PYSAT: Python']Python Satellite Data Analysis Toolkit

被引:16
|
作者
Stoneback, R. A. [1 ]
Burrell, A. G. [1 ]
Klenzing, J. [2 ]
Depew, M. D. [1 ]
机构
[1] Univ Texas Dallas, Phys Dept, WB Hanson Ctr Space Sci, Richardson, TX 75083 USA
[2] NASA, Goddard Space Flight Ctr, Greenbelt, MD USA
基金
美国国家科学基金会;
关键词
SUPERDARN; FUTURE;
D O I
10.1029/2018JA025297
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
A common problem in space science data analysis is combining complementary data sources that are provided and analyzed in different formats and programming languages. The Python Satellite Data Analysis Toolkit (pysat) addresses this issue by providing an open source toolkit that implements the general process of space science data analysis, from beginning to end, in an instrument-independent manner. This toolkit uses an Instrument object that enables systematic analysis of science data from a variety of platforms within a single interface. Basic functions such as downloading, loading, and cleaning are included for all supported instruments. Common analysis routines are also included, which are instrument and data source independent. A nanokernel is used to provide instrument independence, it is attached to the Instrument object and mediates the systematic and arbitrary modification of loaded data. Pysat uses the nanokernel to improve the rigor of time series analysis, support on-the-fly orbit determination, and cleanly span file breaks. Pysat's functions and higher-level scientific analysis features are validated through the use of unit testing. Further adoption by the community provides a set of scientific results produced by a common core, constituting a distributed heritage that supports the validity of the underlying processing and scientific output. These features are used to demonstrate consistency between derived electron density profiles and measured ion drifts, particularly downward ion drifts in the afternoon hours during extreme solar minimum. Pysat builds upon open source Python software that is freely available and encourages community-driven development.
引用
收藏
页码:5271 / 5283
页数:13
相关论文
共 50 条
  • [21] TAT-HUM: Trajectory analysis toolkit for human movements in Python']Python
    Wang, Xiaoye Michael
    Welsh, Timothy N.
    BEHAVIOR RESEARCH METHODS, 2024, 56 (04) : 4103 - 4129
  • [22] Geophysical data analysis using Python']Python
    Sáenz, J
    Zubillaga, J
    Fernández, J
    COMPUTERS & GEOSCIENCES, 2002, 28 (04) : 457 - 465
  • [23] Python']Python Scripting for CIAO Data Analysis
    Galle, Elizabeth C.
    Anderson, Craig S.
    Bonaventura, Nina R.
    Burke, D. J.
    Fruscione, Antonella
    Lee, Nicholas P.
    McDowell, Jonathan C.
    ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XX, 2011, 442 : 131 - 134
  • [24] PyVecContour: A Python']Python toolkit for vectorized isosurface mapping
    Ma, Jinfeng
    Zheng, Hua
    Li, Ruonan
    Rao, Kaifeng
    Yang, Yanzheng
    Li, Weifeng
    SOFTWAREX, 2023, 21
  • [25] Chameleon: A Python']Python Workflow Toolkit for Feature Selection
    Thilakeswaran, Diviya
    McManis, Simon
    Wang, X. Rosalind
    DATA MINING, AUSDM 2021, 2021, 1504 : 121 - 135
  • [26] KinZ an Azure Kinect toolkit for Python']Python and Matlab
    Terven, Juan R.
    Cordova-Esparza, Diana M.
    SCIENCE OF COMPUTER PROGRAMMING, 2021, 211
  • [27] PyAEM: A Python']Python toolkit for aquatic ecosystem modelling
    Huang, Jiacong
    Kong, Ming
    Zhang, Chen
    Cui, Zhen
    Tian, Feng
    Gao, Junfeng
    ECOLOGICAL INFORMATICS, 2020, 60
  • [28] FlowKit: A Python']Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows
    White, Scott
    Quinn, John
    Enzor, Jennifer
    Staats, Janet
    Mosier, Sarah M.
    Almarode, James
    Denny, Thomas N.
    Weinhold, Kent J.
    Ferrari, Guido
    Chan, Cliburn
    FRONTIERS IN IMMUNOLOGY, 2021, 12
  • [29] PyBDR: Set-Boundary Based Reachability Analysis Toolkit in Python']Python
    Ding, Jianqiang
    Wu, Taoran
    Liang, Zhen
    Xue, Bai
    FORMAL METHODS, PT II, FM 2024, 2025, 14934 : 140 - 157
  • [30] pyCSEP: A Python']Python Toolkit for Earthquake Forecast Developers
    Savran, William H.
    Bayona, Jose A.
    Iturrieta, Pablo
    Asim, Khawaja M.
    Bao, Han
    Bayliss, Kirsty
    Herrmann, Marcus
    Schorlemmer, Danijel
    Maechling, Philip J.
    Werner, Maximilian J.
    SEISMOLOGICAL RESEARCH LETTERS, 2022, 93 (05) : 2858 - 2870