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ (movie15) @@ Package `movie15' is obsolete and @@ (movie15) @@ superseded by `media9'. @@ (movie15) @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@.
from http://apple.blogoverflow.com/2012/03/open-and-save-like-a-pro-secrets-of-opensave-dialogs/ :
Let’s move on to a more advanced feature: the Go to Folder dialog. Like in Finder, you can access a prompt for typing a path by pressing ⇧+⌘+G. If you love the keyboard, you’ll love this dialog; frequently, the fastest way to get to where you want to go is by typing its path. This is especially true because the Go to Folder dialog features tab autocompletion: type the beginning of the name of a file or folder and hit tab to fill in the rest of the name automatically. My favorite part about the Go to Folder dialog is that it appears automatically whenever you begin typing a path (/ or ~). When saving, the desired filename can even be included in the path.
The editor of our submitted paper asked for a red-lined article file. Using latexdiff makes this task very easy: Simply grab the 2 versions of your manuscript and issue
| largely a copy-and-paste from http://hints.macworld.com/article.php?story=20070622210507844 ; Jun 25, '07 07:30:00AM • Contributed by: delight1 |
After doing a backup, edit the following file:
sudo vim /System/Library/LaunchDaemons/ssh.plist
| copy and paste from http://hints.macworld.com/article.php?story=20091208050655947 |
Starting in Leopard when you open a file downloaded from the web, OS X asks if you really mean it. While it is intended to stop maliciousness, it is only a source of aggravation for me. While there are some hints here on working around it, it turns out that you can disable it completely using a Terminal command:
Mon projet scientifique s'intéresse aux mécanismes computationnels qui sous-tendent la cognition. C'est-à-dire que l'on sait où se produisent ces mécanismes définissant la système nerveux central en un réseau de neurones connectés par des synapses et qu'ils sont supportés par des signaux électro-chimiques entre ces noeuds, mais on ne connaît pas encore totalement comment l'information qui semble être portée ces signaux peut être interprété. Ce décodage, qui est le fond de notre travail en neurosciences, à un "Graal" qui est la découverte d'un hypothétique "code neural", c'est-à-dire du langage qui est utilisé dans notre cerveau. On ne sait si cette découverte est possible; la question se pose: peut-il exister une connaissance globale du cerveau à la manière d'autres disciplines scientifiques (par exemple, la trajectoire d'une planète avec les lois de Newton?). Il est clair que le cerveau de chaque humain n'est pas assez complexe pour en délimiter la complexité, même les cerveaux mis en réseau avec toutes les communautés neuro-scientifiques, artistiques vont nous permettre dans le futur de mieux comprendre cet objet...
master howto: https://trac.macports.org/wiki/howto/SetupDovecot
Install
sudo aptitude install dovecot
GATSBY UNIT EXTERNAL SEMINAR
Bard Ermentrout Department of Mathematics University of Pittsburgh Wednesday 9 March 2011, 16:00 Seminar Room, Wellcome Trust Centre for Neuroimaging (FIL) 12 Queen Square, London, WC1N 3AR Title: Double or Nothing: Phosphenes and the periodic driving of cortex Abstract: In this talk, I examine two different types of phosphenes - patterns in the visual systems evoked from within it. I first study contour phosphenes in which direct stimulation of the eyeball coupled with a moving bar in the visual field produces slowly propagating waves. The mechanism appears to be due to period doubling which produces an intrinsic bistability. Using averaging, I analyze the dynamics of a one-dimensional analog. In the second part of the talk, I study flicker-induced hallucinations in which diffuse stroboscopic light is capable of evoking spatial patterns in the visual field. I use Floquet theory and symmetric bifurcation theory to explain experiments that indicate different patterns are seen with different temporal frequencies.
Warning
This post is certainly obsolete...
quoting http://ubuntuforums.org/showpost.php?p=10189331&postcount=35 : "For those (like me) who aren't familiar with grub tampering, here's what I did to make the change automatic:"
Warning
This post is certainly obsolete...
To select one of the different sleep modes of the Mac use the command-line tool pmset:
Give detailed information on all python processes:
ps -fp $(pgrep -d, -x python)
with a setup.py package, http://docs.python.org/install/index.html
python setup.py install --home=$HOME/Python2.6
ssh server installed but not starting at boot (I certainly messed up something):
$ ls -l /etc/init.d/*ssh* -rwxr-xr-x 1 root root 3704 2010-09-14 19:20 /etc/init.d/ssh $ ls -l /etc/rc2.d/*ssh* ls: cannot access /etc/rc2.d/*ssh*: No such file or directory $ ls -l /etc/rc1.d/*ssh*
the most simple command is locate :
locate Python.h
; it is based on a database updated regulalry (most often daily).
2010-12-08 10:56:10
Compte rendu annuel d'activité des chercheurs du CNRS Année 2009 - 2010
simply issue
svn propset svn:ignore '*' data/
Because OpenSSH allows you to run commands on remote systems, showing you the results directly, as well as just logging in to systems it's ideal for automating common tasks with shellscripts and cronjobs. One thing that you probably won't want is to do though is store the remote system's password in the script. Instead you'll want to setup SSH so that you can login securely without having to give a password.