Feature/copy wcs output

datalab/datalab_session/analysis/variable_star.py

@@ -45,18 +45,19 @@ def variable_star(input: dict, user: User):
       continue
 
     target_source = find_target_source(cat_hdu, target_ra, target_dec)
-
     if target_source is None:
       log.info(f"No source found matching target coordinates: RA={target_ra}, DEC={target_dec} in image {basename}")
       excluded_images.append(basename)
       continue
 
     try:
       mag = target_source['mag']
+      print(f'mag: {mag}')
       magerr = target_source['magerr']
     except KeyError as e:
       # If mag or magerr is not present, fallback convert flux to mag
       mag, magerr = flux_to_mag(target_source['flux'], target_source['fluxerr'])
+      print(f'flux mag: {mag}')
       flux_fallback = True
     except Exception as e:
       log.warning(f"Invalid magnitude or magnitude error for target in image {basename}")
@@ -69,6 +70,7 @@ def variable_star(input: dict, user: User):
       'julian_date': Time(image.get("observation_date")).jd,
       'observation_date': image.get("observation_date")
     })
+    print(f'light_curve: {light_curve}')
 
   try:
     frequency, power, period, fap = calculate_period(light_curve)

datalab/datalab_session/analysis/wcs.py

@@ -2,6 +2,7 @@
 from astropy.wcs import WCS, WcsError
 from django.contrib.auth.models import User
 
+
 from datalab.datalab_session.exceptions import ClientAlertException
 from datalab.datalab_session.utils.file_utils import get_hdu
 from datalab.datalab_session.utils.filecache import FileCache

datalab/datalab_session/data_operations/fits_output_handler.py

@@ -2,12 +2,14 @@
 import os
 import numpy as np
 from astropy.io import fits
+import logging
 
 from datalab.datalab_session.utils.file_utils import create_jpgs, temp_file_manager
 from datalab.datalab_session.utils.s3_utils import save_files_to_s3
 from datalab.datalab_session.utils.filecache import FileCache
 
-
+log = logging.getLogger()
+log.setLevel(logging.INFO)
 class FITSOutputHandler():
   """A class to handle FITS output files and create jpgs.
 
@@ -35,6 +37,7 @@ def __init__(self, cache_key: str, data: np.array, dir: str, comment: str=None,
       self.primary_hdu = fits.PrimaryHDU(header=fits.Header([('DLAB_KEY', cache_key)]))
       self.image_hdu = fits.CompImageHDU(data=data, header=data_header, name='SCI')
       self.dir = dir
+      log.info(f"[DEBUG] Initializing FITSOutputHandler with header: {data_header}")
 
       if comment: self.set_comment(comment)
 
@@ -45,6 +48,7 @@ def set_comment(self, comment: str):
     """Add a comment to the FITS file."""
     self.primary_hdu.header.add_comment(comment)
 
+  ## add arg: header and call copy wcs method
   def create_and_save_data_products(self, format, index: int=None, large_jpg_path: str=None, small_jpg_path: str=None, tif_path: str=None):
     """
     When you're done with the operation and would like to save the FITS file and jpgs in S3. JPGs are required, any other file is optional.
@@ -65,6 +69,7 @@ def create_and_save_data_products(self, format, index: int=None, large_jpg_path:
       # Create the output FITS file
       fits_output_path = fits_output_file.name
       hdu_list.writeto(fits_output_path, overwrite=True)
+
       FileCache().add_file_to_cache(fits_output_path)
 
       # Create jpgs if not provided

datalab/datalab_session/data_operations/median.py

@@ -56,16 +56,17 @@ def operate(self, submitter: User):
 
         input_fits_list = []
         for index, input in enumerate(input_list, start=1):
+            ## Add method to inputdatahandler to get the header 
             input_fits_list.append(InputDataHandler(submitter, input['basename'], input['source']))
+            log.info(f'input fits list: {input_fits_list}')
             self.set_operation_progress(Median.PROGRESS_STEPS['MEDIAN_MIDPOINT'] * (index / len(input_list)))
 
         cropped_data, shape = crop_arrays([image.sci_data for image in input_fits_list], flatten=True)
         median = np.median(cropped_data, axis=0, overwrite_input=True)
         median = np.reshape(median, shape)
 
         self.set_operation_progress(Median.PROGRESS_STEPS['MEDIAN_CALCULATION_PERCENTAGE_COMPLETION'])
-
-        output = FITSOutputHandler(self.cache_key, median, self.temp, comment).create_and_save_data_products(Format.FITS)
+        output = FITSOutputHandler(self.cache_key, median, self.temp, comment, data_header=input_fits_list[0].sci_hdu.header.copy()).create_and_save_data_products(Format.FITS)
         log.info(f'Median output: {output}')
         self.set_output(output)
         self.set_operation_progress(Median.PROGRESS_STEPS['OUTPUT_PERCENTAGE_COMPLETION'])

datalab/datalab_session/data_operations/normalization.py

@@ -62,7 +62,6 @@ def operate(self, submitter: User):
                 median = np.median(image.sci_data)
                 normalized_image = image.sci_data / median
                 comment = f'Datalab Normalization on file {input_list[index-1]["basename"]}'
-
                 output = FITSOutputHandler(f'{self.cache_key}', normalized_image, self.temp, comment, data_header=image.sci_hdu.header.copy()).create_and_save_data_products(Format.FITS, index=index)
                 output_files.append(output)
                 self.set_output(output_files)

datalab/datalab_session/data_operations/stacking.py

@@ -61,16 +61,17 @@ def operate(self, submitter: User):
 
         input_fits_list = []
         for index, input in enumerate(input_files, start=1):
+            log.info(f'this is index: {index}' f'and input: {input}')
             input_fits_list.append(InputDataHandler(submitter, input['basename'], input['source']))
+            log.info(f'input fits list in normalization: {input_fits_list}')
             self.set_operation_progress(Stack.PROGRESS_STEPS['STACKING_MIDPOINT'] * (index / len(input_files)))
 
         cropped_data, _ = crop_arrays([image.sci_data for image in input_fits_list])
         self.set_operation_progress(Stack.PROGRESS_STEPS['STACKING_PERCENTAGE_COMPLETION'])
-
         stacked_sum = np.sum(cropped_data, axis=0)
         self.set_operation_progress(Stack.PROGRESS_STEPS['STACKING_OUTPUT_PERCENTAGE_COMPLETION'])
 
-        output = FITSOutputHandler(self.cache_key, stacked_sum, self.temp, comment).create_and_save_data_products(Format.FITS)
+        output = FITSOutputHandler(self.cache_key, stacked_sum, self.temp, comment, data_header=input_fits_list[0].sci_hdu.header.copy()).create_and_save_data_products(Format.FITS)
 
         log.info(f'Stacked output: {output}')
         self.set_output(output)

datalab/datalab_session/migrations/0003_alter_dataoperation_options_and_more.py

@@ -0,0 +1,21 @@
+# Generated by Django 4.2.25 on 2025-10-09 22:26
+
+from django.db import migrations
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('datalab_session', '0002_dataoperation_cache_key'),
+    ]
+
+    operations = [
+        migrations.AlterModelOptions(
+            name='dataoperation',
+            options={'ordering': ['pk']},
+        ),
+        migrations.AlterModelOptions(
+            name='datasession',
+            options={'ordering': ['-modified']},
+        ),
+    ]

pyproject.toml

@@ -16,7 +16,6 @@ drf-nested-routers = "^0.93.4"
 ocs-authentication = "^0.2.2"
 psycopg2-binary = "^2.9.9"
 django-dramatiq = "^0.11.6"
-dramatiq = "<2.0"
 redis = "^5.0.1"
 rabbitmq = "^0.2.0"
 pika = "^1.3.2"