Merge pull request #9 from LCOGT/fix/output_file_data

Fix/output file data
LCOGT · May 2, 2024 · 3216981 · 3216981
2 parents befb4aa + 5e902a3
commit 3216981
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Showing 3 changed files with 17 additions and 9 deletions.
diff --git a/datalab/datalab_session/data_operations/data_operation.py b/datalab/datalab_session/data_operations/data_operation.py
@@ -122,7 +122,12 @@ def create_and_store_fits(self, hdu_list: fits.HDUList, percent=None, cur_percen
             large_jpg_url       = add_file_to_bucket(f'{self.cache_key}/{self.cache_key}-{index}-large.jpg', large_jpg_path)
             thumbnail_jpg_url   = add_file_to_bucket(f'{self.cache_key}/{self.cache_key}-{index}-small.jpg', thumbnail_jpg_path)
 
-            output.append({'large_url': large_jpg_url, 'thumbnail_url': thumbnail_jpg_url})
+            output.append({
+                'large_url': large_jpg_url,
+                'thumbnail_url': thumbnail_jpg_url,
+                'basename': f'{self.cache_key}-{index}',
+                'source': 'datalab'}
+            )
 
             if percent is not None and cur_percent is not None:
                 self.set_percent_completion(cur_percent + index/total_files * percent)

diff --git a/datalab/datalab_session/data_operations/long.py b/datalab/datalab_session/data_operations/long.py
@@ -37,7 +37,7 @@ def wizard_description():
         }
 
     def operate(self):
-        num_files = len(self.input_data.get('input_files', []))
+        num_files = max(len(self.input_data.get('input_files', [])), 1)
         per_image_timeout = ceil(float(self.input_data.get('duration', 60.0)) / num_files)
         for i, file in enumerate(self.input_data.get('input_files', [])):
             print(f"Processing long operation on file {file.get('basename', 'No basename found')}")

diff --git a/datalab/datalab_session/data_operations/median.py b/datalab/datalab_session/data_operations/median.py
@@ -44,18 +44,21 @@ def operate(self):
 
         log.info(f'Executing median operation on {len(input)} files')
 
-        image_data_list = self.get_fits_npdata(input, percent=0.4, cur_percent=0.0)
+        if len(input) > 0:
+            image_data_list = self.get_fits_npdata(input, percent=0.4, cur_percent=0.0)
 
-        stacked_data = stack_arrays(image_data_list)
+            stacked_data = stack_arrays(image_data_list)
 
-        # using the numpy library's median method
-        median = np.median(stacked_data, axis=2)
+            # using the numpy library's median method
+            median = np.median(stacked_data, axis=2)
 
-        hdu_list = create_fits(self.cache_key, median)
+            hdu_list = create_fits(self.cache_key, median)
 
-        output = self.create_and_store_fits(hdu_list, percent=0.6, cur_percent=0.4)
+            output = self.create_and_store_fits(hdu_list, percent=0.6, cur_percent=0.4)
 
-        output =  {'output_files': output}
+            output =  {'output_files': output}
+        else:
+            output = {'output_files': []}
 
         log.info(f'Median operation output: {output}')
         self.set_percent_completion(1.0)