In my early days of psychedelic exploration I was as eager to try out new modes of being and ways of exploring the interface between reality and myself as I am now, but I had much less access to psychoactive chemicals. So, like many other people in similar circumstances I decided to explore the over the counter options available to me at the time. My only sources of information were my friends and acquaintances. The Internet didn't really exist yet, for the most part. There weren't any books I could find about the subject. But, I was brave and fairly foolish, so I didn't let my ignorance get in the way of my desire to explore.
My first OTC drug high was with Dramamine. A friend had told me about his experiences with it and it sounded good to me. He described it as 'like acid' when he'd taken twelve pills of this common anti nausea medication. One of my roommates and I went out and bought a few boxes. We each swallowed ten of the pills inside and waited for something to happen.
It took a while to come on. I was looking for acid like effects, of course. My roommate started feeling it first. He described seeing things jumping off the carpet. I wasn't feeling much at this point. Nothing moved or 'breathed' and I didn't feel particularly high. I did feel weird, however, and slightly uncomfortable.
 My roommate went off to bed and I sat in our living room smoking cigarettes and feeling weird. I started having conversations. I was alone, but I kept forgetting it. I'd be a few minutes into an exceedingly odd conversation and then realize the person I was talking to wasn't there. I talked to a few inanimate objects, including the table lamp. It would all seem to make sense, and then I would realize what I was doing and stop. I wasn't actually speaking, the whole conversation was taking place inside my head.
After an hour of this sort of thing I decided to go to bed.
I spent another twenty five minutes in the bathroom taking my contact lenses out. It was a near impossible task. I kept forgetting what I was doing and slipping off into more conversations with folks that weren't there. I was very confused and disoriented. As I left the bathroom I thought I heard voices in another roommate's room. I stopped outside the door and listened for a few minutes to what seemed to be a conversation between my roommate and friend of ours. I couldn't quite make out what was being said, but I had a strange feeling they were talking about me. I wanted to open the door and confront them, but knew this wasn't a good idea. Part of my mind was arguing that there was no one in there, since I hadn't seen anyone come in. The other part was insisting that it heard voices. Finally, reason prevailed and I went to bed.
Lying in bed I continued on my strange journey. I was having detailed and strange thoughts, delusions and fantasies. The main one I remember was of reading (or trying to read) a newspaper in a language I didn't understand or recognize. I continued in this vein for a while before drifting off. The next day I felt hungover and out of sorts. It was not an experience I was eager to repeat. I can't imagine a set of circumstances where I would willingly have that experience again. It was monumentally lame and foul.
Some months later I decided to try Robitussin (DXM) cough syrup. This enjoyed something of a renaissance among some members of my circle of friends and acquaintances. Again I heard it described as 'like acid'. Well, I doubted it, but I felt it was worth looking into, considering how many of my friends had tried it and liked it.
I bought a four ounce bottle of Robitussin DM at the grocery store (I know now I should have gotten the Maximum Strength). I took it home and drank it. Or tried to. It was incredibly nasty tasting. I managed to drink most of the bottle, leaving less than a quarter of it. I went into my room and lay down to await effects and try hard not to puke.
I must have fallen asleep. I awoke feeling strange. I got out of bed and turned on my lamp, which had a purple light bulb. I sat in front of the lamp, staring at it as my mind zoomed over a thousand strange thoughts. I felt weird, somewhat high in a not unpleasant way. There were no visuals or anything, just a strange high and a lot of weird thoughts rolling through my head as I sat in front of the light. I stayed up for an hour or so, enjoying the mild trippiness of my thoughts rambling over assorted oddities before heading to bed. It was interesting, but nothing like acid.
A few weeks later I decided to try it again. This time, I was doing it with two of my friends, M and R. Neither had tried it before. We acquired more robo, this time a generic brand that had a fair amount of alcohol in it as well.
It didn't take long before I was feeling something. The alcohol gave me a bit of a buzz and the DXM was definitely warping me. The room looked like I was viewing it through a fish eye lens. The air appeared to be thick in some strange say.  It wasn't unpleasant, but it wasn't particularly pleasant either.
My stomach had been churning since I drank the cough syrup and it finally decided it had had enough. I asked R what would be a good place to puke and he told me to go outside. I staggered up the stairs and out into his yard where I collapsed and began puking. I puked three or four (or more) times before someone came out to check on me. They asked if I was OK and I mumbled something about being fine, sorry I puked on your tree… I asked to be taken home, and someone obliged. I still felt weird and spacey, but it wasn't in any way fun.
My early and uneducated forays into OTC intoxication were almost universally unpleasant. Even the most pleasant and positive moments were only mildly interesting. I am certain that if I had been better informed I would have almost definitely had better and more interesting experiences.
I understand that many people find DXM to be a pleasant recreational drug and/or positive spiritual tool, but it has never seriously interested me since the second time I tried it. I have considered doing it several times, but with so many other interesting things available to me, it isn't any kind of priority.
The Dramamine experience was pretty much unpleasant in every way and I have no interest in ever repeating it, even if it were the only psychoactive available to me. It wasn't fun or insightful, and was only interesting for the utterly bizarre nature of the effects.
I sincerely hope that anyone reading this who is interested in trying these things takes the time to look for useful information before proceeding. Even OTC drugs like this are extremely powerful and you can seriously injure yourself if you proceed without the proper information on dosage, side effects, etc. I was lucky, but someone else might not be so lucky in my place.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


| Note: while some workshops are delivered in other languages, slides are always in English. |
| Free Kubernetes intro course |
| Getting Started With Kubernetes and Container Orchestration |
|
|
| This is a live recording of a 1-day workshop that took place at QCON London in March 2019. |
| If you're interested, we can deliver that workshop (or longer courses) to your team or organization. |
| Contact Jérôme Petazzoni to make that happen! |
| Past workshops |
| Kubernetes Intensive Online Workshop |
|
| Delivered September 12th-13th, 2020 at fwdays in streaming. |
| Intensive Docker Online Workshop |
|
| Delivered August 29th-30th, 2020 at fwdays in streaming. |
| Intensive Docker Bootcamp |
|
| Delivered July 7th-9th, 2020 at Ardan Live in streaming. |
| Opérer Kubernetes (en français) |
|
| Delivered June 25th-26th, 2020 at ENIX SAS in streaming. |
| Kubernetes avancé (en français) |
|
| Delivered June 23rd-24th, 2020 at ENIX SAS in streaming. |
| Packaging pour Kubernetes (en français) |
|
| Delivered June 22nd, 2020 at ENIX SAS in streaming. |
| Fondamentaux Kubernetes (en français) |
|
| Delivered June 17th-19th, 2020 at ENIX SAS in streaming. |
| Docker intensif (en français) |
|
| Delivered June 15th-16th, 2020 at ENIX SAS in streaming. |
| Intensive Kubernetes Bootcamp |
|
| Delivered June 9th-11th, 2020 at Ardan Live in streaming. |
| Intensive Kubernetes - Advanced Concepts |
|
| Delivered May 4th-8th, 2020 at Ardan Live in streaming. |
| Intensive Docker and Kubernetes |
|
| Delivered March 30th-April 2nd, 2020 at Ardan Live in streaming. |
| Kubernetes Intensive Course |
|
| Delivered March 6th, 2020 at QCON in London. |
| Docker Intensive Course |
|
| Delivered March 5th, 2020 at QCON in London. |
| Opérer Kubernetes (in French) |
|
| Delivered February 7th, 2020 at ENIX SAS in Paris. |
| Kubernetes Avancé (in French) |
|
| Delivered February 6th, 2020 at ENIX SAS in Paris. |
| Kubernetes et Méthodologies DevOps (in French) |
|
| Delivered February 5th, 2020 at ENIX SAS in Paris. |
| Fondamentaux Orchestration et Kubernetes (in French) |
|
| Delivered February 4th, 2020 at ENIX SAS in Paris. |
| Fondamentaux Conteneurs et Docker (in French) |
|
| Delivered February 3rd, 2020 at ENIX SAS in Paris. |
| Déployer ses applications avec Kubernetes (in French) |
|
| Delivered November 13th, 2019 at DevopsDDay in Marseille. |
| Deploying and scaling applications with Kubernetes |
|
| Delivered November 4th-5th, 2019 at Velocity in Berlin. |
| Deep Dive into Kubernetes Internals for Builders and Operators |
|
| Delivered October 30th, 2019 at LISA in Portland, OR. |
| Kubernetes Training |
|
| Delivered October 22nd-24th, 2019 at Ardan Labs in Charlotte, NC. |
| Docker & Containers Training |
|
| Delivered October 22nd, 2019 at Ardan Labs in Charlotte, NC. |
| Kubernetes or Swarm? Build Both, Deploy Apps, Learn The Differences |
|
| Delivered October 22nd, 2019 at GOTO in Berlin. |
| Déployer ses applications avec Kubernetes (in French) |
|
| Delivered September 24th-25th, 2019 at ENIX SAS in Paris. |
| Deploying and scaling applications with Kubernetes (in Turkish) |
|
| Delivered August 27th, 2019 at HacknBreak in Izmir. |
| Container Orchestration with Docker and Swarm (in Turkish) |
|
| Delivered August 26th, 2019 at HacknBreak in Izmir. |
| Introduction to Docker and Containers (in Turkish) |
|
| Delivered August 25th, 2019 at HackBreak in Izmir. |
| Kubernetes 201: Production tooling |
|
| Delivered July 16th, 2019 at OSCON in Portland, OR. |
| Getting Started With Kubernetes |
|
| Delivered June 17th, 2019 at Zenika in Montréal. |
| Kubernetes for administrators and operators |
|
| Delivered June 10th-11th, 2019 at Velocity in San Jose, CA. |
| Getting started with Kubernetes and container orchestration |
|
|
| Delivered May 1st, 2019 at PyCon in Cleveland, OH. |
| Getting Started With Kubernetes and Container Orchestration |
|
| Delivered April 28th, 2019 at GOTO in Chicago, IL. |
| Opérer et administrer Kubernetes |
|
| Delivered April 26th, 2019 at ENIX SAS in Paris. |
| Déployer ses applications avec Kubernetes (in French) |
|
| Delivered April 23rd-24th, 2019 at ENIX SAS in Paris. |
| Bien démarrer avec les conteneurs (in French) |
|
| Delivered April 15th-16th, 2019 at ENIX SAS in Paris. |
| Getting Started With Kubernetes and Container Orchestration |
|
|
| Delivered March 8th, 2019 at QCON in London. |
| Bien démarrer avec les conteneurs (in French) |
|
| Delivered January 7th-8th, 2019 at ENIX SAS in Paris. |
| Déployer ses applications avec Kubernetes |
|
| Delivered December 17th-18th, 2018 at ENIX SAS in Paris. |
| Getting Started With Kubernetes and Container Orchestration |
|
| Delivered November 9th, 2018 at QCON in San Francisco, CA. |
| Introduction to Docker and Containers |
|
| Delivered November 8th, 2018 at QCON in San Francisco, CA. |
| Getting Started With Kubernetes and Container Orchestration |
|
| Delivered November 8th, 2018 at QCON in San Francisco, CA. |
| Kubernetes 101 |
|
| Delivered October 31st, 2018 at Velocity EU in London, UK. |
| Docker Zero to Hero: Docker, Compose and Production Swarm |
|
| Delivered October 30th, 2018 at Velocity EU in London, UK. |
| Kubernetes 101 |
|
| Delivered October 1st, 2018 at Velocity in New York, NY. |
| Kubernetes Bootcamp - Deploying and Scaling Microservices |
|
| Delivered October 1st, 2018 at Velocity in New York, NY. |
| Docker Zero to Hero: Docker, Compose and Production Swarm |
|
| Delivered October 1st, 2018 at Velocity in New York, NY. |
| Déployer ses applications avec Kubernetes |
|
| Delivered September 17th, 2018 at ENIX SAS in Paris. |
| Kubernetes 101 |
|
| Delivered July 17th, 2018 at OSCON in Portland, OR. |
| Kubernetes 101 |
|
| Delivered July 12th, 2018 at devopsdays Minneapolis in Minneapolis, MN. |
| Kubernetes 101 |
|
| Delivered June 27th, 2018 at devopsdays in Amsterdam. |
| Kubernetes 101 |
|
| Delivered June 12th, 2018 at Velocity in San Jose, CA. |
| Kubernetes two-day kickstart: Deploying and Scaling Microservices with Kubernetes |
|
| Delivered June 12th, 2018 at Velocity in San Jose, CA. |
| Kubernetes two-day kickstart: Introduction to Docker and Containers |
|
| Delivered June 11th, 2018 at Velocity in San Jose, CA. |
| Docker 101 |
|
| Delivered May 17th, 2018 at Revolution Conf in Virginia Beach, FL. |
| Kubernetes 101 |
|
| Delivered May 10th, 2018 at NDC Minnesota in Saint Paul, MN. |
| Swarm Orchestration |
|
| Delivered May 8th, 2018 at CRAFT in Budapest. |
| Swarm Orchestration |
|
| Delivered April 27th, 2018 at GOTO in Chicago, IL. |
| Kubernetes 101 |
|
| Delivered April 24th, 2018 at GOTO in Chicago, IL. |
| Introduction à l'orchestration |
|
| Delivered April 13th, 2018 at in Paris. |
| Introduction aux conteneurs |
|
| Delivered April 11th, 2018 at in Paris. |
| Docker 101 |
|
| Delivered April 6th, 2018 at MuraCon in Sacramento, CA. |
| Kubernetes 101 |
|
| Delivered March 27th, 2018 at SREcon Americas in Santa Clara, CA. |
| Kubernetes 101 |
|
| Delivered March 27th, 2018 at Boosterconf in Bergen. |
| Kubernetes 101 |
|
| Delivered February 22nd, 2018 at IndexConf in San Francisco, CA. |
| Orchestrating Microservices with Docker Swarm |
|
| Delivered November 17th, 2017 at QCON SF in San Francisco, CA. |
| Introduction to Docker and Containers |
|
|
| Delivered November 16th, 2017 at QCON SF in San Francisco, CA. |
| (T9) Build, Ship, and Run Microservices on a Docker Swarm Cluster |
|
| Delivered October 31st, 2017 at LISA in San Franciso, CA. |
| (M7) Getting Started with Docker and Containers |
|
| Delivered October 30th, 2017 at LISA in San Franciso, CA. |
| Deploying and scaling microservices with Docker and Kubernetes |
|
|
| Delivered October 26th, 2017 at Open Source Summit Europe in Prague. |
| Swarm from Zero to Hero |
|
| Delivered October 16th, 2017 at DockerCon in Copenhagen. |
| Orchestration for Advanced Users |
|
| Delivered October 16th, 2017 at DockerCon in Copenhagen. |
| Deploying & Scaling microservices with Docker Swarm |
|
|
| Delivered July 25th, 2017 at devopsdays in Minneapolis, MN. |
| Deploying and scaling containerized Microservices with Docker and Swarm |
|
| Delivered June 12th, 2017 at DevOpsCon in Berlin. |
| Deploy and scale containers with Docker native, open source orchestration |
|
|
| Delivered May 18th, 2017 at PyCon in Portland, OR. |
| Deploying and scaling applications in containers with Docker |
|
| Delivered May 8th, 2017 at OSCON in Austin, TX. |
| Container deployment, scaling, and orchestration with Docker Swarm |
|
| Delivered May 4th, 2017 at GOTO in Chicago, IL. |
| Orchestration Workshop |
|
| Delivered April 17th, 2017 at DockerCon in Austin, TX. |
| Container deployment, scaling, and orchestration with Docker Swarm |
|
| Delivered March 22nd, 2017 at Devoxx in San Jose, CA. |
| Container deployment, scaling, and orchestration with Docker Swarm |
|
| Delivered March 3rd, 2017 at SCALE in Pasadena, CA. |
| Deploying and Scaling Applications with Docker Swarm |
|
|
| Delivered December 6th, 2016 at LISA in Boston, MA. |
| Orchestrating Containers in Production at Scale with Docker Swarm |
|
| Delivered October 7th, 2016 at LinuxCon in Berlin. |
| Deployment and orchestration at scale with Docker |
|
| Delivered September 20th, 2016 at Velocity in New York, NY. |
| Orchestrating Containers in Production at Scale with Docker Swarm |
|
| Delivered August 25th, 2016 at LinuxCon in Toronto. |
| Orchestration Workshop |
|
| Delivered June 22nd, 2016 at DockerCon in Seattle, WA. |
| Introduction to Docker and containers |
|
|
| Delivered May 29th, 2016 at PyCon in Portland, OR. |
| Deployment and orchestration at scale with Docker Swarm |
|
| Delivered May 17th, 2016 at OSCON in Austin, TX. |
| Advanced Docker concepts and container orchestration |
|
| Delivered April 27th, 2016 at CRAFT in Budapest. |
| Orchestration Workshop |
|
| Delivered April 22nd, 2016 at Neofonie in Berlin. |
| Orchestration Workshop |
|
| Delivered April 5th, 2016 at Praqma in Stockholm. |
| Orchestration Workshop |
|
| Delivered March 22nd, 2016 at Stylight in Munich. |
| Containers in production with Docker Swarm |
|
| Delivered March 11th, 2016 at QCON in London. |
| Orchestration Workshop |
|
| Delivered February 19th, 2016 at Container Solutions in Amsterdam. |
| Orchestration Workshop |
|
| Delivered February 15th, 2016 at Zenika in Paris. |
| Advanced Docker concepts and container orchestration |
|
| Delivered January 22nd, 2016 at SCALE in Pasadena, CA. |
|


Ocean [ay52], ocean, in The Inner Sea
Last updated Turn #136
Ocean [ay52], ocean, in The Inner Sea
------------------------------------------------------------------------
7: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails, arrived
7: from Ocean [az52], owner:
7: Dorothy [v1b], Pen, with one worker, 94 sailors
7: Apollo [b5e], Imperial Empire, with 11 workers, 63 lt. xbowmen,
7: accompanied by:
7: Lutra [a4w], Imperial Empire, with 13 paladins, 40 pikemen
7: Hera [g3f], Imperial Empire, with 20 soldiers, 40 pikemen
7: Thor [r4g], Imperial Empire, with three soldiers, 46 pikemen
7: Eos [a2t], Imperial Empire, with ten workers, 48 lt. xbowmen
7: Khaki Blazer [w4j], Imperial Empire, with 17 soldiers,
7: seven pikemen, 35 lt. foot
7: Banian [e7n], Imperial Empire, duke, the High Priest of the
7: Church of Eres [151], with five soldiers, ten angels
7: Dunlop [j5s], Mandor Confederacy, prisoner
8: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails,
8: departed for Ocean [ay53].
Routes leaving Ocean:
North, to Ocean [ax52], 6 days
East, to Ocean [ay53], 6 days
South, to Ocean [az52], 6 days
West, to Ocean [ay51], 6 days
The province is blanketed in fog.
No one can be seen through the fog.


Layout Thrashing means: Forcing the browser to calculate a layout that is never rendered to the screen.
Normal Thrashing Situation
fastdom read and write applied
Normal Thrashing Situation
fastdom read and write applied
fastdom write only applied
# Questions and Thoughts?
* Put every DOM/CSSOM changing code into a `requestAnimationFrame`.
* Never put style/layout reading code into a `requestAnimationFrame`.
* Use helpers/ best practice patterns to write maintainable async code.
* Try to follow these rules but use your brain in the first place.
# Thank you!!!


Snookes1516.github.io
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--- layout: post title: How to move the Confluence "Create" button back to the pre-Confluence 5 location date: 2013-09-26 10:11:13.000000000 +01:00 type: post published: true status: publish categories: [] tags: [] meta: tmac_last_id: '618797341827448834' _edit_last: '1' author: David Simpson excerpt_separator: ---
Upon release of Confluence 5, I really couldn't handle the new location for the "Create button". A few months in, I'm happy and it now seems natural, but for users making the change, it can seem counter-intuitive.
Several clients have asked how to get the create button back to where "it should be". This is a hack to do just that.
Browse to Confluence Admin | Look and Feel | Custom HTML | At the END of the BODY and paste the following:
Save and you're back to the "good old days":
This post originally appeared on the AppFusions blog here


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Parentheses ()
These are known as "grouping operators" and are used around expressions and sub-expressions to control operator precedence. Grouping operators could be thought of like a id bracelet.
If there's a long line of people (expressions) waiting to enter a concert, some people (expressions) are given priority access over others depending on the colour of your id bracelet and are processed first.
TOP
Brackets []
Brackets are indicative of arrays||lists and are used to create, access, loop, append, remove, prepend, find by index, remove by index, copy.
Continuing on with the analogy of the id bracelet, these bracelets entitle you to do a range of actions||things while in use.
TOP
Braces {}
These are predominantly used to define key value pairs in objects like:
var car = {"make" : "cadillac", "model" : "escalade"}
as well as for loops, while loops, if (conditional) statements like:
if (var i > 10) { do this }.
So, anything contained within braces {} or wearing this type of bracelet either hold info (object variable/s) or do something (method).
TOP
Single Quotes ' ' | Double Quotes " "
Quotation marks can usually be seen around strings. There is no explicit difference between which one should be used over the other (except where JSON requires the double quotes), however it is important to note that when opening with one you must close with the same.
I personally use "" as it reinforces keymapping for my fingers.
TOP


Pep Lijnders lifts lid on Liverpool's approach to Carabao Cup and Club World Cup clash
Liverpool are due to face Aston Villa in the Carabao Cup and then play in the Club World Cup within 24 hours, so Jurgen Klopp will split up his squad
by Alex SmithLiverpool assistant manager Pep Lijnders has explained how the Reds will contend with their Carabao Cup and Club World Cup fixture pile-up.
Jurgen Klopp's men are due to face Aston Villa on December 17 in their Carabao Cup quarter-final before tackling a yet to be decided side in the FIFA Club World Cup the following day.
The Reds have already revealed that they will split their squad for the quick turnaround from Villa Park to Qatar.
And Lijnders has let slip that plans are already being put in place and offers slightly more information.
"It is important with a 'culture of victory' because each prize we can win is important, so we try to attack each competition we play," Lijnders told Liverpool FC official magazine .
"But, for me, the most important thing is the chance to show, in a different part of the world, our identity and our philosophy and a real spirit of initiative.
"In a perfect world people should look forward to seeing us play. As regards [to] changing between England and Qatar, we already have the people in charge who are preparing to make this process as smooth as possible.
"We have to ignore what's beyond our control whatever the circumstances. This is what we always try to do, but in these periods, it will be even more the case."
Liverpool qualified for the Club World Cup by virtue of winning the Champions League last season.
And Lijnders wants to add the international prize to the Reds' burgeoning trophy cabinet.
"It would be incredible to become champions of the world," says the Reds assistant. "Not many big teams get this chance. It's a mark, a stamp of a strong process over many years.
"We have to think about the semi-final first, it's the first step and the only one that counts at the moment.
"We will show we care and we will show what we want to achieve. We will fight for a result and make it an intense game from out side.
"It's different when you come back as the winning team, that's for sure!
"For me, the most important aspect is to keep the team fresh during this period, knowing that what puts us above the rest is our mentality. We will always focus and what we can control."


{% extends "base.html" %} {% block title %}About{% endblock %} {% block content %}
Marvin runs Ubuntu applications on devices so developers don't have to.
The goal of Marvin is to make it easier for Ubuntu app developers to find out if their application will run successfully on various devices without having to own one of each of them.
{% endblock %}


Grasp Technologies, Inc.
Job Description
Job Title: Support Data Analyst
Reports To: Director, Client Services
FLSA Status: Exempt
Summary: Responsible for using your broad knowledge of technology and business
processes to think of creative ways to solve problems and uses your impact assessment
and communication skills to appropriately escalate and implement change by performing
the following duties.
Duties and Responsibilities include the following. Other duties may be assigned.
1. Assesses all client support requests effectively and prioritizes accordingly. *
2. Ensures all basic or “first-tier” support questions are being properly handled
within the appropriate time frame. *
3. Grows first level supporter’s skills and abilities consistently to be able to handle
the client the first time without escalation. *
4. Performs “second-tier” support by researching and resolving more complex
issues and must be able to discern when something should be escalated to
development or management. *
5. Identifies system modification opportunities to reduce end-user problems,
streamlines applications, and helps make the customer, both internal and
external, more self-sufficient. *
6. Communicates system and process improvement opportunities to internal Grasp
teams, provides training, infrastructure network services, and application
development, and has the ability to translate the customer’s needs into engineer
speak, aka “the interpreter”. *
Qualifications:
To perform this job successfully, an individual must be able to perform each essential
duty satisfactorily. The requirements listed below are representative of the knowledge,
skill, and/or ability required. Reasonable accommodations may be made to enable
individuals with disabilities to perform the essential functions.
Language Ability:
Ability to read, analyze, and interpret general business periodicals, professional journals,
technical procedures, or governmental regulations. Ability to write reports, business
correspondence, and procedure manuals. Ability to effectively present information and
respond to questions from groups of managers, clients, customers, and the general
public.
Math Ability:
Ability to work with mathematical concepts such as probability and statistical inference,
and fundamentals of plane and solid geometry and trigonometry. Ability to apply
concepts such as fractions, percentages, ratios, and proportions to practical situations.
Reasoning Ability:
Ability to solve practical problems and deal with a variety of concrete variables in
situations where only limited standardization exists. Ability to interpret a variety of
instructions furnished in written, oral, diagram, or schedule form.
Computer Skills:
To perform this job successfully, an individual should have knowledge of word
processing software, spreadsheet software, database software, and Microsoft Visual
Studio.
Education/Experience:
Bachelor's degree (B.A./B.S.) from four-year college or university; and two to four years
related experience and/or training; or equivalent combination of education and
experience.
Specialized Training:
• Writing and interpreting SQL
Management Studio
Physical Demands:
The physical demands described here are representative of those that must be met by
an employee to successfully perform the essential functions of this job. Reasonable
accommodations may be made to enable individuals with disabilities to perform the
essential functions.
While performing the duties of this job, the employee is regularly required to stand, walk,
sit, use hands, and talk or hear. The employee is occasionally required to reach with
hands and arms, climb or balance, stoop, kneel, crouch or crawl, and taste or smell. The
employee must occasionally lift and/or move up to 25 pounds. Specific vision abilities
required by this job include close vision, distance vision, peripheral vision, depth
perception, ability to adjust focus, and ability to see color.
Work Environment:
The work environment characteristics described here are representative of those an
employee encounters while performing the essential functions of this job. Reasonable
accommodations may be made to enable individuals with disabilities to perform the
essential functions.
The noise level in the work environment is usually moderate.
Grasp Technologies Standards
Disclaimer
The above statements are intended to describe the general nature and level of work
being performed by people assigned to this classification. They are not to be construed
as an exhaustive list of all responsibilities, duties, and skills required of personnel so
classified. All personnel may be required to perform duties outside of their normal
responsibilities from time to time, as needed.
Additional information
Pay Range $60,000 – $75,000/year
Commensurate with experience
Full Time Employment
How to Apply
Please submit a resume and cover letter to<EMAIL_ADDRESS>We look forward to
speaking with you!


MB.MacroB module
#!/usr/bin/env python
# coding=utf-8
from Bio.PDB.PDBParser import PDBParser
from os import listdir
import Bio.PDB.NeighborSearch
from Bio.pairwise2 import align
import random
import sys
from MB.CustomPDB import CustomModel, CustomChain
import os
def read_pdbs(directory, verbose=False):
"""Reads the input directory and generates pdb models"""
if verbose:
print("Reading pdb input files from %s" % directory)
parser = PDBParser(PERMISSIVE=1, QUIET=True)
if os.path.isdir(directory) and directory.endswith("/"):
try:
pdbmodels = [parser.get_structure("Model_pair", directory + f)[0] for
f in listdir(directory) if f.endswith(".pdb")] # Generates pdb objects for files that end with .pdb
except:
sys.stderr.write("PDB files couldn't be opened. Please, revise that their format is correct.")
sys.exit(1)
else:
sys.stderr.write("Directory %s doesn't exists, please select a valid directory." % directory)
sys.exit(1)
if not bool(pdbmodels): # If no pdb instance is generated
sys.stderr.write("No pdb files where read. Please make sure the given directory contains pdb files. ")
sys.exit(1)
for model in pdbmodels:
if len(model.child_list) != 2:
sys.stderr.write("A pdb input file doesn't contains two chains. Please, all input pdbs must only contain "
"two chains.")
sys.exit(1)
if verbose:
print("Pdb objects stored")
return pdbmodels
def get_new_id(iterator):
"""Returns a new id that is not in the given iterator"""
letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789ñÑçÇ'
for l in letters:
if l not in iterator:
return l
sys.stderr.write("Too many different chains given. The program can only handle modeling"
" a maximum of 66 different sequences")
exit(1)
def has_homolgs(target_seq, known_seqs):
"""Checks if a given sequence is an homolog of any of the known sequences and returns it"""
for k_seq in known_seqs:
alignment = align.globalxx(target_seq, k_seq)[0] # Generates and selects the alignment with the best score
aln_seq_1 = alignment[0] # Get the first sequence of the alignment (with - as gaps )
aln_seq_2 = alignment[1] # Get the second one
al_length = len(alignment[0])
ident = sum(base1 == base2 for base1, base2 in zip(aln_seq_1, aln_seq_2)) # Calculate number of identities
if ident/al_length >= 0.95: # If 95% of identity, return known sequence
return k_seq
def unify_ids(pdbmodels, verbose=False):
"""Unifies chain identifiers and updates the pdb models to CustomModel class"""
seq_dict = dict() # Dictionary where sequences are keys and ids are values
if verbose:
print("Unifying Ids")
for i in range(len(pdbmodels)):
pdb = pdbmodels[i]
model = CustomModel(str(i)) # Transforms model to CustomModel instance
for chain in pdb:
chain = CustomChain(chain) # Transforms chain to CustomChain instance
chain.parent = None # Removes previous parent from chain
chain_seq = chain.get_sequence()
if chain_seq not in seq_dict:
if not seq_dict: # If the sequence dictionary is empty
new_id = get_new_id(seq_dict.values()) # Get first id (A)
seq_dict[chain_seq] = new_id # Set the first sequence as key and A as value
chain.id = new_id # Also update chain id to A
else: # If dictionary is not empty
sequences = seq_dict.keys()
homolog_seq = has_homolgs(chain_seq, sequences) # Check if sequence has homology
if homolog_seq:
homolog_id = seq_dict[homolog_seq] # Get homolog id from seq_dict
seq_dict[chain_seq] = homolog_id # Set this sequence with the homolog id as value
chain.id = homolog_id # Also change chain id to homolog's
else:
new_id = get_new_id(seq_dict.values()) # Otherwise generate a new id
seq_dict[chain_seq] = new_id
chain.id = new_id
else:
chain.id = seq_dict[chain_seq] # If chain is already in seq_dict, update chain object id
model.add(chain)
pdbmodels[i] = model # Update pdbmodels list with the updated model
if verbose:
print("Ids unified")
return seq_dict
def get_interaction_dict(clean_pdbs, verbose=False):
"""Generates interaction dictionary. This is a dictionary of dictionaries. Each chain id is the key of the first
dictionary, and as value a dictionary. The dictionary inside has tuples of interactiong resiudes (their number)
from chain 1 to 2 as keys and for value it has a tuple of chain object 1, chain object 2 and interaction resiudes from 2 to 1:
For instance:
{Chain1 id : {
(first interacting residues number from chain 1 to 2): (Chain1 instance,
Chain2 instance,
interacting residues number from chain 2 to 1)
}
(second interacting resiudes...) : (... , ... , ... )
...
Chain2 id : {
(first interacting residues number from chain 2 to 1): (Chain2 instance,
(Chain1 instance,
interacting residues number from chain 1 to 1)
(second interacting resiudes...) : (... , ... , ... )
...
"""
interaction_dict = dict()
if verbose:
print("Generating interaction dictionary...")
for pdb in clean_pdbs:
chain1, chain2 = list(pdb.get_chains())
inter1_2, inter2_1 = chain1.get_interactions(chain2) # Generates interaction tuples,
# from chain 1 to 2 and from 2 to 1. For instance:
# inter1_2 = (2,40,120)
# inter2_1=(34, 20)
if inter1_2 != (): # If tuple is not empty (there is an interaction)
interaction_dict.setdefault(chain1.id, dict())[inter1_2] = (chain1, chain2, inter2_1) # Update dictionary
if inter2_1 != (): # Same for the other interaction
interaction_dict.setdefault(chain2.id, dict())[inter2_1] = (chain2, chain1, inter1_2)
if verbose:
print("Interaction dictionary generated")
return interaction_dict
def update_interactions_dict(interaction_dict, verbose=False):
"""Updates the interactions attribute of each chain inside the dictionary"""
if verbose:
print("Updating interaction dictionary...")
for chain in interaction_dict:
for interaction_tple in interaction_dict[chain]:
chain1, chain2, ref_inter = interaction_dict[chain][interaction_tple] # De-packs the interaction tuple value
# Generates a list with all interactions of that key minus the current one
chain1_filtered_interactions_lst = [x for x in interaction_dict[chain1.id].keys() if x != interaction_tple]
# Updates chain_interaction attribute with the list of interaction tuples
chain1.add_interaction_lst(chain1_filtered_interactions_lst)
# Generates a list with all interactions minus the one from chain2 to chain1
chain2_filtered_interactions_lst = [x for x in interaction_dict[chain2.id].keys() if x != ref_inter]
chain2.add_interaction_lst(chain2_filtered_interactions_lst)
parent = chain1.get_parent()
parent.child_list = [chain1, chain2] # Updates the model chains with these new updated chains
interaction_dict[chain][interaction_tple] = chain1, chain2 # Updates the dictionary value
# (now without the interaction from chain 2 to 1)
if verbose:
print("Interaction dictionary updated")
def has_clashes(move_atoms, model):
"""Compares the atoms backbone atoms of the moving chain with the backbone atoms of the model"""
backbone = {"CA", "C1\'"}
chain_atoms = [atom for atom in move_atoms if atom.id in backbone] # Gets only the backbone atoms
model_atoms = [atom for atom in model.get_atoms() if atom.id in backbone]
ns = Bio.PDB.NeighborSearch(model_atoms) # Generates a neigbour search tree to speed up distance calculations
clashes = 0
for atom in chain_atoms:
clashes += bool(ns.search(atom.coord, 2)) # If this atom shows clashes, add 1 to the clashes counter
if clashes/len(chain_atoms) >= 0.03: # If more than 3% of atoms show clashes return yes
return True
else: # Otherwise return no
return False
def get_starting_model(interaction_dict, verbose=False):
"""Returns as a starting model one of the CustomModel of the chain with more recorded interactions"""
if verbose:
print("Selecting best interaction from where to start modeling...")
max_len = 0
interaction_key = None
for key in interaction_dict:
length = len(interaction_dict[key])
if length > max_len: # If a chain has more interactions
max_len = length # Updates maximum interaction umber
interaction_key = key # Updates maximum interaction chain key
inter_tple = next(iter(interaction_dict[interaction_key])) # Gets an interaction tuple of the chain
# with more interactions
if verbose:
print("First two chains added")
return interaction_dict[interaction_key][inter_tple][0].parent.copy() # Returns the model of the interaction tuple
def generate_model_profile(model):
"""Generates a dictionary with the id chain as key and the number of repetitions of this chain as values"""
profile = {} # { "A":1, "B":4, ...}
for chain in model:
profile.setdefault(chain.id, 0) # If id not in dic, set it to 0
profile[chain.id] += 1 # Sum 1 to the counter of the id
return profile
def save_results(out_models, output):
"""Saves the resulting models into cif files (at the current working directory"""
i = 1
print("Saving models...")
path = os.getcwd()
for model in out_models: # Saves all models in the current working directory
model.save_to_mmCIF(path+"/"+output + "_" + str(i))
i += 1
print("Done\n")
def main_loop(num_models, output, interaction_dict, verbose=False, max_chains=100, dirty=False,
stech_dict=False):
"""Using the interaction dictionary, this function generates macrocomplex model/s. It begins with a template model
and starts adding chains until conditions allow. Finally it returns a list of pdb instance models."""
out_models = []
for i in range(1, num_models + 1):
print("Macrocomplex " + str(i) + " ...")
macrocomplex = get_starting_model(interaction_dict, verbose).copy() # Selects a starting model
model_stech = generate_model_profile(macrocomplex) # Generates the stechometry of the first two chains
macrocomplex.id = "Model_" + str(i)
run = True # WHile this variable is true, the program will keep trying to add chains to the macrocomplex
num_of_chains = 2 # The model starts with 2 chains already
num_empty_chains = 0 # NUmber of chains that have all their interactions depleted
while run:
for chain in macrocomplex: # Iterates the macrocomplex chains
if num_of_chains < max_chains: # If the number of chains still hasn't reached the maximum allowed
if chain.interactions: # If this chain still has pending interactions
random.shuffle(chain.interactions) # Shuffle the interactions list (to avoid
# repetitive behaviour)
for inter_tple in chain.interactions:
if stech_dict: # If there is stechometry input (either as stirng or template pdb)
target_chain_id = interaction_dict[chain.id][inter_tple][1].id # chain to be added
model_stech.setdefault(target_chain_id, 0)
model_number_chain = model_stech[target_chain_id] # Get the number of repetitions
stech_dict.setdefault(target_chain_id, 0)
if stech_dict[target_chain_id] <= model_number_chain: # If the number of this target
# chain would surpass the stechemestry given, don't add the chain and
if verbose:
print("(S) Chain NOT added: interaction " + chain.id + ": " +
str(inter_tple[:1]) + " ... " + str(inter_tple[-1]) + " to " + target_chain_id)
continue # jump to the next interaction tuple
fix, to_move = interaction_dict[chain.id][inter_tple] # Get the interaction chain instances
sup = Bio.PDB.Superimposer() # Generates a superimposer instance
chain_atoms, fix_atoms = chain.get_common_atoms(fix) # Get common atoms between the
# macrocomplex chain and the one in the interaction dictionary
sup.set_atoms(chain_atoms, fix_atoms) # Generate the superposition
move = to_move.copy() # Make a copy of the chain to move
sup.apply(move) # Apply superposition matrix
move_atoms = sorted(move.get_atoms())
# Now it checks if the target chain has clashes with the model
if not has_clashes(move_atoms, macrocomplex): # If it hasn't
if verbose:
print("Chain " + str(num_of_chains) + " added: interaction " + chain.id + ": " +
str(inter_tple[0]) + " ... " + str(inter_tple[-1]) + " to " + move.id)
move.parent = None # Sets the parent to none to evade biopython's strict id policy
macrocomplex.add(move) # Adds the target chain to the model
model_stech.setdefault(move.id, 0) # Updates stech dict
model_stech[move.id] += 1
num_of_chains += 1
if dirty: # Generates a cif file for each step in the building of the model
macrocomplex.save_to_mmCIF(output + str(i) + "_tmp_" + str(num_of_chains))
elif verbose: # If it has don't add the target chain
print("Chain NOT added: interaction " + chain.id + ": " +
str(inter_tple[:1]) + " ... " + str(inter_tple[-1]) + " to " + move.id)
chain.interactions = False # Set the interaction attribute to 0, this chain now will be ignored
else:
if verbose:
print("Chain " + chain.id + " empty")
num_empty_chains += 1
else:
run = False # When the maximum chain treshold is reached stop running
break
if num_empty_chains >= len(macrocomplex): # If all chains are empty of interactions stop running
run = False
if verbose:
stechometry_string = "" # Print the model's stechometry
for key in sorted(model_stech.keys()):
stechometry_string += key + ":" + str(model_stech[key]) + ","
stechometry_string = stechometry_string[:-1]
print("Macrocomplex's"+str(i)+" Stoichiometry is: "+stechometry_string)
print("Macrocomplex " + str(i) + " finished")
out_models.append(macrocomplex) # Add model to the models list
return out_models
def get_template_stech_dict(template, seq_dict, verbose=False):
"""Generates a stechometry dictionary for a given pdb template"""
template_stech_dict = {} # Format: { "A": 2, "B": 3, ...}, where key is chain id and value
# is the number of repetitions
parser = PDBParser(PERMISSIVE=1, QUIET=True)
template_object = parser.get_structure("template", template)[0] # Generates pdb template object
for chain in template_object:
chain = CustomChain(chain) # Transforms pdb chain object to CustomChain instance
chain.parent = None # Removes previous parent to evade biopython errors of id repetitions
chain_seq = chain.get_sequence()
if chain_seq in seq_dict:
chain.id = seq_dict[chain_seq] # Updates the template chain id to the corresponding by its sequence
template_stech_dict.setdefault(chain.id, 0)
template_stech_dict[chain.id] += 1 # Adds to chain id counter
if verbose: # Transforms the stech_dict to a string to be printed
stechometry_string = ""
for key in sorted(template_stech_dict.keys()):
stechometry_string += key+":"+str(template_stech_dict[key])+","
stechometry_string = stechometry_string[:-1]
print("Template's Stoichiometry is: "+stechometry_string)
return template_stech_dict
def get_string_stech_dict(stech_string):
"""Given a stechometry string it transsforms it to a dictionary"""
stech_dict = {}
try:
stech_lst = stech_string.split(",") # Generates a stech list: ["A:3", "B:2", ...]
for stech in stech_lst:
chain, number = stech.split(":")
stech_dict[chain] = int(number) # Chain id as key and number as value: { "A": 3, "B": 2, ...}
return stech_dict
except:
sys.stderr.write("Stechometry string format is wrong, please follow this format: A:2,B:11,C:4, ...")
sys.exit(1)
def build_macrocomplex(directory, output, max_chains=300, num_models=1, template=False, dirty=False, verbose=False, stech_string=False):
"""Main function that integrates all the important steps. First it reads the pdb models ans stores them in a list.
Then it compares all the chains and unifies the chain ids of the pdb list updating them, it also generates a sequence
key dictionary. Then it checks at each pdb model for chain interactions and stores them in a dictionary. After it
updates the dictionary with information of itself. Next it generates the model/s using this interactions. Finally it
saves the model/s in cif format."""
print("Program is running, please wait...")
# Reads and stores pdb objects in a list
in_pdbmodels = read_pdbs(directory, verbose)
# Unifies all ids by sequence, updates the pdb list with new chain ids and returns a sequence dictionary: {seq: id,}
seq_dict = unify_ids(in_pdbmodels, verbose)
# Checks each pdb object for chain interactions and stores it in a dictionary of dictionaries:
# {
# Chain1_id : { residues_tuple_1_to_2 : chain1_object, chain2_object, residues_tuple_2_to_1}
# Chain2_id : {residues_tuple_2_to_1 : chain2_object, chain1_object, residues_tuple_1_to_2}
# ...
# }
interaction_dict = get_interaction_dict(in_pdbmodels, verbose=verbose)
# Changes interaction_dict chain objects to CustomChain instances and adds the interactions to each instance
update_interactions_dict(interaction_dict, verbose)
stech_dict = {}
# If a template or a string has been given to set Stoichometry, it generates a dictionary of it
# { "A":5, "B":2, "C":6, .. }
if template:
stech_dict = get_template_stech_dict(template, seq_dict, verbose=verbose)
elif stech_string:
stech_dict = get_string_stech_dict(stech_string)
# Starts iterating the interaction pair with more known interactions and generates the model/s
out_pdbmodels = main_loop(num_models, output, interaction_dict, verbose, max_chains, dirty, stech_dict=stech_dict)
# Saves the model/s to ciff format
save_results(out_pdbmodels, output)
Functions
def build_macrocomplex(
directory, output, max_chains=300, num_models=1, template=False, dirty=False, verbose=False, stech_string=False)
Main function that integrates all the important steps. First it reads the pdb models ans stores them in a list. Then it compares all the chains and unifies the chain ids of the pdb list updating them, it also generates a sequence key dictionary. Then it checks at each pdb model for chain interactions and stores them in a dictionary. After it updates the dictionary with information of itself. Next it generates the model/s using this interactions. Finally it saves the model/s in cif format.
def build_macrocomplex(directory, output, max_chains=300, num_models=1, template=False, dirty=False, verbose=False, stech_string=False):
"""Main function that integrates all the important steps. First it reads the pdb models ans stores them in a list.
Then it compares all the chains and unifies the chain ids of the pdb list updating them, it also generates a sequence
key dictionary. Then it checks at each pdb model for chain interactions and stores them in a dictionary. After it
updates the dictionary with information of itself. Next it generates the model/s using this interactions. Finally it
saves the model/s in cif format."""
print("Program is running, please wait...")
# Reads and stores pdb objects in a list
in_pdbmodels = read_pdbs(directory, verbose)
# Unifies all ids by sequence, updates the pdb list with new chain ids and returns a sequence dictionary: {seq: id,}
seq_dict = unify_ids(in_pdbmodels, verbose)
# Checks each pdb object for chain interactions and stores it in a dictionary of dictionaries:
# {
# Chain1_id : { residues_tuple_1_to_2 : chain1_object, chain2_object, residues_tuple_2_to_1}
# Chain2_id : {residues_tuple_2_to_1 : chain2_object, chain1_object, residues_tuple_1_to_2}
# ...
# }
interaction_dict = get_interaction_dict(in_pdbmodels, verbose=verbose)
# Changes interaction_dict chain objects to CustomChain instances and adds the interactions to each instance
update_interactions_dict(interaction_dict, verbose)
stech_dict = {}
# If a template or a string has been given to set Stoichometry, it generates a dictionary of it
# { "A":5, "B":2, "C":6, .. }
if template:
stech_dict = get_template_stech_dict(template, seq_dict, verbose=verbose)
elif stech_string:
stech_dict = get_string_stech_dict(stech_string)
# Starts iterating the interaction pair with more known interactions and generates the model/s
out_pdbmodels = main_loop(num_models, output, interaction_dict, verbose, max_chains, dirty, stech_dict=stech_dict)
# Saves the model/s to ciff format
save_results(out_pdbmodels, output)
def generate_model_profile(
model)
Generates a dictionary with the id chain as key and the number of repetitions of this chain as values
def generate_model_profile(model):
"""Generates a dictionary with the id chain as key and the number of repetitions of this chain as values"""
profile = {} # { "A":1, "B":4, ...}
for chain in model:
profile.setdefault(chain.id, 0) # If id not in dic, set it to 0
profile[chain.id] += 1 # Sum 1 to the counter of the id
return profile
def get_interaction_dict(
clean_pdbs, verbose=False)
Generates interaction dictionary. This is a dictionary of dictionaries. Each chain id is the key of the first dictionary, and as value a dictionary. The dictionary inside has tuples of interactiong resiudes (their number) from chain 1 to 2 as keys and for value it has a tuple of chain object 1, chain object 2 and interaction resiudes from 2 to 1: For instance: {Chain1 id : { (first interacting residues number from chain 1 to 2): (Chain1 instance, Chain2 instance, interacting residues number from chain 2 to 1) } (second interacting resiudes...) : (... , ... , ... ) ... Chain2 id : { (first interacting residues number from chain 2 to 1): (Chain2 instance, (Chain1 instance, interacting residues number from chain 1 to 1) (second interacting resiudes...) : (... , ... , ... ) ...
def get_interaction_dict(clean_pdbs, verbose=False):
"""Generates interaction dictionary. This is a dictionary of dictionaries. Each chain id is the key of the first
dictionary, and as value a dictionary. The dictionary inside has tuples of interactiong resiudes (their number)
from chain 1 to 2 as keys and for value it has a tuple of chain object 1, chain object 2 and interaction resiudes from 2 to 1:
For instance:
{Chain1 id : {
(first interacting residues number from chain 1 to 2): (Chain1 instance,
Chain2 instance,
interacting residues number from chain 2 to 1)
}
(second interacting resiudes...) : (... , ... , ... )
...
Chain2 id : {
(first interacting residues number from chain 2 to 1): (Chain2 instance,
(Chain1 instance,
interacting residues number from chain 1 to 1)
(second interacting resiudes...) : (... , ... , ... )
...
"""
interaction_dict = dict()
if verbose:
print("Generating interaction dictionary...")
for pdb in clean_pdbs:
chain1, chain2 = list(pdb.get_chains())
inter1_2, inter2_1 = chain1.get_interactions(chain2) # Generates interaction tuples,
# from chain 1 to 2 and from 2 to 1. For instance:
# inter1_2 = (2,40,120)
# inter2_1=(34, 20)
if inter1_2 != (): # If tuple is not empty (there is an interaction)
interaction_dict.setdefault(chain1.id, dict())[inter1_2] = (chain1, chain2, inter2_1) # Update dictionary
if inter2_1 != (): # Same for the other interaction
interaction_dict.setdefault(chain2.id, dict())[inter2_1] = (chain2, chain1, inter1_2)
if verbose:
print("Interaction dictionary generated")
return interaction_dict
def get_new_id(
iterator)
Returns a new id that is not in the given iterator
def get_new_id(iterator):
"""Returns a new id that is not in the given iterator"""
letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789ñÑçÇ'
for l in letters:
if l not in iterator:
return l
sys.stderr.write("Too many different chains given. The program can only handle modeling"
" a maximum of 66 different sequences")
exit(1)
def get_starting_model(
interaction_dict, verbose=False)
Returns as a starting model one of the CustomModel of the chain with more recorded interactions
def get_starting_model(interaction_dict, verbose=False):
"""Returns as a starting model one of the CustomModel of the chain with more recorded interactions"""
if verbose:
print("Selecting best interaction from where to start modeling...")
max_len = 0
interaction_key = None
for key in interaction_dict:
length = len(interaction_dict[key])
if length > max_len: # If a chain has more interactions
max_len = length # Updates maximum interaction umber
interaction_key = key # Updates maximum interaction chain key
inter_tple = next(iter(interaction_dict[interaction_key])) # Gets an interaction tuple of the chain
# with more interactions
if verbose:
print("First two chains added")
return interaction_dict[interaction_key][inter_tple][0].parent.copy() # Returns the model of the interaction tuple
def get_string_stech_dict(
stech_string)
Given a stechometry string it transsforms it to a dictionary
def get_string_stech_dict(stech_string):
"""Given a stechometry string it transsforms it to a dictionary"""
stech_dict = {}
try:
stech_lst = stech_string.split(",") # Generates a stech list: ["A:3", "B:2", ...]
for stech in stech_lst:
chain, number = stech.split(":")
stech_dict[chain] = int(number) # Chain id as key and number as value: { "A": 3, "B": 2, ...}
return stech_dict
except:
sys.stderr.write("Stechometry string format is wrong, please follow this format: A:2,B:11,C:4, ...")
sys.exit(1)
def get_template_stech_dict(
template, seq_dict, verbose=False)
Generates a stechometry dictionary for a given pdb template
def get_template_stech_dict(template, seq_dict, verbose=False):
"""Generates a stechometry dictionary for a given pdb template"""
template_stech_dict = {} # Format: { "A": 2, "B": 3, ...}, where key is chain id and value
# is the number of repetitions
parser = PDBParser(PERMISSIVE=1, QUIET=True)
template_object = parser.get_structure("template", template)[0] # Generates pdb template object
for chain in template_object:
chain = CustomChain(chain) # Transforms pdb chain object to CustomChain instance
chain.parent = None # Removes previous parent to evade biopython errors of id repetitions
chain_seq = chain.get_sequence()
if chain_seq in seq_dict:
chain.id = seq_dict[chain_seq] # Updates the template chain id to the corresponding by its sequence
template_stech_dict.setdefault(chain.id, 0)
template_stech_dict[chain.id] += 1 # Adds to chain id counter
if verbose: # Transforms the stech_dict to a string to be printed
stechometry_string = ""
for key in sorted(template_stech_dict.keys()):
stechometry_string += key+":"+str(template_stech_dict[key])+","
stechometry_string = stechometry_string[:-1]
print("Template's Stoichiometry is: "+stechometry_string)
return template_stech_dict
def has_clashes(
move_atoms, model)
Compares the atoms backbone atoms of the moving chain with the backbone atoms of the model
def has_clashes(move_atoms, model):
"""Compares the atoms backbone atoms of the moving chain with the backbone atoms of the model"""
backbone = {"CA", "C1\'"}
chain_atoms = [atom for atom in move_atoms if atom.id in backbone] # Gets only the backbone atoms
model_atoms = [atom for atom in model.get_atoms() if atom.id in backbone]
ns = Bio.PDB.NeighborSearch(model_atoms) # Generates a neigbour search tree to speed up distance calculations
clashes = 0
for atom in chain_atoms:
clashes += bool(ns.search(atom.coord, 2)) # If this atom shows clashes, add 1 to the clashes counter
if clashes/len(chain_atoms) >= 0.03: # If more than 3% of atoms show clashes return yes
return True
else: # Otherwise return no
return False
def has_homolgs(
target_seq, known_seqs)
Checks if a given sequence is an homolog of any of the known sequences and returns it
def has_homolgs(target_seq, known_seqs):
"""Checks if a given sequence is an homolog of any of the known sequences and returns it"""
for k_seq in known_seqs:
alignment = align.globalxx(target_seq, k_seq)[0] # Generates and selects the alignment with the best score
aln_seq_1 = alignment[0] # Get the first sequence of the alignment (with - as gaps )
aln_seq_2 = alignment[1] # Get the second one
al_length = len(alignment[0])
ident = sum(base1 == base2 for base1, base2 in zip(aln_seq_1, aln_seq_2)) # Calculate number of identities
if ident/al_length >= 0.95: # If 95% of identity, return known sequence
return k_seq
def main_loop(
num_models, output, interaction_dict, verbose=False, max_chains=100, dirty=False, stech_dict=False)
Using the interaction dictionary, this function generates macrocomplex model/s. It begins with a template model and starts adding chains until conditions allow. Finally it returns a list of pdb instance models.
def main_loop(num_models, output, interaction_dict, verbose=False, max_chains=100, dirty=False,
stech_dict=False):
"""Using the interaction dictionary, this function generates macrocomplex model/s. It begins with a template model
and starts adding chains until conditions allow. Finally it returns a list of pdb instance models."""
out_models = []
for i in range(1, num_models + 1):
print("Macrocomplex " + str(i) + " ...")
macrocomplex = get_starting_model(interaction_dict, verbose).copy() # Selects a starting model
model_stech = generate_model_profile(macrocomplex) # Generates the stechometry of the first two chains
macrocomplex.id = "Model_" + str(i)
run = True # WHile this variable is true, the program will keep trying to add chains to the macrocomplex
num_of_chains = 2 # The model starts with 2 chains already
num_empty_chains = 0 # NUmber of chains that have all their interactions depleted
while run:
for chain in macrocomplex: # Iterates the macrocomplex chains
if num_of_chains < max_chains: # If the number of chains still hasn't reached the maximum allowed
if chain.interactions: # If this chain still has pending interactions
random.shuffle(chain.interactions) # Shuffle the interactions list (to avoid
# repetitive behaviour)
for inter_tple in chain.interactions:
if stech_dict: # If there is stechometry input (either as stirng or template pdb)
target_chain_id = interaction_dict[chain.id][inter_tple][1].id # chain to be added
model_stech.setdefault(target_chain_id, 0)
model_number_chain = model_stech[target_chain_id] # Get the number of repetitions
stech_dict.setdefault(target_chain_id, 0)
if stech_dict[target_chain_id] <= model_number_chain: # If the number of this target
# chain would surpass the stechemestry given, don't add the chain and
if verbose:
print("(S) Chain NOT added: interaction " + chain.id + ": " +
str(inter_tple[:1]) + " ... " + str(inter_tple[-1]) + " to " + target_chain_id)
continue # jump to the next interaction tuple
fix, to_move = interaction_dict[chain.id][inter_tple] # Get the interaction chain instances
sup = Bio.PDB.Superimposer() # Generates a superimposer instance
chain_atoms, fix_atoms = chain.get_common_atoms(fix) # Get common atoms between the
# macrocomplex chain and the one in the interaction dictionary
sup.set_atoms(chain_atoms, fix_atoms) # Generate the superposition
move = to_move.copy() # Make a copy of the chain to move
sup.apply(move) # Apply superposition matrix
move_atoms = sorted(move.get_atoms())
# Now it checks if the target chain has clashes with the model
if not has_clashes(move_atoms, macrocomplex): # If it hasn't
if verbose:
print("Chain " + str(num_of_chains) + " added: interaction " + chain.id + ": " +
str(inter_tple[0]) + " ... " + str(inter_tple[-1]) + " to " + move.id)
move.parent = None # Sets the parent to none to evade biopython's strict id policy
macrocomplex.add(move) # Adds the target chain to the model
model_stech.setdefault(move.id, 0) # Updates stech dict
model_stech[move.id] += 1
num_of_chains += 1
if dirty: # Generates a cif file for each step in the building of the model
macrocomplex.save_to_mmCIF(output + str(i) + "_tmp_" + str(num_of_chains))
elif verbose: # If it has don't add the target chain
print("Chain NOT added: interaction " + chain.id + ": " +
str(inter_tple[:1]) + " ... " + str(inter_tple[-1]) + " to " + move.id)
chain.interactions = False # Set the interaction attribute to 0, this chain now will be ignored
else:
if verbose:
print("Chain " + chain.id + " empty")
num_empty_chains += 1
else:
run = False # When the maximum chain treshold is reached stop running
break
if num_empty_chains >= len(macrocomplex): # If all chains are empty of interactions stop running
run = False
if verbose:
stechometry_string = "" # Print the model's stechometry
for key in sorted(model_stech.keys()):
stechometry_string += key + ":" + str(model_stech[key]) + ","
stechometry_string = stechometry_string[:-1]
print("Macrocomplex's"+str(i)+" Stoichiometry is: "+stechometry_string)
print("Macrocomplex " + str(i) + " finished")
out_models.append(macrocomplex) # Add model to the models list
return out_models
def read_pdbs(
directory, verbose=False)
Reads the input directory and generates pdb models
def read_pdbs(directory, verbose=False):
"""Reads the input directory and generates pdb models"""
if verbose:
print("Reading pdb input files from %s" % directory)
parser = PDBParser(PERMISSIVE=1, QUIET=True)
if os.path.isdir(directory) and directory.endswith("/"):
try:
pdbmodels = [parser.get_structure("Model_pair", directory + f)[0] for
f in listdir(directory) if f.endswith(".pdb")] # Generates pdb objects for files that end with .pdb
except:
sys.stderr.write("PDB files couldn't be opened. Please, revise that their format is correct.")
sys.exit(1)
else:
sys.stderr.write("Directory %s doesn't exists, please select a valid directory." % directory)
sys.exit(1)
if not bool(pdbmodels): # If no pdb instance is generated
sys.stderr.write("No pdb files where read. Please make sure the given directory contains pdb files. ")
sys.exit(1)
for model in pdbmodels:
if len(model.child_list) != 2:
sys.stderr.write("A pdb input file doesn't contains two chains. Please, all input pdbs must only contain "
"two chains.")
sys.exit(1)
if verbose:
print("Pdb objects stored")
return pdbmodels
def save_results(
out_models, output)
Saves the resulting models into cif files (at the current working directory
def save_results(out_models, output):
"""Saves the resulting models into cif files (at the current working directory"""
i = 1
print("Saving models...")
path = os.getcwd()
for model in out_models: # Saves all models in the current working directory
model.save_to_mmCIF(path+"/"+output + "_" + str(i))
i += 1
print("Done\n")
def unify_ids(
pdbmodels, verbose=False)
Unifies chain identifiers and updates the pdb models to CustomModel class
def unify_ids(pdbmodels, verbose=False):
"""Unifies chain identifiers and updates the pdb models to CustomModel class"""
seq_dict = dict() # Dictionary where sequences are keys and ids are values
if verbose:
print("Unifying Ids")
for i in range(len(pdbmodels)):
pdb = pdbmodels[i]
model = CustomModel(str(i)) # Transforms model to CustomModel instance
for chain in pdb:
chain = CustomChain(chain) # Transforms chain to CustomChain instance
chain.parent = None # Removes previous parent from chain
chain_seq = chain.get_sequence()
if chain_seq not in seq_dict:
if not seq_dict: # If the sequence dictionary is empty
new_id = get_new_id(seq_dict.values()) # Get first id (A)
seq_dict[chain_seq] = new_id # Set the first sequence as key and A as value
chain.id = new_id # Also update chain id to A
else: # If dictionary is not empty
sequences = seq_dict.keys()
homolog_seq = has_homolgs(chain_seq, sequences) # Check if sequence has homology
if homolog_seq:
homolog_id = seq_dict[homolog_seq] # Get homolog id from seq_dict
seq_dict[chain_seq] = homolog_id # Set this sequence with the homolog id as value
chain.id = homolog_id # Also change chain id to homolog's
else:
new_id = get_new_id(seq_dict.values()) # Otherwise generate a new id
seq_dict[chain_seq] = new_id
chain.id = new_id
else:
chain.id = seq_dict[chain_seq] # If chain is already in seq_dict, update chain object id
model.add(chain)
pdbmodels[i] = model # Update pdbmodels list with the updated model
if verbose:
print("Ids unified")
return seq_dict
def update_interactions_dict(
interaction_dict, verbose=False)
Updates the interactions attribute of each chain inside the dictionary
def update_interactions_dict(interaction_dict, verbose=False):
"""Updates the interactions attribute of each chain inside the dictionary"""
if verbose:
print("Updating interaction dictionary...")
for chain in interaction_dict:
for interaction_tple in interaction_dict[chain]:
chain1, chain2, ref_inter = interaction_dict[chain][interaction_tple] # De-packs the interaction tuple value
# Generates a list with all interactions of that key minus the current one
chain1_filtered_interactions_lst = [x for x in interaction_dict[chain1.id].keys() if x != interaction_tple]
# Updates chain_interaction attribute with the list of interaction tuples
chain1.add_interaction_lst(chain1_filtered_interactions_lst)
# Generates a list with all interactions minus the one from chain2 to chain1
chain2_filtered_interactions_lst = [x for x in interaction_dict[chain2.id].keys() if x != ref_inter]
chain2.add_interaction_lst(chain2_filtered_interactions_lst)
parent = chain1.get_parent()
parent.child_list = [chain1, chain2] # Updates the model chains with these new updated chains
interaction_dict[chain][interaction_tple] = chain1, chain2 # Updates the dictionary value
# (now without the interaction from chain 2 to 1)
if verbose:
print("Interaction dictionary updated")


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fopen
—open a file#include <stdio.h> FILE *fopen(const char *file, const char *mode); FILE *_fopen_r(struct _reent *reent, const char *file, const char *mode);
Description
fopen
initializes the data structures needed to read or write a
file. Specify the file's name as the string at file, and the kind
of access you need to the file with the string at mode.
The alternate function _fopen_r
is a reentrant version.
The extra argument reent is a pointer to a reentrancy structure.
Three fundamental kinds of access are available: read, write, and append.
*
mode must begin with one of the three characters `r
',
`w
', or `a
', to select one of these:
r
w
a
fseek
.
Some host systems distinguish between “binary” and “text” files.
Such systems may perform data transformations on data written to, or
read from, files opened as “text”.
If your system is one of these, then you can append a `b
' to any
of the three modes above, to specify that you are opening the file as
a binary file (the default is to open the file as a text file).
`rb
', then, means “read binary”; `wb
', “write binary”; and
`ab
', “append binary”.
To make C programs more portable, the `b
' is accepted on all
systems, whether or not it makes a difference.
Finally, you might need to both read and write from the same file.
You can also append a `+
' to any of the three modes, to permit
this. (If you want to append both `b
' and `+
', you can do it
in either order: for example, "rb+"
means the same thing as
"r+b"
when used as a mode string.)
Use "r+"
(or "rb+"
) to permit reading and writing anywhere in
an existing file, without discarding any data; "w+"
(or "wb+"
)
to create a new file (or begin by discarding all data from an old one)
that permits reading and writing anywhere in it; and "a+"
(or
"ab+"
) to permit reading anywhere in an existing file, but writing
only at the end.
Returns
fopen
returns a file pointer which you can use for other file
operations, unless the file you requested could not be opened; in that
situation, the result is NULL
. If the reason for failure was an
invalid string at mode, errno
is set to EINVAL
.
Portability
fopen
is required by ANSI C.
Supporting OS subroutines required: close
, fstat
, isatty
,
lseek
, open
, read
, sbrk
, write
.


Product Description
Almost works anywhere.Can be mounted to porch or lawn, and also handy if you are working in the dark.
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LED quantity: 60


| Methods' Summary |
| setFilter |
used by interaction to set selected filter
|
| getFilter |
used by detection to get selected filter
|
| Methods' Details |
setFilter
| void |
| setFilter( |
[in] string |
Name ); |
- Description
- used by interaction to set selected filter
This value must be save till another one will be set
and must be provided on getFilter() for interest users.
|
|
getFilter
- Description
- used by detection to get selected filter
|
|
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Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


|
iDreamSky Game Engine
v1.0.0
|
TCB Spline data driven Animation. 更多...
#include <SkeletalAnimation.h>
公開方法(Public Methods) | |
| SkeletalAnimation () | |
| Constructor. | |
| virtual | ~SkeletalAnimation () |
| Destructor. | |
| bool | construct (const char *path_) |
| void | destruct () |
| virtual void | setTime (const float &time_) |
| This function set the animation time. 更多... | |
| virtual void | setSpeed (const float &speed) |
| This function set the animation play back speed. 更多... | |
| virtual void | setLoopBehavior (AnimBase::LoopBehavior lb_) |
| This function set the loop behaviour of animation. 更多... | |
| virtual float | getTime () const |
| This function get the animation time. 更多... | |
| virtual float | getSpeed () const |
| This function get the animation play back speed. 更多... | |
| virtual AnimBase::LoopBehavior | getLoopBehavior () const |
| This function get the loop behaviour of animation. 更多... | |
| virtual float | getDuration () const |
| This function get the duration in frame of the animation data. 更多... | |
| virtual int | getNumBones () const |
| This function get the Number of Bones in the animation data. 更多... | |
| virtual const char * | getBoneName (int boneIdx_) const |
| This function get the Name of Bone in the animation data. 更多... | |
| virtual int | getBoneIndex (const char *name_) const |
| This function try to find the position of Bone of animation data. 更多... | |
| virtual int | getBoneParentIndex (int boneIdx__) const |
| This function try to find the position of parent of Bone in of animation data. 更多... | |
| virtual void | getVector10 (int boneIdx_, Vector10 &v_) const |
| This function get the Vector10 (scale, rotation and translation) of the i-th bone. 更多... | |
| virtual void | update () |
| This function is implemented thing need to do in each cycle of animation. 更多... | |
| virtual bool | isPlaying () const |
| This function get the playing status of the Animation. 更多... | |
| virtual void | play (AnimBase::LoopBehavior lb_=AnimBase::LOOP, const float &speed_=1, const float &starttime_=0) |
| This function start or resume the Animation. 更多... | |
| virtual void | stop () |
| This function stop the Animation. 更多... | |
| virtual bool | stopped () |
| virtual void | pause () |
| This function pause the Animation. 更多... | |
| virtual void | unPause () |
| virtual void | rewind () |
| This function resets the Animation to its first frame (0 time) | |
| virtual bool | isPaused () const |
| This function get the pause status of the Animation. 更多... | |
| 公開方法(Public Methods) 繼承自 Magnum::SkeletalKeyFrameAnimation | |
| SkeletalKeyFrameAnimation () | |
| Constructor. | |
| virtual | ~SkeletalKeyFrameAnimation () |
| Destructor. | |
| 公開方法(Public Methods) 繼承自 Magnum::SkeletalAnimBase | |
| SkeletalAnimBase () | |
| Constructor. | |
| virtual | ~SkeletalAnimBase () |
| Destructor. | |
| virtual void | getLocalTransform (int boneIdx__, Matrix4 &mat_) const |
| This function get the local Transform of of the i-th bone. 更多... | |
| virtual void | getSystemTransform (int boneIdx__, Matrix4 &mat_) const |
| This function get the global Transform of of the i-th bone. 更多... | |
| 公開方法(Public Methods) 繼承自 Magnum::AnimBase | |
| AnimBase () | |
| Constructor. | |
| virtual | ~AnimBase () |
| Destructor. | |
| virtual void | setEventCallBack (Functor1< AnimBase * > *cb_) |
| This function set the animation call back. 更多... | |
| virtual Functor1< AnimBase * > * | getEventCallBack () |
| This function get the animation call back. 更多... | |
| void | invokeCallBack () |
| This function invoke the Animation CallBack. 更多... | |
保護屬性 | |
| SkeletalAnimModel::Resource * | resource |
| bool | _isPlaying |
| bool | _isPaused |
| float | speed |
| AnimBase::LoopBehavior | lb |
| float | lastTime |
| float | time |
| float | timedir |
| 保護屬性 繼承自 Magnum::AnimBase | |
| Functor1< AnimBase * > * | cb |
| This function retrieve the Animation that Triggered the callback. 更多... | |
額外的繼承成員 | |
| 公開型態 繼承自 Magnum::AnimBase | |
| enum | LoopBehavior { ONESHOT, CLAMP, LOOP, PINGPONG } |
| This Enum specified the way the animation is played. 更多... | |
TCB Spline data driven Animation.
This is the animation driven by TCB key. It data file type that this calss uses is *.splineanimv2
|
virtual |
This function try to find the position of Bone of animation data.
| name_ | the name of Bone to find; |
|
virtual |
This function get the Name of Bone in the animation data.
| i | the idx to bone array; |
|
virtual |
This function try to find the position of parent of Bone in of animation data.
| i_ | the index of the bone; |
|
virtual |
This function get the duration in frame of the animation data.
|
virtual |
This function get the loop behaviour of animation.
The loop behaviour of animation
|
virtual |
This function get the Number of Bones in the animation data.
|
virtual |
This function get the animation play back speed.
|
virtual |
This function get the animation time.
|
virtual |
|
virtual |
This function get the pause status of the Animation.
|
virtual |
This function get the playing status of the Animation.
|
virtual |
This function pause the Animation.
After this function is called, the animation will be set at pause state. We can resume it by calling resume or start
|
virtual |
This function start or resume the Animation.
After this function is called, the animation will be set at playing state
| lb_ | the behaviour when animation is played to last frame |
| speed_ | the speed for the animation |
| starttime_ | from which the animation is played |
|
virtual |
This function set the loop behaviour of animation.
| lb_ | loop behaviour |
|
virtual |
This function set the animation play back speed.
| speed | the speed of the animation, set to 1.0 for the same speed as exported data |
|
virtual |
This function set the animation time.
| time | the time of the animation |
|
virtual |
This function stop the Animation.
After this function is called, the animation will be set at stop state
|
virtual |
This function is implemented thing need to do in each cycle of animation.
when this function is called, the system time will be record and internal time elapsed is update. getVector10 function use this internal time value to calculate animation


Nexmo API Demo by OBS
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Mr.Star Lord
SGP ***123A
Lamborghini Veneno Roadster (Luxury car, Orange)
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Join hundreds of fellow mobile developers for Xamarin’s first worldwide developer conference.
Our one-on-one room will be staffed with Xamarin experts to help you troubleshoot and accelerate your app projects. And get hands-on with 3rd party libraries and services during Hack Attacks — programming challenges — happening throughout the conference. Enjoy special recognition at the closing ceremony by earning 5 or more T-shirts that are unique to each Hack Attack.
Conference breaks and evening events provide great opportunities to learn from and contribute to one of the best developer communities on the planet. You only have one opportunity to be part of Xamarin’s inaugural developer conference - claim your right to say “I was there when it all started.”
Relax with other attendees, speakers and Xamarin engineers in the Darwin Lounge.
The self-proclaimed music capital of the world, Austin, TX provides the perfect environment for the evolution of your fun traits. The conference takes place at the swanky Hilton Austin which is just 3 blocks from famous 6th Street, known for dozens of music venues including Pete’s Dueling Piano Bar. Can’t get that in your native habitat. Don’t leave without sampling at least one of Austin’s barbecue joints. Your DNA will never be the same.
Get ready for the fantastic food of Austin, Texas. This city is famous for its innovative food scene as well as the country’s best barbeque. At Xamarin’s Evolve 2013 you’ll feast on authentic Austin grub with as much Austin craft beer as you can hold. Imagine slow roasted pork with onion marmalade, chicken & waffles, miso glazed salmon, Texas Hawaiian sandwiches (that’s just wrong!) or jalapeno smoked beef brisket.


Download Anaconda
Anaconda is a completely free Python distribution (including for commercial use and redistribution). It includes over 195 of the most popular Python packages for science, math, engineering, data analysis.
You can find MD5 information for Anaconda installers here.
*Anaconda comes with installers for Python 2.7 and 3.4. You can use Python 2.6 and 3.3 by installing either the 2.7 or 3.4 version of Anaconda and using the conda command. You can also create a 3.4 environment with the conda command if you've downloaded 2.7 and vice versa.
If you do not want to download the entire distribution, Miniconda is also available. Miniconda contains only conda and Python, so that you can install only the individual packages you want through the conda command. You can download Miniconda here.
For older versions of Anaconda installers, visit the installer archive. For long-term support of the packages found in the Anaconda archives, please visit the Anaconda Server page or contact<EMAIL_ADDRESS>
Using a cloud service account on Amazon AWS, Microsoft Azure, or VMDepot? You can easily use one of our pre-created Anaconda or Miniconda images, with no installation required! For more info please click here.
Looking for more?
Getting started with Anaconda Anaconda Quick Start Guide
Free Community Support for Anaconda Anaconda mailing list
Information on Installation and Use online documentation
Issues or Feature Requests Anaconda Issue Tracker
Install or Technical Support Support page
Anaconda Behind Your Firewall Anaconda Server
NumFOCUS
NumFOCUS is a charitable organization with the purpose of supporting and promoting world-class, innovative, open-source scientific software. It provides the critical service of helping to remove the financial burden of continual development for many projects, including NumPy, SciPy, IPython, PyTables, pandas, Matplotlib, scikit-learn, and more. Donate to NumFOCUS.