Version bumps

arrakis/imager.py

@@ -580,14 +580,26 @@ def make_cube(
     logger = get_run_logger()
 
     logger.info(f"Creating cube for {pol=} {image_set.ms=}")
-    image_list = image_set.image_lists[pol]
+    image_list = [Path(i) for i in image_set.image_lists[pol]]
 
     image_type = "restored" if aux_mode is None else aux_mode
 
+    # Create a cube name
+    old_name = image_list[0]
+    out_dir = os.path.dirname(old_name)
+    old_base = os.path.basename(old_name)
+    new_base = old_base
+    b_idx = new_base.find("beam") + len("beam") + 2
+    sub = new_base[b_idx:]
+    new_base = new_base.replace(sub, ".conv.fits")
+    new_base = new_base.replace("image", f"image.{image_type}.{pol.lower()}")
+    new_name = Path(out_dir) / new_base
+
     # First combine images into cubes
-    hdu_list, freqs = combine_fits(file_list=image_list, create_blanks=True)
-    new_header = hdu_list[0].header
-    data_cube = hdu_list[0].data
+    _ = combine_fits(file_list=image_list, out_cube=new_name, create_blanks=True)
+    with fits.open(new_name, mode="denywrite", memmap=True) as hdu_list:
+        new_header = hdu_list[0].header
+        data_cube = hdu_list[0].data
 
     # Add pol angle to header
     new_header["INSTRUMENT_RECEPTOR_ANGLE"] = (
@@ -610,16 +622,6 @@ def make_cube(
     # Calculate rms noise
     rmss_arr = mad_std(data_cube, axis=(1, 2, 3), ignore_nan=True)
 
-    # Create a cube name
-    old_name = image_list[0]
-    out_dir = os.path.dirname(old_name)
-    old_base = os.path.basename(old_name)
-    new_base = old_base
-    b_idx = new_base.find("beam") + len("beam") + 2
-    sub = new_base[b_idx:]
-    new_base = new_base.replace(sub, ".conv.fits")
-    new_base = new_base.replace("image", f"image.{image_type}.{pol.lower()}")
-    new_name = os.path.join(out_dir, new_base)
     # Deserialise beam
     with open(common_beam_pkl, "rb") as f:
         common_beam = pickle.load(f)
@@ -633,9 +635,9 @@ def make_cube(
     # 0 1234.5
     # 1 6789.0
     # etc.
-    new_w_name = new_name.replace(
-        f"image.{image_type}", f"weights.{image_type}"
-    ).replace(".fits", ".txt")
+    new_w_name = Path(
+        new_name.as_posix().replace(f"image.{image_type}", f"weights.{image_type}")
+    ).with_suffix(".txt")
     data = dict(
         Channel=np.arange(len(rmss_arr)),
         Weight=1 / rmss_arr**2,  # Want inverse variance

pyproject.toml

@@ -63,8 +63,8 @@ RMTable = ">=1.2.1"
 RM-Tools = ">=1.4.2"
 PolSpectra = ">=1.1.0"
 setuptools = "*"
-fixms = ">=0.2.6"
-fitscube = ">=0.3"
+fixms = ">=0.3.3"
+fitscube = ">=0.5.1"
 psycopg2-binary = "*"
 sqlalchemy = "*"
 scikit-image = ">=0.23"