Fixed bug when redshifts argument to Classify() is empty.

Raised error when trying to use the (Agnostic redshift & Classify Host) model until it is included in the package.
daniel-muthukrishna · Mar 27, 2019 · 6b8ca6d · 6b8ca6d
1 parent bfbd2b7
commit 6b8ca6d
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Showing 2 changed files with 11 additions and 4 deletions.
diff --git a/astrodash/classify.py b/astrodash/classify.py
@@ -37,6 +37,8 @@ def __init__(self, filenames=[], redshifts=[], smooth=6, minWave=3500, maxWave=1
             self.knownZ = True
         else:
             self.knownZ = False
+        if not self.redshifts:
+            self.redshifts = [None] * len(filenames)
         self.rlapScores = rlapScores
         self.pars = get_training_parameters()
         self.nw, w0, w1 = self.pars['nw'], self.pars['w0'], self.pars['w1']
@@ -54,8 +56,11 @@ def __init__(self, filenames=[], redshifts=[], smooth=6, minWave=3500, maxWave=1
                                                   "models/zeroZ/tensorflow_model.ckpt")
         else:
             if self.classifyHost:
-                self.modelFilename = os.path.join(self.scriptDirectory, data_files,
-                                                  "models/agnosticZ_classifyHost/tensorflow_model.ckpt")
+                raise ValueError("A model that classifies the host while simulatenously not knowing redshift does not "
+                                 "exist currently. Please try one of the other 3 models or check back at a later "
+                                 "date. Contact the author for support or further queries.")
+                # self.modelFilename = os.path.join(self.scriptDirectory, data_files,
+                #                                   "models/agnosticZ_classifyHost/tensorflow_model.ckpt")
             else:
                 self.modelFilename = os.path.join(self.scriptDirectory, data_files,
                                                   "models/agnosticZ/tensorflow_model.ckpt")
@@ -103,7 +108,7 @@ def list_best_matches(self, n=5, saveFilename='DASH_matches.txt'):
             for i in range(20):
                 host, name, age = classification_split(bestTypes[specNum][i])
                 if not self.knownZ:
-                    redshift, _, redshiftErr = self.calc_redshift(inputImages[i], name, age, inputMinMaxIndexes[i])[0]
+                    redshift, _, redshiftErr = self.calc_redshift(inputImages[i], name, age, inputMinMaxIndexes[i])
                     redshifts.append(redshift)
                     redshiftErrs.append(redshiftErr)
                 prob = softmaxes[specNum][i]
@@ -125,8 +130,10 @@ def list_best_matches(self, n=5, saveFilename='DASH_matches.txt'):
 
         if not redshifts:
             redshifts = self.redshifts
+            redshiftErrs = [None] * len(self.redshifts)
         else:
             redshifts = np.array(redshifts)
+            redshiftErrs = np.array(redshiftErrs)
 
         if saveFilename:
             self.save_best_matches(bestMatchLists, redshifts, bestBroadTypes, rlapLabels, matchesReliableLabels,

diff --git a/setup.py b/setup.py
@@ -9,7 +9,7 @@
     long_description = f.read()
 
 setup(name='astrodash',
-    version='1.0.14',
+    version='1.0.15',
     description='Deep Learning for Automated Spectral Classification of Supernovae',
     long_description=long_description,
     url='https://github.com/daniel-muthukrishna/DASH',