Planet SciPy

Loops in Python explained with examples

for i in range(1,10,2):
  print(i)

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MOA: a theory for composable and verifiable tensor computations

NumFOCUS Hires Executive Operations Administrator

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Some questions and thoughts on journal peer review.

What's up with current peer review practice?

Create Dummy Data in Python

import pandas as pd
data = pd.DataFrame({"A" : ["John","Deep","Julia","Kate","Sandy"], 
                     "MonthSales" : [25,30,35,40,45]})

       A  MonthSales
0   John          25
1   Deep          30
2  Julia 

The Human Element in AI

The over 45 speakers at AnacondaCON 2019 delved into how machine learning, artificial intelligence, enterprise, and open source communities are accomplishing great things with data — from optimizing urban farming to identifying the elements in…

The post The Human Element in AI appeared first on Anaconda.

Things to think about when developing shotgun metagenome classifiers

Thoughts on goals and tradeoffs in classifying shotgun metagenome data.

Our submission for the NHGRI Human Genome Reference Center call

We wrote a big grant proposal!

PIP connection Error : SSL CERTIFICATE VERIFY FAILED

The most common issue in installing python package in a company's network is failure of verification of SSL Certificate. Sometimes company blocks some websites in their network so employees can't access these websites. Whenever they try to visit these websites, it shows "Access Denied because of company's policy". It causes connection error in reaching main python website.

Error looks like this :

Could not fetch URL https://pypi.python.org/: connection error: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:598)

PIP SSL Certification Issue

Solution :

Run the following command. Make sure to specify package name in <package_name>

pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org <package_name> -vvv

Suppose you want to install pandas package, you should submit the following line of command

pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org pandas -vvv

The --trusted-host option mark the host as trusted, even though it does not have valid or any HTTPS

AnacondaCON 2019 Day 3 Recap: The Need for Speed, “Delightful UX” in Dev Tools, LOTR Jokes and More.

Everyone at Anaconda is still feeling the love AnacondaCON 2019. Day 3 wrapped up last Friday with one more day of talks and sessions, highlighted by some powerhouse keynotes. Let’s get right to the good…

The post AnacondaCON 2019 Day 3 Recap: The Need for Speed, “Delightful UX” in Dev Tools, LOTR Jokes and More. appeared first on Anaconda.

Install Python Package

Python is one of the most popular programming language for data science and analytics. It is widely used for a variety of tasks in startups and many multi-national organizations. The beauty of this programming language is that it is open-source which means it is available for free and has very active community of developers across the world. Python developers share their solutions in the form of package or module with other python users. This tutorial explains various ways how to install python package.

Ways to Install Python Package

Method 1 : If Anaconda is already installed on your System

Anaconda is the data science platform which comes with pre-installed popular python packages and powerful IDE (Spyder) which has user-friendly interface to ease writing of python programming scripts.

If Anaconda is installed on your system (laptop), click on Anaconda Prompt as shown in the image below.

Anaconda Prompt

To install a python package or module, enter the code below in Anaconda Prompt -

pip install package-name

Install

News from the NIH Data Commons Pilot Phase Consortium

The NIH Data Commons Pilot Phase Consortium is dead! (Long live the NIH Data Commons!)

NumFOCUS Releases 2018 Annual Report

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NumFOCUS Announces Cambridge Spark will Host a PyData Conference in 2019

The post NumFOCUS Announces Cambridge Spark will Host a PyData Conference in 2019 appeared first on NumFOCUS.

Thoughts on joining Quansight Labs

Critically assessing open science - the CAOS meeting.

A summary of the CAOS open science meeting

Etch-A-Snap — The Raspberry Pi powered Etch-A-Sketch camera

Etch-A-Snap is (probably) the worlds first Etch-A-Sketch Camera. Powered by a Raspberry Pi Zero (or Zero W) it snaps photos just like any other camera, but outputs them by drawing to an Pocket Etch-A-Sketch screen. Quite slowly.

Photos are processed down to 240x144 pixel 1-bit (black & white) line drawings using …

Anaconda 2019.03 Release

Windows is the most popular operating system in the world and consistently has 75% or more of the worldwide desktop market. According to the JetBrains Python Developers Survey, 49% of Python developers use Windows as…

The post Anaconda 2019.03 Release appeared first on Anaconda.

AnacondaCON 2019 Day 2 Recap: AI in Medicine, Cataloging the Contents of Stars, and More!

What You Missed at AnacondaCON Day 2 We’re back with a recap of Day 2 of our annual AnacondaCON. (In case you missed it, you can read our Day 1 recap here). Things started off…

The post AnacondaCON 2019 Day 2 Recap: AI in Medicine, Cataloging the Contents of Stars, and More! appeared first on Anaconda.

AnacondaCON 2019 Day 1 Recap: Big-Time Learning

AnacondaCON 2019 is off to a great start. As in past years, we programmed Day 1 with product- and package-specific tutorials for those looking to get hands-on learning with Anaconda Enterprise tools. Spots in these…

The post AnacondaCON 2019 Day 1 Recap: Big-Time Learning appeared first on Anaconda.

3 Ways to Upskill in Python with DataCamp and Anaconda

DataCamp is proud to partner with Anaconda to offer eight courses on Conda and Python—in addition to the more than 70 total Python courses in DataCamp’s ever-expanding data science and analytics curriculum. Not sure where…

The post 3 Ways to Upskill in Python with DataCamp and Anaconda appeared first on Anaconda.

Now Hiring: Development Director

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Facebook joins NumFOCUS Corporate Sponsors

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NumFOCUS Welcomes Sam Brice as inaugural Visiting Fellow

The post NumFOCUS Welcomes Sam Brice as inaugural Visiting Fellow appeared first on NumFOCUS.

Anaconda Enterprise 5.3 Release

The Anaconda Product Team has released Anaconda Enterprise 5.3, an upgrade focused on platform stability and reliability to support our customers’ needs for continuous innovation. Customizable Static Endpoints Users can now customize a static endpoint,…

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Announcing Public Anaconda Package Download Data

I’m very happy to announce that starting today, we will be publishing summarized download data for all conda packages served in the Anaconda Distribution, as well as the popular conda-forge and bioconda channels.  The dataset…

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Computational Psychiatry: Combining multiple levels of analysis to understand brain disorders - PhD thesis

I noticed that as my personal website at my former university went down that my PhD thesis could not be found anywhere, so I'm posting it here.

During my PhD I explored how machine learning and computational modeling of the brain can be used to improve our understanding, and diagnostics …

NIXML: nix + YAML for easy reproducible environments

NumFOCUS Hires New Staff to Support Events and Financial Services

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Understanding and Improving Conda’s performance

Lately, we have been responding to issues about Conda’s speed.  We’re working on it and we wanted to explain a few of the facets that we’re looking at to solve the problem.   TL;DR: make…

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End of Life (EOL) for Python 2.7 is coming. Are you ready?

End of Life (EOL) for Python 2.7 is coming. Are you ready? We all knew it was coming. Back in 2014 when Guido van Rossum, Python’s creator and principal author, made the announcement, January 1,…

The post End of Life (EOL) for Python 2.7 is coming. Are you ready? appeared first on Anaconda.

Sustaining open source: thinking about communities of effort

Thinking about how to sustain open source.

My recent reading re sustaining open communities

What has Titus been reading lately?

Blosc joins NumFOCUS Sponsored Projects

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Gina Helfrich on the Changelog Podcast

The post Gina Helfrich on the Changelog Podcast appeared first on NumFOCUS.

Reduzindo a pilha

Threat models for open online scientific engagement?

What threats are there for scientists in engaging in open online discussions?

Intake released on Conda-Forge

Intake is a package for cataloging, finding and loading your data. It has been developed recently by Anaconda, Inc., and continues to gain new features. To read general information about Intake and how to use…

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Announcement: Audio TK 3.1.0

NumFOCUS Announces Netflix as Corporate Sponsor

The post NumFOCUS Announces Netflix as Corporate Sponsor appeared first on NumFOCUS.

RPM and Debian Repositories for Miniconda

Conda, the package manager from Anaconda, is now available as either a RedHat RPM or as a Debian package. The packages are the equivalent to the Miniconda installer which only contains Conda and its dependencies.…

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Telemulta

My foreword to "Bayesian Analysis with Python, 2nd Edition" by Osvaldo Martin

When Osvaldo asked me to write the foreword to his new book I felt honored, excited, and a bit scared, so naturally I accepted. What follows is my best attempt to convey what makes probabilistic programming so exciting to me. Osvaldo did a great job with the book, it is …

Call for Answers: Survey About Task Assignment

Revisiting authorship, and JOSS software publications

The question du jour: how should authorship on software papers be decided?

Using Bayesian Decision Making to Optimize Supply Chains

Gyroscopic 3D wireframe cube — Using a 3-axis gyro for live 3D perspective

This little project combines the previous accelerometer-gyroscope code with the 3D rotating OLED cube to produce a 3D cube which responds to gyro input, making it possible to "peek around" the cube with simulated perspective, or make it spin with a flick of the wrist.

Take a look at those …

Julia Co-Creators To Receive 2019 James H. Wilkinson Prize for Numerical Software

The post Julia Co-Creators To Receive 2019 James H. Wilkinson Prize for Numerical Software appeared first on NumFOCUS.

Mestrado em Ciência da Computação na UFPA 2019: Inteligência Computacional para Smart Grids; Metaheurísticas

LaKademy 2018

LaKademy 2018

GPU Dask Arrays, first steps

The following code creates and manipulates 2 TB of randomly generated data.

import dask.array as da

rs = da.random.RandomState()
x = rs.normal(10, 1, size=(500000, 500000), chunks=(10000, 10000))
(x + 1)[::2, ::2].sum().compute(scheduler='threads')

On a single CPU, this computation takes two hours.

On an eight-GPU single-node system this computation takes nineteen seconds.

Actually this computation isn’t that impressive. It’s a simple workload, for which most of the time is spent creating and destroying random data. The computation and communication patterns are simple, reflecting the simplicity commonly found in data processing workloads.

What is impressive is that we were able to create a distributed parallel GPU array quickly by composing these three existing libraries:

1. CuPy provides a partial implementation of Numpy on the GPU.

2. Dask Array provides chunked algorithms on top of Numpy-like libraries like Numpy and CuPy.

   This enables us to operate on more data than we could fit in memory by operating on that data in

3-axis Accelerometer-Gyro — Measuring acceleration and orientation with an MPU6050

Measuring acceleration and rotation has a lot of useful applications, from drone or rocket stablisation to making physically interactive handheld games.

An accelerometer measures proper acceleration, meaning the rate of change of velocity relative to it's own rest frame. This is in contrast to coordinate acceleration, which is relative to …

Manage project dependencies with conda environments

TL;DR: Create a conda environment for each project, capture exact versions when possible, automate activation and updating with a bash function.

I often work on several different projects involving software: Python libraries, papers, presentations, posters, this website, etc. Each project has different dependencies and there is a non-zero chance that these dependencies might be in conflict with each other. For example, I need Python 2.7 to work on a tesseroid modeling paper with a student, while my current work on

First Impressions of GPUs and PyData

I recently moved from Anaconda to NVIDIA within the RAPIDS team, which is building a PyData-friendly GPU-enabled data science stack. For my first week I explored some of the current challenges of working with GPUs in the PyData ecosystem. This post shares my first impressions and also outlines plans for near-term work.

First, lets start with the value proposition of GPUs, significant speed increases over traditional CPUs.

Like many PyData developers, I’m loosely aware that GPUs are sometimes fast, but don’t deal with them often enough to have strong feeling about them.

To get a more visceral feel for the performance differences, I logged into a GPU machine, opened up CuPy (a Numpy-like GPU library developed mostly by Chainer in Japan) and cuDF (a Pandas-like library in development at NVIDIA) and did a couple of small speed comparisons:

>>> import numpy, cupy

>>> x = numpy.random.random((10000, 10000))
>>> y = cupy.random.random((10000, 10000))

>>> %timeit bool((numpy.sin(x) ** 2 + numpy.cos(x) ** 2 == 1).all())
446 ms ± 53.1 ms per

Double Your Donation: NumFOCUS Receives $5,000 Matching Gift Challenge (updated: SUCCESS!)

The post Double Your Donation: NumFOCUS Receives $5,000 Matching Gift Challenge (updated: SUCCESS!) appeared first on NumFOCUS.

A quick read of _The genomic and proteomic landscape of the rumen microbiome_

Using short and long reads to assemble genomes from metagenomes!

Support Python 2 with Cython

Many popular Python packages are dropping support for Python 2 next month. This will be painful for several large institutions. Cython can provide a temporary fix by letting us compile a Python 3 codebase into something usable by Python 2 in many cases.

It’s not clear if we should do this, but it’s an interesting and little known feature of Cython.

Many major numeric Python packages are dropping support for Python 2 at the end of this year. This includes packages like Numpy, Pandas, and Scikit-Learn. Jupyter already dropped Python 2 earlier this year.

For most developers in the ecosystem this isn’t a problem. Most of our packages are Python-3 compatible and we’ve learned how to switch libraries. However, for larger companies or government organizations it’s often far harder to switch. The PyCon 2017 keynote by Lisa Guo and Hui Ding from Instagram gives a good look into why this can be challenging for large production codebases and also gives a good

Anatomy of an OSS Institutional Visit

I recently visited the UK Meteorology Office, a moderately large organization that serves the weather and climate forecasting needs of the UK (and several other nations). I was there with other open source colleagues including Joe Hamman and Ryan May from open source projects like Dask, Xarray, JupyterHub, MetPy, Cartopy, and the broader Pangeo community.

This visit was like many other visits I’ve had over the years that are centered around showing open source tooling to large institutions, so I thought I’d write about it in hopes that it helps other people in this situation in the future.

My goals for these visits are the following:

1. Teach the institution about software projects and approaches that may help them to have a more positive impact on the world
2. Engage them in those software projects and hopefully spread around the maintenance and feature development burden a bit

We were invited by early adopters within the institution, both within the UK Met Office’s Informatics Lab

The story behind the spacegraphcats project and paper

The TRUE story!

Notes on the Frank-Wolfe Algorithm, Part II: A Primal-dual Analysis

This blog post extends the convergence theory from the first part of my notes on the Frank-Wolfe (FW) algorithm with convergence guarantees on the primal-dual gap which generalize and strengthen the convergence guarantees obtained in the first part.

Creating a welcoming teaching/learning environment in workshops

It takes constant work to make a welcoming teaching/learning environment!

Repeatability in Practice (2018 version)

How we do repeatability in the DIB Lab

Linking to emails in org-mode (using neomutt)

Where we store links to emails in org-mode, and open them using neomutt.

Ode ao ódio

Eleições 2018: Minha carta para a família

A arquitetura de compartilhamentos do Telegram para mitigar as fake news no WhatsApp

So you want to contribute to open source

Welcome new open source contributor!

I appreciated receiving the e-mail where you said you were excited about getting into open source and were particularly interested in working on a project that I maintain. This post has a few thoughts on the topic.

First, please forgive me for sending you to this post rather than responding with a personal e-mail. Your situation is common today, so I thought I’d write up thoughts in a public place, rather than respond personally.

This post has two parts:

1. Some pragmatic steps on how to get started
2. A personal recommendation to think twice about where you focus your time

Most open source software (OSS) projects have a “Good first issue” label on their Github issue tracker. Here is a screenshot of how to find the “good first issue” label on the Pandas project:

(note that this may be named something else like “Easy to fix”)

This contains a list of issues that are important, but also

Ciro em frente!

Announcement: Audio TK 3.0.0

3D wireframe cube with MicroPython — Basic 3D model rotation and projection

An ESP2866 is never going to compete with an actual graphics card. But it has more than enough oomph to explore the fundamentals of 3D graphics. In this short tutorial we'll go through the basics of creating a 3D scene and displaying it on an OLED screen using MicroPython.

This …

QtConsole 4.4 Released!

We're excited to announce a significant update to QtConsole—the package that powers Spyder's IPython Console interface—which the Spyder team maintains in collaboration with Project Jupyter. Two of the biggest changes—user-selectable syntax highlighting themes, and enhanced external editor/IDE integration—are already built right into Spyder, so they'll likely be of more interest if you use QtConsole standalone or with another editor/IDE. However, most of the other changes should prove quite useful within Spyder as well, and many were in fact suggested and even implemented by users of our IDE. Particular highlights include a block indent/unindent feature, Select-All (Ctrl-Shift-A) being made cell-specific, Ctrl-Backspace and Ctrl-Delete behaving more intelligently across whitespace and line boundaries, Ctrl-D allowing you to easily exit ipdb, input() and the like, and numerous smaller enhancements and bug fixes. If you'd like to learn more about what's new, please check out our article over on the Jupyter blog, where we go over the major changes in more detail, with plenty

Dask Development Log

This work is supported by Anaconda Inc

To increase transparency I’m trying to blog more often about the current work going on around Dask and related projects. Nothing here is ready for production. This blogpost is written in haste, so refined polish should not be expected.

Since the last update in the 0.19.0 release blogpost two weeks ago we’ve seen activity in the following areas:

1. Update Dask examples to use JupyterLab on Binder
2. Render Dask examples into static HTML pages for easier viewing
3. Consolidate and unify disparate documentation
4. Retire the hdfs3 library in favor of the solution in Apache Arrow.
5. Continue work on hyper-parameter selection for incrementally trained models
6. Publish two small bugfix releases
7. Blogpost from the Pangeo community about combining Binder with Dask
8. Skein/Yarn Update

The new dask-labextension embeds Dask’s dashboard plots into a JupyterLab session so that you can get easy access to information

A foundation for scikit-learn at Inria

We have just announced that a foundation will be supporting scikit-learn at Inria [1]: scikit-learn.fondation-inria.fr

This is an exciting turn for us, because it enables us to receive private funding. As a result, we will be able to have secure employment for some existing core …

Introducing Verde

Verde is a Python library for processing spatial data (bathymetry, geophysics surveys, etc) and interpolating it on regular grids (i.e., gridding).

It implements Green's functions based interpolation methods and other data processing routines. The type of gridding implemented in Verde is essentially fitting various linear models to spatial data and using them to predict new data on regular grids, which is what a lot of machine learning is all about. So Verde's gridder API is inspired on scikit-learn, the state-of-the-art for machine learning in Python. The Green's functions that make up the Jacobian matrix (aka sensitivity or feature matrix) of the linear models generally come from elastic deformation theory. For example, the bi-harmonic spline (Sandwell, 1987) implemented in verde.Spline comes from the deformation of a thin elastic plate.

I submitted a

The Waiting Time Paradox, or, Why Is My Bus Always Late?

Image Source: Wikipedia License CC-BY-SA 3.0

If you, like me, frequently commute via public transit, you may be familiar with the following situation:

You arrive at the bus stop, ready to catch your bus: a line that advertises arrivals every 10 minutes. You glance at your watch and note the time... and when the bus finally comes 11 minutes later, you wonder why you always seem to be so unlucky.

Naïvely, you might expect that if buses are coming every 10 minutes and you arrive at a random time, your average wait would be something like 5 minutes. In reality, though, buses do not arrive exactly on schedule, and so you might wait longer. It turns out that under some reasonable assumptions, you can reach a startling conclusion:

When waiting for a bus that comes on average every 10 minutes, your average waiting time will be 10 minutes.

This is what is sometimes known as the waiting time paradox.

I've encountered this idea before, and always wondered

Three Operator Splitting

Dask Release 0.19.0

This work is supported by Anaconda Inc.

I’m pleased to announce the release of Dask version 0.19.0. This is a major release with bug fixes and new features. The last release was 0.18.2 on July 23rd. This blogpost outlines notable changes since the last release blogpost for 0.18.0 on June 14th.

You can conda install Dask:

conda install dask

or pip install from PyPI:

pip install dask[complete] --upgrade

Full changelogs are available here:

• dask/dask
• dask/distributed

A ton of work has happened over the past two months, but most of the changes are small and diffuse. Stability, feature parity with upstream libraries (like Numpy and Pandas), and performance have all significantly improved, but in ways that are difficult to condense into blogpost form.

That being said, here are a few of the more exciting changes in the new release.

We’ve dropped official support for Python 3.4 and added official support for Python 3.7.

Over the past few months Jim Crist has bulit a suite of

Book: Building Machine Learning Systems with Python – third edition

Summer school announcement: 2nd Advanced Scientific Programming in Python (ASPP) Asia Pacific!

Abstract for SIAM: Supporting and Sustaining Open Source Software Development: the Commons Perspective

How do we support and sustain open source software development?

Analog modelling: The Moog ladder filter emulation in Python

High level performance of Pandas, Dask, Spark, and Arrow

This work is supported by Anaconda Inc

How does Dask dataframe performance compare to Pandas? Also, what about Spark dataframes and what about Arrow? How do they compare?

I get this question every few weeks. This post is to avoid repetition.

1. This answer is likely to change over time. I’m writing this in August 2018
2. This question and answer are very high level. More technical answers are possible, but not contained here.

If you’re coming from Python and have smallish datasets then Pandas is the right choice. It’s usable, widely understood, efficient, and well maintained.

The performance benefit (or drawback) of using a parallel dataframe like Dask dataframes or Spark dataframes over Pandas will differ based on the kinds of computations you do:

1. If you’re doing small computations then Pandas is always the right choice. The administrative costs of parallelizing will outweigh any benefit. You should not parallelize if your computations are taking less

Displaying images on OLED screens — Using 1-bpp images in MicroPython

We've previously covered the basics of driving OLED I2C displays from MicroPython, including simple graphics commands and text. Here we look at displaying monochrome 1 bit-per-pixel images and animations using MicroPython on a Wemos D1.

Processing the images and correct choice of image-formats is important to get the most detail …

Driving I2C OLED displays with MicroPython — I2C monochrome displays with SSD1306

These mini monochrome OLED screens make great displays for projects — perfect for data readout, simple UIs or monochrome games.

Raindar — Desktop daily weather, forecast app in PyQt

The Raindar UI was created using Qt Designer, and saved as .ui file, which is available for download. This was converted to an importable Python file using pyuic5.

Before running the application you need to obtain a API key from OpenWeatherMap.org. This key is unique to you …

Public Institutions and Open Source Software

As general purpose open source software displaces domain-specific all-in-one solutions, many institutions are re-assessing how they build and maintain software to support their users. This is true across for-profit enterprises, government agencies, universities, and home-grown communities.

While this shift brings opportunities for growth and efficiency, it also raises questions and challenges about how these institutions should best serve their communities as they grow increasingly dependent on software developed and controlled outside of their organization.

• How do they ensure that this software will persist for many years?
• How do they influence this software to better serve the needs of their users?
• How do they transition users from previous all-in-one solutions to a new open source platform?
• How do they continue to employ their existing employees who have historically maintained software in this field?
• If they have a mandate to support this field, what is the best role for them to play, and how can they justify their efforts to the groups that control their budget?

This blogpost

Can bits be the basis for a digital commons? (No.)

Bits cannot be the basis for a digital commons, because they are not rivalrous.

Spyder 3.3.0 and 3.3.1 released!

We're pleased to release the next significant update in the stable Spyder 3 line, 3.3.0, along with its follow-on bugfix point release, 3.3.1, which is now live on PyPI and conda. As always, you can update with conda update spyder in the Anaconda Prompt/Terminal/command line (on Windows/macOS/Linux, respectively) if on Anaconda (recommended), or pip update spyder otherwise. If you run into any trouble, please carefully read our new installation documentation and consult our Troubleshooting Guide, which contains straightforward solutions to the vast majority of install-related issues users have reported.

As a new minor version (3.3), it makes several substantial changes to Spyder's underpinnings that deserve some explanation, particularly the newly modular and portable console system that's now separated into its own spyder-kernels package, opening up several new options for users running Spyder in different environments. There's also a brand-new error reporting process, new options in the IPython console, usability and performance improvements for the Variable Explorer, multiple new and changed dependency requirements

Hierarchical Bayesian Neural Networks with Informative Priors

Spyder featured on Episode 1 of Open Source Directions web show

Quansight, the company recently founded by NumPy, SciPy and Anaconda creator Travis Oliphant to help connect companies with open source communities built around data science and machine learning, just released Episode 1 of its live webcast series, and it was all about Spyder! Spyder maintainer Carlos Córdoba, recently hired by Quansight and funded part-time to work on Spyder development as we announced a few weeks ago, was the featured guest on the show.

Carlos first shared his perspective on some of the key moments in Spyder's nearly 10-year development history, from its original creation by Pierre Raybaut and Carlos' initial involvement in the project to its more recent challenges and successes. He also demonstrated basic usage of Spyder, as well as some of its standout features, in a live on-screen demo. Carlos then went on to outline the current roadmap for Spyder 4 in the near future, and explained some of the key new features planned for it. Finally, he took

NeuroML2/LEMS is moving into Neural Mass Models and whole brain networks

"Labor" and "Engaged effort"

Are "effort" and "labor" the same?

Sprint on scikit-learn, in Paris and Austin

Two weeks ago, we held a scikit-learn sprint in Austin and Paris. Here is a brief report, on progresses and challenges.

Websites for Earth Scientists on the academic job hunt

This is a list of the websites I use to search for academic jobs in the Earth Sciences (geophysics, geology, oceanography, meteorology, etc). They've been very useful to me (I found my current position through the CIG mailing list) and I hope that this post can help others who are looking to take the next step in their academic careers.

These sites list everything from Masters and PhD scholarships to postdoc positions and tenure-track professorships. Note that they are biased toward the US, Canada, Oceania, and Europe.

Sign up for these and get email updates when new opportunities are posted (most are updated daily):

• ES_JOBS_NET: I get around 10 emails from this list a day. Lately, I'm seeing a lot of

State of the Spyder, Part 2: Looking up

After sharing some major milestones, development progress, and other tidbits from the past six months in Part 1 of this series (check that one out first if you haven't already), we now have some amazing news to share with you all here in Part 2, along with other status updates. That's not all, though—Part 3 will look ahead toward Spyder 4 and beyond, unveiling and explaining our full roadmap and going over the future possibilities even further afield.

First up, we're thrilled to announce a major part of what's making that plan possible (along with your support, of course!). This May, Spyder was awarded a $3000 development grant from NumFOCUS, an organization promoting better science through open code, to help with finishing Spyder 4! NumFOCUS is a nonprofit dedicated to supporting key scientific computing projects; promoting sustainability in the open source ecosystem; educating the next generation of scientists, engineers, developers and data analysts through their flagship

Introducing Pooch

A friend to fetch your sample data files.

Pooch is a Python package that manages downloading data files over HTTP and storing them in a local directory. It is meant to be used by other Python libraries that ship sample data files for use in documentation, workshops, demos, etc.

For example, your package could define a datasets.py module that has functions to load sample data (like scikit-learn does). If you want the data to live on the web (like in the Github repo) instead of shipping it with your package, Pooch can keep track of it and download it to the user's computer only when it's needed.

This is what a datasets.py module would look like using Pooch:

"""
Module mypackage/datasets.py
"""
import pooch

# Get the version string from your project. You have one of these, right?
from

The road to scikit-image 1.0

The Open Source Anti-Sisyphean League

We need an Open Source Anti-Sisyphean League!

Does batting order matter in Major League Baseball? A simulation approach