March 20, 2018

Matthieu Brucher

Writing custom checks for clang-tidy

I started taking a heavier interest in clang-tidy a few months ago, as I was looking at static analyzers. I found at the time that it was quite complicated to work on clang internal AST. It is a wonderful tool, but it is also a very complex one. Thankfully, the cfe-dev mailing list is full of nice people.

I also started my journey in the LLVM/clang land with the help of this blog post.

Quick setup

The previous blog post is very great to explain how to setup a build:

git clone
cd llvm/tools/
git clone
cd clang/tools/
git clone extra
cd ../../../
mkdir build && cd build/
cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo ..
make check-clang-tools

A new checker can be created with the following command line:

./ misc catch-by-const-ref

A new folder can easily be created manually, and each checker consists of two sections:

  1. A matcher that will select AST sections
  2. A checker that will add additional checks on top of the matcher, like macro or file

Now let’s try to implement two rules:

  • The first one will check that we catch exceptions by const ref (a good practice)
  • The second will allow functions detections (C functions that are now replaced by C++, or functions that should be replaced by safer ones).

A simple matcher for catching by const ref

The best reference for the matchers in clang is (unfortunately) the doxygen for the last_matcher namespace. As you can see, it is quite difficult to navigate, but it’s not as complicated.

Let’s start with what we need to scan:

  • A variable declaration
  • Inside a catch statement
  • That is a reference
  • But not const

My first trial was to match all catch statement. This is easily done by using cxxCatchStmt. When I did that, the issue that I could check that the variable underneath was declared const or not. So instead, I asked some help from the cfe-dev people.

So let’s start over again. This is what we need:

  • variable declaration is matched with varDecl (for a type VarDecl, notice the case difference)
  • inside a catch statement is matched by isExceptionVariable
  • a reference type is matched by references
  • and the const aspect is matched by isConstQualified

varDecl is an instance of VariadicDynCastAllOfMatcher that matches VarDecl. It can take several parameters. So the first parameter will be isExceptionVariable. The second will describe that type of access we are looking for hasType(references(qualType(unless(isConstQualified())))). If you unroll this match, we are looking for a reference on a qualifier type that is not (unless) const qualified.

The result is then:

  1. void CatchByConstRefCheck::registerMatchers(MatchFinder *Finder) {
  2.   // This is a C++ only check thus we register the matchers only for C++
  3.   if (!getLangOpts().CPlusPlus)
  4.     return;
  6.   Finder->addMatcher(varDecl(isExceptionVariable(),hasType(references(qualType(unless(isConstQualified()))))).bind("catch"), this);
  7. }

Now that we have a good matcher, the checker is easy to write. We want to warn for all these variables, and we can even easily propose a fix.

  1. void CatchByConstRefCheck::check(const MatchFinder::MatchResult &Result) {
  3.   const VarDecl* varCatch = Result.Nodes.getNodeAs<VarDecl>("catch");
  5.   const char *diagMsgCatchReference = "catch handler catches by non const reference; "
  6.                                         "catching by const-reference may be more efficient";
  8.   // Emit error message if the type is not const (ref)s
  9.   diag(varCatch->getLocStart(), diagMsgCatchReference)
  10.     << FixItHint::CreateInsertion(varCatch->getLocStart(), "const ");
  11. }

Of course, I’ve written a few examples that are tested by clang testing framework (make check-clang-tools).

Using check options for matching deprecated functions

Now, for a second rule, I wanted to detect some C functions that have a C++ equivalent. For instance, exp() should be replaced by std::exp(), or fabs() by std::abs(). As the list can be different for different projects (and as you may want to replace other functions by others).

When using options, there are two things to do. First getting the options in the constructor, and also use a store call:

  1. DetectCFunctionsCheck::DetectCFunctionsCheck(StringRef Name, ClangTidyContext *Context)
  2.     : ClangTidyCheck(Name, Context),
  3.       stdNamespaceFunctions(Options.get("stdNamespaceFunctions", "floor,exp")),
  4.       functionsToChange(Options.get("functionsToChange", "fabs>std::abs"))
  5. {
  6.     parseStdFunctions();
  7.     parseFunctionToChange();
  8. }
  10. void DetectCFunctionsCheck::storeOptions(ClangTidyOptions::OptionMap &Opts)
  11. {
  12., "stdNamespaceFunctions", stdNamespaceFunctions);
  13., "functionsToChange", functionsToChange);
  14. }

I have two calls here to parse the option strings. They are in charge of splitting them at ‘,’ and then for the replacement functions, we split them at ‘>’. Here, the default options are very simple, and it is easy to change it.

The matchers are very similar:

  1. void DetectCFunctionsCheck::registerMatchers(MatchFinder *Finder) {
  2.     // Should check if there are duplicates.
  3.     for(auto fun: stdNamespaceFunctionsSet)
  4.     {
  5.       Finder->addMatcher(callExpr(callee(functionDecl(hasName(fun), unless(cxxMethodDecl()), isExternC()))).bind(fun), this);
  6.     }
  7.     for(auto fun: functionsToChangeMap)
  8.     {
  9.       Finder->addMatcher(callExpr(callee(functionDecl(hasName(fun.first), unless(cxxMethodDecl()), isExternC()))).bind(fun.first), this);
  10.     }
  11. }

So we select call expression that use a function whose name contains the name required, that is not a call to a method and that is extern C (as I want to replace C functions). Then the check is very easy as well as the fix-it hint:

  2. void DetectCFunctionsCheck::check(const MatchFinder::MatchResult &Result) {
  4.     for(const auto& fun: stdNamespaceFunctionsSet)
  5.     {
  6.         const CallExpr* call = Result.Nodes.getNodeAs<CallExpr>(fun);
  7.         if(call)
  8.         {
  9.             diag(call->getLocStart(), "this function has a corresponding std version. Consider using it (std::" + fun + ")")
  10.                 << FixItHint::CreateInsertion(call->getLocStart(), "std::");
  11.         }
  12.     }
  13.     for(const auto& fun: functionsToChangeMap)
  14.     {
  15.         const CallExpr* call = Result.Nodes.getNodeAs<CallExpr>(fun.first);
  16.         if(call)
  17.         {
  18.             auto start = call->getLocStart();
  19.             diag(start, "this function has a better version. Consider using it (" + fun.second + ")")
  20.                 << FixItHint::CreateReplacement(SourceRange(start, start.getLocWithOffset(fun.first.size() - 1)), fun.second);
  21.         }
  22.     }
  23. }

It would be easy to add a third category, for instance for C unsafe functions, but I don’t need this for now.

I have additional functional tests as well in the repository.


I like writing rules, as clang-tidy is very powerful. Unfortunately, it is sometimes difficult to figure out what query you want to write. Although clang-query helps on this a lot, it is not very nice to use (there is no history of previous rules, you can’t go back on the same line…). I suppose dumping the AST helps as you can figure out what is the match you really want.

These two rules are available on github.

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by Matt at March 20, 2018 08:19 AM

Matthew Rocklin

Summer Student Projects 2018

Around this time of year students look for Summer projects. Often they get internships at potential future employers. Sometimes they become more engaged in open source software.

This blogpost contains some projects that I think are appropriate for a summer student in a computational field. They reflect my biases (which, assuming you read my blog, you’re ok with) and are by no means comprehensive of opportunities within the Scientific Python ecosystem. To be perfectly clear I’m only providing ideas and context here, I offer neither funding nor mentorship.

Criteria for a good project

  1. Is well defined and tightly scoped to reduce uncertainty about what a successful outcome looks like, and to reduce the necessity for high-level advising
  2. Is calibrated so that an industrious student can complete it in a few months
  3. It’s useful, but also peripheral. It has value to the ecosystem but is not critical enough that a core devs is likely to complete the task in the next few months, or be overly picky about the implementation.
  4. It’s interesting, and is likely to stimulate thought within the student
  5. It teaches valuable skills that will help the student in a future job search
  6. It can lead to future work, if the student makes a strong connection

The projects listed here target someone who already has decent knowledge of the fundamentals PyData or SciPy ecosystem (numpy, pandas, general understanding of programming, etc..). They are somewhat focused around Dask and other projects that I personally work on.

Distributed GPU NDArrays with CuPy, Bohrium, or other

Dask arrays coordinate many NumPy arrays to operate in parallel. It includes all of the parallel algorithms, leaving the in-memory implementation to NumPy chunks.

But the chunk arrays don’t actually have to be NumPy arrays, they just have to look similar enough to NumPy arrays to fool Dask Array. We’ve done this before with sparse arrays which implement a subset of the numpy.ndarray API, but with sparse storage, and it has worked nicely.

There are a few GPU NDArray projects out there that satisfy much of the NumPy interface:

It would be valuable to do the same thing with Dask Array with them. This might give us a decent general purpose distributed GPU array relatively cheaply. This would engage the following:

  1. Knowledge of GPUs and performance implications of using them
  2. NumPy protocols (assuming that the GPU library will still need some changes to make it fully compatible)
  3. Distributed performance, focusing on bandwidths between various parts of the architecture
  4. Profiling and benchmarking

Github issue for conversation is here: dask/dask #3007

Use Numba and Dask for Numerical Simulations

While Python is very popular in data analytics it has been less successful in hard-core numeric algorithms and simulation, which are typically done in C++/Fortran and MPI. This is because Python is perceived to be too slow for serious numerical computing.

Yet with recent advances in Numba for fast in-core computing and Dask for parallel computing things may be changing. Certainly fine-tuned C++/Fortran + MPI can out-perform Numba and Dask, but by how much? If the answer is only 10% or so then it could be that the lower barrier to entry of Numba, or the dynamic scaling of Dask, can make them competitive in fields where Python has not previously had a major impact.

For which kinds of problems is a dynamic JITted language almost-as-good as C++/MPI? For which kinds of problems is the dynamic nature of these tools valuable, either due to more rapid development, greater flexibility in accepting community created modules, dynamic load balancing, or other reasons?

This project would require the student to come in with an understanding of their own field, the kinds of computational problems that are relevant there, and an understanding of the performance characteristics that might make dynamic systems tolerable. They would learn about optimization and profiling, and would characterize the relevant costs of dynamic languages in a slightly more modern era.

Blocked Numerical Linear Algebra

Dask arrays contain some algorithms for blocked linear algebra, like least squares, QR, LU, Cholesky, etc.., but no particular attention has been paid to them.

It would be interesting to investigate the performance of these algorithms and compare them to proper distributed BLAS/LAPACK implementations. This will very likely lead to opportunities to improve the algorithms and possibly some of Dask’s internal machinery.

Dask-R and Dask-Julia

Someone with understanding of R’s or Julia’s networking stack could adapt Dask’s distributed scheduler for those languages. Recall that the dask.distributed network consists of a central scheduler, many distributed workers, one or more user-facing clients. Currently these are all written in Python and only really useful from that language.

Making this system useful in another language would require rewriting the client and worker code, but would not require rewriting the scheduler code, which is intentionally language agnostic. Fortunately the client and worker are both relatively simple codebases (relative to the scheduler at least) and minimal implementations could probably be written in around 1-2k lines each.

This would not provide the high-level collections like dask.array or dask.dataframe, but would provide all of the distributed networking, load balancing, resilience, etc.. that is necessary to build a distributed computing stack. It would also allow others to come later and build the high level collections that would be appropriate for that language (presumably R and Julia user communities don’t want exactly Pandas-style dataframe semantics anyway).

This is discussed further in dask/distributed #586 and has actually been partially implemented in Julia in the Invenia project.

This would require some knowledge of network programming and, ideally, async programming in either R or Julia.

High-Level NumPy Optimizations

Projects like Numpy and Dask array compute what the user says, even if a more efficient solution exists.

(x + 1)[:5]  # what user said
x[:5] + 1    # faster and equivalent solution

It would be useful to have a project that exactly copies the Numpy API, but constructs a symbolic representation of that computation instead of performs work. This would enable a few important use cases that we’ve seen arise recently. These include both applications from just analyzing the symbolic representation and also applications from changing it to a more optimal form:

  1. You could analyze this representation and warn users about intermediate stages that require a lot of RAM or compute time
  2. You could suggest ideal chunking patterns based on the full computation
  3. You could communicate this computation over the network to a remote server to perform the computation
  4. You could visualize the computation to help users or students understand what they’re computing
  5. You could manipulate the representation into more efficient forms (such as what is shown above)

The first part of this would be to construct a class that behaves like a Numpy array but constructs a symbolic tree representation instead. This would be similar to Sympy, Theano, Tensorflow, Blaze.expr or similar projects, but it would have much smaller scope and would not be at all creative in designing new APIs. I suspect that you could bootstrap this project quickly using systems like dask.array, which already do all of the shape and dtype computations for you. This is also a good opportunity to connect to recent and ongoing work in Numpy to establish protocols that allow other array libraries (like this one) to work smoothly with existing Numpy code.

Then you would start to build some of the analyses listed above on top of this representation. Some of these are harder than others to do robustly, but presumably they would get easier in time.

March 20, 2018 12:00 AM

March 19, 2018

Continuum Analytics

Anaconda included in Gartner’s 2018 Magic Quadrant for Data Science and Machine Learning Platforms

Gartner recently released its 2018 Magic Quadrant for Data Science and Machine Learning Platforms, featuring Anaconda for the first time. For those unfamiliar with the process, vendors complete an extensive survey (150+ questions) and submit financial data and customer references to Gartner for evaluation. There’s a qualification bar based on revenue and customer traction, and …
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by Rory Merritt at March 19, 2018 03:37 PM

March 18, 2018

Titus Brown

My approach to community building and coordination

For the last three months I've been knee-deep (neck deep? thoroughly underwater?) in the #CommonsPilot project, where I have been funded to take on a community coordinating role.

Someone asked today about how my coordination approach dovetails with the immense complexity of the project, and I put together the following answer, which I liked, and am now sharing with y'all.

I am trying to use the following process:

  • put in place some loose guidelines (we'll be using this platform for e-mail, that project for documents, etc.);

  • define a community code of conduct, and start things off the way you want to continue with communication;

  • devise some simple on-boarding to connect people with guidelines;

  • watch carefully to see what communication avenues are actually being used / work well (slack has been a success, google calendar... not so much); fine tune accordingly (we're now using for both mailing lists and calendars);

  • then, observe & extract the emergent "Desire Paths" and bring them into the on-boarding docs ;

  • layer more structure on as need becomes apparent, but don't do it too early, because each layer of structure acts as a straitjacket on the project and limits flexibility and adaptation;

  • use training and in-person / 1-1 meetings to inculcate culture and process;

  • iterate!

This process has emerged from my participation in open source projects over the last 30 years, as well as from watching Greg Wilson grow Software Carpentry from scratch over a decade. It is as different from the way academics think about building collaborations as the Carpentry teaching method is from "sage on a stage"-style teaching :).


p.s. I just started taking scuba lessons (PADI Open Water cert), which may have been a subconscious reaction to this #CommonsPilot thing... "breathe deeply and slowly. don't panic. and when your air runs out? bail to the surface." :)

by C. Titus Brown at March 18, 2018 11:00 PM

March 15, 2018

Continuum Analytics

March 2018 Kubernetes Security Vulnerabilities and Anaconda Enterprise

The Anaconda team tracks security vulnerabilities and CVEs via the National Vulnerability Database (NVD) on an ongoing basis. Our team is committed to the security of Anaconda Enterprise by making updates available in a timely manner in response to security vulnerabilities and similar incidents. Two security vulnerabilities (CVE-2017-1002101 and CVE-2017-1002102) were recently identified in Kubernetes, which …
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by Mathew Lodge at March 15, 2018 02:09 PM

Leonardo Uieda

A template for reproducible papers

Thumbnail image for publication.

At the PINGA lab, we have been experimenting with ways to increase the reproducibility of our research by publishing the git repositories that accompany our papers. You can find them on our Github organzation. I've synthesized the experience of the last 4 years into a template in the pinga-lab/paper-template repository.

Screenshot of the paper-template Github repository.

The template reflects the tools we've been using and the type of research that we do:

  • Most papers are proposing a new methodology rather than the analysis of a dataset.
  • There is always an application to a dataset to show the method works. We can't always publish the data but we include it in the repository whenever we can.
  • All papers include an implementation of the proposed method.
  • Our code is usually written in Python and executed in Jupyter notebooks.
  • The focus of the paper is usually on the methodology, not the code. As such, the code is more of a proof-of-concept than a full blown application or library.
  • The paper itself is written in LaTeX with the source usually included in the repository.

This certainly won't fit everyone's needs but I hope that you can at least use a few bits and pieces for inspiration. Of course, the template code is open-source (BSD license) and you are free to reuse it however you like. The template includes a sample application to climate change data, complete with a Python package, automated tests, an analysis notebook, a notebook that generates the paper figure, raw data, and a LaTeX text. Everything, from compilation to building the final PDF, can be done with a single make command.

Screenshot of running

We've been using different versions of this template for a few years and I've been tweaking it to address some of the difficulties we encountered along the way.

  • Running experiments in Jupyter notebooks can get messy when people aren't diligent about the execution order. It can be hard to remember to "Reset and run all" before using the results.
  • The execution was done manually so you had to remember and document in what order the notebooks need to be run.
  • Experimental parameters (e.g., number of data points, inversion parameters, model configuration) were copied into the text manually. This sometimes led to values getting out of sync between the notebooks and paper.
  • We only had integration tests implemented in notebooks. More often than not, the checks were visual and not automated. I think a big reason for this is the lack of experience in writing tests within the group and setting up all of the testing infrastructure (mainly how to use pytest and what kind of test to perform).

The latest update addresses all of these pain points. The main features of the new template are:

  • Uses Makefiles to automate the workflow. You can build and test the software, generate results and figures, and compile the PDF with a single make command.
  • A Makefile for building the manuscript PDF with extra rules for running proselint, counting words, and opening the PDF.
  • A starter conda environment for managing dependencies and making sure everyone gets the same version of the dependencies.
  • Boilerplate instructions for downloading the code and reproducing the results.
  • A Makefile for building the Python package, testing it with pytest, running static code checks (flake8 and pylint), and generating results and figures from the notebooks.
  • The code Makefile can run the notebooks using jupyter nbconvert to guarantee that the notebooks are executed in sequential order (top to bottom). I would love to use nbflow but the SCons requirement puts me off a bit. make works fine and the basic syntax is easier to understand.
  • An example of using code to write experimental parameters in a .tex file. The file defines new variables that are used in the main text. This guarantees that the values cited in the text are the ones that you actually used to produce the results.

This last feature is my favorite. For example, the notebook code/notebooks/estimate-hawaii-trend.ipynb has the following code:

tex = r"""
% Generated by code/notebooks/estimate-hawaii-trend.ipynb
\newcommand{{\HawaiiLinearCoef}}{{{linear:.3f} C}}
\newcommand{{\HawaiiAngularCoef}}{{{angular:.3f} C/year}}
""".format(linear=trend.linear_coef, angular=trend.angular_coef)

with open('../../manuscript/hawaii_trend.tex', 'w') as f:

It defines the LaTeX commands \HawaiiLinearCoef and \HawaiiAngularCoef that can be used in the paper to insert the values estimated by the Python code. The commands are saved to a .tex file that can be included in the main manuscript.tex. Since this file is generated by the code, the values are guaranteed to be up-to-date.

If you want to use the template to start a new project:

  1. Create a new git repository:

    mkdir mypaper
    cd mypaper
    git init
  2. Pull in the template code:

    git pull master
  3. Create a new repository on Github.

  4. Push the template code to Github:

    git remote add origin
    git push -u origin master
  5. Follow the instruction in the

Alternatively, you can use the "Import repository" option on Github.

Screenshot of the Github page for importing code from an existing repository.

I hope that this template will be useful to people outside of our lab. There is definitely still room for improvement and I'm looking forward to trying it out on my next project.

What other features would you like to see in the template? Let me know in the comments (or better yet, submit a pull request). I'd love to know about your experiences and workflows for computational papers.

Comments? Leave one below or let me know on Twitter @leouieda.

Found a typo/mistake? Send a fix through Github and I'll happily merge it (plus you'll feel great because you helped someone). All you need is an account and 5 minutes!

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March 15, 2018 12:00 PM

March 14, 2018

March 13, 2018

Matthieu Brucher

Playing with a Bela (2): Compile last Audio Toolkit on Bela with Clang

More than a year ago, I started playing with the Bela board. At the time, I had issues compiling Audio ToolKit with clang. The issue was that the gcc shipped with the Debian image the BeagleBoard used was too old and didn’t fully support C++11. The one that ships now is GCC 6, which is even C++14 compliant. Meaning that everything is available to build Audio Toolkit with Python support.

Setting up Bela

Starting from a fresh image, the main things to install are:

  • Boost
  • FFTW
  • Python and Numpy

With the BeagleBoard, it’s easy as one apt-get line:

apt-get install python3-dev python3-numpy python3-scipy python3-nose libfftw3-dev libboost-dev libboost-system1.62-dev libboost-test1.62-dev

And one line to install nosetests:
pip3 install nosetests

After all the dependent packages are downloaded, the git repository can be cloned:

git clone
git submodule init
git submodule update

Direct build

Let’s create a new folder named AudioTK-build on the same level as AudioTK. We can then run cmake from it:

cmake ../AudioTK

Once this is done, let’s build ATK:


It will take some time, but then we can run the tests. First, we want to export the building Python path to be able to test in place before the install step.
export PYTHONPATH=/root/local/src/AudioTK-build/Python/:$PYTHONPATH

And then:

make test

The result should look something like this:

Test project /root/local/src/AudioTK-build
Start 1: Adaptive
1/23 Test #1: Adaptive ......................... Passed 9.54 sec
Start 2: Core
2/23 Test #2: Core ............................. Passed 481.68 sec
Start 3: Delay
3/23 Test #3: Delay ............................ Passed 168.64 sec
Start 4: Distortion
4/23 Test #4: Distortion ....................... Passed 0.50 sec
Start 5: Dynamic
5/23 Test #5: Dynamic .......................... Passed 23.90 sec
Start 6: EQ
6/23 Test #6: EQ ............................... Passed 158.12 sec
Start 7: IO
7/23 Test #7: IO ............................... Passed 0.66 sec
Start 8: Mock
8/23 Test #8: Mock ............................. Passed 2.98 sec
Start 9: Preamplifier
9/23 Test #9: Preamplifier ..................... Passed 1.45 sec
Start 10: PyAdaptive
10/23 Test #10: PyAdaptive ....................... Passed 10.51 sec
Start 11: PyCore
11/23 Test #11: PyCore ........................... Passed 6.26 sec
Start 12: PyDelay
12/23 Test #12: PyDelay .......................... Passed 5.78 sec
Start 13: PyDistortion
13/23 Test #13: PyDistortion ..................... Passed 7.13 sec
Start 14: PyDynamic
14/23 Test #14: PyDynamic ........................ Passed 16.39 sec
Start 15: PyEQ
15/23 Test #15: PyEQ ............................. Passed 9.83 sec
Start 16: PyPreamplifier
16/23 Test #16: PyPreamplifier ................... Passed 13.20 sec
Start 17: PyReverberation
17/23 Test #17: PyReverberation .................. Passed 4.45 sec
Start 18: PySpecial
18/23 Test #18: PySpecial ........................ Passed 4.41 sec
Start 19: PyTools
19/23 Test #19: PyTools .......................... Passed 5.79 sec
Start 20: Reverberation
20/23 Test #20: Reverberation .................... Passed 12.18 sec
Start 21: Special
21/23 Test #21: Special .......................... Passed 1295.59 sec
Start 22: Tools
22/23 Test #22: Tools ............................ Passed 13.43 sec
Start 23: Utility
23/23 Test #23: Utility .......................... Passed 0.13 sec

100% tests passed, 0 tests failed out of 23

Obviously, this is very slow. It’s more or less 50 times slower than the same on my old MacBook Pro!


Clearly I’m not in a place where I can use ATK on the BeagleBoard. While looking at the assembler code, it seems that almost no Neon instructions were generated.

So the next entry in this series will tackle optimizing ATK on ARM!

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by Matt at March 13, 2018 08:38 AM

March 09, 2018

Leonardo Uieda

Podcasts in my playlist (2018 edition)

Thumbnail image for publication.

Last year, I posted my podcast playlist in response to a similar post by John Leeman (of Don't Panic Geocast fame). In a recent episode (maybe episode 158), John asked listeners for an updated list of recommendations. Here are mine.

I'll start with the new additions since last year, then the ones that stayed with me throughout 2017, and finally the ones that I'm looking to get started this year.

New additions:

  • Gastropod: A podcast that "looks at food through the lens of science and history". In each episode, the hosts dive deep into the science behind a type of food/process/ingredient and how it became what it is today. One of my favorite episodes is about koji, the fungus behind sake, miso, shoyu, and more.
  • The Unmade Podcast: A podcast about insane ideas for new podcasts. Very meta and silly but a fun way to pass the time and get a few laughs.
  • We Martians: A podcast all about the science and exploration of Mars. Just listened to a few episodes but I'm enjoying it so far.

The survivors:

  • Undersampled Radio: Geeky and fun interviews, mostly about geo/science/technology.
  • Don't Panic Geocast: All things geoscience (sometimes with very interesting guests).
  • Hello Internet: A light conversation between two friends who make science videos on YouTube with a surprisingly common discussion of flags.
  • Imaginary Worlds: "A show about how we create them and why we suspend our disbelief". Still one of my favorites.
  • Invisibilia: A series about the forces that shape our lives. Also one of my favorites.
  • Talk Python To Me: The title pretty much says it all.
  • Radiolab: Interesting stories about all sorts of topics. Very high quality production.

The ones I haven't tried yet:

  • Fieldwork Diaries: Interviews with scientists about their field experiences.
  • In Defense of Plants: I'm curious to learn more about the weird world of botany.
  • Ologies: Each episode is about a different field of knowledge. I think it's based on an idea from the Unmade Podcast.
  • The Truth: "Movies for your ears".

That's it for my list. Do you have any recommendations?

Comments? Leave one below or let me know on Twitter @leouieda.

Found a typo/mistake? Send a fix through Github and I'll happily merge it (plus you'll feel great because you helped someone). All you need is an account and 5 minutes!

Please enable JavaScript to view the comments powered by Disqus.

March 09, 2018 12:00 PM

March 06, 2018

Continuum Analytics

Anaconda Repository Changes Afoot

In August 2017, Continuum Analytics announced it is now Anaconda, Inc. Here at Anaconda, we are all excited about the change, and have spent the last several months switching everything over to the Anaconda name. One of the last big changes we need to make is to switch our default conda repository from to …
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by Rory Merritt at March 06, 2018 05:49 PM

Travis Oliphant

Reflections on Anaconda as I start a new chapter with Quansight

Leaving the company you founded is always a tough decision and a tough process that involves many people. It requires a series of potentially emotional "crucial-conversations."  It is actually not that uncommon in venture-backed companies for one or more of the original founders to leave at some point.  There is a decent article on the topic here:

Still it is extremely difficult to let go. You live and breathe the company you start.  Years of working to connect as many people as possible to the dream gives you a feeling of "ownership" and connection that no stock certificate can replace. Starting a company is a lot of work.  It takes a lot of effort. There are many decisions to make and many voices to incorporate. Hiring, firing, raising money, engaging customers, engaging employees, planning projects, organizing events, and aligning a pastiche of personalities while staying relevant in a rapidly evolving technology jungle is difficult.

As a founder over 40 with modest means, I had a family of 6 children who relied on me.  That family had teenage children who needed my attention and pre-school and elementary-school children that I could not simply leave only in the hands of my wife. I look back and sometimes wonder how we pulled it off. The truth probably lies in the time we borrowed: time from exercise, time from sleep, time from vacations, and time from family. I'd like to say that this dissonance against "work-life-harmony" was always a bad choice, but honestly, I don't see how I could have made too may different choices and still have created Anaconda.

Work life harmony

Several things drove me. I could not let the people associated with the company down. I would not lose the money for those that invested in us. I could not let down the people who worked their tail off to build manage, document, market, and sell the technology and products that we produced. Furthermore, I would not let the community of customers and users down that had enabled us to continue to thrive.

The only way you succeed as a founder is through your customers being served by the efforts of those who surround you. It is only the efforts of the talented people who joined us in our journey that has allowed Anaconda to succeed so far. It is critical to stay focused on what is in the best interests of those people.

Permit me to use the name Continuum to describe the angel-funded and bootstrapped early-stage company that Peter and I founded in 2012 and Anaconda to describe the venture-backed company that Continuum became (This company we called Continuum 2.0 internally that really got started in the summer of 2015 after we raised the first tranche of $22 million from VCs.)

Back in 2012, Peter and I knew a few things: 1) we had to connect Python to the Big Data movement; 2) we needed to help the scientific programmer, or a data-scientist developer build visualization-based applications quickly in the web; and 3) we needed to scale the stack of code around the PyData community to bigger hardware and multiple machines. We had big visions of an interconnected data-web, distributed schedulers, and data-structures that traversed the internet which could be analyzed across the cloud with simple Python scripts. We talked and talked about these things and grew misty-eyed in our enthusiasm for the potential of what was possible if we just built the right technology and sold just the right product to fund it.

We knew that we wanted to build a product-company -- though we didn't know exactly what those products would be at the outset.  We had some ideas, only portions of which actually worked out.  I knew how to run a consulting and training company around Python and open-source. Because of this, I felt comfortable raising money from family members. While consulting companies are not "high-growth" they can make real returns for investors. I was pretty confident that I would not lose their money.

We raised $2.25million from a few dozen investors consisting of Peter's family, my family, and a host of third-parties from our mutual networks.  Peter's family was critical to this early stage because they basically "led the early round" and ensured that we could get off the ground.   After they put their money in the bank, we could finish raising the rest of the seed round which took about 6 months to finish.

It is interesting (and somewhat embarrassing and so not detailed here) to go back and look at what products we thought we would be making. Some of the technologies we ended up building (like Excel integration, Numba, Bokeh, and Dask) were reflected in those early product dreams.  However, the real products and commercial success that Anaconda has had so far are only a vague resemblance to what we thought we would do.

Building a Python distribution was the last thing on our minds. I had been building Python distributions since I released SciPy in 2001.  As I have often repeated, SciPy was actually the first Python distribution masquerading as a library. The single biggest effort in releasing SciPy was building the binary installers and making sure everything compiled well.  With Fortran compilers still more scarce than they should be, it can still be difficult to compile and build SciPy.

Fortunately, with conda, conda-forge, and Anaconda, along with the emergence of wheels, almost nobody needs to build SciPy anymore.  It is so easy today to get started with a data-science project and get all the software you need to do amazing work fast. You still have to work to maintain your dependencies and keep that workflow reproducible.  But, I'm so happy that Anaconda makes that relatively straightforward today.

This was only possible because General Catalyst and BuildGroup joined us in the journey in the spring of 2015 to really grow the Anaconda story.  Their investment allowed us to 1) convert to a serious product-company from a bootstrapped consulting company with a few small products and 2) continue to invest heavily in conda, conda-forge, and Anaconda.

There is nothing like real-world experience as a teacher, and the challenge of converting to a serious product company was a tremendous experience that taught me a great deal. I'm grateful to all the people who brought their best to the company and taught me everyday.  It was a privilege and an honor to be a part of their success.  I am grateful for their patience with me as my "learning experiences" often led to real struggles for them.

There are many lasting learnings that I look forward to applying in future endeavors. The one that deserves mention in this post, however, is that building enterprise software that helps open-source communities should be done by selling a complementary product to the open-source.  The "open-core" model does not work as well.  I'm a firm believer that there will always be software to sell, but infrastructure should be and will be open-source --- sustained vibrantly from the companies that depend on it.  Joel Spolsky has written about complementary products before. You should read his exposition.

Early on at Anaconda, Peter and I decided to be a board-led company. This board which includes Peter and I has the final say in company leadership and made the important decision to transition Anaconda from being founder-led to being led by a more experienced CEO.  After this transition and through multiple conversations over many months we all concluded that the best course of action that would maximize my energy and passion while also allowing Anaconda to focus on its next chapter would be for me to spin-out of Anaconda and start a new services and open-source company where I could pursue a broader mission.

This new company is Quansight (short for Quantitative Insight). Our place-holder homepage is at and we are @quansightai on Twitter. I'm excited to tell you more about the company in future blog-posts and announcements.  A few paragraphs will suffice for now.

Our overall mission is to develop people, build technology, and discover products to empower people with knowledge and data to solve the world’s most challenging problems.  We are doing that currently by connecting organizations sustainably with open source communities to solve their hardest problems by enabling teams to transparently apply science to their data.

One of the things we are doing is to help companies get started with AI and ML by applying the entire PyData stack to the fundamental data organization, data visualization, and model management problem that is required for practical success with ML and AI in business.  We also help companies generally improve their data-science practice by leveraging all the power of the Python, PyData, and related ecoystems.

We are also hard at work on the sustainability problem by continuing the tradition we started at Continuum Analytics of building successful and sustainable open-source "practices" that synchronize company needs with open-source technology development.   We have some innovative business approaches to this that we will be announcing in the coming weeks and months.

I'm excited that we have several devs working hard to help bring JupyterLab to 1.0 this year along with a vibrant community. There are many exciting extensions to this remarkable platform that remain to be written.

We also expect to continue to contribute to the PyViz activities that continue to explode in the Python ecosystem as visualization is a critical first step to understanding and using any data you care about.

Finally, Stefan Krah has joined us at Quansight.  Stefan is an award-winning Python core developer who has been steadily working over the past 18 months on a small but powerful collection of projects collectively called Plures.  These will be more broadly available in the next few months and published under the xnd brand.  Xnd is a generic container concept in C with a Python binding that together with its siblings ndtypes and gumath allows building flexible array-computing pipelines over many kinds of data-types.

This technology will serve to underly any array-computing framework and be a glue between machine-learning and data-science frameworks of all kinds.  Our plan is to use this tool to help reduce the data and computational silos that currently exist across the open-source ecosystem.

There is still much to work on and many more technologies to emerge.  It's an exciting time to work in machine learning, data-science, and scientific computing.  I'm thrilled that I continue to get the opportunity to be part of it.  Let me know if you'd like to be a part of our journey.

by Travis Oliphant ( at March 06, 2018 07:51 AM

February 28, 2018

Matthew Rocklin

Craft Minimal Bug Reports

Following up on a post on supporting users in open source this post lists some suggestions on how to ask a maintainer to help you with a problem.

You don’t have to follow these suggestions. They are optional. They make it more likely that a project maintainer will spend time helping you. It’s important to remember that their willingness to support you for free is optional too.

Crafting minimal bug reports is essential for the life and maintenance of community-driven open source projects. Doing this well is an incredible service to the community.

Minimal Complete Verifiable Examples

I strongly recommend following Stack Overflow’s guidelines on Minimal Complete Verifiable Exmamples. I’ll include brief highlights here:

… code should be …

  • Minimal – Use as little code as possible that still produces the same problem
  • Complete – Provide all parts needed to reproduce the problem
  • Verifiable – Test the code you’re about to provide to make sure it reproduces the problem

Lets be clear, this is hard and takes time.

As a question-asker I find that creating an MCVE often takes 10-30 minutes for a simple problem. Fortunately this work is usually straightforward, even if I don’t know very much about the package I’m having trouble with. Most of the work to create a minimal example is about removing all of the code that was specific to my application, and as the question-asker I am probably the most qualified person to do that.

When answering questions I often point people to StackOverflow’s MCVE document. They sometimes come back with a better-but-not-yet-minimal example. This post clarifies a few common issues.

As an running example I’m going to use Pandas dataframe problems.

Don’t post data

You shouldn’t post the file that you’re working with. Instead, try to see if you can reproduce the problem with just a few lines of data rather than the whole thing.

Having to download a file, unzip it, etc. make it much less likely that someone will actually run your example in their free time.


I’ve uploaded my data to Dropbox and you can get it here: my-data.csv.gz

import pandas as pd
df = pd.read_csv('my-data.csv.gz')


You should be able to copy-paste the following to get enough of my data to cause the problem:

import pandas as pd
df = pd.DataFrame({'account-start': ['2017-02-03', '2017-03-03', '2017-01-01'],
                   'client': ['Alice Anders', 'Bob Baker', 'Charlie Chaplin'],
                   'balance': [-1432.32, 10.43, 30000.00],
                   'db-id': [1234, 2424, 251],
                   'proxy-id': [525, 1525, 2542],
                   'rank': [52, 525, 32],

Actually don’t include your data at all

Actually, your data probably has lots of information that is very specific to your application. Your eyes gloss over it but a maintainer doesn’t know what is relevant and what isn’t, so it will take them time to digest it if you include it. Instead see if you can reproduce your same failure with artificial or random data.


Here is enough of my data to reproduce the problem

import pandas as pd
df = pd.DataFrame({'account-start': ['2017-02-03', '2017-03-03', '2017-01-01'],
                   'client': ['Alice Anders', 'Bob Baker', 'Charlie Chaplin'],
                   'balance': [-1432.32, 10.43, 30000.00],
                   'db-id': [1234, 2424, 251],
                   'proxy-id': [525, 1525, 2542],
                   'rank': [52, 525, 32],


My actual problem is about finding the best ranked employee over a certain time period, but we can reproduce the problem with this simpler dataset. Notice that the dates are out of order in this data (2000-01-02 comes after 2000-01-03). I found that this was critical to reproducing the error.

import pandas as pd
df = pd.DataFrame({'account-start': ['2000-01-01', '2000-01-03', '2000-01-02'],
                   'db-id': [1, 2, 3],
                   'name': ['Alice', 'Bob', 'Charlie'})

As we shrink down our example problem we often discover a lot about what causes the problem. This discovery is valuable and something that only the question-asker is capable of doing efficiently.

See how small you can make things

To make it even easier, see how small you can make your data. For example if working with tabular data (like Pandas), then how many columns do you actually need to reproduce the failure? How many rows do you actually need to reproduce the failure? Do the columns need to be named as you have them now or could they be just “A” and “B” or descriptive of the types within?


import pandas as pd
df = pd.DataFrame({'datetime': ['2000-01-03', '2000-01-02'],
                   'id': [1, 2]})

Remove unnecessary steps

Is every line in your example absolutely necessary to reproduce the error? If you’re able to delete a line of code then please do. Because you already understand your problem you are much more efficient at doing this than the maintainer is. They probably know more about the tool, but you know more about your code.


The groupby step below is raising a warning that I don’t understand

df = pd.DataFrame(...)

df = df[df.value > 0]
df = df.fillna(0)

df.groupby(df.x).y.mean()  # <-- this produces the error


The groupby step below is raising a warning that I don’t understand

df = pd.DataFrame(...)

df.groupby(df.x).y.mean()  # <-- this produces the error

Use Syntax Highlighting

When using Github you can enclose code blocks in triple-backticks (the character on the top-left of your keyboard on US-standard QWERTY keyboards). It looks like this:

x = 1

Provide complete tracebacks

You know all of that stuff between your code and the exception that is hard to make sense of? You should include it.


I get a ZeroDivisionError from the following code:

def div(x, y):
    return x / y

div(1, 0)


I get a ZeroDivisionError from the following code:

def div(x, y):
    return x / y

div(1, 0)

ZeroDivisionError                         Traceback (most recent call last)
<ipython-input-4-7b96263abbfa> in <module>()
----> 1 div(1, 0)

<ipython-input-3-7685f97b4ce5> in div(x, y)
      1 def div(x, y):
----> 2     return x / y

ZeroDivisionError: division by zero

If the traceback is long that’s ok. If you really want to be clean you can put it in <details> brackets.

I get a ZeroDivisionError from the following code:

def div(x, y):
    return x / y

div(1, 0)

### Traceback


ZeroDivisionError                         Traceback (most recent call last)
<ipython-input-4-7b96263abbfa> in <module>()
----> 1 div(1, 0)

<ipython-input-3-7685f97b4ce5> in div(x, y)
      1 def div(x, y):
----> 2     return x / y

ZeroDivisionError: division by zero


Ask Questions in Public Places

When raising issues you often have a few possible locations:

  1. GitHub issue tracker
  2. Stack Overflow
  3. Project mailing list
  4. Project Chat room
  5. E-mail maintainers directly (never do this)

Different projects handle this differently, but they usually have a page on their documentation about where to go for help. This is often labeled “Community”, “Support” or “Where to ask for help”. Here are the recommendations from the Pandas community.

Generally it’s good to ask questions where many maintainers can see your question and help, and where other users can find your question and answer if they encounter a similar bug in the future.

While your goal may be to solve your problem, the maintainer’s goal is likely to create a record of how to solve problems like yours. This helps many more users who will have a similar problem in the future, see your well-crafted bug report, and learn from the resulting conversation.

February 28, 2018 12:00 AM

February 27, 2018

Continuum Analytics

Introducing Microsoft R Open as Default R for Anaconda Distribution

Although Anaconda, Inc. is best known as the creator of the world’s most popular Python data science platform, for many years we also have been creating conda packages for R. In September 2017, we announced a partnership with Microsoft that included bringing Microsoft R Open (MRO) to Anaconda users as our default R. We are …
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by Rory Merritt at February 27, 2018 04:38 PM

February 26, 2018

Titus Brown

Assessment report for ANGUS 2017

Note: This is an invited blog post by Dr. Karen Word on our 2017 sequence analysis workshop, ANGUS. Our 2018 schedule has now been posted!

DIBSI Assessment: preliminary ANGUS report

There are two sorts of stories I find myself telling based on my own experience talking to people & reading through things:

1) This workshop catered to learners of many abilities, and no group was categorically dissatisfied. Satisfaction for these different groups was based on different outcomes, e.g. for novices building a mental model & confidence was considered success; for practitioners validating practices and acquiring new tools, tricks, and contacts was more likely to constitute success.

2) From our perspective, a desirable outcome would be the creation of a community within which learners can support each other in solving problems to minimize the strain on experts. Learners largely did not seem to be seeking this kind of community -- even where they valued socialization, it was rarely with an eye towards technical support. The exception seemed to be active practitioners who had already been seeking support within their own community and were able to understand other learners as a resource for help.

Basic demographics:

Note: demographics were collected in the pre-assessment only. Individuals who did not take the pre-assessment are not represented here.

The "other" category above was selected mainly by postdocs, but also includes DVM, MD, BS-holding professional, and one "Assistant professor." This is a similar mixture as those cited under "PhD-holding professional" hence with few exceptions these categories could be combined (yellow & green) indicating similar representation of advanced degree-holders relative to graduate students.

A few response summaries:

People were overwhelmingly satisfied with the workshop:

Curiously, even learners who indicated that they left with "no ability" to install and run genome assembly software still largely felt that their needs had been met:

Other metrics are similarly supportive. 89% of respondants indicated that they learned what they hoped to learn, and 96% say they would recommend the workshop to colleagues.

We asked two open-response content questions on the pre- and post-assessments. One of them was as follows:

Suppose that you are using Illumina to sequence DNA from a mouse sample that should have genetic differences from the mouse reference genome. Discuss one or more approaches you would take to analyze the data, as well as your expected sensitivity to SNPs and indels. Include in your discussion how much you will miss, and how much you find that will be wrong.

This word cloud shows the most common terms from pre-assesment responses to the question above, scrubbed of most terminology present in the question:

vs. Post-assessment responses to same, reflecting common details acquired:

The figure below shows pre vs. post self-evaluation of ability in various relevant subject areas. Substantial changes are evident in all categories except Python programming, a skill that is not directly taught in the workshop.

The figure below shows more specific responses to "I know how to" or "I understand" questions on a Likert-type scale. Progress is evident in all categories, again with python scripting ("Qscriptpy") taking up the rear. The greatest progress is in knowing what relevant tools do.

A few quotes:

In response to "please comment on the extent to which you learned what you expected to learn":

Success as defined by a learner who does not plan to analyze their own data:

1 . I wanted to understand the generic pipeline for analyzing RNA sequencing data- SUCCESS 2. I wanted to learn the fundamental skills needed to use tools in the shell/R- SUCCESS 3. I wanted to feel comfortable talking with bioinformaticists about how they performed analyses, so I can be sure that they did what I need them to do- SUCCESS Additionally, I received a great introduction to the concepts of "open science" and "making stuff actually reproducible," things which are not emphasized in my program!

Success for learner who has data that have previously been analyzed, but wants to become more independent:

I came with zero to none programming skills and had no idea how to get started with analyzing next-gen sequencing data. I was sitting on RNA-Seq datasets that someone else had analyzed for me and felt so powerless and overwhelmed. Since attending the workshop, I was able to get my own Jetstream allocation, move my data into the new server, do genome assembly --> Diff gene expression all on my own, while generating more than decent plots using R. I feel immensely relieved that I have all the basic resources I need to get started on exploring Bioinformatics and all the possible research directions that can open up as a result of this. I also met some freaking awesome people, helping build a network of skilled Bioinformaticians I can reach out to if I ever need advice/help. For someone coming from an obscure univ which doesn't have any bioinformaticians at all, this is huge. Thank you DIBSI organizers!!

Another learner who wants to analyze their own data and came in with some computational skill:

I work with non model organism and this workshop turned out to be great to answer my queries and how to tackle genome and rna seq data analysis of such organisms. Also, the knowledge of tools used would further help me perform assembly and analysis of my own data set. I feel way more confident in running the tools for analysis after participating in this workshop

In response to "Please comment on how your understanding of computational science changed":

The perspective of an advanced learner:

This workshop pushed me from being an adequate programmer who can google things and piece together a decent pipeline to get the necessary data, to understanding what's going on under the hood at each step and analyze data quality much better.

And another:

I had some prior experience of bioinformatics tools used to analysis rna seq and DNA seq data, but this workshop further helped me with understanding new tools such as R studio , markdown and writing own scripts.

Whereas from people reporting lower computational skills, one benefit was gaining focus on what was important or learning what they did not need to know:

My initial thought was that I had to obtain a year long computational course in order to be able to assess genetic data. With this course I learned that all I need is the tools specific to proceed with the process of mapping, and analyzing data. I Was also impressed that with working on the cloud instance there is no need for a huge memory computer. And many people spoke of reducing a barrier of fear for computational processes and/or seeking help: I have a base understanding now of what forms the data comes in and how to appropriately prepare the reads for downstream analyses. There are many steps in the pipeline and many options for programs. I do feel that I have more confidence to try to tackle analysis of my own data and to write scripts. Accessing the cloud or shell used to be very intimidating for me, but I feel more comfortable now. Most importantly, I have gained enough knowledge to actually be able to ask the appropriate questions of my systems admin and colleagues that are more computationally inclined when I get stuck on certain analyses.

Complaints & Suggestions:

A request for consistent embedded pipeline visualization recurs in various responses (this was discussed in Tigers room, possibly explaining this specific trend). Other learners referred to wanting more "big picture" introductions or wanting to know "why" things were being done.

Several people would also like to get more practice with the tools or more opportunity to work with their own data.

One person observed that we target computational novices well but not necessarily genetics novices.

There were two individuals who indicated that they would not recommend this workshop to colleagues. One of them indicated that they do not have colleagues who work with NGS data. The other indicated objections to instructional style and a sense that the workshop was "too casual" and lacked organization. However, elsewhere they seem almost to be responding on behalf of less experienced learners, stating: "I learn some good tricks and got some good tricks. If this was my first workshop, I will felt lost after the first week."

A few pertinent recommendations:

I suggest articulating plans for advance embedding of formative assessessment in the curriculum since:

  • Formative assessment can functionally provide practice with the tools in small ways -- while not providing exactly what is requested, this may resolve the feeling that learners have not "played" with the tools.

  • Formative assessment can also raise "why" questions to prompt discussions of broader connectivity as necessary

It would be relatively straightforward to provide a pipeline roadmap and a vocabulary list (or glossary) for computational and genetic terms and abbreviations. (I suggest having these on paper to avoid adding to overcrowded screens)

We had very few people who reported lacking background in genetics, but we also did not survey for this directly. We should consider whether ths is something we plan to address, and consider adding language in the course description if it is not.

Finally, regarding assessment in future years, I recommend that we more directly inquire as to the goals that attendees have coming into the workshop. I also suggest that we ask about ways in which their home community expects to rely upon their training. Given that success appears to take such different forms for different learners, this would help us to more precisely assess the extent to which we are meeting those varying needs. Our plans for retrospective surveys and interviews will also help tease apart the impact that these different kinds of experiences have on careers and communities in the long term.

by Karen R. Word at February 26, 2018 11:00 PM

February 23, 2018

Continuum Analytics

Harness the Power of Data Science at AnacondaCON 2018!

Last spring, Anaconda celebrated the inaugural AnacondaCON, where over 400 people descended upon Austin to connect with peers and thought leaders within the Python data science community. This year’s event promises to be even bigger and better! Taking place in Austin on April 8-11, AnacondaCON 2018 is shaping up to be one of the hottest …
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by Rory Merritt at February 23, 2018 05:57 PM

February 20, 2018

Matthieu Brucher

Announcement: ATKAutoSwell 2.0.0

I’m happy to announce the update of ATK Auto Swell based on the Audio Toolkit and JUCE. They are available on Windows (AVX compatible processors) and OS X (min. 10.9, SSE4.2) in different formats.

This plugin requires the universal runtime on Windows, which is automatically deployed with Windows update (see tis discussion on the JUCE forum). If you don’t have it installed, please check Microsoft website.

ATK Auto Swell 2.0.0

The supported formats are:

  • VST2 (32bits/64bits on Windows, 32/64bits on OS X)
  • VST3 (32bits/64bits on Windows, 32/64bits on OS X)
  • Audio Unit (32/64bits, OS X)

Direct link for ATKGuitarPreamp.

The files as well as the previous plugins can be downloaded on SourceForge, as well as the source code.

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by Matt at February 20, 2018 08:09 AM

February 16, 2018

February 15, 2018

Continuum Analytics

VS Code in Anaconda Distribution 5.1

A few months ago, Anaconda, Inc., creator of the world’s most popular Python data science platform, announced a partnership with Microsoft that included providing Anaconda Distribution users easy access to Visual Studio Code (VS Code). We are pleased to announce that, with the February 15th release of Anaconda Distribution 5.1, this goal is now a …
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by Rory Merritt at February 15, 2018 04:39 PM

Titus Brown

Do software and data products advance biology more than papers?

There are many outputs from our lab and our collaborators - off the top of my head, the big ones are:

  • papers and preprints
  • software
  • data sets
  • blog posts and tweets
  • talk slides and videos
  • grant proposal text
  • training materials and tutorials
  • trainees (core lab members, rotation students, people who attend our workshops, etc)

Traditionally, only the first (papers) and some small part of the last (trainees who get a PhD or do a postdoc in the lab) are explicitly recognized in biology as "products". I personally value all of them to some degree.

In terms of actual effect I believe that software, trainees, blog posts, and training materials are more impactful products than our papers.

In terms of taming the chaos of science, I view advances in our software's capabilities, and the development and evolution of our perspectives on data analysis, as a kind of ratchet that inexorably advances our science.

Papers, unless they accomplish the very difficult task of nailing down a concept and explaining it well, do very little to advance our lab's science. They are merely artifacts that we produce because they meet metrics, with the side effect of being one relatively ineffective way to communicate methods and results.

A question that I've been considering is this:

To what extent is the focus on papers as a primary output in biology (or at least genomics and bioinformatics) skewing our field's perspectives and slowing progress by distracting us from more useful outputs?

A companion question:

How (if at all) is the rise of software and data products as putative equivalents to papers leading to epistemic confusion as to what constitutes actual progress in biology?

To explain this last point a bit more,

it's not clear that many papers really advance biology directly, given the flood of papers and results and the resulting loss of ability to read and comprehend them all in a particular subject. (This is more true in some areas than in others, but you could also argue that big fields are maybe getting subdivided into more narrow fields because of our inability to comprehend the results in big fields.)

More and more, the results of papers need to be incorporated into theory (difficult in bio) or databases and software before they become useful in biology.

From this perspective, good data and software papers actually advance biology more than a specific finding.

I don't think this is entirely right but I feel like the field is trending in this direction.

But most senior people are really focused on papers as outputs and ignore software and data. This makes it hard for me to talk to them sometimes.

Ultimately, of course, insight and cures, for lack of a better word, are the rightful end products of basic research and biomedical science, respectively. So the question is how to get there faster.

Are papers the best way? Probably not.

Some side notes

I've been pretty happy with the way UC Davis handles merit and promotion, in that faculty in my department really get to explain what they're doing and why. It's not all about papers here, although of course for research-intensive profs that's still a major component.


This blog post was greatly inspired by conversations with Becca Calisi-Rodriguez and Tracy Teal, as well as (as always) the members of the DIB Lab. Thanks!! (I'm not implying that they agree with me, of course!)

I'm particularly indebted to Dr. Tamer Mansour, who, a year ago, said (paraphrased): "This lab is not a research lab. Mostly we train people, and do software engineering. Research is a distinct third." I disagree but it sure was hard to figure out why :)


by C. Titus Brown at February 15, 2018 09:20 AM

February 13, 2018

Matthieu Brucher

Announcement: Audio TK 2.3.0

ATK is updated to 2.3.0 with major fixes and code coverage improvement (see here). Lots of bugs were fixed during that effort and native build on embedded platforms was also fixed.

CMake builds on Linux don’t have to be installed before Python tests have to be ran. SIMD filters are now also easier to implement.

Download link: ATK 2.3.0

* Increased test coverage and fix lots of small mistakes in the API
* Allow in place Python tests (before make install) on Linux
* Split big files to allow native compilation on embedded platforms
* Fix a TDF2 IIR filter bug when the state was not reinitialized, leading to instabilities
* Fix a bug when delays were changed but not the underlying buffers, leading to buffer underflows
* Adding a new Broadcast filter (filling all SIMD vector lines with the same input value)
* Adding a new Reduce filter (summing all SIMD vector lines to the output value)
* Fix alignment issues in SIMD filters
* Fix SIMD EQ dispatcher export issues on Windows (too many possible filters!)
* Implemented relevant Tools SIMD filters

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by Matt at February 13, 2018 08:00 AM

February 12, 2018

Matthew Rocklin

Dask Release 0.17.0

This work is supported by Anaconda Inc. and the Data Driven Discovery Initiative from the Moore Foundation.

I’m pleased to announce the release of Dask version 0.17.0. This a significant major release with new features, breaking changes, and stability improvements. This blogpost outlines notable changes since the 0.16.0 release on November 21st.

You can conda install Dask:

conda install dask -c conda-forge

or pip install from PyPI:

pip install dask[complete] --upgrade

Full changelogs are available here:

Some notable changes follow.


  • Removed dask.dataframe.rolling_* methods, which were previously deprecated both in dask.dataframe and in pandas. These are replaced with the rolling.* namespace
  • We’ve generally stopped maintenance of the dask-ec2 project to launch dask clusters on Amazon’s EC2 using Salt. We generally recommend kubernetes instead both for Amazon’s EC2, and for Google and Azure as well

  • Internal state of the distributed scheduler has changed significantly. This may affect advanced users who were inspecting this state for debugging or diagnostics.

Task Ordering

As Dask encounters more complex problems from more domains we continually run into problems where its current heuristics do not perform optimally. This release includes a rewrite of our static task prioritization heuristics. This will improve Dask’s ability to traverse complex computations in a way that keeps memory use low.

To aid debugging we also integrated these heuristics into the GraphViz-style plots that come from the visualize method.

x = da.random.random(...)
x.visualize(color='order', cmap='RdBu')

Nested Joblib

Dask supports parallelizing Scikit-Learn by extending Scikit-Learn’s underlying library for parallelism, Joblib. This allows Dask to distribute some SKLearn algorithms across a cluster just by wrapping them with a context manager.

This relationship has been strengthened, and particular attention has been focused when nesting one parallel computation within another, such as occurs when you train a parallel estimator, like RandomForest, within another parallel computation, like GridSearchCV. Previously this would result in spawning too many threads/processes and generally oversubscribing hardware.

Due to recent combined development within both Joblib and Dask, these sorts of situations can now be resolved efficiently by handing them off to Dask, providing speedups even in single-machine cases:

from sklearn.externals import joblib
import distributed.joblib  # register the dask joblib backend

from dask.distributed import Client
client = Client()

est = ParallelEstimator()
gs = GridSearchCV(est)

with joblib.parallel_backend('dask'):

See Tom Augspurger’s recent post with more details about this work:

Thanks to Tom Augspurger, Jim Crist, and Olivier Grisel who did most of this work.

Scheduler Internal Refactor

The distributed scheduler has been significantly refactored to change it from a forest of dictionaries:

priority = {'a': 1, 'b': 2, 'c': 3}
dependencies = {'a': {'b'}, 'b': {'c'}, 'c': []}
nbytes = {'a': 1000, 'b': 1000, 'c': 28}

To a bunch of objects:

tasks = {'a': Task('a', priority=1, nbytes=1000, dependencies=...),
         'b': Task('b': priority=2, nbytes=1000, dependencies=...),
         'c': Task('c': priority=3, nbytes=28, dependencies=[])}

(there is much more state than what is listed above, but hopefully the examples above are clear.)

There were a few motivations for this:

  1. We wanted to try out Cython and PyPy, for which objects like this might be more effective than dictionaries.
  2. We believe that this is probably a bit easier for developers new to the schedulers to understand. The proliferation of state dictionaries was not highly discoverable.

Goal one ended up not working out. We have not yet been able to make the scheduler significantly faster under Cython or PyPy with this new layout. There is even a slight memory increase with these changes. However we have been happy with the results in code readability, and we hope that others find this useful as well.

Thanks to Antoine Pitrou, who did most of the work here.

User Priorities

You can now submit tasks with different priorities.

x = client.submit(f, 1, priority=10)   # Higher priority preferred
y = client.submit(f, 1, priority=-10)  # Lower priority happens later

To be clear, Dask has always had priorities, they just weren’t easily user-settable. Higher priorities are given precedence. The default priority for all tasks is zero. You can also submit priorities for collections (like arrays and dataframes)

df = df.persist(priority=5)  # give this computation higher priority.

Several related projects are also undergoing releases:

  • Tornado is updating to version 5.0 (there is a beta out now). This is a major change that will put Tornado on the Asyncio event loop in Python 3. It also includes many performance enhancements for high-bandwidth networks.
  • Bokeh 0.12.14 was just released.

    Note that you will need to update Dask to work with this version of Bokeh

  • Daskernetes, a new project for launching Dask on Kubernetes clusters


The following people contributed to the dask/dask repository since the 0.16.0 release on November 14th:

  • Albert DeFusco
  • Apostolos Vlachopoulos
  • castalheiro
  • James Bourbeau
  • Jon Mease
  • Ian Hopkinson
  • Jakub Nowacki
  • Jim Crist
  • John A Kirkham
  • Joseph Lin
  • Keisuke Fujii
  • Martijn Arts
  • Martin Durant
  • Matthew Rocklin
  • Markus Gonser
  • Nir
  • Rich Signell
  • Roman Yurchak
  • S. Andrew Sheppard
  • sephib
  • Stephan Hoyer
  • Tom Augspurger
  • Uwe L. Korn
  • Wei Ji
  • Xander Johnson

The following people contributed to the dask/distributed repository since the 1.20.0 release on November 14th:

  • Alexander Ford
  • Antoine Pitrou
  • Brett Naul
  • Brian Broll
  • Bruce Merry
  • Cornelius Riemenschneider
  • Daniel Li
  • Jim Crist
  • Kelvin Yang
  • Matthew Rocklin
  • Min RK
  • rqx
  • Russ Bubley
  • Scott Sievert
  • Tom Augspurger
  • Xander Johnson

February 12, 2018 12:00 AM

February 09, 2018

Continuum Analytics

Credit Modeling with Dask

I’ve been working with a large retail bank on their credit modeling system. We’re doing interesting work with Dask to manage complex computations (see task graph below) that I’d like to share. This is an example of using Dask for complex problems that are neither a big dataframe nor a big array, but are still …
Read more →

by Rory Merritt at February 09, 2018 03:56 PM

February 07, 2018

February 06, 2018

Continuum Analytics

Easy Distributed Training with Joblib and Dask

This past week, I had a chance to visit some of the scikit-learn developers at Inria in Paris. It was a fun and productive week, and I’m thankful to them for hosting me and Anaconda for sending me there. This article will talk about some changes we made to improve training scikit-learn models using a …
Read more →

by Rory Merritt at February 06, 2018 03:32 PM

Matthew Rocklin

HDF in the Cloud

Multi-dimensional data, such as is commonly stored in HDF and NetCDF formats, is difficult to access on traditional cloud storage platforms. This post outlines the situation, the following possible solutions, and their strengths and weaknesses.

  1. Cloud Optimized GeoTIFF: We can use modern and efficient formats from other domains, like Cloud Optimized GeoTIFF
  2. HDF + FUSE: Continue using HDF, but mount cloud object stores as a file system with FUSE
  3. HDF + Custom Reader: Continue using HDF, but teach it how to read from S3, GCS, ADL, …
  4. Build a Distributed Service: Allow others to serve this data behind a web API, built however they think best
  5. New Formats for Scientific Data: Design a new format, optimized for scientific data in the cloud

Not Tabular Data

If your data fits into a tabular format, such that you can use tools like SQL, Pandas, or Spark, then this post is not for you. You should consider Parquet, ORC, or any of a hundred other excellent formats or databases that are well designed for use on cloud storage technologies.

Multi-Dimensional Data

We’re talking about data that is multi-dimensional and regularly strided. This data often occurs in simulation output (like climate models), biomedical imaging (like an MRI scan), or needs to be efficiently accessed across a number of different dimensions (like many complex time series). Here is an image from the popular XArray library to put you in the right frame of mind:

This data is often stored in blocks such that, say, each 100x100x100 chunk of data is stored together, and can be accessed without reading through the rest of the file.

A few file formats allow this layout, the most popular of which is the HDF format, which has been the standard for decades and forms the basis for other scientific formats like NetCDF. HDF is a powerful and efficient format capable of handling both complex hierarchical data systems (filesystem-in-a-file) and efficiently blocked numeric arrays. Unfortunately HDF is difficult to access from cloud object stores (like S3), which presents a challenge to the scientific community.

The Opportunity and Challenge of Cloud Storage

The scientific community generates several petabytes of HDF data annually. Supercomputer simulations (like a large climate model) produce a few petabytes. Planned NASA satellite missions will produce hundreds of petabytes a year of observational data. All of these tend to be stored in HDF.

To increase access, institutions now place this data on the cloud. Hopefully this generates more social value from existing simulations and observations, as they are ideally now more accessible to any researcher or any company without coordination with the host institution.

Unfortunately, the layout of HDF files makes them difficult to query efficiently on cloud storage systems (like Amazon’s S3, Google’s GCS, or Microsoft’s ADL). The HDF format is complex and metadata is strewn throughout the file, so that a complex sequence of reads is required to reach a specific chunk of data. The only pragmatic way to read a chunk of data from an HDF file today is to use the existing HDF C library, which expects to receive a C FILE object, pointing to a normal file system (not a cloud object store) (this is not entirely true, as we’ll see below).

So organizations like NASA are dumping large amounts of HDF onto Amazon’s S3 that no one can actually read, except by downloading the entire file to their local hard drive, and then pulling out the particular bits that they need with the HDF library. This is inefficient. It misses out on the potential that cloud-hosted public data can offer to our society.

The rest of this post discusses a few of the options to solve this problem, including their advantages and disadvantages.

  1. Cloud Optimized GeoTIFF: We can use modern and efficient formats from other domains, like Cloud Optimized GeoTIFF

    Good: Fast, well established

    Bad: Not sophisticated enough to handle some scientific domains

  2. HDF + FUSE: Continue using HDF, but mount cloud object stores as a file system with Filesystem in Userspace, aka FUSE

    Good: Works with existing files, no changes to the HDF library necessary, useful in non-HDF contexts as well

    Bad: It’s complex, probably not performance-optimal, and has historically been brittle

  3. HDF + Custom Reader: Continue using HDF, but teach it how to read from S3, GCS, ADL, …

    Good: Works with existing files, no complex FUSE tricks

    Bad: Requires plugins to the HDF library and tweaks to downstream libraries (like Python wrappers). Will require effort to make performance optimal

  4. Build a Distributed Service: Allow others to serve this data behind a web API, built however they think best

    Good: Lets other groups think about this problem and evolve complex backend solutions while maintaining stable frontend API

    Bad: Complex to write and deploy. Probably not free. Introduces an intermediary between us and our data.

  5. New Formats for Scientific Data: Design a new format, optimized for scientific data in the cloud

    Good: Fast, intuitive, and modern

    Bad: Not a community standard

Now we discuss each option in more depth.

Use Other Formats, like Cloud Optimized GeoTIFF

We could use formats other than HDF and NetCDF that are already well established. The two that I hear most often proposed are Cloud Optimized GeoTIFF and Apache Parquet. Both are efficient, well designed for cloud storage, and well established as community standards. If you haven’t already, I strongly recommend reading Chris Holmes’ (Planet) blog series on Cloud Native Geospatial.

These formats are well designed for cloud storage because they support random access well with relatively few communications and with relatively simple code. If you needed to you could look at the Cloud Optimized GeoTIFF spec, and within an hour of reading, get an image that you wanted using nothing but a few curl commands. Metadata is in a clear centralized place. That metadata provides enough information to issue further commands to get the relevant bytes from the object store. Those bytes are stored in a format that is easily interpreted by a variety of common tools across all platforms.

However, neither of these formats are sufficiently expressive to handle some of the use cases of HDF and NetCDF. Recall our image earlier about atmospheric data:

Our data isn’t a parquet table, nor is it a stack of geo-images. While it’s true that you could store any data in these formats, for example by saving each horizontal slice as a GeoTIFF, or each spatial point as a row in a Parquet table, these storage layouts would be inefficient for regular access patterns. Some parts of the scientific community need blocked layouts for multi-dimensional array data.

HDF and Filesystems in Userspace (FUSE)

We could access HDF data on the cloud now if we were able to convince our operating system that S3 or GCS or ADL were a normal file system. This is a reasonable goal; cloud object stores look and operate much like normal file systems. They have directories that you can list and navigate. They have files/objects that you can copy, move, rename, and from which you can read or write small sections.

We can achieve this using an operating systems trick, FUSE, or Filesystem in Userspace. This allows us to make a program that the operating system treats as a normal file system. Several groups have already done this for a variety of cloud providers. Here is an example with the gcsfs Python library

$ pip install gcsfs --upgrade
$ mkdir /gcs
$ gcsfuse bucket-name /gcs --background
Mounting bucket bucket-name to directory /gcs

$ ls /gcs

Now we point our HDF library to a NetCDF file in that directory (which actually points to an object on Google Cloud Storage), and it happily uses C File objects to read and write data. The operating system passes the read/write requests to gcsfs, which goes out to the cloud to get data, and then hands it back to the operating system, which hands it to HDF. All normal HDF operations just work, although they may now be significantly slower. The cloud is further away than local disk.

This slowdown is significant because the HDF library makes many small 4kB reads in order to gather the metadata necessary to pull out a chunk of data. Each of those tiny reads made sense when the data was local, but now that we’re sending out a web request each time. This means that users can sit for minutes just to open a file.

Fortunately, we can be clever. By buffering and caching data, we can reduce the number of web requests. For example, when asked to download 4kB we actually download 100kB or 1MB. If some of the future 4kB reads are within this 1MB then we can return them immediately., Looking at HDF traces it looks like we can probably reduce “dozens” of web requests to “a few”.

FUSE also requires elevated operating system permissions, which can introduce challenges if working from Docker containers (which is likely on the cloud). Docker containers running FUSE need to be running in privileged mode. There are some tricks around this, but generally FUSE brings some excess baggage.

HDF and a Custom Reader

The HDF library doesn’t need to use C File pointers, we can extend it to use other storage backends as well. Virtual File Layers are an extension mechanism within HDF5 that could allow it to target cloud object stores. This has already been done to support Amazon’s S3 object store twice:

  1. Once by the HDF group, S3VFD (currently private),
  2. Once by Joe Jevnik and Scott Sanderson (Quantopian) at (highly experimental)

This provides an alternative to FUSE that is better because it doesn’t require privileged access, but is worse because it only solves this problem for HDF and not all file access.

In either case we’ll need to do look-ahead buffering and caching to get reasonable performance (or see below).

Centralize Metadata

Alternatively, we might centralize metadata in the HDF file in order to avoid many hops throughout that file. This would remove the need to perform clever file-system caching and buffering tricks.

Here is a brief technical explanation from Joe Jevnik:

Regarding the centralization of metadata: this is already a feature of hdf5 and is used by many of the built-in drivers. This optimization is enabled by setting the H5FD_FEAT_AGGREGATE_METADATA and H5FD_FEAT_ACCUMULATE_METADATA feature flags in your VFL driver’s query function. The first flag says that the hdf5 library should pre-allocate a large region to store metadata, future metadata allocations will be served from this pool. The second flag says that the library should buffer metadata changes into large chunks before dispatching the VFL driver’s write function. Both the default driver (sec2) and h5s3 enable these optimizations. This is further supported by using the H5FD_FLMAP_DICHOTOMY free list option which uses one free list for metadata allocations and another for non-metadata allocations. If you really want to ensure that the metadata is aggregated, even without a repack, you can use the built-in ‘multi’ driver which dispatches different allocation flavors to their own driver.

Distributed Service

We could offload this problem to a company, like the non-profit HDF group or a for-profit cloud provider like Google, Amazon, or Microsoft. They would solve this problem however they like, and expose a web API that we can hit for our data.

This would be a distributed service of several computers on the cloud near our data, that takes our requests for what data we want, perform whatever tricks they deem appropriate to get that data, and then deliver it to us. This fleet of machines will still have to address the problems listed above, but we can let them figure it out, and presumably they’ll learn as they go.

However, this has both technical and social costs. Technically this is complex, and they’ll have to handle a new set of issues around scalability, consistency, and so on that are already solved(ish) in the cloud object stores. Socially this creates an intermediary between us and our data, which we may not want both for reasons of cost and trust.

The HDF group is working on such a service, HSDS that works on Amazon’s S3 (or anything that looks like S3). They have created a h5pyd library that is a drop-in replacement for the popular h5py Python library.

Presumably a cloud provider, like Amazon, Google, or Microsoft could do this as well. By providing open standards like OpenDAP they might attract more science users onto their platform to more efficiently query their cloud-hosted datasets.

The satellite imagery company Planet already has such a service.

New Formats for Scientific Data

Alternatively, we can move on from the HDF file format, and invent a new data storage specification that fits cloud storage (or other storage) more cleanly without worrying about supporting the legacy layout of existing HDF files.

This has already been going on, informally, for years. Most often we see people break large arrays into blocks, store each block as a separate object in the cloud object store with a suggestive name, and store a metadata file describing how the blocks relate to each other. This looks something like the following:


There are many variants:

  • They might extend this to have groups or sub-arrays in sub-directories.
  • They might choose to compress the individual blocks in the .dat files or not.
  • They might choose different encoding schemes for the metadata and the binary blobs.

But generally most array people on the cloud do something like this with their research data, and they’ve been doing it for years. It works efficiently, is easy to understand and manage, and transfers well to any cloud platform, onto a local file system, or even into a standalone zip file or small database.

There are two groups that have done this in a more mature way, defining both modular standalone libraries to manage their data, as well as proper specification documents that inform others how to interpret this data in a long-term stable way.

These are both well maintained and well designed libraries (by my judgment), in Python and Java respectively. They offer layouts like the layout above, although with more sophistication. Entertainingly their specs are similar enough that another library, Z5, built a cross-compatible parser for each in C++. This unintended uniformity is a good sign. It means that both developer groups were avoiding creativity, and have converged on a sensible common solution. I encourage you to read the Zarr Spec in particular.

However, technical merits are not alone sufficient to justify a shift in data format, especially for archival datasets of record that we’re discussing. The institutions in charge of this data and have multi-decade horizons and so move slowly. For them, moving off of the historically community standard would be major shift.

And so we need to answer a couple of difficult questions:

  1. How hard is it to make HDF efficient in the cloud?
  2. How hard is it to shift the community to a new standard?

A Sense of Urgency

These questions are important now. NASA and other agencies are pushing NetCDF data into the Cloud today and will be increasing these rates substantially in the coming years.

From (via Ryan Abernathey)

From its current cumulative archive size of almost 22 petabytes (PB), the volume of data in the EOSDIS archive is expected to grow to almost 247 PB by 2025, according to estimates by NASA’s Earth Science Data Systems (ESDS) Program. Over the next five years, the daily ingest rate of data into the EOSDIS archive is expected to reach 114 terabytes (TB) per day, with the upcoming joint NASA/Indian Space Research Organization Synthetic Aperture Radar (NISAR) mission (scheduled for launch by 2021) contributing an estimated 86 TB per day of data when operational.

This is only one example of many agencies in many domains pushing scientific data to the cloud.


Thanks to Joe Jevnik (Quantopian), John Readey (HDF Group), Rich Signell (USGS), and Ryan Abernathey (Columbia University) for their feedback when writing this article. This conversation started within the Pangeo collaboration.

February 06, 2018 12:00 AM

February 02, 2018

Continuum Analytics

The Case for Numba in Community Code

The numeric Python community should consider adopting Numba more widely within community code. Numba is strong in performance and usability, but historically weak in ease of installation and community trust. This blog post discusses these strengths and weaknesses from the perspective of an OSS library maintainer. It uses other more technical blog posts written on …
Read more →

by Rory Merritt at February 02, 2018 03:17 PM

February 01, 2018

Prabhu Ramachandran

VTK-8.1.0 wheels for all platforms on pypi!

I cannot believe it has been 6 years since my last blog post!  Anyway, I have some good news to announce here.

In the Python community, VTK has always been somewhat difficult to install (in comparison to pure Python packages). One has required to either use a specific package management tool or resort to source builds. This has been a major problem when trying to install tools that rely on VTK, like Mayavi.

During the SciPy 2017 conference held at Austin last year, a few of the Kitware developers, notably Jean-Christophe Fillion-Robin  (JC for short) and some of the VTK developers got together with some of us from the SciPy community and decided to try and put together wheels for VTK.

JC did the hard work of figuring this out and setting up a nice VTKPythonPackage during the sprints to make this process easy. As of last week (Jan 27, 2018) Mac OS X wheels were not supported. Last weekend, I finally got the time (thanks to Enthought) to play with JC's work. I figured out how to get the wheels working on OS X. With this, in principle, we could build VTK wheels on all the major platforms.

We decided to try and push wheels at least for the major VTK releases. This in itself would be a massive improvement in making VTK easier to install. Over the last few days, I have built wheels on Linux, OS X, and Windows. All of these are 64 bit wheels for VTK-8.1.0.

Now, VTK 8.x adds a c++11 dependency, and so we cannot build these versions of VTK for Python 2.7 on Windows.

So now we have 64 bit wheels on Windows for Python versions 3.5.x and 3.6.x.
Unfortunately, 3.4.x required a different Visual Studio installed and I lost patience setting things up on my Windows VM.

On Linux, we have 64 bit wheels for Python 2.7.x, 3.4.x, 3.5.x, and 3.6.x.

On MacOS, we have 64 bit wheels for Python 2.7.x, 3.4.x, 3.5.x, and 3.6.x.

So if you are using a 64 bit Python, you can now do

   $ pip install vtk

and have VTK-8.1.0 installed!

This is really nice to have and should hopefully make VTK and other tools a lot easier to install.

A big thank you to JC, the other Kitware developers, the VTK Python developers, especially David Gobbi who has worked on the VTK Python wrappers for many many years now,  for making this happen. Apologies if I missed anyone but thank you all!


by Prabhu Ramachandran ( at February 01, 2018 12:18 AM

January 30, 2018

Matthieu Brucher

Book review: Python for Finance: Analyze Big Financial Data

Recently, I moved to the finance industry. As usually when I start in a new domain, I look at the Python books for it. And Python for Finance from Yves Hilpisch is one of the most known ones.


The book is split in 3 unequal parts. The first one is short and presents the usage of Python in the finance industry, how to install it and a few example of its usage for finance. The Python code is quite simple, strangely the author decided to go for global variables and almost no parameters. Why not presenting classes here? At least he uses examples through IPython/Jupyter, so that’s good!

The second part tackles finance applications to Python and the useful modules. Obviously, the first chapter here handles Numpy. I liked the fact that vectorization is an important part here (not using explicit loops). Then of course an important point is visualizing plot, and especially time series. The third chapter tackles pandas, a library that was originally written for finance analysis, so obviously it has to be used!

Strangely, the chapter after that one is about reading and writing data. I’m not really sure it is worth spending so much time on some functions that are already in numpy and pandas. I agree that I/O is important, but I’m not sure it deserves so much space in a Python book. Or even talk about SQL.

The next chapter tackles performance in Python. The author compares different ways of make your code faster. I liked the IPython example, as lots of people would work from Jupyter with several available cores behind. The multiprocessing module is nice, but can sometimes be… awkward to use. Not sure that the NumbaPro example was useful, as not many people will be able to use them (I felt this was more an ad than actually useful pages).

After this chapter, we are back to math tools for finance. The strange part is that the previous chapter may not really be used for this chapter. Not many algorithms can be efficiently parallelized when they come out of available packages (except when they are meant for this like sklearn pipeline model). So the chapter here will talk about regression (one of the main tool to understand a trend in time series; although the prediction may be completely bogus), interpolation or optimization. The latter one is what you need for lots of models. Later in the chapter, symbolic computation is also introduced, and I have to say that if you know an analytical approach to a problem, then this is quite effective (I always take a similar route for my electronic models).

The tenth chapter dives into the core of finance maths with stochastic equations (and the Black-Scholes one!). Of course here, it’s basically using random number generators, and then applying some rules on top of them. The chapter after that handles puts several of the previous topics together, like normality test for stats, or portfolio optimization for… optimization. There is a part on PCA, but I’m biased, I hate PCA since lots of people use it for dimensionality reduction on data that is not Euclidian…

There is also a chapter on Excel, probably because lots of people use it to analyze data, and you need to be able to exchange data with it. I guess.

And then, the chapter where the author finally tackles classes!! Really!! And by saying that it’s an important aspect of Python. That’s what I don’t understand. Especially the way it’s presented. The part with traits is OK, although the online tutorials are just as good.

Then, there is a chapter on web apps, not sure exactly why there is, to be franc.

After this part with ups and down, there is a part on creating a derivative library. This is the part where there is some real finance computation, although the author refers back to his other book for the theory itself. The chapters are quite small and try to wrap everything from the previous part in a unique framework.

I just wish this integration was done in the second part instead.


So basically the content of the book is on some kind of Python. If you don’t know about finance, you want to know much more at the end of this book. But if want to learn about Python, you will know about modules, but actually not about good Python.

So unfortunately, avoid.

by Matt at January 30, 2018 08:39 AM

Matthew Rocklin

The Case for Numba in Community Code

The numeric Python community should consider adopting Numba more widely within community code.

Numba is strong in performance and usability, but historically weak in ease of installation and community trust. This blogpost discusses these these strengths and weaknesses from the perspective of a OSS library maintainer. It uses other more technical blogposts written on the topic as references. It is biased in favor of wider adoption given recent changes to the project.

Let’s start with a wildly unprophetic quote from Jake Vanderplas in 2013:

I’m becoming more and more convinced that Numba is the future of fast scientific computing in Python.

– Jake Vanderplas, 2013-06-15

We’ll use the following blogposts by other community members throughout this post. They’re all good reads and are more technical, showing code examples, performance numbers, etc..

At the end of the blogpost these authors will also share some thoughts on Numba today, looking back with some hindsight.

Disclaimer: I work alongside many of the Numba developers within the same company and am partially funded through the same granting institution.

Compiled code in Python

Many open source numeric Python libraries need to write efficient low-level code that works well on Numpy arrays, but is more complex than the Numpy library itself can express. Typically they use one of the following options:

  1. C-extensions: mostly older projects like NumPy and Scipy
  2. Cython: probably the current standard for mainline projects, like scikit-learn, pandas, scikit-image, geopandas, and so on
  3. Standalone C/C++ codebases with Python wrappers: for newer projects that target inter-language operation, like XTensor and Arrow

Each of these choices has tradeoffs in performance, packaging, attracting new developers and so on. Ideally we want a solution that is …

  1. Fast: about as fast as C/Fortran
  2. Easy: Is accessible to a broad base of developers and maintainers
  3. Builds easily: Introduces few complications in building and packaging
  4. Installs easily: Introduces few install and runtime dependencies
  5. Trustworthy: Is well trusted within the community, both in terms of governance and long term maintenance

The two main approaches today, Cython and C/C++, both do well on most of these objectives. However neither is perfect. Some issues that arise include the following:

  • Cython
    • Often requires effort to make fast
    • Is often only used by core developers. Requires expertise to use well.
    • Introduces mild packaging pain, though this pain is solved frequently enough that experienced community members are used to dealing with it
  • Standalone C/C++
    • Sometimes introduces complex build and packaging concerns
    • Is often only used by core developers. These projects have difficulty attracting the Python community’s standard developer pool (though they do attract developers from other communities).

There are some other options out there like Numba and Pythran that, while they provide excellent performance and usability benefits, are rarely used. Let’s look into Numba’s benefits and drawbacks more closely.

Numba Benefits

Numba is generally well regarded from a technical perspective (it’s fast, easy to use, well maintained, etc.) but has historically not been trusted due to packaging and community concerns.

In any test of either performance or usability Numba almost always wins (or ties for the win). It does all of the compiler optimization tricks you expect. It supports both for-loopy code as well as Numpy-style slicing and bulk operation code. It requires almost no additional information from the user (assuming that you’re ok with JIT behavior) and so is very approachable, and very easy for novices to use well.

This means that we get phrases like the following:

    • “This is rightaway faster than NumPy.”
    • “In fact, we can infer from this that numba managed to generate pure C code from our function and that it did it already previously.”
    • “Numba delivered the best performance on this problem, while still being easy to use.”
    • “Using numba is very simple; just apply the jit decorator to the function you want to get compiled. In this case, the function code is exactly the same as before”
    • “Wow! A speedup by a factor of about 400, just by applying a decorator to the function. “
    • “Much better! We’re now within about a factor of 3 of the Fortran speed, and we’re still writing pure Python!”
    • “I should emphasize here that I have years of experience with Cython, and in this function I’ve used every Cython optimization there is … By comparison, the Numba version is a simple, unadorned wrapper around plainly-written Python code.”
    • Numba is extremely simple to use. We just wrap our python function with autojit (JIT stands for “just in time” compilation) to automatically create an efficient, compiled version of the function
    • Adding this simple expression speeds up our execution by over a factor of over 1400! For those keeping track, this is about 50% faster than the version of Numba that I tested last August on the same machine.
    • The Cython version, despite all the optimization, is a few percent slower than the result of the simple Numba decorator!
    • “Using Numba is usually about as simple as adding a decorator to your functions”
    • “Numba is usually easier to write for the simple cases where it works”
    • “Numba allows for speedups comparable to most compiled languages with almost no effort”
    • “We find that Numba is more than 100 times as fast as basic Python for this application. In fact, using a straight conversion of the basic Python code to C++ is slower than Numba.”

In all cases where authors compared Numba to Cython for numeric code (Cython is probably the standard for these cases) Numba always performs as-well-or-better and is always much simpler to write.

Here is a code example from Jake’s second blogpost:

Example: Code Complexity

# From

# Numba                                 # Cython
import numpy as np                      import numpy as np
import numba                            cimport cython
                                        from libc.math cimport sqrt

@numba.jit                              @cython.wraparound(False)
def pairwise_python(X):                 def pairwise_cython(double[:, ::1] X):
    M = X.shape[0]                          cdef int M = X.shape[0]
    N = X.shape[1]                          cdef int N = X.shape[1]
                                            cdef double tmp, d
    D = np.empty((M, M), dtype=np.float)    cdef double[:, ::1] D = np.empty((M, M),
    for i in range(M):                      for i in range(M):
        for j in range(M):                      for j in range(M):
            d = 0.0                                 d = 0.0
            for k in range(N):                      for k in range(N):
                tmp = X[i, k] - X[j, k]                 tmp = X[i, k] - X[j, k]
                d += tmp * tmp                          d += tmp * tmp
            D[i, j] = np.sqrt(d)                    D[i, j] = sqrt(d)
    return D                                return np.asarray(D)

The algorithmic body of each function (the nested for loops) is identical. However the Cython code is more verbose with annotations, both at the function definition (which we would expect for any AOT compiler), but also within the body of the function for various utility variables. The Numba code is just straight Python + Numpy code. We could remove the @numba.jit decorator and step through our function with normal Python.

Example: Numpy Operations

Additionally Numba lets us use Numpy syntax directly in the function, so for example the following function is well accelerated by Numba, even though it already fits NumPy’s syntax well.

# from

def laplace_numba(image):
    """Laplace operator in NumPy for 2D images. Accelerated using numba."""
    laplacian = ( image[:-2, 1:-1] + image[2:, 1:-1]
                + image[1:-1, :-2] + image[1:-1, 2:]
                - 4*image[1:-1, 1:-1])
    thresh = np.abs(laplacian) > 0.05
    return thresh

Mixing and matching Numpy-style with for-loop style is often helpful when writing complex numeric algorithms.

Benchmarks in the these blogposts show that Numba is both simpler to use and often as-fast-or-faster than more commonly used technologies like Cython.

Numba drawbacks

So, given these advantages why didn’t Jake’s original prophecy hold true?

I believe that there are three primary reasons why Numba has not been more widely adopted among other open source projects:

  1. LLVM Dependency: Numba depends on LLVM, which was historically difficult to install without a system package manager (like apt-get, brew) or conda. Library authors are not willing to exclude users that use other packaging toolchains, particularly Python’s standard tool, pip.
  2. Community Trust: Numba is largely developed within a single for-profit company (Anaconda Inc.) and its developers are not well known by other library maintainers.
  3. Lack of Interpretability: Numba’s output, LLVM, is less well understood by the community than Cython’s output, C (discussed in original-author comments in the last section)

All three of these are excellent reasons to avoid adding a dependency. Technical excellence alone is insufficient, and must be considered alongside community and long-term maintenance concerns.

But Numba has evolved recently


Numba now depends on the easier-to-install library, llvmlite which, as of a few months ago is pip installable with binary wheels on Windows, Mac, and Linux. The llvmlite package is still a heavy-ish runtime dependency (42MB), but that’s significantly less than large Cython libraries like Pandas or SciPy.

If your concern was about the average user’s inability to install Numba, then I think that this concern has been resolved.


Numba has three community problems:

  1. Development of Numba has traditionally happened within the closed walls of Anaconda Inc (formerly Continuum Analytics)
  2. The Numba maintainers are not well known within the broader Python community
  3. There used to be a proprietary version, Numba Pro

This combination strongly attached Numba’s image to Continuum’s for-profit ventures, making community-oriented software maintainers understandably wary of dependence, for fear that dependence on this library might be used for Continuum’s financial gain at the expense of community users.

Things have changed significantly.

Numba Pro was abolished years ago. The funding for the project today comes more often from Anaconda Inc. consulting revenue, hardware vendors looking to ensure that Python runs as efficiently as possible on their systems, and from generous donations from the Gordon and Betty Moore foundation to ensure that Numba serves the open source Python community.

Developers outside of Anaconda Inc. now have core commit access, which forces communication to happen in public channels, notably GitHub (which was standard before) and Gitter chat (which is relatively new).

The maintainers are still fairly relatively unknown within the broader community. This isn’t due to any sort of conspiracy, but is instead due more to shyness or having interests outside of OSS. Antoine, Siu, Stan, and Stuart are all considerate, funny, and clever fellows with strong enthusiasm for compilers, OSS, and performance. They are quite responsive on the Numba mailing list should you have any questions or concerns.

If your concern was about Numba trapping users into a for-profit mode, then that seems to have been resolved years ago.

If your concern is more about not knowing who is behind the project then I encourage you to reach out. I would be surprised if you don’t walk away pleased.

The Continued Cases Against Numba

For completeness, let’s list a number of reasons why it is still quite reasonable to avoid Numba today:

  1. It isn’t a community standard
  2. Numba hasn’t attracted a wide developer base (compilers are hard), and so is probably still dependent on financial support for paid developers
  3. You want to speed up non-numeric code that includes classes, dicts, lists, etc. for which I need Cython or PyPy
  4. You want to build a library that is useful outside of Python, and so plan to build most numeric algorithms on C/C++/Fortran
  5. You prefer ahead-of-time compilation and want to avoid JIT times
  6. While llvmlite is cheaper than LLVM, it’s still 50MB
  7. Understanding the compiled results is hard, and you don’t have good familiarity with LLVM

Numba features we didn’t talk about

  1. Multi-core parallelism
  2. GPUs
  3. Run-time Specialization to the CPU you’re running on
  4. Easy to swap out for other JIT compilers, like PyPy, if they arise in the future

Update from the original blogpost authors

After writing the above I reached out both to Stan and Siu from Numba and to the original authors of the referenced blogposts to get some of their impressions now having the benefit of additional experience.

Here are a few choice responses:

  1. Stan:

    I think one of the biggest arguments against Numba still is time. Due to a massive rewrite of the code base, Numba, in its present form, is ~3 years old, which isn’t that old for a project like this. I think it took PyPy at least 5-7 years to reach a point where it was stable enough to really trust. Cython is 10 years old. People have good reason to be conservative with taking on new core dependencies.

  2. Jake:

    One thing I think would be worth fleshing-out a bit (you mention it in the final bullet list) is the fact that numba is kind of a black box from the perspective of the developer. My experience is that it works well for straightforward applications, but when it doesn’t work well it’s *extremely difficult to diagnose what the problem might be.*

    Contrast that with Cython, where the html annotation output does wonders for understanding your bottlenecks both at a non-technical level (“this is dark yellow so I should do something different”) and a technical level (“let me look at the C code that was generated”). If there’s a similar tool for numba, I haven’t seen it.

  • Florian:

    Elaborating on Jake’s answer, I completely agree that Cython’s annotation tool does wonders in terms of understanding your code. In fact, numba does possess this too, but as a command-line utility. I tried to demonstrate this in my blogpost, but exporting the CSS in the final HTML render kind of mangles my blog post so here’s a screenshot:

    Numba HTML annotations

    This is a case where jit(nopython=True) works, so there seems to be no coloring at all.

    Florian also pointed to the SciPy 2017 tutorial by Gil Forsyth and Lorena Barba

  • Dion:

    I hold Numba in high regard, and the speedups impress me every time. I use it quite often to optimize some bottlenecks in our production code or data analysis pipelines (unfortunately not open source). And I love how Numba makes some functions like scipy.optimize.minimize or scipy.ndimage.generic_filter well-usable with minimal effort.

    However, I would never use Numba to build larger systems, precisely for the reason Jake mentioned. Subjectively, Numba feels hard to debug, has cryptic error messages, and seemingly inconsistent behavior. It is not a “decorate and forget” solution; instead it always involves plenty of fiddling to get right.

    That being said, if I were to build some high-level scientific library à la Astropy with some few performance bottlenecks, I would definitely favor Numba over Cython (and if it’s just to spare myself the headache of getting a working C compiler on Windows).

  • Stephan:

    I wonder if there are any examples of complex codebases (say >1000 LOC) using Numba. My sense is that this is where Numba’s limitations will start to become more evident, but maybe newer features like jitclass would make this feasible.

As a final take-away, you might want to follow Florian’s advice and watch Gil and Lorena’s tutorial here:

January 30, 2018 12:00 AM

January 27, 2018

Matthew Rocklin

Write Dumb Code

The best way you can contribute to an open source project is to remove lines of code from it. We should endeavor to write code that a novice programmer can easily understand without explanation or that a maintainer can understand without significant time investment.

As students we attempt increasingly challenging problems with increasingly sophisticated technologies. We first learn loops, then functions, then classes, etc.. We are praised as we ascend this hierarchy, writing longer programs with more advanced technology. We learn that experienced programmers use monads while new programmers use for loops.

Then we graduate and find a job or open source project to work on with others. We search for something that we can add, and implement a solution pridefully, using the all the tricks that we learned in school.

Ah ha! I can extend this project to do X! And I can use inheritance here! Excellent!

We implement this feature and feel accomplished, and with good reason. Programming in real systems is no small accomplishment. This was certainly my experience. I was excited to write code and proud that I could show off all of the things that I knew how to do to the world. As evidence of my historical love of programming technology, here is a linear algebra language built with a another meta-programming language. Notice that no one has touched this code in several years.

However after maintaining code a bit more I now think somewhat differently.

  1. We should not seek to build software. Software is the currency that we pay to solve problems, which is our actual goal. We should endeavor to build as little software as possible to solve our problems.
  2. We should use technologies that are as simple as possible, so that as many people as possible can use and extend them without needing to understand our advanced techniques. We should use advanced techniques only when we are not smart enough to figure out how to use more common techniques.

Neither of these points are novel. Most people I meet agree with them to some extent, but somehow we forget them when we go to contribute to a new project. The instinct to contribute by building and to demonstrate sophistication often take over.

Software is a cost

Every line that you write costs people time. It costs you time to write it of course, but you are willing to make this personal sacrifice. However this code also costs the reviewers their time to understand it. It costs future maintainers and developers their time as they fix and modify your code. They could be spending this time outside in the sunshine or with their family.

So when you add code to a project you should feel meek. It should feel as though you are eating with your family and there isn’t enough food on the table. You should take only what you need and no more. The people with you will respect you for your efforts to restrict yourself. Solving problems with less code is a hard, but it is a burden that you take on yourself to lighten the burdens of others.

Complex technologies are harder to maintain

As students, we demonstrate merit by using increasingly advanced technologies. Our measure of worth depends on our ability to use functions, then classes, then higher order functions, then monads, etc. in public projects. We show off our solutions to our peers and feel pride or shame according to our sophistication.

However when working with a team to solve problems in the world the situation is reversed. Now we strive to solve problems with code that is as simple as possible. When we solve a problem simply we enable junior programmers to extend our solution to solve other problems. Simple code enables others and boosts our impact. We demonstrate our value by solving hard problems with only basic techniques.

Look! I replaced this recursive function with a for loop and it still does everything that we need it to. I know it’s not as clever, but I noticed that the interns were having trouble with it and I thought that this change might help.

If you are a good programmer then you don’t need to demonstrate that you know cool tricks. Instead, you can demonstrate your value by solving a problem in a simple way that enables everyone on your team to contribute in the future.

But moderation, of course

That being said, over-adherence to the “build things with simple tools” dogma can be counter productive. Often a recursive solution can be much simpler than a for-loop solution and often times using a Class or a Monad is the right approach. But we should be mindful when using these technologies that we are building for ourselves our own system; a system with which others have had no experience.

January 27, 2018 12:00 AM

January 24, 2018

Bruno Pinho

Fast and Reliable Top of Atmosphere (TOA) calculations of Landsat-8 data in Python

How to efficiently extract reflectance information from Landsat-8 Level-1 Data Product images.

by Bruno Ruas de Pinho at January 24, 2018 04:01 PM

January 22, 2018

Pierre de Buyl

Testing a NumPy-based code on Travis with plain pip and wheels

Installing the scientific Python stack is not the most obvious task in a scientist's routine. This is especially annoying for automated deployments such as for continuous integration testing. I present here a short way to deploy Travis CI testing for a small library that depends only on NumPy.

The goal

I developed a small library that relies only on Python and NumPy, as a design requirement. I wanted a simple pip-based deployment of my Python package testing via continuous integration, including the version of NumPy of my choice and with no rebuild of NumPy.

I started by performing the tests on my machines, simply issuing python -m pytest when changing the code. This is a limitation, mostly because I am limited to a few Python/NumPy versions.

How to set up Travis

Travis has instructions and support for Python-based projects. The typical "SciPy stack" is not covered (except for one version of NumPy that ships with their images), so most Python-based scientific software downloads Anaconda or Miniconda as part of their continuous integration testing, getting access to plently of binary packages.

I have no specific argument against the conda solution apart that it is a large dependency in terms of download size, and that I believe "plain pip" is the most general solution for Python and I like to stick to it.

So, I set up Travis with a test matrix for Python 2.7, 3.5 and 3.6. I wanted to test several NumPy versions as well. I couldn't find a lightweight solution (i.e. a nice sample .travis.yml file) as most projects use (ana/mini)conda. Since the arrival of manylinux wheels, it is actually easy to rely on "plain pip" to install NumPy on Travis. Make sure to update pip itself first and to install "wheels" as well.

The timing of the build on travis is between 30 and 80s, so there is obviously no build of NumPy occurring there and this is a reasonable use of resources.

In the example, I exclude NumPy 1.11.0 from the Python 3.6 test because there are no "Python 3.6 NumPy 1.11.0" manylinux wheels.

language: python

 - 2.7
 - 3.5
 - 3.6

  - NUMPY_VERSION=1.11.0
  - NUMPY_VERSION=1.12.1
  - NUMPY_VERSION=1.14.0

    - python: 3.6
      env: NUMPY_VERSION=1.11.0

  - virtualenv --python=python venv
  - source venv/bin/activate
  - python -m pip install -U pip
  - pip install -U wheel
  - pip install numpy==$NUMPY_VERSION
  - pip install pytest
  - python build
  - python -m pytest


I hope that this solution will be useful to others. If you want to see the repository itself, it is here (with a badge to the travis-ci builds).

The resulting .travis.yml file is really short, which is (in my opinion) a benefit. As SciPy also provides manylinux wheels, this is really a powerful and easy way to deploy. Any scientific package that depends on NumPy/SciPy can use it and add a build of the compiled package with, for instance, an extra dependency on GCC or Cython.

by Pierre de Buyl at January 22, 2018 03:00 PM

Matthew Rocklin

Pangeo: JupyterHub, Dask, and XArray on the Cloud

This work is supported by Anaconda Inc, the NSF EarthCube program, and UC Berkeley BIDS

A few weeks ago a few of us stood up, an experimental deployment of JupyterHub, Dask, and XArray on Google Container Engine (GKE) to support atmospheric and oceanographic data analysis on large datasets. This follows on recent work to deploy Dask and XArray for the same workloads on super computers. This system is a proof of concept that has taught us a great deal about how to move forward. This blogpost briefly describes the problem, the system, then describes the collaboration, and finally discusses a number of challenges that we’ll be working on in coming months.

The Problem

Atmospheric and oceanographic sciences collect (with satellites) and generate (with simulations) large datasets that they would like to analyze with distributed systems. Libraries like Dask and XArray already solve this problem computationally if scientists have their own clusters, but we seek to expand access by deploying on cloud-based systems. We build a system to which people can log in, get Jupyter Notebooks, and launch Dask clusters without much hassle. We hope that this increases access, and connects more scientists with more cloud-based datasets.

The System

We integrate several pre-existing technologies to build a system where people can log in, get access to a Jupyter notebook, launch distributed compute clusters using Dask, and analyze large datasets stored in the cloud. They have a full user environment available to them through a website, can leverage thousands of cores for computation, and use existing APIs and workflows that look familiar to how they work on their laptop.

A video walk-through follows below:

We assembled this system from a number of pieces and technologies:

  • JupyterHub: Provides both the ability to launch single-user notebook servers and handles user management for us. In particular we use the KubeSpawner and the excellent documentation at Zero to JupyterHub, which we recommend to anyone interested in this area.
  • KubeSpawner: A JupyterHub spawner that makes it easy to launch single-user notebook servers on Kubernetes systems
  • JupyterLab: The newer version of the classic notebook, which we use to provide a richer remote user interface, complete with terminals, file management, and more.
  • XArray: Provides computation on NetCDF-style data. XArray extends NumPy and Pandas to enable scientists to express complex computations on complex datasets in ways that they find intuitive.
  • Dask: Provides the parallel computation behind XArray
  • Daskernetes: Makes it easy to launch Dask clusters on Kubernetes
  • Kubernetes: In case it’s not already clear, all of this is based on Kubernetes, which manages launching programs (like Jupyter notebook servers or Dask workers) on different machines, while handling load balancing, permissions, and so on
  • Google Container Engine: Google’s managed Kubernetes service. Every major cloud provider now has such a system, which makes us happy about not relying too heavily on one system
  • GCSFS: A Python library providing intuitive access to Google Cloud Storage, either through Python file interfaces or through a FUSE file system
  • Zarr: A chunked array storage format that is suitable for the cloud


We were able to build, deploy, and use this system to answer real science questions in a couple weeks. We feel that this result is significant in its own right, and is largely because we collaborated widely. This project required the expertise of several individuals across several projects, institutions, and funding sources. Here are a few examples of who did what from which organization. We list institutions and positions mostly to show the roles involved.

  • Alistair Miles, Professor, Oxford: Helped to optimize Zarr for XArray on GCS
  • Jacob Tomlinson, Staff, UK Met Informatics Lab: Developed original JADE deployment and early Dask-Kubernetes work.
  • Joe Hamman, Postdoc, National Center for Atmospheric Research: Provided scientific use case, data, and work flow. Tuned XArray and Zarr for efficient data storing and saving.
  • Martin Durant, Software developer, Anaconda Inc.: Tuned GCSFS for many-access workloads. Also provided FUSE system for NetCDF support
  • Matt Pryor, Staff, Centre for Envronmental Data Analysis: Extended original JADE deployment and early Dask-Kubernetes work.
  • Matthew Rocklin, Software Developer, Anaconda Inc. Integration. Also performance testing.
  • Ryan Abernathey, Assistant Professor, Columbia University: XArray + Zarr support, scientific use cases, coordination
  • Stephan Hoyer, Software engineer, Google: XArray support
  • Yuvi Panda, Staff, UC Berkeley BIDS and Data Science Education Program: Provided assistance configuring JupyterHub with KubeSpawner. Also prototyped the Daskernetes Dask + Kubernetes tool.

Notice the mix of academic and for-profit institutions. Also notice the mix of scientists, staff, and professional software developers. We believe that this mixture helps ensure the efficient construction of useful solutions.


This experiment has taught us a few things that we hope to explore further:

  1. Users can launch Kubernetes deployments from Kubernetes pods, such as launching Dask clusters from their JupyterHub single-user notebooks.

    To do this well we need to start defining user roles more explicitly within JupyterHub. We need to give users a safe an isolated space on the cluster to use without affecting their neighbors.

  2. HDF5 and NetCDF on cloud storage is an open question

    The file formats used for this sort of data are pervasive, but not particulary convenient or efficent on cloud storage. In particular the libraries used to read them make many small reads, each of which is costly when operating on cloud object storage

    I see a few options:

    1. Use FUSE file systems, but tune them with tricks like read-ahead and caching in order to compensate for HDF’s access patterns
    2. Use the HDF group’s proposed HSDS service, which promises to resolve these issues
    3. Adopt new file formats that are more cloud friendly. Zarr is one such example that has so far performed admirably, but certainly doesn’t have the long history of trust that HDF and NetCDF have earned.
  3. Environment customization is important and tricky, especially when adding distributed computing.

    Immediately after showing this to science groups they want to try it out with their own software environments. They can do this easily in their notebook session with tools like pip or conda, but to apply those same changes to their dask workers is a bit more challenging, especially when those workers come and go dynamically.

    We have solutions for this. They can bulid and publish docker images. They can add environment variables to specify extra pip or conda packages. They can deploy their own pangeo deployment for their own group.

    However these have all taken some work to do well so far. We hope that some combination of Binder-like publishing and small modification tricks like environment variables resolve this problem.

  4. Our docker images are very large. This means that users sometimes need to wait a minute or more for their session or their dask workers to start up (less after things have warmed up a bit).

    It is surprising how much of this comes from conda and node packages. We hope to resolve this both by improving our Docker hygeine and by engaging packaging communities to audit package size.

  5. Explore other clouds

    We started with Google just because their Kubernetes support has been around the longest, but all major cloud providers (Google, AWS, Azure) now provide some level of managed Kubernetes support. Everything we’ve done has been cloud-vendor agnostic, and various groups with data already on other clouds have reached out and are starting deployment on those systems.

  6. Combine efforts with other groups

    We’re actually not the first group to do this. The UK Met Informatics Lab quietly built a similar prototype, JADE (Jupyter and Dask Environment) many months ago. We’re now collaborating to merge efforts.

    It’s also worth mentioning that they prototyped the first iteration of Daskernetes.

  7. Reach out to other communities

    While we started our collaboration with atmospheric and oceanographic scientists, these same solutions apply to many other disciplines. We should investigate other fields and start collaborations with those communities.

  8. Improve Dask + XArray algorithms

    When we try new problems in new environments we often uncover new opportunities to improve Dask’s internal scheduling algorithms. This case is no different :)

Much of this upcoming work is happening in the upstream projects so this experimentation is both of concrete use to ongoing scientific research as well as more broad use to the open source communities that these projects serve.

Community uptake

We presented this at a couple conferences over the past week.

We found that this project aligns well with current efforts from many government agencies to publish large datasets on cloud stores (mostly S3). Many of these data publication endeavors seek a computational system to enable access for the scientific public. Our project seems to complement these needs without significant coordination.


While we encourage people to try out we also warn you that this system is immature. In particular it has the following issues:

  1. it is insecure, please do not host sensitive data
  2. it is unstable, and may be taken down at any time
  3. it is small, we only have a handful of cores deployed at any time, mostly for experimentation purposes

However it is also open, and instructions to deploy your own live here.

Come help

We are a growing group comprised of many institutions including technologists, scientists, and open source projects. There is plenty to do and plenty to discuss. Please engage with us at

January 22, 2018 12:00 AM

January 19, 2018

January 10, 2018

Titus Brown

Some resources for the data science ecosystem

ref my talk at CSUPERB, "Data Science is, like, the new critical thinking!" --

Training and teaching - the home base for Data Carpentry, which runs two day workshops around the world and has an instructor training program that teaches people to teach their materials. - the home base for the UC Berkeley "Foundations of Data Science" course. All open/free materials running with open source tools.

Reading and background

Influential works in Data-Driven Discovery, a paper by Mark Stalzer and Chris Mentzel, that outlines topics that probably fit within the "data science" field.

Project Jupyter: Computational Narratives as the Engine of Collaborative Data Science, a grant proposal by the Jupyter team.

Some Web sites worth visiting - a Web site for running Jupyter Notebooks in the cloud, for free! Try out my Monty Hall problem notebook! (or see the source).

Media for thinking the unthinkable by Bret Victor - an inspiring video lecture that I happened across as I was preparing my talk...

Please add resources below in the comments!


by C. Titus Brown at January 10, 2018 11:00 PM

Bruno Pinho

Automated Bulk Downloads of Landsat-8 Data Products in Python

Earth Explorer provides a very good interface to download Landsat-8 data. However, we usually want to automate the process and run everything without spending time with GUIs. In this tutorial, I will show how to automate the bulk download of low Cloud Covered Landsat-8 images, in Python, using Amazon S3 or Google Storage servers.

by Bruno Ruas de Pinho at January 10, 2018 01:00 PM

January 09, 2018

Titus Brown

Some interview questions for a job building data analysis pipelines

Recently we interviewed for a staff job that involved building bioinformatics data analysis pipelines. We came up with the following interview questions, which seemed to work quite well for a first round interview, & I thought I'd share --

Question 1

Scenario: you've been maintaining a data analysis pipeline that involves running a shell script by hand. The shell script works perfectly about 95% of the time, and breaks the remaining 5% of the time because of many small issues. This is OK so far because you've been asked to process 1 data set a week and the rest of the time is spent on other tasks. But now the job has changed and you're working 50% or more of your time on this and expected to analyze 100 data sets a month. How would you allocate your time and efforts? Feel free to fill in backstory from your own previous work experiences.

Question 2

Scenario: You're running the same data analysis pipeline as above, and after two months, you suddenly get feedback from your bosses boss that the results are wrong now. How do you approach this situation?

Bonus: Question 3

You are building a workflow or pipeline with a bunch of software that is incompatible in its dependencies and installation requirements. What approaches would you consider, what kinds of questions would you ask about the workflow and pipeline to choose between the approaches, and what are the drawbacks of the various approaches?


by C. Titus Brown at January 09, 2018 11:00 PM

January 07, 2018

Titus Brown

Reassessing the ‘Digital Commons’

Part I -- Sustainability and Funding

Funding FLOSS contributions

Individual contributors

Sustainability of Free/Libre and Open Source Software (FLOSS) has been an ongoing subject of concern. In 2017, Sustain released a practical report on the topic, sharing findings and recommendations pertaining to the sustainability of FLOSS [Nickolls, 2017]. The authors of this report, also known as the Sustainers, use the term 'FOSS' and, more often, 'OSS.' We prefer using the term 'FLOSS' to express neutrality [Stallman, 2013].

At the lowest level, FLOSS consists of lines of code contributed by individuals. The latter contribute either voluntarily or because they are paid to do so [Schweik, 2011]. Some organizations, whether for-profit or not-for-profit, hire people to work on FLOSS projects, either part-time or full-time. To quote the Sustainers, "contributions are often made on the basis of immediate and individual needs." And so is the funding of these contributions, from a standpoint where we equate a contribution with its funding: That is, a contribution would not have landed, had it not been funded somehow, whether directly or indirectly, whether in the form of money or time.

This individual-centric perspective makes funding, if not sustainability, a non-issue. It is only good at answering, as an individual, the all-too-common question "How do you make money working on FLOSS?" We consider that sustainability includes, but is not restricted to, funding. Indeed, what if you have funding, but no talent existing or available to take advantage of it? Now, we may ask the holistic question: Isn't a FLOSS project more than the sum of its individual contributions?

Collective projects

FLOSS projects which see communities of practice emerge and organize around them are definitely much more. An example we cherish would be SciPy, a Python-based ecosystem for scientific computing. Interactions between members of these communities create value, knowledge, and culture. These members do not have to be code contributors; they may be end users, power users, or contributors in a broader sense. Remarkably, the yt project has pushed the definition of its "members" (yt is a Python package for analyzing and visualizing high-dimensional scientific data): Quoting [Turk, 2016], "yt has a model in place for recognizing contributions that go beyond code."

So, can we grasp this collective dimension? We sense that sustainability should be a concern shared throughout the community. When, additionally, whole segments of technological, cultural, educational, and economic activities rely on FLOSS projects, we agree with the Sustainers that the concern for sustainability (including funding) should be shared by "stakeholders" who are many and diverse, far beyond the small circle of (code) contributors. The Sustainers call this key subset of FLOSS our "essential digital infrastructure." Further, they identify it as a "public good." For each piece of this infrastructure, the circle of contributors is indeed very small with respect to its end-user base, made up of "consumers" or "users" [Nickolls, 2017].

Although the Sustainers link to Elinor Ostrom's "8 Principles for Managing A Commmons [sic]" when recommending good governance, we argue that their report is a missed opportunity for leveraging the concept of Commons. In the following, we explain why we care about viewing FLOSS as a digital commons (rather than a public good). We note that other digital (information, knowledge) commons have been approached as public goods. One example would be information acquisition, as studied by [Ramachandran and Chaintreau, 2015]. They also report very low ratios of "contributors" to "consumers," falling within a production/consumption view.

Bringing the Commons into play

They say FLOSS is a digital commons

Historically, the Commons have described natural resources that were shared within a community---not only as a matter of fact, but through intentional rules and collective self-management which ensured their sustainability and fair access [Maurel, 2016]. As more and more commons were enclosed and sacrificed to private interests (including that of the State), they all but disappeared from the official economic discourse. Instead, the discussion narrowed down to the private/public dichotomy. In that paradigm, "public" goods merely qualified what could not (at the time) be realistically privatized, such as air or water. They were described in contrast to private goods, as non-rivalrous and non-excludable [Hess and Ostrom, 2011a].

The concept of Commons reappeared with the rise of environmental concerns [Bollier, 2011] as well as the development of technologies, which suddenly enabled "the capture of what were once free and open public goods" [Hess and Ostrom, 2011a]. In a somewhat similar way, knowledge has long been able to straddle the ambiguous border between private and public: the necessity of print grounding it in the private property realm, while the public domain materialized its non-rivalrous, non-excludable nature.

The Internet and the advent of the digital era have changed this situation. Once the limitations inherent to print are gone, the complex status of knowledge is revealed. Defining it as a commons is an attempt to grasp and honor this complexity. Indeed, the term "Commons" translates a desire to move away and beyond the simplistic understanding of private vs public. By speaking of commons, its advocates seek to build a new framework for analysis, which integrates the philosophical, political, and social dimensions along with the traditional, market-centred economic one [Bollier, 2011].

How is the collective dimension enforced?

In 2017, we celebrated the 10th anniversary of the GNU General Public License version 3 (GPLv3). It is one of the most popular Free Software licenses. To remain neutral, we wish we could use the term 'FLOSS license' to mean any software license approved by both the Free Software Foundation and the Open Source Initiative. Free Software licenses are tools designed to safeguard and advance the freedom of software users. Indeed, user freedom is the ultimate motivation underlying Free Software. But, since it is not that of Open Source, we cannot casually replace 'Free Software' with 'FLOSS' in our second-to-last sentence.

What we can highlight is that FLOSS licenses grant individual freedoms. There is no built-in mechanism to account for a community. The sense of community is typically derived from the practice of sharing (allowed by FLOSS licensing), in-person or remote participation in events (conferences, hackathons, etc.), and collaboration on certain contributions (possibly event organization, project maintenance, etc.). We can describe FLOSS as pro-sharing, alongside other movements such as Creative Commons or Open Science. We note that the concern for sharing has been at the heart of Free Software since the very beginning [Stallman, 1983].

At the end of the day, distribution and dissemination are one-way ideas. They do not bear on collective responsibility. Still, we recognize that copyleft---and the related ShareAlike offered by Creative Commons licenses---represent a means to extend some responsibility to all community members. Therefore, we hypothesize that, even though copyleft and related tools do serve the project of building digital commons, they might not be sufficient. And, although FLOSS has been a great source of inspiration to other digital commons [Laurent, 2012], the FLOSS way does not have to be the only way to the digital Commons.

Generally, most of Commons literature seems to present copyright enclosure as the one big threat to the digital Commons. Since digital knowledge is in essence non-rivalrous, there is a presumption that Hardin's famous "tragedy of commons"1 does not apply [Hess and Ostrom, 2011a]. In fact, the opposite is considered more likely to be true: "the tragedy of the anticommons (...) lies in the potential underuse of scarce scientific resources caused by excessive intellectual property rights and overpatenting in biomedical research" [Hess and Ostrom, 2011a]. As a reaction, commons-oriented initiatives tend to overemphasize accessibility, to the expense of sustainability and governance---as if these concerns ranked second in the definition of commons.

Issues, solutions, and questions

A critical take on the priorities put forward for these commons

Commons movements deemed successful include FLOSS, Open Access (OA), or free culture. Why does their focus on free access and use fall short? Mostly because they reflect only the authors' or maintainers' intentions, with little regard for or feedback from the other stakeholders' needs. First of all, the very definitions of what constitutes 'freedom' (in FLOSS and free culture) or 'open access' (in eponymous OA) are subject to a cultural bias. Open Access, for instance, operates a hierarchy between so-called barriers. While the removal of some (price and permission) is a compulsory prerequisite to be labelled OA, others ("handicap access," "connectivity," language, etc.), arguably harder obstacles to overcome, are merely acknowledged as works in progress [Suber, 2011].

This helps us see "free and unfettered access" [Hess and Ostrom, 2011a] as a relative concept, and the set of criteria which determine it as mere guidelines, rather than objective conditions. In this light, we would like to argue for a more comprehensive view of "accessibility." If we are to treat digital Commons as commons, then we may need to do more (or less, depending) than giving up privileges traditionally associated with copyright---a privilege unto itself, ironically! We want to find whatever specific provisions are most likely to serve and engage the community. Yet the 'free/open' argument, insofar as it is arbitrary and partial, necessarily promotes the concerns of some over those of others.

Here is an example of different interests conflicting. In his contribution on OA to book [Hess and Ostrom, 2011b], Suber posits that the concept of open access can be extended to royalty-producing literature [Suber, 2011]. Yet the focus on eliminating the "price barrier" creates a contention. His argument that OA does not adversely affect sales is based on the assumption that people do not read whole books in electronic format---a surprising opinion, which seems irrevocably outdated. Moreover, if maximum dissemination is the goal, then distribution and searchability are more important factors than price---or rather, lack thereof. In many fields where traditional sales channels are still the norm, putting a price on something---even a symbolic one---remains the best guarantee of effectively sharing one's work.

As a matter of fact, in the chapter that follows Suber's, Ghosh argues that a well-regulated marketplace can help realize the process of exchange, which is crucial to the Commons [Ghosh, 2011]. This does not negate Suber's defense of scholars' "insulation from the market", but it does put it into perspective.

Implications for funding and sustainability

In reality, the issue is cyclical. "Consumers" might object to a price which does not appropriately meet their means, or their perception of the work's value. On the other hand, commoners, who may have a say in setting the price and defining exactly what they are paying for (access, use of a resource, or the work which made both possible), would, presumably, agree to such a (financial) contribution. Here, we are drawing on the now well-known research on common-pool resource systems. It has shown success not to be linked with any specific set of rules, but broader principles [Hess and Ostrom, 2011a]. One of them is of particular interest to us: "Individuals affected by these rules can usually participate in modifying the rules."

But are such findings relevant to digital commons? Usually, the latter are considered a separate category, on the basis of their non-rivalry (or low subtractabilty) [Hess and Ostrom, 2011a]. Unlike common-pool resources, they cannot be depleted or destroyed through overuse. This might be somewhat true of the resource itself, but what about the human labour needed to create and maintain it? We want to point out that time and work capacity are uniquely rivalrous resources. The Sustainers recognize this duality as well ("the sustainability of resources and the sustainability of people"). Therefore, wouldn't a certain level of institutions still be in order, if not to regulate the use of the digital resource, then at least to take care of the human resource?

We are led to believe that a strong sense of community, implying shared values and adherence to the rules in place, is as significant for the sustainability of digital commons, as it is for other types of commons'. When it comes to funding, we have already mentioned that the more engaged users are, the less they should be tempted to free-ride. We also think that, in the case of collective projects, treating every potential user as another commoner can only help with the recruitment and long-term integration of contributors. The orientation of the pandas project (Python data analysis library), as stated in their governance document, seems to support this claim: "we strive to keep the barrier between Contributors and Users as low as possible"; "In general all Project decisions are made through consensus among the Core Team with input from the Community." Evidently, they see value in doing so.

However, we may note that the funding of the project is left to the care of a distinct organization, i.e., NumFOCUS (which, as a side note, Marianne loves). We can also concede that each digital commons has its own specific requirements and culture. For example, formal, centralized types of institutions, which have worked well for environmental commons, will not necessarily be successful with FLOSS commons [Schweik and English, 2007]. Again, rules and systems will be diverse, since they must above all be designed to "[match] local needs and conditions", to quote Hess and Ostrom.


In this article, we chose to target funding as a key to digital commons' sustainability. However, it is obviously not the only issue. Preservation, legitimate use, and diversity should all be core concerns to anyone looking to build and enrich the Commons. For, when we speak of 'the Knowledge Commons', we never mean a particular piece of knowledge, but rather the entire ecosystem which allows as many people as possible to keep creating and sharing knowledge.


Bollier, David. 2011. “The Growth of the Commons Paradigm.” In Understanding Knowledge as a Commons: From Theory to Practice, edited by Charlotte Hess and Elinor Ostrom. MIT Press.

Ghosh, Shubha. 2011. “How to Build a Commons: Is Intellectual Property Constrictive, Facilitating, or Irrelevant?” In Understanding Knowledge as a Commons: From Theory to Practice, edited by Charlotte Hess and Elinor Ostrom. MIT Press.

Hess, Charlotte, and Elinor Ostrom. 2011a. “Introduction: An Overview of the Knowledge Commons.” In Understanding Knowledge as a Commons: From Theory to Practice, edited by Charlotte Hess and Elinor Ostrom. MIT Press.

———, eds. 2011b. Understanding Knowledge as a Commons: From Theory to Practice. MIT Press.

Laurent, Philippe. 2012. “Free and Open Source Software Licensing: A Reference for the Reconstruction of ‘Virtual Commons’?” In Conference for the 30th Anniversary of the CRID, 1–19. s.n.

Maurel, Lionel. 2016. “Les Little Free Libraries, victimes d’une Tragédie des Communs ?” Accessed on Thu, December 21, 2017.

Nickolls, Ben. 2017. A One Day Conversation for Open Source Software Sustainers. Sustain. GitHub HG (SF). Accessed on Thu, December 21, 2017.

Ramachandran, Arthi, and Augustin Chaintreau. 2015. “Who Contributes to the Knowledge Sharing Economy?” In Proceedings of the 2015 ACM on Conference on Online Social Networks, 37–48. COSN ’15. New York, NY: ACM.

Schweik, Charles M. 2011. “Free/Open-Source Software as a Framework for Establishing Commons in Science.” In Understanding Knowledge as a Commons: From Theory to Practice, edited by Charlotte Hess and Elinor Ostrom. MIT Press.

Schweik, Charles M., and Robert English. 2007. “Tragedy of the FOSS Commons? Investigating the Institutional Designs of Free/Libre and Open Source Software Projects.” First Monday 12 (2).

Stallman, Richard. 1983. “Why Programs Should be Shared.” Accessed on Thu, December 21, 2017.

———. 2013. “FLOSS and FOSS.” Accessed on Thu, December 21, 2017.

Suber, Peter. 2011. “Creating an Intellectual Commons Through Open Access.” In Understanding Knowledge as a Commons: From Theory to Practice, edited by Charlotte Hess and Elinor Ostrom. MIT Press.

Turk, Matthew. 2016. “The Royal ‘We’ in Scientific Software Development.” Accessed on Thu, December 21, 2017.

  1. The tragedy of commons describes the overexploitation or free-riding that lead to a shared resource's destruction. 

by Marianne Corvellec and Jeanne Corvellec at January 07, 2018 11:00 PM

Filipe Saraiva

Discussing the future of Cantor

Hello devs! Happy new year!

It is common to use the new year date to start new projects or give new directions for old ones. The last one is the case for Cantor.

Since when I got the maintainer status for Cantor, I was working to improve the community around the software. Because the great plugins systems of Qt, it is easy to write new backends for Cantor, and in fact in last years Cantor reached the number of 11 backends.

If in a hand it is a nice thing because Cantor can run different mathematical engines, in other hand it is very common developers create backends, release them with Cantor upstream, and forget this piece of software after some months. The consequence of this is a lot of unsolved bugs in Bugzilla, unexpected behaviours of some backends, and more.

For instance, R backend is broken from some years right now (thanks Rishabh it was fixed during his GSoC/KDE Edu Sprint 2017 but not released yet). Sage backend breaks for each new release of Sage.

Different backends use different technologies. Scilab and Octave backends use QProcess + Standard Streams; Python 2 uses Python/C API; Python 3, R, and Julia use D-Bus.

In addition to these, remember each programming language used as mathematical engine for Cantor has their respective release schedule and it is very common new versions break the way as backends are implemented.

So, yes, the mainternhip of Cantor is a hell.

In order to remedy it I invited developers to be co-maintainer of these respective backends, but it does not have the effect I was suposed to. I implemented a way to present the versions of programming languages supported in the backend but it does not work well too.

So, my main work in Cantor during these years was try to solve bugs of backends I don’t use and, sometimes, I don’t know how they work, while new features were impossible to be planned and implemented.

If we give a look to Jupyter, the main software for notebook-based mathematical computation, it is possible to see this software supports several programming languages. But, in fact, this support is provide by the community – Jupyter focus effort in Python support only (named the ipython kernel) and in new features for Jupyter itself.

So, I would like to hear the KDE and Cantor community about the future of Cantor. My proposal is split the code of the others backends and put them as third-party plugins, maintained by their respective community. Only the Python 3 backend would be “officially” maintaned and delivered in KDE Applications bundle.

This way I could focus in provide new features and I could to say “well, this bug with X backend must be reported to the X backend community because they are accountable for this piece of software”.

So, what do you think about?

by Filipe Saraiva at January 07, 2018 02:07 PM

January 03, 2018

Jarrod Millman

BIDS is hiring NumPy developers

The Berkeley Institute for Data Science (BIDS) is hiring Open Source Scientific Python Developers to contribute to NumPy.  You can read more about the new positions here.  For more information about the work this grant will support, please see this NumPy lecture by BIDS Computational Fellow Nathaniel Smith.  Interested applicants can find more information in the job posting.

by Jarrod Millman ( at January 03, 2018 11:46 AM

January 02, 2018

January 01, 2018

William Stein

Low latency local CoCalc and SageMath on the Google Pixelbook: playing with Crouton, Gallium OS, Rkt, Docker

I just got CoCalc fully working locally on my Google Pixelbook Chromebook! I want this, since (1) I was inspired by a recent blog post about computer latency, and (2) I'm going to be traveling a lot next week (the JMM in San Diego -- come see me at the Sage booth), and may have times without Internet during which I want to work on CoCalc's development.

I first tried Termux, which is a "Linux in Android" userland that runs on the Pixelbook (via Android), but there were way, way too many problems for CoCalc, which is a very complicated application, so this was out. The only option was to enable ChromeOS dev mode.

I next considered partitioning the hard drive, installing Linux natively (in addition to ChromeOS), and dual booting. However, it seems the canonical option is Gallium OS and it nobody has got that to work with Pixelbook yet (?). In fact, it appears that Gallium OS development made have stopped a year ago (?). Bummer. So I gave up on that approach...

The next option was to try Crouton + Docker, since we have a CoCalc Docker image. Unfortunately, it seems currently impossible to use Docker with the standard ChromeOS kernel.  The next thing I considered was to use Crouton + Rkt, since there are blog posts claiming Rkt can run vanilla Docker containers on Crouton.

I setup Crouton, installed the cli-extra chroot, and easily installed Rkt. I learned how Rkt is different than Docker, and tried a bunch of simple standard Docker containers, which worked. However, when I tried running the (huge) CoCalc Docker container, I hit major performance issues, and things broke down. If I had the 16GB Chromebook and more patience, maybe this would have worked. But with only 8GB RAM, it really wasn't feasible.

The next idea was to just use Crouton Linux directly (so no containers), and fix whatever issues arose. I did this, and it worked well, with CoCalc giving me a very nice local browser-based interface to my Crouton environment. Also, since we've spent so much time optimizing CoCalc to be fast over the web, it feels REALLY fast when used locally. I made some changes to the CoCalc sources and added a directory, to hopefully make this easier if anybody else tries. This is definitely not a 1-click solution.

Finally, for SageMath I first tried the Ubuntu PPA, but realized it is hopelessly out of date. I then downloaded and extracted the Ubuntu 16.04 binary and it worked fine. Of course, I'm also building Sage from source (I'm the founder of SageMath after all), but that takes a long time...

Anyway, Crouton works really, really well on the Pixelbook, especially if you do not need to run Docker containers.

by William Stein ( at January 01, 2018 10:29 PM

December 20, 2017

December 19, 2017

December 18, 2017

Jake Vanderplas

Simulating Chutes & Ladders in Python

[img: Chutes and Ladders animated simulation]

This weekend I found myself in a particularly drawn-out game of Chutes and Ladders with my four-year-old. If you've not had the pleasure of playing it, Chutes and Ladders (also sometimes known as Snakes and Ladders) is a classic kids board game wherein players roll a six-sided die to advance forward through 100 squares, using "ladders" to jump ahead, and avoiding "chutes" that send you backward. It's basically a glorified random walk with visual aids to help you build a narrative. Thrilling. But she's having fun practicing counting, learning to win and lose gracefully, and developing the requisite skills to be a passionate sports fan, so I play along.

On the approximately twenty third game of the morning, as we found ourselves in a near endless cycle of climbing ladders and sliding down chutes, never quite reaching that final square to end the game, I started wondering how much longer the game could last: what is the expected length of a game? How heavy are the tails of the game length distribution? How succinctly could I answer those questions in Python? And then, at some point, it clicked: Chutes and Ladders is memoryless — the effect of a roll depends only on where you are, not where you've been — and so it can be modeled as a Markov process! By the time we (finally) hit square 100, I basically had this blog post written, at least in my head.

When I tweeted about this, people pointed me to a number of similar treatments of Chutes & Ladders, so I'm under no illusion that this idea is original. Think of this as a blog post version of a dad joke: my primary goal is not originality, but self-entertainment, and if anyone else finds it entertaining that's just an added bonus.

by Jake VanderPlas at December 18, 2017 06:00 PM

Continuum Analytics

The Most Popular Anaconda Webinars of 2017

Happy holidays, #AnacondaCREW! Our experts love participating in the Python data science community by sharing their experiences through live, interactive webinars. Below are our most-viewed Anaconda webinars of the year. They’re now available on-demand, so even if you missed them the first time around, you can still watch and learn! Taming the Python Data Visualization …
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by Janice Zhang at December 18, 2017 02:43 PM

December 14, 2017

Paul Ivanov

SciPy 2018 dates and call for abstracts

I'm helping with next year's SciPy conference, so here are the details:

July 9-15, 2018 | Austin, Texas

Tutorials: July 9-10, 2018
Conference (Talks and Posters): July 11-13, 2018
Sprints: July 14-15, 2018

SciPy 2018, the 17th annual Scientific Computing with Python conference, will be held July 9-15, 2018 in Austin, Texas. The annual SciPy Conference brings together over 700 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The call for abstracts for SciPy 2018 for talks, posters and tutorials is now open. The deadline for submissions is February 9, 2018.

Talks and Posters (July 11-13, 2018)

In addition to the general track, this year will have specialized tracks focused on:

  • Data Visualization
  • Reproducibilty and Software Sustainability

Mini Symposia

  • Astronomy
  • Biology and Bioinformatics
  • Data Science
  • Earth, Ocean and Geo Science
  • Image Processing
  • Language Interoperability
  • Library Science and Digital Humanities
  • Machine Learning
  • Materials Science
  • Political and Social Sciences

There will also be a SciPy Tools Plenary Session each day with 2 to 5 minute updates on tools and libraries.

Tutorials (July 9-10, 2018)

Tutorials should be focused on covering a well-defined topic in a hands-on manner. We are looking for awesome techniques or packages, helping new or advanced Python programmers develop better or faster scientific applications. We encourage submissions to be designed to allow at least 50% of the time for hands-on exercises even if this means the subject matter needs to be limited. Tutorials will be 4 hours in duration. In your tutorial application, you can indicate what prerequisite skills and knowledge will be needed for your tutorial, and the approximate expected level of knowledge of your students (i.e., beginner, intermediate, advanced). Instructors of accepted tutorials will receive a stipend.

Mark Your Calendar for SciPy 2018!

by Paul Ivanov at December 14, 2017 08:00 AM

December 13, 2017


Cheat Sheets: Pandas, the Python Data Analysis Library

Download all 8 Pandas Cheat Sheets

Learn more about the Python for Data Analysis and Pandas Mastery Workshop training courses

Pandas (the Python Data Analysis library) provides a powerful and comprehensive toolset for working with data. Fundamentally, Pandas provides a data structure, the DataFrame, that closely matches real world data, such as experimental results, SQL tables, and Excel spreadsheets, that no other mainstream Python package provides. In addition to that, it includes tools for reading and writing diverse files, data cleaning and reshaping, analysis and modeling, and visualization. Using Pandas effectively can give you super powers, regardless of whether you’re working in data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, or engineering.

However, learning Pandas can be a daunting task because the API is so rich and large. This is why we created a set of cheat sheets built around the data analysis workflow illustrated below. Each cheat sheet focuses on a given task. It shows you the 20% of functions you will be using 80% of the time, accompanied by simple and clear illustrations of the different concepts. Use them to speed up your learning, or as a quick reference to refresh your mind.

Here’s the summary of the content of each cheat sheet:

  1. Reading and Writing Data with Pandas: This cheat sheet presents common usage patterns when reading data from text files with read_table, from Excel documents with read_excel, from databases with read_sql, or when scraping web pages with read_html. It also introduces how to write data to disk as text files, into an HDF5 file, or into a database.
  2. Pandas Data Structures: Series and DataFrames: It presents the two main data structures, the DataFrame, and the Series. It explain how to think about them in terms of common Python data structure and how to create them. It gives guidelines about how to select subsets of rows and columns, with clear explanations of the difference between label-based indexing, with .loc, and position-based indexing, with .iloc.
  3. Plotting with Series and DataFrames: This cheat sheet presents some of the most common kinds of plots together with their arguments. It also explains the relationship between Pandas and matplotlib and how to use them effectively. It highlights the similarities and difference of plotting data stored in Series or DataFrames.
  4. Computation with Series and DataFrames: This one codifies the behavior of DataFrames and Series as following 3 rules: alignment first, element-by-element mathematical operations, and column-based reduction operations. It covers the built-in methods for most common statistical operations, such as mean or sum. It also covers how missing values are handled by Pandas.
  5. Manipulating Dates and Times Using Pandas: The first part of this cheatsheet describes how to create and manipulate time series data, one of Pandas’ most celebrated features. Having a Series or DataFrame with a Datetime index allows for easy time-based indexing and slicing, as well as for powerful resampling and data alignment. The second part covers “vectorized” string operations, which is the ability to apply string transformations on each element of a column, while automatically excluding missing values.
  6. Combining Pandas DataFrames: The sixth cheat sheet presents the tools for combining Series and DataFrames together, with SQL-type joins and concatenation. It then goes on to explain how to clean data with missing values, using different strategies to locate, remove, or replace them.
  7. Split/Apply/Combine with DataFrames: “Group by” operations involve splitting the data based on some criteria, applying a function to each group to aggregate, transform, or filter them and then combining the results. It’s an incredibly powerful and expressive tool. The cheat sheet also highlights the similarity between “group by” operations and window functions, such as resample, rolling and ewm (exponentially weighted functions).
  8. Reshaping Pandas DataFrames and Pivot Tables: The last cheatsheet introduces the concept of “tidy data”, where each observation, or sample, is a row, and each variable is a column. Tidy data is the optimal layout when working with Pandas. It illustrates various tools, such as stack, unstack, melt, and pivot_table, to reshape data into a tidy form or to a “wide” form.

Download all 8 Pandas Cheat Sheets

Data Analysis Workflow

Ready to accelerate your skills with Pandas?

Enthought’s Pandas Mastery Workshop (for experienced Python users) and Python for Data Analysis (for those newer to Python) classes are ideal for those who work heavily with data. Contact us to learn more about onsite corporate or open class sessions.


The post Cheat Sheets: Pandas, the Python Data Analysis Library appeared first on Enthought Blog.

by admin at December 13, 2017 10:12 PM

December 12, 2017

Continuum Analytics

Parallel Python with Numba and ParallelAccelerator

With CPU core counts on the rise, Python developers and data scientists often struggle to take advantage of all of the computing power available to them. CPUs with 20 or more cores are now available, and at the extreme end, the Intel® Xeon Phi™ has 68 cores with 4-way Hyper-Threading. (That’s 272 active threads!) To …
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by Janice Zhang at December 12, 2017 06:28 PM