2022-12-23_video-abstract.py

"""
# 2022-12-23_video-abstract


 Using the template @ https://laurentperrinet.github.io/sciblog/posts/2019-09-11_video-abstract-vision.html
and using that @ https://github.com/chloepasturel/AnticipatorySPEM/blob/master/2020-03_video-abstract/2020-03-24_video-abstract.ipynb

cp 2022-12-23_polychrony-review_video-abstract.mp4  /Users/laurentperrinet/metagit/blog/hugo_academic/content/publication/grimaldi-22-polychronies/

"""
# Creating the movie using the (*excellent*) [MoviePy](http://zulko.github.io/moviepy/index.html) library:

videoname = "2022-12-23_polychrony-review_video-abstract"
gifname = videoname + ".gif"
gifname = None
fps = 30
from moviepy.editor import VideoFileClip, ImageClip, TextClip, CompositeVideoClip

H, W = 500, 800
H_fig, W_fig = int(H-H/(1.618*3)), int(W-W/(1.618*3))


opt_t = dict(font="Arial", size=(W,H), method='caption')
opt_st = dict(font="Arial", size=(W,H), method='caption')
# opt_t = dict(size=(W,H), method='caption')
# opt_st = dict(size=(W,H), method='caption')

clip = []
t = 0 

#################################################################################
# TITRE
#################################################################################
# 
texts = ["""Precise spiking motifs in neurobiological and neuromorphic data





""", """Precise spiking motifs in neurobiological and neuromorphic data

   Grimaldi, Gruel, Besnainou, 
   Jérémie, Martinet, and Perrinet


""", """Precise spiking motifs in neurobiological and neuromorphic data

   Grimaldi, Gruel, Besnainou, 
   Jérémie, Martinet, and Perrinet

Brain Sciences (2022)
"""]
txt_opts = dict(align='center', color='white', **opt_t) #stroke_color='gray', stroke_width=.5
duration = 1.5
for text in texts:
    txt = TextClip(text, fontsize=35, **txt_opts).set_start(t).set_duration(duration)
    t += duration
    clip.append(txt)

#################################################################################
# INTRO
#################################################################################

intro_subs = ["""
The majority of information passing 
in the brain is mediated by all-or-none events,




""", """
The majority of information passing 
in the brain is mediated by all-or-none events,
*spikes*.



""", """
The majority of information passing 
in the brain is mediated by all-or-none events,
*spikes*.
We review here novel evidence for the 
role of their precise timing from 
neurobiological and neuromorphic data.
"""]

sub_opts = dict(fontsize=32, align='center', color='white', **opt_t)
sub_durations = [1.5, 1.5, 2.5]
for subtitle, sub_duration in zip(intro_subs, sub_durations):
    sub = TextClip(subtitle, **sub_opts).set_start(t).set_duration(sub_duration)
    t += sub_duration
    clip.append(sub)
clip.append(sub) # another bit on the last bit

#################################################################################
#################################################################################
chapters = [dict(title="Visual system", color='green',
            content=[dict(figure='figures/visual-latency_bg.jpg', duration=3, subtitle=[
                            "The visual system is very efficient in generating a...", 
                            "...decision from the retinal image to the different ..."]),
                    dict(figure='figures/visual-latency.jpg', duration=3, subtitle=[
                            "...stages of the visual pathways, here a reaction...", 
                            "...of finger muscles in about 300 milliseconds."]),
                     dict(figure='/Users/laurentperrinet/metagit/ABCD/AG_figures/animated_neurons/LIF.mp4', duration=5, subtitle=[
                            "It is thought that this efficiency is achieved by spikes...", 
                            "...that is, brief all-or-none events which are passed...", 
                            "...in the very large network which forms the brain...", 
                            "...from assemblies of neurons to others."])]), 
           dict(title="Polychrony", color='orange',
            content=[dict(figure='figures/izhikevich.png', duration=5, subtitle=[
                            "We review here an hypothesis in which, rather than the...", 
                            "...firing frequency of spikes, it is their precise timing...", 
                            "...which would allow or not this message passing."]),
                    dict(figure='figures/THC_1a_k.png', duration=3, subtitle=[
                            "We present different mathematical models for extracting...", 
                            "...such precise spiking motifs and..."]),
                    dict(figure='figures/THC_1a.png', duration=5, subtitle=[
                            "...also some novel tools for extracting these motifs...", 
                            "...in arbitrary raster plots from neurobiology...", 
                            "...or neuromorphic engineering."])]), 
           dict(title="Neurobiology", color='red',
            content=[dict(figure='figures/haimerl2019.jpg', duration=5, subtitle=[
                            "This hypothesis is reviewed with respect to our ...", 
                            "...knowledge of the neurobiology, for instance in the...", 
                            "...hippocampus of rodents. We also review..."]),
                    dict(figure='figures/Ikegaya2004zse0150424620001.jpeg', duration=5, subtitle=[
                            "...numerous and extensive work on mechanisms...", 
                            "...which may allow the neural system to learn...", 
                            "...to actually use that precise spiking motifs", 
                            "...by attuning the delay between pairs of neurons."])]), 
           dict(title="Neuromorphic", color='blue',
            content=[dict(figure='figures/event_driven_computations.png', duration=5, subtitle=[
                            "We also review evidence from computational neuroscience...", 
                            "...and neuromorphic engineering, in particular for novel...", 
                            "...device such as event-based cameras which directly...", 
                            "...transform lumnious information into spikes."]),
                    dict(figure='/Users/laurentperrinet/metagit/ABCD/pyTERtorch/2022-11-30_BiolCybernetics/figures/2022-11-10_MotionDetection_input.mp4', duration=5, subtitle=[
                            "For instance, we show how precise spike times may be...", 
                            "...used to detect the direction of motion from such...", 
                            "...a stream of events in an ultrafast fashion."])]), 
           ]


# http://zulko.github.io/moviepy/ref/VideoClip/VideoClip.html?highlight=compositevideoclip#textclip
txt_opts = dict(fontsize=65, bg_color='white', align='center', **opt_st)
sub_opts = dict(fontsize=28, align='South', color='white', **opt_st)

for chapter in chapters:
    duration = 1
    txt = TextClip(chapter['title'], color=chapter['color'], **txt_opts).set_start(t).set_duration(duration)
    t += duration
    clip.append(txt)

    for content in chapter['content']:
        # set the figure
        figname = content['figure']
        duration = content['duration']
        if figname[-4:]=='.mp4' :
            img = VideoFileClip(figname, audio=False)
            print(figname, '--> duration:', img.duration, ', fps:', img.fps)
            # duration = img.duration
        else:
            img = ImageClip(figname)
        
        H_clip, W_clip, three = img.get_frame(0).shape
        if H_clip/W_clip > H_fig/W_fig: # portrait-like
            img = img.resize(height=H_fig)
        else: # landscape-like
            img = img.resize(width=W_fig)

        img = img.set_duration(duration)
        img = img.set_start(t).set_pos('center')#.resize(height=H_fig, width=W_fig)

        t += duration
        clip.append(img)

        # write the subtitles
        t_sub = t - duration
        sub_duration = duration / len(content['subtitle'])
        for subtitle in content['subtitle']:
            sub = TextClip(subtitle, **sub_opts).set_start(t_sub).set_duration(sub_duration)
            t_sub += sub_duration
            clip.append(sub)

#################################################################################
#################################################################################


#################################################################################
# OUTRO
#################################################################################
texts = ["""
Overall, this manuscript reviews the potential
of using precise spiking motifs to design




""",
"""
Overall, this manuscript reviews the potential
of using precise spiking motifs to design
more efficient machine learning algorithms, 
that is, faster and more energy-frugal,


""",
"""
Overall, this manuscript reviews the potential
of using precise spiking motifs to design
more efficient machine learning algorithms, 
that is, faster and more energy-frugal,
but also to better understand 
neurobiological processes and their disorders...
"""]

txt_opts = dict(fontsize=30, align='center', **opt_t)
duration = 3
for text in texts:
    txt = TextClip(text, color='white', **txt_opts).set_start(t).set_duration(duration)
    t += duration
    clip.append(txt)
    
# FIN
texts = ["""
For more info, and the full, open-sourced code... visit


""", """
For more info, and the full, open-sourced code... visit

https://laurentperrinet.github.io/publication/grimaldi-22-polychronies
""",
]

txt_opts = dict(align='center', **opt_t)
duration = 2.5
for text in texts:
    txt = TextClip(text, color='orange', fontsize=26, **txt_opts).set_start(t).set_duration(duration)
    t += duration
    clip.append(txt)    

# QRCODE
#
# qrencode -o figures/qrcode.png -d 200 https://laurentperrinet.github.io/publication/grimaldi-22-polychronies
img = ImageClip('figures/qrcode.png').set_duration(duration)
img = img.resize(width=(W_fig*2)//3).set_start(t).set_pos('center')
clip.append(img)

#################################################################################
# COMPOSITING
#################################################################################

video = CompositeVideoClip(clip)
video.write_videofile(videoname + '.mp4', fps=fps)

if not(gifname is None):
    video.write_gif(gifname, fps=fps)
    from pygifsicle import optimize
    optimize(gifname)