| Charlie K. | Charlie started his career in highschool when he began working for Verdeeco, a smart-grid data anayltics startup company. As the company grew and eventually acquired Sensus, Charlie remained as a software developer. Further on, his role transitioned from software development to development operations when Xylem acquired Sensus. Since then, Charlie has remained on the team as a coordinator between the devops and development teams. |
|---|---|
| Dawit A. | As an IT and customer service representative at the CATLab of Georgia State University, Dawit spends his time maintaining and overlooking the handling of technical equipment reserved in the CATLab. Due to his time at the university, his communicating, team building, and social skills have strengthed. |
| Juwan S. | As a student at Georgia State University, Juwan is a junior computer science major. Being skilled in Java and knowing relevant knowledge in front-end development, releveant courses that he has taken to strengthen is skills include Data Structures and Systems Programming. |
| Jiawei S. | Jiawei is a recently graduate student of Georgia State University whom majored in Computer Science. As a current research assistant, focusing on modeling and stimulation, Jiwaei is knowledgeable in Java, C and some front-end languages. Relevant coursework he has studied includes Automata, Modeling and Stimulation Theory and Application, and Programming Language Concepts. |
| Ashanti R | Ashanti is a current senior at Georgia State University pursuing a bachelors' degree in computer science. As a designer, Ashanti is experienced in front-end development as well as having knowledge in programming in Java. Studying to become an UI/UX designer, relevant coursework that she has taken includes Data Mining and Digital Image Processing. |


|
The Oculus Rift is a virtual reality system that completely immerses the user inside virtual worlds. CHAI3D supports for the Oculus Rift through a module based on API version 0.8 (beta) of the display. The current implementation is limited to the Windows operating system, but support for Linux and Mac OS X is expected once the product rolls into production. Since the Oculus Rift is still under development, please check their website for more updated information about the product. |
OCULUS Module Resources
CHAI3D Library |


Droid Arabic Naskh is an Arabic type designed for use in Google™ products such as Google ChromeOS™ and Android™. Designed to complement the Latin, Greek and Cyrillic provided in the Droid Serif family, the Arabic matches the color, alignment and design detail of the Droid Serif allowing them to be used together for multi-lingual typesetting. This Naskh style is optimized for reading Arabic script on screen. The large ‘loop height’ and ‘tooth height’ help prevent readers from having to zoom web pages to a larger size in order to read them. The traditional Naskh forms are softened for less formal documents such as periodicals and journals. The letterforms structures are based on the calligraphic grammatical rules of the Naskh writing style while drawn with a contemporary feel.
@import url(http://fonts.googleapis.com/earlyaccess/droidarabicnaskh.css);
font-family: 'Droid Arabic Naskh', serif;Apache License, version 2.0 | Download


@(graphLabels: play.api.libs.json.JsValue, graphData: play.api.libs.json.JsValue)(implicit request: RequestHeader) @import play.api.libs.json.Json @import controllers.handlers.EveryoneTotals @import controllers.handlers.OneFiveOneDateUtils @import controllers.handlers.OneFiveOneConstants @import utils.Exercises._ @main("151 Challenge") {
Welcome to the 151 Challenge. If you're just getting started, check out the Rules Page to get an idea what you're in for. We start on on @OneFiveOneDateUtils.printableDate(OneFiveOneConstants.START_OF_CHALLENGE) at 12:00am.
@defining(EveryoneTotals.everyoneToday()) { everyoneToday =>
Today (PST) so far we have done
@everyoneToday.reps1 @yearsExercises._1.displayName,
@everyoneToday.reps2 @yearsExercises._2.displayName,
@everyoneToday.reps3 @yearsExercises._3.displayName
and @everyoneToday.reps4 @yearsExercises._4.displayName.
}
@defining(EveryoneTotals.everyoneBestDay()) { everyoneBestDay =>
Our best day was @OneFiveOneDateUtils.printableDate(everyoneBestDay._1) when we did
@everyoneBestDay._2.reps1 @yearsExercises._1.displayName,
@everyoneBestDay._2.reps2 @yearsExercises._2.displayName,
@everyoneBestDay._2.reps3 @yearsExercises._3.displayName
and @everyoneBestDay._2.reps4 @yearsExercises._4.displayName.
}
You can join the Group Me chat for all the contestants to ask questions, stay up to date and talk trash. If you've already joined, you can go there now.
}


SPASE Model Specification Search
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<longdescref>
The <longdescref>
element supports a reference to a text description of
the graphic or object. This element replaces the deprecated @longdescref
attribute on <image>
and <object>
elements.
Category: Body elements


Hi, my name is
Kalash Sharma.
I love to develop and build things.
Developer who is passionate about
writing clean, elegant and efficient code.
I want to make things that make a difference.
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I am originally from Wilson, North Carolina but moved to Snow Hill, North Carolina last year. I was originally working for a Bridgestone tire plant running a tire assembly machine but the job was taking a turn for the worst and after 13 years i figured it was time to go back to school. I wanted to get into coding full time, im currently working on getting a certificate in Full stack development with MERN from MIT. I have been messing around with some form of coding for the last 15+ years. I originally started making mIRC scripts and took a Visual Basics class in high school. While messing around with the mIRC scripts I started making basic web pages with HTML, CSS and very little PHP. Messed around with web design for a few years off and on. Some of my friends started messing around with Kodi(a popular media center program), so i figured I would give it a chance. I started learning python so i could create a maintenance style plugins for the Kodi. After that with the help of a bootstrap template I found online I created a website for a local gun shop DeadOnTacticalArms. Outside of what is included with the template and bootstrap i did all the frontend HTML, Javascript, and Jquery. I also did all the backend PHP coding and created an admin panel for them. Now im working on getting my certificate from MIT xPro so i can become a Full Stack Developer.
- HTML
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- GitHub: DennisAMayo
- LinkedIn: Dennis Mayo
- Email<EMAIL_ADDRESS>- Name: Dennis Mayo
- Age: 33
- Location: Snow Hill, NC


sphinx.ext.graphviz
– Add Graphviz graphs¶
New in version 0.6.
This extension allows you to embed Graphviz graphs in your documents.
It adds these directives:
-
.. graphviz::
¶ Directive to embed graphviz code. The input code for
dot
is given as the content. For example:.. graphviz:: digraph foo { "bar" -> "baz"; }
In HTML output, the code will be rendered to a PNG or SVG image (see :confval:`graphviz_output_format`). In LaTeX output, the code will be rendered to an embeddable PDF file.
You can also embed external dot files, by giving the file name as an argument to
graphviz
and no additional content:.. graphviz:: external.dot
As for all file references in Sphinx, if the filename is absolute, it is taken as relative to the source directory.
Changed in version 1.1: Added support for external files.
-
.. graph::
¶ Directive for embedding a single undirected graph. The name is given as a directive argument, the contents of the graph are the directive content. This is a convenience directive to generate
graph <name> { <content> }
.For example:
.. graph:: foo "bar" -- "baz";
Note
The graph name is passed unchanged to Graphviz. If it contains non-alphanumeric characters (e.g. a dash), you will have to double-quote it.
-
.. digraph::
¶ Directive for embedding a single directed graph. The name is given as a directive argument, the contents of the graph are the directive content. This is a convenience directive to generate
digraph <name> { <content> }
.For example:
.. digraph:: foo "bar" -> "baz" -> "quux";
New in version 1.0: All three directives support an alt
option that determines the image’s
alternate text for HTML output. If not given, the alternate text defaults to
the graphviz code.
New in version 1.1: All three directives support a caption
option that can be used to give a
caption to the diagram.
Changed in version 1.4: All three directives support a graphviz_dot
option that can be switch the
dot
command within the directive.
New in version 1.5: All three directives support a align
option to align the graph horizontal.
The values “left”, “center”, “right” are allowed.
New in version 1.6: All three directives support a name
option to set the label to graph.
There are also these new config values:


Condition - {{car.condition}}
Color - {{car.color}}
Transmission - {{car.transmission}}
Year Of Production - {{car.yearOfProduction}}
Kilometers - {{car.kilometers}}
Price - {{car.price}}


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A PERFECT MURDER (1998)
A film review by Steve Kong Edited by Cher Johnson Copyright 1998 Steve Kong
With a name like A Perfect Murder, a movie has a lot to live up to. Is A Perfect Murder perfect?
Stephen Taylor (Michael Douglas) is a big money mover who is married to Emily (Gwyneth Paltrow), the daughter of a rich family. Emily is having an affair with a grimy looking painter, David (Viggo Mortenson).
The questions posed by the script are: Does Emily's husband know about the affair? What would he do if he found out? How long can the affair last? And is it really love that David is searching for?
If you have seen the previews for A Perfect Murder, you'll know that Emily's husband does find out and he wants Emily dead. He plans the "perfect murder" in which David is the killer. The plan is foolproof -- or is it?
The plot unfolds slowly and is deliciously evil. Which makes the ending feel somewhat like a cheat.
Director Andrew Davis (The Fugitive) tries to redeem himself here after his horrible Chain Reaction. And Davis does an admirable job with A Perfect Murder -- though, it never reaches the heights that his The Fugitive did.
The look of the film is engrossing with its high contrast lighting. This has been done before and better -- look at Douglas's last film, The Game, for this.
Performance-wise the best comes from Viggo Mortensen. Mortensen is one of the most underrated and underused actors in Hollywood -- this is a shame because Mortensen is a versatile and talented actor. If you've never seen Mortensen, check out Crimson Tide and G.I. Jane also.
If Gwyneth Paltrow's Sliding Doors character had a third story line, A Perfect Murder would have been it. Paltrow does nothing new here. Following in Paltrow's footsteps, Douglas is not doing anything either. Douglas almost seems like he didn't leave the set of his last film, The Game.
Paltrow and Douglas's lack in the performance arena doesn't hurt the film at all though. The script is well written enough to make us overlook these flaws.
One performance to look for is David Suchet as the head police investigator. His part is small, but he makes a good impression.
Overall, I really liked A Perfect Murder and I recommend it. The film is not a perfect murder, but it is still quite a good killing.
--- Steve Kong<EMAIL_ADDRESS>reviews from a guy who loves the cinema http://boiled.sbay.org/boiled/
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"In order to succeed, your desire for success needs to be greater than your fear of failure"
--Albert Einstein
Albert Einstein ( 1879 - 1955) the most recognized scientists and considered the most influential physicist of the 20th century. He is well known for his special and general theories of relativity that proposed revolutionary changes in human concepts of time, space and gravity. He stands behind probably the most famous formula E = mc² . In 1921, Einstein won the Nobel Prize for Physics for explanation of the phenomenon known as the photoelectric effect. He worked on many other influential theories and projects including: the deflection of light by gravity, the quantum theory of atomic motion in solids, Brownian motion, an explanation for capillary action and much more.Throughout his life, Einstein published more than 300 scientific and 150 non-scientific papers.
Helped to explain that time and motion are relative to their observers, as long as the speed of light remains constant and natural laws are the same throughout the universe.
Proposed that gravity is a curved field in the space-time continuum created by the existence of mass.
Demonstrates that mass and energy are the same physical entity and can be changed into each other.Concept that led to the development of nuclear energy.
Proposed that light is composed of small packets of energy(photons) that have wave like properties. This theory later led to the invention of the Television).
Oservation of the zigzag movement particles in suspension, helped to prove the existence of atoms and molecules- fundamental discovery for almost every branch of science.
Research supported by the U.S., that led to the development of the atomic bomb in 1945. After realizing atomic bomb's destruction power, Einstein campaigned for a ban on nuclear weapon.


Frequently Asked Questions¶
The following are a collection of frequently asked questions about FOOOF.
These answers focus on the ideas and concepts relating to parameterizing neural power spectra.
Code specific and hands-on aspects of using FOOOF are covered in the tutorials and examples.
Table of Contents¶
What if I don’t identify a peak? Is there no periodic activity?
How should I interpret peaks? Are they equivalent to oscillations?
Why is this different from other methods / what makes it work?
I’m interested in a particular oscillation band, should I fit a small range?
What does the ‘aperiodic’ component of the signal mean / reflect?
What is FOOOF?¶
FOOOF is an open-source Python toolbox to parameterize neural power spectra.
In particular, FOOOF is a model driven approach that assumes that neurophysiological time series are comprised of two separable components, reflecting periodic and aperiodic activity. FOOOF does therefore rely on the assumption that these two components are indeed separable components of the underlying data, though it is agnostic to their physiological origin and putative functional roles.
FOOOF operates on frequency representation of neurophysiological times series (power spectra). At it’s core, FOOOF is a fitting procedure to measure these two components - the periodic and aperiodic components - in power spectra. The full model fit consists of parameterizing these aperiodic and periodic components, as well as a full (combined) model fit of the whole power spectrum.
The full mathematical description of the model is described in the tutorials.
What do you mean by ‘periodic’ and ‘aperiodic’ activity?¶
By ‘periodic’ activity we mean features of the signal that are rhythmic, that are typically referred to as oscillations. This does not require that periodic activity be continuous, as oscillatory activity often exhibits as ‘bursts’ in the time series. In practice, putative oscillations, reflecting periodic activity, are operationally defined and detectable by FOOOF if they exhibit as band-limited power over and above the aperiodic component of the signal in the power spectrum.
By ‘aperiodic’ activity we mean the arrhythmic component of the signal, that typically follow a 1/f like function, whereby the power systematically decreases with increasing frequencies.
Why should I use FOOOF?¶
Though we often focus on the periodic (rhythmic or oscillatory) components, neurophysiological recordings of electromagnetic activity in the brain are composed of not only this periodic activity, which is only sometimes present, but also includes aperiodic activity.
Both of these components of the signal, periodic and aperiodic activity, are known to be dynamic and can vary both within and between subjects. These two components of the signal have very different properties and interpretations, however many commonly applied analyses often conflate changes between them, as they do not systematically separate these two aspects of the data, potentially leading to misinterpretation of the results.
It is therefore of the utmost importance to explicitly measure both components of the signal to be sure what is actually changing in the data. This is what FOOOF is designed to do. By explicitly modeling and measuring both aperiodic and periodic components of the signal, FOOOF allows for separating out and quantifying which features of the data are changing, and in what ways.
Why is it important to measure aperiodic activity?¶
Historically, much more work has focused on periodic components of neural signals, with a focus on measuring putative oscillatory activity and investigating their properties and potential functional roles.
Though the aperiodic, or 1/f, component of the signal has long known to be present in the data, more recent work has demonstrated that this aperiodic activity is dynamic, and systematically varies both within [1] and between [2] subjects.
The dynamic properties of the aperiodic activity means that even if periodic activity is still the focus of the analysis, quantification of such data must explicitly account for aperiodic activity to appropriately measure which components of the data are actually changing.
In addition, aperiodic components of neural signals may be important and interesting in their own right as an interesting signal to investigate as potential biomarker. For example, recent work has been investigating potential physiological interpretations of aperiodic activity [3] (see also below for more on this).
Why call it ‘aperiodic’? Why not call it ‘1/f’ or ‘noise’?¶
What we now call the ‘aperiodic’ component of the signal has variously been called, by us and others, either the ‘1/f’ component activity of the signal (also referred to as ‘scale free’), or sometimes the ‘background’ activity, or ‘1/f noise’ or ‘background noise’.
We have moved away from these terms, as they are imprecise, and/or theoretically loaded. In terms of calling it the ‘1/f’, though the signal often approximates ‘1/f’ over at least some frequency ranges, it is not truly ‘1/f’ across all frequencies.
From the physics perspective, ‘1/f’ activity is sometimes referred to as ‘noise’, as shorthand for ‘statistical noise’ or coloured noise. As well as not necessarily meeting technical definitions, referring to aperiodic neural activity as noise in this way is also different to how the terms ‘signal’ & ‘noise’ are typically used in neuroscience. In neuroscience, the terms ‘signal’ & ‘noise’ are typically used to refer to data of interest, and unwanted or artefactual data, respectively. We consider that the aperiodic could be the signal of interest for some investigations. Therefore, we have moved away from this term so as not to imply it is either strictly statistical noise, nor is it merely unwanted ‘noise’ in the recording.
For similar reasons, we have moved away from calling it ‘background’ activity, as this implies a signal of interest in the foreground, and we consider that aperiodic activity is of theoretical interest itself, and not merely the ‘background’ to periodic activity.
For these reasons, we prefer and use the term ‘aperiodic’ as a neutral description of this component of the signal.
Why should I look for a peak to consider periodic activity?¶
Due to neural signals containing aperiodic neural activity, there will always be power within any given frequency range. If the aperiodic activity changes, this measured power could also change. All this can happen without any truly periodic activity being present in the data. Even if there is periodic activity, quantifications of it can be confounded by aperiodic activity.
To be able to argue that there is periodic activity, and measure whether it changes, one should be able to observe a peak in the power spectrum, reflecting band specific power, over and above the aperiodic activity in the data. Using peaks in the power spectrum as evidence for periodic activity is an established idea (see, for example [4]), which we here formalize into a model quantification.
What if I don’t identify a peak? Is there no periodic activity?¶
If a peak is not detected, in the power spectrum, within a given frequency band, this is consistent with there being no periodic activity at that frequency. Without a detected peak, we argue that there is no evidence of periodic activity, at that frequency, over and above the power as expected by the aperiodic activity. In this situation, one should be very wary of interpreting activity at this frequency, as it is most likely reflects aperiodic activity.
However, we cannot prove a negative, and so the absence of a detected peak does not imply that there is definitively no periodic activity at that particular frequency. There could be very low power periodic activity, and/or periodic activity that is variable through time (bursty) such as to not display a prominent peak across the analyzed time sample.
How should I interpret peaks? Are they equivalent to oscillations?¶
Peaks, defined as regions of power over and above the aperiodic background are considered to be putative periodic activity. However, there is not necessarily a one-to-one mapping of peaks, as detected by the FOOOF algorithm, and oscillations in the data.
One reason for this is that peaks are fit as gaussians, and multiple overlapping gaussians can, in some cases, be fit to what one might otherwise consider to likely comprise a single oscillatory component in the data. This is a consequence of fitting a symmetric function (gaussians) to what can be an asymmetric peak power spectrum.
Because of this, it is often useful to focus on the dominant (highest power) peak within a given frequency band from a FOOOF analysis, as this peak will offer the best estimate of the putative oscillations center frequency and power. If analyzing bandwidth of extracted peaks, than overlapping peaks should always be considered. FOOOF is not currently optimized for inferring whether multiple peaks within a frequency band likely reflect distinct oscillations or not.
It can also be the case that peaks in the power spectrum may reflect harmonic power from an asymmetric oscillation in the time domain [5], and so a peak in a particular frequency range does not necessarily imply that there is a true oscillation at that particular frequency in the data. For example, an asymmetric wave at 10 Hz can exhibit power at a 20 Hz harmonic, but this does not necessarily imply there are any 20 Hz rhythmic components in the signal. To investigate potential harmonics arising from asymmetric periodic activity, one can use ByCycle a Python tool for analyzing neural oscillations and their properties cycle-by-cycle [5].
Why is this different from other methods / what makes it work?¶
Many existing approaches do not attempt to separate the periodic and aperiodic components of the signal. Of methods that do attempt to measure periodic and/or aperiodic signal properties, one difference is that FOOOF does not prioritize one or the other component, but attempts to jointly learn both components.
As a quick version, the joint learning procedure and some developments in fitting the aperiodic component are why we think FOOOF seems to do better at measuring these signal properties. More in depth analysis of the properties of FOOOF, and systematic comparisons with other methods (through simulations) are upcoming.
Are there settings for the FOOOF algorithm?¶
There are some settings for the algorithm that need to be set before it can be run, though the default values are often good enough to get started on most datasets.
A full description of the settings - what they are and how to choose them - is covered in the tutorials.
How do I pick settings?¶
There is often at least some level of picking the parameter settings that is needed to get FOOOF to fit well. To do so, we recommend selecting a subset of power spectra from your dataset, fitting FOOOF models, and tuning the settings on this dataset, like a training set. Once you have chosen the parameters for the dataset, you can apply these settings to the data to be analyzed.
In order to be able to systematically compare FOOOF model outputs between conditions / tasks / subjects, etc, we recommend using the same FOOOF settings across any particular dataset.
FOOOF tends not to be overly sensitive to small changes in parameter settings. You can also perform a sensitivity analysis - repeating the analysis with different settings - to examine if the outputs are strongly dependent on the settings you choose.
I’m interested in a particular oscillation band, should I fit a small range?¶
Generally, no, it is better to always try and fit a broad range, rather than to fit a small frequency range, even if one is interested in a specific oscillation band in particular.
This is because if a small frequency range is used, it becomes much more difficult to estimate the aperiodic component of the data, and without a good estimate of the aperiodic component, it can also be more difficult to effectively estimate the periodic components.
Therefore, if one is interested in, for example, alpha oscillations (approximately 7-14 Hz), then we still recommend fitting a broad range (for example, 3-40 Hz), and then extracting the alpha oscillations post-hoc. There are utilities in fooof.analysis to extract oscillations from particular bands, and examples of this on the examples page.
What does the ‘aperiodic’ component of the signal mean / reflect?¶
We don’t know. Exactly what the ‘aperiodic’ component of the signal is, in terms of where it comes from, and what reflects is an open research question.
Descriptively, we know that aperiodic activity is always there, and is a prominent component of the signal. This has been known for a long time, and there are many hypotheses and ideas around about ‘1/f’-like and aperiodic properties of neural time series, and what they might mean. Many of the ideas regarding the potential functional properties of 1/f or ‘scale-free’ systems comes from work in physics and from the context of dynamical systems [6].
We, and others, also work on physiological models of where aperiodic activity might come from. One such model, from the VoytekLab, explores the hypothesis that the aperiodic properties of the local field potential arise from balanced activity in excitatory and inhibitory activity (EI balance), and shows how changes in the aperiodic properties of a signal can be predicted from changes in EI balance [3].
What data can I use with FOOOF?¶
FOOOF operates on power spectra derived from electrophysiological or magnetophysiological time series, that measure local field potential (LFP) data - in the broad sense, covering intracranial LFP data, electroencephalography (EEG), magnetoencephalography (MEG), and electrocorticography (ECoG) / intracranial EEG (iEEG).
FOOOF should work across all of these modalities, and is broadly agnostic to the details of the data. However, data from different modalities may require different settings.
Does it matter how I calculate the power spectra?¶
For the most part, no, it does not matter exactly how you calculate power spectra that you will fit with FOOOF. For example, using different methods to estimate the power spectrum, such as Welch’s or wavelet approaches, should all be fine.
Regardless of how you calculate them, the properties of the power spectra do matter somewhat to FOOOF - for example, the better the frequency resolution the more resolution you will have for estimating center frequencies and bandwidths of detected peaks, and the ‘smoother’ the spectra, the better FOOOF will be able to fit.
Does this work on epoched data? Can I fit single trials?¶
Yes, FOOOF can be used in task based analyses.
There are broadly two approaches you can take:
Calculate FFT’s or power spectra per trial, and average across all trials in a condition, to fit one FOOOF model per condition
This approach is better if you want to use FOOOF to characterize short time segments in a task design
Calculate power spectra per trial, and fit FOOOF models per trial, analyzing the distribution of FOOOF outputs per condition
This approach can be used when you have relatively long time segments to fit. We currently recommend at least about 500 ms for using this approach, though it is somewhat dependent on the cleanliness of the data, and what aspects of the FOOOF outputs you want to analyze.
Ultimately these two approaches should converge to be the same, though depending on the data and analysis goals, one or the other might be more appropriate.
Is there a time resolved version?¶
Since it operates on frequency representations (power spectra) FOOOF is not, by construction, a time resolved method. However, similar to other frequency estimation approaches that are used in a time-resolved manner, it can be applied in a sliding window fashion, which could be used to estimate results analogous to a spectrogram. This functionality is not currently directly included in FOOOF, but is a topic of ongoing work, and will hopefully be available soon.
Why is it called FOOOF?¶
FOOOF stands for “fitting oscillations & one-over f”.
This was a working title for the project that stuck as the name of the code and the tool. We have somewhat moved away from referring to the components that FOOOF fits in this way, now preferring to talk about periodic and aperiodic activity. FOOOF therefore is something of a legacy name, for a tool for parameterizing neural power spectra.
How do I cite FOOOF?¶
If you use FOOOF for analyses, or reference it’s approach, please cite the bioRxiv preprint.
What colour is FOOOF?¶
FOOOF is orange.
References¶
[1] Podvalny et al (2017). A Unifying Principle Underlying the Extracellular Field Potential Spectral Responses in the Human Cortex. DOI: 10.1152/jn.00943.2014
[2] Voytek et al (2015). Age-Related Changes in 1/f Neural Electrophysiological Noise. DOI: 10.1523/JNEUROSCI.2332-14.2015
[3] Gao, Peterson & Voytek (2017). Inferring synaptic excitation/inhibition balance from field potentials. DOI: 10.1016/j.neuroimage.2017.06.078
[4] Buzsaki, Logothetis & Singer (2013). Scaling Brain Size, Keeping Timing: Evolutionary Preservation of Brain Rhythms. DOI: 10.1016/j.neuron.2013.10.002
[5] Cole & Voytek (2019). Cycle-by-cycle analysis of neural oscillations. DOI: 10.1152/jn.00273.2019
[6] He (2014). Scale-free Brain Activity: Past, Present and Future. DOI: 10.1016/j.tics.2014.04.003


A New York Company, makers of Kifu, member and event management for communities. For services, email us at contact@shukaico.com.


AMERICAN HISTORY X (1998)
Rating: 3 stars (out of 4.0) ******************************** Key to rating system: 2.0 stars - Debatable 2.5 stars - Some people may like it 3.0 stars - I liked it 3.5 stars - I am biased in favor of the movie 4.0 stars - I felt the movie's impact personally or it stood out ********************************* A Movie Review by David Sunga
Directed by: Tony Kaye
Written by: David McKenna
Ingredients: neo-Nazi, redemption, legacy, family
Starring: Edward Norton, Edward Furlong
Synopsis: When Venice Beach high school student Danny Vinyard (Edward Furlong) becomes drawn to the neo-Nazi movement, the principal puts Danny in a personal tutoring program whereby he must write about the life his older brother Derek (Edward Norton), whom he idolizes. Through Danny's eyes, AMERICAN HISTORY X tells the tale of how wayward brother Derek became involved with the neo-Nazi movement, was sentenced to prison for a shooting, served time, and faced the legacy of his mistakes.
Opinion: AMERICAN HISTORY X is a non-formula movie that approaches neo-Nazism from a non-stereotypical view. It shows how seemingly average teens with empty lives might get caught up in a racist social movement perpetrated by demagogues with political ambitions. Aside from redemption, another central theme of AMERICAN HISTORY X is the concept of legacy. A man might serve time in prison, but for both victims and aggressors, the consequences of former actions continue. Although the pace of the film somewhat uneven at times, and there is perhaps one unnecessary scene in the last third of the movie, AMERICAN HISTORY X is an excellent family drama that is well photographed and poignant as well as superbly acted.
Reviewed by David Sunga November 21, 1998
Copyright © 1998 by David Sunga This review and others like it can be found at THE CRITIC ZOO: http://www.criticzoo.com email<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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{% extends 'books/shelf.html' %} {% load static %} {% load extra_tags %} {% block nav %}
Important Instruction: Please take care to upload the files with an appropriate name so that it is easy for others to understand.
NOTE: You can upload multiple files of the same type, like multiple lectures, notes, assignments, tutorials or books for the same course.


Off-by-One Overflows Welcome to the OWASP Hotel! Can you find out which room a VIP guest is staying in? In order to access the Internet, you need to provide us the following information: Step 1/2 Ensure that your first and last names are entered exactly as they appear in the hotel's registration system. First Name: * Last Name: * Room Number: * * The above fields are required for login.


The menu Hybrid contains the following commands:
| RSA-AES Encryption | Hybrid encryption based on AES and RSA. |
| RSA-AES Decryption | Hybrid decryption based on AES and RSA. |
| ECC-AES Encryption | Hybrid encryption based on AES and ECC. |
| ECC-AES Decryption | Hybrid decryption based on AES and ECC. |
More information on hybrid encryption can be found in the CT Book.


Wifi: username / password
| Install the Tools | http://bit.ly/CnCTheTools |
| This presentation | http://bit.ly/CnCAprSlides |
| The worksheet | http://bit.ly/CnCAprWork |
All slides in this presentation are licensed by http://kcwomenintech.org under a Creative Commons Attribution-ShareAlike 4.0 International License.
Program Director
Program Co-Director
Curriculum Director
Technical Materials Director
Mentor Director
Marketing Director
A utility that tracks changes in files
Changes to source code are recorded
If a mistake is made, you can rollback changes
Concurrently work on code
Work without a network connection
Fast
Branches
Staging area*
Stashing*
Clone a repo from Github using GitKraken & see the tree visualization of commits and branches from collaborators.
Wifi: username / password
| This presentation | http://bit.ly/CnCAprSlides |
| The worksheet | http://bit.ly/CnCAprWork |
| Install the Tools | http://bit.ly/CnCTheTools |
All slides in this presentation are licensed by http://kcwomenintech.org under a Creative Commons Attribution-ShareAlike 4.0 International License.
git init
git clone [url]
Git Term: origin
When the clone is pulled down to your machine, it keeps a reference to the parent, which the default term in git is origin
git pull
Fetches and merges: Updates references & merges those updates into your local repo
git fetch
Updates references in your local repo, but does not change any files
git status
Shows information about status, including staged and unstaged changes
git add [filepath]
git add [dir]
git add .

Wifi: username / password
| This presentation | http://bit.ly/CnCAprSlides |
| The worksheet | http://bit.ly/CnCAprWork |
| Install the Tools | http://bit.ly/CnCTheTools |
All slides in this presentation are licensed by http://kcwomenintech.org under a Creative Commons Attribution-ShareAlike 4.0 International License.
git diff
git diff [filepath]
git difftool
git diff --staged
Git Term: remote
Where your code is stored
The path of where the code came from [origin]
The path where the code is going [upstream]
git merge
Take the code that has diverged from origin and apply it (merge) it back into the parent branch
git merge [branch name]
Take the code from [branch name] that has diverged from origin and apply it (merge) it back into the parent branch
git rebase -i HEAD~4
Apply the last 4 commits, "squashed" as 1 commit
git log
Shows commit history
git push
Updates remote using local and sends necessary objects
Edit a file and push changes.
Wifi: username / password
| This presentation | http://bit.ly/CnCAprSlides |
| The worksheet | http://bit.ly/CnCAprWork |
| Install the Tools | http://bit.ly/CnCTheTools |
All slides in this presentation are licensed by http://kcwomenintech.org under a Creative Commons Attribution-ShareAlike 4.0 International License.
A branch is a pointer to a commit
git checkout -b [new-branch-name]
git checkout [existing-branch-name]
Allows teammates to review your code changes & easily make comments/suggestions
Allows reviewers to approve, comment, or request changes to the overall PR
For when you want to work on a repo without affecting the original repo
git clone [fork_url]
Create a branch, complete a merge, open a pull request, and fork a repo.
Wifi: username / password
| This presentation | http://bit.ly/CnCAprSlides |
| The worksheet | http://bit.ly/CnCAprWork |
| Install the Tools | http://bit.ly/CnCTheTools |
All slides in this presentation are licensed by http://kcwomenintech.org under a Creative Commons Attribution-ShareAlike 4.0 International License.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/erased.html ---
@Gabriel Ebner What would be the best way to define the following type:
erased A
is a type with a computable function A -> erased A
and a noncomputable function erased A -> A
A
and erased A
are (noncomputably) equivalent with those functionserased A
is VM-erased, meaning elements of this type are stored as the "proof object" / "neutral element"Attempt 1:
import data.set.basic data.equiv def erased (α : Type*) : Type* := set.range (singleton : α → set α) namespace erased @[inline] def mk {α} (a : α) : erased α := ⟨_, a, rfl⟩ noncomputable def out {α} (a : erased α) : α := classical.some a.2 theorem mk_out {α} (a : α) : (mk a).out = a := eq.symm $ set.mem_singleton_iff.1 $ by unfold out; rw [classical.some_spec (mk a).2]; simp [mk] theorem out_mk {α} (a : erased α) : mk (out a) = a := subtype.eq $ set.ext $ λ b, by simp [out, mk, classical.some_spec a.2] noncomputable def equiv (α) : erased α ≃ α := ⟨out, mk, out_mk, mk_out⟩ end erased
The problem with this encoding is that erased A
is essentially a set A
, which is a A -> Prop
, which is stored as a closure returning a proof object. Thus it isn't fully erased, so it still causes its arguments to be evaluated:
#eval erased.mk (2+2) -- (erased.mk 4)
Do you know why type families aren't erased like types are?
Why not use nonempty
?
it's not noncomputably equivalent to A
I withdraw my question, I get it
The goal is to have a piece of "data" that is actually erased but still exists from the lean modeling POV
I see
Wait, this no longer works? Let me check.
Mmmh, I can't reproduce this here. The 2+2 computation is completely erased. Let me upgrade Lean.
Still can't reproduce.
Actually that #eval
doesn't work, lean complains about too few arguments since it's a closure which is waiting for an argument. This shows the closure creation:
set_option trace.compiler.code_gen true def f := let x := erased.mk (2+2) in x -- [compiler.code_gen] f._lambda_1 1 -- 0: scnstr #0 -- 1: ret -- [compiler.code_gen] f 0 -- 0: closure f._lambda_1 0 -- 1: localinfo x @ 0 -- 2: push 0 -- 3: drop 1 -- 4: ret
What do you get with these changes (added has_repr
and the set_option
):
set_option trace.compiler.optimize_bytecode true def erased (α : Type*) : Type* := set.range (singleton : α → set α) namespace erased @[inline] def mk {α} (a : α) : erased α := ⟨_, a, rfl⟩ noncomputable def out {α} (a : erased α) : α := classical.some a.2 theorem mk_out {α} (a : α) : (mk a).out = a := eq.symm $ (set.mem_singleton_iff _ _).1 $ by unfold out; rw [classical.some_spec (mk a).2]; simp [mk] theorem out_mk {α} (a : erased α) : mk (out a) = a := subtype.eq $ set.ext $ λ b, by simp [out, mk, classical.some_spec a.2] noncomputable def equiv (α) : erased α ≃ α := ⟨out, mk, out_mk, mk_out⟩ instance (α): has_repr (erased α) := ⟨λ _, "erased"⟩ end erased #eval (erased.mk (2+2)) -- (erased.mk 4)
Hmm, as a workaround you can use have ..., from ..., ...
instead of let.
Well, the let was just to force the closure creation out of tail call position
if you use have
instead it just adds an additional argument to the main function
I think the problem is just that prop-erasure is not implemented for let
, because nobody ever uses lets except for data.
Hm, you can still clearly tell the difference between these two pieces of code:
set_option trace.compiler.code_gen true #eval let x := 2 + 2 = 4 in x #eval let x := erased.mk (2+2) in x
I'm glad to see that 2+2
is not evaluated anywhere in the generated code, but it is still creating a closure returning #0
rather than #0
itself
Now I remember: we can't erase type families to #0
. We need to erase them to a function type, but I don't think such an erasure is implemented. If mk
is not inlined, then we actually compute 2+2
here.
Here's attempt number 2, which tries to encode each element of erased A as a straight type:
import set_theory.ordinal universe u def erased (α : Type u) : Type (u+1) := {o // o < cardinal.ord (cardinal.mk α)} namespace erased open cardinal ordinal noncomputable def wo' (α : Type u) : {r : α → α → Prop // ∃ [wo : is_well_order α r], ord (mk α) = @ordinal.type α r wo} := classical.choice $ let ⟨r, wo⟩ := cardinal.ord_eq α in ⟨⟨r, wo⟩⟩ def wo (α : Type u) (x y) := (wo' α).1 x y instance {α} : is_well_order α (wo α) := (wo' α).2.fst theorem wo_eq (α : Type u) : ord (mk α) = type (wo α) := (wo' α).2.snd @[inline] def type {α} (r : α → α → Prop) [wo : is_well_order α r] : ordinal := quot.mk _ ⟨α, r, wo⟩ @[inline] def typein {α} (r : α → α → Prop) [is_well_order α r] (a : α) : ordinal := type (subrel r {b | r b a}) @[inline] def mk {α} (a : α) : erased α := ⟨typein (wo α) a, (wo_eq α).symm ▸ typein_lt_type (wo α) a⟩ noncomputable def out {α} (a : erased α) : α := enum (wo α) a.1 (wo_eq α ▸ a.2) theorem out_mk {α} (a : α) : (mk a).out = a := enum_typein _ _ theorem mk_out {α} (a : erased α) : mk (out a) = a := subtype.eq $ typein_enum _ _ noncomputable def equiv (α) : erased α ≃ α := ⟨out, mk, mk_out, out_mk⟩ end erased
Since you can't really (provably) distinguish types except by their cardinality, this approach is rather more involved
It also requires a lot of inlining; I had to restate the definitions of ordinal.type
and ordinal.typein
so they would be inlined
Anything that relies on inlining will have the closure problem.
Not true:
set_option trace.compiler.code_gen true def f := @erased.mk ℕ #eval let x := f (2+2) in x
Note that even though f
is not inlined 2+2
is not evaluated
If you're okay with a bit of extra ugliness, you can define an erase
function in both versions:
@[inline] def erase {α} (a : erased α) : erased α := ⟨λ x, a.1 x, a.2⟩ -- 👌 def f' := let x := erased.mk (2+2) in x.erase
Mmh, I still get a call to erased.mk
and 2+2
, but no call to f
since it is (of course) inlined.
You should set the optimize_bytecode
option instead of code_gen
, otherwise you miss out on all the optimizations.
I'm confused: why does erase
work? The argument count goes down, but it still returns #0
. I thought you said type/proof lambdas can't be implemented as #0
?
set_option trace.compiler.optimize_bytecode true def f1 : erased ℕ := (erased.mk (2+2)) def f2 : erased ℕ := (erased.mk (2+2)).erase
I'm a bit lost as well. Apparently the apply
operation is special-cased to also work on #0
.
That's why I would expect that type families should also compile as #0
rather than lam x, #0
You know, type families are actually erased, it's just that subtypes aren't because they haven't been erased yet....:
def f (x : set ℕ) := x def g (x : { s : set ℕ // true }) := x
Last updated: Apr 24 2020 at 19:02 UTC


Kieran Tierney's Scotland absence explained by Steve Clarke as Arsenal star misses out again
The left-back has made a phased return to action after a sustained injury lay-off.
by Liam Bryce, https://www.dailyrecord.co.uk/authors/liam-bryce/Steve Clarke has revealed Arsenal requested Kieran Tierney again be left out of the Scotland squad – despite now being a regular in Unai Emery’s side.
The former Celtic left-back is not among the 25-man contingent selected to face Cyprus and Kazakhstan in Euro 2020 qualifying, having sat out during the last international break.
Tierney is recovering from a long-term pelvic issue and also underwent surgery on a double hernia before making a £25million move to London during the summer.
His return is being carefully managed by Arsenal, although he has now made six appearances totalling 426 minutes.
Clarke admits the 22-year-old's continued absence is frustrating but insists he has plenty of depth in the shape of Andy Robertson and Greg Taylor.
The Scotland boss said: "There's an ongoing issue. Arsenal asked us not to select him. It’s a bit frustrating but we have good cover in that position."
Clarke welcomed Steven Naismith back into the fold after he returned to the pitch for Hearts at the weekend.
The striker is another whose season has been plagued by injury but Clarke is convinced he's fit enough to make an impact.
Clarke added: "He hasn’t played much but I spoke to him yesterday and his fitness is good. He expects to start the next game and he’ll be a good addition to the squad."
Also returning are Ryan Jack, Oli McBurnie and Liam Cooper after injury ruled them out of clashes with Russia, Belgium and San Marino.
Full squad
Goalkeepers
Craig MacGillivray (Portsmouth), David Marshall (Wigan), Jon McLaughlin (Sunderland)
Defenders
Liam Cooper (Leeds Utd), Michael Devlin (Aberdeen), Declan Gallagher (Motherwell), Scott McKenna (Aberdeen), Stephen O’Donnell (Kilmarnock), Liam Palmer (Sheff Wed), Andy Robertson (Liverpool), Greg Taylor (Celtic).
Midfielders
Stuart Armstrong (Southampton), Ryan Christie (Celtic), John Fleck (Sheff Utd), Ryan Jack (Rangers), John McGinn (Aston Villa), Callum McGregor (Celtic), Kenny McLean (Nowrich City), Scott McTominay (Manchester Utd), Oliver Burke (Alves*), James Forrest (Celtic), Ryan Fraser (Bournemouth).
Strikers
Oli McBurnie (Sheff Utd), Steven Naismith (Hearts), Lawrence Shankland (Dundee Utd).


Talking about your hometown or a city that is interesting to you is a great way to drive conversation to something you are confortable talking about. Grab a fun fact or two and practice introducing them. "I really like traveling and I'm new to tech. I just read that Berlin has a thriving tech startup community. What has your experience been with traveling?"
{{stat.name}}: {{stat.score_out_of_10 | number : '1.2-2'}}/10
Step 1 - Data: Choose a few categories that strike you. Don't try to memorize all of the info.
Step 2 - Opening: Personalize your intro or response. Even if you've never visited {{selection}} something drew you to it. Don't be afraid to share. "I've always wanted to learn a language and I love Brazilian music. Rio de Janeiro seems like it has a great vacation vibe. Where would you want to travel in South America?"
Step 3 - Continuing the conversation: Try to ask open-ended questions. "What's your favorite part of {{selection}}?" versus "Have you ever been to {{selection}}?"
Step 4 - Individualize: Add any anecdotal information. People can talk about the weather, sports, etc with anyone. Sharing your personal experience is the first step to a genuine relationship or at minimum keeping someone from falling asleep mid-conversation with you.
Step 5 - Tact: Boost your self-confidence by rehearsing with a few friends or writing notes for yourself. If you don't get the reaction you intended...Dont worry! Regroup and try again.


Handles all requests relating to compute resources (e.g. guest VMs,
networking and storage of VMs, and compute hosts on which they run).
-
class API(image_service=None, network_api=None, volume_api=None, security_group_api=None, **kwargs)
Bases: nova.db.base.Base
API for interacting with the compute manager.
-
add_fixed_ip(context, target, *args, **kwargs)
Add fixed_ip from specified network to given instance.
-
attach_volume(context, target, *args, **kwargs)
Attach an existing volume to an existing instance.
-
backup(context, target, *args, **kwargs)
Backup the given instance
| Parameters: |
- instance – nova.db.sqlalchemy.models.Instance
- name – name of the backup or snapshot
name = backup_type # daily backups are called ‘daily’
- rotation – int representing how many backups to keep around;
None if rotation shouldn’t be used (as in the case of snapshots)
- extra_properties – dict of extra image properties to include
|
-
confirm_resize(context, target, *args, **kwargs)
Confirms a migration/resize and deletes the ‘old’ instance.
-
create(context, instance_type, image_href, kernel_id=None, ramdisk_id=None, min_count=None, max_count=None, display_name=None, display_description=None, key_name=None, key_data=None, security_group=None, availability_zone=None, user_data=None, metadata=None, injected_files=None, admin_password=None, block_device_mapping=None, access_ip_v4=None, access_ip_v6=None, requested_networks=None, config_drive=None, auto_disk_config=None, scheduler_hints=None)
Provision instances, sending instance information to the
scheduler. The scheduler will determine where the instance(s)
go and will handle creating the DB entries.
Returns a tuple of (instances, reservation_id)
-
create_db_entry_for_new_instance(context, instance_type, image, base_options, security_group, block_device_mapping)
Create an entry in the DB for this new instance,
including any related table updates (such as security group,
etc).
This is called by the scheduler after a location for the
instance has been determined.
-
delete(context, target, *args, **kwargs)
Terminate an instance.
-
delete_instance_metadata(context, target, *args, **kwargs)
Delete the given metadata item from an instance.
-
detach_volume(context, volume_id)
Detach a volume from an instance.
-
force_delete(context, target, *args, **kwargs)
Force delete a previously deleted (but not reclaimed) instance.
-
get(context, instance_id)
Get a single instance with the given instance_id.
-
get_active_by_window(context, begin, end=None, project_id=None)
Get instances that were continuously active over a window.
-
get_all(context, search_opts=None, sort_key='created_at', sort_dir='desc', limit=None, marker=None)
Get all instances filtered by one of the given parameters.
If there is no filter and the context is an admin, it will retrieve
all instances in the system.
Deleted instances will be returned by default, unless there is a
search option that says otherwise.
The results will be returned sorted in the order specified by the
‘sort_dir’ parameter using the key specified in the ‘sort_key’
parameter.
-
get_console_output(context, target, *args, **kwargs)
Get console output for an instance.
-
get_diagnostics(context, target, *args, **kwargs)
Retrieve diagnostics for the given instance.
-
get_instance_bdms(context, instance)
Get all bdm tables for specified instance.
-
get_instance_faults(context, instances)
Get all faults for a list of instance uuids.
-
get_instance_metadata(context, target, *args, **kwargs)
Get all metadata associated with an instance.
-
get_instance_type(context, instance_type_id)
Get an instance type by instance type id.
-
get_lock(context, target, *args, **kwargs)
Return the boolean state of given instance’s lock.
-
get_vnc_console(context, target, *args, **kwargs)
Get a url to an instance Console.
-
inject_file(context, target, *args, **kwargs)
Write a file to the given instance.
-
inject_network_info(context, target, *args, **kwargs)
Inject network info for the instance.
-
is_volume_backed_instance(context, instance, bdms)
-
live_migrate(context, instance, *args, **kw)
Migrate a server lively to a new host.
-
lock(context, target, *args, **kwargs)
Lock the given instance.
-
pause(context, target, *args, **kwargs)
Pause the given instance.
-
reboot(context, target, *args, **kwargs)
Reboot the given instance.
-
rebuild(context, target, *args, **kwargs)
Rebuild the given instance with the provided attributes.
-
remove_fixed_ip(context, target, *args, **kwargs)
Remove fixed_ip from specified network to given instance.
-
rescue(context, target, *args, **kwargs)
Rescue the given instance.
-
reset_network(context, target, *args, **kwargs)
Reset networking on the instance.
-
resize(context, target, *args, **kwargs)
Resize (ie, migrate) a running instance.
If flavor_id is None, the process is considered a migration, keeping
the original flavor_id. If flavor_id is not None, the instance should
be migrated to a new host and resized to the new flavor_id.
-
restore(context, target, *args, **kwargs)
Restore a previously deleted (but not reclaimed) instance.
-
resume(context, target, *args, **kwargs)
Resume the given instance.
-
revert_resize(context, target, *args, **kwargs)
Reverts a resize, deleting the ‘new’ instance in the process.
-
set_admin_password(context, target, *args, **kwargs)
Set the root/admin password for the given instance.
-
snapshot(context, target, *args, **kwargs)
Snapshot the given instance.
| Parameters: |
- instance – nova.db.sqlalchemy.models.Instance
- name – name of the backup or snapshot
- extra_properties – dict of extra image properties to include
|
| Returns: | A dict containing image metadata
|
-
snapshot_volume_backed(context, instance, *args, **kw)
Snapshot the given volume-backed instance.
| Parameters: |
- instance – nova.db.sqlalchemy.models.Instance
- image_meta – metadata for the new image
- name – name of the backup or snapshot
- extra_properties – dict of extra image properties to include
|
| Returns: | the new image metadata
|
-
soft_delete(context, target, *args, **kwargs)
Terminate an instance.
-
start(context, target, *args, **kwargs)
Start an instance.
-
stop(context, target, *args, **kwargs)
Stop an instance.
-
suspend(context, target, *args, **kwargs)
Suspend the given instance.
-
trigger_provider_fw_rules_refresh(context)
Called when a rule is added/removed from a provider firewall
-
unlock(context, target, *args, **kwargs)
Unlock the given instance.
-
unpause(context, target, *args, **kwargs)
Unpause the given instance.
-
unrescue(context, target, *args, **kwargs)
Unrescue the given instance.
-
update(context, target, *args, **kwargs)
Updates the instance in the datastore.
| Parameters: |
- context – The security context
- instance – The instance to update
- kwargs – All additional keyword args are treated
as data fields of the instance to be
updated
|
| Returns: | None
|
-
update_instance_metadata(context, target, *args, **kwargs)
Updates or creates instance metadata.
If delete is True, metadata items that are not specified in the
metadata argument will be deleted.
-
class AggregateAPI(**kwargs)
Bases: nova.db.base.Base
Sub-set of the Compute Manager API for managing host aggregates.
-
add_host_to_aggregate(context, aggregate_id, host)
Adds the host to an aggregate.
-
create_aggregate(context, aggregate_name, availability_zone)
Creates the model for the aggregate.
-
delete_aggregate(context, aggregate_id)
Deletes the aggregate.
-
get_aggregate(context, aggregate_id)
Get an aggregate by id.
-
get_aggregate_list(context)
Get all the aggregates.
-
remove_host_from_aggregate(context, aggregate_id, host)
Removes host from the aggregate.
-
update_aggregate(context, aggregate_id, values)
Update the properties of an aggregate.
-
update_aggregate_metadata(context, aggregate_id, metadata)
Updates the aggregate metadata.
If a key is set to None, it gets removed from the aggregate metadata.
-
class HostAPI
Bases: nova.db.base.Base
-
get_host_uptime(context, host)
Returns the result of calling “uptime” on the target host.
-
host_power_action(context, host, action)
Reboots, shuts down or powers up the host.
-
set_host_enabled(context, host, enabled)
Sets the specified host’s ability to accept new instances.
-
set_host_maintenance(context, host, mode)
Start/Stop host maintenance window. On start, it triggers
guest VMs evacuation.
-
class KeypairAPI(**kwargs)
Bases: nova.db.base.Base
Sub-set of the Compute Manager API for managing key pairs.
-
create_key_pair(context, user_id, key_name)
Create a new key pair.
-
delete_key_pair(context, user_id, key_name)
Delete a keypair by name.
-
get_key_pair(context, user_id, key_name)
Get a keypair by name.
-
get_key_pairs(context, user_id)
List key pairs.
-
import_key_pair(context, user_id, key_name, public_key)
Import a key pair using an existing public key.
-
class SecurityGroupAPI(**kwargs)
Bases: nova.db.base.Base
Sub-set of the Compute API related to managing security groups
and security group rules
-
add_rules(context, id, name, vals)
-
add_to_instance(context, target, *args, **kwargs)
Add security group to the instance
-
create(context, name, description)
-
destroy(context, security_group)
-
ensure_default(context)
Ensure that a context has a security group.
Creates a security group for the security context if it does not
already exist.
| Parameters: | context – the security context |
-
get(context, name=None, id=None, map_exception=False)
-
get_rule(context, id)
-
is_associated_with_server(security_group, instance_uuid)
Check if the security group is already associated
with the instance. If Yes, return True.
-
list(context, names=None, ids=None, project=None, search_opts=None)
-
static new_cidr_ingress_rule(grantee_cidr, protocol, from_port, to_port)
-
static new_group_ingress_rule(grantee_group_id, protocol, from_port, to_port)
-
parse_cidr(cidr)
-
static raise_group_already_exists(msg)
-
static raise_invalid_cidr(cidr, decoding_exception=None)
-
static raise_invalid_group(msg)
-
static raise_invalid_property(msg)
-
static raise_not_found(msg)
-
static raise_over_quota(msg)
-
remove_from_instance(context, target, *args, **kwargs)
Remove the security group associated with the instance
-
remove_rules(context, security_group, rule_ids)
-
rule_exists(security_group, values)
Indicates whether the specified rule values are already
defined in the given security group.
-
trigger_handler(event, *args)
-
trigger_members_refresh(context, group_ids)
Called when a security group gains a new or loses a member.
Sends an update request to each compute node for each instance for
which this is relevant.
-
trigger_rules_refresh(context, id)
Called when a rule is added to or removed from a security_group.
-
validate_property(value, property, allowed)
Validate given security group property.
| Parameters: |
- value – the value to validate, as a string or unicode
- property – the property, either ‘name’ or ‘description’
- allowed – the range of characters allowed
|
-
check_instance_lock(function)
-
check_instance_state(vm_state=None, task_state=(None, ))
Decorator to check VM and/or task state before entry to API functions.
If the instance is in the wrong state, the wrapper will raise an exception.
-
check_policy(context, action, target, scope='compute')
-
policy_decorator(scope)
Check corresponding policy prior of wrapped method to execution
-
wrap_check_policy(func)
-
wrap_check_security_groups_policy(func)


|
JQ8400 MP3 Player Arduino Library
A simple library to control a JQ8400 MP3 Player Module from an Arduino.
|
Public Member Functions | |
| JQ8400_Serial (Stream &_Stream) | |
| Create JQ8400 object with a given serial object to communicate to the JQ8400. More... | |
| void | play () |
| Start playing the current file, if paused the playing is resumed. More... | |
| void | restart () |
| Restart the current track from the beginning. | |
| void | pause () |
| Pause the current file. More... | |
| void | stop () |
| Stop the current playing (if any). | |
| void | fastForward (uint16_t seconds=5) |
| Fast Forward by a number of seconds (default 5). | |
| void | rewind (uint16_t seconds=5) |
| Rewind by a number of seconds (default 5). | |
| void | next () |
| Play the next file. | |
| void | prev () |
| Play the previous file. | |
| void | nextFolder () |
| Play the next folder. | |
| void | prevFolder () |
| Play the previous folder. | |
| void | playFileByIndexNumber (uint16_t fileNumber) |
| Play a specific file based on it's FAT index number. More... | |
| void | interjectFileByIndexNumber (uint16_t fileNumber) |
| Interject the currently playing file (if any) with the given FAT index number file. More... | |
| void | playFileNumberInFolderNumber (uint16_t folderNumber, uint16_t fileNumber) |
| Play a specific file in a specific folder based on the name of those folder and file. More... | |
| void | playInFolderNumber (uint16_t folderNumber) |
| Play the first (?) file in a specific folder 00 to 99. More... | |
| void | seekFileByIndexNumber (uint16_t fileNumber) |
| Seek to a specific file based on it's FAT index number. More... | |
| void | abLoopPlay (uint16_t secondsStart, uint16_t secondsEnd) |
| A-B Loop for the file currently playing. More... | |
| void | abLoopClear () |
| Use this to break a currently running A-B loop. More... | |
| void | volumeUp () |
| Increase the volume by 1 (volume ranges 0 to 30). More... | |
| void | volumeDn () |
| Decrease the volume by 1 (volume ranges 0 to 30). More... | |
| void | setVolume (byte volumeFrom0To30) |
| Set the volume to a specific level (0 to 30). More... | |
| void | setEqualizer (byte equalizerMode) |
| Set the equalizer to one of 6 preset modes. More... | |
| void | setLoopMode (byte loopMode) |
| Set the looping mode. More... | |
| void | setSource (byte source) |
| Set the source to read mp3 data from. More... | |
| uint8_t | getSource () |
| Return the currently selected source. More... | |
| uint8_t | sourceAvailable (uint8_t source) |
Return boolean indicating if the given source is available (can be selected using setSource() ) More... | |
| void | sleep () |
| Put the device to sleep. More... | |
| void | reset () |
| Reset the device (softly). More... | |
| byte | getStatus () |
| Get the status from the device. More... | |
| uint8_t | busy () |
| Return if the device is busy (playing) or not. More... | |
| byte | getVolume () |
| Get the current volume level. More... | |
| byte | getEqualizer () |
| Get the equalizer mode. More... | |
| byte | getLoopMode () |
| Get loop mode. More... | |
| uint16_t | countFiles () |
| Count the number of files on the current media. More... | |
| uint16_t | currentFileIndexNumber () |
| For the currently playing (or paused, or file that would be played next if stopped) file, return the file's FAT index number. More... | |
| uint16_t | currentFilePositionInSeconds () |
| For the currently playing or paused file, return the current position in seconds. More... | |
| uint16_t | currentFileLengthInSeconds () |
| For the currently playing or paused file, return the total length of the file in seconds. More... | |
| void | currentFileName (char *buffer, uint16_t bufferLength) |
| Get the name of the "current" file. More... | |
| void | playSequenceByFileNumber (uint8_t playList[], uint8_t listLength) |
| Play a sequence of files, which must all exist in a folder called "ZH" and be named 00.mp3 through 99.mp3. More... | |
| void | playSequenceByFileName (const char *playList[], uint8_t listLength) |
| Play a sequence of files, which must all exist in a folder called "ZH" and have 2 character names. More... | |
Protected Member Functions | |
| void | sendCommandData (uint8_t command, uint8_t *requestBuffer, uint8_t requestLength, uint8_t *responseBuffer, uint8_t bufferLength) |
| Send a command to the JQ8400 module,. More... | |
| void | sendCommand (uint8_t command, uint8_t *responseBuffer=0, uint8_t bufferLength=0) |
| Send a command with no arguments and no response. More... | |
| void | sendCommand (uint8_t command, uint8_t arg, uint8_t *responseBuffer=0, uint8_t bufferLength=0) |
| Send a command with a single 8 bit argument and no response. More... | |
| void | sendCommand (uint8_t command, uint16_t arg, uint8_t *responseBuffer=0, uint8_t bufferLength=0) |
| Send a command with a 16 bit integer argument. More... | |
| uint16_t | sendCommandWithUnsignedIntResponse (byte command) |
| Send a command to the JQ8400 module, and get a 16 bit integer response. More... | |
| uint8_t | sendCommandWithByteResponse (uint8_t command) |
| Send a command to the JQ8400 module, and get an 8 bit integer response. More... | |
| uint8_t | getAvailableSources () |
| Return a bitmask of the available sources. More... | |
| int | waitUntilAvailable (uint16_t maxWaitTime=1000) |
| Blocking wait with a timeout for serial input. More... | |
Protected Attributes | |
| Stream * | _Serial |
| Set in the constructor, the stream (eg HardwareSerial or SoftwareSerial object) that connects us to the device. | |
| uint8_t | currentVolume = 20 |
| Record of current volume level (JQ8400 has no way to query) | |
| uint8_t | currentEq = 0 |
| Record of current equalizer (JQ8400 has no way to query) | |
| uint8_t | currentLoop = 2 |
| Record of current loop mode (JQ8400 has no way to query) | |
|
inline |
Create JQ8400 object with a given serial object to communicate to the JQ8400.
#include <SoftwareSerial.h> SoftwareSerial mySerial(8,9); #include <JQ8400_Serial.h> JQ8400_Serial mp3(mySerial); void setup() { mySerial.begin(9600); mp3.reset(); }
#include <JQ8400_Serial.h> JQ8400_Serial mp3(Serial); void setup() { Serial.begin(9600); mp3.reset(); }
#include <JQ8400_Serial.h> JQ8400_Serial mp3(Serial2); void setup() { Serial2.begin(9600); mp3.reset(); }
Of course, power and ground are also required, VCC on JQ8400 is 5v tolerant (but RX isn't totally, hence the resistor above). *
The TX voltage from the JQ8400 is 3.3v, you do not need to level-shift this as 3.3v will still read as high for a 5v Arduino (and is safe for a 3.3v one).
| void JQ8400_Serial::abLoopClear | ( | ) |
Use this to break a currently running A-B loop.
Pause, stop, play will also break the A-B loop, this will do it without requiring any break the play will just continue through the previously set end marker without looping.
| void JQ8400_Serial::abLoopPlay | ( | uint16_t | secondsStart, |
| uint16_t | secondsEnd | ||
| ) |
A-B Loop for the file currently playing.
For the track that is already playing (and not paused) loop play between two marks defined by a start and end second.
If you want to start a file in an ab loop, start it playing and immediately issue the abLoopPlay.
mp3.playFileByIndexNumber(1); mp3.abLoopPlay(25,50);
will work, similarly if you want to resume from a pause into an ab loop
mp3.play(); mp3.abLoopPlay(25,50);
if you pause in an abLoop when you play again the abLoop is terminated.
There seems to be some sort of odd granularity with this function of the device, the first time through the ab-loop the start point is sometimes different to subsequent loops through, perhaps a second "out", that is if you specify start at 25 seconds, the first loop might start at 24, and subsequent ones at 25 (or vice-versa). In short, experimentation might be needed here.
| secondsStart | Second to set as start marker. |
| secondsEnd | Second to set as end marker. |
|
inline |
Return if the device is busy (playing) or not.
Just a convenience test, equivalent to getStatus() == MP3_STATUS_PLAYING
| uint16_t JQ8400_Serial::countFiles | ( | ) |
Count the number of files on the current media.
| uint16_t JQ8400_Serial::currentFileIndexNumber | ( | ) |
For the currently playing (or paused, or file that would be played next if stopped) file, return the file's FAT index number.
This number can be used with playFileByIndexNumber();
About The FAT Index Number
Note that the index number has nothing to do with the file name (except if you uploaded/copied them to the media in order of file name).
It is the index of the file in the drive's File Allocation Table (FAT), loosely this is usually the order in which you copied the files across, but due to operating systems that's not entirely assured.
To sort the FAT (after copying files), or view the FAT order, search for a FAT sorting utility for your operating system.
Specifically on Linux fatsort is the tool you want, but be sure to get a recent version which supports FAT-12, older ones do not.
| uint16_t JQ8400_Serial::currentFileLengthInSeconds | ( | ) |
For the currently playing or paused file, return the total length of the file in seconds.
| void JQ8400_Serial::currentFileName | ( | char * | buffer, |
| uint16_t | bufferLength | ||
| ) |
Get the name of the "current" file.
The name returned is shortened significantly to 8+3 format without a dot, all caps... in other words, this isn't very useful probably.
The current file is the one that is playing, paused, or if stopped then could be next to play or last played, uncertain.
Example
char buf[12]; mp3.currentFileName(buf, sizeof(buf)); Serial.println(buf);
| buffer | character buffer of 12 bytes or more (eg char buf[12] ) |
| bufferLength | length of the buffer (eg 12) |
| uint16_t JQ8400_Serial::currentFilePositionInSeconds | ( | ) |
For the currently playing or paused file, return the current position in seconds.
|
protected |
Return a bitmask of the available sources.
bit 0 = USB bit 1 = SD bit 2 = FLASH
| byte JQ8400_Serial::getEqualizer | ( | ) |
Get the equalizer mode.
| byte JQ8400_Serial::getLoopMode | ( | ) |
Get loop mode.
| uint8_t JQ8400_Serial::getSource | ( | ) |
Return the currently selected source.
| byte JQ8400_Serial::getStatus | ( | ) |
Get the status from the device.
| byte JQ8400_Serial::getVolume | ( | ) |
Get the current volume level.
| void JQ8400_Serial::interjectFileByIndexNumber | ( | uint16_t | fileNumber | ) |
Interject the currently playing file (if any) with the given FAT index number file.
Like a special announcement comes over, it interrupts the music playing, when the announcement finishes, the music continues.
About The FAT Index Number
Note that the index number has nothing to do with the file name (except if you uploaded/copied them to the media in order of file name).
It is the index of the file in the drive's File Allocation Table (FAT), loosely this is usually the order in which you copied the files across, but due to operating systems that's not entirely assured.
To sort the FAT (after copying files), or view the FAT order, search for a FAT sorting utility for your operating system.
Specifically on Linux fatsort is the tool you want, but be sure to get a recent version which supports FAT-12, older ones do not.
| fileNumber | FAT index of the file to interject. |
| void JQ8400_Serial::pause | ( | ) |
| void JQ8400_Serial::play | ( | ) |
Start playing the current file, if paused the playing is resumed.
If stopped or playing the playing is started from beginning.
| void JQ8400_Serial::playFileByIndexNumber | ( | uint16_t | fileNumber | ) |
Play a specific file based on it's FAT index number.
About The FAT Index Number
Note that the index number has nothing to do with the file name (except if you uploaded/copied them to the media in order of file name).
It is the index of the file in the drive's File Allocation Table (FAT), loosely this is usually the order in which you copied the files across, but due to operating systems that's not entirely assured.
To sort the FAT (after copying files), or view the FAT order, search for a FAT sorting utility for your operating system.
Specifically on Linux fatsort is the tool you want, but be sure to get a recent version which supports FAT-12, older ones do not.
| fileNumber | FAT index of the file to play next. |
| void JQ8400_Serial::playFileNumberInFolderNumber | ( | uint16_t | folderNumber, |
| uint16_t | fileNumber | ||
| ) |
Play a specific file in a specific folder based on the name of those folder and file.
To use this function, folders must be named from 00 to 99, and the files in those folders must be named from 000.mp3 to 999.mp3
Example
The device contains the file...
/03/006.mp3
then the following code will play that file...
mp3.playFileNumberInFolderNumber(3, 6);
Note that zero padding of your folder and file names is required - "01/002.mp3" good, "1/2.mp3" bad.
| folderNumber | 0 to 99 |
| fileNumber | 0 to 999 |
| void JQ8400_Serial::playInFolderNumber | ( | uint16_t | folderNumber | ) |
Play the first (?) file in a specific folder 00 to 99.
To use this function, folders must be named from 00 to 99.
So to play the folder "/03" use mp3.playInFolderNumber(3);
Note that zero padding of your folder and file required - "01/002.mp3" good, "1/2.mp3" bad.
| folderNumber | 0 to 99 |
| void JQ8400_Serial::playSequenceByFileName | ( | const char * | playList[], |
| uint8_t | listLength | ||
| ) |
Play a sequence of files, which must all exist in a folder called "ZH" and have 2 character names.
Don't ask me why the folder must be called "ZH", that's what the JQ8400 wants.
Example
Given that the JQ8400 contains the files...
/ZH/A1.mp3 /ZH/1B.mp3 /ZH/AZ.mp3
then the following code will play them in the order 1B.mp3, A1.mp3, AZ.mp3
const char * playList[] = { "1B", "A1", "AZ" }; mp3.playSequenceByFileName(playList, sizeof(playList)/sizeof(char *));
| playList | An array of the two character names (as strings). |
| listLength | Number of filenames in the list. |
| void JQ8400_Serial::playSequenceByFileNumber | ( | uint8_t | playList[], |
| uint8_t | listLength | ||
| ) |
Play a sequence of files, which must all exist in a folder called "ZH" and be named 00.mp3 through 99.mp3.
Don't ask me why the folder must be called "ZH", that's what the JQ8400 wants.
Example
Given that the JQ8400 contains the files...
/ZH/01.mp3 /ZH/02.mp3 /ZH/03.mp3
then the following code will play them in the order 03.mp3, 01.mp3, 02.mp3
uint8_t playList[] = { 3, 1, 2 }; mp3.playSequenceByFileNumber(playList, sizeof(playList));
pay attention that the file names are 2 digits, "`1.mp3`" is not valid.
| playList | An array of the numbers of files in the "ZH" folder. |
| listLength | Number of filenames in the list. |
| void JQ8400_Serial::reset | ( | ) |
Reset the device (softly).
It may be necessary in practice to actually power-cycle the device in case it gets confused or something.
So if designing a PCB/circuit including JQ8400 modules it might be worth while to include such ability (ie, power the device through a MOSFET which you can turn on/off at will).
| void JQ8400_Serial::seekFileByIndexNumber | ( | uint16_t | fileNumber | ) |
Seek to a specific file based on it's FAT index number.
The file will not start playing until you issue play()
Note that any currently playing file will stop immediately.
It seems (undocumented) that seeking to a track will wake up the device's output and keep it awake until you play or stop (or it stops after playing), you may wish to issue a sleep() after seeking.
About The FAT Index Number
Note that the index number has nothing to do with the file name (except if you uploaded/copied them to the media in order of file name).
It is the index of the file in the drive's File Allocation Table (FAT), loosely this is usually the order in which you copied the files across, but due to operating systems that's not entirely assured.
To sort the FAT (after copying files), or view the FAT order, search for a FAT sorting utility for your operating system.
Specifically on Linux fatsort is the tool you want, but be sure to get a recent version which supports FAT-12, older ones do not.
| fileNumber | FAT index of the file to play next. |
|
inlineprotected |
Send a command with no arguments and no response.
| command | Byte value of to send as from the datasheet. |
|
inlineprotected |
Send a command with a single 8 bit argument and no response.
| command | Byte value of to send as from the datasheet. |
| arg | Single byte of data |
|
inlineprotected |
Send a command with a 16 bit integer argument.
| command | Byte value of to send as from the datasheet. |
| arg | 16 bit uint16_teger data |
|
protected |
Send a command to the JQ8400 module,.
| command | Byte value of to send as from the datasheet. |
| requestBuffer | Pointer to (or NULL) request data bytes. |
| requestLength | Number of bytes in the request buffer. |
| responseBuffer | Buffer to store a single line of response, if NULL, no response is read. Note that the response is NOT a null-terminated string, if you want that, do it yourself (and specify length-1). |
| buffLength | Length of response buffer. |
|
protected |
Send a command to the JQ8400 module, and get an 8 bit integer response.
| command | Byte value of to send as from the datasheet. |
|
protected |
Send a command to the JQ8400 module, and get a 16 bit integer response.
| command | Byte value of to send as from the datasheet. |
| void JQ8400_Serial::setEqualizer | ( | byte | equalizerMode | ) |
Set the equalizer to one of 6 preset modes.
| equalizerMode | One of the following, |
| void JQ8400_Serial::setLoopMode | ( | byte | loopMode | ) |
Set the looping mode.
| loopMode | One of the following, |
Note that the random options are perhaps not that optimal especially with low file counts, a track playing twice back to back is not unusual, and the randomness does not anecdotally seem all that random. (NB: These random options are implemented inside the JQ8400 device itself, not in code, you could make your own randomiser and just use play by index etc...)
| void JQ8400_Serial::setSource | ( | byte | source | ) |
Set the source to read mp3 data from.
Note that the datasheet calls this "drive".
| source | One of the following... |
| void JQ8400_Serial::setVolume | ( | byte | volumeFrom0To30 | ) |
Set the volume to a specific level (0 to 30).
| volumeFrom0To30 | Level of volume to set from 0 to 30 |
| void JQ8400_Serial::sleep | ( | ) |
Put the device to sleep.
This will stop all playing. When you play() again it will be from the beginning of the current track.
Note that the JQ8400 seems to automatically sleep when it stops playing except when you seek to a track (or pause).
|
inline |
Return boolean indicating if the given source is available (can be selected using setSource()
)
| source | One of the following
|
| void JQ8400_Serial::volumeDn | ( | ) |
Decrease the volume by 1 (volume ranges 0 to 30).
| void JQ8400_Serial::volumeUp | ( | ) |
Increase the volume by 1 (volume ranges 0 to 30).
|
protected |
Blocking wait with a timeout for serial input.
| maxWaitTime | Milliseconds |


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Owning Palette: Advanced Classes, Controls, and VIs
Requires: Labstache
The Lexer class represents an object that can read in a stream of bytes and break groups of bytes into identified tokens.
| Class Item | Description |
|---|---|
| Create | Creates a new Lexer object. |
| Destroy | Safely destroys a Lexer object and cleans up any internally used resources. Once a Lexer object is destroyed, it cannot be used again and a new Lexer object must be created. |
| Run | Reads a Mustache template and creates tokens based on the content. |


Check out Larry Chase’s Top 10 Trends for the Next 10 Years. Of course, he focusses on marketing.
He does have some interesting points:
1. Pay Per Call Rings In: Any salesperson worth his or her salt knows that a call is worth many times more than a click. […]
That’s a bit very simplistic but all in all. Absolutely agreed.  Feed Marketing Flourishes: You’ve got RSS (Real Simple Syndication). You’ve got Podcasting (where you can download and time-shift audio content to your iPod or MP3 player). Now you’ve even got Video Podcasting where …
With Apple now having gone into Podcasting with iTunes 4.9, I really believe that this will start taking off. I still prefer blogs without audio as I don’t have to listen to something, but for a walk in the park it might be interesting. And the attention that consumers give the podcasts are likely higher than a web site, making them prime targets for advertisement.
3. Email Marketing Will Survive: Spam issues will recede dramatically, because they have to. Too much is at stake. […]
Yes it will survive but it needs to get way less expensive! And Blogs will enable companies to build their own lists more easily. For new customer acquistion, even the targeted lists are just not performing on par with other systems out there. With performance I mean the ROI is just not the same, so if the conversion is worse than in SEM, then the price should be less too.
4. Personal Agents Propogate: Watch for the growth of "agent software" to help you sift through the morass of online information.[…]
We already have stuff like Froogle or personalized search but a real agent needs to go far beyond that. Maybe. We’ll see. He’s talking about 10 years so that’s ok.
5. Reverb Marketing, In Stereo: […] Smart marketers will synchronize their messaging so the end user hears and sees complementary messages at or near the same time. This will be the new definition of what media planners call "Road Blocking." […]
Very cool indeed and it will really need some big players or big partnerships to pull that of. But I did wonder before why instead of spreading your ads out over weeks and months, you don’t just block an entire day on all channels. Ok, maybe not that much but you could do show sponsorships including ads around the show, the shows web site, web sites the target audience visit and radio stations the next morning.
7. IPTV Adds Interactivity: Microsoft and others are currently exploring TV over Internet protocol.[…] IPTV will be much more interactive. In addition to an 800 number, with IPTV you’ll be able to click and buy right then and there.[…]
Yes, yes, yes.
8. Commercial Content On Demand: Messages from marketers need to be so appealing that the audience actually requests the message.[…] The advertisers in Web Digest For Marketers generate sales leads by offering high-value PDF downloads on subjects of particular interest to the target audience they’re trying to reach.[…]
Very good point and an interesting idea how the content producers can leverage their assets a bit more. If you have your content in XML and easily transferable in other media, then you are in a good position to now leverage APIs that are out there.
9. Publishing Faces Tectonic Shifts: Research is already showing that many people in their 20s are not picking up the newspaper habit the way their parents did.
Target your audience, like in 8, with special info you have about them. Then you can do targeting of the ads automatically too.
10. Direct Marketers Will Take Over the Internet: Oops, this has already happened, but not the way I predicted 10 years ago.[…]
What he believes is that the real old school direct marketers have a lot of knowledge that is not yet leveraged in the online world. The way you work is different online, but what works largely isn’t. The two fields need to mix.
Very good points. I like it.


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How to Manage 100k+ Experiments on Brain Imaging Datasets
Ph.D. students studying machine learning on the human brain must conduct massive experiments on brain imaging datasets. When dealing with large datasets, students often face challenges managing numerous experiments, requiring a systematic approach for script development, result storage, and experiment visualization.
To address these challenges, we propose a set of guidelines that enable researchers to effectively manage and visualize a vast number of experiments. In this context, we discuss a brain imaging study that involved conducting over 100k experiments and generating extensive visualizations.
Rule 1: Make sure you do not start writing a script unless a function for your code has been defined.
Let’s consider the task of mapping brain time series data from a source atlas (with several dimensions p1) to the corresponding form in a target atlas (with several dimensions p2). Indeed, we are looking for a number of two-dimensional matrices that can accurately map two time series into each other given the cost matrix, which represents the geometry of our brain.
To accomplish this, we train transformation matrices for all possible pairs of atlases, as illustrated in the following graph:
A simple function for calculating the mappings is as follows:
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However, the Sinkhorn function is not specific to this particular study and can be widely used in later analysis.
For example, you can define another function named sinkhorn2
that can map two time series data into one using the predefined sinkhorn
function and some appropriate lines of code.
This principle can significantly reduce the chunks of code that you are supposed to write later in your analysis.
Rule 2: Make sure you avoid creating separate images that need to be merged, aligned, or captioned later. Whenever possible, consolidate your visuals into a single script that handles these settings automatically.
With the exception of the surface plots, all our plots are generated using R. However, we do not create individual plots with the intention of merging them later in Adobe Illustrator or similar tools.
For instance, in R, we have a function called my_plot
that takes inputs such as c_data
, source
, target
, and task
, and it saves a collection of eight plots (arranged in a 2x4 grid) into a list called my_plots
:
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Subsequently, to conjure up an array of visualizations, we call this function repeatedly:
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and finally, we attach all these plots into each other using ggarrange
:
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Rule 3: Ensure that your scripts include arguments: establish default values for them, specify their types, and invoke your functions through a central function using suitable instructions.
Now, it’s time to visualize a number of experiments like these:
Each graph in the visualization displays the red line, which represents the optimal value achieved in a baseline scenario, while the size of each dot corresponds to the standard deviation observed in 100 trials.
Collectively, these graphs illustrate the outcomes of a total of $8 \times 8 \times 100$ experiments!
Now, how can you store the results into files (namely .csv
files) in a way that allows for multiple visualizations? Consider a scenario where your supervisor requests similar plots for all possible combinations of atlases. Would you manually modify your scripts each time? Certainly not!
To address this, let’s discuss the approach for your scripts. We recommend incorporating as many arguments as possible to maintain flexibility. In Python, you can write this as follows:
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Here’s an example of a bash script that uses the Shen atlas as the source, Craddock as the target, and working memory as the specified task.
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Having naming conventions for your result files is an excellent idea! It allows you to call your scripts multiple times, perhaps utilizing multiple CPUs or GPUs.
In our example, we’ve implemented a function called shuffle_list(atlases)
. Whenever we execute our script, it’s highly probable that different combinations of arguments will be invoked, leading to the creation of relevant files. This approach ensures the generation of appropriate files for each run, making your workflow smooth and efficient.
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Rule 4: It is highly advisable to employ naming conventions for your files that provide sufficient information about your experiments.
By incorporating relevant details in the file names, you can easily load them into your visualization units along with the corresponding arguments. This approach promotes clarity and facilitates a better understanding of your work.
Rule 5: In the event that an error is detected in the initial stages, it is recommended to remove the files associated with the error and restart the script afresh.
By adhering to the aforementioned guidelines, such as employing proper file management and following established rules, none of the important steps, including the visualization process, will be impacted. They will simply replace the files with new results or figures with new plots!
This approach ensures a robust and reliable workflow, minimizing the impact of any errors or faults encountered during the execution of the script.