Small updates to the scrub_deveny_pickup

obstools/scrub_deveny_pickup.py

@@ -60,7 +60,7 @@
 import ccdproc.utils.slices
 import matplotlib.pyplot as plt
 import numpy as np
-from pypeit import msgs
+# from pypeit import msgs
 import pypeit.spec2dobj
 import scipy.fft
 import scipy.ndimage
@@ -127,18 +127,18 @@ def iterative_pypeit_clean(
 
     try:
         # Look for the spec2d file
-        spec2d_file = utils.flatten_comprehension(
+        spec2d_file = utils.flatten_itertools(
             [
                 sorted(d.joinpath("Science").glob(f"spec2d_{filename.stem}-*.fits"))
                 for d in pyp_dir
             ]
         )[0]
     except (StopIteration, IndexError):
         # And... fail.
-        msgs.warn(
-            f"File {filename.name} does not have a corresponding PypeIt-processed 2D spectrum. "
-            "Check the image type and whether you have `run_pypeit`."
-        )
+        # msgs.warn(
+        #     f"File {filename.name} does not have a corresponding PypeIt-processed 2D spectrum. "
+        #     "Check the image type and whether you have `run_pypeit`."
+        # )
         return
     # Define (and create, if needed) the QA directory for these plots
     qa_dir = spec2d_file.parents[1] / "QA" / "PDFs"
@@ -1012,7 +1012,7 @@ def package_into_fits(
         suffix  (Default: False)
     """
     # Add a little history
-    time_str = datetime.datetime.utcnow().isoformat(sep=" ", timespec="seconds")
+    time_str = datetime.datetime.now(datetime.UTC).isoformat(sep=" ", timespec="seconds")
     history_str = f"Written by package obstools: {time_str} UTC"
     # For the image HDUs, include a basic header
     img_hdr = astropy.io.fits.Header({"BUNIT": "ADU", "HISTORY": history_str})

obstools/utils.py

@@ -25,6 +25,7 @@
 import argparse
 from functools import reduce
 from importlib import resources
+import itertools
 import pathlib
 import textwrap
 import sys
@@ -139,6 +140,22 @@ def flatten_comprehension(nested_list: list[list]) -> list:
     return [item for row in nested_list for item in row]
 
 
+def flatten_itertools(nested_list: list[list]) -> list:
+    """Flatten a single-depth nested list via itertools
+
+    Parameters
+    ----------
+    nested_list : :obj:`list`
+        The single-depth nested list to flatten
+
+    Returns
+    -------
+    :obj:`list`
+        The flattened list
+    """
+    return [itertools.chain.from_iterable(nested_list)]
+
+
 def gaussfit(
     x: np.ndarray,
     y: np.ndarray,