WIP: Discussion PR on SpectrumCollections decorator

specutils/manipulation/resample.py

@@ -6,9 +6,9 @@
 from astropy.nddata import StdDevUncertainty, VarianceUncertainty, InverseVariance
 
 from ..spectra import Spectrum1D
+from ..spectra.spectrum_collection import runs_on_spectrum_collection
 
-__all__ = ['ResamplerBase', 'FluxConservingResampler',
-           'LinearInterpolatedResampler', 'SplineInterpolatedResampler']
+__all__ = ['ResamplerBase', 'FluxConservingResampler', 'LinearInterpolatedResampler', 'SplineInterpolatedResampler']
 
 
 class ResamplerBase(ABC):
@@ -137,6 +137,7 @@ def _resample_matrix(self, orig_lamb, fin_lamb):
 
         # set bins that don't overlap 100% with original bins
         # to zero by checking edges, and applying generated mask
+        # to zero by checking edges, and applying generated mask
         left_clip = np.where(fin_edges[:-1] - orig_edges[0] < 0, 0, 1)
         right_clip = np.where(orig_edges[-1] - fin_edges[1:] < 0, 0, 1)
         keep_overlapping_matrix = left_clip * right_clip
@@ -145,6 +146,7 @@ def _resample_matrix(self, orig_lamb, fin_lamb):
 
         return resamp_mat
 
+    @runs_on_spectrum_collection
     def resample1d(self, orig_spectrum, fin_lamb):
         """
         Create a re-sampling matrix to be used in re-sampling spectra in a way

specutils/spectra/spectrum_collection.py

@@ -1,4 +1,5 @@
 import logging
+import functools
 
 import astropy.units as u
 import numpy as np
@@ -244,3 +245,34 @@ def __repr__(self):
     Uncertainty type:    {}""".format(
             self.ndim, self.shape, self.flux.unit, self.spectral_axis.unit,
             self.uncertainty.uncertainty_type if self.uncertainty is not None else None)
+
+
+def runs_on_spectrum_collection(func):
+    """
+    Decorator to wrap Spectrum1D manipulation functionality so that it can
+    run on a SpectrumCollection
+
+    For this decorator to work, the first argument in your function/method
+    should be the Spectrum1D or SpectrumCollection object, although if it's
+    not it will simply pass the original function.
+
+    Another fallout, right now it's expecting this to be attached to a class
+    method, since it's expecting the first argument to be  self, and the second
+    argument to be the spectrum1D or SpectrumCollection
+    """
+    @functools.wraps(func)
+
+    def wrapper(sarg, farg, *args, **kwargs):
+
+        if isinstance(farg, SpectrumCollection):
+            new_spectra = []
+
+            for spec in farg:
+                new_spectra.append(func(sarg, spec, *args, **kwargs))
+
+            return SpectrumCollection.from_spectra(new_spectra)
+
+        else:
+            return func(sarg, farg, *args, **kwargs)
+
+    return wrapper

specutils/tests/test_resample.py

@@ -4,7 +4,7 @@
 from astropy.nddata import InverseVariance, StdDevUncertainty
 from astropy.tests.helper import assert_quantity_allclose
 
-from ..spectra.spectrum1d import Spectrum1D
+from ..spectra  import Spectrum1D, SpectrumCollection
 from ..tests.spectral_examples import simulated_spectra
 from ..manipulation.resample import FluxConservingResampler, LinearInterpolatedResampler, SplineInterpolatedResampler
 
@@ -162,3 +162,20 @@ def test_expanded_grid_interp_spline():
     assert_quantity_allclose(results.flux,
                             np.array([np.nan, 3.98808594, 6.94042969, 6.45869141,
                                       5.89921875, 7.29736328, np.nan, np.nan, np.nan])*u.mJy)
+
+
+def test_spectrum_collection():
+    """
+    Test if spectrum collection decorator is working with resample
+    """
+
+    spec = Spectrum1D(spectral_axis=np.linspace(0, 50, 50) * u.AA,
+                      flux = np.random.randn(50) * u.Jy,
+                      uncertainty = StdDevUncertainty(np.random.sample(50), unit='Jy'))
+    spec1 = Spectrum1D(spectral_axis=np.linspace(20, 60, 50) * u.AA,
+                       flux = np.random.randn(50) * u.Jy,
+                       uncertainty = StdDevUncertainty(np.random.sample(50), unit='Jy'))
+    spec_coll = SpectrumCollection.from_spectra([spec, spec1])
+
+    inst = FluxConservingResampler()
+    results = inst(spec_coll, np.linspace(20,40,40))