added NRSSIntegrator with accompanying minor tweaks to WPIntegrator

docs/source/getting_started/integration.rst

@@ -23,7 +23,14 @@ For grazing-incidence experiments ``PGGeneralIntegrator`` uses
 ``pygix`` to output either reciprocal-space ``q_xy``/``q_z`` data or
 caked ``q`` vs ``chi``.  Datasets already in ``qx``/``qy`` coordinates
 can be integrated with ``WPIntegrator`` which relies on
-``skimage.transform.warp_polar`` (or its GPU version).
+``skimage.transform.warp_polar`` (or its GPU version).  ``WPIntegrator``
+is a detector-plane ``qx``/``qy`` to ``chi``/``q_perp`` remesher, so it
+is the right choice for reciprocal-plane / 2D NRSS outputs.
+
+For NRSS detector-aware 3D outputs use ``NRSSIntegrator`` instead.
+It keeps the same polar remesh approach but relabels the radial axis
+with detector-corrected ``|q|`` so the reduced curve can be compared to
+geometry-aware analytical form factors.
 
 All integrators return properly labeled xarray objects so the rest of
 the workflow can use normal xarray indexing and visualization.

src/PyHyperScattering/Fitting.py

@@ -94,15 +94,15 @@ def fit_lorentz(x,guess=None,pos_int_override=False,silent=False):
     except RuntimeError:
         if not silent:
             print("Fit failed to converge")
-        retval = xr.DataArray(data=np.nan,coords=x.coords).to_dataset(name='intensity')
-        retval['pos'] = xr.DataArray(data=np.nan,coords=x.coords)
-        retval['width'] = xr.DataArray(data=np.nan,coords=x.coords)
+        retval = xr.full_like(x, np.nan, dtype=float).to_dataset(name='intensity')
+        retval['pos'] = xr.full_like(x, np.nan, dtype=float)
+        retval['width'] = xr.full_like(x, np.nan, dtype=float)
         return retval
     if not silent:
         print(f"Fit completed, coeff = {coeff}")
-    retval = xr.DataArray(data=coeff[0],coords=x.coords).to_dataset(name='intensity')
-    retval['pos'] = xr.DataArray(data=coeff[1],coords=x.coords)
-    retval['width'] = xr.DataArray(data=coeff[2],coords=x.coords)
+    retval = xr.full_like(x, coeff[0], dtype=float).to_dataset(name='intensity')
+    retval['pos'] = xr.full_like(x, coeff[1], dtype=float)
+    retval['width'] = xr.full_like(x, coeff[2], dtype=float)
     return retval
 def fit_lorentz_bg(x,guess=None,pos_int_override=False,silent=False):
     '''
@@ -131,17 +131,17 @@ def fit_lorentz_bg(x,guess=None,pos_int_override=False,silent=False):
     except RuntimeError:
         if not silent:
             print("Fit failed to converge")
-        retval = xr.DataArray(data=np.nan,coords=x.coords).to_dataset(name='intensity')
-        retval['pos'] = xr.DataArray(data=np.nan,coords=x.coords)
-        retval['width'] = xr.DataArray(data=np.nan,coords=x.coords)
-        retval['bg'] = xr.DataArray(data=np.nan,coords=x.coords)
+        retval = xr.full_like(x, np.nan, dtype=float).to_dataset(name='intensity')
+        retval['pos'] = xr.full_like(x, np.nan, dtype=float)
+        retval['width'] = xr.full_like(x, np.nan, dtype=float)
+        retval['bg'] = xr.full_like(x, np.nan, dtype=float)
         return retval
     if not silent:
         print(f"Fit completed, coeff = {coeff}")
-    retval = xr.DataArray(data=coeff[0],coords=x.coords).to_dataset(name='intensity')
-    retval['pos'] = xr.DataArray(data=coeff[1],coords=x.coords)
-    retval['width'] = xr.DataArray(data=coeff[2],coords=x.coords)
-    retval['bg'] = xr.DataArray(data=coeff[3],coords=x.coords)
+    retval = xr.full_like(x, coeff[0], dtype=float).to_dataset(name='intensity')
+    retval['pos'] = xr.full_like(x, coeff[1], dtype=float)
+    retval['width'] = xr.full_like(x, coeff[2], dtype=float)
+    retval['bg'] = xr.full_like(x, coeff[3], dtype=float)
     return retval
 
 def fit_cos_anisotropy(data,qL,qU,qspacing,Enlist,ChiL,ChiU,binnumber,Chilim):
@@ -329,4 +329,4 @@ def fit_cos(x_data, y_data):
         Ani_unc=0
         Chisq=100
 
-    return params, Ani, Ani_unc, Chisq
+    return params, Ani, Ani_unc, Chisq