From 35d13fc5c849197a89e2c3e813501d3a234205a5 Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Wed, 1 May 2024 13:49:50 -0400
Subject: [PATCH 1/3] Add better dead time correction

---
 reduction/lr_reduction/DeadTimeCorrection.py |   2 +-
 reduction/lr_reduction/event_reduction.py    | 117 +++++++------------
 reduction/lr_reduction/template.py           |   7 ++
 reduction/lr_reduction/workflow.py           |   1 +
 reduction/notebooks/workflow.ipynb           | 107 ++++++++++-------
 5 files changed, 116 insertions(+), 118 deletions(-)

diff --git a/reduction/lr_reduction/DeadTimeCorrection.py b/reduction/lr_reduction/DeadTimeCorrection.py
index 8318c83..caf09f6 100644
--- a/reduction/lr_reduction/DeadTimeCorrection.py
+++ b/reduction/lr_reduction/DeadTimeCorrection.py
@@ -86,7 +86,7 @@ def PyExec(self):
 
         # We don't compute an error on the dead time correction, so set it to zero
         counts_ws.setE(0, 0 * corr)
-
+        counts_ws.setDistribution(True)
         self.setProperty('OutputWorkspace', counts_ws)
 
 
diff --git a/reduction/lr_reduction/event_reduction.py b/reduction/lr_reduction/event_reduction.py
index 549de2f..beee160 100644
--- a/reduction/lr_reduction/event_reduction.py
+++ b/reduction/lr_reduction/event_reduction.py
@@ -41,6 +41,44 @@ def get_q_binning(q_min=0.001, q_max=0.15, q_step=-0.02):
         return q_min * np.asarray([_step**i for i in range(n_steps)])
 
 
+def get_dead_time_correction(ws, template_data):
+    """
+        Compute dead time correction to be applied to the reflectivity curve.
+        The method will also try to load the error events from each of the
+        data files to ensure that we properly estimate the dead time correction.
+        :param ws: workspace with raw data to compute correction for
+        :param template_data: reduction parameters
+    """
+    tof_min = ws.getTofMin()
+    tof_max = ws.getTofMax()
+
+    run_number = ws.getRun().getProperty("run_number").value
+    error_ws = api.LoadErrorEventsNexus("REF_L_%s" % run_number)
+    corr_ws = mantid_algorithm_exec(DeadTimeCorrection.SingleReadoutDeadTimeCorrection,
+                                    InputWorkspace=ws,
+                                    InputErrorEventsWorkspace=error_ws,
+                                    Paralyzable=template_data.paralyzable,
+                                    DeadTime=template_data.dead_time_value,
+                                    TOFStep=template_data.dead_time_tof_step,
+                                    TOFRange=[tof_min, tof_max],
+                                    OutputWorkspace="corr")
+    corr_ws = api.Rebin(corr_ws, [tof_min, 10, tof_max])
+    return corr_ws
+
+def apply_dead_time_correction(ws, template_data):
+    """
+        Apply dead time correction, and ensure that it is done only once
+        per workspace.
+        :param ws: workspace with raw data to compute correction for
+        :param template_data: reduction parameters
+    """
+    if not "dead_time_applied" in ws.getRun():
+        corr_ws = get_dead_time_correction(ws, template_data)
+        ws = api.Multiply(ws, corr_ws, OutputWorkspace=str(ws))
+        api.AddSampleLog(Workspace=ws, LogName="dead_time_applied", LogText='1', LogType="Number")
+    return ws
+    
+
 class EventReflectivity(object):
     """
         Event based reflectivity calculation.
@@ -238,61 +276,6 @@ def to_dict(self):
                     dq0=dq0, dq_over_q=dq_over_q, sequence_number=sequence_number,
                     sequence_id=sequence_id)
 
-    def get_dead_time_correction(self):
-        """
-            Compute dead time correction to be applied to the reflectivity curve.
-            The method will also try to load the error events from each of the
-            data files to ensure that we properly estimate the dead time correction.
-        """
-        # Scattering workspace
-        tof_min = self._ws_sc.getTofMin()
-        tof_max = self._ws_sc.getTofMax()
-
-        run_number = self._ws_sc.getRun().getProperty("run_number").value
-        error_ws = api.LoadErrorEventsNexus("REF_L_%s" % run_number)
-        corr_ws = mantid_algorithm_exec(DeadTimeCorrection.SingleReadoutDeadTimeCorrection,
-                                        InputWorkspace=self._ws_sc,
-                                        InputErrorEventsWorkspace=error_ws,
-                                        DeadTime=self.dead_time_value,
-                                        TOFStep=self.dead_time_tof_step,
-                                        Paralyzable=self.paralyzable,
-                                        TOFRange=[tof_min, tof_max],
-                                        OutputWorkspace="corr")
-        corr_sc = corr_ws.readY(0)
-        wl_bins = corr_ws.readX(0) / self.constant
-
-        # Direct beam workspace
-        run_number = self._ws_db.getRun().getProperty("run_number").value
-        error_ws = api.LoadErrorEventsNexus("REF_L_%s" % run_number)
-        corr_ws = mantid_algorithm_exec(DeadTimeCorrection.SingleReadoutDeadTimeCorrection,
-                                        InputWorkspace=self._ws_db,
-                                        InputErrorEventsWorkspace=error_ws,
-                                        DeadTime=self.dead_time_value,
-                                        TOFStep=self.dead_time_tof_step,
-                                        Paralyzable=self.paralyzable,
-                                        TOFRange=[tof_min, tof_max],
-                                        OutputWorkspace="corr")
-        corr_db = corr_ws.readY(0)
-
-        # Flip the correction since we are going from TOF to Q
-        dead_time_per_tof = np.flip(corr_sc / corr_db)
-
-        # Compute Q for each TOF bin
-        d_theta = self.gravity_correction(self._ws_sc, wl_bins)
-        q_values_edges = np.flip(4.0 * np.pi / wl_bins * np.sin(self.theta - d_theta))
-        q_values = (q_values_edges[1:] + q_values_edges[:-1]) / 2
-
-        # Interpolate to estimate the dead time correction at the Q values we measured
-        q_middle = (self.q_bins[1:] + self.q_bins[:-1]) / 2
-
-        dead_time_corr = np.interp(q_middle, q_values, dead_time_per_tof)
-
-        # Cleanup
-        api.DeleteWorkspace(corr_ws)
-        api.DeleteWorkspace(error_ws)
-
-        return dead_time_corr
-
     def specular(self, q_summing=False, tof_weighted=False, bck_in_q=False,
                  clean=False, normalize=True):
         """
@@ -306,11 +289,6 @@ def specular(self, q_summing=False, tof_weighted=False, bck_in_q=False,
             :param clean: if True, and Q summing is True, then leading artifact will be removed
             :param normalize: if True, and tof_weighted is False, normalization will be skipped
         """
-        # First, let's compute the dead-time correction if we need it
-        # We do this first because the specular calls below may modify the input workspace
-        if self.dead_time:
-            dead_time_corr = self.get_dead_time_correction()
-
         if tof_weighted:
             self.specular_weighted(q_summing=q_summing, bck_in_q=bck_in_q)
         else:
@@ -323,17 +301,6 @@ def specular(self, q_summing=False, tof_weighted=False, bck_in_q=False,
         self.d_refl = self.d_refl[trim:]
         self.q_bins = self.q_bins[trim:]
 
-        # Dead time correction
-        if self.dead_time:
-            i_max = np.argmax(dead_time_corr[trim:])
-            i_min = np.argmin(dead_time_corr[trim:])
-            print("Dead time correction: [%g -> %g] at [%g -> %g]" % (dead_time_corr[trim:][i_min],
-                                                                      dead_time_corr[trim:][i_max],
-                                                                      self.q_bins[i_min],
-                                                                      self.q_bins[i_max]))
-            self.refl *= dead_time_corr[trim:]
-            self.d_refl *= dead_time_corr[trim:]
-
         # Remove leading artifact from the wavelength coverage
         # Remember that q_bins is longer than refl by 1 because
         # it contains bin boundaries
@@ -522,6 +489,7 @@ def _reflectivity(self, ws, peak_position, peak, low_res, theta, q_bins=None, q_
 
                 # Gravity correction
                 d_theta = self.gravity_correction(ws, wl_list)
+                event_weights = evt_list.getWeights()
 
                 # Sign will depend on reflect up or down
                 x_distance = _pixel_width * (j - peak_position)
@@ -531,8 +499,9 @@ def _reflectivity(self, ws, peak_position, peak, low_res, theta, q_bins=None, q_
 
                 if wl_dist is not None and wl_bins is not None:
                     wl_weights = 1.0/np.interp(wl_list, wl_bins, wl_dist, np.inf, np.inf)
-                    hist_weigths = wl_weights * qz / wl_list
-                    _counts, _ = np.histogram(qz, bins=_q_bins, weights=hist_weigths)
+                    hist_weights = wl_weights * qz / wl_list
+                    hist_weights *= event_weights
+                    _counts, _ = np.histogram(qz, bins=_q_bins, weights=hist_weights)
                     _norm, _ = np.histogram(qz, bins=_q_bins)
                     if sum_pixels:
                         refl += _counts
@@ -541,7 +510,7 @@ def _reflectivity(self, ws, peak_position, peak, low_res, theta, q_bins=None, q_
                         refl[j-peak[0]] += _counts
                         counts[j-peak[0]] += _norm
                 else:
-                    _counts, _ = np.histogram(qz, bins=_q_bins)
+                    _counts, _ = np.histogram(qz, bins=_q_bins, weights=event_weights)
                     if sum_pixels:
                         refl += _counts
                     else:
diff --git a/reduction/lr_reduction/template.py b/reduction/lr_reduction/template.py
index 4e78e36..037909d 100644
--- a/reduction/lr_reduction/template.py
+++ b/reduction/lr_reduction/template.py
@@ -155,11 +155,18 @@ def process_from_template_ws(ws_sc, template_data, q_summing=False,
     if isinstance(template_data, str):
         template_data = read_template(template_data, sequence_number)
 
+    if template_data.dead_time:
+        ws_sc = event_reduction.apply_dead_time_correction(ws_sc, template_data)
+
     # Load normalization run
     normalize = normalize and template_data.apply_normalization
     if ws_db is None and normalize:
         ws_db = api.LoadEventNexus("REF_L_%s" % template_data.norm_file)
 
+    # Apply dead time correction
+    if template_data.dead_time:
+        ws_db = event_reduction.apply_dead_time_correction(ws_db, template_data)
+
     # If we run in theta-theta geometry, we'll need thi
     thi_value = ws_sc.getRun()['thi'].value[0]
     ths_value = ws_sc.getRun()['ths'].value[0]
diff --git a/reduction/lr_reduction/workflow.py b/reduction/lr_reduction/workflow.py
index 0e78150..082b7ae 100644
--- a/reduction/lr_reduction/workflow.py
+++ b/reduction/lr_reduction/workflow.py
@@ -279,6 +279,7 @@ def reduce_explorer(ws, ws_db, theta_pv=None, center_pixel=145, db_center_pixel=
 
     theta = theta_value * np.pi / 180.
 
+    #TODO: dead time correction should be applied here
     event_refl = event_reduction.EventReflectivity(ws, ws_db,
                                                    signal_peak=peak, signal_bck=peak_bck,
                                                    norm_peak=norm_peak, norm_bck=norm_bck,
diff --git a/reduction/notebooks/workflow.ipynb b/reduction/notebooks/workflow.ipynb
index e0c7ce5..bbc7855 100644
--- a/reduction/notebooks/workflow.ipynb
+++ b/reduction/notebooks/workflow.ipynb
@@ -12,11 +12,11 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-25T17:40:34.734887Z",
-     "iopub.status.busy": "2024-03-25T17:40:34.734474Z",
-     "iopub.status.idle": "2024-03-25T17:40:35.334084Z",
-     "shell.execute_reply": "2024-03-25T17:40:35.333606Z",
-     "shell.execute_reply.started": "2024-03-25T17:40:34.734864Z"
+     "iopub.execute_input": "2024-04-30T17:38:21.119550Z",
+     "iopub.status.busy": "2024-04-30T17:38:21.119259Z",
+     "iopub.status.idle": "2024-04-30T17:38:21.761642Z",
+     "shell.execute_reply": "2024-04-30T17:38:21.761169Z",
+     "shell.execute_reply.started": "2024-04-30T17:38:21.119533Z"
     },
     "tags": []
    },
@@ -44,11 +44,11 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-25T17:40:36.697898Z",
-     "iopub.status.busy": "2024-03-25T17:40:36.697605Z",
-     "iopub.status.idle": "2024-03-25T17:40:37.474343Z",
-     "shell.execute_reply": "2024-03-25T17:40:37.473873Z",
-     "shell.execute_reply.started": "2024-03-25T17:40:36.697881Z"
+     "iopub.execute_input": "2024-04-30T17:38:44.315416Z",
+     "iopub.status.busy": "2024-04-30T17:38:44.315083Z",
+     "iopub.status.idle": "2024-04-30T17:38:45.143497Z",
+     "shell.execute_reply": "2024-04-30T17:38:45.142829Z",
+     "shell.execute_reply.started": "2024-04-30T17:38:44.315396Z"
     },
     "tags": []
    },
@@ -72,11 +72,11 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-25T17:40:37.475371Z",
-     "iopub.status.busy": "2024-03-25T17:40:37.475152Z",
-     "iopub.status.idle": "2024-03-25T17:40:37.477884Z",
-     "shell.execute_reply": "2024-03-25T17:40:37.477504Z",
-     "shell.execute_reply.started": "2024-03-25T17:40:37.475353Z"
+     "iopub.execute_input": "2024-04-30T17:38:45.848964Z",
+     "iopub.status.busy": "2024-04-30T17:38:45.848389Z",
+     "iopub.status.idle": "2024-04-30T17:38:45.852393Z",
+     "shell.execute_reply": "2024-04-30T17:38:45.851860Z",
+     "shell.execute_reply.started": "2024-04-30T17:38:45.848939Z"
     },
     "tags": []
    },
@@ -85,8 +85,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6.9.20240320.1649\n",
-      "3.10.13 | packaged by conda-forge | (main, Oct 26 2023, 18:07:37) [GCC 12.3.0]\n"
+      "6.9.20240429.1322\n",
+      "3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0]\n"
      ]
     }
    ],
@@ -100,11 +100,11 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-25T17:41:13.123436Z",
-     "iopub.status.busy": "2024-03-25T17:41:13.123205Z",
-     "iopub.status.idle": "2024-03-25T17:41:13.126079Z",
-     "shell.execute_reply": "2024-03-25T17:41:13.125690Z",
-     "shell.execute_reply.started": "2024-03-25T17:41:13.123419Z"
+     "iopub.execute_input": "2024-04-30T17:38:46.505577Z",
+     "iopub.status.busy": "2024-04-30T17:38:46.505163Z",
+     "iopub.status.idle": "2024-04-30T17:38:46.925951Z",
+     "shell.execute_reply": "2024-04-30T17:38:46.925235Z",
+     "shell.execute_reply.started": "2024-04-30T17:38:46.505554Z"
     },
     "tags": []
    },
@@ -124,11 +124,11 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-25T17:40:58.550522Z",
-     "iopub.status.busy": "2024-03-25T17:40:58.550179Z",
-     "iopub.status.idle": "2024-03-25T17:41:13.122243Z",
-     "shell.execute_reply": "2024-03-25T17:41:13.121719Z",
-     "shell.execute_reply.started": "2024-03-25T17:40:58.550502Z"
+     "iopub.execute_input": "2024-04-30T17:42:31.648353Z",
+     "iopub.status.busy": "2024-04-30T17:42:31.647865Z",
+     "iopub.status.idle": "2024-04-30T17:42:47.545359Z",
+     "shell.execute_reply": "2024-04-30T17:42:47.544598Z",
+     "shell.execute_reply.started": "2024-04-30T17:42:31.648321Z"
     },
     "tags": []
    },
@@ -186,15 +186,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-25T17:43:52.028530Z",
-     "iopub.status.busy": "2024-03-25T17:43:52.028194Z",
-     "iopub.status.idle": "2024-03-25T17:43:53.800532Z",
-     "shell.execute_reply": "2024-03-25T17:43:53.799842Z",
-     "shell.execute_reply.started": "2024-03-25T17:43:52.028511Z"
+     "iopub.execute_input": "2024-04-30T17:43:08.805937Z",
+     "iopub.status.busy": "2024-04-30T17:43:08.805448Z",
+     "iopub.status.idle": "2024-04-30T17:43:10.910024Z",
+     "shell.execute_reply": "2024-04-30T17:43:10.909169Z",
+     "shell.execute_reply.started": "2024-04-30T17:43:08.805911Z"
     },
+    "scrolled": false,
     "tags": []
    },
    "outputs": [
@@ -205,29 +206,45 @@
       "wl=4.25; ths=0.599896; thi=-0.0140861; No offset\n",
       "Background on both sides: [132 134] [147 149]\n",
       "Background on both sides: [132 134] [144 146]\n",
-      "Dead time correction: [4.96042e-321 -> 0.917482] at [0.0220792 -> 0.0225208]\n",
+      "Normalization options: True True\n",
+      "Template data was passed instead of a file path: template data not saved\n",
+      "wl=4.25; ths=0.599896; thi=-0.0140861; No offset\n",
+      "Background on both sides: [132 134] [147 149]\n",
+      "Background on both sides: [132 134] [144 146]\n",
       "Normalization options: True True\n",
       "Template data was passed instead of a file path: template data not saved\n",
       "wl=4.25; ths=1.1832; thi=-0.0140861; No offset\n",
       "Background on both sides: [132 134] [147 149]\n",
       "Background on both sides: [132 134] [144 146]\n",
-      "Dead time correction: [0 -> 0.903899] at [0.099448 -> 0.045039]\n",
+      "Normalization options: True True\n",
+      "Template data was passed instead of a file path: template data not saved\n",
+      "wl=4.25; ths=1.1832; thi=-0.0140861; No offset\n",
+      "Background on both sides: [132 134] [147 149]\n",
+      "Background on both sides: [132 134] [144 146]\n",
+      "Normalization options: True True\n",
+      "Template data was passed instead of a file path: template data not saved\n",
+      "wl=4.25; ths=2.34186; thi=-0.0140861; No offset\n",
+      "Background on both sides: [132 134] [147 149]\n",
+      "Background on both sides: [132 134] [144 146]\n",
       "Normalization options: True True\n",
       "Template data was passed instead of a file path: template data not saved\n",
       "wl=4.25; ths=2.34186; thi=-0.0140861; No offset\n",
       "Background on both sides: [132 134] [147 149]\n",
       "Background on both sides: [132 134] [144 146]\n",
-      "Dead time correction: [2.69374e-318 -> 0.911283] at [0.0865754 -> 0.088307]\n",
       "Normalization options: True True\n",
       "Template data was passed instead of a file path: template data not saved\n"
      ]
     }
    ],
    "source": [
+    "import lr_reduction.background as background\n",
+    "import lr_reduction.DeadTimeCorrection as DeadTimeCorrection\n",
     "importlib.reload(workflow)\n",
     "importlib.reload(output)\n",
     "importlib.reload(event_reduction)\n",
     "importlib.reload(template)\n",
+    "importlib.reload(background)\n",
+    "importlib.reload(DeadTimeCorrection)\n",
     "\n",
     "# To test dead time correction\n",
     "# run=[206593,206594,206595,206596,206597,206598,206599,206600]\n",
@@ -246,12 +263,15 @@
     "    template_data = template.read_template(template_path, sequence_number)\n",
     "    template_data.dead_time = True\n",
     "    template_data.paralyzable = True\n",
-    "    workflow.reduce(ws, template_data, output_dir=data_dir, average_overlap=False)"
+    "    ws_db = api.LoadEventNexus(\"REF_L_%s\" % template_data.norm_file)\n",
+    "    workflow.reduce(ws, template_data, output_dir=data_dir, average_overlap=False, ws_db=ws_db)\n",
+    "    workflow.reduce(ws, template_data, output_dir=data_dir, average_overlap=False, ws_db=ws_db)\n",
+    "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 24,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-03-07T18:54:58.331335Z",
@@ -265,7 +285,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1500x1500 with 1 Axes>"
       ]
@@ -277,6 +297,7 @@
    "source": [
     "reduced_path = os.path.join(data_dir, 'reference_rq.txt')\n",
     "reduced_path = os.path.join(data_dir, 'ref_206780_206594.txt')\n",
+    "reduced_path = os.path.join(os.path.expanduser('~/REFL_206784_reduced_data-with-deadtime.txt'))\n",
     "\n",
     "if os.path.isfile(reduced_path):\n",
     "    _data = np.loadtxt(reduced_path).T\n",
@@ -799,9 +820,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "mantid-framework",
-   "language": "",
-   "name": "mantid"
+   "display_name": "Python [conda env:mantid-dev]",
+   "language": "python",
+   "name": "conda-env-mantid-dev-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -813,7 +834,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

From 98c9600fa0ecd31ed7488be67fb96bfb8e689830 Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Wed, 1 May 2024 14:00:29 -0400
Subject: [PATCH 2/3] Add better dead time correction

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index c32ea2e..cbc02ef 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,7 @@ Reduction scripts for the Liquids Reflectometer. This includes both automated re
 
 
 ## Release notes:
+ - reduction v2.0.26 [05/2024] Implement a better dead time correction using weighted events
  - reduction v2.0.25 [04/2024] Use dead time parameters from template
  - reduction v2.0.24 [04/2024] Fix issue with errors when using dead time correction
  - reduction v2.0.22 [04/2024] Add dead time correction to scaling factor calculation

From bac745eec25e50e52a56fc1f106679fbbc80480e Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Wed, 1 May 2024 14:04:08 -0400
Subject: [PATCH 3/3] Add better dead time correction

---
 reduction/lr_reduction/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/reduction/lr_reduction/__init__.py b/reduction/lr_reduction/__init__.py
index 56fc0f1..c062e27 100644
--- a/reduction/lr_reduction/__init__.py
+++ b/reduction/lr_reduction/__init__.py
@@ -1 +1 @@
-__version__ = '2.0.25'
+__version__ = '2.0.26'