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Abstract Over the past few decades, the measurement precision of some pulsar timing experiments has advanced from ∼10 μ s to ∼10 ns, revealing many subtle phenomena. Such high precision demands both careful data handling and sophisticated timing models to avoid systematic error. To achieve these goals, we present PINT ( P INT I s N ot T empo3 ), a high-precision Python pulsar timing data analysis package, which is hosted on GitHub and available on the Python Package Index (PyPI) as pint-pulsar . PINT is well tested, validated, object oriented, and modular, enabling interactive data analysis and providing an extensible and flexible development platform for timing applications. It utilizes well-debugged public Python packages (e.g., the N um P y and A stropy libraries) and modern software development schemes (e.g., version control and efficient development with git and GitHub) and a continually expanding test suite for improved reliability, accuracy, and reproducibility. PINT is developed and implemented without referring to, copying, or transcribing the code from other traditional pulsar timing software packages (e.g., Tempo / Tempo2 ) and therefore provides a robust tool for cross-checking timing analyses and simulating pulse arrival times. In this paper, we describe the design, use, and validation of PINT , and we compare timing results between it and Tempo and Tempo2 .
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nGauge: Integrated and Extensible Neuron Morphology Analysis in Python
The study of neuron morphology requires robust and comprehensive methods to quantify the differences between neurons of different subtypes and animal species. Several software packages have been developed for the analysis of neuron tracing results stored in the standard SWC format. The packages, however, provide relatively simple quantifications and their non-extendable architecture prohibit their use for advanced data analysis and visualization. We developed nGauge, a Python toolkit to support the parsing and analysis of neuron morphology data. As an application programming interface (API), nGauge can be referenced by other popular open-source software to create custom informatics analysis pipelines and advanced visualizations. nGauge defines an extendable data structure that handles volumetric constructions (e.g. soma), in addition to the SWC linear reconstructions, while remaining lightweight. This greatly extends nGauge’s data compatibility.
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- Award ID(s):
- 1707316
- PAR ID:
- 10353054
- Date Published:
- Journal Name:
- Neuroinformatics
- ISSN:
- 1539-2791
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s12021-022-09573-8
