Numerical Python

Dubois, Paul F.; Hinsen, Konrad; Hugunin, James

doi:10.1063/1.4822400

Cited by 110 publications

(67 citation statements)

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“…The background gate had in total the same number of channels as the γ gate. Equation 1 was fit to experimental points with a non-linear least-squares algorithm using Numerical Python [15] and Lmfit libraries [16]. The correctness of the fit was confirmed by changing background gates as well as by choosing different minimalization algorithms and comparing the results.…”

Section: Resultsmentioning

confidence: 99%

β-decay study of neutron-rich bromine and krypton isotopes

Miernik¹,

Rykaczewski²,

Grzywacz³

et al. 2013

Phys. Rev. C

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Section: Resultsmentioning

confidence: 99%

β-decay study of neutron-rich bromine and krypton isotopes

Miernik¹,

Rykaczewski²,

Grzywacz³

et al. 2013

Phys. Rev. C

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“…Hierarchical Clustering of Proteomics Data-Enhanced proteomics data was clustered using the Python (56) and Python modules numpy (57) and scipy (58) combined in the Python module pyGCluster (Jaeger et al, manuscript in prep). Briefly, agglomerative hierarchical clustering for different distance-linkage combinations was performed using a pool of (1) six methods to define the distance among clusters (linkage): "complete," "average," "weighted," "centroid," "median," "ward," and (2) two functions to calculate the distance between clusters (metric): "Euclidean" and "correlation."…”

Section: Methodsmentioning

confidence: 99%

The Metabolic Status Drives Acclimation of Iron Deficiency Responses in Chlamydomonas reinhardtii as Revealed by Proteomics Based Hierarchical Clustering and Reverse Genetics

Höhner

Barth

Magneschi

et al. 2013

Molecular & Cellular Proteomics

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Iron is a crucial cofactor in numerous redox-active proteins operating in bioenergetic pathways including respiration and photosynthesis. Cellular iron management is essential to sustain sufficient energy production and minimize oxidative stress. To produce energy for cell growth, the green alga Chlamydomonas reinhardtii possesses the metabolic flexibility to use light and/or carbon sources such as acetate. To investigate the interplay between the iron-deficiency response and growth requirements under distinct trophic conditions, we took a quantitative proteomics approach coupled to innovative hierarchical clustering using different "distance-linkage combinations" and random noise injection. Protein co-expression analyses of the combined data sets revealed insights into cellular responses governing acclimation to iron deprivation and regulation associated with photosynthesis dependent growth. Photoautotrophic growth requirements as well as the iron deficiency induced specific metabolic enzymes and stress related proteins, and yet differences in the set of induced enzymes, proteases, and redoxrelated polypeptides were evident, implying the establishment of distinct response networks under the different conditions. Moreover, our data clearly support the notion that the iron deficiency response includes a hierarchy for iron allocation within organelles in C. reinhardtii. Importantly, deletion of a bifunctional alcohol and acetaldehyde dehydrogenase (ADH1), which is induced under low iron based on the proteomic data, attenuates the remodeling of the photosynthetic machinery in response to iron deficiency, and at the same time stimulates expression of stress-related proteins such as NDA2, LHCSR3, and PGRL1. This finding provides evidence that the coordinated regulation of bioenergetics pathways and iron deficiency response is sensitive to the cellular and chloroplast metabolic and/or redox status, consistent with systems approach data. Molecular &

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“…We are also interested in extending our work with the Python scripting language and exploring extensions such as Numerical Python which provide high-level mathematical operations on arrays and matrices [22]. We have no plans to build a sophisticated graphical user interface at this time.…”

Section: Discussionmentioning

confidence: 99%

Lightweight computational steering of very large scale molecular dynamics simulations

Beazley

Lomdahl

1996

Proceedings of the 1996 ACM/IEEE Conference on Supercomputing

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Abstract:We present a computational steering approach for controlling, analyzing, and visualizing very large scale molecular dynamics simulations involving tens to hundreds of millions of atoms. Our approach relies on extensible scripting languages and an easy to use tool for building extensions and modules. The system is extremely easy to modify, works with existing C code, is memory efficient, and can be used from inexpensive workstations and networks. We demonstrate how we have used this system to manipulate data from production MD simulations involving as many as 104 million atoms running on the CM-5 and Cray T3D. We also show how this approach can be used to build systems that integrate common scripting languages (including Tcl/Tk, Perl, and Python), simulation code, user extensions, and commercial data analysis packages.

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Numerical Python

Cited by 110 publications

References 0 publications

β-decay study of neutron-rich bromine and krypton isotopes

β-decay study of neutron-rich bromine and krypton isotopes

The Metabolic Status Drives Acclimation of Iron Deficiency Responses in Chlamydomonas reinhardtii as Revealed by Proteomics Based Hierarchical Clustering and Reverse Genetics

Lightweight computational steering of very large scale molecular dynamics simulations

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