rename self.det_distance for clarity, some formatting

reduction/lr_reduction/event_reduction.py

@@ -404,7 +404,7 @@ def extract_meta_data(self):
         Extract meta data from the loaded data file.
         """
         # Get instrument parameters
-        if self.instrument_settings is None or self.instrument_settings.apply_instrument_settings:
+        if self.instrument_settings is None or not self.instrument_settings.apply_instrument_settings:
             settings = read_settings(self._ws_sc)
         else:
             settings = self.instrument_settings
@@ -451,13 +451,13 @@ def extract_meta_data_4A(self):
         4A-specific meta data
         """
         run_object = self._ws_sc.getRun()
-        self.det_distance = run_object["SampleDetDis"].getStatistics().mean
+        self.sample_detector_distance = run_object["SampleDetDis"].getStatistics().mean
         source_sample_distance = run_object["ModeratorSamDis"].getStatistics().mean
         if run_object["SampleDetDis"].units not in ["m", "meter"]:
-            self.det_distance /= 1000.0
+            self.sample_detector_distance /= 1000.0
         if run_object["ModeratorSamDis"].units not in ["m", "meter"]:
             source_sample_distance /= 1000.0
-        self.source_detector_distance = source_sample_distance + self.det_distance
+        self.source_detector_distance = source_sample_distance + self.sample_detector_distance
 
     def extract_meta_data_4B(self):
         """
@@ -472,9 +472,9 @@ def extract_meta_data_4B(self):
             settings = self.instrument_settings
 
         if settings.apply_instrument_settings:
-            self.det_distance = settings.sample_detector_distance
+            self.sample_detector_distance = settings.sample_detector_distance
         else:
-            self.det_distance = self.DEFAULT_4B_SAMPLE_DET_DISTANCE
+            self.sample_detector_distance = self.DEFAULT_4B_SAMPLE_DET_DISTANCE
 
         # Check that we have the needed meta data for the emission delay calculation
         if self.use_emission_time:
@@ -503,9 +503,9 @@ def __repr__(self):
             String representation of the reduction settings
         """
         output = "Reduction settings:\n"
-        output += "    sample-det: %s\n" % self.det_distance
+        output += "    sample-det: %s\n" % self.sample_detector_distance
         output += "    source-det: %s\n" % self.source_detector_distance
-        output += "    pixel: %s\n" % self.pixel_width
+        output += "    pixel-width: %s\n" % self.pixel_width
         output += "    WL: %s %s\n" % (self.wl_range[0], self.wl_range[1])
         output += "    Q: %s %s\n" % (self.q_min_meas, self.q_max_meas)
         theta_degrees = self.theta * 180 / np.pi
@@ -887,7 +887,7 @@ def _reflectivity(
                 event_weights = evt_list.getWeights()
 
                 x_distance = _pixel_width * (j - peak_position)
-                delta_theta_f = np.arctan(x_distance / self.det_distance) / 2.0
+                delta_theta_f = np.arctan(x_distance / self.sample_detector_distance) / 2.0
 
                 # Sign will depend on reflect up or down
                 ths_value = ws.getRun()["ths"].value[-1]
@@ -919,8 +919,8 @@ def _reflectivity(
         if q_summing:
             x0 = _pixel_width * (peak_position - peak[0])
             x1 = _pixel_width * (peak_position - peak[1])
-            delta_theta_f0 = np.arctan(x0 / self.det_distance) / 2.0
-            delta_theta_f1 = np.arctan(x1 / self.det_distance) / 2.0
+            delta_theta_f0 = np.arctan(x0 / self.sample_detector_distance) / 2.0
+            delta_theta_f1 = np.arctan(x1 / self.sample_detector_distance) / 2.0
 
             qz_max = 4.0 * np.pi / self.tof_range[1] * self.constant * np.fabs(np.sin(theta + delta_theta_f0))
             qz_min = 4.0 * np.pi / self.tof_range[1] * self.constant * np.fabs(np.sin(theta + delta_theta_f1))
@@ -1051,7 +1051,7 @@ def _off_specular(self, ws, wl_dist, wl_bins, x_bins, z_bins, peak_position, the
             wl_weights = 1.0 / np.interp(wl_list, wl_bins, wl_dist, np.inf, np.inf)
 
             x_distance = float(j - peak_position) * self.pixel_width
-            delta_theta_f = np.arctan(x_distance / self.det_distance)
+            delta_theta_f = np.arctan(x_distance / self.sample_detector_distance)
             # Sign will depend on reflect up or down
             ths_value = ws.getRun()["ths"].value[-1]
             delta_theta_f *= np.sign(ths_value)

reduction/lr_reduction/template.py

@@ -291,7 +291,7 @@ def process_from_template_ws(
         instrument_settings=instrument_settings,
         use_emission_time=template_data.use_emission_time,
     )
-    print(event_refl)
+    print(f"{'*'*88}\nevent_refl:\n{event_refl}\n{'*'*88}")
 
     # R(Q)
     qz, refl, d_refl = event_refl.specular(

reduction/notebooks/workflow-fixed-tthd.ipynb

@@ -540,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-01T22:56:07.736455Z",
@@ -561,7 +561,7 @@
    ],
    "source": [
     "_pixel_width = 0.0007\n",
-    "det_distance = 1.355\n",
+    "sample_detector_distance = 1.355\n",
     "dirpix = 261\n",
     "peak_position = 85.5\n",
     "#peak_position = 210\n",
@@ -571,21 +571,21 @@
     "\n",
     "\n",
     "x0 = _pixel_width * (peak_position - dirpix)\n",
-    "theta = np.arctan(x0 / det_distance) / 2.0 * 180 / np.pi\n",
+    "theta = np.arctan(x0 / sample_detector_distance) / 2.0 * 180 / np.pi\n",
     "print(theta)\n",
     "\n",
     "\n",
-    "    \n",
+    "\n",
     "if False:\n",
     "    for i in range(304):\n",
     "        x0 = _pixel_width * (i - dirpix)\n",
-    "        theta = np.arctan(x0 / det_distance) / 2.0 * 180 / np.pi\n",
+    "        theta = np.arctan(x0 / sample_detector_distance) / 2.0 * 180 / np.pi\n",
     "        print(\"Pixel:%g     Theta = %g\" % (i, theta))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-08-30T01:39:22.045519Z",
@@ -628,10 +628,10 @@
     "all_pix = np.arange(300)\n",
     "\n",
     "x0 = _pixel_width * (pix - dirpix)\n",
-    "calc = np.arctan(x0 / det_distance) / 2.0 * 180 / np.pi\n",
+    "calc = np.arctan(x0 / sample_detector_distance) / 2.0 * 180 / np.pi\n",
     "\n",
     "x0 = _pixel_width * (all_pix - dirpix)\n",
-    "all_calc = np.arctan(x0 / det_distance) / 2.0 * 180 / np.pi\n",
+    "all_calc = np.arctan(x0 / sample_detector_distance) / 2.0 * 180 / np.pi\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(10,5))\n",
     "plt.plot(pix, ths, 'o', label='ths')\n",
@@ -656,9 +656,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "sans",
-   "language": "",
-   "name": "sans"
+   "display_name": "lr_reduction",
+   "language": "python",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -670,7 +670,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,