Merge branch 'enh/slicing_methods'

astrodata/nddata.py

@@ -499,3 +499,13 @@ def transpose(self):
             mask=None if self.mask is None else self.mask.T, wcs=new_wcs,
             meta=self.meta, copy=False
         )
+
+    def _slice(self, item):
+        """Additionally slice things like OBJMASK"""
+        kwargs = super()._slice(item)
+        if 'other' in kwargs['meta']:
+            kwargs['meta'] = deepcopy(self.meta)
+            for k, v in kwargs['meta']['other'].items():
+                if isinstance(v, np.ndarray) and v.shape == self.shape:
+                    kwargs['meta']['other'][k] = v[item]
+        return kwargs

astrodata/tests/test_nddata.py

@@ -149,5 +149,16 @@ def test_access_to_other_planes_when_windowed(testnd):
     assert len(ndwindow.OBJCAT) == 3
 
 
+# Basically the same test as above but using slicing.
+def test_access_to_other_planes_when_sliced(testnd):
+    ndwindow = testnd[1:, 1:]
+    assert ndwindow.data.shape == (4, 4)
+    assert ndwindow.data[0, 0] == testnd.shape[1] + 1
+    assert ndwindow.OBJMASK.shape == (4, 4)
+    assert ndwindow.OBJMASK[0, 0] == testnd.shape[1] + 1
+    assert isinstance(ndwindow.OBJCAT, Table)
+    assert len(ndwindow.OBJCAT) == 3
+
+
 if __name__ == '__main__':
     pytest.main()

gempy/gemini/gemini_tools.py

@@ -1663,7 +1663,7 @@ def mark_history(adinput=None, keyword=None, primname=None, comment=None):
 
 
 def measure_bg_from_image(ad, sampling=10, value_only=False, gaussfit=True,
-                          separate_ext=True, ignore_mask=False):
+                          separate_ext=True, ignore_mask=False, section=None):
     """
     Return background value, and its std deviation, as measured directly
     from pixels in the SCI image. DQ plane are used (if they exist)
@@ -1683,6 +1683,8 @@ def measure_bg_from_image(ad, sampling=10, value_only=False, gaussfit=True,
         return information for each extension, rather than the whole AD?
     ignore_mask : bool
         if True, ignore the mask and OBJMASK
+    section: slice/None
+        region to use for statistics
 
     Returns
     -------
@@ -1704,17 +1706,18 @@ def measure_bg_from_image(ad, sampling=10, value_only=False, gaussfit=True,
         flags = None
         # Use DQ and OBJMASK to flag pixels
         if not single and not separate_ext:
-            bg_data = np.array([ext.data for ext in ad]).ravel()
+            bg_data = np.array([ext.data[section] for ext in ad]).ravel()
             if not ignore_mask:
                 flags = np.array([ext.mask | getattr(ext, 'OBJMASK', 0)
                                   if ext.mask is not None
-                    else getattr(ext, 'OBJMASK', np.empty_like(ext.data, dtype=bool))
-                                  for ext in ad]).ravel()
+                    else getattr(ext, 'OBJMASK', np.zeros_like(ext.data, dtype=bool))
+                                  for ext in ad])[section].ravel()
         else:
             if not ignore_mask:
-                flags = ext.mask | getattr(ext, 'OBJMASK', 0) if ext.mask is not None \
-                    else getattr(ext, 'OBJMASK', None)
-            bg_data = ext.data.ravel()
+                flags = ((ext.mask | getattr(ext, 'OBJMASK', 0))[section]
+                    if ext.mask is not None else
+                         ext.OBJMASK[section] if hasattr(ext, 'OBJMASK') else None)
+            bg_data = ext.data[section].ravel()
 
         if flags is None:
             bg_data = bg_data[::sampling]

gempy/gemini/tests/test_gemini_tools.py

@@ -15,6 +15,8 @@
     ('N20180118S0344.fits', 1.32),
 ]
 
+# For making a fake test image, and testing it.
+datavalue = 10000
 
 caltype_data = [
     ('arc', 'Arc spectrum'),
@@ -25,6 +27,16 @@
     ('flat', 'Flat Frame'),
 ]
 
+@pytest.fixture
+def fake_image(astrofaker):
+    np.random.seed(4) # A number that works with all the later tests
+    ad = astrofaker.create('NIRI', ['IMAGE'])
+    ad.init_default_extensions()
+    ad[0].data += datavalue
+    ad.add_poisson_noise()
+
+    return ad
+
 
 @pytest.mark.dragons_remote_data
 @pytest.mark.parametrize("caltype, obj", caltype_data)
@@ -74,18 +86,30 @@ def test_fit_continuum_slit_image(fname, fwhm, change_working_dir):
 
 
 @pytest.mark.parametrize("gaussfit", [True, False])
-def test_measure_bg_from_image_fake(gaussfit, astrofaker):
-    datavalue = 10000
-    ad = astrofaker.create('NIRI', ['IMAGE'])
-    ad.init_default_extensions()
-    ad[0].data += datavalue
-    ad.add_poisson_noise()
+def test_measure_bg_from_image_fake(gaussfit, fake_image):
+
+    ad = fake_image
     mean, stddev, nsamples = gt.measure_bg_from_image(ad[0], gaussfit=gaussfit)
     assert abs(mean - datavalue) < 1
     if gaussfit:  # stddev unreliable for non-Gaussfit
         assert abs(stddev - np.sqrt(datavalue / ad[0].gain())) < 0.5
 
 
+@pytest.mark.parametrize("section", [(slice(0, -1), slice(0, -1)),
+                                     (slice(None, 512), slice(512, None)),
+                                     (slice(None, None), slice(None, None)),
+                                     (slice(-512, None), slice(None, -512))])
+@pytest.mark.parametrize("gaussfit", [True, False])
+def test_measure_bg_from_image_fake_sections(section, gaussfit, fake_image):
+
+    ad = fake_image
+    mean, stddev, nsamples = gt.measure_bg_from_image(ad[0], gaussfit=gaussfit,
+                                                      section=section)
+    assert abs(mean - datavalue) < 1
+    if gaussfit:  # stddev unreliable for non-Gaussfit
+        assert abs(stddev - np.sqrt(datavalue / ad[0].gain())) < 0.5
+
+
 @pytest.mark.dragons_remote_data
 @pytest.mark.parametrize("gaussfit", [True, False])
 def test_measure_bg_from_image_real(gaussfit):