schwimmbad: A uniform interface to parallel processing pools in Python

Price-Whelan, Adrian M.; Foreman-Mackey, Daniel

doi:10.21105/joss.00357

Cited by 31 publications

(7 citation statements)

References 4 publications

(2 reference statements)

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“…The data visualization in this work used the software package getdist [85]. The MCMC chains were run in parallel with MPI through the use of the schwimmbad library [86].…”

Section: A More On Perturbation Theorymentioning

confidence: 99%

Constraining Ultralight Axions with Galaxy Surveys

Laguë,

Bond,

Hložek

et al. 2021

Preprint

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Ultralight axions and other bosons are dark matter candidates present in many high energy physics theories beyond the Standard Model. In particular, the string axiverse postulates the existence of up to O(100) light scalar bosons constituting the dark sector. Considering a mixture of axions and cold dark matter, we obtain upper bounds for the axion relic density Ω a h 2 < 0.004 for axions of mass 10 −31 eV ≤ m a ≤ 10 −26 eV at 95% confidence. We also improve existing constraints by a factor of over 4.5 and 2.1 for axion masses of 10 −25 eV and 10 −32 eV, respectively. We use the Fourier-space galaxy clustering statistics from the Baryon Oscillation Spectroscopic Survey (BOSS) and demonstrate how galaxy surveys break important degeneracies in the axion parameter space compared to the cosmic microwave background (CMB). We test the validity of the effective field theory of large-scale structure approach to mixed ultralight axion dark matter by making our own mock galaxy catalogs and find an anisotropic ultralight axion signature in the galaxy quadrupole. We also observe an enhancement of the linear galaxy bias from 1.8 to 2.4 when allowing for 5% of the dark matter to be composed of a 10 −28 eV axion in our simulations. Finally, we develop an augmented interpolation scheme allowing a fast computation of the axion contribution to the linear matter power spectrum leading to a 70% reduction of the computational cost for the full Monte Carlo Markov chains analysis.

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“…The data visualization in this work used the software package getdist [85]. The MCMC chains were run in parallel with MPI through the use of the schwimmbad library [86].…”

Section: A More On Perturbation Theorymentioning

confidence: 99%

Constraining Ultralight Axions with Galaxy Surveys

Laguë,

Bond,

Hložek

et al. 2021

Preprint

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“…Users can find answers to popular and even niche questions on forums. 81 Second, the Python ecosystem is rich in a variety of add-on packages for efficient numerical computing [82][83][84] , image processing 85,86 , scientific analysis 87 , machine-learning 88 , deep-learning [89][90][91] , file-handling 92 , graphical user interfaces (GUI) 93 , graphical processing unit (GPU) computing 94 , parallel computing [95][96][97][98] , and data visualization 99,100 that dramatically simplify and shrink the time necessary to develop code. Third, the majority of the existing packages for material science and microscopy, especially for reading proprietary file formats are sometimes only available in python.…”

Section: Softwarementioning

confidence: 99%

USID and Pycroscopy – Open Source Frameworks for Storing and Analyzing Imaging and Spectroscopy Data

Somnath¹,

Smith

Laanait³

et al. 2019

Microsc Microanal

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Materials science is undergoing profound changes due to advances in characterization instrumentation that have resulted in an explosion of data in terms of volume, velocity, variety and complexity. Harnessing these data for scientific research requires an evolution of the associated computing and data infrastructure, bridging scientific instrumentation with super-and cloudcomputing. Here, we describe Universal Spectroscopy and Imaging Data (USID), a data model capable of representing data from most common instruments, modalities, dimensionalities, and sizes. We pair this schema with the hierarchical data file format (HDF5) to maximize compatibility, exchangeability, traceability, and reproducibility. We discuss a family of community-driven, open-source, and free python software packages for storing, processing and visualizing data. The first is pyUSID which provides the tools to read and write USID HDF5 files in addition to a scalable framework for parallelizing data analysis. The second is Pycroscopy, which provides algorithms for scientific analysis of nanoscale imaging and spectroscopy modalities and is built on top of pyUSID and USID. The instrument-agnostic nature of USID facilitates the development of analysis code independent of instrumentation and task in Pycroscopy which in turn can bring scientific communities together and break down barriers in the age of openscience. The interested reader is encouraged to be a part of this ongoing community-driven effort to collectively accelerate materials research and discovery through the realms of big data.

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“…The pyMBMS mz peaks were annotated as described in Sykes et al (2009), as done previously in Muchero et al (2015).…”

Section: Pymbms Phenotype Datamentioning

confidence: 99%

“…Lignin-related pyMBMS peaks, as described in Sykes et al (2009), Davis et al (2006), and Muchero et al (2015) were identified among the pyMBMS GWAS hits, and are shown in Table S2. Lignin-related metabolites and metabolites in the lignin pathway were also identified among the metabolomics GWAS hits, a list of which can be seen in Table S3.…”

Section: Target Lignin Genes/phenotypesmentioning

confidence: 99%

Pleiotropic and Epistatic Network-Based Discovery: Integrated Networks for Target Gene Discovery

et al. 2018

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Biological organisms are complex systems that are composed of functional networks of interacting molecules and macro-molecules. Complex phenotypes are the result of orchestrated, hierarchical, heterogeneous collections of expressed genomic variants. However, the effects of these variants are the result of historic selective pressure and current environmental and epigenetic signals, and, as such, their co-occurrence can be seen as genome-wide correlations in a number of different manners. Biomass recalcitrance (i.e., the resistance of plants to degradation or deconstruction, which ultimately enables access to a plant's sugars) is a complex polygenic phenotype of high importance to biofuels initiatives. This study makes use of data derived from the re-sequenced genomes from over 800 different Populus trichocarpa genotypes in combination with metabolomic and pyMBMS data across this population, as well as co-expression and co-methylation networks in order to better understand the molecular interactions involved in recalcitrance, and identify target genes involved in lignin biosynthesis/degradation. A Lines Of Evidence (LOE) scoring system is developed to integrate the information in the different layers and quantify the number of lines of evidence linking genes to target functions. This new scoring system was applied to quantify the lines of evidence linking genes to lignin-related genes and phenotypes across the network layers, and allowed for the generation of new hypotheses surrounding potential new candidate genes involved in lignin biosynthesis in P. trichocarpa, including various AGAMOUS-LIKE genes. The resulting Genome Wide Association Study networks, integrated with Single Nucleotide Polymorphism (SNP) correlation, co-methylation, and co-expression networks through the LOE scores are proving to be a powerful approach to determine the pleiotropic and epistatic relationships underlying cellular functions and, as such, the molecular basis for complex phenotypes, such as recalcitrance.

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schwimmbad: A uniform interface to parallel processing pools in Python

Cited by 31 publications

References 4 publications

Constraining Ultralight Axions with Galaxy Surveys

Constraining Ultralight Axions with Galaxy Surveys

USID and Pycroscopy – Open Source Frameworks for Storing and Analyzing Imaging and Spectroscopy Data

Pleiotropic and Epistatic Network-Based Discovery: Integrated Networks for Target Gene Discovery

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