The official Python Cloud Foundry buildpack has support for conda environments using the environment.yml file. This provides a lot of flexibility for Python (and other) dependencies and helps you to use packages from other public and private sources including your own locally built ones. Using other conda channels One thing this allows you to do is reference other channels such as conda-forge, and get packages from there instead of the standard Anaconda provided default channel.

I’m excited to be giving a talk at the Data Science Lab meetup which has recently started in London. The topic is going to be how to use the open source Cloud Foundry platform for data science.

I’m very happy to be giving a talk at the latest PyData conference in New York this weekend.

This is a long post but I wanted a place to collect all the code I am showing in my talk and to provide a few more resources for those interested in trying out Python on Cloud Foundry further.

Resources

• Simple Flask app
• Slides from the talk
• My new Cloud Foundry buildpack using conda
• Video of Flask app deployment
• Update: Video of PyData NY talk

What is Cloud Foundry?

My talk is about how to use Python and the PyData stack on Cloud Foundry the open source cloud platform. Cloud Foundry started life at VMware and development transferred to Pivotal when it was formed. Cloud Foundry has grown much bigger since then with over 30 companies joining together to form the Cloud Foundry Foundation which will guide the development of the open source project.

What tools do you need to bring to a DataDive? The next DataKind UK DataDive is taking place in two weeks time in London. I took part in one of the previous DataDives and I would highly recommend the experience for anyone with data science or analytical skills who wants to help charities use their data.

The DataDives take place over the course of a weekend and in that time you have to decide on a charity to work with, understand their data and goals, perform your analysis and present your results in a usable form. That’s a lot to get through in just over two days so it’s very important to be able to get up and running quickly with the analysis. I thought it might be useful to list the software and tools that I will be packing in my DataDive toolbelt this time around.

The new INSPIRE HEP database has been up and running for a while now, and is going from strength to strength (despite some recent wobbles concerning citation counts). I recently needed to get the BibTeX entries for a few papers and instead of copy-pasting from the results web page each time I wondered whether a more programmatic solution existed. There used to be a few utilities for SPIRES which enabled you to get results using a script but I haven’t seen any that do this with INSPIRE (although there is a plugin for Jabref).

My latest paper has just hit the arXiv and is now available. The paper builds on the numerical work I previously completed on cosmological perturbations beyond linear order. The new results do not assume slow-roll in the calculation of the source term for the second order equations of motion and so allow a much greater range of potentials to be analysed. The paper is called “Second Order Perturbations During Inflation Beyond Slow-roll” and already has a record on SPIRES and Inspire Beta.

This is a slightly more technical post than usual but having figured out how to do something quite esoteric in Matplotlib I thought I would write it down to save me remembering. I have been making quite a few plots recently for a paper which should hit the arXiv very soon. The Python plotting package Matplotlib has been indispensable in this regard, especially as I took the effort of creating a script which creates all the plots.

My new paper, written with Karim Malik, has just been released into the wild. We show that it is possible to numerically simulate second order perturbations for a single scalar field with a canonical action. I’ve been working on this for a long time and learned a lot about the mechanics of inflation in the process. I’ve also churned out quite a lot of python code, and learned some rudimentary parallel programming.