mwaskom/seaborn: v0.9.0 (July 2018)

Waskom, Michael; Botvinnik, Olga; O'Kane, Drew; Hobson, Paul; Östblom, Joel; Lukauskas, Saulius; Gemperline, David C; Augspurger, Tom; Halchenko, Yaroslav; Cole, John B.; Warmenhoven, Jordi; Ruiter, Julian de; Pye, Cameron R.; Hoyer, Stephan; Vanderplas, Jake; Villalba, Santi; Kunter, Gero; Quintero, E. A.; Bachant, Pete; Martin, Matthew J.; Meyer, Kyle; Miles, Alistair; Ram, Yoav; Brunner, Thomas; Yarkoni, Tal; Williams, Mike Lee; Evans, Constantine; Fitzgerald, Clark; Brian,; Qalieh, Adel

doi:10.5281/zenodo.1313201

Cited by 113 publications

(37 citation statements)

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“…The complete source of this paper, including the data, code, and instructions for training the classification model, can be found at github.com/rice-solar-physics/synthetic-observablespaper-observations. Facility: SDO(AIA) Software: astropy (v3.1.0, The Astropy Collaboration et al 2018;The Astropy Collaboration 2018), dask (v1.0.0, Rocklin 2015), drms (v0.5, Glogowski et al 2019b,a), matplotlib (v3.0.2, Hunter 2007Caswell et al 2018), numpy (v1.15.4, Harris et al 2020), PythonTeX (v0.16, Poore 2015), scikit-learn (v0.20, Pedregosa et al 2011Grisel et al 2019), seaborn (v0.9.0, Waskom et al 2018), scipy (v1.1.0, Virtanen et al 2020, SolarSoftware (Freeland & Handy 1998), sunpy (v0.9.5, Mumford et al 2018…”

Section: Acknowledgmentsmentioning

confidence: 99%

Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables

Barnes

Bradshaw

Viall

2019

ApJ

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To adequately constrain the frequency of energy deposition in active region cores in the solar corona, systematic comparisons between detailed models and observational data are needed. In this paper, we describe a pipeline for forward modeling active region emission using magnetic field extrapolations and field-aligned hydrodynamic models. We use this pipeline to predict time-dependent emission from active region NOAA 1158 as observed by SDO/AIA for low-, intermediate-, and high-frequency nanoflares. In each pixel of our predicted multi-wavelength, time-dependent images, we compute two commonly-used diagnostics: the emission measure slope and the time lag. We find that signatures of the heating frequency persist in both of these diagnostics. In particular, our results show that the distribution of emission measure slopes narrows and the mean decreases with decreasing heating frequency and that the range of emission measure slopes is consistent with past observational and modeling work. Furthermore, we find that the time lag becomes increasingly spatially coherent with decreasing heating frequency while the distribution of time lags across the whole active region becomes more broad with increasing heating frequency. In a follow up paper, we train a random forest classifier on these predicted diagnostics and use this model to classify real AIA observations of NOAA 1158 in terms of the underlying heating frequency.

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Section: Acknowledgmentsmentioning

confidence: 99%

Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables

Barnes

Bradshaw

Viall

2019

ApJ

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“…We thank Akshay Agrawal, Shaojie Bai, Shane Barratt, Nicola Bastianello, Priya Donti, Zico Kolter, Christian Kroer, Maximilian Nickel, Alex Peysakhovich, Bartolomeo Stellato, and Mary Williamson for insightful discussions and acknowledge the Python community (Van Rossum and Drake Jr, 1995;Oliphant, 2007) for developing the core set of tools that enabled this work, including PyTorch (Paszke et al, 2019), Hydra (Yadan, 2019), Jupyter (Kluyver et al, 2016), Matplotlib (Hunter, 2007, seaborn (Waskom et al, 2018), numpy (Oliphant, 2006; Van Der Walt et al, 2011), pandas (McKinney, 2012, and SciPy (Jones et al, 2014).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Neural Fixed-Point Acceleration for Convex Optimization

Venkataraman¹,

Amos²

2021

Preprint

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Fixed-point iterations are at the heart of numerical computing and are often a computational bottleneck in real-time applications that typically need a fast solution of moderate accuracy. We present neural fixed-point acceleration which combines ideas from meta-learning and classical acceleration methods to automatically learn to accelerate fixed-point problems that are drawn from a distribution. We apply our framework to SCS, the state-of-the-art solver for convex cone programming, and design models and loss functions to overcome the challenges of learning over unrolled optimization and acceleration instabilities. Our work brings neural acceleration into any optimization problem expressible with CVXPY. The source code behind this paper is available at github.com/facebookresearch/neural-scs.

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“…DATA AVAIL-ABILITY: The simulation data and plotting scripts to reproduce the presented findings are available at Lichtenberg et al (2020a,b,c, osf.io/m4jh7). SOFTWARE: SPI-DER (Bower et al 2018), SOCRATES (Edwards & Slingo 1996), NUMPY (Harris et al 2020;Reddy et al 2020), SCIPY (Virtanen et al 2020a,b), PANDAS (McKinney 2010;Reback et al 2019), MATPLOTLIB (Hunter 2007;Caswell et al 2019), SEABORN (Waskom et al 2018).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Vertically resolved magma ocean-protoatmosphere evolution: H$_2$, H$_2$O, CO$_2$, CH$_4$, CO, O$_2$, and N$_2$ as primary absorbers

Lichtenberg,

Bower,

Hammond

et al. 2021

Preprint

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The earliest atmospheres of rocky planets originate from extensive volatile release during magma ocean epochs that occur during assembly of the planet. These establish the initial distribution of the major volatile elements between different chemical reservoirs that subsequently evolve via geological cycles. Current theoretical techniques are limited in exploring the anticipated range of compositional and thermal scenarios of early planetary evolution, even though these are of prime importance to aid astronomical inferences on the environmental context and geological history of extrasolar planets. Here, we present a coupled numerical framework that links an evolutionary, vertically-resolved model of the planetary silicate mantle with a radiative-convective model of the atmosphere. Using this method we investigate the early evolution of idealized Earth-sized rocky planets with end-member, clear-sky atmospheres dominated by either H 2 , H 2 O, CO 2 , CH 4 , CO, O 2 , or N 2 . We find central metrics of early planetary evolution, such as energy gradient, sequence of mantle solidification, surface pressure, or vertical stratification of the atmosphere, to be intimately controlled by the dominant volatile and outgassing history of the planet. Thermal sequences fall into three general classes with increasing cooling timescale: CO, N 2 , and O 2 with minimal effect, H 2 O, CO 2 , and CH 4 with intermediate influence, and H 2 with several orders of magnitude increase in solidification time and atmosphere vertical stratification. Our numerical experiments exemplify the capabilities of the presented modeling framework and link the interior and atmospheric evolution of rocky exoplanets with multi-wavelength astronomical observations.

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mwaskom/seaborn: v0.9.0 (July 2018)

Cited by 113 publications

References 0 publications

Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables

Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables

Neural Fixed-Point Acceleration for Convex Optimization

Vertically resolved magma ocean-protoatmosphere evolution: H$_2$, H$_2$O, CO$_2$, CH$_4$, CO, O$_2$, and N$_2$ as primary absorbers

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