> The tie-breaker is social, not technical.
The tie-breaker is financial. Jupyter is winning because it's free, not because it's social. It becomes social because of widespread adoption, and it get's widespread adoption because it's free.
I love Jupyter, love love love. But there's a lot of hyperbole and opinion here. Mathematica is just a for-profit business, it's that simple. And it wouldn't be fair to deny the example that Mathematica, Maple & Matlab have set for free solutions like Jupyter.
There's nothing dishonest about a for-profit business. And if Mathematica wants to keep PDF export for themselves, so be it, that's their right. What's dishonest is expecting technical software and service for free and calling people names like vandals if you don't get it. Just celebrate Jupyter and enjoy that people are doing great work you get to use without paying. I don't love Mathematica or it's founder, but there's no real need to impugn Mathematica in order to make this point.
Long time user of Python here and recent user of Mathematica.
Some observations I have are that they're both great. Python is a nice open source scripting language, but getting libraries to work can sometimes be a pain. Mathematica is basically install this and everything is included. The Mathematica documentation is amazing and it is really simple how to do most things. The whole iPhone "there is an app for that" is equivalent to "there is a function for that".
Graph Theory works flawlessly in Mathematica. In Python, there is a module to Graphviz. Let me know if Python has something new though. There are a lot of other examples. Mathematica's Import function can read over 150 different file types including: CSV,.XLS, genetic encoding files, optimization files....whatever. It is usually far easier and more consistent than finding a corresponding Python library and struggling with the install and minimal documentation. Let me be clear that Python is awesome and rocks and i think Jupyter is moving it in the right direction. I just feel like many dismiss Mathematica as something that does Calculus homework rather than what it is today which is a massive 20 million LOC conglomeration of C & Java & Wolfram language that does everything from Statistics, Machine Learning, Visualization, BlockChain, 3D printing, NodeGraphs, data sets and analysis...etc in a single consistent package. It is expensive and proprietary and certainly has its own faults, but a lot of that cash is funneled back into a great product.
This is spot-on:
"Membership in an open source community is like membership in the community of science. There is a straightforward process for finding a true answer to any question. People disagree in public conversations. They must explain clearly and listen to those who response with equal clarity. Members of the community pay more attention to those who have been right in the past, and to those who enhance their reputation for integrity by admitting in public when they are wrong. They shun those who mislead. There is no court of final appeal. The only recourse is to the facts.
It’s a messy process but it works, the only one in all of human history that ever has. No other has ever achieved consensus at scale without recourse to coercion.
In science, anyone can experiment. In open source, anyone can access the facts of the code. Linus Torvalds may supervise a hierarchy that decides what goes into the Linux kernel, but anyone can see what’s there. Because the communities of science and open source accept facts as the ultimate source of truth and use the same public system for resolving disagreements about the facts, they foster the same norms of trust grounded in individual integrity."
The entire blog post is worth a read.
I've been using Mathematica since 1995 and Jupyter/colab for 5+ years. Most recently I've been using them both in parallel. While Jupyter is probably the future in terms of mass adoption, there are still some areas where Jupyter is lagging.
1. Mathematica has an easy way of sharing notebook. I just run "deploy" command which turns notebook into publicly accessible webpage, hosted by wolfram, here's an example -- https://www.wolframcloud.com/objects/user-eac9ee2d-7714-42da...
2. Mathematica has more active community. Mathematica-specific questions are likely to be answered within an hour by experts on https://mathematica.stackexchange.com/
3. Mathematica has better tools for simple interactivity. I like to throw in "Manipulate" for a simple graph with a draggable constant, or go to http://demonstrations.wolfram.com/index.php for an idea for more complicated demonstration to use in a presentation
4. Mathematica has more options for advanced visualization, and interfaces are more uniform since graph drawing, 3D drawing, and other kinds of visualizations are developed within a single system. Some examples https://www.wolfram.com/language/11/new-visualization-domain...
Right, Jupyter is nice to have, but this is really over the top nonsense:
Jupyter encourages individual integrity; Mathematica lets individuals hide behind corporate evasion
I have no idea what he's talking about re PDF export either. I print to PDF all the time, to email people a static document to look at, etc. It works just fine. (Whether you can you make book-quality formatted text easily, I've no idea, never been tempted to try.)
Jupyter is an amazing and useful piece of software. I agree that its openness is important, that its flexibility in producing content is excellent, and that it deserves to be the current hotness. But I'm afraid
> Now, Jupyter is the unambiguous technical leader.
is pure fantasy, imho. SymPy is still two decades behind Mathematica in large swathes of symbolic computation.
It may be that, for the things that the author wanted to do, the Python libraries were a good fit (it seems he was working on NLP), but overall, I just don't see it.
Jupyter isn't a foil for mathematica. It has completely different use cases.
The straight one to one fight is between sagemath, or their rebranded cocal site, and mathematica. In terms of ability to do things sagemath is the glue between all the amazing open source math and science software that has been written over the last 50 years.
But in terms of presentation by far the best is org-mode with sage, julia and everything else tied in. One text file that can be emailed around, put under version control, and can speak two dozen computer languages with babel, and has latex support for both pdf and html output.
I'm surprised there's no mention or discussion of the importance of open-source tooling for replicable science. Without seeing and reviewing the source, how can you tell that a particular calculation is right? Also, relying on costly tools such as Mathematica cuts off a sizable amount of the population from being able to replicate or play with your findings on cost grounds alone.
Responders have been making the argument that Jupyter is beating Mathematica because of financial or social issues. I'd like to posit a different interpretation, which could be construed to encapsulate these reasons, as well as additional other factors.
Jupyter has a lower friction to adoption and usage than Mathematica, for a definition of friction which encompasses ease of acquisition and sharing. I include economic considerations in the ease of acquisition and sharing. Lack of proprietary walled garden lock-in/lock-out factors in as well.
People are also likely considering the longer term scenario, whereby data, model, and information interchange has been hindered by proprietary formats (the "wall" in the walled garden) and lack of complete information on how to get information in and out. Which is what the OP was complaining about, as they were not able to easily construct a publication quality preprint/submission from one, but could do it easily from the other.
Some of these sources of friction are effectively "own-goals", that is, you increase friction in such a way as to prevent something that people need to do, to be effectively impossible. Or you hide it. Or disable certain groups from using that functionality.
Then the question is balancing the longevity of the format, the proprietary value against alternatives. Increasingly, people are less interested in this friction for a number of critical systems.
I am looking at this from the perspective of someone who has a few 10's of MB of data/writings on 25-30 year old 3.5 inch and 5.25 inch floppies. These are in formats for which I may not have an ability to extract the data/information without some significant effort.
The formats that have survived well for me over the last 30 years have been either open, or readable/writeable with open tools. The closed ones, not so much luck with.
FYI there is a not as complete open source implementation of Mathematica called mathics. In fact it is also Python based just like Jupyter(I don't think this is a coincidence).
He should look into Sage.
Open source is only great when it exists. For finite element analysis, there are only two generally useful open source products and neither of them has a remotely modern or easy UI. For $10,000 or so, you can get a proprietary one that's fast to use and doesn't have you hitting a brick wall when you find there's some key feature it can't do.
UI is a major failure of open source - it can hardly ever achieve it, at least not well. Most of the popular open source programs have no UI at all.
> Python libraries let me replicate everything I wanted to do with Mathematica: Matplotlib for graphics, SymPy for symbolic math, NumPy and SciPy for numerical calculations
Are the Python libraries precise enough for professional mathematicians? And do they deal with mathematical 'edge cases', a variety of inputs (formats, notations, etc.), etc.?
On one hand, I could say 'the author uses them therefore they must be sufficiant'. On the other, I've seen plenty of cases where the professionals were not careful about the tools they use (e.g, spreadsheets running critical, large-scale financial operations).
I have chosen the emacs org mode system over Jupyter, but I still like Jupyter regardless. The real tragedy is how dependent people have become on proprietary stacks like Mathematica.
>Which reminds me. If you are a Julia enthusiast, how do you suppose the investors in this new language plan to make their big score?
This is a weird jab at Julia. Open source software is woefully underfunded. Julia Computing was founded in the wake of Heartbleed where people learned that open source needs some kind of funding to keep developers alive (example article: https://arstechnica.com/information-technology/2014/04/tech-... ). Coming from academic backgrounds, the core contributors really had two options if they wanted to devote full time to Julia: either everyone gets an academic job while working on Julia instead of papers (lol), or band together to get R&D funding and use that to fund a life of open source development. They did the latter.
It's quite silly to even imply there's something nefarious that can go on here. Their main product is the language. They can't sabotage that without sabotaging themselves. They may have some priorities swayed, just like how any other individual who's working on open source is doing it for their own reasons. For example, IBM funded them to add PowerPC support, and what do you know Julia works on PowerPC. Is that so awful? With this funding model, what ends up happening is you have a large group of people who dedicate their lives to developing open sourced code for automatic GPU compilation, machine learning libraries, etc. along with compiler support for optimizing scientific computing. Because of this (and other reasons), Julia ends up having a much stronger governance which is one reason why its development ends up being more active. And this activity in turn makes its project more democratic than projects like CPython or Jupyter which have been larger projects for a longer time, but with less contributors (Julia's 686 vs CPython's 524 vs Jupyter's 330).
And most of the Julia contributors aren't even part of that company! Many are academics. A lot of the funding is through NumFOCUS, a non-profit which also helps projects like Jupyter, matplotlib, etc. which the author is for! (And they are great projects as well!)
So while I am happy that the author is pro open source, I think it's necessary to point out that this open source outsider view is both wrong and dangerous. Saying that you love the purity and despise anyone who gets to make a living from it is harmful! Open source is a labor of love, but it has also destroyed many careers. I think society has this view that open source (mathematical) projects are "funded" by academic careers, but even creators of popular projects like SageMath have publicly noted that open source is harmful to academic success (https://escapethetower.wordpress.com/2016/06/13/creator-of-s...).
Instead of being against funding open source contributors, I would like to see the author promote funding for open source. Paul Romer is a leading economist. He has the power to proclaim that open source matters for academic careers and push for it to be put on equal footing with papers for grant applications in his field. People like him should be advocating for jobs dedicated to open source development, not scoffing at the supposed impurity of someone being paid to develop a public good. Someone at the top of the academic hierarchy should start a change and make the development of public tools as valued as the development of (non-public) publications.
R Studio Notebooks are pretty good too; I like that, by default, there is an interactive console connected to the same kernel in addition to the notebook. This allows me to use the console to interactively probe my data, or try out something, and then record a more finished product in the notebook itself. I think this can be done in Juypter (http://jupyter-notebook.readthedocs.io/en/latest/examples/No...), but, not out of the box.
Dan Toomey's Learning Jupyter is free today on https://www.packtpub.com/packt/offers/free-learning btw.
I learned and was fluent with Mathematica early and learned python later. I still run to Mathematica for doing symbolic analysis because there is basically no impedement between my ideas and the keyboard when I am solving that type of problem. I've moved all my numerical analysis to the scipy system since it is a more natural language for those types of problems.
Since the author is talking about using Jupyter for research papers, how do you do basic things, like bibliographies, naming tables and referencing them later?
It always goes like this.
The initial solutions may be proprietary, and financed by investors. They have a business model so of course they don’t give everything away for free.
With time, enough people get together to build an open source alternative. And then like a snowball it eclipses everything proprietary that went before it.
What would a world look like that didn’t apply Capitalism to ideas?
One where companies couldn’t sue one another for Intellectual Property infringements. Like Waymo suing Uber.
One where self driving cars can incorporate improvements made by any other self driving car instead of putting people at risk reinventing the wheel.
Where the long tail of drug research leads to something.
Why would people release their findings? Because if they don’t, others will. And then they won’t get that small measure of input and control and attach their name to it. Jonas Salk is an exception in the biomedical field. Albert Einstein is the norm in physics.
The alternatives to Capitalism do not have to be Socialism. They can be SCIENCE. OPEN SOURCE. WIKI.
Collaboration instead of Competition.
I would like to see the same in web browser engines. WebKit instead of IE. And so on. When that happens, we all win.
Yes free software is good. Software, like knowledge, does not have to be scarce.