"""Public access points for the NF2 framework.
The top-level package intentionally avoids importing heavy astronomy and deep
learning dependencies until a caller asks for the matching helper.
"""
__version__ = "0.4.1"
[docs]
def load(path, device=None):
"""Load an NF2 result and return the matching output helper.
Parameters
----------
path:
Path to a ``.nf2`` checkpoint.
device:
Optional PyTorch device used for evaluation.
Returns
-------
CartesianOutput or SphericalOutput
Geometry-specific output helper selected from checkpoint metadata.
"""
import torch
from nf2.evaluation.output import CartesianOutput, SphericalOutput
state = torch.load(path, map_location="cpu", weights_only=False)
geometry = state.get("data", {}).get("type")
if geometry == "cartesian":
return CartesianOutput(path, device=device)
if geometry == "spherical":
return SphericalOutput(path, device=device)
raise ValueError(f"Unsupported NF2 geometry: {geometry!r}")
[docs]
def run(*args, **kwargs):
"""Run a single NF2 extrapolation.
The function accepts either the normalized runtime configuration used by
:mod:`nf2.extrapolate` or the public v0.4 YAML-style schema. When the
public schema is passed, it is normalized before training starts.
"""
data = kwargs.get("data", {})
if data.get("geometry") is not None or ("data" in kwargs and "boundaries" not in data):
from nf2.train.config import normalize_config
kwargs = normalize_config(kwargs)
from nf2.extrapolate import run as _run
return _run(*args, **kwargs)
[docs]
def run_series(*args, **kwargs):
"""Run an NF2 extrapolation series.
Accepts the same public configuration schema as :func:`run`, with series
placeholders expanded before the data module is built.
"""
data = kwargs.get("data", {})
if data.get("geometry") is not None or ("data" in kwargs and "boundaries" not in data):
from nf2.train.config import normalize_config
kwargs = normalize_config(kwargs)
from nf2.extrapolate_series import run as _run_series
return _run_series(*args, **kwargs)
[docs]
def export_file(*args, **kwargs):
"""Export one NF2 result file to VTK, NPZ, HDF5, or FITS."""
from nf2.export import export_file as _export_file
return _export_file(*args, **kwargs)
[docs]
def export_series(*args, **kwargs):
"""Export multiple NF2 result files matched by one or more glob patterns."""
from nf2.export import export_series as _export_series
return _export_series(*args, **kwargs)
[docs]
def download_sharp_series(*args, **kwargs):
"""Download a SHARP data series through JSOC/DRMS."""
from nf2.data.download import download_SHARP_series
return download_SHARP_series(*args, **kwargs)
[docs]
def download_hmi_full_disk(*args, **kwargs):
"""Download HMI full-disk vector data through JSOC/DRMS."""
from nf2.data.download import download_hmi_full_disk as _download_hmi_full_disk
return _download_hmi_full_disk(*args, **kwargs)
[docs]
def download_hmi_sharp(*args, **kwargs):
"""Download HMI SHARP vector data through JSOC/DRMS."""
from nf2.data.download import download_hmi_sharp as _download_hmi_sharp
return _download_hmi_sharp(*args, **kwargs)
[docs]
def download_hmi_synoptic(*args, **kwargs):
"""Download HMI synoptic vector maps through JSOC/DRMS."""
from nf2.data.download import download_hmi_synoptic as _download_hmi_synoptic
return _download_hmi_synoptic(*args, **kwargs)
def __getattr__(name):
if name in {"CartesianOutput", "DisambiguationOutput", "HeightTransformOutput", "SphericalOutput"}:
from nf2.evaluation import output
return getattr(output, name)
raise AttributeError(name)
__all__ = [
"CartesianOutput",
"DisambiguationOutput",
"HeightTransformOutput",
"SphericalOutput",
"download_hmi_full_disk",
"download_hmi_sharp",
"download_hmi_synoptic",
"download_sharp_series",
"export_file",
"export_series",
"load",
"run",
"run_series",
]
