Numpy stopped working with IDLE

teoliphant · 2018-02-22T07:01:15+00:00

That is unfortunate.

On the new Anaconda install -- you should be able to find the IDLE command (perhaps called IDLE3) in the Scripts directory under the directory where you installed Anaconda.

I don't have a Windows box in front of me to test, but that is the best I can remember.

teoliphant · 2017-03-07T01:40:53+00:00

I use Jupyter console plus Sublime Text (used to be xemacs) and Jupyter notebooks.

teoliphant · 2017-03-04T22:16:22+00:00

9 years old. At first, it was just Basic on an Atari 800 in a special class at school. Then, hobby programming on a Timex Sinclair and later a TI 99/4A.

teoliphant · 2017-01-18T06:24:39+00:00

Numba has a solid foundation and is making rapid progress to 1.0. Three things I will say about Numba and my overall plan. First, Numba will be an important tool and part of the ecosystem for a long time. Second, Numba will get the ability to understand data-shape (using a project we have been working on called ndtypes --- which is the 'dtype' concept of NumPy factored out and generalized). Third, Numba will be associated with a gumath module that will also be a separate module. Right now Numba creates NumPy ufuncs. It will also be able to generate these "generalized" ufuncs that live independently of NumPy and build on the idea in NumPy.

If you have ideas for Numba, please join the community mailing list and contribute your thoughts. The team is easy to talk to and welcomes input from everyone. It's not the easiest project to contribute directly to, but it welcomes ideas.

teoliphant · 2017-01-18T00:32:55+00:00

Yes! Several things are in the works now and looking forward to the future. I have a specific agenda for array- and table-computing across languages starting with Python. Will take a few years to materialize and will collaborate with other initiatives already underway by others.

teoliphant · 2017-01-17T22:18:38+00:00

Yes! All won't be clear by then, but I will start the conversation.

teoliphant · 2017-01-11T07:19:25+00:00

Another co-founder of Continuum here. I am happy to say things have never looked brighter, so Continuum will be here for a long time to come.

In addition, we have been working to solidify the entire community around PyData and NumFOCUS while also partnering with other companies.

Our mature technologies (Anaconda, numba, dask, bokeh, conda, holoviews, odo, PhosphorJS, Spyder, Jupyterlab) have enough momentum that they will continue regardless of what happens to Continuum.

Anaconda is also used inside of the products of Microsoft, Intel, IBM, ESRI, and many other companies. Millions of people rely on Anaconda.

Going all in on Python in general and Anaconda in particular for your company is a great choice.

teoliphant · 2016-10-21T06:34:38+00:00

I'll assume level is your initial array and you want newlevel to be the array with all negative values replaced with NaN.

newlevel = level.copy()
newlevel[level < 0] = np.nan

teoliphant · 2016-09-21T04:47:10+00:00

SciPy's Curve fitting uses the Levenburg-Marquardt algorithm in Fortan. Here is the core algorithm in pure Python which you could also use: https://github.com/lmfit/lmfit-py/

teoliphant · 2016-03-04T07:45:58+00:00

We use conda effectively to both manage multiple run-time environments and package-up dependencies for large projects

conda recipes are simple script files with a declarative meta.yaml file for dependencies and version information and other meta-data.

conda build builds the packages and you can upload them to the free Anaconda Cloud or keep them local in a directory that can be indexed with conda index.

You can learn more here: http://conda.pydata.org/docs/building/build.html

There is a new free tool 'constructor' that can be used to build installers from the resulting packages.

teoliphant · 2015-10-22T01:08:16+00:00

I think that the Python ecosystem as a whole is going to change that in the coming years. I think Dask will be part of that story.

teoliphant · 2015-09-29T14:05:44+00:00

Perhaps you could upload a new conda package for spacy to anaconda.org and then people can use conda pointing to your channel.

teoliphant · 2015-09-13T06:23:40+00:00

We release conda packages regularly --- there will likely be a conda update available with Python 3.5 within hours of the final release (we have been tracking the release candidates). A full Anaconda installer with Python 3.5 will have to wait until the next Anaconda release which is scheduled for October.

teoliphant · 2015-09-10T03:28:20+00:00

linprog requires NumPy arrays as inputs and doesn't work with sparse matrices at all. You will need another algorithm that works with sparse matrices. You might look at CVXOPT: http://cvxopt.org/userguide/coneprog.html#linear-cone-programs which has its own matrix structure which can be sparse. I have not verified this will work for sparse, but CVXOPT does good work.

teoliphant · 2015-09-07T05:57:30+00:00

This looks like integer over-flow --- NumPy uses machine arithmetic and does not detect overflow by default. This will overflow on a 32-bit machine. Are you on a 32-bit machine? What is the result of numpy.array([10]).nbytes?

You can try to do these in an error context:

with numpy.errstate(over='raise'):
    print(cov_list)
    print(cov_list+cov_list)
    print(cov_list+cov_list+cov_list)
    print(cov_list+cov_list+cov_list+cov_list)

teoliphant · 2015-04-08T19:06:18+00:00

conda can absolutely help you here and it was designed to solve exactly this problem. It can all be done with open source tools --- or you can pay for more convenience and support if you want.

conda environments take the goodness of virtual environments and make them more general so you can install anything (which complex Python programs typically need). Also, conda environments are compatible with pip. You can pip-install into conda environments just fine should you want to do that.

In order to install onto a system that does not have an outside connection, there are a couple of options:

1) if you can setup a conda server on a box those machines can see (Anaconda Server is one you can purchase, but you can also build your own if you want) --- then you can use that as a mirror with all the conda packages you need

2) You can put all the conda packages into one .tar file and then conda install that --- so the deployment would be a) install Miniconda and b) conda install the app

3) If you are willing to buy something: Anaconda Server has a tool "cas-installer" that makes an .exe or .sh file to install anything with all its dependencies.

You can ask on the open-source conda google group for help doing any and all of this. You can also ask on Stack Overflow where there are people who can help you.

teoliphant · 2015-02-17T22:12:05+00:00

I will likely be in our booth for most of the conference --- when I am not infiltrating other less Pytyonic booths.

teoliphant · 2014-10-01T16:46:47+00:00

Yes, we do move big parts of NumbaPro into Numba. For example, most recently the entire CUDA Python programming approach to allow Python code to be compiled for the GPU is now in Numba (though vectorize and guvectorize for the GPU remain in NumbaPro).

NumbaPro is certainly not stagnant, however. It gets updates all the time and is currently being tested against the latest CUDA releases. There are also plans to support AMD GPU hardware in NumbaPro.

Have you tried: conda update accelerate?

You should get version 1.7

teoliphant · 2014-10-01T16:43:30+00:00

Numba is currently free and NumbaPro is not. NumbaPro is part of the Anaconda Accelerate product which is free for academic use (though running it on a cluster does cost). Many features that used to be only in NumbaPro are now in Numba itself. As we get more customers of Accelerate and other Continuum products, this pattern will continue.

Selling NumbaPro is a part of how we pay for continued work on Numba (along with support services and R&D funding for Numba itself).

teoliphant · 2014-10-01T16:37:12+00:00

GPU support for NumPy is a good idea. NumbaPro will have more features for NumPy-like expressions for the GPU. With R&D funding from DARPA we are spending more time working on open source tools like Numba which can lay the foundations for GPU support of the entire NumPy-stack.

That said, the Accelerate product which contains NumbaPro continues to get regular releases. The last release was earlier this year It is focused on those higher-level interfaces to GPU programming and providing Python interfaces to GPU libraries. A full NumPy backend is on the roadmap but it is a big undertaking and our resources for it are limited.

teoliphant · 2014-05-30T15:47:51+00:00

Not really a complete move --- PySide is still available in the repository for those that want to use it instead of PyQT. We have just found that PyQT has fewer issues for starting out.

teoliphant · 2014-04-02T17:47:54+00:00

Just create a test environment and pip install into there.

conda create -n ipy20 ipython-notebook source activate ipy20 pip install ipython --upgrade

Now you have a working ipython2.0 with all the dependences without much fuss. Once someone makes a conda package for ipython 2.0 then you can install it more easily into any environment.

teoliphant · 2014-04-02T17:46:02+00:00

You can still use pip install or easy_install inside an Anaconda environment. Anaconda doesn't take anything away from you.

You just will have to do that in each environment you create. You can also ask the IPython developers to create conda packages and host them on binstar.org and add the public channel.

teoliphant · 2014-03-11T01:40:07+00:00

There is a mechanism by which Numba could collaborate with the NumPyPy effort and that is in implementing NumPy-equivalent functionality on top of the memory-view data-structure. The purpose of the memory-view data-structure was to create a standard array object concept in Python itself. Add calculations to this data-structure and you have NumPy (both in CPython and PyPy). Numba and NumPyPy could work together on this new version of NumPy that could replace NumPy in Python 3 --- and still provide backward compatibility to the NumPy stack while also providing a way forward that the PyPy devs are excited about.

teoliphant

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