PR comments

pypeit/core/findobj_skymask.py

@@ -239,7 +239,7 @@ def ech_findobj_ineach_order(
             Good-pixel mask for input image.  Must have the same shape as
             ``image``.  If None, all pixels in ``slitmask`` with non-negative
             values are considered good.
-        std_trace (`astropy.table.Table`_:, optional):
+        std_trace (`astropy.table.Table`_, optional):
             Table with the trace of the standard star on the input detector,
             which is used as a crutch for tracing. The table has two columns:
             `ECH_ORDER` and `TRACE_SPAT`. The shape of each row must be (nspec,).
@@ -489,7 +489,7 @@ def ech_fill_in_orders(sobjs:specobjs.SpecObjs,
             ``np.arange(norders)`` (but this is *not* recommended).
         obj_id (`numpy.ndarray`_):
             Object IDs of the objects linked together.
-        std_trace (`astropy.table.Table`_:, optional):
+        std_trace (`astropy.table.Table`_, optional):
             Table with the trace of the standard star on the input detector,
             which is used as a crutch for tracing. The table has two columns:
             `ECH_ORDER` and `TRACE_SPAT`. The shape of each row must be (nspec,).
@@ -1136,7 +1136,7 @@ def ech_objfind(image, ivar, slitmask, slit_left, slit_righ, slit_spat_id, order
             Plate scale in arcsec/pix. This can either be a single float for
             every order, or an array with shape (norders,) providing the plate
             scale of each order.
-        std_trace (`astropy.table.Table`_:, optional):
+        std_trace (`astropy.table.Table`_, optional):
             Table with the trace of the standard star on the input detector,
             which is used as a crutch for tracing. The table has two columns:
             `ECH_ORDER` and `TRACE_SPAT`. The shape of each row must be (nspec,).

pypeit/find_objects.py

@@ -15,7 +15,6 @@
 
 from pypeit import specobjs
 from pypeit import msgs, utils
-from pypeit import flatfield
 from pypeit.display import display
 from pypeit.core import skysub, qa, parse, flat, flexure
 from pypeit.core import procimg
@@ -326,7 +325,7 @@ def run(self, std_trace=None, show_peaks=False, show_skysub_fit=False):
 
         Parameters
         ----------
-        std_trace : `astropy.table.Table`_:, optional
+        std_trace : `astropy.table.Table`_, optional
             Table with the trace of the standard star on the input detector,
             which is used as a crutch for tracing. For MultiSlit reduction,
             the table has a single column: `TRACE_SPAT`.
@@ -405,7 +404,7 @@ def find_objects(self, image, ivar, std_trace=None,
             Image to search for objects from. This floating-point image has shape
             (nspec, nspat) where the first dimension (nspec) is
             spectral, and second dimension (nspat) is spatial.
-        std_trace : `astropy.table.Table`_:, optional
+        std_trace : `astropy.table.Table`_, optional
             Table with the trace of the standard star on the input detector,
             which is used as a crutch for tracing. For MultiSlit reduction,
             the table has a single column: `TRACE_SPAT`.
@@ -731,7 +730,7 @@ def find_objects_pypeline(self, image, ivar, std_trace=None,
             Image to search for objects from. This floating-point image has shape
             (nspec, nspat) where the first dimension (nspec) is
             spectral, and second dimension (nspat) is spatial.
-        std_trace : `astropy.table.Table`_:, optional
+        std_trace : `astropy.table.Table`_, optional
             Table with the trace of the standard star on the input detector,
             which is used as a crutch for tracing. For MultiSlit reduction,
             the table has a single column: `TRACE_SPAT`.
@@ -886,7 +885,7 @@ def find_objects_pypeline(self, image, ivar, std_trace=None,
             Image to search for objects from. This floating-point image has shape
             (nspec, nspat) where the first dimension (nspec) is
             spectral, and second dimension (nspat) is spatial.
-        std_trace : `astropy.table.Table`_:, optional
+        std_trace : `astropy.table.Table`_, optional
             Table with the trace of the standard star on the input detector,
             which is used as a crutch for tracing. For Echelle reduction,
             the table has two columns: `ECH_ORDER` and `TRACE_SPAT`.

pypeit/specobjs.py

@@ -1070,28 +1070,32 @@ def lst_to_array(lst, mask=None):
     Allows for a list of Quantity objects
 
     Args:
-        lst : list
+        lst (:obj:`list`):
             Should be number or Quantities
         mask (`numpy.ndarray`_, optional):
             Boolean array used to limit to a subset of the list.  True=good
 
     Returns:
         `numpy.ndarray`_, `astropy.units.Quantity`_:  Converted list
     """
-    if mask is None:
-        mask = np.array([True]*len(lst))
+    _mask = np.ones(len(lst), dtype=bool) if mask is None else mask
+
+    # Return a Quantity array
     if isinstance(lst[0], units.Quantity):
-        return units.Quantity(lst)[mask]
-    else:
-        # if all the elements of lst have the same type, np.array(lst)[mask] will work
-        if len(set(map(type, lst))) == 1:
-            return np.array(lst)[mask]
-        else:
-            # if not, we need to use dtype="object"
-            return np.array(lst, dtype="object")[mask]
-        # TODO: The dtype="object" is needed for the case where one element of lst is not a list but None.
-        #  This would prevent the error "ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions..."
-        #  However, this is may introduce other issues, where an array of objects creates problems in other parts of the code.
-        #  I am using this workaround. Any suggestions/ideas?
+        return units.Quantity(lst)[_mask]
+
+    # If all the elements of lst have the same type, np.array(lst)[mask] will work
+    if len(set(map(type, lst))) == 1:
+        return np.array(lst)[_mask]
+
+    # Otherwise, we have to set the array type to object
+    return np.array(lst, dtype=object)[_mask]
+
+    # TODO: The dtype="object" is needed for the case where one element of lst
+    # is not a list but None.  This would prevent the error "ValueError: setting
+    # an array element with a sequence. The requested array has an inhomogeneous
+    # shape after 1 dimensions..." However, this is may introduce other issues,
+    # where an array of objects creates problems in other parts of the code.  I
+    # am using this workaround. Any suggestions/ideas?