SciPy: Open Source Scientific Tools for Python
- E. Jones, T. Oliphant, Pearu Peterson
- Computer Science
- 2001
SciPy 1.0: fundamental algorithms for scientific computing in Python
- Pauli Virtanen, R. Gommers, Y. Vázquez-Baeza
- Computer ScienceNature Methods
- 23 July 2019
An overview of the capabilities and development practices of SciPy 1.0 is provided and some recent technical developments are highlighted.
Array programming with NumPy
- Charles R. Harris, K. Millman, T. Oliphant
- PhysicsThe Naturalist
- 18 June 2020
How a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data is reviewed.
Python for Scientific Computing
- T. Oliphant
- Computer ScienceComputing in science & engineering (Print)
- 1 May 2007
Python is an excellent "steering" language for scientific codes written in other languages. However, with additional basic tools, Python transforms into a high-level language suited for scientific…
Guide to NumPy
- T. Oliphant
- Physics
- 15 September 2015
This is the second edition of Travis Oliphant's A Guide to NumPy, designed to be a reference that can be used by practitioners who are familiar with Python but want to learn more about NumPy and related tools.
Magnetic resonance elastography: Non-invasive mapping of tissue elasticity
- A. Manduca, T. Oliphant, R. Ehman
- BiologyMedical Image Anal.
- 1 December 2001
Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python
- Pauli Virtanen, R. Gommers, Y. Vázquez-Baeza
- Computer ScienceNature Methods
- 24 February 2020
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Complex‐valued stiffness reconstruction for magnetic resonance elastography by algebraic inversion of the differential equation
- T. Oliphant, A. Manduca, R. Ehman, J. Greenleaf
- GeologyMagnetic Resonance in Medicine
- 1 February 2001
It is demonstrated how a collection of data representing the full vector displacement field could be used to potentially estimate the full complex stiffness tensor.
A Bayesian perspective on estimating mean, variance, and standard-deviation from data
- T. Oliphant
- Mathematics
- 2006
After reviewing some classical estimators for mean, variance, and standard-deviation and showing that un-biased estimates are not usually desirable, a Bayesian perspective is employed to determine…
Accuracy of scatterometer-derived winds using the Cramer-Rao bound
- T. Oliphant, D. Long
- Environmental ScienceIEEE Transactions on Geoscience and Remote…
- 1 November 1999
The role of geophysical modeling error is considered and shown to play a significant role in the performance of near-surface wind estimates and the Cramer-Rao (C-R) bound is derived for wind estimation and its implications for wind retrieval are discussed.
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