ivoflipse

From following their issue tracker: some ideas didn't pan out (yet), some ideas turned out to break backwards compatibility, several pyperformance tests broke because of the changes made so they had to fix upstream (eg gevent and cython) and a lot of the work seems to be setting up future optimizations. They're also getting their changes merged directly into CPython main, so I reckon there's more red tape than if they had done the work on a fork. It does seem to inspire fellow core devs to pitch in and try some of their own ideas, so I can see the effort pick up steam as they make more headway.

This is from the latest 3.11 alpha 4 release just this week:

The Faster CPython Project is already yielding some exciting results: this version of CPython 3.11 is ~ 19% faster on the geometric mean of the PyPerformance benchmarks, compared to 3.10.0.

https://pythoninsider.blogspot.com/2022/01/python-3102-3910-...

So this is definitely not languishing as they are getting these changes directly into main for Python 3.11

For whatever reason with our enterprise license agreement, we pay $7/set of documents. I assumed it was flat fee, but nope.

We use DocuSign because FDA requires electronic signatures that follow 21 CFR Part 11. A lot of companies will be very hesitant to roll their own or claim compliance if the supplier (like DocuSign) doesn't claim it's compliant.

Packaging PEPs aren't really tied to the CPython release cycle unless they impact CPython, like the deprecation of distutils. So if this PEP gets approved, you'll see projects using it much sooner than the release of 3.11

Yarn has the following: https://classic.yarnpkg.com/en/docs/selective-version-resolu...

This doesn't allow you to use an alternative index, but it does allow you to override packages that restrict their dependencies versions too strictly

Conda has the advantage that conda packages are typically repackaged from the Pypi package. Especially om conda-forge, they are curated by people with more experience in packaging, that are more likely to use appropriate version pinning for libraries. Furthermore, conda-forge has the ability to create a new build of the same package with improved version pinnings, which typically doesn't happen on Pypi for already released packages.

For anyone interested in following this work, these Discourse threads are interesting:

- https://discuss.python.org/t/preparing-for-new-python-parsin...

- https://discuss.python.org/t/switch-pythons-parsing-tech-to-...

Then maybe those large enterprise organizations should start paying for the long mature process with experienced developers

They're having the discussion on Discuss: https://discuss.python.org/t/preparing-for-new-python-parsin...

Report the issue with CFPB (https://www.consumerfinance.gov). I waited for ~3 months with barely any info from Coinbase's side. After reporting it, the issue got resolved in a timely fashion.

Hopefully this will also mean decent Python support, because with the Kinect for Windows v2 dev kit I'd have to wait until wrappers became available.

The Kinect v1 was also a pain to get working on x64 Windows, so hopefully things will be a bit easier this time around

Any idea of what might be a better approach?

You could buy the book, which isn't that expensive and covers everything from the video's as far as I can tell.

That allows you to study it more at your own pace. I'm taking the Visualizing Algebra course from Udacity, after working halfway through Coding the Matrix, because I found my algebra skills to be lacking.

Too bad its no longer possible to get a certificate on Coursera though, now I have to wait until the next iteration.

I first wanted to link to their homepage, but its just a sign up page for updates.

Their Vimeo has some more nice looking videos like this one: http://vimeo.com/60564990

Too bad they aren't exactly spilling beans on how their tech works, what it takes to run or any details for that matter. I guess we'll have to wait, unless someone else has more info.

I'm helping veterinary scientists with their gait analysis, basically by porting the calculations used for pressure measurements (so no 3D forces) on humans and solving problems like detecting the paws along the way.

An example of what this gets used for are things like lameness detection or diagnosing disorders. Or it could be used to evaluate the effectiveness of the treatment.

While the pressure plate doesn't measure 3D forces, it does have the advantage of giving you information about the distribution, which I expect at least has strong correlations with 3D forces. Obviously you need animals of a certain size to get good data (or systems with very small sensors), so you wouldn't use it for gecko's or even really small dogs.

Perhaps I should try calculating the distance to the body's center of mass, to get an idea how well this distinguishes the different paws. The problem I'm having so far is that the quality of the measurements isn't always optimal and inferring which paws are making contact is difficult if you don't see paws from both sides (for quadrupeds at least).

I'll be sure to check out the research you pointed to!

That looks really interesting! I'm currently trying to cook up a reliable way to label my paw prints (like here: http://stackoverflow.com/questions/4502656/how-to-sort-my-pa...).

I'm wondering if modeling/simulating quadruped locomotion would help make this task a lot easier. Instead of trying some machine learning/computer vision on the pressure data, reason about what kind of walking pattern brought forth the results. Like a paw can't be in two places at the same time and other heuristics like that.

Any suggestions for literature I should read up on for horizontal locomotion? :-)

Do you happen to have any reports or theses about the animal locomotion studies? I'm currently working on a similar topic (https://github.com/ivoflipse/Pawlabeling/) and its always good to learn from past experiences.

If you liked his first chapter, consider supporting his IndieGogo campaign for the whole book (http://www.indiegogo.com/projects/neural-networks-and-deep-l...).

Thanks :-) Your answers on some of those other questions really helped me get into this type of analysis. I'm even still working on my this dogs project (https://github.com/ivoflipse/Pawlabeling), the automatic labeling of the paws still needs a better solution!

He just tweeted it, which is why its showing up. But yes, the attribution is wrong.

My suggestions would be to try running Canopy (Enthought) or Anaconda (Continuum), which come with all 'scientific' batteries included and make scientific libraries easier to install and to keep them up to date.

I can understand that you'd want to manually check what's happening. For example taking the mean over the rows of a 2D array using numpy's mean function and aren't really sure whether axis=0 or axis=1 refers to the rows.

But you'd only have to figure it out once and then learn to trust numpy, instead of rolling your own version every time.

For a more painless install try Continuum's Anaconda or Enthought's Canopy. They bundle together the most important scientific libraries, work cross-platform and in this case include all dependencies.

Unless its a species that ends up doing more harm than good. Like huge swarms of jellyfish.

Through the jump list of the application you opened that document in recently? Obviously this requires you to know both what you opened it in and have it pinned to your taskbar, but I really like that feature for reopening files I recently closed on a per app basis.

I use Ninite to set up a new computer, there are some elements that I generally skip (Office/anti-virus trials), but it can install a lot of useful programs without having to download dozens of installers.

You can actually install it alongside Python(x,y), though I get you don't need two Python installs. But at least you can experiment with some of Anaconda's interesting features, without breaking Python(x,y).

Then try Anaconda[0], I haven't had any problems with it so far and it includes IPython and most scientific libraries a Python programmer needs.

0: https://store.continuum.io/cshop/anaconda/

Probably University of Washington not allowing downloading of their material. Or you should just try it again a bit later.

Isn't the reason those fields mostly see usage of basic stats that they were never taught anything more sophiscated. For example in exercise physiology, which studies a lot of phenomena over time, the results get reduced to minimum/maximum/average because those points match the required input of their basic ANOVA's. Instead they could be comparing time series and find more subtle difference in when a rise starts to occur or at which rate. Numbers that often get obscured by the massive data reductions. Sure you can get by without more advanced data analysis and statistics, but its not exactly something to strive for.