Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use Astropy core for astronomy units, coordinates, times, tables, FITS/WCS files, modeling, statistics, visualization, cosmology, configuration, and public CLIs.
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See how to installReview the instructions the agent will receive before you install.
Use this skill when a task needs the Astropy core Python package for astronomy or astrophysics analysis. Astropy is a CPU scientific-Python library; selected workflows do not require GPU or accelerator backends. Use an installed Astropy package and the bundled references/scripts here rather than reopening the source repository.
import astropy
from astropy import units as u
print(astropy.__version__)
print((42 * u.km / u.s).to(u.m / u.s))
If plotting, fitting, convolution FFT paths, or common dataframe bridges are needed, prefer installing the common optional set:
python -m pip install "astropy[recommended]"
Use astropy[all] only for broad optional integrations such as HDF5, Parquet,
S3/fsspec, Jupyter, or additional astronomy packages.
Time, timescales, SkyCoord, coordinate frames, transformations,
separations, matching, and observation geometry, read
sub-skills/time-coordinates/SKILL.md.Table/QTable, unified I/O, FITS, ASCII/ECSV, VOTable, HDF5/Parquet
notes, pandas/dataframe bridges, and table/FITS CLIs, read
sub-skills/tables-io/SKILL.md.NDData, CCDData,
masks, uncertainties, and image metadata containers, read
sub-skills/wcs-nddata/SKILL.md.fits2bitmap, kernels, direct convolution, and FFT convolution, read
sub-skills/visualization-convolution/SKILL.md..to_value(unit) and record the unit.format=, scale=, frame=, unit=, origin=, and
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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