inline integrate into the hRIXS class

src/toolbox_scs/detectors/hrixs.py

@@ -250,24 +250,6 @@ def parabola(x, a, b, c=0):
     return (a * x + b) * x + c
 
 
-def integrate(image, factor=FACTOR, range=RANGE, curvature=(CURVE_A, CURVE_B)):
-    x = np.arange(image.shape[1])[None, :]
-    y = np.arange(image.shape[0])[:, None]
-    ys = factor * (y - parabola(x, curvature[1], curvature[0]))
-    ysf = np.floor(ys)
-    rang = (factor * range[0], factor * range[1])
-    bins = rang[1] - rang[0]
-    lhy, lhx = np.histogram(ysf.ravel(), bins=bins, range=rang,
-                            weights=((ys - ysf) * image).ravel())
-    rhy, rhx = np.histogram((ysf + 1).ravel(), bins=bins, range=rang,
-                            weights=(((ysf + 1) - ys) * image).ravel())
-    lvy, lvx = np.histogram(ysf.ravel(), bins=bins, range=rang,
-                            weights=(ys - ysf).ravel())
-    rvy, rvx = np.histogram((ysf + 1).ravel(), bins=bins, range=rang,
-                            weights=((ysf + 1) - ys).ravel())
-    return (lhy + rhy) / (lvy + rvy)
-
-
 class hRIXS:
     """The hRIXS analysis, especially curvature correction
 
@@ -489,14 +471,26 @@ class hRIXS:
             data = h.integrate(data)  # create spectrum by summing pixels
             data.spectrum[0, :].plot()  # plot the spectrum of the first image
         """
-        bins = self.Y_RANGE.stop - self.Y_RANGE.start
-        ret = np.zeros((len(data["hRIXS_det"]), bins - 20))
+        bins = self.Y_RANGE.stop - self.Y_RANGE.start - 20
+        ret = np.zeros((len(data["hRIXS_det"]), bins))
         for image, r in zip(data["hRIXS_det"], ret):
             if self.USE_DARK:
                 image = image - self.dark_image
-            r[:] = integrate(image.values[self.X_RANGE, self.Y_RANGE].T,
-                             factor=1, range=(10, bins - 10),
-                             curvature=(self.CURVE_A, self.CURVE_B))
+            image = image.values[self.X_RANGE, self.Y_RANGE].T
+            x = np.arange(image.shape[1])[None, :]
+            y = np.arange(image.shape[0])[:, None]
+            ys = (y - parabola(x, self.CURVE_B, self.CURVE_A))
+            ysf = np.floor(ys)
+            rang = (10, bins + 10)
+            lhy, lhx = np.histogram(ysf.ravel(), bins=bins, range=rang,
+                                    weights=((ys - ysf) * image).ravel())
+            rhy, rhx = np.histogram((ysf + 1).ravel(), bins=bins, range=rang,
+                                    weights=(((ysf + 1) - ys) * image).ravel())
+            lvy, lvx = np.histogram(ysf.ravel(), bins=bins, range=rang,
+                                    weights=(ys - ysf).ravel())
+            rvy, rvx = np.histogram((ysf + 1).ravel(), bins=bins, range=rang,
+                                    weights=((ysf + 1) - ys).ravel())
+            r[:] = (lhy + rhy) / (lvy + rvy)
         data = data.assign_coords(
             energy=np.arange(self.Y_RANGE.start + 10, self.Y_RANGE.stop - 10)
             * self.ENERGY_SLOPE + self.ENERGY_INTERCEPT)