I was reading, and watching, Mike Zamansky's blog post series about org-capture and how he manages his bookmarks. His blog and video series are a big recommendation from me, he is teaching me tons every time I watch his videos. His inspirational videos were what made me dig down on how I could do what he's doing but… my way…
I stumbled across this blog post that describes the process of using org-cliplink to insert the title of the post into an org-mode link. Basically, what I wanted to do is provide a link and get an org-mode link. Sounds simple enough. Let's dig in.
Org Capture Templates
I will assume that you went through Mike's part 1 and part 2 posts to understand what org-capture-templates are and how they work. I essentially learned it from him and I do not think I can do a better job than a teacher.
Now that we understand where we need to start from, let's explain the situation. We need to find a way to call org-capture and provide it with a template. This template will need to take a url and add an org-modeurl in our bookmarks. It will look something like the following.
I formatted it a bit so it would have some properties. I simply used the %t to put the timestamp of when I took the bookmark. I used the %? to drop me at the end for editing. Then some-function-here-to-call a function to call to generate our bookmark section with a title.
The blog post I eluded to earlier solved it by using org-cliplink. While org-cliplink is great for getting titles and manipulating them, I don't really need that functionality. I can do it manually. Sometimes, though, I would like to copy a page… Maybe if there is a project that could attempt to do someth… Got it… org-web-tools.
Configuring org-capture with org-web-tools
You would assume that you would be able to just pop (org-web-tools-insert-link-for-url) in the previous block and you're all done. But uhhh….
Wrong number of arguments: (1 . 1), 0
No dice. What would seem to be the problem ?
We look at the definition and we find this.
(defunorg-web-tools-insert-link-for-url(url)"Insert Org link to URL using title of HTML page at URL.If URL is not given, look for first URL in `kill-ring'."(interactive(list (org-web-tools--get-first-url)))(insert(org-web-tools--org-link-for-urlurl)))
I don't know why, exactly, it doesn't work by calling it straight away because I do not know emacs-lisp at all. If you do, let me know. I suspect it has something to do with (interactive) and the list provided to it as arguments.
Anyway, I can see it is using org-web-tools--org-link-for-url, which the documentation suggests does the same thing as org-web-tools-insert-link-for-url, but is not exposed with (interactive). Okay, we have bits and pieces of the puzzle. Let's put it together.
Now if we copy a link into the clipboard and then call org-capture with the option b, we get prompted to edit the following before adding it to our bookmarks.
** [[https://cestlaz.github.io/stories/emacs/][Using Emacs Series - C'est la Z]]
:PROPERTIES:
:TIMESTAMP: <2020-09-17 do>
:END:
Works like a charm.
Custom URL
What if we need to modify the url in some way before providing it. I have that use case. All I needed to do is create a function that takes input from the user and provide it to org-web-tools--org-link-for-url. How hard can that be ?! uhoh! I said the curse phrase didn't I ?
(defunorg-web-tools-insert-link-for-given-url()"Extend =org-web-tools-inster-link-for-url= to take a user given URL"(interactive)(let ((url(read-string"Link: ")))(org-web-tools--org-link-for-urlurl)))
We can, then, hook the whole thing up to our org-capture-templates and we get.
if we use the B, this time, it will prompt us for input.
Conclusion
I thought this was going to be harder to pull off but, alas, it was simple, even for someone who doesn't know emacs-lisp, to figure out. I hope I'd get more familiar with emacs-lisp with time and be able to do more. Until next time, I recommend you hook org-capture into your workflow. Make sure it fits your work style, otherwise you will not use it, and make your path a more productive one.
The more I get comfortable with emacs and doom, the more I tend to move things to it. This means that I am getting things done faster, without the need to get bogged down in the weeds of things.
This also means that, sometimes, I get to decommission a service that I host for my own personal use. If I can do it with a text file in git, why would I host a full-on service to do it for me ?
You might say, well, then you can access it from anywhere ! Security much ?!
if I don't have my machine, I will not access my passwords. In practice, the reality is that I am tied to my own machine. On one hand, I cannot access my services online without my machine and if I am on the move it is highly unlikely for me to access my rss.
Oh yeah ! rss ! That's what we are here for right ? Let's dive in…
Introduction
I hosted an instance of miniflux on a vps for my rss. Miniflux is a great project, I highly recommend it. I have used it for a number of years without any issues at all; hassle free. I love it !
But with time, we have to move on. I have had my eye on the rss configuration in the doominit.el since I installed it. Now comes the time for me to try it out.
I will go with my process with you so you can see what I did. There might be better ways of doing things than this, if you know how ping me !
Doom documentation
The nice thing about doom is that it is documented. The rss is a doommodule so we will look in the doommodules manual.
We can achieve this by hitting SPC h d m and then searching for rss. The documentation will give us a bit of informaton to get started, like for example that it uses elfeed as a package.
Configure the Elfeed RSS reader with an Orgmode file
Sweet ! That's what I'm talking about. A neatly written org file as configuration.
It is always a good idea to go through documentation, at least quickly. Skim it over, you don't know what you would miss in there. I've been doing this for a long time, there is no way I can miss any… oh wait… I missed this…
Import/Export OPML?
Whaaaat ?
Use elfeed-org-import-opml to import an OPML file to an elfeed-org structured tree.
Alright, that sounds easy. Let's export from miniflux and import in elfeed.
Configuration
Before we import and whatnot, let's figure out what we are importing and where.
After reading the documentation of both elfeed and elfeed-org, it says we need to set rmh-elfeed-org-files which is a list.
In my doom configuration, I think I need to do the following.
This way we can guarantee where the file is, or we can go digging where the default is and copy from there.
This is just another file in my org collection. Nothing special about it, it gets tagged and searched like everything else.
Note
I added the (elfeed-org) in the block to load elfeed-org after I had to load it manually a few times. This made it work on my system, I might be doing it wrong so your milage may vary.
The after! section is doom specific.
I also added the following line above the rmh-elfeed-org-files line.
(setqelfeed-search-filter"@1-month-ago")
I simply wanted to see a span of a month instead of the default 2 weeks.
Elfeed-org by default inherits tagging and ignores text. In this way, I can cascade tags and when it's time to sort I can search for +xkcd and I get only xkcd posts. I can also do something similar to filter on +general +europe for specifically getting Europe's Reddit news.
The other reason for the org integration is the documentation aspect for the future. I have only recently migrated to elfeed so the documentation is still somewhat lacking, even for me. Not to worry though, as is the custom with the other migrations so far I ended up documenting a lot of it in better ways.
The big finish ?
Okay, okay ! That's a lot of babbling let's get to it, shall we ?
Now that everything is configured the way we like. Let's reload everything and try M-xelfeed.
Yeah, I know not very impressive huh ? We didn't add any hooks to update and fetch things. I like to do that manually. The documentation, though, describes how to do that, if you like. For now, let's do it ourselves M-xelfeed-update. You should be greeted with something like this.
There was nothing hard about the setup, whatsoever. It took me a bit to go through the relevant bits of the documentation for my use cases which are, I admit, simple. I can now decommission my miniflux instance as I have already found my future rss reader.
SecureDrop workstation project's packages are reproducible. We use prebuilt
wheels (by us) along with GPG signatures to verify and install them using pip
during the Debian package building step. But, the way we built those wheels
(standard pip command), they were not reproducible.
To fix this problem, Jennifer Helsby (aka
redshiftzero) built a tool and the results
are available at https://reproduciblewheels.com/. Every night her tool is
building the top 100 + our dependency packages on Debian Buster and verifies
the reproducibly of them. She has a detailed write
up on the steps.
While this issue was fixed, a related issue was to have reproducible source
tarballs. python3 setup.py sdist still does not give us a reproducible
tarballs. Conor Schaefer, our CTO at the Freedom of the Press Foundation
decided to tackle that issue using a few more lines of
bash
in our build scripts. Now we have reproducible wheels and source tarballs
(based on specified timestamps) for our projects.
A few days ago, setuptools 50.0.0 release caused
breakage to many projects.
SecureDrop package builds was also
broken. We use
dh-virtualenv tool to build
the packages. Initially, we tried to use the experimental build system from
dh-virtualenv. We could specify the version of the setuptools to be
installed in the virtualenv while creating it.
This approach worked for Xenial builds. As we are working to have proper builds
on Focal (still work in progress), that was
broken due to the
above-mentioned change.
So, we again tried to
use Python's venv module itself to create the virtual environment and use the
wheels from the /usr/share/python-wheels directory to build the virtual
environment. Which works very nicely on Xenial, but on Focal the default
setuptools version is 44.0.0, which also
failed to install the
dependencies.
Now, we are actually getting the setuptools 46.0.0 wheel and replacing the
build container's default setuptools wheel. The team spent a lot of time in
debugging and finding a proper fix for the package builds. Hopefully, we will
not get a similar breakage on the same kind of dependency error soon (the
actual package dependencies are pinned via hashes).
Now we are here at home, in the little nation
of our marriage, swearing allegiance to the table
we set for lunch or the windchime on the porch,
its easy dissonance. Even in our shared country,
the afternoon allots its golden lines
so that we’re seated, both in shadow, on opposite
ends of a couch and two gray dogs between us.
There are acres of opinions in this house.
I make two cups of tea, two bowls of soup,
divide an apple equally. If I were a patriot,
I would call the blanket we spread across our bed
the only flag—some nights we’ve burned it
with our anger at each other. Some nights
we’ve welcomed the weight, a woolen scratch
on both our skins. My love, I am pledging
to this republic, for however long we stand,
I’ll watch with you the rain’s arrival in our yard.
We’ll lift our faces, together, toward the glistening.
I recently read this paper titled, Understanding Real-World Concurrency Bugs in Go (PDF), that studies concurrency bugs in Golang and comments on the new primitives for messages passing that the language is often known for.
I am not a very good Go programmer, so this was an informative lesson in various ways to achieve concurrency and synchronization between different threads of execution. It is also a good read for experienced Go developers as it points out some important gotchas to look out for when writing Go code. The fact that it uses real world examples from well known projects like Docker, Kubernetes, gRPC-Go, CockroachDB, BoltDB etc. makes it even more fun to read!
The authors analyzed a total of 171 concurrency bugs from several prominent Go open source projects and categorized them in two orthogonal dimensions, one each for the cause of the bug and the behavior. The cause is split between two major schools of concurrency
Along the cause dimension, we categorize bugs into those that are caused by misuse of shared memory and those caused by misuse of message passing
and the behavior dimension is similarly split into
we separate bugs into those that involve (any number of ) goroutines that cannot proceed (we call themblocking bugs) and those that do not involve any blocking (non-blocking bugs)
Interestingly, they chose the behavior to be blocking instead of deadlock since the former implies that atleast one thread of execution is blocked due to some concurrency bug, but the rest of them might continue execution, so it is not a deadlock situation.
Go has primitive shared memory protection mechanisms like Mutex, RWMutex etc. with a caveat
Write lock requests in Go have ahigher privilege than read lock requests.
as compared to pthread in C. Go also has a new primitive called sync.Once that can be used to guarantee that a function is executed only once. This can be useful in situations where some callable is shared across multiple threads of execution but it shouldn't be called more than once. Go also hassync.WaitGroups , which is similar to pthread_join to wait for various threads of executioun to finish executing.
Go also uses channels for the message passing between different threads of executions called Goroutunes. Channels can be buffered on un-buffered (default), the difference between them being that in a buffered channel the sender and receiver don't block on each other (until the buffered channel is full).
The study of the usage patterns of these concurrency primitives in various code bases along with the occurence of bugs in the codebase concluded that even though message passing was used at fewer places, it accounted for a larger number of bugs(58%).
Implication 1:With heavier usages of goroutines and newtypes of concurrency primitives, Go programs may potentiallyintroduce more concurrency bugs
Also, interesting to note is this observation in tha paper
Observation 5:All blocking bugs caused by message passing are related to Go’s new message passing semantics like channel. They can be difficult to detect especially when message passing operations are used together with other synchronization mechanisms
The authors also talk about various ways in which Go runtime can detect some of these concurrency bugs. Go runtime includes a deadlock detector which can detect when there are no goroutunes running in a thread, although, it cannot detect all the blocking bugs that authors found by manual inspection.
For shared memory bugs, Go also includes a data race detector which can be enbaled by adding -race option when building the program. It can find races in memory/data shared between multiple threads of execution and uses happened-before algorithm underneath to track objects and their lifecycle. Although, it can only detect a part of the bugs discovered by the authors, the patterns and classification in the paper can be leveraged to improve the detection and build more sophisticated checkers.
It’s a Michael Lewis book.
That alone, is enough for me to tell you to go read it.
Kahneman and Tversky’s work has probably been the biggest influence on my life in recent years, since Taleb.1
We cannot think in probabilities in our daily lives.
We keep fooling ourselves, with various biases.
And the intuition we have, is because we are really amazing biological machines. And even that is subject to error. Unless the intuition is backed by extensive experience. And even then we can easily be fooled.
That basically is the gist of their work (to me, so far).
I flunked nearly every experiment in Danny Kahneman’s Thinking, Fast and Slow.2
We need to think slowly, through every implication, when it comes to the few big decisions in life.
So how did this work come about?
The Undoing Project tells us the story.
Of Kahneman and Tversky’s friendship and years of collaboration.
Of how a MacArthur Fellow and and Nobel Laureate are in the end, only human, and even they could not undo the events in their lives and their relationship.
Of how the world is patently unfair and never treats people equally.
It’s a lovely read.
Taleb was the one, who introduced me to Kahneman’s work, in the first place ↩
TLDR; Trying to learn new things I tried writing a URL shortner called shorty. This is a first draft and I am trying to approach it from first principle basis. Trying to break down everything to the simplest component.
I decided to write my own URL shortner and the reason for doing that was to dive a little more into golang and to learn more about systems. I have planned to not only document my learning but also find and point our different ways in which this application can be made scalable, resilient and robust.
A high level idea is to write a server which takes the big url and return me a short url for the same. I have one more requirement where I do want to provide a slug i.e a custom short url path for the same. So for some links like https://play.google.com/store/apps/details?id=me.farhaan.bubblefeed, I want to have a url like url.farhaan.me/linktray which is easy to remember and distribute.
The way I am thinking to implement this is by having two components, I want a CLI interface which talks to my Server. I don’t want a fancy UI for now because I want it to be exclusively be used through terminal. A Client-Server architecture, where my CLI client sends a request to the server with a URL and an optional slug. If a slug is present URL will have that slug in it and if it doesn’t it generates a random string and make the URL small. If you see from a higher level it’s not just a URL shortner but also a URL tagger.
The way a simple url shortner works:
Flow Diagram
A client makes a request to make a given URL short, server takes the URL and stores it to the database, server then generates a random string and maps the URL to the string and returns a URL like url.farhaan.me/<randomstring>.
Now when a client requests to url.farhaan.me/<randomstring>, it goest to the same server, it searches the original URL and redirects the request to a different website.
The slug implementation part is very straightforward, where given a word, I might have to search the database and if it is already present we raise an error but if it isn’t we add it in the database and return back the URL.
One optimization, since it’s just me who is going to use this, I can optimize my database to see if the long URL already exists and if it does then no need to create a new entry. But this should only happen in case of random string and not in case of slugs. Also this is a trade off between reducing the redundancy and latency of a request.
But when it comes to generating a random string, things get a tiny bit complicated. This generation of random strings, decides how many URLs you can store. There are various hashing algorithms that I can use to generate a string I can use md5, base10 or base64. I also need to make sure that it gives a unique hash and not repeated ones.
Unique hash can be maintained using a counter, the count either can be supplied from a different service which can help us to scale the system better or it can be internally generated, I have used database record number for the same.
If you look at this on a system design front. We are using the same Server to take the request and generate the URL and to redirect the request. This can be separated into two services where one service is required to generate the URL and the other just to redirect the URL. This way we increase the availability of the system. If one of the service goes down the other will still function.
The next step is to write and integrate a CLI system to talk to the server and fetch the URL. A client that can be used for an end user. I am also planning to integrate a caching mechanism in this but not something out of the shelf rather write a simple caching system with some cache eviction policy and use it.
Till then I will be waiting for the feedback. Happy Hacking.
I now have a Patreon open so that you folks can support me to do this stuff for longer time and sustain myself too. So feel free to subscribe to me and help me keeping doing this with added benefits.
TLDR; Link Tray is a utility we recently wrote to curate links from different places and share it with your friends. The blogpost has technical details and probably some productivity tips.
Link Bubble got my total attention when I got to know about it, I felt it’s a very novel idea, it helps to save time and helps you to curate the websites you visited. So on the whole, and believe me I am downplaying it when I say Link Bubble does two things:
Saves time by pre-opening the pages
Helps you to keep a track of pages you want to visit
It’s a better tab management system, what I felt weird was building a whole browser to do that. Obviously, I am being extremely naive when I am saying it because I don’t know what it takes to build a utility like that.
Now, since they discontinued it for a while and I never got a chance to use it. I thought let me try building something very similar, but my use case was totally different. Generally when I go through blogs or articles, I open the links mentioned in a different tab to come back to them later. This has back bitten me a lot of time because I just get lost in so many links.
I thought if there is a utility which could just capture the links on the fly and then I could quickly go through them looking at the title, it might ease out my job. I bounced off the same idea across to Abhishek and we ended up prototyping LinkTray.
Our first design was highly inspired by facebook messenger but instead of chatheads we have links opened. If you think about it the idea feels very beautiful but the design is “highly” not scalable. For example if you have as many as 10 links opened we had trouble in finding our links of interest which was a beautiful design problems we faced.
We quickly went to the whiteboard and put up a list of requirements, first principles; The ask was simple:
To share multiple links with multiple people with least transitions
To be able to see what you are sharing
To be able to curate links (add/remove/open links)
We took inspiration from an actual Drawer where we flick out a bunch of links and go through them. In a serendipitous moment the design came to us and that’s how link tray looks like the way it looks now.
Link Tray
Link Tray was a technical challenge as well. There is a plethora of things I learnt about the Android ecosystem and application development that I knew existed but never ventured into exploring it.
Link Tray is written in Java, and I was using a very loosely maintained library to get the overlay activity to work. Yes, the floating activity or application that we see is called an overlay activity, this allows the application to be opened over an already running application.
The library that I was using doesn’t have support for Android O and above. To figure that out it took me a few nights , also because I was hacking on the project during nights . After reading a lot of GitHub issues I figured out the problem and put in the support for the required operating system.
One of the really exciting features that I explored about Android is Services. I think I might have read most of the blogs out there and all the documentation available and I know that I still don't know enough. I was able to pick enough pointers to make my utility to work.
Just like Uncle Bob says make it work and then make it better. There was a persistent problem, the service needs to keep running in the background for it to work. This was not a functional issue but it was a performance issue for sure and our user of version 1.0 did have a problem with it. People got mislead because there was constant notification that LinkTray is running and it was annoying. This looked like a simple problem on the face but was a monster in the depth.
Architecture of Link Tray
The solution to the problem was simple stop the service when the tray is closed, and start the service when the link is shared back to link tray. Tried, the service did stop but when a new link was shared the application kept crashing. Later I figured out the bound service that is started by the library I am using is setting a bound flag to True but when they are trying to reset this flag , they were doing at the wrong place, this prompted me to write this StackOverflow answer to help people understand the lifecycle of service. Finally after a lot of logs and debugging session I found the issue and fixed it. It was one of the most exciting moment and it help me learn a lot of key concepts.
The other key learning, I got while developing Link Tray was about multi threading, what we are doing here is when a link is shared to link tray, we need the title of the page if it has and favicon for the website. Initially I was doing this on the main UI thread which is not only an anti-pattern but also a usability hazard. It was a network call which blocks the application till it was completed, I learnt how to make a network call on a different thread, and keep the application smooth.
Initially approach was to get a webview to work and we were literally opening the links in a browser and getting the title and favicon out, this was a very heavy process. Because we were literally spawning a browser to get information about links, in the initial design it made sense because we were giving an option to consume the links. Over time our design improved and we came to a point where we don’t give the option to consume but to curate. Hence we opted for web scraping, I used custom headers so that we don’t get caught by robot.txt. And after so much of effort it got to a place where it is stable and it is performing great.
It did take quite some time to reach a point where it is right now, it is full functional and stable. Do give it a go if you haven’t, you can shoot any queries to me.
I have been following GNU Emacs development through Debbugs and Sacha Chua’s newsletter. I always felt that I should use the latest development version of Emacs, instead of sticking to stable release. That way I get to use the latest improvements and also help in testing the changes. If I find any bugs, I can report those.
The motivation for building pretests I was planning to build RPM packages for Fedora from master branch.
I'm on vacation at the North Sea with my family, and like exactly one year ago I was facing the problem of having too many postcards to write. Last year, I had written a small Python script that would take a yaml file and compile it to an HTML postcard.
The yaml describes all adjustable parts of the postcard, like the content and address, but also a title, stamp and front image. A jinja2 template, a bit of CSS and javascript create a flipable postcard that can be sent via email - which is very convenient if you, like me, are too lazy to buy postcards and stamps, and have more email addresses in your address book than physical addresses.
A postcard yaml could look like this (click the card to flip it around):
----name:Holiday Status 2020front_image:'private_images/ninja.jpg'address:|Random ReaderSchubisu's BlogWorld Wide Webtitle:I'm fine, thanks :)content:|Hey there!I'm currently on vacation and was stumbling over the same problem I had last year; writing greeting cards for friends and family. Luckily I've solved that issue last year, I simply had totally forgotten about it.This is an electronic postcard, made of HTML, CSS and a tiny bit of javascript, compiling my private photos and messages to a nice looking card.Feel free to fork, use and add whatever you like!Greets,Schubisustamp:'private_images/leuchtturm_2020.jpg'
and will be rendered by the script to this:
I was curious anyway, how this would be rendered on my blog. I've added a small adjustment to my CSS to scale the iframe tag by 0.75% and I'm okay with the result ;)
20th June 2020 marked a new beginning for PyLadies in India. We had our first meetup.
I started my journey with PyLadies in 2016 as an organizer of PyLadies Pune. It began with a personal itch of the lawyer who wanted to learn Python. I revived the PyLadies Pune. We had meetups within our local limits, and everything was hunky-dory.
But PyCon India 2016 gave us the platform to peep into more larger picture. The Pythonistas in India, though divided by language, culture, geographical location nevertheless, our stories are similar. Men predominate the Indian Python community (then and so as now, unfortunately). And the community members who identify themselves as women found their place in that small PyLadies Pune booth. We shared our stories, our journey, and found out how similar they were. From the first day where I was going and telling who are we “PyLadies” and getting some not so good reaction. At the end of the conference, there was acceptance, respect, and recognition by the same people. We ended PyCon India 2016 in success with the initiation of one new chapter, PyLadies Delhi. And we realized united we stand. From then every PyCon India, we had at least a new chapter coming up. With a little bit of push, support, and help, I had some amazing ladies coming up, leading, and sharing the Pyladies baton in India. Now we have 8 + PyLadies chapters in India and many more to come. They make me feel happy, proud. But more than that, I think I have them to lean on, with whom the future of PyLadies India is safe and secure.
The chapters were running fine in their places and with a meeting every year in PyCon India. We were somewhat happy. But we felt the need for a united organization, with all the chapters being a part of it. It will be the face of PyLadies in India. A place where we will decide the future course of us, PyLadies in India. The talks were on for quite sometimes, but now the pandemic has allowed us to embark on this journey. In these times, it is a necessity to move all our meetups to an online platform. Also, it has given us every chapter to increase their reach and not be bound by the local limits.
But it came with a bunch of challenges to the chapter organizers -
a. to decide on the online platform,
b. increase the bandwidth of organizers to help set up the platform and run the online event,
c. it has opened up a lot more choice of sessions for PyLadies across India and Globe to attend and be a part of.
So in the above circumstances, we, different PyLadies chapters in India, have decided to come up with a single meetup. Each month all the chapters in India will put up a session together. But of course, if any chapter wants to have their meet up, they are free to do it. There is no rationing on the meetups. The only thing we will have to try to make sure that we do not collide on the dates. So here are we with the first Pyladies India session. It will not be an exaggeration to state that we owe this pandemic for this :)
The team got into planning and working for the same. Social media, Creatives, preparing the platform for the broadcast, av, and other settings. I, along with my amazing PyLadies Nancy, Sakshi, Vaishnavi, and Niharika, were busy and on our toes. The choice of my speaker for the first PyLadies India session was natural. We were unanimous the First Women C-Python Core Developer to be our speaker. All of us wanted Marriata. And I can not thank her enough for being ever helpful and saying yes to come to take the session, and all it took me to message her. We wanted the session introductory, so the people who are starting their journey with PyLadies India will be able to start and grow with us. So she suggested that for a tutorial on Github Bots and we readily agreed to the idea.
The plan for 20th was all set. I will introduce us, PyLadies India. Then Marriata will let us know what is happening in the field of Global PyLadies, she will take the session on Github Bot, and I will moderate the whole session. But my health (umm age) pulled my excitement down. I had to undergo gum and tooth surgery, which needed me not to talk. Then Niharika came into rescue. She gave the voice to my thoughts.
The session ended in all happy note. The people very well received Marriata’s session for being detailed and smooth.
We can not wait to meet again, follow us on Twitter, Instagram, and subscribe to our youtube channel. Some of us now may want to thank the pandemic for this opportunity :) Thank you, PyLadies, au revoir.
When I started this blog post, my intention was to compare basic data wrangling
and plotting in Python and R. I write most of my code in Python, doing mostly
scientific stuff, data wrangling, basic statistics and plots. However, although
the pandas framework has been built to resemble R's data.frame class, the
functional programming style that R provides with the tidyverse - or rather
with the %>% operator of the magittr package - often give a way more natural
feeling when handling data.
To be honest, I was sure, that the tidyverse packages would out-perform
pandas; however, looking at the code snippets now, I must admit that the
difference is less obvious than I previously thought. Still, in more complex
situations, Python code - in comparison - looks more verbose, while R seems to
be more concise and consistent.
Plotting may be a different story, but to make a fair comparison here, I would have
to take the variety of plotting libraries into account
that the Python ecosystem offers, however this would be far out of the scope of this
post.
The ggplot2 package on the other hand, has been developed
as an implementation of"Grammar of Graphics", a philosophy by Leland Wilkinson
that divides visualizations into semantic components. This implementation
provides an huge flexibility, as we'll see later.
Setting it up
Traditionally, the popular iris data set is used to demonstrate data wrangling,
but in the given situation let's play around with the Johns Hopkins University
COVID-19 data from github. As an example, the time series of
global confirmed cases can be accessed here.
Let's set up our working environments and load the three data sets confirmed,
recovered and deaths in R and Python.
importpandasaspdbase_url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/"confirmed=pd.read_csv(base_url+"time_series_covid19_confirmed_global.csv")recovered=pd.read_csv(base_url+"time_series_covid19_recovered_global.csv")deaths=pd.read_csv(base_url+"time_series_covid19_deaths_global.csv")confirmed.head()# Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 ...# 0 NaN Afghanistan 33.0000 65.0000 0 0 0 ...# 1 NaN Albania 41.1533 20.1683 0 0 0 ...# 2 NaN Algeria 28.0339 1.6596 0 0 0 ...# 3 NaN Andorra 42.5063 1.5218 0 0 0 ...# 4 NaN Angola -11.2027 17.8739 0 0 0 ...
So far, so similar. When looking at the loaded data sets, we see that the format
is kind of ugly. These are time series with names of states and countries in the
first two columns, latitude and longitude coordinates in the third and forth,
and one column per day for the following rest of the set, containing the numbers
of confirmed / recovered / deaths reported for the country/region.
To make working with the data easier, I want to transform them to a long format,
having a single column date with all the dates and a column confirmed (or
recovered and deaths respectively) with the corresponding number of cases.
confirmed_long=pd.melt(confirmed,id_vars=["Province/State","Country/Region","Lat","Long"],var_name="date",value_name="confirmed")\
.assign(date=lambdax:pd.to_datetime(x.date))confirmed_long.head()# Province/State Country/Region Lat Long date confirmed# 0 NaN Afghanistan 33.0000 65.0000 2020-01-22 0# 1 NaN Albania 41.1533 20.1683 2020-01-22 0# 2 NaN Algeria 28.0339 1.6596 2020-01-22 0# 3 NaN Andorra 42.5063 1.5218 2020-01-22 0# 4 NaN Angola -11.2027 17.8739 2020-01-22 0
As you can see, I took the opportunity to also parse the strings in the date
column to datetime. While in R we can pipe the output of the previous command
into the next with the %>% operator, we achieve a very similar affect using
pandas' assign function.
We can do the same with the remaining two data sets. However, we have plenty of
redundant information, since all three data sets will be the same apart from the
confirmed, reconvered and deaths columns. So let's merge them into one
data frame.
Also I don't care about single provinces and states but rather want to sum up
the data country-wise. This will mess up our latitudes and longitudes, so I just
drop them here.
We're ready to look at some plots. I would like to see a line plot that shows us
the numbers of confirmed, recovered and death cases against the time, on a log
transformed scale. Different countries should be coded in colors, the confirmed,
recovered and deaths lines should be coded in line styles.
Let's have a look at the Python code first:
# first I merge the three data sets into one, drop some columns# I'm not interested in and sum the numbers by country, as I don't# want to look at specific states here.covid_long=pd.merge(pd.merge(confirmed_long,recovered_long,how="left"),deaths_long,how="left")\
.drop(["Lat","Long"],axis=1)\
.groupby(["Country/Region","date"])\
.sum()\
.reset_index()# I define a list of three countries I want to see in the plotcountries=["US","Germany","India"]# and set the y-scale of my axes object to log scale, since we expect# exponential growthfig,ax=plt.subplots()ax.set_yscale('log')forcountryincountries:covid_long.query('`Country/Region` == @country')\
.plot(x="date",y="confirmed",ax=ax,label=country)
So far, these are just the numbers of confirmed cases. For plots in matplotlib,
I know no better way than to plot the different countries sequentially or in a
loop. To add the other cases, I add another loop.
# to plot confirmed, recovered and deaths cases in one plot# we have to put a little effort into it# we need a set of colors for our countries and a set of styles# for the different status attributescolors=plt.get_cmap('Set1',len(countries))styles=['-','--','.']fig,ax=plt.subplots()ax.set_yscale('log')fori,statusinenumerate(['confirmed','recovered','deaths']):forj,countryinenumerate(countries):covid_long.query('`Country/Region` == @country')\
.plot(x="date",y=status,label=f"{country} - {status}",c=colors(j),ax=ax,style=styles[i])
While this is a bit messy in a single plot, we can use multiple subplots instead
of line style, to show different status.
# we can split this up into multiple subplots for better overviewfig,ax=plt.subplots(3,1)fori,statusinenumerate(['confirmed','recovered','deaths']):ax[i].set_yscale('log')ax[i].set_title(status)forj,countryinenumerate(countries):covid_long.query('`Country/Region` == @country')\
.plot(x="date",y=status,label=f"{country}",c=colors(j),ax=ax[i])
Finally, I want to compute the deviation of confirmed cases, to get the daily
growth. Here's how I would do it with pandas:
# we pivot the table and compute the deviation. I don't know# a more elegant way that to do it in multiple stepscovid_even_longer=pd.melt(covid_long,id_vars=["Country/Region","date"],var_name="status",value_name="count")covid_even_longer.loc[:,'lagged']=(covid_even_longer\
.sort_values(by=['date'],ascending=True)\
.groupby(['Country/Region','status'])['count']\
.shift(-1))covid_even_longer=covid_even_longer\
.assign(deviation=lambdax:x['lagged']-x['count'])fig,ax=plt.subplots()fori,countryinenumerate(countries):covid_even_longer.query('`Country/Region` == @country and status == "confirmed"')\
.plot(x="date",y="deviation",label=country,c=colors(i),ax=ax)
So now to have a direct comparison, here's the tidyverse way to achieve the
above results:
## we define a vector of countries we want to look atcountries<-c("US","Germany","India")## plot the confirmed, recovered and deaths cases in one plot## we again pivot our table and make use of ggplots aestheticscovid_long%>%filter(Country.Region%in%countries)%>%pivot_longer(cols=c("confirmed","recovered","deaths"),names_to="status",values_to="count")%>%ggplot(aes(x=date,y=count,color=Country.Region))+geom_line(aes(linetype=status),size=1.2)+scale_y_log10()
## this looks a bit chaotic in a single plot, we can also use## facet_grid to break this up into sub-plotscovid_long%>%filter(Country.Region%in%countries)%>%pivot_longer(cols=c("confirmed","recovered","deaths"),names_to="status",values_to="count")%>%ggplot(aes(x=date,y=count,color=Country.Region))+facet_grid("status")+geom_line(size=1.2)+scale_y_log10()
## it's easy to on-the-fly calculate a deviation to get daily## change rates by groups and pipe the resulting dataframe into## the plotting function againcovid_long%>%filter(Country.Region%in%countries)%>%pivot_longer(cols=c("confirmed","recovered","deaths"),names_to="status",values_to="count")%>%group_by(Country.Region,status)%>%mutate(deviation=lead(count)-count)%>%ungroup()%>%filter(status=="confirmed")%>%ggplot(aes(x=date,y=deviation,color=Country.Region))+geom_line(aes(linetype=status),size=1.2)
Summary
While there hardly any difference visible for simpler data transformations,
merging and pivoting, the Python syntax becomes quite verbose when plotting
slightly more complex data. The R workflow in contrast is straight forward,
readable and arguably more beautiful.
Visualization in semantic letters allows to focus on what to show, not how. No
loops necessary. It's not necessary to create as many temporary variables, since
it simply doesn't take many extra steps to achieve what I want. Computing and
adding new columns in my data frame is just done on the fly.
Of course, there is much more you can do with both libraries. I haven't touched
theming at all, nor different plot types. However, this is a fairly common
set-up that I have to deal with in my daily work, so although not a complete
comparison, it should be a relevant one.
When I was doing a licensing survey in the Fedora ecosystem. I asked a few developers, "What is license according to them?" I got some interesting answers:
"I do not care about the license; it bores me." - a super senior developer says this. (not a very good example to follow)
"You have to fill up the name of a license to make the package in Fedora unless they won't accept the package" (sadly)
"License is something that protects your code." (Ahh finally some optimism)
The answer appeared as a ray of hope to me that yes, there are developers (still) who do care about code ( both their code and law).
But most of the developers (if not all) see licenses as long, huge legal documents filled with complicated words and some never-ending sentences. There are certain tags that people tend to associate with licenses like: MIT is permissive, GPL is strict, BSD is secure. Instead of going through the license document itself, developers choose the license for their project; based on these mental tags, they tag to it.
From today I am going to start a series to explaining different kinds of software licenses. The series will translate legalese into English. Together we will try to understand the Licenses and it's permissions one by one.
Before that, let us know what Open Source License and why it is essential?
Open Sourced software or hardware is the software or hardware that released under open source licenses. Open Source Licenses grant certain Intellectual Property Rights, in this case, Copyright, to their users.
The phrase Open Source software or hardware points to a software or hardware which
has its source code, design open and available to anybody and they can
examine,
modify
enhance and
share.
Open source projects products and communities based on the ethos of
collaboration, open and free exchange, and community sharing. Which results in a transparent process and better product or process. Open source Licenses stand in opposite to Closed Source/ Proprietary Licenses. For the last few years, there has been a surge in the usage of Open Source Licenses because
the product, process, or initiative is having open source licenses believed to be secure and stable.
the community has the control behind it.
the collaborative efforts produce a better product.
trains the programmers, designers to be better at their job through mentorship and community efforts.
Licenses lie at the very core of the Open source and Free Software community. But though many times we say “Open Source” and “Free and Open Source”(FOSS) in the same breath. But they are very different from each other, in the permissions and restrictions.
Choosing a license a developer marks the limit of its product, selects the community he wants to work with. So it is of utmost importance for them to know well. From the next post of this series, we will thrive to that goal.
So, recently I started using windows for work. Why? There are a couple of reasons, one that I needed to use MSVC, that is the Microsoft Visual C++ toolchain and the other being, I wasn’t quite comfortable to ifdef stuff for making it work on GCC aka, the GNU counterpart of MSVC.
Prometheus is a monitoring system and a time series database. It can collect metrics from different places and store it as series of values over time. It uses pull based mechanism to collect the metrics. Applications can expose the metrics in a plain text format using HTTP server, which is then fetched by Prometheus. Fetching of metrics is called scraping.
For other systems which don’t expose the metrics in Prometheus exposition format, we can use exporters.
After an anxious month, I am writing a Krita Weekly again and probably this would be my last one too, though I hope not. Let’s start by talking about bugs. Unlike the trend going about the last couple of months, the numbers have taken a serious dip.
[Published in Open Source For You (OSFY) magazine, October 2017 edition.]
This article is the eighth in the DevOps series. In this issue, we shall learn to set up Docker in the host system and use it with Ansible.
Introduction
Docker provides operating system level virtualisation in the form of containers. These containers allow you to run standalone applications in an isolated environment. The three important features of Docker containers are isolation, portability and repeatability. All along we have used Parabola GNU/Linux-libre as the host system, and executed Ansible scripts on target Virtual Machines (VM) such as CentOS and Ubuntu.
Docker containers are extremely lightweight and fast to launch. You can also specify the amount of resources that you need such as CPU, memory and network. The Docker technology was launched in 2013, and released under the Apache 2.0 license. It is implemented using the Go programming language. A number of frameworks have been built on top of Docker for managing these cluster of servers. The Apache Mesos project, Google’s Kubernetes, and the Docker Swarm project are popular examples. These are ideal for running stateless applications and help you to easily scale them horizontally.
Setup
The Ansible version used on the host system (Parabola GNU/Linux-libre x86_64) is 2.3.0.0. Internet access should be available on the host system. The ansible/ folder contains the following file:
ansible/playbooks/configuration/docker.yml
Installation
The following playbook is used to install Docker on the host system:
The Parabola package repository is updated before proceeding to install the dependencies. The python2-docker package is required for use with Ansible. Hence, it is installed along with the docker package. The Docker daemon service is then started and the library/hello-world container is fetched and executed. A sample invocation and execution of the above playbook is shown below:
With verbose ’-v’ option to ansible-playbook, you will see an entry for LogPath, such as /var/lib/docker/containers//-json.log. In this log file you will see the output of the execution of the hello-world container. This output is the same when you run the container manually as shown below:
$ sudo docker run hello-world
Hello from Docker!
This message shows that your installation appears to be working correctly.
To generate this message, Docker took the following steps:
1. The Docker client contacted the Docker daemon.
2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
3. The Docker daemon created a new container from that image which runs the
executable that produces the output you are currently reading.
4. The Docker daemon streamed that output to the Docker client, which sent it
to your terminal.
To try something more ambitious, you can run an Ubuntu container with:
$ docker run -it ubuntu bash
Share images, automate workflows, and more with a free Docker ID:
https://cloud.docker.com/
For more examples and ideas, visit:
https://docs.docker.com/engine/userguide/
Example
A Deep Learning (DL) Docker project is available (https://github.com/floydhub/dl-docker) with support for frameworks, libraries and software tools. We can use Ansible to build the entire DL container from the source code of the tools. The base OS of the container is Ubuntu 14.04, and will include the following software packages:
Tensorflow
Caffe
Theano
Keras
Lasagne
Torch
iPython/Jupyter Notebook
Numpy
SciPy
Pandas
Scikit Learn
Matplotlib
OpenCV
The playbook to build the DL Docker image is given below:
We first clone the Deep Learning docker project sources. The docker_image module in Ansible helps us to build, load and pull images. We then use the Dockerfile.cpu file to build a Docker image targeting the CPU. If you have a GPU in your system, you can use the Dockerfile.gpu file. The above playbook can be invoked using the following command:
Depending on the CPU and RAM you have, it will take considerable amount of time to build the image with all the software. So be patient!
Jupyter Notebook
The built dl-docker image contains Jupyter notebook which can be launched when you start the container. An Ansible playbook for the same is provided below:
- name: Start Jupyter notebook
hosts: localhost
gather_facts: true
become: true
tags: [notebook]
vars:
DL_DOCKER_NAME: "floydhub/dl-docker"
tasks:
- name: Run container for Jupyter notebook
docker_container:
name: "dl-docker-notebook"
image: "{{ DL_DOCKER_NAME }}:cpu"
state: started
command: sh run_jupyter.sh
You can invoke the playbook using the following command:
The Dockerfile already exposes the port 8888, and hence you do not need to specify the same in the above docker_container configuration. After you run the playbook, using the ‘docker ps’ command on the host system, you can obtain the container ID as indicated below:
$ sudo docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
a876ad5af751 floydhub/dl-docker:cpu "sh run_jupyter.sh" 11 minutes ago Up 4 minutes 6006/tcp, 8888/tcp dl-docker-notebook
You can now login to the running container using the following command:
$ sudo docker exec -it a876 /bin/bash
You can then run an ‘ifconfig’ command to find the local IP address (“172.17.0.2” in this case), and then open http://172.17.0.2:8888 in a browser on your host system to see the Jupyter Notebook. A screenshot is shown in Figure 1:
TensorBoard
TensorBoard consists of a suite of visualization tools to understand the TensorFlow programs. It is installed and available inside the Docker container. After you login to the Docker container, at the root prompt, you can start Tensorboard by passing it a log directory as shown below:
# tensorboard --logdir=./log
You can then open http://172.17.0.2:6006/ in a browser on your host system to see the Tensorboard dashboard as shown in Figure 2:
Docker Image Facts
The docker_image_facts Ansible module provides useful information about a Docker image. We can use it to obtain the image facts for our dl-docker container as shown below:
The ANSIBLE_STDOUT_CALLBACK environment variable is set to ‘json’ to produce a JSON output for readability. Some important image facts from the invocation of the above playbook are shown below:
This is an attempt to summarize the broader software architecture around dependency resolution in pip and how testing is being done around this area.
The motivation behind writing this, is to make sure all the developers working on this project are on the same page, and to have a written record about the state of affairs.
Architecture
The “legacy” resolver in pip, is implemented as part of pip’s codebase and has been a part of it for many years. It’s very tightly coupled with the existing code, isn’t easy to work with and has severe backward compatibility concerns with modifying directly – which is why we’re implementing a separate “new” resolver in this project, instead of trying to improve the existing one.
The “new” resolver that is under development, is not implemented as part of pip’s codebase; not completely anyway. We’re using an abstraction that separates all the metadata-generation-and-handling stuff vs the core algorithm. This allows us to work on the core algorithm logic (i.e. the NP-hard search problem) separately from pip-specific logic (eg. download, building etc). The abstraction and core algorithm are written/maintained in https://github.com/sarugaku/resolvelib right now. The pip-specific logic for implementing the “other side” of the abstraction is in https://github.com/pypa/pip/tree/master/src/pip/_internal/resolution/resolvelib.
Testing
In terms of testing, we have dependency-resolution-related tests in both resolvelib and pip.
resolvelib
The tests in resolvelib are intended more as “check if the algorithm does things correctly” and even contains tests that are agnostic to the Python ecosystem (eg. we’ve borrowed tests from Ruby, Swift etc). The goal here is to make sure that the core algorithm we implement is capable of generating correct answers (for example: not getting stuck in looping on the same “requirement”, not revisiting rejected nodes etc).
pip
The tests in pip is where I’ll start needing more words to explain what’s happening. :)
YAML-based tests
We have “YAML” tests which I’d written back in 2017, as a format to easily write tests for pip’s new resolver when we implement it. However, since we didn’t have a need for it to be working completely back then (there wasn’t a new resolver to test with it!), the “harness” for running these tests isn’t complete and would likely need some work to be as feature complete as we’d want it to be, for writing good tests.
We have some unit tests for the new resolver implementation. These cover very basic “sanity checks” to ensure it follows the “contract” of the abstraction, like “do the candidates returned by a requirement actually satisfy that requirement?”. These likely don’t need to be touched, since they’re fairly well scoped and test fairly low-level details (i.e. ideal for unit tests).
We also have “new resolver functional tests”, which are written as part of the current work. These exist since how-to-work-with-YAML-tests was not an easy question to answer and there needs to be work done (both on the YAML format, as well as the YAML test harness) to flag which tests should run with which resolver (both, only legacy, only new) and make it possible to put run these tests in CI easily.
These files test all the functionality of the install command (like: does it use the right build dependencies, does it download the correct files, does it write the correct metadata etc). There might be some dependency-resolution-related tests in test_install*.py files.
These files contain a lot of tests so, ideally, at some point, someone would go through and de-duplicate tests from this as well.
How can you help?
If you use pip, there are a multiple ways that you can help us!
Right now, even before we release the new resolver as a beta, you can help by running pip check on your current environment. This will report if you have any inconsistencies in your set of installed packages. Having a clean installation will make it much less likely that you will hit issues when the new resolver is released (and may address hidden problems in your current environment!). If you run pip check and run into stuff you can’t figure out, please ask for help in our issue tracker or chat.
Thanks to Paul Moore and Tzu-Ping for help in reviewing and writing this post,
as well as Sumana Harihareswara for suggesting to put this up on my blog!
Co-worked with another developer, in person, for 1 week, on pip!
Triaged pip’s issue tracker (a lot).
Spend some time improving pip’s test suite infrastructure.
Investigated Python 2 usage, to identify anomalies.
Helped with virtualenv 20.0 release (kinda!).
Invested effort to improve pip’s test suite
Helped aggregate test cases for pip’s next generation resolver.
Communication
Managed the pip 20.0 release fiasco.
Helped the UX folks get started with working on pip.
Additional notes on challenges
January has been a very productive month.
Most of the challenges have been the logistics around work, not the work as
such.
My health has been pretty good and there’s a certain flow to my work that I’m
enjoying now. Turns out, if you like what you’re doing, you tend to be pretty
productive! :)
As long as I remember to push my blog posts to the repository, they’ll actually
go live on the day they’re supposed to.
Goals for February 2020
Technical
Internal Cleansing: AKA Technical debt down payment.
Issue triage: Triage a fair number of issues on pip’s issue tracker.
Technical Documentation: improving pip’s technical documentation, for contributors and developers
Communication
Help all the other contractors to get up to “full speed” for working on pip
Get PyPA to participate in GSoC 2020
Python Packaging Summit at PyCon US 2020: help organization.
Move forward on Python Packaging Governance
Other commitments
None.
Help us
How can you help us?
provide test cases where the latest released version of pip (19.3.1, at the time of writing) fails to resolve dependencies properly (on zazo’s issue tracker). They will help us design and test the new resolver.
talk with your company about becoming a PSF sponsor. The Fundable Packaging Improvements page lists fairly well-scoped projects that would happen much faster if we get funding to achieve them.
[NOTE: This post originally appeared on
deepsource.io, and has been
posted here with due permission.]
In the early part of the last century, when David Hilbert was working on
stricter formalization of geometry than Euclid, Georg Cantor had worked out a
theory of different types of infinities, the theory of sets. This
theory would soon unveil a series of confusing paradoxes, leading to
a crisis in the Mathematics community regarding the stability of the
foundational principles of the math of that time.
Central to these paradoxes was the Russell’s paradox (or more generally, as
we’d talk about later, the Epimenides Paradox). Let’s see what it is.
In those simpler times, you were allowed to define a set if you could describe
it in English. And, owing to mathematicians’ predilection for self-reference,
sets could contain other sets.
Russell then, came up with this:
\(R\) is a set of all the sets which do not contain themselves.
The question was "Does \(R \) contain itself?" If it doesn’t, then according to
the second half of the definition it should. But if it does, then it no longer
meets the definition.
The same can symbolically be represented as:
Let \(R = \{ x \mid x \not \in x \} \), then \(R \in R \iff R \not \in R \)
Cue mind exploding.
“Grelling’s paradox” is a startling variant which uses adjectives instead of
sets. If adjectives are divided into two classes, autological
(self-descriptive) and heterological (non-self-descriptive), then, is
‘heterological’ heterological? Try it!
Epimenides Paradox
Or, the so-called Liar Paradox was another such paradox which shred apart
whatever concept of ‘computability’ was, at that time - the notion that things
could either be true or false.
Epimenides was a Cretan, who made one immortal statement:
“All Cretans are liars.”
If all Cretans are liars, and Epimenides was a Cretan, then he was lying when
he said that “All Cretans are liars”. But wait, if he was lying then, how can
we ‘prove’ that he wasn’t lying about lying? Ein?
This is what makes it a paradox: A statement so rudely violating the assumed
dichotomy of statements into true and false, because if you tentatively think
it’s true, it backfires on you and make you think that it is false. And a
similar backfire occurs if you assume that the statement is false. Go ahead,
try it!
If you look closely, there is one common culprit in all of these paradoxes,
namely ‘self-reference’. Let’s look at it more closely.
Strange Loopiness
If self-reference, or what Douglas Hofstadter - whose prolific work on the
subject matter has inspired this blog post - calls ‘Strange Loopiness’ was the
source of all these paradoxes, it made perfect sense to just banish
self-reference, or anything which allowed it to occur. Russell and Whitehead,
two rebel mathematicians of the time, who subscribed to this point of view, set
forward and undertook the mammoth exercise, namely “Principia Mathematica”,
which we as we will see in a little while, was utterly demolished by Gödel’s
findings.
The main thing which made it difficult to ban self-reference was that it was
hard to pin point where exactly did the self-reference occur. It may as well be
spread out over several steps, as in this ‘expanded’ version of Epimenides:
The next statement is a lie.
The previous statement is true.
Russell and Whitehead, in P.M. then, came up with a multi-hierarchy set
theory to deal with this. The basic idea was that a set of the lowest ‘type’
could only contain ‘objects’ as members (not sets). A set of the next type
could then only either contain objects, or sets of lower types. This,
implicitly banished self-reference.
Since, all sets must have a type, a set ‘which contains all sets which are not
members of themselves’ is not a set at all, and thus you can say that Russell’s
paradox was dealt with.
Similarly, if an attempt is made towards applying the expanded Epimenides to
this theory, it must fail as well, for the first sentence to make a reference
to the second one, it has to be hierarchically above it - in which case, the
second one can’t loop back to the first one.
Thirty one years after David Hilbert set before the academia to rigorously
demonstrate that the system defined in Principia Mathematica was both
consistent (contradiction-free) and complete (i.e. every true statement
could be evaluated to true within the methods provided by P.M.), Gödel
published his famous Incompleteness Theorem. By importing the Epimenides
Paradox right into the heart of P.M., he proved that not just the
axiomatic system developed by Russell and Whitehead, but none of the
axiomatic systems whatsoever were complete without being inconsistent.
Clear enough, P.M. lost it’s charm in the realm of academics.
Before Gödel’s work too, P.M. wasn’t particularly loved as well.
Why?
It isn’t just limited to this blog post, but we humans, in general, have a diet
for self-reference - and this quirky theory severely limits our ability to
abstract away details - something which we love, not only as programmers, but
as linguists too - so much so, that the preceding paragraph, “It isn’t …
this blog … we humans …” would be doubly forbidden because the ‘right’
to mention ‘this blog post’ is limited only to something which is
hierarchically above blog posts, ‘metablog-posts’. Secondly, me (presumably a
human) belonging to the class ‘we’ can’t mention ‘we’ either.
Since, we humans, love self-reference so much, let’s discuss some ways in which
it can be expressed in written form.
One way of making such a strange loop, and perhaps the ‘simplest’ is using the
word ‘this’. Here:
This sentence is made up of eight words.
This sentence refers to itself, and is therefore useless.
This blog post is so good.
This sentence conveys you the meaning of ‘this’.
This sentence is a lie. (Epimenides Paradox)
Another amusing trick for creating a self-reference without using the word
‘this sentence’ is to quote the sentence inside itself.
Someone may come up with:
The sentence ‘The sentence contains five words’ contains five words.
But, such an attempt must fail, for to quote a finite sentence inside itself
would mean that the sentence is smaller than itself. However, infinite
sentences can be self-referenced this way.
The sentence
"The sentence
"The sentence
...etc
...etc
is infinitely long"
is infinitely long"
is infinitely long"
There’s a third method as well, which you already saw in the title - the Quine
method. The term ‘Quine’ was coined by Douglas Hofstadter in his book “Gödel
Escher, Bach” (which heavily inspires this blog post). When using this, the
self-reference is ‘generated’ by describing a typographical entity, isomorphic
to the quine sentence itself. This description is carried in two parts - one is
a set of ‘instructions’ about how to ‘build’ the sentence, and the other, the
‘template’ contains information about the construction materials required.
The Quine version of Epimenides would be:
“yields falsehood when preceded by it’s quotation” yields falsehood when preceded by it’s quotation
Before going on with ‘quining’, let’s take a moment and realize how awfully
powerful our cognitive capacities are, and what goes in our head when a
cognitive payload full of self-references is delivered - in order to decipher
it, we not only need to know the language, but also need to work out the
referent of the phrase analogous to ‘this sentence’ in that language. This
parsing depends on our complex, yet totally assimilated ability to handle the
language.
The idea of referring to itself is quite mind-blowing, and we keep doing it all
the time — perhaps, why it feels so ‘easy’ for us to do so. But, we aren’t born
that way, we grow that way. This could better be realized by telling someone
much younger “This sentence is wrong.”. They’d probably be confused - What
sentence is wrong?. The reason why it’s so simple for self-reference to occur,
and hence allow paradoxes, in our language, is well, our language. It allows
our brain to do the heavy lifting of what the author is trying to get through
us, without being verbose.
Back to Quines.
Reproducing itself
Now, that we are aware of how ‘quines’ can manifest as self-reference, it would
be interesting to see how the same technique can be used by a computer program
to ‘reproduce’ itself.
To make it further interesting, we shall choose the language most apt for the
purpose - brainfuck:
Running that program above produces itself as the output. I agree, it isn’t the
most descriptive program in the world, so written in Python below, is the
nearest we can go to describe what’s happening inside those horrible chains of
+’s and >’s:
The first line generates """ on the fly, which marks multiline strings in
Python.
Next two lines define the eniuq function, which prints the argument template
twice - once, plain and then surrounded with triple quotes.
The last 4 lines cleverly call this function so that the output of the program
is the source code itself.
Since we are printing in an order opposite of quining, the name of the function
is ‘quine’ reversed -> eniuq (name stolen from Hofstadter again)
Remember the discussion about how self-reference capitalizes on the processor?
What if ‘quining’ was a built-in feature of the language, providing what we in
programmer lingo call ‘syntactic sugar’?
Let’s assume that an asterisk, * in the brainfuck interpreter would copy the
instructions before executing them, what would then be the output of the
following program?
*
It’d be an asterisk again. You could make an argument that this is silly, and
should be counted as ‘cheating’. But, it’s the same as relying on the
processor, like using “this sentence” to refer to this sentence - you rely on
your brain to do the inference for you.
What if eniuq was a builtin keyword in Python? A perfect self-rep was then
just be a call away:
eniuq('eniuq')
What if quine was a verb in the English language? We could reduce a lot of
explicit cognitive processes required for inference. The Epimenides paradox
would then be:
“yields falsehood if quined” yields falsehood if quined
Now, that we are talking about self-rep, here’s one last piece of entertainment
for you.
If you take that absurd thing above, and move around in the cartesian plane for
the coordinates \(0 \le x \le 106, k \le y \le k + 17\), where \(k\) is a
544 digit integer (just hold on with me here), color every pixel black for
True, and white otherwise, you'd get:
I wanted to attend TUG 2019 for two main reasons - to present my work on the “XeTeX Book Template”, and also to meet my favourite computer scientist, Prof. Donald Knuth. He does not travel much, so, it was one of those rare opportunities for me to meet him in person. His creation of the TeX computer typesetting system, where you can represent any character mathematically, and also be able to program and transform it is beautiful, powerful and the best typesetting software in the world. I have been using TeX extensively for my documentation and presentations over the years.
Day I
I reached the hotel venue only in the afternoon of Friday, August 9, 2019, as I was also visiting Mountain View/San Jose on official work. I quickly checked into the hotel and completed my conference registration formalities. When I entered the hall, Rishi T from STM Document Engineering Private Limited, Thiruvananthapuram was presenting a talk on “Neptune - a proofing framework for LaTeX authors”. His talk was followed by an excellent poetic narration by Pavneet Arora, who happened to be a Vim user, but, also mentioned that he was eager to listen to my talk on XeTeX and GNU Emacs.
After a short break, Shreevatsa R, shared his experiences on trying to understand the TeX source code, and the lessons learnt in the process. It was a very informative, user experience report on the challenges he faced in navigating and learning the TeX code. Petr Sojka, from Masaryk University, Czech Republic, shared his students’ experience in using TeX with a detailed field report. I then proceeded to give my talk on the “XeTeX Book Template” on creating multi-lingual books using GNU Emacs and XeTeX. It was well received by the audience. The final talk of the day was by Jim Hefferon, who analysed different LaTeX group questions from newbies and in StackExchange, and gave a wonderful summary of what newbies want. He is a professor of Mathematics at Saint Michael’s College, and is well-known for his book on Linear Algebra, prepared using LaTeX. It was good to meet him, as he is also a Free Software contributor.
The TUG Annual General Meeting followed with discussions on how to grow the TeX community, the challenges faced, membership fees, financial reports, and plan for the next TeX user group conference.
Day II
The second day of the conference began with Petr Sojka and Ondřej Sojka presenting on “The unreasonable effectiveness of pattern generation”. They discussed the Czech hyphenation patterns along with a pattern generation case study. This talk was followed by Arthur Reutenauer presenting on “Hyphenation patterns in TeX Live and beyond”. David Fuchs, a student who worked with Prof. Donald Knuth on the TeX project in 1978, then presented on “What six orders of magnitude of space-time buys you”, where he discussed the design trade-offs in TeX implementation between olden days and present day hardware.
After a short break, Tom Rokicki, who was also a student at Stanford and worked with Donald Knuth on TeX, gave an excellent presentation on searching and copying text in PDF documents generated by TeX for Type-3 bitmap fonts. This session was followed by Martin Ruckert’s talk on “The design of the HINT file format”, which is intended as a replacement of the DVI or PDF file format for on-screen reading of TeX output. He has also authored a book on the subject - “HINT: The File Format: Reflowable Output for TeX”. Doug McKenna had implemented an interactive iOS math book with his own TeX interpreter library. This allows you to dynamically interact with the typeset document in a PDF-free ebook format, and also export the same. We then took a group photo:
I then had to go to Stanford, so missed the post-lunch sessions, but, returned for the banquet dinner in the evening. I was able to meet and talk with Prof. Donald E. Knuth in person. Here is a memorable photo!
He was given a few gifts at the dinner, and he stood up and thanked everyone and said that “He stood on the shoulders of giants like Isaac Newton and Albert Einstein.”
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I had a chance to meet a number of other people who valued the beauty, precision and usefulness of TeX. Douglas Johnson had come to the conference from Savannah, Georgia and is involved in the publishing industry. Rohit Khare, from Google, who is active in the Representational State Transfer (ReST) community shared his experiences with typesetting. Nathaniel Stemen is a software developer at Overleaf, which is used by a number of university students as an online, collaborative LaTeX editor. Joseph Weening, who was also once a student to Prof. Donald Knuth, and is at present a Research Staff member at the Institute for Defense Analyses Center for Communications Research in La Jolla, California (IDA/CCR-L) shared his experiences in working with the TeX project.
Day III
The final day of the event began with Antoine Bossard talking on “A glance at CJK support with XeTeX and LuaTeX”. He is an Associate Professor of the Graduate School of Science, Kanagawa University, Japan. He has been conducting research regarding Japanese characters and their memorisation. This session was followed by a talk by Jaeyoung Choi on “FreeType MF Module 2: Integration of Metafont and TeX-oriented bitmap fonts inside FreeType”. Jennifer Claudio then presented the challenges in improving Hangul to English translation.
After lunch, Uwe Ziegenhagen demonstrated on using LaTeX to prepare and automate exams. This was followed by a field report by Yusuke Terada, on how they use TeX to develop a digital exam grading system at large scale in Japan. Chris Rowley, from the LaTeX project, then spoke on “Accessibility in the LaTeX kernel - experiments in tagged PDF”. Ross Moore joined remotely for the final session of the day to present on “LaTeX 508 - creating accessible PDFs”. The videos of both of these last two talks are available online.
A number of TeX books were made available for free for the participants, and I grabbed quite a few, including a LaTeX manual written by Leslie Lamport. Overall, it was a wonderful event, and it was nice to meet so many like-minded Free Software people.
A special thanks to Karl Berry, who put in a lot of effort in organizing the conference, but, could not make it to due to a car accident.
I was still up at this unearthly hour, thinking about life for a while now - fumbled thoughts about where I had come, where I started, and quite expectedly, Omar Bhai, your name popped in.
The stream continued. I started thinking about everything I’ve learned from you and was surprised with merely the sheer volume of thoughts that followed. I felt nostalgic!
I made a mental note to type this out the next day.
I wanted to do this when we said our final goodbyes and you left for the States, but thank God, I didn’t - I knew that I would miss you, but never could I have guessed that it would be so overwhelming - I would’ve never written it as passionately as I do today.
For those of you who don’t already know him, here’s a picture:
I’m a little emotional right now, so please bear with me.
You have been warned - the words “thank you” and “thanks” appear irritatingly often below. I tried changing, but none other has quite the same essence.
How do I begin thanking you?
Well, let’s start with this - thank you for kicking me on my behind, albeit civilly, whenever I would speak nuisance (read chauvinism). I can’t thank you enough for that!
I still can’t quite get how you tolerated the bigot I was and managed to be calm and polite. Thank You for teaching me what tolerance is!
Another thing which I learnt from you was what it meant to be privileged. I can no longer see things the way I used to, and this has made a huge difference. Thank You!
I saw you through your bad times and your good. The way you tackled problems, and how easy you made it look. Well, it taught me [drum roll] how to think (before acting and not the other way round). Thank You for that too!
And, thank you for buying me books, and even more so, lending away so many of them! and even more so, educating me about why to read books and how to read them. I love your collection.
You showed all of us, young folks, how powerful effective communication is. Thank You again for that! I know, you never agree on this, but you are one hell of a speaker. I’ve always been a fan of you and your puns.
I wasn’t preparing for the GRE, but I sat in your sessions anyways, just to see you speak. The way you connect with the audience is just brilliant.
For all the advice you gave me on my relationships with people - telling me to back off when I was being toxic and dragging me off when I was on the receiving side - I owe you big time. Thank You!
Also, a hearty thank you for making me taste the best thing ever - yes, fried cheese it is. :D
Thank You for putting your trust and confidence in me!
It’s been a very long time since I wrote here for the last.
The reason is nothing big but mainly because:
Apparently, I was not able to finish some tasks in time that I used to write about.
I was not well for a long time that could be an another reason .
Besides, life happened in many ways which ultimately left me working on some other things first, because they seemed to be *important* for the time.
And, yes, there is no denying the fact that I was procastinating too because writing seems to be really hard at most times.
Though I had worked on many things throughout the time and I’ll try to write them here as short and quick logs below.
Around the second last week of august, I worked on setting up a self-hosted OpenVPN server which supported client scalability. The infrastructure required two servers/VMs, each having a basic firewall setup and a non-root “sudo” priviliged user. One among them was to host the OpenVPN service and another one was to serve as a Certificate Authority (CA). You can refer the following links to check for the related process.
In the last week of August, I worked on another task i.e to read about Syslogs and figure out how each of the systems can email the root mails to a certain email address for log collection. Thus, I read about Syslogs, how it works, its format and various Syslogs message levels. The latter part of the task was accomplished using ssmtp as mail program & writing cron jobs to actually send them to the intended email addresses. Check the following links for resources.
This one question always came up, many times, the students managed to destroy their systems by doing random things. rm -rf is always one of the various commands in this regard.
While I was doing the above task, at one time I ruined my local system’s mail server configs and actually ended up doing something which kushal writes about in one of his recent post (quoted above). I was using the command rm -rf to clean some of the left-over dependencies of some mail packages, but that eventually resulted into machine being crashed. It was not the end of the mess this time. I made an another extremely big mistake meanwhile. I was trying to back up the crashed system, into an external hard disk using dd. But because I had never used dd before, so again I did something wrong and this time, I ended up losing ~500 GBs of backed up data. This is “the biggest mistake” and “the biggest lesson” I have learnt so far. (now I know why one should have multiple backups) And as there was absolutely no way of getting that much data back, the last thing I did was, formatting the hard-disk into 2 partitions, one with ext4 file system for linux backup and the other one as ntfs for everything else.
Thank you so much jasonbraganza for all the help and extremely useful suggestions during the time.
Okay, now after all the hassle bustle above, I got something really nice. This time, I received the “Raspberry Pi 4, 4GB, Complete Kit ” from kushal.
Thank you very much kushal for the RPi and an another huge thanks for providing me with all the guidance and support that made me reach to even what I am today.
During the same time, I attended a dgplug guest session from utkarsh2102. This session gave me a “really” good beginner’s insight of how things actually work in Debian Project. I owe a big thanks to utkarsh2102 as well, for he so nicely voluteered me from there onwards, to actually start with Debian project. I have started with DPMT and have done packaging 4 python modules so far. And now, I am looking forward to start contributing to Debian Ruby Team as well.
With the start of september, I spent some time solving some basic Python problems from kushal’s lymworkbook. Those issues were related to some really simply sys-admins work. But for me, working around and wrapping them in Python was a whole lot of learning. I hope I will continue to solve some more problems/issues from the lab.
And lastly (and currently), I am back to reading and implementing concepts from Ops School curriculum.
Voila, finally, I finish compiling up the logs from some last 20 days of work and other stuffs. (and thus, I am eventually finishing my long pending task of writing this post here as well).
I will definitely try to be more consistent with my writing from now onwards.
I'm an advocate of using SSH authentication and connecting to services like Github, Gitlab, and many others.
On this, I received a bunch of messages over IRC asking why do I prefer SSH for Git over HTTPS.
I find the Github documentation quite helpful when it comes down to learning the basic operation of using Git and Github. So, what has Github to say about "SSH v/s HTTPS"?
Github earlier used to recommend using SSH, but they later changed it to HTTPS. The reason for the Github's current recommendation could be:
Ease to start with: HTTPS is very easy to start with, as you don't have to set up your SSH keys separately. Once the account is created, you can just go over and start working with repositories. Though, the first issue that you hit is that you need to enter your username/password for every operation that you need to perform with git. This can be overcome by caching or storing the password using Git's credential storage. If you cache, then it is cached in memory for a limited period after which it is flushed so you need to enter your credentials again. I would not advise storing the password, as it is stored as plain-text on disk.
Easily accessible: HTTPS in comparison to SSH is easily accessible. Why? You may ask. The reason is a lot of times SSH ports are blocked behind a firewall and the only option left for you might be HTTPS. This is a very common scenario I've seen in the Indian colleges and a few IT companies.
Why do I recommend SSH-way?
SSH keys provide Github with a way to trust a computer. For every machine that I have, I maintain a separate set of keys. I upload the public keys to Github or whichever Git-forge I'm using. I also maintain a separate set of keys for the websites. So, for example, if I have 2 machines and I use Github and Pagure then I end up maintaining 4 keys. This is like a 1-to-1 connection of the website and the machine.
SSH is secure until you end up losing your private key. If you do end up losing your key, even then you can just login using your username/password and delete the particular key from Github. I agree, that the attacker can do nasty things but that would be limited to repositories and you would have control of your account to quickly mitigate the problem.
On the other side, if you end up losing your Github username/password to an attacker, you lose everything.
I also once benefitted from using SSH with Github, but IMO, exposing that also exposes a vulnerability so I'll just keep it a secret :)
Also, if you are on a network that has SSH blocked, you can always tunnel it over HTTPS.
Jest is a really good frontend testing framework and works great with React and Babel out of the box, along with Enzyme for component testing. But, imports with React and Babel can often be filled with nasty imports. I wrote in a previous blog about how to make better more cleaner imports using some webpack tweaks.
But the problem appears when we try to write Jest and Enzyme tests with them. Because Babel can now longer understand and parse the imports. And without Babel parsing them and converting to ES5, jest cannot test the components. So we actually need a mix of Babel configuration and Jest configuration.
Note: This post assumes you already have jest, babel-jest and babel/plugin-transform-modules-commonjs packages installed using your favorite javascript package manager.
Basically, the workaround is first we need to resolve the cleaner imports into the absolute paths for the import using Jest configurations, and then use the Babel configurations to parse the rest code (without the modules) to ES5.
The configuration files look something like these: babel.config.js
In babel.config.js, we make a check to see if the code is right now in test environment. This is helpful because
Jest sets the environment to "test" when running a test so it is easily identifiable
It ensures that the test configuration don't mess up with the non test configurations in our Webpack config (or any other configuration you are using)
So in this case, I am returning the exact same Babel configuration that I need in my Webpack config in non-test environment.
In the test configuration for Babel, we are using a plugin "@babel/plugin-transform-modules-commonjs". This is needed to parse all the non component imports like React, etc. along with parsing the components from ES6 to ES5 after jest does the path resolution. So it helps to convert the modules from ES6 to ES5.
Now, let's see the jest.config.js. The jest configuration allows us to do something called moduleNameMapper. This is a very useful configuration in many different usecases. It basically allows us to convert the module names or paths we use for module import to something that jest understands (or in our case, something that the Babel plugin can parse).
So, the left hand part of the attribute contains a regular expression which matches the pattern we are using for imports. Since our imports look something like '~/path/from/jsRoot/Component', so the regular expression to capture all such imports is '^~/(.*)$'. Now, to convert them to absolute paths, we need to append '<rootDir>/path/to/jsRoot/' in front of the component path.
And, voila! That should allow Jest to properly parse, convert to ES5 and then test.
The best part? We can use the cleaner imports even in the .test.js files and this configuration will work perfectly with that too.
You can bring in modules from different javascript file using require based javascript code, or normal Babel parse-able imports. But the code with these imports often become a little bad because of relative imports like:
importComponentfrom'../../path/to/Component'
But a better, more cleaner way of writing ES6 imports is
importComponentfrom'~/path/from/jsRoot/Component'
This hugely avoids the bad relative paths for importing depending on where the component files are. Now, this is not parse-able by babel itself. But you can parse this by webpack itself using it's resolve attribute. So your webpack should have these two segments of code:
The {modules: false} ensures that babel-preset-env doesn't handle the parsing of the module imports. You can check the following comment in a webpack issue to know more about this.
I'm an advocate of using SSH authentication and connecting to services like Github, Gitlab, and many others. I do make sure that the use the git:// URL while cloning the repo but sometimes I do make mistake of using the https:// instead. Only to later realise when git prompts me to enter my username to authenticate the SSH connection. This is when I have to manually reset my git remote URL.
Today, I found a cleaner solution to this problem. I can use insteadOf to enforce the connection via SSH.
This blog is more like a bookmark for me, the solution was scavenged from internet. Recently I have been working on an analytics project where I had to generate pivot transpose tables from the data. Now this is the first time I faced the limitations set on postgres database. Since its a pivot, one of my column would be transposed and used as column names here, this is where things started breaking. Writing to postgres failed with error stating column names are not unique. After some digging I realized Postgres has a column name limitation of 63 bytes and anything more than that will be truncated hence post truncate multiple keys became the same causing this issue.
Next step was to look at the data in my column, it ranged from 20-300 characters long. I checked with redshift and Bigquery they had similar limitations too, 128 bytes. After looking for sometime found a solution, downloaded the postgres source, changed NAMEDATALEN to 301(remember column name length is always NAMEDATALEN – 1) src/include/pg_config_manual.h, followed the steps from postgres docs to compile the source and install and run postgres. This has been tested on Postgres 9.6 as of now and it works.
Next up I faced issues with maximum number columns, my pivot table had 1968 columns and postgres has a limitation of 1600 total columns. According to this answer I looked into the source comments and that looked quite overwhelming . Also I do not have a control over how many columns will be there post pivot so no matter whatever value i set , in future i might need more columns, so instead I handled the scenario in my application code to split the data across multiple tables and store them.
Flow Diagram
Link Tray
, also because I was hacking on the project during nights 
. After reading a lot of GitHub issues I figured out the problem and put in the support for the required operating system.
Architecture of Link Tray
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(now I know why one should have multiple backups) And as there was absolutely no way of getting that much data back, the last thing I did was, formatting the hard-disk into 2 partitions, one with 
