From 9f6f89738d0bbc64cbfcc43778303afb07f2c401 Mon Sep 17 00:00:00 2001
From: ndilalla <niccolo.dilalla@stanford.edu>
Date: Wed, 18 Feb 2026 19:45:37 -0800
Subject: [PATCH] Skymap inconsistency fixed.

---
 fermipy/skymap.py | 31 +++++++++++++++++++++++--------
 1 file changed, 23 insertions(+), 8 deletions(-)

diff --git a/fermipy/skymap.py b/fermipy/skymap.py
index dc5bbdd1..869d51f0 100644
--- a/fermipy/skymap.py
+++ b/fermipy/skymap.py
@@ -133,11 +133,17 @@ def __init__(self, counts, wcs, ebins=None):
         Parameters
         ----------
         counts : `~numpy.ndarray`
-            Counts array in row-wise ordering (LON is first dimension).
+            Counts array. Internal layout is (energy, lat, lon), i.e. first
+            dimension is energy, then spatial (lat, lon). So for 3D,
+            counts.shape = (n_energy, n_lat, n_lon). For 2D, counts.shape =
+            (n_lat, n_lon). This is the opposite of FITS/WCS axis order (which
+            is lon, lat, energy); see sum_over_energy and create_from_hdu.
         """
         Map_Base.__init__(self, counts)
         self._wcs = wcs
 
+        # _npix is (n_lon, n_lat, n_energy) for 3D or (n_lon, n_lat) for 2D,
+        # i.e. counts.shape reversed (matches WCS axis order).
         self._npix = counts.shape[::-1]
 
         if len(self._npix) == 3:
@@ -364,9 +370,9 @@ def get_map_values(self, lons, lats, ibin=None):
 
         Returns
         ----------
-        vals : numpy.ndarray((n))
-           Values of pixels in the flattened map, np.nan used to flag
-           coords outside of map
+        vals : numpy.ndarray
+           For 3D maps: shape (ne, np) with ne energy bins and np sky coordinates.
+           For 2D maps: shape (np,). np.nan flags coords outside of map.
         """
         pix_idxs = self.get_pixel_indices(lons, lats, ibin)
         idxs = copy.copy(pix_idxs)
@@ -376,10 +382,19 @@ def get_map_values(self, lons, lats, ibin=None):
         for i, p in enumerate(pix_idxs):
             m &= (pix_idxs[i] >= 0) & (pix_idxs[i] < self._npix[i])
             idxs[i][~m] = 0
-
-        vals = self.counts.T[idxs]
-        vals[~m] = np.nan
-        return vals
+        
+        # Use tuple() so NumPy does multi-dim advanced indexing.
+        # counts is (energy, lat, lon) for 3D, (lat, lon) for 2D. pix_idxs is
+        # [lon, lat, energy] or [lon, lat], so index as (energy, lat, lon).
+        if len(self._npix) == 3:
+            vals = self.counts[(idxs[2], idxs[1], idxs[0])]
+            vals[~m] = np.nan
+            # Return (ne, np) to match HEALPix HpxMap.get_map_values convention
+            return vals.T
+        else:
+            vals = self.counts[(idxs[1], idxs[0])]
+            vals[~m] = np.nan
+            return vals
 
     def interpolate(self, lon, lat, egy=None):