59 min
EP027 Scientific Computing with SciPy and NumPy FLOSS for Science
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- Technology
In episode 27, we interviewed Ralf Gommers from the NumPy and SciPy projects. We started our discussion by talking about his past research experience as a physicist and his transition to open source software and programming. This led him to get involved in projects such as PyWavelets, NumPy and SciPy. Following that, we had a great discussion about NumPy, its many features, its target audience and its performance. We learned why NumPy is not included in Python's standard library and its overlap with Scipy. We also compared the combination of Matlab to NumPy and Python and how users could transition to this open source solution. We then had a brief discussion about SciPy and the features it provides. Ralf informed us of the positive results from Google's previous Summer of Code and Season of Docs participations. We discussed how to reach the project and the many kinds of contributions that they are looking for. We talked about the importance of FLOSS for science and attribution of research output. We finished the interview with our classic quick questions and a reflection from Ralf about the need for more sustainability in open source software development as volunteer effort may not be sufficient in the future.
00:00:00 Intro
00:00:18 Introduction
00:00:33 Introducing Ralf Gommers
00:02:05 Research during his PhD and and PostDoc
00:03:20 When he started to use open source tools
00:03:52 Learning to code
00:04:39 PyWavelets, another sideproject he likes
00:05:55 His elevator pitch for NumPy
00:06:55 Vector arrays in Python before NumPy
00:07:49 How he got involved in the NumPy project
00:10:13 Traget users for NumPy
00:11:36 NumPy as part of the standard library?
00:13:24 Features provided by NumPy
00:14:22 Major differences between Python built-in list and NumPy's array
00:16:01 Structured data
00:16:45 Why appending a row to an array is made hard
00:18:09 Multithreaded code with NumPy
00:19:48 Distributed array processing
00:20:50 GPU computation with Python and NumPy
00:22:16 Linear algebra functions in NumPy
00:23:25 Overlap between SciPy and NumPy for linear algebra
00:23:55 Python speed as an interpreted language
00:25:43 Python with NumPy compared to Matlab
00:28:07 How easy is the transition between Matlab and Python Numpy
00:29:26 Performance difference between Matlab and Python
00:31:00 Commercial applications of NumPy
00:32:15 Contributions from the industry ans incentives to contribute
00:34:10 Elevator pitch for SciPy
00:35:37 Overview of some of the submodules in SciPy
00:38:11 The size of the communities
00:39:33 Participation in Google Summer of Code
00:40:24 Participation in Google Season of Docs
00:41:48 Communication channels in the project
00:43:25 Where to ask for support?
00:44:48 Possible contributions
00:46:25 Skills usefull to contribute to the NumPy project
00:48:12 Identifying possible contributions
00:48:52 The importance of FLOSS for science
00:52:02 Possible negative impact of FLOSS on science
00:52:49 Crediting contributions in science
00:53:42 Most notable scientific discovery in recent years
00:54:49 His favourite text processing tool
00:55:30 Volunteer effort may not be sufficient anymore
00:56:58 Contact informations for Ralf Gommers
00:57:27 Outro
In episode 27, we interviewed Ralf Gommers from the NumPy and SciPy projects. We started our discussion by talking about his past research experience as a physicist and his transition to open source software and programming. This led him to get involved in projects such as PyWavelets, NumPy and SciPy. Following that, we had a great discussion about NumPy, its many features, its target audience and its performance. We learned why NumPy is not included in Python's standard library and its overlap with Scipy. We also compared the combination of Matlab to NumPy and Python and how users could transition to this open source solution. We then had a brief discussion about SciPy and the features it provides. Ralf informed us of the positive results from Google's previous Summer of Code and Season of Docs participations. We discussed how to reach the project and the many kinds of contributions that they are looking for. We talked about the importance of FLOSS for science and attribution of research output. We finished the interview with our classic quick questions and a reflection from Ralf about the need for more sustainability in open source software development as volunteer effort may not be sufficient in the future.
00:00:00 Intro
00:00:18 Introduction
00:00:33 Introducing Ralf Gommers
00:02:05 Research during his PhD and and PostDoc
00:03:20 When he started to use open source tools
00:03:52 Learning to code
00:04:39 PyWavelets, another sideproject he likes
00:05:55 His elevator pitch for NumPy
00:06:55 Vector arrays in Python before NumPy
00:07:49 How he got involved in the NumPy project
00:10:13 Traget users for NumPy
00:11:36 NumPy as part of the standard library?
00:13:24 Features provided by NumPy
00:14:22 Major differences between Python built-in list and NumPy's array
00:16:01 Structured data
00:16:45 Why appending a row to an array is made hard
00:18:09 Multithreaded code with NumPy
00:19:48 Distributed array processing
00:20:50 GPU computation with Python and NumPy
00:22:16 Linear algebra functions in NumPy
00:23:25 Overlap between SciPy and NumPy for linear algebra
00:23:55 Python speed as an interpreted language
00:25:43 Python with NumPy compared to Matlab
00:28:07 How easy is the transition between Matlab and Python Numpy
00:29:26 Performance difference between Matlab and Python
00:31:00 Commercial applications of NumPy
00:32:15 Contributions from the industry ans incentives to contribute
00:34:10 Elevator pitch for SciPy
00:35:37 Overview of some of the submodules in SciPy
00:38:11 The size of the communities
00:39:33 Participation in Google Summer of Code
00:40:24 Participation in Google Season of Docs
00:41:48 Communication channels in the project
00:43:25 Where to ask for support?
00:44:48 Possible contributions
00:46:25 Skills usefull to contribute to the NumPy project
00:48:12 Identifying possible contributions
00:48:52 The importance of FLOSS for science
00:52:02 Possible negative impact of FLOSS on science
00:52:49 Crediting contributions in science
00:53:42 Most notable scientific discovery in recent years
00:54:49 His favourite text processing tool
00:55:30 Volunteer effort may not be sufficient anymore
00:56:58 Contact informations for Ralf Gommers
00:57:27 Outro
59 min