Merge 8c8fcb9 into 88df245

.travis.yml

@@ -3,10 +3,9 @@ language: c
 env:
   matrix:
     - PYTHON_VERSION=2.7
-    - PYTHON_VERSION=3.3
-    - PYTHON_VERSION=3.4
     - PYTHON_VERSION=3.5
     - PYTHON_VERSION=3.6
+    - PYTHON_VERSION=3.7
   global:
     - SETUP_XVFB=True
     - CONDA_CHANNELS=conda-forge

CHANGES.rst

@@ -1,7 +1,8 @@
 1.3 (unreleased)
 ----------------
 
-- No changes yet.
+- Fixed an issue that occurred during the convolution if not all
+  SEDs had the same number of wavelengths. [#66]
 
 1.2 (2018-06-16)
 ----------------

appveyor.yml

@@ -16,6 +16,7 @@ environment:
       - PYTHON_VERSION: "2.7"
       - PYTHON_VERSION: "3.5"
       - PYTHON_VERSION: "3.6"
+      - PYTHON_VERSION: "3.7"
 
 platform:
     -x64

sedfitter/convolve/convolve.py

@@ -56,10 +56,10 @@ def _convolve_model_dir_1(model_dir, filters, overwrite=False):
         os.mkdir(model_dir + '/convolved')
 
     # Find all SED files to convolve
-    sed_files = (glob.glob(model_dir + '/seds/*.fits.gz') +
-                 glob.glob(model_dir + '/seds/*/*.fits.gz') +
-                 glob.glob(model_dir + '/seds/*.fits') +
-                 glob.glob(model_dir + '/seds/*/*.fits'))
+    sed_files = sorted(glob.glob(model_dir + '/seds/*.fits.gz') +
+                       glob.glob(model_dir + '/seds/*/*.fits.gz') +
+                       glob.glob(model_dir + '/seds/*.fits') +
+                       glob.glob(model_dir + '/seds/*/*.fits'))
 
     par_table = load_parameter_table(model_dir)
 
@@ -95,7 +95,7 @@ def _convolve_model_dir_1(model_dir, filters, overwrite=False):
         try:
             assert binned_nu is not None
             np.testing.assert_array_almost_equal_nulp(s.nu.value, binned_nu.value, 100)
-        except AssertionError:
+        except (ValueError, AssertionError):
             log.info('Rebinning filters')
             binned_filters = [f.rebin(s.nu) for f in filters]
             binned_nu = s.nu

sedfitter/convolve/monochromatic.py

@@ -48,10 +48,10 @@ def convolve_model_dir_monochromatic(model_dir, overwrite=False, max_ram=8,
         os.mkdir(model_dir + '/convolved')
 
     # Find all SED files to convolve
-    sed_files = (glob.glob(model_dir + '/seds/*.fits.gz') +
-                 glob.glob(model_dir + '/seds/*/*.fits.gz') +
-                 glob.glob(model_dir + '/seds/*.fits') +
-                 glob.glob(model_dir + '/seds/*/*.fits'))
+    sed_files = sorted(glob.glob(model_dir + '/seds/*.fits.gz') +
+                       glob.glob(model_dir + '/seds/*/*.fits.gz') +
+                       glob.glob(model_dir + '/seds/*.fits') +
+                       glob.glob(model_dir + '/seds/*/*.fits'))
 
     par_table = load_parameter_table(model_dir)
 

sedfitter/tests/test_full_pipeline.py

@@ -33,17 +33,26 @@ def generate_random_models_1(models_dir, aperture_dependent=False):
 
         sed.name = 'model_{0:04d}'.format(i)
         sed.distance = 1 * u.kpc
-        sed.wav = np.logspace(-2., 3., 100) * u.micron
+
+        # Make at least one SED have a different size as a regression test for a
+        # bug that caused the convolution to crash if SEDs did not all have the
+        # same size. Note that technically speaking the monochromatic
+        # 'convolution' will be wrong in this case, but this doesn't matter
+        # since the monochromatic convolution is not really going to be
+        # supported going forward.
+        n_wav = 100 if i < 4 else 105
+
+        sed.wav = np.logspace(-2., 3., n_wav) * u.micron
         sed.nu = sed.wav.to(u.Hz, equivalencies=u.spectral())
 
         if aperture_dependent:
             sed.apertures = np.logspace(1., 6., 10) * u.au
-            sed.flux = np.cumsum(np.random.random((10, 100)), axis=0) * u.mJy
+            sed.flux = np.cumsum(np.random.random((10, n_wav)), axis=0) * u.mJy
         else:
             sed.apertures = None
-            sed.flux = (1 + np.random.random((1, 100))) * u.mJy
+            sed.flux = (1 + np.random.random((1, n_wav))) * u.mJy
 
-        sed.error = sed.flux * np.random.random(100) / 100.
+        sed.error = sed.flux * np.random.random(n_wav) / 100.
 
         sed.write(os.path.join(models_dir, 'seds', sed.name + '_sed.fits'))