zarr-developers/zarr-python: v2.4.0

Miles, Alistair; Kirkham, John; Durant, Martin; Bourbeau, J.; Onalan, Tarik; Hamman, Joseph; Patel, Zain; shikharsg,; Rocklin, Matthew; dussin, raphael; Schut, Vincent; Andrade, Elliott Sales de; Abernathey, Ryan; Noyes, Charles; sbalmer,; bot, pyup.io; Tran, Tommy; Saalfeld, Stephan; Swaney, Justin; Moore, Josh; Jevnik, Joe; Kelleher, Jerome; Funke, Jan; Sakkis, George; Barnes, C.; Banihirwe, Anderson

doi:10.5281/zenodo.3773450

Cited by 24 publications

(16 citation statements)

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“…Software: Astropy (The Astropy Collaboration et al (Rocklin 2015), IPython (Pérez & Granger 2007), Matplotlib (Hunter 2007), Numpy (Oliphant 2006), Scipy (Virtanen et al 2020), seaborn (Waskom et al 2018), Zarr (Miles et al 2020)…”

Section: Discussionmentioning

confidence: 99%

Nanoflare Diagnostics from Magnetohydrodynamic Heating Profiles

Knizhnik,

Barnes,

Reep

et al. 2020

Preprint

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The nanoflare paradigm of coronal heating has proven extremely promising for explaining the presence of hot, multi-million degree loops in the solar corona. In this paradigm, localized heating events supply enough energy to heat the solar atmosphere to its observed temperatures. Rigorously modeling this process, however, has proven difficult, since it requires an accurate treatment of both the magnetic field dynamics and reconnection as well as the plasma's response to magnetic perturbations. In this paper, we combine fully 3D magnetohydrodynamic (MHD) simulations of coronal active region plasma driven by photospheric motions with spatially-averaged, time-dependent hydrodynamic (HD) modeling of coronal loops to obtain physically motivated observables that can be quantitatively compared with observational measurements of active region cores. We take the behavior of reconnected field lines from the MHD simulation and use them to populate the HD model to obtain the thermodynamic evolution of the plasma and subsequently the emission measure distribution. We find the that the photospheric driving of the MHD model produces only very low-frequency nanoflare heating which cannot account for the full range of active region core observations as measured by the low-temperature emission measure slope. Additionally, we calculate the spatial and temporal distributions of field lines exhibiting collective behavior, and argue that loops occur due to random energization occurring on clusters of adjacent field lines.

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Section: Discussionmentioning

confidence: 99%

Nanoflare Diagnostics from Magnetohydrodynamic Heating Profiles

Knizhnik,

Barnes,

Reep

et al. 2020

Preprint

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“…On-disk files are loaded lazily using dask 19 , meaning content is only read in memory when requested. The object can be saved as a zarr 53 store. This allows handling of very large files that do not fit in the memory.…”

Section: Sqgrspatial_neighbors(adata Coord_type="generic" Delaunay=true)mentioning

confidence: 99%

Squidpy: a scalable framework for spatial omics analysis

et al. 2022

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Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.

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“…This is particularly needed as even pandas DataFrame has no canonical persistent data storage format. AnnData has chosen the self-describing hierarchical data formats HDF5 (Collette, 2013) and zarr (Miles et al, 2020) for this purpose (Figure 2), which are compatible with non-Python programming environments. The broad compatibility and high stability of the format led to wide adoption, and initiatives like the Human Cell Atlas (Regev et al, 2017) and HuBMAP (Consortium & others, 2019) distribute their single-cell omics datasets through .h5ad.…”

Section: The On-disk Formatmentioning

confidence: 99%