Fix upgrade to numpy 2.0 issues

1. Numpy no longer allows assigning 1D array into a scalar element. We
   have to extract a matching value before assigning.
2. np.trapz was deprecated (removed in 2.4) - replace with np.trapezoid

Signed-off-by: Patrick Avery <patrick.avery@kitware.com>

Author: psavery

hexrd/core/instrument/hedm_instrument.py

@@ -1118,10 +1118,10 @@ def extract_polar_maps(
             # find indices
             idx = np.zeros_like(active_hkls, dtype=int)
             for i, input_hklID in enumerate(active_hkls):
-                try:
-                    idx[i] = np.where(active_hklIDs == input_hklID)[0]
-                except ValueError:
+                matches = np.where(active_hklIDs == input_hklID)[0]
+                if len(matches) != 1:
                     raise RuntimeError(f"hklID '{input_hklID}' is invalid")
+                idx[i] = matches[0]
             tth_ranges = tth_ranges[idx]
 
         delta_eta = eta_edges[1] - eta_edges[0]

hexrd/core/material/crystallography.py

@@ -1608,10 +1608,10 @@ def getHKLs(
             idx = np.zeros(len(self.hklDataList), dtype=int)
             for i, hkl_id in enumerate(hkl_ids):
                 # find ordinal index of current hklID
-                try:
-                    idx[i] = int(np.where(all_hkl_ids == hkl_id)[0])
-                except TypeError:
-                    raise RuntimeError(f"Requested hklID '{hkl_id}'is invalid!")
+                matches = np.where(all_hkl_ids == hkl_id)[0]
+                if len(matches) != 1:
+                    raise RuntimeError(f"Requested hklID '{hkl_id}' is invalid!")
+                idx[i] = matches[0]
                 if sorted_excl[idx[i]] and not allHKLs:
                     raise RuntimeError(f"Requested hklID '{hkl_id}' is excluded!")
                 hkls.append(self.hklDataList[idx[i]]['hkl'])

hexrd/hedm/ipfcolor/sphere_sector.py

@@ -686,14 +686,14 @@ def calc_hue(self, dir3, switch):
         rho = self.calculate_rho(dir3, switch)
         r = np.linspace(0.0, 2 * np.pi, 1000)
         v = self.hue_speed(r)
-        cons = np.trapz(v, r)
+        cons = np.trapezoid(v, r)
 
         h = np.zeros(rho.shape)
 
         for i in range(rho.shape[0]):
             r = np.linspace(0.0, rho[i], 1000)
             v = self.hue_speed(r)
-            h[i] = np.trapz(v, r) / cons
+            h[i] = np.trapezoid(v, r) / cons
 
         return h
 

hexrd/powder/wppf/LeBailCalibration.py

@@ -212,8 +212,8 @@ def computespectrum(self, instr_updated, lp_updated):
 
             weighted_expt = np.nan_to_num(ww * v.spectrum_expt._y**2)
 
-            wss = np.trapz(evec, v.tth_list)
-            den = np.trapz(weighted_expt, v.tth_list)
+            wss = np.trapezoid(evec, v.tth_list)
+            den = np.trapezoid(weighted_expt, v.tth_list)
             r = np.sqrt(wss / den) * 100.0
             if ~np.isnan(r):
                 rwp.append(r)

hexrd/powder/wppf/WPPF.py

@@ -878,7 +878,7 @@ def total_area(self):
         to protect against nans in the values
         '''
         mask = np.isnan(total_intensity)
-        sum_area = np.trapz(total_intensity[~mask], tth[~mask])
+        sum_area = np.trapezoid(total_intensity[~mask], tth[~mask])
         return sum_area
 
     @property

hexrd/powder/wppf/amorphous.py

@@ -339,7 +339,7 @@ def amorphous_lineout(self):
     def integrated_area(self):
         x = self.tth_list
         y = self.amorphous_lineout
-        return np.trapz(y, x)
+        return np.trapezoid(y, x)
 
     @property
     def peak_model(self):

hexrd/powder/wppf/peakfunctions.py

@@ -483,7 +483,7 @@ def pvfcj(
         res += pv * fact
 
     res = np.sin(tth_r) * res / den / 4.0 / HoL / SoL
-    a = np.trapz(res, tth_list)
+    a = np.trapezoid(res, tth_list)
     return res / a
 
 
@@ -580,8 +580,8 @@ def pvoight_pink_beam(
 
     g = _gaussian_pink_beam(alpha_exp, beta_exp, fwhm_g, tth, tth_list)
     l_val = _lorentzian_pink_beam(alpha_exp, beta_exp, fwhm_l, tth, tth_list)
-    ag = np.trapz(g, tth_list)
-    al = np.trapz(l_val, tth_list)
+    ag = np.trapezoid(g, tth_list)
+    al = np.trapezoid(l_val, tth_list)
     if np.abs(ag) < 1e-6:
         ag = 1.0
     if np.abs(al) < 1e-6:
@@ -768,7 +768,7 @@ def calc_Iobs_pvfcj(
         y = Ic * pv
         y = y[mask]
 
-        Iobs[ii] = np.trapz(yo * y / yc, tth_list_mask)
+        Iobs[ii] = np.trapezoid(yo * y / yc, tth_list_mask)
 
     return Iobs
 
@@ -820,7 +820,7 @@ def calc_Iobs_pvtch(
         y = Ic * pv
         y = y[mask]
 
-        Iobs[ii] = np.trapz(yo * y / yc, tth_list_mask)
+        Iobs[ii] = np.trapezoid(yo * y / yc, tth_list_mask)
 
     return Iobs
 
@@ -887,7 +887,7 @@ def calc_Iobs_pvpink(
         y = Ic * pv
         y = y[mask]
 
-        Iobs[ii] = np.trapz(yo * y / yc, tth_list_mask)
+        Iobs[ii] = np.trapezoid(yo * y / yc, tth_list_mask)
 
     return Iobs