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import os
import numpy as np
from . import get_data_home
from .tools import download_with_progress_bar
DATA_URL = ("https://github.com/astroML/astroML-data/raw/main/datasets/"
"spec4000.npz")
def reconstruct_spectra(data):
"""Compute the reconstructed spectra.
Parameters
----------
data: NpzFile
numpy data object returned by fetch_sdss_corrected_spectra.
Returns
-------
spec_recons: ndarray
Reconstructed spectra, using principal components to interpolate
across the masked region.
"""
spectra = data['spectra']
coeffs = data['coeffs']
evecs = data['evecs']
mask = data['mask']
mu = data['mu']
norms = data['norms']
spec_recons = spectra.copy()
nev = coeffs.shape[1]
spec_fill = mu + np.dot(coeffs, evecs[:nev])
spec_fill *= norms[:, np.newaxis]
spec_recons[mask] = spec_fill[mask]
return spec_recons
def compute_wavelengths(data):
"""Compute the wavelength associated with spectra.
Paramters
---------
Parameters
----------
data: NpzFile
numpy data object returned by fetch_sdss_corrected_spectra.
Returns
-------
wavelength: ndarray
One-dimensional wavelength array for spectra.
"""
return 10 ** (data['coeff0']
+ data['coeff1'] * np.arange(data['spectra'].shape[1]))
def fetch_sdss_corrected_spectra(data_home=None,
download_if_missing=True):
"""Loader for Iterative PCA pre-processed galaxy spectra
Parameters
----------
data_home : optional, default=None
Specify another download and cache folder for the datasets. By default
all astroML data is stored in '~/astroML_data'.
download_if_missing : optional, default=True
If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.
Returns
-------
data : NpzFile
The data dictionary
Notes
-----
This is the file created by the example script
examples/datasets/compute_sdss_pca.py
"""
data_home = get_data_home(data_home)
data_file = os.path.join(data_home, os.path.basename(DATA_URL))
if not os.path.exists(data_file):
if not download_if_missing:
raise IOError('data not present on disk. '
'set download_if_missing=True to download')
print("downloading PCA-processed SDSS spectra from %s to %s"
% (DATA_URL, data_home))
databuffer = download_with_progress_bar(DATA_URL)
open(data_file, 'wb').write(databuffer)
data = np.load(data_file)
return data
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