From 4945c90a4c42c54fef24eca67c9671cec3e04b5d Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Tue, 9 Jan 2024 15:44:22 -0500
Subject: [PATCH 01/15] add dead time correction

---
 reduction/lr_reduction/event_reduction.py |  67 +++++++++++++-
 reduction/lr_reduction/template.py        |   9 +-
 reduction/lr_reduction/workflow.py        |   5 +-
 reduction/notebooks/workflow.ipynb        | 103 ++++++++++++----------
 4 files changed, 127 insertions(+), 57 deletions(-)

diff --git a/reduction/lr_reduction/event_reduction.py b/reduction/lr_reduction/event_reduction.py
index 57d0b4e..1e2979c 100644
--- a/reduction/lr_reduction/event_reduction.py
+++ b/reduction/lr_reduction/event_reduction.py
@@ -56,13 +56,14 @@ class EventReflectivity(object):
     INSTRUMENT_4B = 1
     DEFAULT_4B_SAMPLE_DET_DISTANCE = 1.83
     DEFAULT_4B_SOURCE_DET_DISTANCE = 15.75
+    DEAD_TIME = 4.0
 
     def __init__(self, scattering_workspace, direct_workspace,
                  signal_peak, signal_bck, norm_peak, norm_bck,
                  specular_pixel, signal_low_res, norm_low_res,
                  q_min=None, q_step=-0.02, q_max=None,
                  tof_range=None, theta=1.0, instrument=None,
-                 functional_background=False):
+                 functional_background=False, dead_time=False):
         """
             Pixel ranges include the min and max pixels.
 
@@ -80,6 +81,7 @@ def __init__(self, scattering_workspace, direct_workspace,
             :param q_min: value of largest q point
             :param tof_range: TOF range,or None
             :param theta: theta scattering angle in radians
+            :param dead_time: if not zero, dead time correction will be used
         """
         if instrument in [self.INSTRUMENT_4A, self.INSTRUMENT_4B]:
             self.instrument = instrument
@@ -100,6 +102,7 @@ def __init__(self, scattering_workspace, direct_workspace,
         self._offspec_x_bins = None
         self._offspec_z_bins = None
         self.summing_threshold = None
+        self.dead_time = dead_time
 
         # Turn on functional background estimation
         self.use_functional_bck = functional_background
@@ -226,6 +229,52 @@ 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, tof_step=100):
+        """
+            Perform dead time correction using counts per pulse over the whole
+            face of the detector.
+            Interpolate for the Q values we are going to use for the reduction.
+        """
+        # Rebin the data according to the tof_step we want to compute the correction with
+        tof_min = self._ws_sc.getTofMin()
+        tof_max = self._ws_sc.getTofMax()
+        _ws_sc = api.Rebin(InputWorkspace=self._ws_sc, Params="%s,%s,%s" % (tof_min, tof_step, tof_max))
+        _ws_db = api.RebinToWorkspace(WorkspaceToRebin=self._ws_db, WorkspaceToMatch=_ws_sc)
+
+        # Get the total number of counts on the detector for each TOF bin per pulse
+        counts_ws = api.SumSpectra(_ws_sc)
+        t_series = np.asarray(_ws_sc.getRun()['proton_charge'].value)
+        non_zero = t_series > 0
+        n_pulses = np.count_nonzero(non_zero)
+        rate_sc = counts_ws.readY(0) / n_pulses
+        wl_bins = counts_ws.readX(0) / self.constant
+        wl_bins = (wl_bins[1:] + wl_bins[:-1]) / 2
+
+        counts_ws = api.SumSpectra(_ws_db)
+        t_series = np.asarray(_ws_sc.getRun()['proton_charge'].value)
+        non_zero = t_series > 0
+        n_pulses = np.count_nonzero(non_zero)
+        rate_db = counts_ws.readY(0) / n_pulses
+
+        # Compute the dead time correction for each TOF bin
+        corr_sc = 1/(1-rate_sc*self.DEAD_TIME/tof_step)
+        corr_db = 1/(1-rate_db*self.DEAD_TIME/tof_step)
+        if np.min(corr_sc) < 0 or np.min(corr_db) < 0:
+            print("Corrupted dead time correction:")
+            print("Reflected: %s" % corr_sc)
+            print("Direct Beam: %s" % corr_db)
+        dead_time_per_tof = corr_sc / corr_db
+
+        # Compute Q for each TOF bin
+        d_theta = self.gravity_correction(self._ws_sc, wl_bins)
+        q_values = 4.0 * np.pi / wl_bins * np.sin(self.theta - d_theta)
+
+        # 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)
+
+        return dead_time_corr
+
     def specular(self, q_summing=False, tof_weighted=False, bck_in_q=False,
                  clean=False, normalize=True):
         """
@@ -239,6 +288,10 @@ 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
+        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:
@@ -252,9 +305,15 @@ def specular(self, q_summing=False, tof_weighted=False, bck_in_q=False,
         self.q_bins = self.q_bins[trim:]
 
         # Dead time correction
-        # dead_time = 4e-6
-        #self.refl = self.refl * t_corr_sc / t_corr_db
-        #self.d_refl = self.d_refl * t_corr_sc / t_corr_db
+        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
diff --git a/reduction/lr_reduction/template.py b/reduction/lr_reduction/template.py
index 87c7f31..b0b4789 100644
--- a/reduction/lr_reduction/template.py
+++ b/reduction/lr_reduction/template.py
@@ -129,7 +129,7 @@ def _value_check(key, data, reference):
 
 def process_from_template(run_number, template_path, q_summing=False, normalize=True,
                           tof_weighted=False, bck_in_q=False, clean=False, info=False,
-                          functional_background=False):
+                          functional_background=False, dead_time=False):
     """
         The clean option removes leading zeros and the drop when doing q-summing
     """
@@ -142,13 +142,14 @@ def process_from_template(run_number, template_path, q_summing=False, normalize=
     return process_from_template_ws(ws_sc, template_path, q_summing=q_summing,
                                     tof_weighted=tof_weighted, bck_in_q=bck_in_q,
                                     clean=clean, info=info, normalize=normalize,
-                                    functional_background=functional_background)
+                                    functional_background=functional_background,
+                                    dead_time=dead_time)
 
 
 def process_from_template_ws(ws_sc, template_data, q_summing=False,
                              tof_weighted=False, bck_in_q=False, clean=False,
                              info=False, normalize=True, theta_value=None, ws_db=None,
-                             functional_background=False):
+                             functional_background=False, dead_time=False):
     # Get the sequence number
     sequence_number = 1
     if ws_sc.getRun().hasProperty("sequence_number"):
@@ -222,7 +223,7 @@ def process_from_template_ws(ws_sc, template_data, q_summing=False,
                                                    signal_low_res=low_res, norm_low_res=norm_low_res,
                                                    q_min=q_min, q_step=q_step, q_max=None,
                                                    tof_range=[tof_min, tof_max],
-                                                   theta=np.abs(theta),
+                                                   theta=np.abs(theta), dead_time=dead_time,
                                                    functional_background=functional_background,
                                                    instrument=event_reduction.EventReflectivity.INSTRUMENT_4B)
 
diff --git a/reduction/lr_reduction/workflow.py b/reduction/lr_reduction/workflow.py
index 8249fe5..363510b 100644
--- a/reduction/lr_reduction/workflow.py
+++ b/reduction/lr_reduction/workflow.py
@@ -12,7 +12,7 @@
 
 def reduce(ws, template_file, output_dir, average_overlap=False,
            q_summing=False, bck_in_q=False, is_live=False,
-           functional_background=False):
+           functional_background=False, dead_time=False):
     """
         Function called by reduce_REFL.py, which lives in /SNS/REF_L/shared/autoreduce
         and is called by the automated reduction workflow.
@@ -32,7 +32,8 @@ def reduce(ws, template_file, output_dir, average_overlap=False,
                                                                         clean=q_summing,
                                                                         bck_in_q=bck_in_q,
                                                                         functional_background=functional_background,
-                                                                        info=True)
+                                                                        info=True,
+                                                                        dead_time=dead_time)
 
     # Save partial results
     coll = output.RunCollection()
diff --git a/reduction/notebooks/workflow.ipynb b/reduction/notebooks/workflow.ipynb
index a41538a..787aed8 100644
--- a/reduction/notebooks/workflow.ipynb
+++ b/reduction/notebooks/workflow.ipynb
@@ -12,11 +12,11 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-06T17:19:41.400214Z",
-     "iopub.status.busy": "2023-11-06T17:19:41.399831Z",
-     "iopub.status.idle": "2023-11-06T17:19:42.048666Z",
-     "shell.execute_reply": "2023-11-06T17:19:42.047955Z",
-     "shell.execute_reply.started": "2023-11-06T17:19:41.400195Z"
+     "iopub.execute_input": "2024-01-09T20:09:18.843870Z",
+     "iopub.status.busy": "2024-01-09T20:09:18.843578Z",
+     "iopub.status.idle": "2024-01-09T20:09:19.510363Z",
+     "shell.execute_reply": "2024-01-09T20:09:19.509342Z",
+     "shell.execute_reply.started": "2024-01-09T20:09:18.843849Z"
     },
     "tags": []
    },
@@ -44,11 +44,11 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-06T17:19:43.234553Z",
-     "iopub.status.busy": "2023-11-06T17:19:43.234228Z",
-     "iopub.status.idle": "2023-11-06T17:19:43.986702Z",
-     "shell.execute_reply": "2023-11-06T17:19:43.986219Z",
-     "shell.execute_reply.started": "2023-11-06T17:19:43.234536Z"
+     "iopub.execute_input": "2024-01-09T20:09:19.512179Z",
+     "iopub.status.busy": "2024-01-09T20:09:19.511698Z",
+     "iopub.status.idle": "2024-01-09T20:09:20.281547Z",
+     "shell.execute_reply": "2024-01-09T20:09:20.280870Z",
+     "shell.execute_reply.started": "2024-01-09T20:09:19.512156Z"
     },
     "tags": []
    },
@@ -72,11 +72,11 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-06T17:19:44.903069Z",
-     "iopub.status.busy": "2023-11-06T17:19:44.902694Z",
-     "iopub.status.idle": "2023-11-06T17:19:44.905677Z",
-     "shell.execute_reply": "2023-11-06T17:19:44.905279Z",
-     "shell.execute_reply.started": "2023-11-06T17:19:44.903051Z"
+     "iopub.execute_input": "2024-01-09T20:09:20.283083Z",
+     "iopub.status.busy": "2024-01-09T20:09:20.282554Z",
+     "iopub.status.idle": "2024-01-09T20:09:20.285955Z",
+     "shell.execute_reply": "2024-01-09T20:09:20.285414Z",
+     "shell.execute_reply.started": "2024-01-09T20:09:20.283062Z"
     },
     "tags": []
    },
@@ -100,11 +100,11 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-06T17:19:45.605996Z",
-     "iopub.status.busy": "2023-11-06T17:19:45.605646Z",
-     "iopub.status.idle": "2023-11-06T17:19:45.838636Z",
-     "shell.execute_reply": "2023-11-06T17:19:45.838131Z",
-     "shell.execute_reply.started": "2023-11-06T17:19:45.605971Z"
+     "iopub.execute_input": "2024-01-09T20:09:20.541527Z",
+     "iopub.status.busy": "2024-01-09T20:09:20.540968Z",
+     "iopub.status.idle": "2024-01-09T20:09:20.792757Z",
+     "shell.execute_reply": "2024-01-09T20:09:20.791836Z",
+     "shell.execute_reply.started": "2024-01-09T20:09:20.541507Z"
     },
     "tags": []
    },
@@ -120,14 +120,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-08T18:35:17.776593Z",
-     "iopub.status.busy": "2023-11-08T18:35:17.776276Z",
-     "iopub.status.idle": "2023-11-08T18:35:34.006094Z",
-     "shell.execute_reply": "2023-11-08T18:35:34.005627Z",
-     "shell.execute_reply.started": "2023-11-08T18:35:17.776575Z"
+     "iopub.execute_input": "2024-01-09T20:41:05.482380Z",
+     "iopub.status.busy": "2024-01-09T20:41:05.482006Z",
+     "iopub.status.idle": "2024-01-09T20:41:24.400835Z",
+     "shell.execute_reply": "2024-01-09T20:41:24.400041Z",
+     "shell.execute_reply.started": "2024-01-09T20:41:05.482354Z"
     },
     "tags": []
    },
@@ -137,28 +137,36 @@
      "output_type": "stream",
      "text": [
       "wl=15; ths=-0.600382; thi=-0.00812677; No offset\n",
-      "Linear background on both sides: [133 135] [148 150]\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Dead time correction: [0.997982 -> 0.997982] at [0.0078845 -> 0.0078845]\n",
       "Normalization options: True True\n",
       "wl=12.386; ths=-0.600058; thi=-0.00812677; No offset\n",
-      "Linear background on both sides: [133 135] [148 150]\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Dead time correction: [0.993186 -> 0.993186] at [0.0094227 -> 0.0094227]\n",
       "Normalization options: True True\n",
       "wl=9.74; ths=-0.600058; thi=-0.00812677; No offset\n",
-      "Linear background on both sides: [133 135] [148 150]\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Dead time correction: [0.885545 -> 0.992058] at [0.0114862 -> 0.0117159]\n",
       "Normalization options: True True\n",
       "wl=7.043; ths=-0.599896; thi=-0.00812677; No offset\n",
-      "Linear background on both sides: [133 135] [148 150]\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Dead time correction: [0.997344 -> 0.997344] at [0.0151558 -> 0.0151558]\n",
       "Normalization options: True True\n",
       "wl=4.25; ths=-0.599733; thi=-0.00812677; No offset\n",
-      "Linear background on both sides: [133 135] [148 150]\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Dead time correction: [0.991585 -> 0.999528] at [0.0220792 -> 0.0225208]\n",
       "Normalization options: True True\n",
       "wl=4.25; ths=-1.18271; thi=-0.00812677; No offset\n",
-      "Linear background on both sides: [133 135] [148 150]\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Dead time correction: [1.00012 -> 1.00012] at [0.0441559 -> 0.0441559]\n",
       "Normalization options: True True\n",
       "wl=4.25; ths=-2.34284; thi=-0.00812677; No offset\n",
-      "Linear background on both sides: [131 133] [149 151]\n",
+      "Background on both sides: [131 133] [149 151]\n",
+      "Dead time correction: [1.0001 -> 1.00026] at [0.0865754 -> 0.088307]\n",
       "Normalization options: True True\n",
       "wl=4.25; ths=-4.63906; thi=-0.00812677; No offset\n",
-      "Linear background on both sides: [131 133] [149 151]\n",
+      "Background on both sides: [131 133] [149 151]\n",
+      "Dead time correction: [1.0001 -> 1.0001] at [0.173141 -> 0.173141]\n",
       "Normalization options: True True\n"
      ]
     }
@@ -167,6 +175,7 @@
     "importlib.reload(workflow)\n",
     "importlib.reload(output)\n",
     "importlib.reload(event_reduction)\n",
+    "importlib.reload(template)\n",
     "\n",
     "data_dir = os.path.expanduser('~/git/LiquidsReflectometer/reduction/data')\n",
     "template_path = os.path.join(data_dir, 'template.xml')\n",
@@ -175,19 +184,19 @@
     "\n",
     "for i in range(198409, 198417):\n",
     "    ws = api.Load(\"REF_L_%s\" % i)\n",
-    "    workflow.reduce(ws, template_path, output_dir=data_dir, average_overlap=False)\n"
+    "    workflow.reduce(ws, template_path, output_dir=data_dir, average_overlap=False, dead_time=True)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-08T18:35:36.440233Z",
-     "iopub.status.busy": "2023-11-08T18:35:36.439800Z",
-     "iopub.status.idle": "2023-11-08T18:35:36.818267Z",
-     "shell.execute_reply": "2023-11-08T18:35:36.817821Z",
-     "shell.execute_reply.started": "2023-11-08T18:35:36.440206Z"
+     "iopub.execute_input": "2024-01-09T20:41:37.175599Z",
+     "iopub.status.busy": "2024-01-09T20:41:37.175164Z",
+     "iopub.status.idle": "2024-01-09T20:41:37.738393Z",
+     "shell.execute_reply": "2024-01-09T20:41:37.737952Z",
+     "shell.execute_reply.started": "2024-01-09T20:41:37.175574Z"
     },
     "tags": []
    },
@@ -195,18 +204,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fc18b46acaf1488aa42979848bedc841",
+       "model_id": "2032fdcc1e6f48dda9ad0bb24f719598",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
+      "image/png": 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",
       "text/html": [
        "\n",
        "            <div style=\"display: inline-block;\">\n",
        "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
        "                    Figure\n",
        "                </div>\n",
-       "                <img 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' 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+       "                <img 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' width=1000.0/>\n",
        "            </div>\n",
        "        "
       ],
@@ -220,7 +229,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1de12dce63af4376bd8606afe8aae23a",
+       "model_id": "bd761d75cc8a4b01bbae0c8bbe920a47",
        "version_major": 2,
        "version_minor": 0
       },
@@ -247,8 +256,8 @@
      "output_type": "stream",
      "text": [
       "0.0\n",
-      "-0.010348942915170302\n",
-      "0.0012212756757149915\n",
+      "0.1735444705802743\n",
+      "0.012093480426643599\n",
       "-1.3623114389989168e-06\n"
      ]
     }

From 511d6fc0ebe84f6e8bf0dd4a0f5958ca553ccf8f Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Wed, 10 Jan 2024 16:10:38 -0500
Subject: [PATCH 02/15] fix small calculation issue

---
 reduction/lr_reduction/event_reduction.py |  17 +--
 reduction/notebooks/workflow.ipynb        | 145 +++++++++++++++-------
 2 files changed, 108 insertions(+), 54 deletions(-)

diff --git a/reduction/lr_reduction/event_reduction.py b/reduction/lr_reduction/event_reduction.py
index 1e2979c..f3e2454 100644
--- a/reduction/lr_reduction/event_reduction.py
+++ b/reduction/lr_reduction/event_reduction.py
@@ -56,7 +56,7 @@ class EventReflectivity(object):
     INSTRUMENT_4B = 1
     DEFAULT_4B_SAMPLE_DET_DISTANCE = 1.83
     DEFAULT_4B_SOURCE_DET_DISTANCE = 15.75
-    DEAD_TIME = 4.0
+    DEAD_TIME = 8.0 # Nominally 4.0 microseconds
 
     def __init__(self, scattering_workspace, direct_workspace,
                  signal_peak, signal_bck, norm_peak, norm_bck,
@@ -239,7 +239,7 @@ def get_dead_time_correction(self, tof_step=100):
         tof_min = self._ws_sc.getTofMin()
         tof_max = self._ws_sc.getTofMax()
         _ws_sc = api.Rebin(InputWorkspace=self._ws_sc, Params="%s,%s,%s" % (tof_min, tof_step, tof_max))
-        _ws_db = api.RebinToWorkspace(WorkspaceToRebin=self._ws_db, WorkspaceToMatch=_ws_sc)
+        _ws_db = api.Rebin(InputWorkspace=self._ws_db, Params="%s,%s,%s" % (tof_min, tof_step, tof_max))
 
         # Get the total number of counts on the detector for each TOF bin per pulse
         counts_ws = api.SumSpectra(_ws_sc)
@@ -248,10 +248,10 @@ def get_dead_time_correction(self, tof_step=100):
         n_pulses = np.count_nonzero(non_zero)
         rate_sc = counts_ws.readY(0) / n_pulses
         wl_bins = counts_ws.readX(0) / self.constant
-        wl_bins = (wl_bins[1:] + wl_bins[:-1]) / 2
 
+        # Direct beam
         counts_ws = api.SumSpectra(_ws_db)
-        t_series = np.asarray(_ws_sc.getRun()['proton_charge'].value)
+        t_series = np.asarray(self._ws_db.getRun()['proton_charge'].value)
         non_zero = t_series > 0
         n_pulses = np.count_nonzero(non_zero)
         rate_db = counts_ws.readY(0) / n_pulses
@@ -259,20 +259,23 @@ def get_dead_time_correction(self, tof_step=100):
         # Compute the dead time correction for each TOF bin
         corr_sc = 1/(1-rate_sc*self.DEAD_TIME/tof_step)
         corr_db = 1/(1-rate_db*self.DEAD_TIME/tof_step)
+
         if np.min(corr_sc) < 0 or np.min(corr_db) < 0:
             print("Corrupted dead time correction:")
             print("Reflected: %s" % corr_sc)
             print("Direct Beam: %s" % corr_db)
-        dead_time_per_tof = corr_sc / corr_db
+        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 = 4.0 * np.pi / wl_bins * np.sin(self.theta - d_theta)
+        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)
-
+        
         return dead_time_corr
 
     def specular(self, q_summing=False, tof_weighted=False, bck_in_q=False,
diff --git a/reduction/notebooks/workflow.ipynb b/reduction/notebooks/workflow.ipynb
index 787aed8..3096eff 100644
--- a/reduction/notebooks/workflow.ipynb
+++ b/reduction/notebooks/workflow.ipynb
@@ -177,6 +177,10 @@
     "importlib.reload(event_reduction)\n",
     "importlib.reload(template)\n",
     "\n",
+    "# To test dead time correction\n",
+    "# run=[206593,206594,206595,206596,206597,206598,206599,206600]\n",
+    "# rate=[49805,34762,21197,12198,8020,4542,2254,569]\n",
+    "\n",
     "data_dir = os.path.expanduser('~/git/LiquidsReflectometer/reduction/data')\n",
     "template_path = os.path.join(data_dir, 'template.xml')\n",
     "\n",
@@ -189,14 +193,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 229,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-10T21:09:16.876854Z",
+     "iopub.status.busy": "2024-01-10T21:09:16.876345Z",
+     "iopub.status.idle": "2024-01-10T21:09:25.391551Z",
+     "shell.execute_reply": "2024-01-10T21:09:25.390987Z",
+     "shell.execute_reply.started": "2024-01-10T21:09:16.876835Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wl=4.25; ths=-0.599733; thi=-0.00812677; No offset\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Dead time correction: [0.870673 -> 0.979509] at [0.0487517 -> 0.0220792]\n",
+      "Normalization options: True True\n",
+      "wl=4.25; ths=-1.18271; thi=-0.00812677; No offset\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Dead time correction: [0.871634 -> 0.97502] at [0.097498 -> 0.0441559]\n",
+      "Normalization options: True True\n",
+      "wl=4.25; ths=-2.34284; thi=-0.00812677; No offset\n",
+      "Background on both sides: [131 133] [149 151]\n",
+      "Dead time correction: [0.870433 -> 0.977805] at [0.191162 -> 0.0865754]\n",
+      "Normalization options: True True\n",
+      "wl=4.25; ths=-4.63906; thi=-0.00812677; No offset\n",
+      "Background on both sides: [131 133] [149 151]\n",
+      "Dead time correction: [0.872217 -> 0.974492] at [0.374807 -> 0.173141]\n",
+      "Normalization options: True True\n"
+     ]
+    }
+   ],
+   "source": [
+    "importlib.reload(workflow)\n",
+    "importlib.reload(output)\n",
+    "importlib.reload(event_reduction)\n",
+    "importlib.reload(template)\n",
+    "\n",
+    "# To test dead time correction\n",
+    "# run=[206593,206594,206595,206596,206597,206598,206599,206600]\n",
+    "# rate=[49805,34762, 21197, 12198, 8020,  4542,  2254,  569]\n",
+    "\n",
+    "data_dir = os.path.expanduser('~/git/LiquidsReflectometer/reduction/data')\n",
+    "template_path = os.path.join(data_dir, 'template_high_rate_206597.xml')\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/git/LiquidsReflectometer/reduction'))\n",
+    "\n",
+    "for i in range(198413, 198417):\n",
+    "    ws = api.Load(\"REF_L_%s\" % i)\n",
+    "    workflow.reduce(ws, template_path, output_dir=data_dir, average_overlap=False, dead_time=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T20:41:37.175599Z",
-     "iopub.status.busy": "2024-01-09T20:41:37.175164Z",
-     "iopub.status.idle": "2024-01-09T20:41:37.738393Z",
-     "shell.execute_reply": "2024-01-09T20:41:37.737952Z",
-     "shell.execute_reply.started": "2024-01-09T20:41:37.175574Z"
+     "iopub.execute_input": "2024-01-10T21:09:55.881356Z",
+     "iopub.status.busy": "2024-01-10T21:09:55.881033Z",
+     "iopub.status.idle": "2024-01-10T21:09:56.233358Z",
+     "shell.execute_reply": "2024-01-10T21:09:56.232949Z",
+     "shell.execute_reply.started": "2024-01-10T21:09:55.881333Z"
     },
     "tags": []
    },
@@ -204,18 +265,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2032fdcc1e6f48dda9ad0bb24f719598",
+       "model_id": "e40d8d7712a240338a4eb02facebbf72",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
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",
       "text/html": [
        "\n",
        "            <div style=\"display: inline-block;\">\n",
        "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
        "                    Figure\n",
        "                </div>\n",
-       "                <img 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' width=1000.0/>\n",
+       "                <img 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' width=1000.0/>\n",
        "            </div>\n",
        "        "
       ],
@@ -229,18 +290,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bd761d75cc8a4b01bbae0c8bbe920a47",
+       "model_id": "e558a05111b34cf29b1a5ddb8adddf05",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
       "text/html": [
        "\n",
        "            <div style=\"display: inline-block;\">\n",
        "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
        "                    Figure\n",
        "                </div>\n",
-       "                <img 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' width=1000.0/>\n",
        "            </div>\n",
        "        "
       ],
@@ -256,14 +317,16 @@
      "output_type": "stream",
      "text": [
       "0.0\n",
-      "0.1735444705802743\n",
-      "0.012093480426643599\n",
-      "-1.3623114389989168e-06\n"
+      "0.002325716392084999\n",
+      "0.00015451278785510008\n",
+      "0.0\n"
      ]
     }
    ],
    "source": [
-    "reduced_path = os.path.join(data_dir, 'reference_rq.txt')\n",
+    "#reduced_path = os.path.join(data_dir, 'reference_rq.txt')\n",
+    "reduced_path = os.path.join(data_dir, 'ref_rate_206597.txt')\n",
+    "\n",
     "if os.path.isfile(reduced_path):\n",
     "    _data = np.loadtxt(reduced_path).T\n",
     "\n",
@@ -272,8 +335,10 @@
     "    _refl = np.loadtxt(reduced_path).T\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(10,5))\n",
-    "plt.errorbar(_refl[0], _refl[1]*_refl[0]**4, yerr=_refl[2]*_refl[0]**4, markersize=4, marker='.', linestyle='', label='new reduction')\n",
-    "plt.errorbar(_data[0], _data[1]*_data[0]**4, yerr=_data[2]*_data[0]**4, markersize=4, marker='', linestyle='-', label='reference')\n",
+    "#plt.errorbar(_refl[0], _refl[1]*_refl[0]**4, yerr=_refl[2]*_refl[0]**4, markersize=4, marker='.', linestyle='', label='new reduction')\n",
+    "#plt.errorbar(_data[0], _data[1]*_data[0]**4, yerr=_data[2]*_data[0]**4, markersize=4, marker='', linestyle='-', label='reference')\n",
+    "plt.errorbar(_refl[0], _refl[1], yerr=_refl[2], markersize=4, marker='*', linestyle='-', label='new reduction')\n",
+    "plt.errorbar(_data[0], _data[1], yerr=_data[2], markersize=4, marker='*', linestyle='-', label='reference')\n",
     "\n",
     "plt.legend()\n",
     "plt.xlabel('q [$1/\\AA$]')\n",
@@ -482,42 +547,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 31,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-02-03T20:57:28.354196Z",
-     "iopub.status.busy": "2023-02-03T20:57:28.353571Z",
-     "iopub.status.idle": "2023-02-03T20:57:28.778071Z",
-     "shell.execute_reply": "2023-02-03T20:57:28.777020Z",
-     "shell.execute_reply.started": "2023-02-03T20:57:28.354139Z"
+     "iopub.execute_input": "2024-01-10T14:38:54.953223Z",
+     "iopub.status.busy": "2024-01-10T14:38:54.952919Z",
+     "iopub.status.idle": "2024-01-10T14:38:54.972840Z",
+     "shell.execute_reply": "2024-01-10T14:38:54.972299Z",
+     "shell.execute_reply.started": "2024-01-10T14:38:54.953204Z"
     },
     "tags": []
    },
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ef81998856d24e458ce3a97ff8fff480",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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",
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-       "                    Figure\n",
-       "                </div>\n",
-       "                <img 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' width=1000.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'IPTS' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m reduced_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/SNS/REF_L/IPTS-\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m/shared/autoreduce/REFL_\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m_combined_data_auto.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[43mIPTS\u001b[49m, run_number)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(reduced_path):\n\u001b[1;32m      4\u001b[0m     _data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mloadtxt(reduced_path)\u001b[38;5;241m.\u001b[39mT\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'IPTS' is not defined"
+     ]
     }
    ],
    "source": [

From 38a1434bc8b4aeafda8e30e3cec8d0be911b6fcf Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Thu, 7 Mar 2024 09:16:41 -0500
Subject: [PATCH 03/15] add deadtime algo

---
 reduction/lr_reduction/DeadTimeCorrection.py |  98 ++++++
 reduction/lr_reduction/event_reduction.py    |  64 ++--
 reduction/notebooks/workflow.ipynb           | 310 +++++++++++--------
 reduction/test/test_dead_time.py             |  45 +++
 4 files changed, 344 insertions(+), 173 deletions(-)
 create mode 100644 reduction/lr_reduction/DeadTimeCorrection.py
 create mode 100644 reduction/test/test_dead_time.py

diff --git a/reduction/lr_reduction/DeadTimeCorrection.py b/reduction/lr_reduction/DeadTimeCorrection.py
new file mode 100644
index 0000000..31fa0d3
--- /dev/null
+++ b/reduction/lr_reduction/DeadTimeCorrection.py
@@ -0,0 +1,98 @@
+"""
+    Dead time correction algorithm for single-readout detectors.
+"""
+import time
+import math
+import os
+from mantid.api import *
+from mantid.simpleapi import *
+from mantid.kernel import *
+import numpy as np
+import scipy
+
+def call(InputWorkspace, DeadTime=4.2, TOFStep=100, Paralyzable=False, TOFRange=[0, 0], OutputWorkspace='correction'):
+    """
+        Function to make the algorithm call similar to a normal Mantid call
+    """
+    algo = SingleReadoutDeadTimeCorrection()
+    algo.PyInit()
+    algo.setProperty("InputWorkspace", InputWorkspace)
+    algo.setProperty("DeadTime", DeadTime)
+    algo.setProperty("TOFStep", TOFStep)
+    algo.setProperty("Paralyzable", Paralyzable)
+    algo.setProperty("TOFRange", TOFRange)
+    algo.setProperty("OutputWorkspace", OutputWorkspace)
+    algo.PyExec()
+    return algo.getProperty('OutputWorkspace').value
+
+
+class SingleReadoutDeadTimeCorrection(PythonAlgorithm):
+
+    def category(self):
+        return "Reflectometry\\SNS"
+
+    def name(self):
+        return "SingleReadoutDeadTimeCorrection"
+
+    def version(self):
+        return 1
+
+    def summary(self):
+        return "Single read-out dead time correction calculation"
+
+    def PyInit(self):
+        self.declareProperty(WorkspaceProperty("InputWorkspace", "", Direction.Input),
+                             "Optionally, we can provide a workspace directly")
+        self.declareProperty("DeadTime", 4.2, doc="Dead time in microseconds")
+        self.declareProperty("TOFStep", 100,
+                             doc="TOF bins to compute deadtime correction for, in microseconds")
+        self.declareProperty("Paralyzable", False,
+                             doc="If true, paralyzable correction will be applied, non-paralyzing otherwise")
+        self.declareProperty(FloatArrayProperty("TOFRange", [0., 0.],
+                                                FloatArrayLengthValidator(2), direction=Direction.Input),
+                             "TOF range to use")
+        self.declareProperty(MatrixWorkspaceProperty("OutputWorkspace", "", Direction.Output), "Output workspace")
+
+    def PyExec(self):
+        # Event data must include error events (all triggers on the detector)
+        ws_event_data = self.getProperty("InputWorkspace").value
+        dead_time = self.getProperty("DeadTime").value
+        tof_step = self.getProperty("TOFStep").value
+        paralyzing = self.getProperty("Paralyzable").value
+
+        # Rebin the data according to the tof_step we want to compute the correction with
+        tof_min, tof_max = self.getProperty("TOFRange").value
+        if tof_min == 0 and tof_max == 0:
+            tof_min = ws_event_data.getTofMin()
+            tof_max = ws_event_data.getTofMax()
+        logger.notice("TOF range: %f %f" % (tof_min, tof_max))
+        _ws_sc = Rebin(InputWorkspace=ws_event_data, Params="%s,%s,%s" % (tof_min, tof_step, tof_max), PreserveEvents=False)
+
+        # Get the total number of counts on the detector for each TOF bin per pulse
+        counts_ws = SumSpectra(_ws_sc)
+        t_series = np.asarray(_ws_sc.getRun()['proton_charge'].value)
+        non_zero = t_series > 0
+        n_pulses = np.count_nonzero(non_zero)
+        rate = counts_ws.readY(0) / n_pulses
+        tof_bins = counts_ws.readX(0)
+
+         # Compute the dead time correction for each TOF bin
+        if paralyzing:
+            true_rate = -scipy.special.lambertw(-rate * dead_time / tof_step).real / dead_time
+            corr = true_rate / (rate / tof_step)
+            # If we have no events, set the correction to 1 orderwise we will get a nan
+            # from the equation above.
+            corr[rate==0] = 1
+        else:
+            corr = 1/(1-rate * dead_time / tof_step)
+
+        if np.min(corr) < 0:
+            error = ( "Corrupted dead time correction:\n"
+                      +"  Reflected: %s\n" % corr )
+            logger.error(error)
+
+        counts_ws.setY(0, corr)
+        self.setProperty('OutputWorkspace', counts_ws)
+
+
+AlgorithmFactory.subscribe(SingleReadoutDeadTimeCorrection)
\ No newline at end of file
diff --git a/reduction/lr_reduction/event_reduction.py b/reduction/lr_reduction/event_reduction.py
index f3e2454..b61e2aa 100644
--- a/reduction/lr_reduction/event_reduction.py
+++ b/reduction/lr_reduction/event_reduction.py
@@ -5,8 +5,10 @@
 
 import mantid.simpleapi as api
 import numpy as np
+import scipy
 
 from . import background
+from .DeadTimeCorrection import call as DeadTimeCorrection
 
 
 def get_wl_range(ws):
@@ -56,14 +58,15 @@ class EventReflectivity(object):
     INSTRUMENT_4B = 1
     DEFAULT_4B_SAMPLE_DET_DISTANCE = 1.83
     DEFAULT_4B_SOURCE_DET_DISTANCE = 15.75
-    DEAD_TIME = 8.0 # Nominally 4.0 microseconds
+    DEAD_TIME = 4.2 # Nominally 4.0 microseconds
 
     def __init__(self, scattering_workspace, direct_workspace,
                  signal_peak, signal_bck, norm_peak, norm_bck,
                  specular_pixel, signal_low_res, norm_low_res,
                  q_min=None, q_step=-0.02, q_max=None,
                  tof_range=None, theta=1.0, instrument=None,
-                 functional_background=False, dead_time=False):
+                 functional_background=False, dead_time=False,
+                 paralyzable=False):
         """
             Pixel ranges include the min and max pixels.
 
@@ -82,6 +85,7 @@ def __init__(self, scattering_workspace, direct_workspace,
             :param tof_range: TOF range,or None
             :param theta: theta scattering angle in radians
             :param dead_time: if not zero, dead time correction will be used
+            :param paralyzable: if True, the dead time calculation will use the paralyzable approach
         """
         if instrument in [self.INSTRUMENT_4A, self.INSTRUMENT_4B]:
             self.instrument = instrument
@@ -103,6 +107,7 @@ def __init__(self, scattering_workspace, direct_workspace,
         self._offspec_z_bins = None
         self.summing_threshold = None
         self.dead_time = dead_time
+        self.paralyzable = paralyzable
 
         # Turn on functional background estimation
         self.use_functional_bck = functional_background
@@ -229,41 +234,28 @@ 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, tof_step=100):
-        """
-            Perform dead time correction using counts per pulse over the whole
-            face of the detector.
-            Interpolate for the Q values we are going to use for the reduction.
-        """
-        # Rebin the data according to the tof_step we want to compute the correction with
+    def get_dead_time_correction(self, tof_step=100, paralyzing=False):
+        # Scattering workspace
         tof_min = self._ws_sc.getTofMin()
         tof_max = self._ws_sc.getTofMax()
-        _ws_sc = api.Rebin(InputWorkspace=self._ws_sc, Params="%s,%s,%s" % (tof_min, tof_step, tof_max))
-        _ws_db = api.Rebin(InputWorkspace=self._ws_db, Params="%s,%s,%s" % (tof_min, tof_step, tof_max))
-
-        # Get the total number of counts on the detector for each TOF bin per pulse
-        counts_ws = api.SumSpectra(_ws_sc)
-        t_series = np.asarray(_ws_sc.getRun()['proton_charge'].value)
-        non_zero = t_series > 0
-        n_pulses = np.count_nonzero(non_zero)
-        rate_sc = counts_ws.readY(0) / n_pulses
-        wl_bins = counts_ws.readX(0) / self.constant
-
-        # Direct beam
-        counts_ws = api.SumSpectra(_ws_db)
-        t_series = np.asarray(self._ws_db.getRun()['proton_charge'].value)
-        non_zero = t_series > 0
-        n_pulses = np.count_nonzero(non_zero)
-        rate_db = counts_ws.readY(0) / n_pulses
-
-        # Compute the dead time correction for each TOF bin
-        corr_sc = 1/(1-rate_sc*self.DEAD_TIME/tof_step)
-        corr_db = 1/(1-rate_db*self.DEAD_TIME/tof_step)
-
-        if np.min(corr_sc) < 0 or np.min(corr_db) < 0:
-            print("Corrupted dead time correction:")
-            print("Reflected: %s" % corr_sc)
-            print("Direct Beam: %s" % corr_db)
+
+        corr_ws = DeadTimeCorrection(InputWorkspace=self._ws_sc,
+                                     DeadTime=self.DEAD_TIME,
+                                     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
+        corr_ws = DeadTimeCorrection(InputWorkspace=self._ws_db,
+                                     DeadTime=self.DEAD_TIME,
+                                     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
@@ -277,7 +269,7 @@ def get_dead_time_correction(self, tof_step=100):
         dead_time_corr = np.interp(q_middle, q_values, dead_time_per_tof)
         
         return dead_time_corr
-
+        
     def specular(self, q_summing=False, tof_weighted=False, bck_in_q=False,
                  clean=False, normalize=True):
         """
diff --git a/reduction/notebooks/workflow.ipynb b/reduction/notebooks/workflow.ipynb
index 3096eff..73da2f7 100644
--- a/reduction/notebooks/workflow.ipynb
+++ b/reduction/notebooks/workflow.ipynb
@@ -9,14 +9,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T20:09:18.843870Z",
-     "iopub.status.busy": "2024-01-09T20:09:18.843578Z",
-     "iopub.status.idle": "2024-01-09T20:09:19.510363Z",
-     "shell.execute_reply": "2024-01-09T20:09:19.509342Z",
-     "shell.execute_reply.started": "2024-01-09T20:09:18.843849Z"
+     "iopub.execute_input": "2024-03-07T14:00:41.160719Z",
+     "iopub.status.busy": "2024-03-07T14:00:41.160296Z",
+     "iopub.status.idle": "2024-03-07T14:00:41.189068Z",
+     "shell.execute_reply": "2024-03-07T14:00:41.188511Z",
+     "shell.execute_reply.started": "2024-03-07T14:00:41.160699Z"
     },
     "tags": []
    },
@@ -32,7 +32,7 @@
     "import matplotlib.lines as mlines\n",
     "\n",
     "#%matplotlib notebook\n",
-    "%matplotlib ipympl\n",
+    "%matplotlib inline\n",
     "\n",
     "import warnings\n",
     "warnings.filterwarnings('ignore', module='numpy')\n",
@@ -41,23 +41,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T20:09:19.512179Z",
-     "iopub.status.busy": "2024-01-09T20:09:19.511698Z",
-     "iopub.status.idle": "2024-01-09T20:09:20.281547Z",
-     "shell.execute_reply": "2024-01-09T20:09:20.280870Z",
-     "shell.execute_reply.started": "2024-01-09T20:09:19.512156Z"
+     "iopub.execute_input": "2024-03-07T14:00:42.162602Z",
+     "iopub.status.busy": "2024-03-07T14:00:42.161984Z",
+     "iopub.status.idle": "2024-03-07T14:00:42.165544Z",
+     "shell.execute_reply": "2024-03-07T14:00:42.165028Z",
+     "shell.execute_reply.started": "2024-03-07T14:00:42.162584Z"
     },
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "ConfigService-[Error] logging set to error priority\n"
+      "logging set to error priority\n"
      ]
     }
    ],
@@ -69,14 +69,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T20:09:20.283083Z",
-     "iopub.status.busy": "2024-01-09T20:09:20.282554Z",
-     "iopub.status.idle": "2024-01-09T20:09:20.285955Z",
-     "shell.execute_reply": "2024-01-09T20:09:20.285414Z",
-     "shell.execute_reply.started": "2024-01-09T20:09:20.283062Z"
+     "iopub.execute_input": "2024-03-07T14:00:43.027342Z",
+     "iopub.status.busy": "2024-03-07T14:00:43.026880Z",
+     "iopub.status.idle": "2024-03-07T14:00:43.030004Z",
+     "shell.execute_reply": "2024-03-07T14:00:43.029464Z",
+     "shell.execute_reply.started": "2024-03-07T14:00:43.027325Z"
     },
     "tags": []
    },
@@ -97,14 +97,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T20:09:20.541527Z",
-     "iopub.status.busy": "2024-01-09T20:09:20.540968Z",
-     "iopub.status.idle": "2024-01-09T20:09:20.792757Z",
-     "shell.execute_reply": "2024-01-09T20:09:20.791836Z",
-     "shell.execute_reply.started": "2024-01-09T20:09:20.541507Z"
+     "iopub.execute_input": "2024-03-07T14:00:43.895394Z",
+     "iopub.status.busy": "2024-03-07T14:00:43.895102Z",
+     "iopub.status.idle": "2024-03-07T14:00:44.186351Z",
+     "shell.execute_reply": "2024-03-07T14:00:44.185649Z",
+     "shell.execute_reply.started": "2024-03-07T14:00:43.895378Z"
     },
     "tags": []
    },
@@ -115,62 +115,22 @@
     "from lr_reduction import template\n",
     "from lr_reduction import output\n",
     "from lr_reduction import event_reduction\n",
-    "from lr_reduction import reduction_template_reader"
+    "from lr_reduction import reduction_template_reader\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T20:41:05.482380Z",
-     "iopub.status.busy": "2024-01-09T20:41:05.482006Z",
-     "iopub.status.idle": "2024-01-09T20:41:24.400835Z",
-     "shell.execute_reply": "2024-01-09T20:41:24.400041Z",
-     "shell.execute_reply.started": "2024-01-09T20:41:05.482354Z"
+     "iopub.status.busy": "2024-03-07T14:00:35.270890Z",
+     "iopub.status.idle": "2024-03-07T14:00:35.271335Z",
+     "shell.execute_reply": "2024-03-07T14:00:35.271215Z",
+     "shell.execute_reply.started": "2024-03-07T14:00:35.271204Z"
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "wl=15; ths=-0.600382; thi=-0.00812677; No offset\n",
-      "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [0.997982 -> 0.997982] at [0.0078845 -> 0.0078845]\n",
-      "Normalization options: True True\n",
-      "wl=12.386; ths=-0.600058; thi=-0.00812677; No offset\n",
-      "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [0.993186 -> 0.993186] at [0.0094227 -> 0.0094227]\n",
-      "Normalization options: True True\n",
-      "wl=9.74; ths=-0.600058; thi=-0.00812677; No offset\n",
-      "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [0.885545 -> 0.992058] at [0.0114862 -> 0.0117159]\n",
-      "Normalization options: True True\n",
-      "wl=7.043; ths=-0.599896; thi=-0.00812677; No offset\n",
-      "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [0.997344 -> 0.997344] at [0.0151558 -> 0.0151558]\n",
-      "Normalization options: True True\n",
-      "wl=4.25; ths=-0.599733; thi=-0.00812677; No offset\n",
-      "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [0.991585 -> 0.999528] at [0.0220792 -> 0.0225208]\n",
-      "Normalization options: True True\n",
-      "wl=4.25; ths=-1.18271; thi=-0.00812677; No offset\n",
-      "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [1.00012 -> 1.00012] at [0.0441559 -> 0.0441559]\n",
-      "Normalization options: True True\n",
-      "wl=4.25; ths=-2.34284; thi=-0.00812677; No offset\n",
-      "Background on both sides: [131 133] [149 151]\n",
-      "Dead time correction: [1.0001 -> 1.00026] at [0.0865754 -> 0.088307]\n",
-      "Normalization options: True True\n",
-      "wl=4.25; ths=-4.63906; thi=-0.00812677; No offset\n",
-      "Background on both sides: [131 133] [149 151]\n",
-      "Dead time correction: [1.0001 -> 1.0001] at [0.173141 -> 0.173141]\n",
-      "Normalization options: True True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "importlib.reload(workflow)\n",
     "importlib.reload(output)\n",
@@ -193,14 +153,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-10T21:09:16.876854Z",
-     "iopub.status.busy": "2024-01-10T21:09:16.876345Z",
-     "iopub.status.idle": "2024-01-10T21:09:25.391551Z",
-     "shell.execute_reply": "2024-01-10T21:09:25.390987Z",
-     "shell.execute_reply.started": "2024-01-10T21:09:16.876835Z"
+     "iopub.execute_input": "2024-03-07T14:03:41.675586Z",
+     "iopub.status.busy": "2024-03-07T14:03:41.675238Z",
+     "iopub.status.idle": "2024-03-07T14:03:50.953438Z",
+     "shell.execute_reply": "2024-03-07T14:03:50.952865Z",
+     "shell.execute_reply.started": "2024-03-07T14:03:41.675567Z"
     },
     "tags": []
    },
@@ -211,19 +171,19 @@
      "text": [
       "wl=4.25; ths=-0.599733; thi=-0.00812677; No offset\n",
       "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [0.870673 -> 0.979509] at [0.0487517 -> 0.0220792]\n",
+      "Dead time correction: [0.530554 -> 0.882112] at [0.0487517 -> 0.0220792]\n",
       "Normalization options: True True\n",
       "wl=4.25; ths=-1.18271; thi=-0.00812677; No offset\n",
       "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [0.871634 -> 0.97502] at [0.097498 -> 0.0441559]\n",
+      "Dead time correction: [0.5229 -> 0.863203] at [0.099448 -> 0.0441559]\n",
       "Normalization options: True True\n",
       "wl=4.25; ths=-2.34284; thi=-0.00812677; No offset\n",
       "Background on both sides: [131 133] [149 151]\n",
-      "Dead time correction: [0.870433 -> 0.977805] at [0.191162 -> 0.0865754]\n",
+      "Dead time correction: [0.37933 -> 0.880062] at [0.194985 -> 0.0865754]\n",
       "Normalization options: True True\n",
       "wl=4.25; ths=-4.63906; thi=-0.00812677; No offset\n",
       "Background on both sides: [131 133] [149 151]\n",
-      "Dead time correction: [0.872217 -> 0.974492] at [0.374807 -> 0.173141]\n",
+      "Dead time correction: [0.522303 -> 0.839429] at [0.389949 -> 0.176604]\n",
       "Normalization options: True True\n"
      ]
     }
@@ -239,7 +199,7 @@
     "# rate=[49805,34762, 21197, 12198, 8020,  4542,  2254,  569]\n",
     "\n",
     "data_dir = os.path.expanduser('~/git/LiquidsReflectometer/reduction/data')\n",
-    "template_path = os.path.join(data_dir, 'template_high_rate_206597.xml')\n",
+    "template_path = os.path.join(data_dir, 'template_high_rate_206594.xml')\n",
     "\n",
     "os.chdir(os.path.expanduser('~/git/LiquidsReflectometer/reduction'))\n",
     "\n",
@@ -250,33 +210,125 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-10T21:09:55.881356Z",
-     "iopub.status.busy": "2024-01-10T21:09:55.881033Z",
-     "iopub.status.idle": "2024-01-10T21:09:56.233358Z",
-     "shell.execute_reply": "2024-01-10T21:09:56.232949Z",
-     "shell.execute_reply.started": "2024-01-10T21:09:55.881333Z"
+     "iopub.execute_input": "2024-03-07T14:01:02.633645Z",
+     "iopub.status.busy": "2024-03-07T14:01:02.633268Z",
+     "iopub.status.idle": "2024-03-07T14:01:03.059066Z",
+     "shell.execute_reply": "2024-03-07T14:01:03.058471Z",
+     "shell.execute_reply.started": "2024-03-07T14:01:02.633623Z"
     },
     "tags": []
    },
    "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "len(ref) = 160; len(new) = 217\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (160,) (217,) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 39\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlen(ref) = \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m; len(new) = \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mlen\u001b[39m(_data[\u001b[38;5;241m1\u001b[39m]), \u001b[38;5;28mlen\u001b[39m(_refl[\u001b[38;5;241m1\u001b[39m])))\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m4\u001b[39m):\n\u001b[0;32m---> 39\u001b[0m     \u001b[38;5;28mprint\u001b[39m(np\u001b[38;5;241m.\u001b[39msum(\u001b[43m_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m_refl\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m))\n",
+      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (160,) (217,) "
+     ]
+    }
+   ],
+   "source": [
+    "reduced_path = os.path.join(data_dir, 'reference_rq.txt')\n",
+    "reduced_path = os.path.join(data_dir, 'ref_rate_206594.txt')\n",
+    "\n",
+    "if os.path.isfile(reduced_path):\n",
+    "    _data = np.loadtxt(reduced_path).T\n",
+    "\n",
+    "reduced_path = os.path.join(data_dir, 'REFL_198409_combined_data_auto.txt')\n",
+    "if os.path.isfile(reduced_path):\n",
+    "    _refl = np.loadtxt(reduced_path).T\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(15,5))\n",
+    "#plt.errorbar(_refl[0], _refl[1]*_refl[0]**4, yerr=_refl[2]*_refl[0]**4, markersize=4, marker='.', linestyle='', label='new reduction')\n",
+    "#plt.errorbar(_data[0], _data[1]*_data[0]**4, yerr=_data[2]*_data[0]**4, markersize=4, marker='', linestyle='-', label='reference')\n",
+    "plt.errorbar(_refl[0], _refl[1], yerr=_refl[2], markersize=4, marker='*', linestyle='-', label='new reduction')\n",
+    "plt.errorbar(_data[0], _data[1], yerr=_data[2], markersize=4, marker='*', linestyle='-', label='reference')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.xlabel('q [$1/\\AA$]')\n",
+    "plt.ylabel('R(q)')\n",
+    "ax.set_yscale('log')\n",
+    "ax.set_xscale('log')\n",
+    "plt.show()\n",
+    "\n",
+    "if False and len(_data[1])==len(_refl[1]):\n",
+    "    # dQ is computed for each run in the new implementation\n",
+    "    fig, ax = plt.subplots(figsize=(10,5))\n",
+    "    plt.plot(_refl[0], _refl[3]/_refl[0], label=\"new_reduction\")\n",
+    "    plt.plot(_data[0], _data[3]/_data[0], label=\"reference\")\n",
+    "\n",
+    "    plt.xlabel('q [$1/\\AA$]')\n",
+    "    plt.ylabel('$\\Delta q$')\n",
+    "    ax.set_yscale('linear')\n",
+    "    ax.set_xscale('log')\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"len(ref) = %s; len(new) = %s\" % (len(_data[1]), len(_refl[1])))\n",
+    "\n",
+    "for i in range(4):\n",
+    "    print(np.sum(_data[i]-_refl[i]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-02-01T16:37:32.948206Z",
+     "iopub.status.busy": "2024-02-01T16:37:32.947877Z",
+     "iopub.status.idle": "2024-02-01T16:37:33.115736Z",
+     "shell.execute_reply": "2024-02-01T16:37:33.115324Z",
+     "shell.execute_reply.started": "2024-02-01T16:37:32.948185Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R_max 0.23809523809523808\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e40d8d7712a240338a4eb02facebbf72",
+       "model_id": "df2fc77cbcb4455b99b3b2bc0e41b39b",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
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",
       "text/html": [
        "\n",
        "            <div style=\"display: inline-block;\">\n",
        "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
        "                    Figure\n",
        "                </div>\n",
-       "                <img 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' width=1000.0/>\n",
+       "                <img 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vMOAZ74e7k26LUNBgN9IwPpGxnIwxfGsXjbQb5K2c9PaTnsOljKcwu28dyCbfTpGMCExA5c0KMdQT7uDVqDiDNQQBcRERERkT91pvPPT5erycjI7m0Z2b0tJZU1zE/N4auUfSzbkce6rELWZRUy49t0zokNZkLvDoyOa6vF5aTF0FeyiIiIiIj8qYaYf366fNxduKxvOJf1DSe3uIJvN2bzdco+Nu4t4retB/lt60E8XU2MjmvLhMT2nBMbgqsWl5NmTAFdRERERET+VFP1oJ9IqJ8HNw2J5qYh0ew8WMLXKfv5OsW2uNw3G/bzzYb9BHq5cn6Pdlzcqz39otpgNGpxOWleFNBFREREROSkqmosbM89DDRtD/qJdArxYfroLkwbFcuGvUV8tX4f323MJq+kkg9WZfHBqiza+Xtwca/2XNy7PXHt/LQSvDQLCugiIiIiInJSO3JLqDZb8fVwITzQ09Hl2BkMBnpHBNA7IoCHLujOyl0FfJ2yjx/TcsguqmD24l3MXryLTiHeXNSrPRf3ak9MiI+jyxY5IU3QEGlB3nrrLcaMGePoMhrdf//7Xy6++GJHlyEiItJqHDv/3Fl7ol1MRobEBvP0X3rx+4OjeO2avpzfIwx3FyM7D5byws/bGfHsIi58aQmvL97J/sJyR5csUo8CujR7w4YNY+rUqY4u44w8+uij9O7du0GuVVlZySOPPMLDDz/cINdzZlOmTOH3339n6dKlji5FRESkVXD0/PPT5eFqYlxCGK9c3Zc1D43iuYm9GNolBJPRQOq+Yv49bwuDnvyVv7y2nP+tyCSvpNLRJYsAGuIurYTVasVsNuPi0jRf8lVVVbi5uTXJvY76/PPP8fHx4ZxzzmnS+zalo/+O7u7uXHXVVbz00ksMGTLE0WWJiIi0eOnZRYBzzD8/Xb4erlzaJ5xL+4STX1LJvNQcvt2wn9UZBfyeeYjfMw/xz2/SGNw5mIt6tmdsQhj+ng27z7vIqVIPujRrN9xwA4sWLWLWrFkYDAYMBgOZmZn89ttvGAwG5s+fT1JSEu7u7ixZsoQbbriBCRMm1LnG1KlTGTZsmP2x1WrlP//5DzExMXh6etKrVy8+++yzk9YRFRXFE088wQ033IC/vz9TpkwB4L777qNLly54eXkRExPDww8/THV1NQBz5sxhxowZbNiwwV77nDlzACgqKuKWW24hNDQUPz8/RowYwYYNG05aw9y5c+sN+z76ep955hnatWtHUFAQt99+u70GgEOHDnHdddcRGBiIl5cX5513Htu3b7cfnzNnDgEBAcyfP5/u3bvj4+PDuHHjyM7OPmk9w4YN44477uCOO+4gICCAoKAgHnroIaxWq/2c999/n6SkJHx9fQkLC+Oqq64iNzfXfvxE/44AF198MV999RXl5RqeJiIi0pisVmuz60E/kSAfd64dGMknf01mxQMjePD87vQM98dihSXb87j3840kPbGAm99dw9cp+yitrHF0ydLKKKBLszZr1iySk5OZMmUK2dnZZGdnExERYT9+7733MnPmTDZv3kzPnj1P6ZoPPfQQ77zzDq+++ippaWlMmzaNa665hkWLFp30eU8//TQJCQmsXbvWPszc19eXOXPmkJ6ezqxZs3jjjTd4/vnnAZg0aRJ///vfiY+Pt9c+adIkrFYrF1xwATk5OcybN4+1a9fSp08fRo4cSUFBwQnvv2TJEpKSkuq1L1y4kJ07d7Jw4ULeffdd5syZY38jAGwhfs2aNXzzzTesWLECq9XK+eefXyfEl5WV8cwzz/C///2PxYsXk5WVxd133/2nn8t3330XFxcXVq1axYsvvsjzzz/Pm2++aT9eVVXF448/zoYNG/jqq6/IyMjghhtuqHed4/07JiUlUV1dzerVq/+0DhERETlz+wrLKa6owdVkIDbU19HlNJh2/p5MOTeGb+4Ywm93D+PuMV3o2taXarOVnzcf4K65KfR9YgG3f7COHzZlU1FtdnTJ0gpoiLucnNUK1WVNf19XLziFBUj8/f1xc3PDy8uLsLCwescfe+wxRo8efcq3LS0t5bnnnuPXX38lOTkZgJiYGJYuXcrs2bMZOnToCZ87YsSIeqH1oYcesn8cFRXF3//+dz7++GPuvfdePD098fHxwcXFpU7tv/76K5s2bSI3Nxd3d3cAnnnmGb766is+++wzbrnllnr3LiwspLCwkPbt29c7FhgYyH//+19MJhPdunXjggsu4JdffmHKlCls376db775hmXLljFo0CAAPvjgAyIiIvjqq6/4y1/+AkB1dTWvvfYanTp1AuCOO+7gscce+9PPZ0REBM8//zwGg4GuXbuyadMmnn/+efsIg8mTJ9vPjYmJ4cUXX6R///6UlJTg41O7wurx/h29vb0JCAggMzPzpP8uIiIicnaO9p53DvXFzaVl9u9FBXtzx4hY7hgRy9acw3y3cT/fbthPZn4Z32/K5vtN2Xi7mRgV15YLe7bn3C7BuLuYHF22tEAK6HJy1WXw7/qhr9H9Yz+4eZ/1ZY7Xo3wy6enpVFRU1AuDVVVVJCYmnva9PvvsM1544QV27NhBSUkJNTU1+PmdfGjY2rVrKSkpISgoqE57eXk5O3fuPO5zjg7z9vDwqHcsPj4ek6n2P5B27dqxadMmADZv3oyLiwsDBgywHw8KCqJr165s3rzZ3ubl5WUP50evcXQo+pIlSzjvvPPsx2bPns3VV18NwMCBA+us9JqcnMyzzz6L2WzGZDKxfv16Hn30UVJSUigoKMBisQCQlZVFXFyc/Xkn+nf09PSkrMwBbyCJiIi0Iseu4N4adA3zpWtYV6aP7kLa/mK+3bif7zZks6+wnK9T9vN1yn58PVwYExfGhb3aMbhTcIt940KangK6tGje3nVDvtForDMHGqgzlPtoQPz+++/p0KFDnfOO9maf6r1WrlzJFVdcwYwZMxg7diz+/v7MnTuXZ5999qTXsVgstGvXjt9++63esYCAgOM+JygoCIPBwKFDh+odc3Wtu8iJwWCwv84/fi6OslqtdYL18a5x9LlJSUmkpKTYj7Vt2/a41/yj0tJSxowZw5gxY3j//fcJCQkhKyuLsWPHUlVVVefcP35ujyooKCAkJOSU7iciIiJnpqXMPz9dBoOBhA7+JHTw5/5x3Vi/p5DvNmTz/ab9HCiu5PN1e/l83V78PV0ZFx/GBT3bkdwpCFeTwrqcOQV0OTlXL1tvtiPue4rc3Nwwm09tTlBISAipqal12lJSUuwBNC4uDnd3d7Kyss562PSyZcuIjIzkwQcftLft3r37T2vv06cPOTk5uLi4EBUVdUr3cnNzIy4ujvT09NPaBz0uLo6amhpWrVplH+Ken5/Ptm3b6N69+yldw9PTk86dOx/32MqVK+s9jo2NxWQysWXLFvLy8njyySft6wasWbPmlGvfuXMnFRUVfzqyQURERM5Oa+tBPx6DwUCfjoH06RjIQxd0Z83uQ3y3cT/zNuWQV1LJx2v28PGaPQR6uTIuIYwLe7ZnQHQbXBTW5TQpoMvJGQwNMtS8MUVFRbFq1SoyMzPx8fGhTZs2Jzx3xIgRPP3007z33nskJyfz/vvvk5qaag95vr6+3H333UybNg2LxcKQIUMoLi5m+fLl+Pj4cP31159yXZ07dyYrK4u5c+fSr18/vv/+e7788st6tWdkZJCSkkJ4eDi+vr6MGjWK5ORkJkyYwFNPPUXXrl3Zv38/8+bNY8KECScc7j127FiWLl16WnvCx8bGMn78eKZMmcLs2bPx9fXl/vvvp0OHDowfP/6Ur3Mie/bsYfr06fz1r39l3bp1vPTSS/YRBB07dsTNzY2XXnqJW2+9ldTUVB5//PFTvvaSJUuIiYmpM/ReREREGlZReTV7D9mm0rXmgH4so9FA/+g29I9uwz8vimd1RgHfb9rPD5tyyC+t4qPVe/ho9R6CvN0YmxDGBT3aKazLKdNXiTR7d999NyaTibi4OPsw6RMZO3YsDz/8MPfeey/9+vXj8OHDXHfddXXOefzxx3nkkUeYOXMm3bt3Z+zYsXz77bdER0efVl3jx49n2rRp3HHHHfTu3Zvly5fbV3c/6rLLLmPcuHEMHz6ckJAQPvroIwwGA/PmzePcc89l8uTJdOnShSuuuILMzMyTDh+fMmUK8+bNo6io6LTqfOedd+jbty8XXnghycnJWK1W5s2bV29Y+5m47rrrKC8vp3///tx+++3ceeed9kXuQkJCmDNnDp9++ilxcXE8+eSTPPPMM6d87Y8++si+2JyIiIg0js1Hes87BHji76W9wf/IZDSQ3CmIJyb0YNU/RvLBzQO4sn9HAr1cyS+t4sNVWVz95ioG/PsX/vHlJpbtyKPGbHF02eLEDNYTTUKVFqm4uBh/f3+KiorqLVZWUVFBRkYG0dHRx11sTJzfxIkTSUxM5IEHHnB0KQwbNozevXvzwgsvNPi1U1NTGTlyJNu2bcPf3/+E5+lrWkRE5Oy8vTSDx75LZ3RcW9647vQW323NaswWVuzKZ96mbH5MzeFQWe2aR0HeboxLCGPGxfEO71U/WTYQx1APukgL8vTTT9fZnqyl2r9/P++9995Jw7mIiIicPc0/PzMuJiPnxIYw89KerH5wFP+7qT9X9o+w96yn7ityeDgX56Q56CItSGRkJHfeeaejy2h0p7MQnoiIiJy51rqCe0NyPRLWz4kN4bHxCazYmY+GMMuJKKCLSKM43jZxIiIi0nxU1VjYnnsYUA96Q3E1GTm3i7aIlRPTuAoREREREalnR24J1WYrvh4uhAd6OrockVZBAV1EREREROo5dv65wWBwcDUirYMCuoiIiIiI1KP55yJNTwFdRERERETqSc8uAjT/XKQpKaCLiIiIiEgdVqtVPegiDqCALiIiIiIidewrLKe4ogZXk4HYUF9HlyPSaiigi7QAv/32GwaDgcLCQkeXIiIiIi3A0d7zzqG+uLkoMog0FX23ichJ3XDDDUyYMMHRZYiIiEgTSt2n+ecijqCALuIkrFYrNTU1TXa/6urqJruXiIiINC+Lth0EoF9UoIMrEWldFNCl2Rs2bBh33nknU6dOJTAwkLZt2/L6669TWlrKjTfeiK+vL506deKHH36o87z09HTOP/98fHx8aNu2Lddeey15eXn24z/++CNDhgwhICCAoKAgLrzwQnbu3Gk/XlVVxR133EG7du3w8PAgKiqKmTNnApCZmYnBYCAlJcV+fmFhIQaDgd9++w2oHZY+f/58kpKScHd3Z8mSJVitVv7zn/8QExODp6cnvXr14rPPPqtT+7x58+jSpQuenp4MHz6czMzMP/08GQwGXnvtNcaPH4+3tzdPPPEEZrOZm266iejoaDw9PenatSuzZs2yP+fRRx/l3Xff5euvv8ZgMNSpf9++fUyaNInAwECCgoIYP378KdUhIiIizu1AcQUb9tp60Ed0D3VwNSKtiwK6tAjvvvsuwcHBrF69mjvvvJPbbruNv/zlLwwaNIh169YxduxYrr32WsrKygDIzs5m6NCh9O7dmzVr1vDjjz9y4MABJk6caL9maWkp06dP5/fff+eXX37BaDRyySWXYLFYAHjxxRf55ptv+OSTT9i6dSvvv/8+UVFRp137vffey8yZM9m8eTM9e/bkoYce4p133uHVV18lLS2NadOmcc0117Bo0SIA9uzZw6WXXsr5559PSkoKN998M/fff/8p3euf//wn48ePZ9OmTUyePBmLxUJ4eDiffPIJ6enpPPLII/zjH//gk08+AeDuu+9m4sSJjBs3juzsbLKzsxk0aBBlZWUMHz4cHx8fFi9ezNKlS/Hx8WHcuHFUVVWd9udAREREnMevW3IB6BURQKivh4OrEWldXBxdgDQPZdVlJzxmMppwN7mf0rlGgxEPF4+Tnuvl6nXa9fXq1YuHHnoIgAceeIAnn3yS4OBgpkyZAsAjjzzCq6++ysaNGxk4cCCvvvoqffr04d///rf9Gm+//TYRERFs27aNLl26cNlll9W5x1tvvUVoaCjp6ekkJCSQlZVFbGwsQ4YMwWAwEBkZedp1Azz22GOMHj0asL0p8Nxzz/Hrr7+SnJwMQExMDEuXLmX27NkMHTqUV199lZiYGJ5//nkMBgNdu3Zl06ZNPPXUU396r6uuuorJkyfXaZsxY4b94+joaJYvX84nn3zCxIkT8fHxwdPTk8rKSsLCwuznvf/++xiNRt58800MBgMA77zzDgEBAfz222+MGTPmjD4XIiIi4ni/bD4AwKhu6j0XaWoK6HJKBnw44ITHzulwDq+MesX+eNgnwyivKT/uuUltk3hn3Dv2x+M+H8ehykN1ztl0/abTrq9nz572j00mE0FBQfTo0cPe1rZtWwByc23vCK9du5aFCxfi4+NT71o7d+6kS5cu7Ny5k4cffpiVK1eSl5dn7znPysoiISGBG264gdGjR9O1a1fGjRvHhRdeeEbBNCkpyf5xeno6FRUV9sB+VFVVFYmJiQBs3ryZgQMH2oMxYA/zp3Ovo1577TXefPNNdu/eTXl5OVVVVfTu3fuk11m7di07duzA17futisVFRV1pgGIiIhI81JeZWbpDtuUv1FxbR1cjUjro4AuLYKrq2udxwaDoU7b0TB7NGRbLBYuuuii4/Y6t2vXDoCLLrqIiIgI3njjDdq3b4/FYiEhIcE+hLtPnz5kZGTwww8/8PPPPzNx4kRGjRrFZ599htFomz1itVrt1z3Romze3t72j4/W9/3339OhQ4c657m7u9e75uk69l4An3zyCdOmTePZZ58lOTkZX19fnn76aVatWnXS61gsFvr27csHH3xQ71hISMgZ1yciIiKOtWxHHhXVFjoEeNItTPufizQ1BXQ5JauuOnFgMxlNdR7/NvG3E55rNNRd9uDHy348q7rOVJ8+ffj888+JiorCxaX+t0F+fj6bN29m9uzZnHPOOQAsXbq03nl+fn5MmjSJSZMmcfnllzNu3DgKCgrsITU7O9ve833sgnEnEhcXh7u7O1lZWQwdOvSE53z11Vd12lauXPmn1z6eJUuWMGjQIP72t7/Z2/7YA+7m5obZbK7T1qdPHz7++GNCQ0Px89P2KyIiIi3FL1tsw9tHdg+tM1pPRJqGFomTU+Ll6nXCP8fOP/+zc4+df36ic5vC7bffTkFBAVdeeSWrV69m165d/PTTT0yePBmz2Wxfmfz1119nx44d/Prrr0yfPr3ONZ5//nnmzp3Lli1b2LZtG59++ilhYWEEBATg6enJwIEDefLJJ0lPT2fx4sX2OfIn4+vry9133820adN499132blzJ+vXr+fll1/m3XffBeDWW29l586dTJ8+na1bt/Lhhx8yZ86cM/o8dO7cmTVr1jB//ny2bdvGww8/zO+//17nnKioKDZu3MjWrVvJy8ujurqaq6++muDgYMaPH8+SJUvIyMhg0aJF3HXXXezdu/eMahERERHHslis/LLZNh1wZHcNbxdxBAV0aZXat2/PsmXLMJvNjB07loSEBO666y78/f0xGo0YjUbmzp3L2rVrSUhIYNq0aTz99NN1ruHj48NTTz1FUlIS/fr1IzMzk3nz5tmHt7/99ttUV1eTlJTEXXfdxRNPPHFKtT3++OM88sgjzJw5k+7duzN27Fi+/fZboqOjAejYsSOff/453377Lb169eK1116rs9jd6bj11lu59NJLmTRpEgMGDCA/P79ObzrAlClT6Nq1K0lJSYSEhLBs2TK8vLxYvHgxHTt25NJLL6V79+5MnjyZ8vJy9aiLiIg0U6n7i8g9XIm3m4mBMW0cXY5Iq2Swns2EVml2iouL8ff3p6ioqF6QqqioICMjg+joaDw8tKWGNH/6mhYRETl1zy3Yxou/bOe8hDBevaavo8uRJnCybCCOoR50ERERERHh5/Sj8881vF3EURTQRURERERauf2F5aRnF2MwwPCu2pFFxFEU0BvRK6+8Yh9a27dvX5YsWXLCc7/44gtGjx5NSEgIfn5+JCcnM3/+/Hrnff755/aVvuPi4vjyyy8b8yWIiIiISCvwyxbb4nB9OwYS5OP+J2eLSGNRQG8kH3/8MVOnTuXBBx9k/fr1nHPOOZx33nlkZWUd9/zFixczevRo5s2bx9q1axk+fDgXXXQR69evt5+zYsUKJk2axLXXXsuGDRu49tprmThx4p/uWS0iIiIicjK/bNbwdhFnoEXiGsmAAQPo06cPr776qr2te/fuTJgwgZkzZ57SNeLj45k0aRKPPPIIAJMmTaK4uJgffvjBfs64ceMIDAzko48+OqVrapE4aU30NS0iIvLnSitrSHx8AVU1FhZMO5fYtr6OLkmaiBaJcz7qQW8EVVVVrF27ljFjxtRpHzNmDMuXLz+la1gsFg4fPkybNrVbXKxYsaLeNceOHXvK1zxVes9GWgp9LYuIiPy5JdvzqKqx0LGNF51DfRxdjkir5uLoAlqivLw8zGYzbdvWHSLUtm1bcnJyTukazz77LKWlpUycONHelpOTc9rXrKyspLKy0v64uLj4hOe6uroCUFZWhqen5ynVKeLMysrKgNqvbREREanv6PD2Ud3bYjAYHFyNSOumgN6I/vgDzmq1ntIPvY8++ohHH32Ur7/+mtDQ0LO65syZM5kxY8Yp1WsymQgICCA317ZIiJeXl35IS7NktVopKysjNzeXgIAATCaTo0sSERFxShaLlYVbbb/7jeoe+idni0hjU0BvBMHBwZhMpno927m5ufV6wP/o448/5qabbuLTTz9l1KhRdY6FhYWd9jUfeOABpk+fbn9cXFxMRETECc8PCwuzX1ekuQsICLB/TYuIiEh9KXsLySupwtfDhX7Rbf78CSLSqBTQG4Gbmxt9+/ZlwYIFXHLJJfb2BQsWMH78+BM+76OPPmLy5Ml89NFHXHDBBfWOJycns2DBAqZNm2Zv++mnnxg0aNAJr+nu7o67+6lvlWEwGGjXrh2hoaFUV1ef8vNEnI2rq6t6zkVERP7E0eHtQ7uE4GrS8lQijqaA3kimT5/OtddeS1JSEsnJybz++utkZWVx6623Arae7X379vHee+8BtnB+3XXXMWvWLAYOHGjvKff09MTf3x+Au+66i3PPPZennnqK8ePH8/XXX/Pzzz+zdOnSBq/fZDIp3IiIiIi0cD+n20ZNjo7T9moizkBvkzWSSZMm8cILL/DYY4/Ru3dvFi9ezLx584iMjAQgOzu7zp7os2fPpqamhttvv5127drZ/9x11132cwYNGsTcuXN555136NmzJ3PmzOHjjz9mwIABTf76RERERKR521NQxtYDhzEZDQzrovnnIs5A+6C3MtrrUEREREQA5izL4NFv0xkQ3YaP/5rs6HLEAZQNnI960EVEREREWqHvN2UDtu3VRMQ5KKCLiIiIiLQym/YW8XvmIVyMBi7q1d7R5YjIEQroIiIiIiKtzFtLdwFwUa/2hPl7OLgaETlKAV1EREREpBXJKargu4224e03DYl2cDUiciwFdBERERGRVuTdFZnUWKwMiG5DQgd/R5cjIsdQQBcRERERaSXKqmr4cJVtq1/1nos4HwV0EREREZFW4vO1eykqryYqyIuRWr1dxOkooIuIiIiItAIWi5W3l2UCcOPgaExGg2MLEpF6FNBFRERERFqBX7fkkpFXip+HC5f3DXd0OSJyHAroIiIiIiKtwFtLMwC4ckBHvN1dHFyNiByPArqIiIiISAuXtr+IFbvycTEauGFQlKPLEZETUEAXEREREWnhjvaen9+jHe38PR1cjYiciAK6iIiIiEgLlltcwbcb9gNw8znaWk3EmSmgi4iIiIi0YO+t2E212Uq/qEB6hgc4uhwROQkFdBERERGRFqq8ysz7q3YDcNOQGAdXIyJ/RgFdRERERKSF+mL9XgrLqunYxovRcW0dXY6I/AkFdBERERGRFqjabOHNJbbF4W4YFIXJaHBwRSLyZxTQRURERERaoLm/7yEjr5Q23m5M7Bfh6HJE5BQooIuIiIiItDCHK6p5YcE2AKaOisXH3cXBFYnIqVBAFxERERFpYV79bSf5pVXEBHtzZf+Oji5HRE6RArqIiIiISAuyr7Cct5ba5p7ff143XE36lV+kudB3q4iIiIhIC/Ls/K1U1lgYEN1GK7eLNDMK6CIiIiIiLcSmvUV8sX4fAA9e0B2DQSu3izQnCugiIiIiIi2A1WrlX/PSAZjQuz09wwMcW5CInDYFdBERERGRFuCXzbms3FWAm4uRe8Z1c3Q5InIGFNBFRERERJq5arOFf/+wGYCbhkTTIcDTwRWJyJlQQBcRERERaebmrs5i18FS2ni7cduwTo4uR0TOkAK6iIiIiEgzVlxRzfM/bwdg6qhY/DxcHVyRiJwpBXQRERERkWbstd92UlBaRUyIN1f27+jockTkLCigi4iIiIg0Uxl5pby1NAOAB87rjqtJv96LNGf6DhYRERERaYYsFiv3fb6RyhoLQzoHM6p7qKNLEpGzpIAuIiIiItIMfbBqN6szCvByMzHz0h4YDAZHlyQiZ0kBXURERESkmdl7qIwnf9gCwL1juxLRxsvBFYlIQ1BAFxERERFpRqxWKw98sYnSKjNJkYFclxzl6JJEpIEooIuIiIiINCOfrt3Lku15uLsY+c/lPTEaNbRdpKVQQBcRERERaSYOFFfw+HfpAEwf3YWYEB8HVyQiDUkBXURERESkGbBarTz4ZSqHK2roGe7PTUOiHV2SiDQwBXQRERERkWbg243Z/Lz5AK4mA/+5vCcu2vO8eSo5CIcPOLoKcVL6rhYRERERcXL5JZU8+k0aAHcMj6VbmJ+DK5LTZq6BVa/DS33hh3sdXY04KRdHFyAiIiIiIif36LfpFJRW0S3Ml9uGdXJ0OXK6slbC93fDgU22xwW7oLIE3LWGgNSlgC4iIiIi4sS+3bCfbzfsx2Q08PTlvXBz0SDYZqMkFxb8EzZ8aHvs4Q8jHoakyWA0ObY2cUoK6CIiIiIiTiozr5QHvrD1uv5tWCd6hPs7uCI5JeYaWPMW/PovqCyytSVeC6MeBe9gh5Ymzk0BXURERETECVXWmLnjo3WUVNbQP6oNd42MdXRJcip2r4B599QOZ2/XC85/FiL6ObYuaRYU0EVEREREnNDMeVtI3VdMoJcrs67srVXbnV1xNix4BDZ9YnvsEQAjH4a+N2o4u5wyBXQRERERESfzY2o2c5ZnAvDcxN608/d0bEFyYjVVsOpVWPQfqCoBDNDnOhj5iIazy2lTQBcRERERcSJ7Csq457ONAPz13BiGdwt1cEVyQjt+hh/uh/zttsfh/eC8/0CHPo6tS5otBXQRERERESdRVWPhjo/Wc7iihj4dA7h7bFdHlyTHcygTfvwHbP3e9tg7BEbNgF5XglFTEeTMKaCLiIiIiDiJ//y4hQ17CvH3dOXFKxNx1bxz51JZAkufh+UvgbkSDCYYcCsMu8+2hZrIWVJAFxERERFxAj+nH+DNpRkAPH15T8IDvRxckdhZrbDpM9sicIf329qih8J5T0Fod8fWJi2KArqIiIiIiIPtKyzn759uAGDy4GjGxIc5uCKx258CP9wHe1baHgdEwth/QbcLwWBwaGnS8iigi4iIiIg4UFlVDbe8t4ai8mp6hvtz/3ndHF2SAJQchF8fg3X/A6zg6gXnTIfkO8HVw9HVSQulgC4iIiIi4iAWi5W/f7KBtP3FBHm78crVfXBz0bxzh6qpgtWzbdumVRbb2nr8xbYInH8Hx9YmLZ4CuoiIiIiIg7zwy3Z+SM3B1WRg9rV9Ne/ckaxW2PYjzH8QCnba2sJ62rZNi0x2bG3Saiigi4iIiIg4wLcb9vPiL7b9s/99SQ+Soto4uKJWLHcz/PgA7Fpoe+wdCiMfgd5XgdHk2NqkVVFAFxERERFpYhv3FnL3kUXhbjk3hr8kRTi4olaqrAAW/hvWvA1WM5jcYODf4Jy/g4efo6uTVkgBXURERESkCeUUVTDlvTVU1lgY0S2U+8ZpUbgmV1MFv78Ji56CikJbW7cLYczj0CbGoaVJ66aALiIiIiLSRCqqzdzyvzUcKK4kNtSHWVf0xmTUVl1NxmqFrT/ATw/VzjMPjYdxMyFmqGNrE0EBXURERESkSVitVu75bCMb9xYR6OXKW9f3w9fD1dFltR45m2D+PyBjse2xdwiMeAgSr9U8c3EaCugiIiIiIk3ghZ+38+2G/bgYDbx6TV86BmnF9iZx+AAsfKJ2P3OTOyT/DYZM1zxzcToK6CIiIiIijez9lbuZdWTF9scnJDAwJsjBFbUC1eWw4mVY+jxUldja4i+BUY9CYJQjKxM5IQV0EREREZFG9MOmbB7+OhWA/xvRmSv7d3RwRS2cxQKbPoFfHoPifba29n1s88w7DnRsbSJ/QgFdRERERKSRrNiZz11zU7Ba4cr+HZk2uoujS2rZMpfC/AchO8X22D8CRv4TEi4Do9GhpYmcCgV0EREREZFGkLa/iFveW0OV2cLY+LY8MSEBg0ErtjeKvB3w8z9hy3e2x26+cM50GHgbuHo6tjaR06C3kRrRK6+8QnR0NB4eHvTt25clS5ac8Nzs7GyuuuoqunbtitFoZOrUqfXOmTNnDgaDod6fioqKRnwVIiIiInK6svLLuP7t3zlcWUP/6DbMuiJR26k1htI8mHcPvDLAFs4NJuh3M/zfeltAVziXZkY96I3k448/ZurUqbzyyisMHjyY2bNnc95555Genk7HjvXnHVVWVhISEsKDDz7I888/f8Lr+vn5sXXr1jptHh4eDV6/iIiIiJyZg4crufbtVeSVVNItzJc3rkvCw1XbeDWo6nJY+QosfQEqi21tsWNhzOMQ0tWhpYmcDQX0RvLcc89x0003cfPNNwPwwgsvMH/+fF599VVmzpxZ7/yoqChmzZoFwNtvv33C6xoMBsLCwhqnaBERERE5K4crqrnhndXszi8joo0n703uj7+n9jpvMBYzbPwYfn2idgG4dr1gzBMQfa5jaxNpABri3giqqqpYu3YtY8aMqdM+ZswYli9fflbXLikpITIykvDwcC688ELWr19/VtcTERERkYZRXmVmyntrSNtfTJC3G+9NHkCon0Y6Npidv8LsofDVbbZw7h8Bl74BU35TOJcWQz3ojSAvLw+z2Uzbtm3rtLdt25acnJwzvm63bt2YM2cOPXr0oLi4mFmzZjF48GA2bNhAbGzscZ9TWVlJZWWl/XFxcfEZ319EREREjq+i2hbOV+4qwMfdhTk39ic62NvRZbUM2RttC8Dt/NX22N0fzv079P8ruOoNEGlZFNAb0R9X6bRarWe1cufAgQMZOLB278bBgwfTp08fXnrpJV588cXjPmfmzJnMmDHjjO8pIiIiIidXUW3mlv+tZemOPLzcTMy5sR89wv0dXVbzV5hlG8q+8RPACkZX2wJwQ+8FrzaOrk6kUSigN4Lg4GBMJlO93vLc3Nx6vepnw2g00q9fP7Zv337Ccx544AGmT59uf1xcXExERESD1SAiIiLSmlXWmLnt/bUs3nYQT1cTc27sT1KUwuNZKSuAJc/C6tfBXGVrS7gMRjwEbWIcW5tII1NAbwRubm707duXBQsWcMkll9jbFyxYwPjx4xvsPlarlZSUFHr06HHCc9zd3XF3d2+we4qIiIiITVWNhb+9v46FWw/i4Wrk7Rv60T9a4fyMVZfDqtmw9DmoKLK1RZ0Dox+DDn0cW5tIE1FAbyTTp0/n2muvJSkpieTkZF5//XWysrK49dZbAVvP9r59+3jvvffsz0lJSQFsC8EdPHiQlJQU3NzciIuLA2DGjBkMHDiQ2NhYiouLefHFF0lJSeHll19u8tcnIiIi0ppVmy3c/uE6ftmSi7uLkbeu70dypyBHl9U8mWtgw0fw28zaldlD42H0DOg8Cs5iiqhIc6OA3kgmTZpEfn4+jz32GNnZ2SQkJDBv3jwiIyMByM7OJisrq85zEhMT7R+vXbuWDz/8kMjISDIzMwEoLCzklltuIScnB39/fxITE1m8eDH9+/dvstclIiIi0tpVmy3c+eF6FqQfwM3FyBvXJTG4c7Cjy2p+rFbY8j388hjkbbW1+YXDiAeh5yQwau94aX0MVqvV6ugipOkUFxfj7+9PUVERfn5+ji5HREREpFmpNluYOjeF7zdl42Yy8vp1fRnWNdTRZTU/mcvg50dh72rbY89AOOfv0G+KVmZvQsoGzkc96CIiIiIip6Ci2sztH9iGtbuaDLx2bR+F89OVk2rrMd8+3/bYxROS/waD7wIPrXwvooAuIiIiIvInSiprmPLuGlbsysfdxchr1/RleDeF81NWkAEL/w2bPgWsYDBBn+tg6H3g187R1Yk4DQV0EREREZGTKCyr4oZ3fidlTyE+7i68eX0SA2O0INwpOZwDi5+GtXPAUmNri5sAIx6G4M6OrEzEKSmgi4iIiIicwMHDlVz71iq25BwmwMuVd2/sT6+IAEeX5fzKC2HZLFj1GlSX2do6jYSRD0P7xJM+VaQ1U0AXERERETmOfYXlXPPmKjLySgn2ceeDmwfQNczX0WU5t6pSWP06LH2+di/z8H4w8p8QfY5jaxNpBhTQRURERET+ICOvlGveXMW+wnI6BHjy/s0DiA72dnRZzqumEta+axvOXpprawuNsw1l73qe9jIXOUUK6CIiIiIix0jdV8QN7/xOXkklMcHevH/zANoHeDq6LOdkroGNc+G3J6Foj60tIBKGPQA9J2ovc5HTpIAuIiIiInLE4m0Hue39tZRWmenezo/3JvcnxNfd0WU5H4sF0r+yrcyev93W5hMGQ++BxOvAxc2h5Yk0VwroIiIiIiLAZ2v3cv/nG6mxWBnUKYjXru2Ln4ero8tyLlYrbJsPC5+AnE22Ns82MGQa9J8CrhppIHI2FNBFREREpFWzWq28vHAHz/y0DYDxvdvz9OW9cHMxOrgyJ2K1wq6F8Ou/YN8aW5ubLwy6Awb+DTz8HFufSAuhgP4HO3fu5J133mHnzp3MmjWL0NBQfvzxRyIiIoiPj3d0eSIiIiLSgGrMFh75Jo0PV2UBcOvQTtw7titGoxY1s9u9HH59AnYvsz128YQBt8Cgu8Bb+8GLNCS9LXiMRYsW0aNHD1atWsUXX3xBSUkJABs3buSf//yng6sTERERkYZUVlXDre+v5cNVWRgM8Nj4eO4/r5vC+VF718L/LoF3zrOFc5MbDLgN7toAox9TOBdpBOpBP8b999/PE088wfTp0/H1rd3jcvjw4cyaNcuBlYmIiIhIQ8orqeTmd9eQsqcQdxcjs65IZFxCmKPLcg7ZG2DhTNj2g+2x0QUSr4Vz7wb/cMfWJtLCKaAfY9OmTXz44Yf12kNCQsjPz3dARSIiIiLS0LbkFHPTnDXsKywnwMuVt65Pom9kG0eX5Xg5qfDbTNjyne2xwQi9roRz74E20Y6tTaSVUEA/RkBAANnZ2URH1/0BtH79ejp06OCgqkRERESkofyy+QD/99F6SqvMRAV58dYN/egU4uPoshwrd4stmKd/daTBAD0uh6H3Q3BnR1Ym0uoooB/jqquu4r777uPTTz/FYDBgsVhYtmwZd999N9ddd52jyxMRERGRM2S1WnljyS5m/rAFqxWSY4J49Zo+BHi14v2687bDoqdg02eA1dYWf4ktmId2c2hpIq2VwWq1Wh1dhLOorq7mhhtuYO7cuVitVlxcXDCbzVx11VXMmTMHk8nk6BLPWnFxMf7+/hQVFeHnp+0wREREpOWrqrHw4Jeb+HTtXgCu7N+Rx8bH42pqpesl522HxU/Dpk/BarG1db8Ihj0AbbVrUWuibOB8FNCPY9euXaxbtw6LxUJiYiKxsbGOLqnB6JtQREREWpOC0ipu/d9aVmcWYDTAwxfGccOgKAyGVrhSe94OWPyfusG86/kw7H5o18uxtYlDKBs4Hw1xP8Zjjz3G3XffTUxMDDExMfb28vJynn76aR555BEHViciIiIip2PbgcPc9O7v7Ckox9fdhZeuSmRY11BHl9X08nYc6TH/pG4wH3oftO/t0NJEpC71oB/DZDKRnZ1NaGjdH9z5+fmEhoZiNpsdVFnD0btkIiIi0hp8vzGbez7bQFmVmY5tvHjr+iRi2/r++RNbkuMF8y7nwbD7oH2iY2sTp6Bs4HzUg34Mq9V63OFOGzZsoE0bbb0hIiIi4uxqzBaenr+V2Yt3ATCoUxD/vaoPbbxb0WJwB7fZgnnqZ8cE83G2HvMOfRxbm4iclAI6EBgYiMFgwGAw0KVLlzoh3Ww2U1JSwq233urACkVERETkzxSUVnHnR+tYtiMfgL+eG8M9Y7vi0loWg8vdYptjnvoF9lXZu4yDofdCh74OLU1ETo0COvDCCy9gtVqZPHkyM2bMwN/f337Mzc2NqKgokpOTHVihiIiIiJzMpr1F3Pr+WvYVluPlZuLpy3txQc92ji6raRxItwXztK+wB/OuF9iCueaYizQrCujA9ddfD0B0dDSDBg3C1dXVwRWJiIiIyKn6bO1e/vHlJqpqLEQFeTH72iS6hrWC+ebZG2DRf2DLd7Vt3S+Cc++Fdj0dV5eInDEF9GMMHTrU/nF5eTnV1dV1jmvhBBERERHnUVFt5vHv0vlgVRYAI7uF8tyk3vh7tvDOlr1rbMF8+/wjDQaIu9gWzMMSHFqaiJwdBfRjlJWVce+99/LJJ5+Qn59f73hLWMVdREREpCXYnV/K3z5YR9r+YgCmjorl/0bEYjS24P3Ndy+3BfNdC22PDUZIuAzOuRtCuzm2NhFpEArox7jnnntYuHAhr7zyCtdddx0vv/wy+/btY/bs2Tz55JOOLk9EREREgHmbsrnvs40crqyhjbcbz0/qzdAuIY4uq3FYrbDrN1jyLGQusbUZTNDrCjjn7xDUyaHliUjDUkA/xrfffst7773HsGHDmDx5Mueccw6dO3cmMjKSDz74gKuvvtrRJYqIiIi0WpU1Zv79/WbeXbEbgH5Rgbx4ZSLt/D0dXFkjsFph24+w+BnYt8bWZnSFxKthyDQIjHJoeSLSOBTQj1FQUEB0dDRgm29eUFAAwJAhQ7jtttscWZqIiIhIq5aVX8YdH61j494iAG4b1om/j+7S8rZQs5gh/WtY8hwc2GRrc/GAPtfD4P8D/3DH1icijUoB/RgxMTFkZmYSGRlJXFwcn3zyCf379+fbb78lICDA0eWJiIiItEo/puZwz2cbOFxRQ4CXK89P7M3wbqGOLqthmath06e2YJ6/3dbm5gP9boLkO8Cnhb1eETkuBfRj3HjjjWzYsIGhQ4fywAMPcMEFF/DSSy9RU1PDc8895+jyRERERFqV8iozj3+fzodHVmnv0zGA/17Vh/YBLWhIe3U5rH8flr0IRbbXiUcADLgVBvwVvNo4tDwRaVoGq9VqdXQRziorK4s1a9bQqVMnevXq5ehyGkRxcTH+/v4UFRVp2zgRERFxWmn7i7hrbgo7cksA+OvQGO4e0xXXljKkvaIY1rwNK16G0lxbm3cIJN8O/W4G91awj7s4nLKB81EP+hHV1dWMGTOG2bNn06VLFwA6duxIx44dHVyZiIiISOthsVh5Z3kmT/2whSqzhVBfd56b2JshscGOLq1hlObDqtdg9WyosM2nxz8CBt8FideAawsaHSAip00B/QhXV1dSU1MxGFrw3pkiIiIiTuzg4Uru/nQDi7YdBGBU97b85/KetPF2c3BlDaBor623fO0cqC6ztQV3sa3I3uMvYHJ1aHki4hwU0I9x3XXX8dZbb2nPcxEREZEmtnBrLvd8uoG8kircXYw8dGEc1wzo2Pw7Tw5ug2WzYOPHYKm2tbXrBefcDd0uBGMLGbIvIg1CAf0YVVVVvPnmmyxYsICkpCS8vb3rHNdCcSIiIiINq6yqhn/P28z7K20LpHUL8+XFKxPp0raZz8Het9a2IvuW74EjSz5FnQNDpkKnkdDc33gQkUahgH6M1NRU+vTpA8C2bdvqHGv2796KiIiIOJm1uwuY/skGdufbhnzfMCiK+8/rhoerycGVnSGrFXYthKXPQ8bi2vZuF8LgqRDRz2GliUjzoIB+jIULFzq6BBEREZEWr7LGzKyft/Paop1YrNDO34Nn/tKLwZ2b6UJw5hrY/DUsfQFyNtrajC7QY6Jt8bfQbg4tT0SaDwV0EREREWkym7OLmfZxCltyDgNwaZ8O/POiePw9m+EiaUf3MF/xXziUaWtz9YI+10HyHRAQ4dDyRKT5UUAXERERkUZntlh5Y8kunvtpG1VmC2283fj3JQmMS2jn6NJOX1kB/P6Wbbu0sjxbm2cbGHAr9J8CXm0cW5+INFsK6CIiIiLSqLYfOMw9n20kZU8hAKO6hzLz0p6E+Lo7trDTVZgFK16Bde9BdamtLaAjJN9p28Pczcux9YlIs6eALiIiIiKNosZsYfbiXcz6eTtVZgu+7i48fGEcf0kKb14L8GZvhOUvQuoXYDXb2tom2BZ+i78ETPqVWkQahn6aiIiIiEiD25JTzD2fbmTTviIAhncN4d+X9qCdv6eDKztFR1dkX/ai7e+joofaFn7rNEJbpYlIg1NA/4P//e9/vPbaa2RkZLBixQoiIyN54YUXiI6OZvz48Y4uT0RERMSpVZstvLJwJ/9duJ1qsxU/Dxf+eVE8l/bp0Dx6zc3VkPalrcc8Z5OtzWCy9ZQPuhPa93ZoeSLSsimgH+PVV1/lkUceYerUqfzrX//CbLYNYQoICOCFF15QQBcRERE5idR9Rdz72UbSs4sBGB3Xln9NSCDUz8PBlZ2CimJY9y6sfBWK99najq7IPvBvEBjp2PpEpFVQQD/GSy+9xBtvvMGECRN48skn7e1JSUncfffdDqxMRERExHmVVdXwws/beWtpBmaLlUAvVx69OJ6Le7V3/l7zor221djXvguVtjcW8A6FAX+FpMlakV1EmpQC+jEyMjJITEys1+7u7k5paakDKhIRERFxbou3HeTBrzaxp6AcgIt6teeRC+Ocf4X27A2w4mVI/RwsNba24K62Yew9J4KLk9cvIi2SAvoxoqOjSUlJITKy7hCmH374gbi4OAdVJSIiIuJ88ksqeeL7zXy53jYcvEOAJ09MSGB4t1AHV3YSFgts/wlW/Bcyl9S2R50Dg/4POo8Co9Fx9YlIq6eAfox77rmH22+/nYqKCqxWK6tXr+ajjz5i5syZvPnmm44uT0RERMThrFYrn6/bxxPfp1NYVo3RADcMiubvY7rg7e6kv1pWlcHGubY9zPO329oMJki4FJJvh/b1R1CKiDiCk/4UdYwbb7yRmpoa7r33XsrKyrjqqqvo0KEDs2bN4oorrnB0eSIiIiIOtSO3hEe+TmX5znwAurfz48lLe9ArIsCxhZ3I4QPw+xvw+1tQXmBrc/eHvtfb5pj7hzu2PhGRPzBYrVaro4twRnl5eVgsFkJDnXiY1hkoLi7G39+foqIi/Pz8HF2OiIiINAPlVWZeXriD2Yt3Um224u5iZOqoLtx8TjSuJiccEp6zydZbnvoZmKtsbQEdbauxJ14D7r6OrU/ESSgbOB/1oJ9AcHCwo0sQERERcbhftxzgka/T2HvItgjciG6hzLg4nog2Xg6u7A+Ozi9f+TJkLK5tD+9nG8be7SIw6VdfEXFu+il1jOjo6JNuBbJr164mrEZERETEcfYXljPj2zTmpx0AoJ2/B/+8KJ6x8W2da+u0qlJI+dC2VVr+DlubwQRxF8PA2yGin2PrExE5DQrox5g6dWqdx9XV1axfv54ff/yRe+65xzFFiYiIiDShqhoL7yzLYNYv2ymrMuNiNHDTkGj+b2Sscy0CV7jHNr987RyoKLK1uftD3+ug/18hIMKh5YmInAkn+inreHfddddx219++WXWrFnTxNWIiIiINK1F2w4y49s0dh0sBaBfVCBPTOhB1zAnmbNttcKe1bDyFdj8LVjNtvbAaBh4G/S+Gtx9HFujiMhZ0CJxp2DXrl307t2b4uJiR5dy1rQQhIiIiPzRnoIyHv8unZ/SbcPZg33cuG9cNy7rE47R6ATD2WuqIP0rWzDfv762Pfpc28JvsWPAaHJYeSLNlbKB81EP+in47LPPaNOmjaPLEBEREWlQ5VVmXl20k9mLdlJZY8FkNHDDoCjuGhWLn4ero8uDklzbEPbf34KSHFubyR16TrT1mLeNd2h5IiINTQH9GImJiXUWPbFareTk5HDw4EFeeeUVB1YmIiIi0nCsVivz03J4/LvN7Cu0rc4+qFMQj14cT5e2TjCcfX+KbdG31M9rt0nzCYP+N0PfG8Fbu+2ISMukgH6MCRMm1HlsNBoJCQlh2LBhdOvWzTFFiYiIiDSgtP1FPP5dOit3FQDQ3t+Dhy6M47yEMMeuzm6uts0rXzUb9qysbQ/vBwNuhe4Xg4ub4+oTEWkCCuhH1NTUEBUVxdixYwkLC3N0OSIiIiIN6uDhSp5bsJW5v+/BagV3FyO3nBvDbcM64eXmwF8JS/Nsw9jXvA3F+2xtRleIv8QWzMP7Oq42EZEmpoB+hIuLC7fddhubN292dCkiIiIiDaayxsycZZm89OsOSiprALiwZzvuP68b4YFejits3zpY/XrdYezeIZA02fbHVx0mItL6KKAfY8CAAaxfv57IyEhHlyIiIiJyVmzzzA/w73mbySooA6BnuD+PXBhHUpSDFr+tqYL0r2H1bNj7e217+z4w4K+2XnMXd8fUJiLiBBTQj/G3v/2Nv//97+zdu5e+ffvi7e1d53jPnj0dVJmIiIjIqUvZU8i/v9/M6kzbPPNQX3fuHdeNSxM7OGbbtOJs2zD2te9AiW0rt9ph7H+F8KSmr0lExAkZHV2AM5g8eTLFxcVMmjSJjIwM/u///o/BgwfTu3dvEhMT7X+frldeeYXo6Gg8PDzo27cvS5YsOeG52dnZXHXVVXTt2hWj0cjUqVOPe97nn39OXFwc7u7uxMXF8eWXX552XSIiItIy7Sko4/8+Ws+El5exOrMAdxcjdwzvzMK7h3F53ybe09xqhd3L4dMb4IUEWPSkLZz7hMGwf8C0NLjsDYVzEZFjqAcdePfdd3nyySfJyMhosGt+/PHHTJ06lVdeeYXBgwcze/ZszjvvPNLT0+nYsWO98ysrKwkJCeHBBx/k+eefP+41V6xYwaRJk3j88ce55JJL+PLLL5k4cSJLly5lwIABDVa7iIiINC9FZdW8/NsO5izLpMpswWCASxPD+fuYLrQP8GzaYqpKYeMnsPoNyE2rbe+YDP2nQLeLtBq7iMgJGKxWq9XRRTia0WgkJyeH0NDQBrvmgAED6NOnD6+++qq9rXv37kyYMIGZM2ee9LnDhg2jd+/evPDCC3XaJ02aRHFxMT/88IO9bdy4cQQGBvLRRx+dUl3FxcX4+/tTVFSEn5/fqb8gERERcTpVNRbeX7mbF3/dTmFZNWDbz/wf53cnoYN/0xaTtwPWvAXrP4DKIlubiyf0nGgL5mE9mrYeEflTygbORz3oRzTkvp9VVVWsXbuW+++/v077mDFjWL58+Rlfd8WKFUybNq1O29ixY+sFeREREWnZLBYr327czzM/bWVPQTkAsaE+/OP87gzrGtJ0+5mba2Dbj/D7m7BrYW17YLQtlPe+CjwDm6YWEZEWQAH9iC5duvzpf2YFBQWndK28vDzMZjNt27at0962bVtycnLOuMacnJzTvmZlZSWVlZX2x8XFxWd8fxEREXEsq9XK4u15PPXDFtKzbf+nh/i6M21UFyYmheNiaqLlhUpyYd27sGYOFO890miALmOh383QaSQYtdSRiMjpUkA/YsaMGfj7N+xQsD8GfqvVetbvaJ/uNWfOnMmMGTPO6p4iIiLieBv2FPLUj1tYvjMfAF93F24d1okbB0fh5dYEv9JZrZC1An5/y7ZVmsU2pB7PNtDnOki6EQKjGr8OkWbKarXy0ZaP8Hb1Znzn8Y4uR5yUAvoRV1xxRYPNQQ8ODsZkMtXr2c7Nza3XA346wsLCTvuaDzzwANOnT7c/Li4uJiIi4oxrEBERkaa162AJz/60je83ZQPgZjJyXXIkfxvemTbeTbDYWkUxbPzYFswPbq5t75BkG8YeNwFcPRq/DpFmotpSzY5DO0jLT6Osuozr4q8DbB1t76W/h7+7vwK6nJACOg07/xzAzc2Nvn37smDBAi655BJ7+4IFCxg//sy/GZOTk1mwYEGdeeg//fQTgwYNOuFz3N3dcXd3P+N7ioiIiGPsKyznxZ+389m6vZgtVvvK7NNGxxIe6NX4BWRvtC36tvFTqC61tbl6QY/LIekmaN+78WsQaQYyizLZmLeR1LxU0vLT2FqwlUqzbYqpr5sv18Zda88bf+nyFyxWiyPLFSengI5tuElDmz59Otdeey1JSUkkJyfz+uuvk5WVxa233grYerb37dvHe++9Z39OSkoKACUlJRw8eJCUlBTc3NyIi4sD4K677uLcc8/lqaeeYvz48Xz99df8/PPPLF26tMHrFxEREcc4eLiSlxfu4MNVWVSZbb/Ij+wWyj3jutItrJFXWa4uh7QvYc3bsPf32vbgrtDvJug5CTwDGrcGESdltVrZW7KX7Ye2M6LjCHv7v1b9i5XZK+uc6+vqS1xwHAlBCVSaK/FwsY0yuanHTU1aszQ/CuiAxdLw72JNmjSJ/Px8HnvsMbKzs0lISGDevHlERkYCkJ2dTVZWVp3nJCYm2j9eu3YtH374IZGRkWRmZgIwaNAg5s6dy0MPPcTDDz9Mp06d+Pjjj7UHuoiISAtQVFbN7MU7eWdZJuXVZsC2ZdrdY7vSp2Mjr4R+cBusfQdSPoSKQlub0QW6X2TrLY8aAk21MryIk8gty7X3iqflpZGWn0ZhZSEAiyYtoo1HGwCS2iZRaa4kPiiehOAEEoITiPCNwGjQQoly+rQPeiujvQ5FREScS0llDXOWZTB78S4OV9QA0DsigHvGdmVw5+DGu3FNFWz5Fta8A5lLatv9O0LSDdD7GvA987VzRJqTwopCvN28cTW6AvDcmud4J+2deue5GF3oGtiVfw35F50COjV1mQ1O2cD5qAddRERExAFKK2t4d0Umry/eRWGZbUX0bmG+/H1MV0Z1D228vczzd9q2SFv/AZTl2doMRugyDpImQ6cRYDQ1zr1FnEBpdSnp+emk5aWRmp9KWl4ae0v28sH5H9AzpCcA0f7RGA1GYvxjbL3iQbae8djAWNxMTbA4o7RaCugiIiIiTai8ysz/Vmby2qJdFJRWARAT4s1dI2O5qGd7jMZGCOY1VbD1e1g7B3b9VtvuEwZ9r7dtk+Yf3vD3FXEiS/Yu4Zk1z5BRlIGV+oOIdxXtsgf0sVFjGRs1Fi/XJliQUeQYCugiIiIiTaCi2sz7K3fz2qJd5JXYVniOCvLirlGxXNyrA6bGCOYFGbW95aW5RxoN0Hkk9L0RuowFk2vD31fEAaot1ews3Fln3vjkHpMZFzUOAA8XD3YV7QIgzDvMPmc8LiiO+KB4/N397ddSMBdHUUAXERERaUTlVWY+XJ3Fa4t2cvCwLZhHtPHk/0bEckliB1xMDbyQlL23/F3YtbC23actJF5r6y0PjGzYe4o4SE5pDu+kvkNqfmqd7c2O2pC7wR7Q44Pi+e+I/xIfHE+wZyOu7yByFhTQRURERBpBWVUNH6zMYvbi2h7zDgGe3DmiM5f1Dce1oYN5/k7bEPaUD2vnlmOwzSlPutE2x1y95dIMWa1W9pXsIzU/lfS8dGIDY7mo00UAGDDw4ZYP7ef6uPoQHxRPfLCtd7xXSC/7MS9XL4ZGDG3y+kVOhwK6iIiISAMqrazhvRW7eWNJ7Rzz8EBPbh/emcv6hOPm0oDBvLoCtnxnC+bHrsTuEwZ9rrX1mKu3XJqZaks1S/cutS3gdmSo+tHtzQCGhg+1B/RQr1Cm9JhCTEAMCUEJdPTrqO3NpFlTQBcRERFpAMUV1fxvxW7eXLKLQ0dWZY8M8uL24Z25JLFDw/aYH0iHde/BxrlQfsjWZjBC59G2Rd9ix4JJv+aJ8yuqLCItL41KcyXDOw4HbL3i9y25j/Kacvt5LkYXugR2ISEogX7t+tnbDQYD/9fn/5q8bpHGop/cIiIiImehoLSKd5ZlMGd5pn0f8+hgb+4Y3pnxvds33BzzyhJI+8IWzPf+Xtvu18HWU554DQRENMy9RBpBaXUpm/M3k5afRmpeKql5qewt2QtAjH+MPaC7GF0YHTkaAwbbUPWgBLq06YK7yd2R5Ys0CQV0ERERkTOQW1zBG0t28cGqLMqqzAB0DvXh9uGduKhnAwVzqxX2rbOtxJ76OVSV2NqNLtD1POhzvfYtF6dUaa5k7+G9dAroZG+7Zt417CjcUe/cjr4d6damGxarxT48/V9D/tVktYo4EwV0ERERkdOwp6CM2Yt38smavVTVWABI6ODHHcM7MyYurGH2MS/Nh40fw/r/QW56bXubTrZV2HtdCb5tz/4+Ig2gxlLDzsKddXrGtxdux8XgwoqrVuBitEWOuKA4Dlcdtm9vFh8cX297M5HWTgFdRERE5BRsO3CY1xbt5JuU/dRYrAAkRQZy+4jODOsSgsFwlsHcYrZti7buf7Dle7DY5rHj4gHdL7bNLY8cDGd7H5GzYLFaMGCwf70/v/Z5Ptz8IRXminrn+rr7cqDsAB18OgDwaPKjuGonAZGTUkAXEREROYm1uw/x6m87+XnzAXvbkM7B3DGiMwOi25x9MD+Uadsabf0HULy3tr1dL9vc8h5/Ac+As7uHyBmwWq3sL91PWl6abUX1vDTS89P5cvyXhHmHAeDl4kWFucK+vVlccBwJQQkkBCfQzrtdne8PhXORP6eALiIiIvIHVquVRdsO8spvO1mdUQDYOq7HxYdx69BO9IoIOLsbVJfD5m9tQ9gzFte2ewRAz4m2YN6u59ndQ+QMLdu3jPc3v096fjoFFQX1jqfmpdoD+iWxlzAmagyRfpHa3kykASigi4iIiBxRY7bw/aZsXlu0i83ZxQC4mgxcktiBvw7tRKcQnzO/uNUK+9fD+vdh02dQWXTkgAFihtpCebcLwdXj7F+IyJ8oqiyy7zGempfK5B6T6RXSy35s6b6lQO32ZvZ540HxdRZ+C/UKdUj9Ii2VArqIiIi0eqWVNXz8+x7eWprBvkLb3stebiau6t+Rm86Jpp2/55lfvOQgbPrENoQ9N6223b8jJF5tW/AtMPIsX4HIyR0oPcCPmT/ah6vvObynzvHeob3tAT0pLIl/DPiHtjcTcQAFdBEREWm1cg9X8O7yTN5fmUVRuW1RtiBvN64fFMV1yZEEeLmd2YXN1bD9J1so3z4fLLb90TG5Q9zFtj3Lo84Fo4YES8OqMlextWArqfmpdA7oTL+wfgAcLD/IM2ueqXNuhG+EvWd8cPvB9vZQr1Cu7HZlk9YtIjYK6CIiItLq7DxYwptLdvH5un32rdKig725+ZxoLusTjofrGe4rfiAdUj6wbZFWerC2vUNf6H0VJFwGnoEN8ApEwGwxs6Nwh317s7T8NLYd2kbNkTeELu9yuT2gdwnswsiOI4kLsi3iFh+s7c1EnJECuoiIiLQKVquVFbvyeWtJBr9sybW3J3YM4K/ndmJ0XFtMZ7KHeVmBbU75hg9tc8yP8g6BnpNsveWh3RvgFUhrZrFayCrOosJcQbc23QAorirm8m8vr3dugHsA8cHxdG9T+3XnZnLjheEvNFW5InKGFNBFRESkRas2W/hu437eXJJB2n7bwm8GA4zs1pa/Do0hKTLw9LdKM1fDjl9sveVbf6jds9zoAl3GQe+rIXY0aFspOQNWq5Xs0mx7r/jR7c0OVx9mQLsBvDnmTQACPQLp1qYbfm5+dbY46+DT4ey3/xMRh1BAFxERkRapqKyaD1dn8e7yTHKKKwDwcDVyed9wJg+OJuZMVmTPSYUNH8HGT6C0theesJ62UN7jcvAObqBXIK1FaXUp3q7e9sfjvx5PRlFGvfPcTe64Guu+6fPJhZ8ojIu0IAroIiIi0qLsOljCnOWZfLZ2L2VVZgBCfN25PjmSqwdEEuh9mgu/leTCpk9twTxnU227V7BtCHvvKyGsRwO+AmnJjm5vlp6fTmpeKql5qZgMJuZfPt9+TphXGHuK9xAbGEt8cDwJQQkkBCcQExBTL6ArnIu0LAroIiIi0uxZrVaW7sjj7aUZLNxauzhbtzBfbhoSzcW92+PuchoLv9VU2oaub/gIti8Aqy3oY3SFrkeGsHcepSHscsr+u/6//JDxA1mHs+odM2CgqLLIvmjb44MfJ8AjQNubibRCCugiIiLSbFVUm/ly/T7eWZbBtgMlwNH55aFMHhxNcqegU+9htFph7++2UJ76BVQU1h7r0Ne2X3nCZeDVpuFfiDR7VeYqth3aZt9nfHP+Zt4//308XDwAOFRxyB7Ow33CSQi29YrHBcURFxRXZ4h7W++2DnkNIuJ4CugiIiLS7OwrLOf9lbuZuzqLQ2W2Bdq83ExMTIrg+kFRRAd7/8kVjnEoEzZ8DBvnQsGu2nbf9tBrki2Yh3Rt2BcgLcLvOb/zY8aPpOWnsfXQVvv2ZkdtPbSVXiG9AJjYdaJ9m7MAjwAHVCsizYECuoiIiDQLVquV1RkFzFmeyfy0HCxWW3uHAE9uHBzFX5Ii8Pc8xSHnFUWQ9hVsmAtZy2vbXb2g+8W2YB49FIxnuB+6tBhHtzdLzU8lLS+Na+KuoYNPBwDS89P5ZNsn9nMD3AOID4q3zxuP8Y+xH+vaRm/yiMifU0AXERERp1ZRbebrlH28syyTLTmH7e3JMUFcPyiKUd1DcTEZ//xCNVWw8xdbKN/6A5grjxwwQMxQW095twvB/QxWd5cWo7CikDUH1tgWcMtPJT3Ptr3ZUXFBcfaAPqDdAG6Iv8EeyLW9mYicLQV0ERERcUp7Csp4f+VuPl6zh8Ijw9g9XI1ckhjO9YMi6Rbm9+cXsVph7xrY+DGkfg7lBbXHQrrZQnmPv4B/h0Z6FeLM8svzSctPo6NvR6L8owBYm7uWab9Nq3Oeu8mdrm26khCUQJRflL29W5tudGvTrQkrFpGWTgFdREREnIbFYmXx9oP8b8Vuft2ai/XIMPbwQE+uS45kYlIEAV6nsE1awS7Y+KktmBfsrG33DrUF8l6TbHuXq7ez1SiuKrZvbXZ0Ibec0hwA/tb7b9zW6zYAEoIS6NamG/FB8faF3DoFdKq3vZmISGNQQBcRERGHKyqr5tO1e3h/5W4y88vs7efEBnNdchQjuoViMv5JmC7Ng7QvbaF87++17a5etqHrvSZB9DAw6deflq6suoyymjKCPYMByCjK4OKvLq53ngED0f7R+LjWTmto692WTy/6tMlqFRE5lv6HEhEREYfZuLeQ91fu5psN+6motgDg6+HCX/pGcM3AjsSE/Ml88Koy2DoPNn5im19+dBVtgxFihkGPidD9Is0rb8GqzdVsO7TNPmc8LT+NnYU7uSD6Av59zr8B6OjbEU8XT4I8guzzxeOD4+nepjs+bvraEBHnoYAuIiIiTaq8ysy3G/bz/qrdbNxbZG/v3s6P65IjGd+7PV5uJ/kVxVwDu36DTZ/Clu+gqqT2WLve0HOSbb9yX+0l3ZKZLWau++E6NhdsptpSXe94dmm2/WOT0cTCiQvr7DUuIuKMFNBFRESkSezILeGDVbv5fO1eiitsPd1uJiPn9wjjmoGR9I0MPPEK2FYr7Ftr6ylP+wJKD9YeC+hoC+U9JkJIlyZ4JdIUrFYrWYezbHPG89NIy0vD3eTO62NeB2yhu6ymjGpLNf7u/vZe8aNzx0O9QutcT+FcRJoDBXQRERFpNJU1ZuanHeDDVbtZuat2BfWINp5cPSCSv/QNJ8jH/cQXOLgVNn1m6y0/lFHb7hUE8ZfYQnlEfy321oLMSZ3D0v1LSc9P53DV4TrHPEwe1FhqcDHafoV9fPDjBLgHaHszEWkxFNBFRESkwWXmlfLR6iw+XbuXgtIqAIwGGNGtLdcM7Mi5sSEYT7ToW9Fe25Zomz6FnE217UcXe+vxF+g0HExaVbu5Orq9WVpeGhnFGTx1zlP2gL0udx2rslcB4GZ0s62ofkzPuNFQu+d9QnCCQ+oXEWksCugiIiLSIKpqLCxIP8CHq3ezbEe+vb2tnzuT+nVkUr8IOgR4Hv/JpfmQ/pWttzxreW270QU6jbSF8m7ng5uGKTdH6fnprNi/grT8NFLzUuvMDweY3nc6Yd5hAFze5XLOCT+HhKAEOgd0xlVvxIhIK6KALiIiImdl18ESPv59D5+v20teia233GCAYV1CuLJ/R0Z0C8XFZKz/xMrDsOV7W2/5zl9rV2AHiBwMPS6HuAng1aZpXoictfKacrYUbCEtL41LYi+xz/v+ftf3vJf+nv08Awai/KPs88Y9TB72Y+eGn9vkdYuIOAsFdBERETltFdVmfkzN4aPVWazKqJ1bHurrzqR+EUzqF0F4oFf9J1aXw/afbKF823yoqag9FtbT1lOecCn4hzfBq5CzUW2uZlvhNtLy0uxbnO0s3InFatsur0tgF/q36w/AgHYDyCnNsW9xFhcUp+3NRESOQwFdRERETtmWnGLmrt7Dl+v3UVRu29rKaIBhXUO5ol/E8XvLzdVHtkX7zNZjfuzCX0GdIeFy27ZoWoHdaZktZnYV7SLYM5hAj0AAvtj+BU+seqLeucGewSQEJeBmcrO3nRt+rnrGRUROgQK6iIiInFRxRTXfbtjPJ7/vYcMx+5Z3CPBkYlIEE/uF087/D3PLLWbIXGrbEi39ayg/VHvML9zWS97jcluvuVbfdipWq5W9h/eSmp9q6xnPS2VzwWbKa8r5Z/I/ubzL5QDEB8fj5+ZHQnAC8UHx9t7xtt7af15E5EwpoIuIiEg9VquV1RkFfLxmD/M2ZVNRbRu27GI0MKp7W64c0JEhnYMxHbsSu8UCe3+3DV9P/wpKDtQe8w6BuPG23vKIAWA8zpx0aXJWq5UaS419Iba0/DSm/DSl3vZmAF4uXnXa44LiWHrFUm1vJiLSgBTQRURExO5AcQWfr9vLp2v2kpFXam/vHOrDpKQILunTgeBj9y23WmHfOltPedpXULy39phHAMRdbBu+HjkETPq1w9EKKgpsc8bzU+1zx8d3Hs+0vtMACPcJ53DVYdyMbnRt09W+tVlCcAJRflGYjCb7tY7d7kxERBqG/qcUERFp5SprzPycnsuna/eweNtBLFZbu7ebiQt7tmdivwj6dAyo7Sm1WiFnI6R+AWlfQuHu2ou5+UK3C2xD2GOGg4tb/RtKkyqtLuXhZQ+TlpfG/tL99Y6n5afZP/Z39+fziz8n2i9a25uJiDiAArqIiEgrZLVaSdtfzKdr9vD1hv0UllXbj/WLCuQvfSO4oGc7vN1djj4BclJtgTztSyjYWXsxVy/oMs4WyjuPAtcT7HUujaa8ppytBVvt+4wHegRyb797AdvQ9NU5qymqtK0fEOUXZe8Vjw+Kp2ubrnWu1SVQi/WJiDiKArqIiEgrkldSyVfr9/H5un1szi62t4f5eXBpnw5c3jecmJAj219ZrXAgzTZ0Pe1LyN9eeyEXD4gdYwvlsWPAzbtpX4jw5fYvSTmYQlpeGjsKd2C2mu3H2nu3twd0g8HAQwMeItAjkLigOHzdfB1VsoiI/AkFdBERkRaussbMr5tz+XzdXn7bepCaI2PY3UxGRse35S99wzknNqR2wbfczbU95Xnbai9kcrf1kMdfAl3HgbuCXmMzW8xkFGWQlp/GwfKD3NzjZvuxj7d+XGd4+tHtzeKC40gISsBqtdqnJYyLHtfktYuIyOlTQBcREWmBrFYrG/cW8fm6vXzzhyHsvcL9uaxvOBf3ak+Al5utpzx3s23l9bSvIG9r7YVMbrWhvMs48PBr8tfSmuwr2cfGgxtJzUslLT+NzfmbKaspA8BkMHFN92vwcPEA4OJOFzOo/SD7FmdtvdpqRXURkWZOAV1ERKQF2V9Yzlcp+/hi3T525JbY29v6uTMhsQOX9wkntq3vkVCeDiu/sgXzOj3lbtBpBMRfausp9/Bv8tfR0lmtVg6UHSAtP43hEcPtK6LPWjuLHzJ/qHOup4sn3dt0JyE4gUpzpT2gX9X9qiavW0REGpcCuoiISDNXUlnDj6k5fLFuLyt25WM9sgq7u4uRMfFhXN433LZnuQE4kAq/fm3rKT92TrnJDTqNhPgJ0PU8hfIGdqjikL1X/Og2Z3nleQB8M+Ebov2jAUhsm8jekr32XvGEoASi/aPrbG8mIiItlwK6iIhIM1RjtrB8Zz5frt/Hj6k5lFfXLhDWP7oNl/XpwHk92uHn7gLZKfDry5D+NRTsqr2IyR06j4S4Ceopb0AlVSW4mlxxN9n2i3879W2eX/t8vfNMBhOdAjpRXFW7WN+V3a7kym5XNlmtIiLiXBTQRUREmomjW6N9uX4f32zYz8HDlfZj0cHeXJrYgQmJHYgI8ID962Dx67ZQXphVexEXD9uc8rjxmlPeACpqKthSsMW+vVlqXiqZxZm8PPJlzg0/F7Bta3b077igOPsWZ93adMPTRVvSiYhILQV0ERERJ7enoIxvNuzny/V155UHerlyQc92XNonnMQOvhj2rIJVb8Lmb6F4X+0FXL1sW6HFjbf97e7jgFfRsqTkpvDEyifqbW921K7CXfaAPqj9IJZduQw/N70ZIiIiJ6eALiIi4oQOlVbx/aZsvk7Zx++Zh+ztbi5GRse15ZLeHTi3UwBue5fBhrfg4++g9GDtBdx8oMtYWyjvPBrcvBzwKpovs8VMZnFmnXnj4zuPZ2LXiQD4uPqw9ZBttfsgjyASghPs88bjg+IJ8gyyX8vDxQMPPBzyOkREpHlRQBcREXES5VVmFmw+wNfr97FoW+1+5QYDJMcEMSGxA+O6BeC3bymkvwHfzIOKwtoLePhD1wsg7mKIGQ6uCoWno7CikDc3vUlqfmqd7c2O6ujX0R7Qo/2jeX7Y8yQEJ2h7MxERaTAK6CIiIg5UbbawbEceX6fsZ35aDmVVtcOl49r5MSGxPRd39yPswBLY/Br8tACqaoe54xUM3Y6E8qhzwcXNAa+ieckty7XPFw/zDrOHbjeTG//b/D8sVgtQu73Z0dXUe4X2sl/DZDQxKnKUQ+oXEZGWSwFdRESkiVksVtbsPsQ3G/Yxb1MOBaVV9mMRbTwZ36sDl3TzpFPBEtj8Miz6Fcy1C8Lh1wG6X2T70zEZtAXXCVmtVpbtX2bf2iwtL42D5bVTAXqF9LIHdC9XL27tdSthXmEkBCcQ4x+j7c1ERKRJKaCLiIg0gaMrsH+zYT/fbthPdlGF/ViQtxsX9GzH5bFGehxegmHL87ByGRy7+FibGOh+se1P+0QwGh3wKpxbaXUp6fnpHKo4xJioMQAYDAb+ueyf5Jbn2s8zGox0CuhEQlACiaGJda5xW6/bmrRmERGRYymgi4iINKLtBw7z7cZsvtuwn115pfZ2X3cXxiaEMSm6gj5lSzFtnQnr19Z9ctse0P1C6HYhtI23TUYXwLa92dZDW22LuB3pHc8sysSKlTYebRgdOdo+L3xU5CgKKwvt25t1DeyKl6sWzRMREeejgC4iItLAMvNK+W7jfr7bmM2WnMP2dncXI6O6hXJ1x3z6VyzHZdv3kLbtmGcaIGJAbShvE930xTuhaks1u4t20zmws73t9l9uZ3XO6nrntvNuR0JwAuU15fYQ/sCAB5qsVhERkbOhgC4iItIA9hWW8/2RUL5xb5G93dVkYHjnQK5rv5f+lStw2/4D7Nhf+0SjK0SfawvlXS8A37YOqN55mC1mdhfvts8XT81PZWvBVirNlSy/cjm+br4AdG/TnR2FO2y94kEJx93eTEREpLlRQBcRETlD2UXlfL8xm+83ZbM+q9DebjIaGBHtyY1td5FUsRy3XQtgd21ox80HOo+yLfIWO9q2PVorZLUe3UbONhT9rU1v8frG1+ttbwbg6+rL3sN76R7UHYD/6/N//D3p79reTEREWhQFdBERkdNwoLiCeZuy+X5jNmt2H7K3GwwwOgImh2yhT9ly3LKWwL5jVl73CoZu59uGrkcPbZV7lOeW5dZZTT0tP43XR79uD90+rj6U1ZTZtzeLC4qzzxuP8I3AaKhdGM/NpO3kRESk5VFAFxER+RM5RRX8kJrNvE22UH6k4xeDAcZ3KOXawFR6lizDNXst5FprnxgYXTufPLxfq9wObX3uet5JfYe0vLQ6K6kflZqfag/ooyJHkdg2kRj/GFyM+hVFRERaH/3vJyIichzZReX8sCnHHsqPMmLhqnbZXOG3ie7Fy3DJ2wl5xzyxfR/odoHtT0i3VrHy+tHtzY72jl/S+RIGdxgMQHl1OQv3LARqtzeLD4onIcjWMx4bGGu/TpBnkOaQi4hIq6aA3oheeeUVnn76abKzs4mPj+eFF17gnHPOOeH5ixYtYvr06aSlpdG+fXvuvfdebr31VvvxOXPmcOONN9Z7Xnl5OR4erW+opIhIQ9tXWM6PqbZQvvaYUO5JBde3zeRSrw10KlyK6VA+HD18dJG3budDl/PAv4Njim9ChyoO8UPGD6Tlp5Gal0pGUQZWakcOtPdpbw/oCSEJ3JN0DwnBCXRr003bm4mIiJyEAnoj+fjjj5k6dSqvvPIKgwcPZvbs2Zx33nmkp6fTsWPHeudnZGRw/vnnM2XKFN5//32WLVvG3/72N0JCQrjsssvs5/n5+bF169Y6z1U4FxE5c7vzS/khNYcfNmWz4ZjV10MNh5gcspXz3VKIOLQaQ1EFHD3s4Q+xY6Dr+bbF3jz8HFN8I6u2VLOzcCepeamEeYcxpMMQAEqqS5i5emadc8O8w+yrqSe3T7a3+7n5cV38dU1at4iISHOlgN5InnvuOW666SZuvvlmAF544QXmz5/Pq6++ysyZM+ud/9prr9GxY0deeOEFALp3786aNWt45pln6gR0g8FAWFhYk7wGEZGWakduCT+mZjNvUw7p2cVHWq10N+7hujabGWVcS0hxKhQf86SAjrZt0LqeB5GDwOTqiNIbjcVqIbMo094rfuz2ZgCjOo6yB/Rwn3DGRo0lxj+GhOAE4oLiCPYMdmT5IiIiLYICeiOoqqpi7dq13H///XXax4wZw/Lly4/7nBUrVjBmzJg6bWPHjuWtt96iuroaV1fbL4IlJSVERkZiNpvp3bs3jz/+OImJiY3zQkREWgir1Ura/mLmp+XwY2oO23NLAHClhnNNm7kmIJ1B5t/xqdgPJcc8sUNfWyDvegGEdm8x88mtViv7SvZRVFlEfHA8YNt//PJvL6faUl3nXF9XX+KC4+gV0sveZjAYeGboM01as4iISGuggN4I8vLyMJvNtG3btk5727ZtycnJOe5zcnJyjnt+TU0NeXl5tGvXjm7dujFnzhx69OhBcXExs2bNYvDgwWzYsIHY2NjjXreyspLKytptfoqLi497nohIS2OxWFm/5xA/pubwY1oOewrKAQikmMtdNjLRdxOJ1etwrSmF0iNPcvGAmGHQZZwtmPu2jBFLx9verLCykK6BXfns4s8AcDW50iukFzWWGhKCbUPVE4IS6OjXsc72ZiIiItJ4FNAbkeEPPS1Wq7Ve25+df2z7wIEDGThwoP344MGD6dOnDy+99BIvvvjica85c+ZMZsyYcUb1i4g0N9VmCyt35TM/LYef0g6Qe7gSsBJr2MedbusZ77WJThXpGLBA+ZEn+bSFLmNt88mjh4Jb817ErKy6rM5CbNf/cD3rctfVO8/F6IK7izsWq8UewN8e+/ZJ/58SERGRxqWA3giCg4MxmUz1estzc3Pr9ZIfFRYWdtzzXVxcCAo6/pYzRqORfv36sX379hPW8sADDzB9+nT74+LiYiIiIk71pYiIOL2yqhoWbzvI/LQD/LL5AMUVNbhRTX/jFu5yX89Ytw0EV2fbTq448qSwHrW95O0Swdg8e4jLqsts25sdmTeelp9GYUUhS69cag/dbb3bYjQYifGPsW1vFpxAfFA8Xdp0wd3kXud6CuciIiKOpYDeCNzc3Ojbty8LFizgkksusbcvWLCA8ePHH/c5ycnJfPvtt3XafvrpJ5KSkuzzz//IarWSkpJCjx49TliLu7s77u7uJzwuItIcHSqt4pctucxPy2HJ9oNUVFsIoZCxphTO80xhsGET7pYjXeTVgMkdYobaesq7jAP/cIfWf7beTXuXL7d/ya6iXXW2Nztq7+G9dPSz7Rhyd9LdPJr8qLY3ExERaQYU0BvJ9OnTufbaa0lKSiI5OZnXX3+drKws+77mDzzwAPv27eO9994D4NZbb+W///0v06dPZ8qUKaxYsYK33nqLjz76yH7NGTNmMHDgQGJjYykuLubFF18kJSWFl19+2SGvUUSkKe0pKGNB+gF+Ss9hdUYBFquVeEMmtxjXM84rhTjLDtuJ1iN/jg5d7zLONq/czduB1Z+eGksNOwt31q6onpfKK6Nesa+UXlxVzM6inQC09WpLQnCCfTX1+KB4/N397dcK9Qp1yGsQERGR06eA3kgmTZpEfn4+jz32GNnZ2SQkJDBv3jwiIyMByM7OJisry35+dHQ08+bNY9q0abz88su0b9+eF198sc4Wa4WFhdxyyy3k5OTg7+9PYmIiixcvpn///k3++kREGpvVaiU9u9gWytMOkJ5djDflDDGm8m/Teka7biTIWmA72XLkSe37HAnlYyGsV7Maur7p4Ca+z/ie1Dzb9mYV5oo6x9Pz0zk3/FwALoi5gJ7BPYkPjtf2ZiIiIi2IwXp0JTJpFYqLi/H396eoqAg/Pz9HlyMiUke12cKqXQX8vPkAC9IPsK+wnChDNiOMKYwwrWegcQsu1NQ+wdUbOg239ZLHjgHf46/z4SysViv7S/fb54tfFHMRsYG2XTi+3vE1Dy17yH6uj6sP8UHxxAXHkRCUQL+wfgR6BDqqdBERaYGUDZyPetBFRMShiiuqWbT1IAvSD7Bway6VFeUMMG7mJmMKI9xTiDL8YXvKNjEQOxa6jIHIweDivOtsHK46zNoDa2sXcctL41DlIfvxUM9Qe0DvE9qHa7pfQ3xwPPFB8UT6RWp7MxERkVZGAV1ERJrcnoIyftl8gJ8357IqI59gcx7DTSk8Z0xhiEcqnlTWnmx0hchBtmHrsWMhuLPjCj+Josoi0vLTCPYMpktgFwC2H9rOnb/eWec8F4MLsYGxJAQn0LVNV3t7hF8E9/W/r0lrFhEREeeigC4iIo3OYrGyaV+Rfej6jpxDJBm3Mcy4gQdNKXR33VP3CT5hEDvaFsqjh4KHcw27K6suY3PBZnuveGp+KnsO217DVd2u4oEBDwDQrU03Ogd0Ji4ozraQW1DCcbc3ExEREQEFdBERaSRlVTUs3Z7Hr1ty+WVLLsbD2Qw1beAuYwpD3FPxNZTXnmwwQnh/WyiPHWPbp9xJ9uSuMldRVFlEiFcIAIcqDjHsk2FYrJZ650b4RtRZQd3L1Ysvx3/ZZLWKiIhI86aALiIiDWZ/YTm/bMnl180HWLXzAD3MWxlmSuFd40biPHbXPdkrGDqPsoXyTiPAq41jij7GH7c3S8tPY9uhbSS3S+aVUa8AEOgRaF85PSHItr3Z0Xnjx4ZzERERkdOlgC4iImfMbLGyYW8hC7fk8svmXPKzMxlm2sBEYwqzTKn4udT2klsxYOjQ90gv+Whol+hU26Dd+vOtrM1ZW297M4D9JfvrPP5q/Ff4uvk2VWkiIiLSSiigi4jIaSmuqGbJtjx+2XKAZVv2E1ORylDjRp41ptDdo+5ccqtXEIZOIyF2NIZOI8DbMXt2W61WskuzSc1LJTU/lfS8dMpqyvjwgg/t51TWVFJhrsDH1Ye4oDjig+NJCLL1jrf3bl/negrnIiIi0hgU0EVE5KSsVis7D5aycEsuv27JJTtzC0MMKZxn3MjjxlS83WpXXK/TS955NIb2vcFocljtn2z9hIV7FpKen05BRUG948VVxfi52Ragu6ffPXi4eBDlF6XtzURERMQhFNBFRKSeimozK3fls3BLLsu3ZNGhaB1DjRt5wriRTm7Zdc61eodg6DwKOo080kse1KS1Ht3eLD0/nfT8dJ465ylcTa4ApOalsnTfUqB2e7Nje8a9Xbzt14kLimvSukVERET+SAFdREQA2HuojN+2HmTh5gMc3LWeAZYURhk38g/jFtzdauznWQ0mDBEDIHYUdB6FoW2PJp1LvuPQDpbtX0ZaXhpp+WlkHc6qc3xKjyl0D+oOwIUxF9I9qDvxQfF0bdNV25uJiIiIU1NAFxFppapqLKzJLGDh1lzWbt5Jh4KVnGPcxL9MGwkzHYJjRqZb/CMwdh5p6yWPGQoejb9aeZW5im2HtpGal8qoyFH2ldN/2/sbs9bNqnNuuE+4bTX1oHjaeNSuBt+/XX/6t+vf6LWKiIiINAQFdBGRVmR/YTmLth1k8eb9lOxcQX9LChcYN/KAIQOjm9V+nsXkgSH6HAxHQrkxOLZR9yWvsdSwq2gXaXlp9oXcth3aRo3F1nMf6BHI2KixAPRt25fhEcPtgTw+KJ4Aj4BGq01ERESkqSigi4i0YJU1ZtZkHmLRtoNsTd9AxKGVnGvcyH+M6fgay+GYkenm4O6YYkdC55EYOw4CV49GqclitbDn8B68XLwI8QoB4Oesn7ln0T31zg1wDyA+KB4fVx97W2JoIokjEhulNhERERFHUkAXEWlh9hSUsWjbQVZtzoCMxQywpHC1cRORxlxwrT2vxj0QU+cRR3rJh2Pya3/ii54hq9VKTmkOaflpdbY4O1x9mLv63MXNPW4GID4oHm9Xb+KC4kgISiAu2PZ3B58OGBqx515ERETEmSigi4g0c0dXXF+8NZuDW5bTqXg1Q4ypXGHYgYvRYu8ltxhcsIT3xyXWFshd2vVu8C3Qqi3VuBpt7wLsKd7DNT9cc9ztzdxN7pRUldgfh/uEs/zK5dreTERERFo1BXQRkWbGarWyI7eERVtz2bp5Az57l5DMBqYa0/EzlNf5yV7p3wm3riMxdBqJMWowRnffBqujuKrYvpJ6Wl4aqfmpDGo/iBmDZgAQ5h3G4arD9u3N4oLiSAhOICE4gU4BnexBHsBgMGBAPeUiIiLSuimgi4g0A0Vl1Szdkcea9B1U71hIfMU6xpk2cbMhr85q61Vu/hAzDLcjveTuAR0btA6zxcw/lv6DtPw0dhfvrnc8LS/N/rGryZW5F86lo29HPFwaZz67iIiISEuigC4i4oRqzBY27C1i2Za95G1eTPu8FQw2buI8w26MBqv9p7fZ4EJlu354dhuNodNw3Nr1Outh69Xmavv2Zqn5qZgMJh4d9CgAJqOJ1LxU+97j4T7hxAfHkxCUQHxwPHFBcXWu1SWwy1nVIiIiItKaKKCLiDiJPQVlLN52gMzUlXjuXUKSeSO3GLfgYaiu89O6xL8LHl1H4tJ5BKaowXi5eZ/1vX/M/JE1OWtIzbNtb1ZtqbYf83b15pHkR+zzw6f3nY6Hi4e2NxMRERFpYAroIiIOcriimhU780lL24Bl5290LVvLecY02hiOLJ52pCO8zD0UYobi1W00xAzDx7ftGd3ParWSdTiL1LxUMoszub337fZjX+34imX7ltkf+7v721ZTPzJv3GK12AP6yMiRZ/aCRUREROSkFNBFRJqIbdh6IWvSt1Oy5VfCC1aRbEhljPGg7YQjgbzK5E15h2R8u4/C2Gk4XiFd4Qy2Gssty2XjwY2k5qXaFnLLT+Nw1WH78UldJxHsGQzAuKhxxAbEEh8cT3xQPOE+4dreTERERKSJKaCLiDQSq9VKRl4pq7ZkcTBtIf7Zy+ln3cRfjUcWVzsSyM2YKA5OxKvbSNy7jMStQx/cTK4nvvBx5Jfnk5afRv+w/vYF2d7Y+AZzt86tc56b0Y1uQd2ID4qnxlJjb5/QecIZv04RERERaRgK6CIiDej/27vzsKiu+3/g79kZVlkEBtkERNlUBCWIu0asTdQ0v8QmqSGJTYImdeu3Rr+NmppoTFIbzdNoo/WXpI3RLDbVJjauwShg3ABlERBBQEAEREC2Wc73D2R0BFQiAyO+X88zz+Oc+7nnnnP9iPPh3rmnsq4JyTmlKD5zCMrCIwjTpuL/SfKgkOgBCdC6ktgVu0DIA8bDLmgSZD7RcOzE8me1zbXIqMxAekU6MiszkV6RjtJrpQCAf/ziHwh3DQcADHEdgtTLqQhxDjE+yC3AMcBkeTMiIiIishws0ImI7kF9sw7Hz1cg73QSkH8IAXUnMVGaDbWkuSWg5WvbuGrVDzqfMXAMfRjS/mPgaNv3rvpv0DUAANRyNQDgm9xvsDxpeZs4CSTwdfBFvbbe2PaI3yN4xO+Re5gdEREREXUnFuhERJ2g0xuQVlSNs6ePoflcAvpVH0eUJBNjJdcL4+u3rdfJHVHfLwYOIZOgGjAeDo6+d+xbq9cipzoHGRUZxu+N51Xn4U8j/4TpAdMBAL4OLf30s+1nfIBb68PcbJW2ZpgxEREREXUXFuhERLchhEBOWS1On0lFfc4P6Hv5JwxHOiIkV1sCrl8hb5DaoNp1BOyCJsJ20ETYugbB9i4fspZ7JRfLE5cj+0q2yfJmrfKu5hn/HOocih9n/ghHK8d7nhsRERERWRYW6EREtyiqqkdKejquZh5En0vJCDek4wlJRcvG6zV3s0SFCqdhUAeOQ5+QSVBrhkIta/9HauvyZhkVGUivTEdGRQZGe47Gb8N+CwBwtHJEemU6AMBeaY9Ql1CT74272dxYVk0hU8BRxuKciIiIqDdigU5ED7zLtU04mZGFqoyDsC1NRlhzGqZJL90IkAA6yFHuEAaZ3xi4DJ4MpddweMhVHfbZoGvAptObjLeq37y8GQBYK6yNBbqL2gXvj3sfAx0HwtOOy5sRERERPahYoBPRA+dqvRYnz+ai4swBqC8mIqgxDVOkJTcCpIAeUpTbBsPgOwp9Bz8MpW80PJQ2bfqqaqwyXhm3llsjLiQOAKCSqfB51ueo17V8N10hVWCQU8vyZqEuoRjcd7BJP5N8JplvwkRERER0X2CBTkS9Xl2TDqnZ53Dp9EEoixMRWJ+KCdLiGwFSwAAJyq0HoNkrBn0HT4bafxQ0VvZt+jpedhxnKs60XBmvyEDJtRuFva+9r7FAl0qkeHnIy7BV2CLUJRQD+gyAopNrmxMRERHRg4UFOhH1Oo1aPdJyzqM07QAURYnwv5aCUdKiGwHXH+xWZhWARs+RcAmdBNuBY+CuvvHd7gZdA7LLU1FSV4KpflON7WuOrUHOlRyT4/na+7Y8Td0lFEII4y3qL4S+YL5JEhEREVGvwwKdiO57jVo9zuScR+mZA5AXJsHvWgqiJIU3AloLclV/1HtEwylkIvoEjYe7jTOAluXNMqtzkV64DxmVLUuc5VXnQS/0UEqVeNjnYePV77GeY+Fj72Nc3izIOQh2SrvunjIRERER9UIs0InovtOk0yMjNx+lafshLUpE/7pUDL+5IL/+jLUypS/qNA+hT/BEuISMh7ttX+gNehTUFMDB2qk1DEsOL8HeC3vbHMdF7YJQ51DUNNfAWd1SzM8bNs/MsyMiIiKiBxULdCKyeE06PTJyzuHS6QOQFSXBty4FwyQ3fYf8eqVdqvRFrXsUHIInwDVkPNxsXaGtLcbxynSkZ32CjMoMZFVmoV5Xj//+6r/wtPMEAAQ5B+Fo6VHjA9xCXEIQ4hwCN2s3PlGdiIiIiLoNC3QisjiNWj0ys8+i/MxByIuS4HctFcMkNz1l/XrNXKL0Ra37Q3AIGg/X0PFwt3WF5npB/XXO13j/5Puoaa5p079arsbFuovGAv3Z4GcxO3Q2i3EiIiIi6lEs0ImoxzU265CRlY6K9INQFifDrz4NwySm65AbIEGJyg+17lHoM2gcVIERyGsoRXplOjIrEpH+34+wetRqRHtEAwDslHaoaa4xLm8W7Bxs/N54f4f+kEllxu6VMmV3T5mIiIiIqA0W6ETU7eqbtMhMP4WqzARYXTyKgIY0REgqbwRIWtYhL1YF4Jr7CDgGj4d72ARca7yMzac/QsbF/4+SnLfa9JtRmWEs0KM9orH9ke0I7BPI5c2IiIiI6L7AAp2IzK62vhFZaT/h6tlDsCn7CYGNpxEpuenWcwmggwxFVgNRrRmOSt8BKOujRkZNHsZ5jUOwbywAQN9Qhn0X9hl387X3RYhLCEKdW5Y4G+g00LjNXmmPEOeQbpsjEREREdG9YoFORF2uqqYOOamHUZd9GA7lxxDYnIERkvobARKgCQoUWYegWjMMqRp7FKl0yLiSjXPVP0Jf8IMxVClTIvZ6gT7AcQAWDFuAUJdQBDsHc3kzIiIiIupVWKAT0T27VFGJvJQENJ47DKeKExioy8ZDkuYbARKgFlb4ySEY2a7e0PgMw68eikeAXIWa5ho8vy3GpD9nK2fj09Sj3KOM7QqpArPDZnfXtIiIiIiIuhULdCLqFCEECouLUJR6ELr8JLhWn8IA/Xm4SfQ3YiRApsIBSX38ca6PC4rUepxruIh6XQWgq0BknRS/kqsAtNyKPt1/Ovpa90Woc0tRzuXNiIiIiOhBxAKdiG5Lrzcg71wmys4kQFqYDI+aVPihGD43xVySy3BW4Qon63BIfUfCY/AExB99FVeaWgpy1LbEqeVqBDkFIcItwuQYb41q+8A3IiIiIqIHDQt0IjLR2NSM3PTjqMo6BFXJT/C9dgaBkkoEXt9+RSrFEZUVEtWuyLBxQIFShyuGa3CzdsP+J7409hN+Phzl9eUIcQlBiHMIQl1C4efgZ7K8GRERERER3cACnegBd/VqDfLSfkRt7hHYXjqBAU0ZCLv+QLd6iQTWEgGtkOGCagDe0qhxUlp5896AAZBKpLBX2aNeWw9rhTUAYN34dbxNnYiIiIioE1igEz1AhBAoLS1GYWoCmvOT4FJ1CgG6XAyT6NEokSBbqcB/HJRIU/ZFmtoWl+QGbBv0JgYMmYgAK1sE/rQaJ89ug4+9j/GqeKhLKAY6DjQW5q1YnBMRERERdQ4LdKJeTK834Hx2GsozDkFa/BM8atLgIy7C46aYf9nZ4J/2fZCvlEFvUlO3PPSt2c8XcitbAMBLg1/Cq+Gvwl5p321zICIiIiJ6ULBAJ+pF6uuvIS/tCK7mHIG67Dh86zPgL6mBTCFHukqFBEcl0lVumFOlgtouHIr+I1HhWI9zuR8DAJysnFquil9/mnqwczBc1C7G/m/+MxERERERdS0W6ET3sUulxShM+wFN+clwqkyBvzYXYRItchUK7LK1QbqDFbJU9rgmlZrsV/nEW5geMB0A4FRbDL9+gxHiHAJ3G3femk5ERERE1ENYoBPdJ/R6PfLPnkJ55mFIi4/BoyYNVtJLqFUpkaFUYoysASqdFldgj0S7AHziUGHct3V5sxCXEIQ6h2K4+3DjNk87T3jaefbElIiIiIiI6CYs0IksVE1NNfLTfkRtbhJsy09C05iBS2o90lVKZKiUyPBUolzezxhfphkP9xGL4eYbjEcbq1CcttH4ILf+Dv0hl/KfOxERERGRJeMndiILIAwGFBfkoPRMAvSFP8HmairqZSWwE3qMatYCAM4r5Ih39zDZTwop/B39EeIcgod9HoabZwgAwFntjNcfer27p0FERERERPeABTpRD2iov4bzpxNxNTcJktLjaNZnoVjVhEylEulWKuTbySEkrhhfp8NzTYOg9xgOp6BRGHThA/j18TdeGR/kNKjN8mZERERERHR/YoFOZGZCCJQV5aH4TAIaLxyFvDoNEU15CJHooQUw0scTjVIbADYm+7laucAjKBbDRiwxtn01ZEy3jp2IiIiIiLoPC3SiLtbYUI/8M0moyj6CqstHcdVwHkXKZqSrlMhWKxAo0+LzUj2q4IBCm1D0lV7FVZkBYa6DMdh1CEKcQxDiEsIlzYiIiIiIHjAs0InugTAYUFach4tnfoT2wk9wrEqDn/YcPnF1wCFrNa45SwGorr9aXFD3QXHcN+jnEwQnqRTbm2tgp7Dj8mZERERERA84FuhEnVB/rRb5pxNRmHMQF6tPoFJcRL5Kj4tyOXaVl0ICABKgTqLANakUSsjQ38oT4Z5RGKoZhhDnEHjbe0MqubEuub3SvsfmQ0REREREloMFOlEHhMGAorx0lGUmwlB0DJlNKUhV1yFDpUC5XA70AQClMX636y/h7jUGmpAxmOcgxTyJBP59/Lm8GRERERER3RVWDkTXXa2qwNm0fcjK34+Sa1m4JK3EyspyeBsMAIAkxz74wablardEAB4SewQ6DMRwvzEI94jEQKeBUEgVPTkFIiIiIiK6j7FApweStrkJF7KOIytzD9KvHMUlUYICpQ7nFQoImQSwBwAlflWrhrveG9VOQ+Dl4YV4F2s85D8GQc7BXN6MiIiIiIi6FAt06vWEwYDiC1k4fmYX8sp+wvCqMkQ15CFA0owUOxt87uIMQIrW29Ud9XL4KtwR7BoOr0degJ9zAAAgquemQEREREREDwAW6NTrVF+5jJ9O7URm0SEUNZxDibQa55RSNEmlgArwUVRB3diMGljDWgQgzCBDQJ8gRA2YhCj/MVzejIiIiIiIegQLdLqvNTU24MTpPag5fwoupdlwrc1AieIy/sfDFZAAsAZa09zaAHgb7FDpPQmFQ+Lg6R+GX8pk+GVPToCIiIiIiOg6Fuh03xAGA06fTcTxzG+RdyUNxfpSFCi1qJbJEHe1Bv9ztRoA4KiVQGUQ8NKr4aP0RLBmBMYMnoZA1yCT5c2IiIiIiIgsCQt0M9qwYQPee+89lJaWIiQkBOvWrcPo0aM7jD906BAWLVqEjIwMeHh4YPHixYiPjzeJ2bFjB5YtW4a8vDz4+/tj1apVeOyxx8w9lR5xqSQfpZlH0VBwDIbKFCx3KUe5XNay0bi6mQwyIVCocMVRz8eh9hsBr5BR+KmvBjKprKeGTkRERERE1Gks0M3kiy++wIIFC7BhwwbExMTgo48+wi9+8QtkZmbC29u7TXx+fj6mTp2KF198EZ999hkSExMxd+5c9O3bF48//jgAIDk5GTNnzsSbb76Jxx57DN988w2efPJJHDlyBFFR9/cjzC5VFuPHUzuQWZKMosZ8FMnqENTciHXlFQAAAUAn6QeJEPDUSeEFZ/jZByNywCTEhE6BlULdsxMgIiIiIiK6RxIhhOjpQfRGUVFRGDZsGDZu3GhsCwoKwowZM/D222+3iX/ttdewa9cuZGVlGdvi4+ORlpaG5ORkAMDMmTNRU1OD//73v8aYKVOmwNHREdu2bburcdXU1MDBwQFXr16Fvb39z53ePRMGA4599R62Vn+LPOkVFCsAg0RiEuOq02HLRTnK7UMhPIahztsbEaEPo4+tUw+NmoiIiIio97CU2oBu4BV0M2hubsbJkyexZMkSk/bJkycjKSmp3X2Sk5MxefJkk7bY2Fhs2bIFWq0WCoUCycnJWLhwYZuYdevWden4u4NEKoV79j9R6daMQqUKAOCiM8BHbwsvK18Ea6IxJvwx9HPxgW/PDpWIiIiIiKhbsEA3g4qKCuj1eri5uZm0u7m5oaysrN19ysrK2o3X6XSoqKiARqPpMKajPgGgqakJTU1Nxvc1NTWdnY7ZlPo/ibFNeZjk4o+YsBkI9B7c00MiIiIiIiLqMSzQzUhyyy3bQog2bXeKv7W9s32+/fbb+NOf/nTXY+5ODz2zHA/19CCIiIiIiIgsBNecMgMXFxfIZLI2V7bLy8vbXAFv5e7u3m68XC6Hs7PzbWM66hMAli5diqtXrxpfRUVFP2dKREREREREZGYs0M1AqVQiIiIC+/btM2nft28fRo4c2e4+0dHRbeL37t2LyMhIKBSK28Z01CcAqFQq2Nvbm7yIiIiIiIjI8vAWdzNZtGgRZs2ahcjISERHR2PTpk0oLCw0rmu+dOlSXLx4Ef/4xz8AtDyx/a9//SsWLVqEF198EcnJydiyZYvJ09nnz5+PMWPG4J133sH06dOxc+dO7N+/H0eOHOmRORIREREREVHXYYFuJjNnzkRlZSVWrlyJ0tJShIaGYvfu3fDx8QEAlJaWorCw0Bjfv39/7N69GwsXLsSHH34IDw8PfPDBB8Y10AFg5MiR2L59O15//XUsW7YM/v7++OKLL+77NdCJiIiIiIiI66A/cLjWIRERERERAawNLBG/g05ERERERERkAVigExEREREREVkAFuhEREREREREFoAFOhEREREREZEFYIFOREREREREZAFYoBMRERERERFZABboRERERERERBaABToRERERERGRBWCBTkRERERERGQB5D09AOpeQggAQE1NTQ+PhIiIiIiIelJrTdBaI1DPY4H+gKmtrQUAeHl59fBIiIiIiIjIEtTW1sLBwaGnh0EAJIK/LnmgGAwGlJSUwM7ODhKJxOzHq6mpgZeXF4qKimBvb2/241Hvwvyhe8H8oZ+LuUP3gvlD96K780cIgdraWnh4eEAq5befLQGvoD9gpFIpPD09u/249vb2/E+KfjbmD90L5g/9XMwduhfMH7oX3Zk/vHJuWfhrEiIiIiIiIiILwAKdiIiIiIiIyAKwQCezUqlUWLFiBVQqVU8Phe5DzB+6F8wf+rmYO3QvmD90L5g/xIfEEREREREREVkAXkEnIiIiIiIisgAs0ImIiIiIiIgsAAt0IiIiIiIiIgvAAp2IiIiIiIjIArBAp07ZsGED+vfvDysrK0RERODw4cO3jT906BAiIiJgZWUFPz8//O1vf2sTs2PHDgQHB0OlUiE4OBjffPONuYZPPayr82fz5s0YPXo0HB0d4ejoiEmTJuHYsWPmnAL1IHP8/Gm1fft2SCQSzJgxo4tHTZbCHPlTXV2NV155BRqNBlZWVggKCsLu3bvNNQXqQebIn3Xr1mHgwIFQq9Xw8vLCwoUL0djYaK4pUA/pTO6Ulpbi6aefxsCBAyGVSrFgwYJ24/jZuZcTRHdp+/btQqFQiM2bN4vMzEwxf/58YWNjIy5cuNBu/Pnz54W1tbWYP3++yMzMFJs3bxYKhUJ8/fXXxpikpCQhk8nE6tWrRVZWlli9erWQy+Xi6NGj3TUt6ibmyJ+nn35afPjhhyIlJUVkZWWJ559/Xjg4OIji4uLumhZ1E3PkT6uCggLRr18/MXr0aDF9+nQzz4R6gjnyp6mpSURGRoqpU6eKI0eOiIKCAnH48GGRmpraXdOibmKO/Pnss8+ESqUSW7duFfn5+WLPnj1Co9GIBQsWdNe0qBt0Nnfy8/PFvHnzxKeffiqGDh0q5s+f3yaGn517PxbodNdGjBgh4uPjTdoGDRoklixZ0m784sWLxaBBg0zaXn75ZfHQQw8Z3z/55JNiypQpJjGxsbHi17/+dReNmiyFOfLnVjqdTtjZ2YlPP/303gdMFsVc+aPT6URMTIz4+9//LuLi4lig91LmyJ+NGzcKPz8/0dzc3PUDJotijvx55ZVXxIQJE0xiFi1aJEaNGtVFoyZL0NncudnYsWPbLdD52bn34y3udFeam5tx8uRJTJ482aR98uTJSEpKanef5OTkNvGxsbE4ceIEtFrtbWM66pPuT+bKn1vV19dDq9XCycmpawZOFsGc+bNy5Ur07dsXs2fP7vqBk0UwV/7s2rUL0dHReOWVV+Dm5obQ0FCsXr0aer3ePBOhHmGu/Bk1ahROnjxp/FrW+fPnsXv3bvzyl780wyyoJ/yc3Lkb/Ozc+8l7egB0f6ioqIBer4ebm5tJu5ubG8rKytrdp6ysrN14nU6HiooKaDSaDmM66pPuT+bKn1stWbIE/fr1w6RJk7pu8NTjzJU/iYmJ2LJlC1JTU801dLIA5sqf8+fP4+DBg3jmmWewe/du5Obm4pVXXoFOp8Py5cvNNh/qXubKn1//+te4fPkyRo0aBSEEdDod5syZgyVLlphtLtS9fk7u3A1+du79WKBTp0gkEpP3Qog2bXeKv7W9s33S/csc+dPq3XffxbZt25CQkAArK6suGC1Zmq7Mn9raWvzmN7/B5s2b4eLi0vWDJYvT1T9/DAYDXF1dsWnTJshkMkRERKCkpATvvfceC/ReqKvzJyEhAatWrcKGDRsQFRWFc+fOYf78+dBoNFi2bFkXj556kjk+5/Kzc+/GAp3uiouLC2QyWZvfzpWXl7f5LV4rd3f3duPlcjmcnZ1vG9NRn3R/Mlf+tPrzn/+M1atXY//+/Rg8eHDXDp56nDnyJyMjAwUFBXj00UeN2w0GAwBALpcjOzsb/v7+XTwT6gnm+vmj0WigUCggk8mMMUFBQSgrK0NzczOUSmUXz4R6grnyZ9myZZg1axZ++9vfAgDCwsJw7do1vPTSS/jjH/8IqZTfQr3f/ZzcuRv87Nz78V8/3RWlUomIiAjs27fPpH3fvn0YOXJku/tER0e3id+7dy8iIyOhUChuG9NRn3R/Mlf+AMB7772HN998E99//z0iIyO7fvDU48yRP4MGDcKZM2eQmppqfE2bNg3jx49HamoqvLy8zDYf6l7m+vkTExODc+fOGX+xAwA5OTnQaDQsznsRc+VPfX19myJcJpNBtDzAuQtnQD3l5+TO3eBn5wdA9z+Xju5XrUtFbNmyRWRmZooFCxYIGxsbUVBQIIQQYsmSJWLWrFnG+NZlRhYuXCgyMzPFli1b2iwzkpiYKGQymVizZo3IysoSa9as4VIRvZQ58uedd94RSqVSfP3116K0tNT4qq2t7fb5kXmZI39uxae4917myJ/CwkJha2srXn31VZGdnS2+/fZb4erqKt56661unx+ZlznyZ8WKFcLOzk5s27ZNnD9/Xuzdu1f4+/uLJ598stvnR+bT2dwRQoiUlBSRkpIiIiIixNNPPy1SUlJERkaGcTs/O/d+LNCpUz788EPh4+MjlEqlGDZsmDh06JBxW1xcnBg7dqxJfEJCgggPDxdKpVL4+vqKjRs3tunzq6++EgMHDhQKhUIMGjRI7Nixw9zToB7S1fnj4+MjALR5rVixohtmQ93NHD9/bsYCvXczR/4kJSWJqKgooVKphJ+fn1i1apXQ6XTmngr1gK7OH61WK9544w3h7+8vrKyshJeXl5g7d664cuVKN8yGulNnc6e9zzU+Pj4mMfzs3LtJhOB9NEREREREREQ9jd9BJyIiIiIiIrIALNCJiIiIiIiILAALdCIiIiIiIiILwAKdiIiIiIiIyAKwQCciIiIiIiKyACzQiYiIiIiIiCwAC3QiIiIiIiIiC8ACnYiIiIiIiMgCsEAnIiJ6gDz33HOYMWNGTw/jvvHGG29g6NChPT0MIiJ6QLBAJyIi6kIFBQWQSCRITU29574aGhpgbW2Ns2fP3vvArlu/fj0++eSTLuuPiIiIuo68pwdARERE7du3bx+8vLwwaNCgLuvTwcHhnvbXarVQKBRdNBoiIiK6Ga+gExFRr2EwGPDOO+8gICAAKpUK3t7eWLVqlXH7mTNnMGHCBKjVajg7O+Oll15CXV2dcfu4ceOwYMECkz5nzJiB5557zvje19cXq1evxgsvvAA7Ozt4e3tj06ZNxu39+/cHAISHh0MikWDcuHEAgISEBIwYMQI2Njbo06cPYmJicOHChdvOZ+fOnZg2bVq721qv1H/55ZcYPXo01Go1hg8fjpycHBw/fhyRkZGwtbXFlClTcPnyZeN+t97ifrtzdvMxxo0bBysrK3z22WcwGAxYuXIlPD09oVKpMHToUHz//fe3nQvQcru4t7c3VCoVPDw8MG/ePOO25uZmLF68GP369YONjQ2ioqKQkJBgsn9iYiLGjh0La2trODo6IjY2FleuXAEANDU1Yd68eXB1dYWVlRVGjRqF48ePG/dNSEiARCLBgQMHEBkZCWtra4wcORLZ2dkmx1izZg3c3NxgZ2eH2bNno7Gx8Y7zIiIi6ios0ImIqNdYunQp3nnnHSxbtgyZmZn4/PPP4ebmBgCor6/HlClT4OjoiOPHj+Orr77C/v378eqrr3b6OGvXrkVkZCRSUlIwd+5czJkzx3gb+rFjxwAA+/fvR2lpKf71r39Bp9NhxowZGDt2LE6fPo3k5GS89NJLkEgkHR7DYDDg22+/xfTp0287lhUrVuD111/HqVOnIJfL8dRTT2Hx4sVYv349Dh8+jLy8PCxfvrzD/W93zlq99tprmDdvHrKyshAbG4v169dj7dq1+POf/4zTp08jNjYW06ZNQ25ubofH+frrr/H+++/jo48+Qm5uLv79738jLCzMuP35559HYmIitm/fjtOnT+OJJ57AlClTjH2mpqZi4sSJCAkJQXJyMo4cOYJHH30Uer0eALB48WLs2LEDn376KU6dOoWAgADExsaiqqrKZBx//OMfsXbtWpw4cQJyuRwvvPCCcduXX36JFStWYNWqVThx4gQ0Gg02bNhw2/NPRETUpQQREVEvUFNTI1Qqldi8eXO72zdt2iQcHR1FXV2dse27774TUqlUlJWVCSGEGDt2rJg/f77JftOnTxdxcXHG9z4+PuI3v/mN8b3BYBCurq5i48aNQggh8vPzBQCRkpJijKmsrBQAREJCwl3PJzExUbi4uAi9Xt/u9tbj/P3vfze2bdu2TQAQBw4cMLa9/fbbYuDAgcb3cXFxYvr06UKIO5+z1mOsW7fOpN3Dw0OsWrXKpG348OFi7ty5Hc5n7dq1IjAwUDQ3N7fZdu7cOSGRSMTFixdN2idOnCiWLl0qhBDiqaeeEjExMe32XVdXJxQKhdi6dauxrbm5WXh4eIh3331XCCHEDz/8IACI/fv3G2O+++47AUA0NDQIIYSIjo4W8fHxJn1HRUWJIUOGdDgvIiKirsQr6ERE1CtkZWWhqakJEydO7HD7kCFDYGNjY2yLiYmBwWBoc5vznQwePNj4Z4lEAnd3d5SXl3cY7+TkhOeeew6xsbF49NFHsX79epSWlt72GDt37sQjjzwCqfT2/1XfPJbWK983X5l2c3PrcGx3OmetIiMjjX+uqalBSUkJYmJiTGJiYmKQlZUFAFi9ejVsbW2Nr8LCQjzxxBNoaGiAn58fXnzxRXzzzTfQ6XQAgFOnTkEIgcDAQJP9Dh06hLy8PAA3rqC3Jy8vD1qt1mRMCoUCI0aMMI6pvfOl0WgAwHh+srKyEB0dbRJ/63siIiJzYoFORES9glqtvu12IUSHt5S3tkulUgghTLZptdo28bc+JE0ikcBgMNz2+B9//DGSk5MxcuRIfPHFFwgMDMTRo0c7jN+1a9cdb2+/dSyt87i1raOx3emctbr5lxq3HqvVzec3Pj4eqampxpeHhwe8vLyQnZ2NDz/8EGq1GnPnzsWYMWOg1WphMBggk8lw8uRJk/2ysrKwfv36O4619e/sdmNq1d75utPfHRERUXdhgU5ERL3CgAEDoFarceDAgXa3BwcHIzU1FdeuXTO2JSYmQiqVIjAwEADQt29fkyvber0e6enpnRqHUqk07nur8PBwLF26FElJSQgNDcXnn3/ebh+5ubkoKCjA5MmTO3XszrrTOWuPvb09PDw8cOTIEZP2pKQkBAUFAWi5YyAgIMD4kstbFo1Rq9WYNm0aPvjgAyQkJCA5ORlnzpxBeHg49Ho9ysvLTfYLCAiAu7s7gJYr3x2NMyAgAEql0mRMWq0WJ06cMI7pbgQFBbX5pcntfolCRETU1bjMGhER9QpWVlZ47bXXsHjxYiiVSsTExODy5cvIyMjA7Nmz8cwzz2DFihWIi4vDG2+8gcuXL+N3v/sdZs2aZbw1fMKECVi0aBG+++47+Pv74/3330d1dXWnxuHq6gq1Wo3vv/8enp6esLKyQlVVFTZt2oRp06bBw8MD2dnZyMnJwbPPPttuHzt37sSkSZNgbW19r6fltu50zjryhz/8AStWrIC/vz+GDh2Kjz/+GKmpqdi6dWuH+3zyySfQ6/WIioqCtbU1/vnPf0KtVsPHxwfOzs545pln8Oyzz2Lt2rUIDw9HRUUFDh48iLCwMEydOhVLly5FWFgY5s6di/j4eCiVSvzwww944okn4OLigjlz5uAPf/gDnJyc4O3tjXfffRf19fW3ncet5s+fj7i4OERGRmLUqFHYunUrMjIy4Ofn16nzSkRE9HOxQCciol5j2bJlkMvlWL58OUpKSqDRaBAfHw8AsLa2xp49ezB//nwMHz4c1tbWePzxx/GXv/zFuP8LL7yAtLQ0PPvss5DL5Vi4cCHGjx/fqTHI5XJ88MEHWLlyJZYvX47Ro0fjiy++wNmzZ/Hpp5+isrISGo0Gr776Kl5++eV2+9i5cyfi4uJ+/onohNuds47MmzcPNTU1+P3vf4/y8nIEBwdj165dGDBgQIf79OnTB2vWrMGiRYug1+sRFhaG//znP3B2dgbQ8hWAt956C7///e9x8eJFODs7Izo6GlOnTgUABAYGYu/evfjf//1fjBgxAmq1GlFRUXjqqacAtCyPZjAYMGvWLNTW1iIyMhJ79uyBo6PjXZ+LmTNnIi8vD6+99hoaGxvx+OOPY86cOdizZ89d90FERHQvJOLWL9sRERFRj6moqIBGo0FRUZHx9m4iIiJ6MPA76ERERBakqqoKf/nLX1icExERPYB4BZ2IiIiIiIjIAvAKOhEREREREZEFYIFOREREREREZAFYoBMRERERERFZABboRERERERERBaABToRERERERGRBWCBTkRERERERGQBWKATERERERERWQAW6EREREREREQWgAU6ERERERERkQVggU5ERERERERkAVigExEREREREVmA/wN0gsLB2ZzhxwAAAABJRU5ErkJggg==' 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        "            </div>\n",
        "        "
       ],
@@ -290,18 +342,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e558a05111b34cf29b1a5ddb8adddf05",
+       "model_id": "894fb3e194544ca1a0b6dfb65b5b6e1b",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
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",
       "text/html": [
        "\n",
        "            <div style=\"display: inline-block;\">\n",
        "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
        "                    Figure\n",
        "                </div>\n",
-       "                <img 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' 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' width=1000.0/>\n",
        "            </div>\n",
        "        "
       ],
@@ -311,58 +363,42 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0\n",
-      "0.002325716392084999\n",
-      "0.00015451278785510008\n",
-      "0.0\n"
-     ]
     }
    ],
    "source": [
-    "#reduced_path = os.path.join(data_dir, 'reference_rq.txt')\n",
-    "reduced_path = os.path.join(data_dir, 'ref_rate_206597.txt')\n",
+    "import scipy\n",
+    "# Rate is counts per microsecond\n",
+    "rate_db = np.arange(0.001, .1, 0.001)\n",
+    "dead_time = 4.2\n",
     "\n",
-    "if os.path.isfile(reduced_path):\n",
-    "    _data = np.loadtxt(reduced_path).T\n",
     "\n",
-    "reduced_path = os.path.join(data_dir, 'REFL_198409_combined_data_auto.txt')\n",
-    "if os.path.isfile(reduced_path):\n",
-    "    _refl = np.loadtxt(reduced_path).T\n",
+    "meas_par = -scipy.special.lambertw(-rate_db * dead_time).real / dead_time\n",
+    "corr_npar = 1/(1 - rate_db * dead_time )\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(10,5))\n",
-    "#plt.errorbar(_refl[0], _refl[1]*_refl[0]**4, yerr=_refl[2]*_refl[0]**4, markersize=4, marker='.', linestyle='', label='new reduction')\n",
-    "#plt.errorbar(_data[0], _data[1]*_data[0]**4, yerr=_data[2]*_data[0]**4, markersize=4, marker='', linestyle='-', label='reference')\n",
-    "plt.errorbar(_refl[0], _refl[1], yerr=_refl[2], markersize=4, marker='*', linestyle='-', label='new reduction')\n",
-    "plt.errorbar(_data[0], _data[1], yerr=_data[2], markersize=4, marker='*', linestyle='-', label='reference')\n",
+    "meas_npar = corr_npar * rate_db \n",
     "\n",
+    "r_max = 1/dead_time\n",
+    "\n",
+    "print(\"R_max\", r_max)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10,5))\n",
+    "plt.plot(rate_db, meas_par, label='true rate (par)')\n",
+    "plt.plot(rate_db, meas_npar, label='true rate (non-par)')\n",
+    "plt.plot(rate_db, rate_db, linestyle='--', label='measured rate')\n",
     "plt.legend()\n",
-    "plt.xlabel('q [$1/\\AA$]')\n",
-    "plt.ylabel('R(q)')\n",
-    "ax.set_yscale('log')\n",
-    "ax.set_xscale('log')\n",
+    "plt.xlabel('counts / micro-second')\n",
+    "plt.ylabel('True rate')\n",
     "plt.show()\n",
     "\n",
-    "if len(_data[1])==len(_refl[1]):\n",
-    "    # dQ is computed for each run in the new implementation\n",
-    "    fig, ax = plt.subplots(figsize=(10,5))\n",
-    "    plt.plot(_refl[0], _refl[3]/_refl[0], label=\"new_reduction\")\n",
-    "    plt.plot(_data[0], _data[3]/_data[0], label=\"reference\")\n",
     "\n",
-    "    plt.xlabel('q [$1/\\AA$]')\n",
-    "    plt.ylabel('$\\Delta q$')\n",
-    "    ax.set_yscale('linear')\n",
-    "    ax.set_xscale('log')\n",
-    "    plt.show()\n",
-    "else:\n",
-    "    print(\"len(ref) = %s; len(new) = %s\" % (len(_data[1]), len(_refl[1])))\n",
+    "fig, ax = plt.subplots(figsize=(10,5))\n",
+    "plt.plot(rate_db, meas_par/rate_db, label='Corr (par)')\n",
+    "plt.plot(rate_db, meas_npar/rate_db, label='Corr (non-par)')\n",
     "\n",
-    "for i in range(4):\n",
-    "    print(np.sum(_data[i]-_refl[i]))\n"
+    "plt.legend()\n",
+    "plt.xlabel('counts / micro-second')\n",
+    "plt.ylabel('Correction')\n",
+    "plt.show()"
    ]
   },
   {
diff --git a/reduction/test/test_dead_time.py b/reduction/test/test_dead_time.py
new file mode 100644
index 0000000..d838bb5
--- /dev/null
+++ b/reduction/test/test_dead_time.py
@@ -0,0 +1,45 @@
+import os
+import numpy as np
+from lr_reduction.DeadTimeCorrection import SingleReadoutDeadTimeCorrection
+
+import mantid
+import mantid.simpleapi as mtd_api
+
+def test_deadtime():
+    """
+        Test the time-resolved reduction that uses a measured reference.
+        It is generally used at 30 Hz but it also works at 60 Hz.
+    """
+    ws = mtd_api.Load("REF_L_198409")
+
+    algo = SingleReadoutDeadTimeCorrection()
+    algo.PyInit()
+    algo.setProperty("InputWorkspace", ws)
+    algo.setProperty("OutputWorkspace", "dead_time_corr")
+    algo.PyExec()
+    corr_ws = algo.getProperty('OutputWorkspace').value
+    corr = corr_ws.readY(0)
+    for c in corr:
+        assert(c>0)
+        assert(c<1.001)
+
+def test_deadtime_paralyzable():
+    """
+        Test the time-resolved reduction that uses a measured reference.
+        It is generally used at 30 Hz but it also works at 60 Hz.
+    """
+    ws = mtd_api.Load("REF_L_198409")
+
+    algo = SingleReadoutDeadTimeCorrection()
+    algo.PyInit()
+    algo.setProperty("InputWorkspace", ws)
+    algo.setProperty("Paralyzable", True)
+    algo.setProperty("OutputWorkspace", "dead_time_corr")
+    algo.PyExec()
+    corr_ws = algo.getProperty('OutputWorkspace').value
+    corr = corr_ws.readY(0)
+    with open("dc.txt", 'w') as fd:
+        fd.write(str(corr))
+    for c in corr:
+        assert(c>0)
+        assert(c<1.001)

From 47a3edefa798308464afec78348d026572afff25 Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Thu, 7 Mar 2024 09:45:51 -0500
Subject: [PATCH 04/15] fix test

---
 reduction/test/test_dead_time.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/reduction/test/test_dead_time.py b/reduction/test/test_dead_time.py
index d838bb5..47f5833 100644
--- a/reduction/test/test_dead_time.py
+++ b/reduction/test/test_dead_time.py
@@ -4,6 +4,9 @@
 
 import mantid
 import mantid.simpleapi as mtd_api
+mtd_api.config["default.facility"] = "SNS"
+mtd_api.config["default.instrument"] = "REF_L"
+
 
 def test_deadtime():
     """

From 3dbdbaea1a6d9135f660bc73766b3906ff3d633b Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Thu, 7 Mar 2024 10:00:32 -0500
Subject: [PATCH 05/15] fix test

---
 reduction/test/test_dead_time.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/reduction/test/test_dead_time.py b/reduction/test/test_dead_time.py
index 47f5833..52eeb43 100644
--- a/reduction/test/test_dead_time.py
+++ b/reduction/test/test_dead_time.py
@@ -7,13 +7,16 @@
 mtd_api.config["default.facility"] = "SNS"
 mtd_api.config["default.instrument"] = "REF_L"
 
+from lr_reduction.utils import amend_config
+
 
 def test_deadtime():
     """
         Test the time-resolved reduction that uses a measured reference.
         It is generally used at 30 Hz but it also works at 60 Hz.
     """
-    ws = mtd_api.Load("REF_L_198409")
+    with amend_config(data_dir=nexus_dir):
+        ws = mtd_api.Load("REF_L_198409")
 
     algo = SingleReadoutDeadTimeCorrection()
     algo.PyInit()
@@ -31,7 +34,8 @@ def test_deadtime_paralyzable():
         Test the time-resolved reduction that uses a measured reference.
         It is generally used at 30 Hz but it also works at 60 Hz.
     """
-    ws = mtd_api.Load("REF_L_198409")
+    with amend_config(data_dir=nexus_dir):
+        ws = mtd_api.Load("REF_L_198409")
 
     algo = SingleReadoutDeadTimeCorrection()
     algo.PyInit()

From fc25d2e494ce0759b14d389806925dc26fc15d5d Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Thu, 7 Mar 2024 10:20:40 -0500
Subject: [PATCH 06/15] fix test

---
 reduction/test/test_dead_time.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/reduction/test/test_dead_time.py b/reduction/test/test_dead_time.py
index 52eeb43..86fd71d 100644
--- a/reduction/test/test_dead_time.py
+++ b/reduction/test/test_dead_time.py
@@ -10,7 +10,7 @@
 from lr_reduction.utils import amend_config
 
 
-def test_deadtime():
+def test_deadtime(nexus_dir):
     """
         Test the time-resolved reduction that uses a measured reference.
         It is generally used at 30 Hz but it also works at 60 Hz.
@@ -29,7 +29,7 @@ def test_deadtime():
         assert(c>0)
         assert(c<1.001)
 
-def test_deadtime_paralyzable():
+def test_deadtime_paralyzable(nexus_dir):
     """
         Test the time-resolved reduction that uses a measured reference.
         It is generally used at 30 Hz but it also works at 60 Hz.

From 7e552479edb0c811e458d4f8000588f28fad0554 Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Thu, 7 Mar 2024 13:56:24 -0500
Subject: [PATCH 07/15] clean up

---
 reduction/lr_reduction/DeadTimeCorrection.py  |   4 +-
 reduction/lr_reduction/event_reduction.py     |  10 +-
 .../lr_reduction/reduction_template_reader.py |  14 ++
 reduction/lr_reduction/template.py            |  11 +-
 reduction/lr_reduction/workflow.py            |   5 +-
 reduction/notebooks/workflow.ipynb            | 141 +++++++++++-------
 6 files changed, 122 insertions(+), 63 deletions(-)

diff --git a/reduction/lr_reduction/DeadTimeCorrection.py b/reduction/lr_reduction/DeadTimeCorrection.py
index 31fa0d3..8e25b28 100644
--- a/reduction/lr_reduction/DeadTimeCorrection.py
+++ b/reduction/lr_reduction/DeadTimeCorrection.py
@@ -42,7 +42,7 @@ def summary(self):
 
     def PyInit(self):
         self.declareProperty(WorkspaceProperty("InputWorkspace", "", Direction.Input),
-                             "Optionally, we can provide a workspace directly")
+                             "Input workspace use to compute dead time correction")
         self.declareProperty("DeadTime", 4.2, doc="Dead time in microseconds")
         self.declareProperty("TOFStep", 100,
                              doc="TOF bins to compute deadtime correction for, in microseconds")
@@ -80,7 +80,7 @@ def PyExec(self):
         if paralyzing:
             true_rate = -scipy.special.lambertw(-rate * dead_time / tof_step).real / dead_time
             corr = true_rate / (rate / tof_step)
-            # If we have no events, set the correction to 1 orderwise we will get a nan
+            # If we have no events, set the correction to 1 otherwise we will get a nan
             # from the equation above.
             corr[rate==0] = 1
         else:
diff --git a/reduction/lr_reduction/event_reduction.py b/reduction/lr_reduction/event_reduction.py
index b61e2aa..156d7d6 100644
--- a/reduction/lr_reduction/event_reduction.py
+++ b/reduction/lr_reduction/event_reduction.py
@@ -5,7 +5,6 @@
 
 import mantid.simpleapi as api
 import numpy as np
-import scipy
 
 from . import background
 from .DeadTimeCorrection import call as DeadTimeCorrection
@@ -59,6 +58,7 @@ class EventReflectivity(object):
     DEFAULT_4B_SAMPLE_DET_DISTANCE = 1.83
     DEFAULT_4B_SOURCE_DET_DISTANCE = 15.75
     DEAD_TIME = 4.2 # Nominally 4.0 microseconds
+    DEAD_TIME_TOF_STEP = 100
 
     def __init__(self, scattering_workspace, direct_workspace,
                  signal_peak, signal_bck, norm_peak, norm_bck,
@@ -234,13 +234,17 @@ 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, tof_step=100, paralyzing=False):
+    def get_dead_time_correction(self):
+        """
+            Compute dead time correction to be applied to the reflectivity curve.
+        """
         # Scattering workspace
         tof_min = self._ws_sc.getTofMin()
         tof_max = self._ws_sc.getTofMax()
 
         corr_ws = DeadTimeCorrection(InputWorkspace=self._ws_sc,
                                      DeadTime=self.DEAD_TIME,
+                                     TOFStep=self.DEAD_TIME_TOF_STEP,
                                      Paralyzable=self.paralyzable,
                                      TOFRange=[tof_min, tof_max],
                                      OutputWorkspace="corr")
@@ -250,6 +254,7 @@ def get_dead_time_correction(self, tof_step=100, paralyzing=False):
         # Direct beam workspace
         corr_ws = DeadTimeCorrection(InputWorkspace=self._ws_db,
                                      DeadTime=self.DEAD_TIME,
+                                     TOFStep=self.DEAD_TIME_TOF_STEP,
                                      Paralyzable=self.paralyzable,
                                      TOFRange=[tof_min, tof_max],
                                      OutputWorkspace="corr")
@@ -284,6 +289,7 @@ def specular(self, q_summing=False, tof_weighted=False, bck_in_q=False,
             :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()
         
diff --git a/reduction/lr_reduction/reduction_template_reader.py b/reduction/lr_reduction/reduction_template_reader.py
index 3c0f32f..049ba6a 100644
--- a/reduction/lr_reduction/reduction_template_reader.py
+++ b/reduction/lr_reduction/reduction_template_reader.py
@@ -65,6 +65,10 @@ def __init__(self):
         self.incident_medium_list = ['air']
         self.incident_medium_index_selected = 0
 
+        # Dead time correction
+        self.dead_time:bool = False
+        self.paralyzable:bool = False
+
     def from_dict(self, data_dict, permissible=True):
         r"""
         Update object's attributes with a dictionary with entries of the type  attribute_name: attribute_value.
@@ -147,6 +151,10 @@ def to_xml(self):
         _xml += "<incident_medium_list>%s</incident_medium_list>\n" % str(self.incident_medium_list[0])
         _xml += "<incident_medium_index_selected>%s</incident_medium_index_selected>\n" % str(self.incident_medium_index_selected)
 
+        # Dead time correction
+        _xml += "<dead_time_correction>%s</dead_time_correction>\n" % str(self.dead_time)
+        _xml += "<dead_time_paralyzable>%s</dead_time_paralyzable>\n" % str(self.paralyzable)
+
         _xml += "</RefLData>\n"
 
         return _xml
@@ -248,6 +256,12 @@ def from_xml_element(self, instrument_dom):
             self.incident_medium_list = ['H2O']
             self.incident_medium_index_selected = 0
 
+        # Dead time correction
+        self.dead_time = getBoolElement(instrument_dom, "dead_time_correction",
+                                        default=self.dead_time)
+        self.paralyzable = getBoolElement(instrument_dom, "dead_time_paralyzable",
+                                          default=self.paralyzable)
+
 
 ###### Utility functions to read XML content ########################
 def getText(nodelist):
diff --git a/reduction/lr_reduction/template.py b/reduction/lr_reduction/template.py
index b0b4789..2224265 100644
--- a/reduction/lr_reduction/template.py
+++ b/reduction/lr_reduction/template.py
@@ -129,7 +129,7 @@ def _value_check(key, data, reference):
 
 def process_from_template(run_number, template_path, q_summing=False, normalize=True,
                           tof_weighted=False, bck_in_q=False, clean=False, info=False,
-                          functional_background=False, dead_time=False):
+                          functional_background=False):
     """
         The clean option removes leading zeros and the drop when doing q-summing
     """
@@ -142,14 +142,13 @@ def process_from_template(run_number, template_path, q_summing=False, normalize=
     return process_from_template_ws(ws_sc, template_path, q_summing=q_summing,
                                     tof_weighted=tof_weighted, bck_in_q=bck_in_q,
                                     clean=clean, info=info, normalize=normalize,
-                                    functional_background=functional_background,
-                                    dead_time=dead_time)
+                                    functional_background=functional_background)
 
 
 def process_from_template_ws(ws_sc, template_data, q_summing=False,
                              tof_weighted=False, bck_in_q=False, clean=False,
                              info=False, normalize=True, theta_value=None, ws_db=None,
-                             functional_background=False, dead_time=False):
+                             functional_background=False):
     # Get the sequence number
     sequence_number = 1
     if ws_sc.getRun().hasProperty("sequence_number"):
@@ -223,7 +222,9 @@ def process_from_template_ws(ws_sc, template_data, q_summing=False,
                                                    signal_low_res=low_res, norm_low_res=norm_low_res,
                                                    q_min=q_min, q_step=q_step, q_max=None,
                                                    tof_range=[tof_min, tof_max],
-                                                   theta=np.abs(theta), dead_time=dead_time,
+                                                   theta=np.abs(theta),
+                                                   dead_time=template_data.dead_time,
+                                                   paralyzable=template_data.paralyzable,
                                                    functional_background=functional_background,
                                                    instrument=event_reduction.EventReflectivity.INSTRUMENT_4B)
 
diff --git a/reduction/lr_reduction/workflow.py b/reduction/lr_reduction/workflow.py
index 363510b..8249fe5 100644
--- a/reduction/lr_reduction/workflow.py
+++ b/reduction/lr_reduction/workflow.py
@@ -12,7 +12,7 @@
 
 def reduce(ws, template_file, output_dir, average_overlap=False,
            q_summing=False, bck_in_q=False, is_live=False,
-           functional_background=False, dead_time=False):
+           functional_background=False):
     """
         Function called by reduce_REFL.py, which lives in /SNS/REF_L/shared/autoreduce
         and is called by the automated reduction workflow.
@@ -32,8 +32,7 @@ def reduce(ws, template_file, output_dir, average_overlap=False,
                                                                         clean=q_summing,
                                                                         bck_in_q=bck_in_q,
                                                                         functional_background=functional_background,
-                                                                        info=True,
-                                                                        dead_time=dead_time)
+                                                                        info=True)
 
     # Save partial results
     coll = output.RunCollection()
diff --git a/reduction/notebooks/workflow.ipynb b/reduction/notebooks/workflow.ipynb
index 73da2f7..07e6f2a 100644
--- a/reduction/notebooks/workflow.ipynb
+++ b/reduction/notebooks/workflow.ipynb
@@ -9,14 +9,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T14:00:41.160719Z",
-     "iopub.status.busy": "2024-03-07T14:00:41.160296Z",
-     "iopub.status.idle": "2024-03-07T14:00:41.189068Z",
-     "shell.execute_reply": "2024-03-07T14:00:41.188511Z",
-     "shell.execute_reply.started": "2024-03-07T14:00:41.160699Z"
+     "iopub.execute_input": "2024-03-07T18:47:15.346898Z",
+     "iopub.status.busy": "2024-03-07T18:47:15.346475Z",
+     "iopub.status.idle": "2024-03-07T18:47:15.997310Z",
+     "shell.execute_reply": "2024-03-07T18:47:15.996658Z",
+     "shell.execute_reply.started": "2024-03-07T18:47:15.346876Z"
     },
     "tags": []
    },
@@ -41,14 +41,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T14:00:42.162602Z",
-     "iopub.status.busy": "2024-03-07T14:00:42.161984Z",
-     "iopub.status.idle": "2024-03-07T14:00:42.165544Z",
-     "shell.execute_reply": "2024-03-07T14:00:42.165028Z",
-     "shell.execute_reply.started": "2024-03-07T14:00:42.162584Z"
+     "iopub.execute_input": "2024-03-07T18:47:16.748753Z",
+     "iopub.status.busy": "2024-03-07T18:47:16.748380Z",
+     "iopub.status.idle": "2024-03-07T18:47:17.489202Z",
+     "shell.execute_reply": "2024-03-07T18:47:17.488585Z",
+     "shell.execute_reply.started": "2024-03-07T18:47:16.748722Z"
     },
     "tags": []
    },
@@ -69,14 +69,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T14:00:43.027342Z",
-     "iopub.status.busy": "2024-03-07T14:00:43.026880Z",
-     "iopub.status.idle": "2024-03-07T14:00:43.030004Z",
-     "shell.execute_reply": "2024-03-07T14:00:43.029464Z",
-     "shell.execute_reply.started": "2024-03-07T14:00:43.027325Z"
+     "iopub.execute_input": "2024-03-07T18:47:17.616398Z",
+     "iopub.status.busy": "2024-03-07T18:47:17.615822Z",
+     "iopub.status.idle": "2024-03-07T18:47:17.619379Z",
+     "shell.execute_reply": "2024-03-07T18:47:17.618866Z",
+     "shell.execute_reply.started": "2024-03-07T18:47:17.616377Z"
     },
     "tags": []
    },
@@ -97,14 +97,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T14:00:43.895394Z",
-     "iopub.status.busy": "2024-03-07T14:00:43.895102Z",
-     "iopub.status.idle": "2024-03-07T14:00:44.186351Z",
-     "shell.execute_reply": "2024-03-07T14:00:44.185649Z",
-     "shell.execute_reply.started": "2024-03-07T14:00:43.895378Z"
+     "iopub.execute_input": "2024-03-07T18:47:18.651068Z",
+     "iopub.status.busy": "2024-03-07T18:47:18.650512Z",
+     "iopub.status.idle": "2024-03-07T18:47:18.972007Z",
+     "shell.execute_reply": "2024-03-07T18:47:18.971380Z",
+     "shell.execute_reply.started": "2024-03-07T18:47:18.651049Z"
     },
     "tags": []
    },
@@ -120,17 +120,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.status.busy": "2024-03-07T14:00:35.270890Z",
-     "iopub.status.idle": "2024-03-07T14:00:35.271335Z",
-     "shell.execute_reply": "2024-03-07T14:00:35.271215Z",
-     "shell.execute_reply.started": "2024-03-07T14:00:35.271204Z"
+     "iopub.execute_input": "2024-03-07T18:47:19.256059Z",
+     "iopub.status.busy": "2024-03-07T18:47:19.255641Z",
+     "iopub.status.idle": "2024-03-07T18:47:34.587879Z",
+     "shell.execute_reply": "2024-03-07T18:47:34.587198Z",
+     "shell.execute_reply.started": "2024-03-07T18:47:19.256036Z"
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wl=15; ths=-0.600382; thi=-0.00812677; No offset\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Normalization options: True True\n",
+      "wl=12.386; ths=-0.600058; thi=-0.00812677; No offset\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Normalization options: True True\n",
+      "wl=9.74; ths=-0.600058; thi=-0.00812677; No offset\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Normalization options: True True\n",
+      "wl=7.043; ths=-0.599896; thi=-0.00812677; No offset\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Normalization options: True True\n",
+      "wl=4.25; ths=-0.599733; thi=-0.00812677; No offset\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Normalization options: True True\n",
+      "wl=4.25; ths=-1.18271; thi=-0.00812677; No offset\n",
+      "Background on both sides: [133 135] [148 150]\n",
+      "Normalization options: True True\n",
+      "wl=4.25; ths=-2.34284; thi=-0.00812677; No offset\n",
+      "Background on both sides: [131 133] [149 151]\n",
+      "Normalization options: True True\n",
+      "wl=4.25; ths=-4.63906; thi=-0.00812677; No offset\n",
+      "Background on both sides: [131 133] [149 151]\n",
+      "Normalization options: True True\n"
+     ]
+    }
+   ],
    "source": [
     "importlib.reload(workflow)\n",
     "importlib.reload(output)\n",
@@ -148,19 +180,19 @@
     "\n",
     "for i in range(198409, 198417):\n",
     "    ws = api.Load(\"REF_L_%s\" % i)\n",
-    "    workflow.reduce(ws, template_path, output_dir=data_dir, average_overlap=False, dead_time=True)\n"
+    "    workflow.reduce(ws, template_path, output_dir=data_dir, average_overlap=False)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T14:03:41.675586Z",
-     "iopub.status.busy": "2024-03-07T14:03:41.675238Z",
-     "iopub.status.idle": "2024-03-07T14:03:50.953438Z",
-     "shell.execute_reply": "2024-03-07T14:03:50.952865Z",
-     "shell.execute_reply.started": "2024-03-07T14:03:41.675567Z"
+     "iopub.execute_input": "2024-03-07T18:54:38.824105Z",
+     "iopub.status.busy": "2024-03-07T18:54:38.823533Z",
+     "iopub.status.idle": "2024-03-07T18:54:48.835710Z",
+     "shell.execute_reply": "2024-03-07T18:54:48.834905Z",
+     "shell.execute_reply.started": "2024-03-07T18:54:38.824083Z"
     },
     "tags": []
    },
@@ -171,20 +203,24 @@
      "text": [
       "wl=4.25; ths=-0.599733; thi=-0.00812677; No offset\n",
       "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [0.530554 -> 0.882112] at [0.0487517 -> 0.0220792]\n",
+      "Dead time correction: [0.753782 -> 0.938569] at [0.0487517 -> 0.0220792]\n",
       "Normalization options: True True\n",
+      "Template data was passed instead of a file path: template data not saved\n",
       "wl=4.25; ths=-1.18271; thi=-0.00812677; No offset\n",
       "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [0.5229 -> 0.863203] at [0.099448 -> 0.0441559]\n",
+      "Dead time correction: [0.749746 -> 0.92837] at [0.099448 -> 0.0441559]\n",
       "Normalization options: True True\n",
+      "Template data was passed instead of a file path: template data not saved\n",
       "wl=4.25; ths=-2.34284; thi=-0.00812677; No offset\n",
       "Background on both sides: [131 133] [149 151]\n",
-      "Dead time correction: [0.37933 -> 0.880062] at [0.194985 -> 0.0865754]\n",
+      "Dead time correction: [0.753723 -> 0.937186] at [0.194985 -> 0.0865754]\n",
       "Normalization options: True True\n",
+      "Template data was passed instead of a file path: template data not saved\n",
       "wl=4.25; ths=-4.63906; thi=-0.00812677; No offset\n",
       "Background on both sides: [131 133] [149 151]\n",
-      "Dead time correction: [0.522303 -> 0.839429] at [0.389949 -> 0.176604]\n",
-      "Normalization options: True True\n"
+      "Dead time correction: [0.7496 -> 0.928125] at [0.389949 -> 0.173141]\n",
+      "Normalization options: True True\n",
+      "Template data was passed instead of a file path: template data not saved\n"
      ]
     }
    ],
@@ -205,26 +241,29 @@
     "\n",
     "for i in range(198413, 198417):\n",
     "    ws = api.Load(\"REF_L_%s\" % i)\n",
-    "    workflow.reduce(ws, template_path, output_dir=data_dir, average_overlap=False, dead_time=True)"
+    "    sequence_number = ws.getRun().getProperty(\"sequence_number\").value[0]\n",
+    "    template_data = template.read_template(template_path, sequence_number)\n",
+    "    template_data.dead_time = True\n",
+    "    workflow.reduce(ws, template_data, output_dir=data_dir, average_overlap=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T14:01:02.633645Z",
-     "iopub.status.busy": "2024-03-07T14:01:02.633268Z",
-     "iopub.status.idle": "2024-03-07T14:01:03.059066Z",
-     "shell.execute_reply": "2024-03-07T14:01:03.058471Z",
-     "shell.execute_reply.started": "2024-03-07T14:01:02.633623Z"
+     "iopub.execute_input": "2024-03-07T18:54:58.331335Z",
+     "iopub.status.busy": "2024-03-07T18:54:58.330918Z",
+     "iopub.status.idle": "2024-03-07T18:54:58.757370Z",
+     "shell.execute_reply": "2024-03-07T18:54:58.756785Z",
+     "shell.execute_reply.started": "2024-03-07T18:54:58.331298Z"
     },
     "tags": []
    },
    "outputs": [
     {
      "data": {
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3b1/IajeHw4Hb7Q4ar6xPnz68+eabFBUV+cLDJUuWYLFYOProo+v2wetZsw7dKlRNNKumnBdccAEXXHBBre4VERHRLNrOHooxA1J5bcmWgBApKdrOvhCVaqGCIcMwmHXT0FoFVtEOO3arJWDc4/UC3qC5lw5O4+8fBZ9rVnUN+4qcIbe5/u3so/l87e6AMK9Tmyh2HgieO338gIB/YBqGQWykLehzVa4Qqxoydq5SaZbeKR6LYZCSEBXwue4YeTRLN+8N+L4z0hJ478aTcNgsAe/lsFn44E8nB62htmt12Cy1/r0JdV/DMLBYDN/cN5ZuJWt/CUVON+9VqvbTtlMREREREWnJqv5dqTEDN4CRI0dy0kknMXbsWKZOncoxxxxDVlYWc+bMYezYsb5tocOHD+ef//wnF154IYZhkJiYSJ8+fZg5cybPPPNMnd/3lltu4eWXX+ayyy7jrrvuIjk5mU2bNvHuu+/y8ssvExsby/XXX89dd91F27Zt6dChA/fffz8WS+D3dfrppzNjxgxOPPFEPB4Pd999d0DF2WWXXcakSZMYO3YskydPJiUlhRUrVpCamspJJ51Et27d2LJlCytXriQtLY24uLigzOb3v/89Dz30EFdffTUTJ05kz549/PnPf+bKK68MKtI60pp16JacnIzVavVVtVXIyclp9C+2KaoIkqqGSJ2qVKpVFwxVVtcQqOL9q47ZrQb/XZ5ZYzjldHm45MVlQfOuH9aDz9ftDvpcSTGOoM81ZkBqyO8m+B+sZmhb2yDrnRtODKhICxfaVdy/IhSu/F7h1lDbtdYloAt1z6pznS4PT3z+M68u2Ro0f/r4ASHXJiIiIiIiIvXHMAzmzJnD/fffz3XXXceePXvo2LEjp556akDuMWLECKZNm8bw4cN9Y6eddhorV648pEq31NRUlixZwt13383ZZ59NWVkZXbt25ZxzzvEFa//4xz8oLCzkggsuIC4ujjvvvJO8vMDjn5566imuvfZaTj31VFJTU/nnP//J8uXLfa87HA6++OIL7rzzTs4991xcLhd9+vTh2WefBWDcuHF88MEHjBgxggMHDvD6669zzTXXBLxHdHQ0c+fO5bbbbuO4444jOjqacePGMW3atDp/7vpmeEO192yiDMPgww8/ZOzYsb6xE044gcGDB/Pcc8/5xvr06cOYMWOYPHnyYb9nfn4+CQkJ5OXlER8ff9j3a2xOl8cXrHi9Xl+IBAQEQ7NuGnrEEn2ny8PFLyytcQ3VzQOO2Oeq+h2GC7LqOrcp8nq9nPfMNwHn+GWkJfDRLSe3+i7AIiIiIiLSNJSWlrJlyxa6d+9OZGTkYd2r2Omiz4NzAVj/yNl1PhdOWobq/kzVJSdq8qFbYWEhmzZtAmDgwIFMmzaNESNGkJSURJcuXZg5cyZXXnklL7zwAieddBIvvfQSL7/8MuvWraNr166H/f4tLXQLpSkEQ7VdQ2sKvJqCiqCzoNTFllyzeUTPdjF8dtup+i5FRERERKRJqM/QTQTqL3Rr8pHtjz/+yIgRI3zPK5ocXH311bzxxhuMHz+evXv38sgjj5CdnU16ejpz5sypl8CttajttsamsIa6rLUpfK7mrvK207/NWs17yzMPNmho0lm9iIiIiIiISKNr8pVuja01VLqJ1EZecTkjn17EnoIybh1xFH89+5jGXpKIiIiIiIgq3aTe1Velm/aHiUitJETbeeSCvgC8sGgzG7LzG3lFIiIiIiIiIk2XQjcRqbVR/VI4u28HXB4vN//7J854aiGLN+Y29rJEREREREREmhyFbiJSJ38a3pNoh5UtuUVs3lPEpDkbWJOZR+b+4sZemoiIiIiIiEiT0eQbKYhI0zL22aUBz9dn5zN6xmIAtk45rzGWJCIiIiIiItLkqNJNROpk+vgBIcefvrT/kV2IiIiIiIhIfXMWwcQE85ezqLFXI82cQjcRqZMxA1LJSEsIGl+8KZcFP+cwctoinfMmIiIiIiLSQLxeL3/84x9JSkrCMAxWrlzZ2EuSMBS6iUidlLu9AGSkJfD4hel0ahMFwPs/7eTW//zEppxCpnyuc95EREREREQawueff84bb7zBJ598QnZ2Nunp6Y29JAlDZ7qJSJ04bBZm3TQUu9XAMAwuP74L3e+dA0BRmRuAtTt1zpuIiIiIiEhdOZ1OHA5HtXM2b95MSkoKQ4cOPeT38Xq9uN1ubDbFQg1JlW4iUmcOmwXDMAAwDCPsOW/hxkVERERERJq8LV83+FsMHz6cW2+9lQkTJpCcnMyZZ57J+vXrOffcc4mNjaVDhw5ceeWV5OaaR/hcc801/PnPf2b79u0YhkG3bt0AM0R74okn6NGjB1FRUfTv359Zs2b53mfhwoUYhsHcuXMZMmQIERERfPPNN7W+7quvvmLIkCFER0czdOhQfvnll4DP8b///Y8hQ4YQGRlJcnIyF110ke81p9PJ3/72Nzp16kRMTAwnnHACCxcubLgvtQlR6CYih23MgFTSU+MDxjq1iWLMgNRGWpGIiIiIiLRqXq/ZCKEuv/b8Atu/999j/mOwbZk5Xpf7eL11Wuqbb76JzWZjyZIlTJkyhdNOO40BAwbw448/8vnnn7N7924uvfRSAP75z3/yyCOPkJaWRnZ2Nj/88AMAf//733n99dd5/vnnWbduHXfccQdXXHEFixYtCnivv/3tb0yePJkNGzaQkZFR6+vuv/9+nnrqKX788UdsNhvXXXed77VPP/2Uiy66iPPOO48VK1b4AroK1157LUuWLOHdd99l9erVXHLJJZxzzjls3LixTt9Tc2R4vXX809DK5Ofnk5CQQF5eHvHx8TVfINIKOV0eLn5hKQCdE6P5dE02BvD57adwTEf970ZERERERBpOaWkpW7ZsoXv37kRGRpqDziKY1EhFAPdlgSOmVlOHDx9OXl4eK1asAODBBx/ku+++Y+7cub45mZmZdO7cmV9++YWjjz6a6dOnM336dLZu3QpAUVERycnJzJ8/n5NOOsl33Q033EBxcTHvvPMOCxcuZMSIEcyePZsxY8bU+bp58+ZxxhlnADBnzhzOO+88SkpKiIyMZOjQofTo0YO333476PNt3ryZXr16kZmZSWqq//dj5MiRHH/88UyaNKmWX+qRFfLP1EF1yYm0eVdEDlvlc968XthfXMbSzfv42/treP+mk7BZVVQrIiIiIiISSuWqsOXLl7NgwQJiY2OD5m3evJmjjz46aHz9+vWUlpZy5plnBow7nU4GDhwY9r3qcl1GRobvcUpKCgA5OTl06dKFlStX8oc//CHkZ/vpp5/wer1B6y4rK6Nt27Yhr2lJFLqJSL1w2MxgzTDgqUsHcPbTX7NqxwGeXbCZ20b2auTViYiIiIhIq2KPNivO6sLrhdfOgd1r/GMp/eGaOeZfdOry3nUQE+OvivN4PIwePZqpU6cGzasIu6ryeDyAuc2zU6dOAa9FRERU+161vc5ut/seV5zvXXF9VFRUyHVVzLFarSxfvhyr1RrwWqhgsaVR6CYi9S4lIYpHx6Zz27sreWb+RtpE2/nXt9uYOLovw3olN/byRERERESkpTOMWm/x9HE5wVJpl05KfzAsYHWArfqOovVl0KBBvP/++3Tr1q3WnUX79OlDREQE27dv57TTTqv1ex3qdVVlZGTw1Vdfce211wa9NnDgQNxuNzk5OZxyyimH/B7NlUI3EWkQYwZ04n+rsvhqQw6Pf7oBp9vDlM83MDkqg8QYO2mJdfuvPyIiIiIiIg3K5oCrPoKp3czn18w5ooEbwC233MLLL7/MZZddxl133UVycjKbNm3i3Xff5eWXXw6qFgOIi4vjr3/9K3fccQcej4dhw4aRn5/P0qVLiY2N5eqrrw75Xod6XVUPPfQQZ5xxBj179uR3v/sdLpeLzz77jL/97W8cffTR/P73v+eqq67iqaeeYuDAgeTm5jJ//nz69evHueeee1jfV1On0E1EGsxXG3IAcLrNsuO1O/MZPWMxAFunnNdo6xIREREREQnJWilgM4wjGrgBpKamsmTJEu6++27OPvtsysrK6Nq1K+eccw4WS/izsh999FHat2/P5MmT+e2332jTpg2DBg3ivvvuq/b9DvW6yoYPH857773Ho48+ypQpU4iPj+fUU0/1vf7666/z2GOPceedd7Jz507atm3LSSed1OIDN1D30hqpe6nIoZu9Yie3z1wZNP7n04/i+O5JPPzxem05FRERERGRw1Jdp8k6q9z1tA5dSKVlqa/upWopKCINZsyAVNJTg/8h9H/zN3Hlq9+zKaeQSXPWsyYzj8z9xY2wQhEREREREZGGodBNRBpMuduLxWKQkZbA4xemc0yHuKA567MLGD1jMcOmLgBg8cZcRk5bxOKNuUd6uSIiIiIi0to5YmBinvlLVW5ymHSmm4g0GIfNwqybhmK3GhiGweXHd+H95Zn8ddbqoLnRDgt//3ANy7fvZ1NOoZouiIiIiIiISLOmSjcRaVAOmwXDMAAwDINxg9PISEsImGO3GhQ7Pbz93XY2ZBcA/qYLFRVwIiIiIiIiIs2JQjcROaLK3WbvlootpxlpCRzbMY7Lj+8ccv708QOO4OpERERERKS5Up9IqS/19WdJ20tF5IgKteW03O3FbjVYnZnH2qx839xoh5WTj2ob8j6LN+Yy8eN16n4qIiIiItLK2e12AIqLi4mKimrk1UhL4HQ6AbBarYd1H4VuInLEOWz+IlvDMHDYDJwuj6/pwqAuiby1bCvFTjdjZizh1jOO4rXFW5k4ui/dkqPJzivhile/A+DxOet5Ylx/nf0mIiIiItJKWa1W2rRpQ05ODgDR0dG+I25E6srj8bBnzx6io6Ox2Q4vNjO8qr+sVn5+PgkJCeTl5REfH9/YyxFp0Zwuj68CbuPuAq557Tt25pX5Xu+TEs/67Pyw12+dct6RWKaIiIiIiDQxXq+XXbt2ceDAgcZeirQAFouF7t2743A4gl6rS06kSjcRaTIqV8D16hAXELgB1QZu/7g4o8HWJSIiIiIiTZthGKSkpNC+fXvKy8sbeznSzDkcDiyWw2+DoNBNRJqs6eMHcPvMlUHjHeMj2JUfGMh9ujqLCwakEmE7vD33IiIiIiLSfFmt1sM+h0ukvqh7qYg0WWMGpJKeGlyuuyu/DLvVPKMhwmbBABb+msvNb//Egp93M3LaIhZvzPXNX7wxN2hMREREREREpCEpdBORJqvc7fU1V3j8wnT6pMQRE2H1vQYQE2HloQv6YLfAVz/n8Jd3V7Ipp5DJn21g1vIdvLBoM1e8+h2bcgp5fM561mTmkbm/uDE/loiIiIiIiLQCaqRQAzVSEGlclZsreL1eut87p17uu3XKeSzemMvEj9cxcXRfhvVKrpf7ioiIiIiISMtVl5xIlW4i0qQ5bBZfu2/DMJg+fkDIeVec0KXW97QYcMObPzDx43VsyilkyucbVAEnIiIiIiIi9Uqhm4g0K6HOectIS+CRMX2DxvulxpPRKSFgLMpuxeOFeRty2JRTCMDanfmMnrGYYVMXNOziRUREREREpNVQ6CYizUrVc94y0sxQrdjpCRr3GuDFGzDWq0Mst444KuS9p13a/0h+FBEREREREWnBdKZbDXSmm0jTU/Wct3K3F4fNEnIcCBqzWw3GPLuE1Zl5Afcd3KUNlw7pzMuLt+icNxEREREREQmiM91EpEWres6bw2YJOx5qrCKMy0hL4NEx6aQmRGIAy7cf4O4P1rApp5BHP1mnc95ERERERETkkKnSrQaqdBNpmerSFXXL5HNZsmmvOp2KiIiIiIi0cqp0ExGpQW27ogKMeGohV7z6HZtyCpk0x9/pdPHGXEZOW8TijblHaNUiIiIiIiLSXKjSrQaqdBNpHbxeb9A5b4nRdvYXl4e95ugOsfy6u5D0TvFMvjCDxBg7aYnRR2K5IiIiIiIi0ghU6SYiUkeVz3mr6HTaOSmaRy7oG/aaX3cXArB2Zz6jZyxm2NQFR2StIiIiIiIi0vTZGnsBIiJNgcNmYdZNQ33nvF1+fBdfp9NZP2UGVMDFRVgpKHMH3ePSIWks+Hk3j8/5WWe/iYiIiIiItHKqdBMROaimTqcVFXDdkmPomxpcRvzfHzO59o0fQ3Y/1flvIiIiIiIirYvOdKuBznQTkaqdTovK3Fz+yrcAjD+uM//5bjtrs/LDXv/mtcfxjy9+Ye3OfJ3/JiIiIiIi0ozVJSdS6FYDhW4iEkrVIO795Zn8ddbqOt3j7etPYOLH67QVVUREREREpJlQIwURkQZWdSvquMFpZKQlBMxJSYikS1JUyOs7xkdyxavfsSmnkElz1gdsRRUREREREZHmT6GbiEg9CHX2W7u4CL684zSO6RAXNH9Xfqnv8frsAnU/FRERERERaWHUvVREpB6E634KEGG3kJGWwPjjOjPz+x3sKSwjO6806B69U+L4eVc+uQXOoG2nizfmaiuqiIiIiIhIM6Iz3WqgM91E5HBVPf/N6fJwyYvLWJ2ZV+11XZOi+f0JXYiOsPH2t9v4eVdBUCMGhXEiIiIiIiJHjhop1COFbiJS35wuDxe/sBQwu5/O/GEHZeUeftldUKf73DGyFx+vzmJTTpG6ooqIiIiIiBwBCt3qkUI3EWkIVavfyt1e5qzJ5vaZKw/73lunnHf4CxQREREREZEg6l4qItLEVe1+6rBZGDMgNagDar9O8aSnBv6DPDUhkpN6JIW87/TxAxpkvSIiIiIiIlI3Ct1ERJqIUB1QvYBhBI4lx0XwxrXHBwV0BpBfWs43G/cwctoiFm/MPfIfQkRERERERABtL62RtpeKyJEUatspEHKs4ly4C/qn8sxXG8kvdQGQEGUnr6Rc57yJiIiIiIjUM53pVo8UuolIU1U5oPN4PPS477Owc9++/gR1ORURERERETlMOtNNRKQVqHwunMViCXue24DObXjkk3VsyilkyucbWJOZR+b+YgAWb8zVVlQREREREZEGoNBNRKSFGDMgNajpAsDKHQf4dXchAGt35jN6xmKGTV3Amsw8pny+ISiMUxAnIiIiIiJy+LS9tAbaXioizYXT5fGd8zb+uM7M/H4H2Xml7Cksq9N90jvFs3Znfq3OhFu8MVfbVkVEREREpNXQmW71SKGbiDQnVRsxOF0exj2/lLVZ+b45DquB0123f/RvnXJewPPM/cXsLyrn3g9X1zqgExERERERae7qkhPZjtCaRETkCHDY/KcGGIYZvlksBhlpCWb12w87AHjt6iFc8sIytuwtrjQfQv1nmGmX9g8aGzZ1QcDzim2rEBzQiYiIiIiItEYK3UREWjCHzcKsm4b6qt8uP74L5Qer3OKi7AFhnNfrpdzt5eddBQH3mPnDdo7rlsS2vcVM/Hgd1wzt5tuCWlW4Zg4iIiIiIiKtjbaX1kDbS0Wkpaq6FbWozM3lr3wLwPghnXlu4Way8krweiHSZqHU5anxnveOOoY+qQk8/PF6nfMmIiIiIiItjs50q0cK3USkNakaxG3eU8jIaV+HnZ+RlsCFAzrxz682cqCkHID4SBv5pa6gc97UdEFERERERJo7hW71SKGbiLR2H/yUyYT/rgoaf/LiDMYNTsMwDDweDz3u+yzsPd676UQuecGsouuTEs/UcWq6ICIiIiIizU9dciJLta+KiEird+HATqSnBv6fSUZagi9wA7BYLNWe51YRuAGszzabLgybuoDFG3MZOW0RizfmNsjaRUREREREGotCNxERqVa52+vrgPr4helkpCX4xisbMyA1ZDj3dIjupwB3nnk0Uz7fwKacQqZ8voE1mXlk7i8OOVdERERERKS50fbSGmh7qYhI8Flv5W4vDpslaM7FLyxl5/4S9hY56RAXQYeESN678SRGTlvEjv0ltXqvrVPOa4iPICIiIiIicti0vbSSHTt2MHz4cPr06UNGRgbvvfdeYy9JRKTZcdgsvq2khmEEBW4Vc2bdNJRR6R0BGH9cZ2bdNBTDMHxVcdEOKxlpCXROigr5PtVtURUREREREWlOWnzoZrPZmD59OuvXr2fevHnccccdFBUVNfayRERapFDhnMNm4ey+ZhAXabfy0S0nM++O00JuRR0zIPWIr1lERERERKQh2Bp7AQ0tJSWFlJQUANq3b09SUhL79u0jJiamkVcmItJ65BaV+R4bhrlN1WIxMAAv0OVg5Zu5bdVonEWKiIiIiIjUo0avdPv6668ZPXo0qampGIbB7Nmzg+Y899xzdO/encjISAYPHsw333xzSO/1448/4vF46Ny582GuWkRE6mJPflnAc4fNwmvXHEfFoaJ/PesYZt00NOS2VRERERERkeao0f92U1RURP/+/ZkxY0bI12fOnMntt9/O/fffz4oVKzjllFMYNWoU27dv980ZPHgw6enpQb+ysrJ8c/bu3ctVV13FSy+91OCfSUREAu0uKA0a21/k9D0Od06ciIiIiIhIc9Xo20tHjRrFqFGjwr4+bdo0rr/+em644QYApk+fzty5c3n++eeZPHkyAMuXL6/2PcrKyrjwwgu59957GTp0aI1zy8r8FRn5+fm1/SgiIhKC1+slp0qlG8DuEGMiIiIiIiItRZMuK3A6nSxfvpyzzjorYPyss85i6dKltbqH1+vlmmuu4fTTT+fKK6+scf7kyZNJSEjw/dJWVBGRw1NY5qKk3B00nhOi+k1ERERERKSlaNKhW25uLm63mw4dOgSMd+jQgV27dtXqHkuWLGHmzJnMnj2bAQMGMGDAANasWRN2/r333kteXp7v144dOw7rM4iItHbhKtpU6SYiIiIiIi1Zo28vrQ3DCOxk5/V6g8bCGTZsGB6Pp9bvFRERQURERJ3WJyIi4YWraNudr0o3ERERERFpuZp0pVtycjJWqzWoqi0nJyeo+k1ERJqmUOe5AewpUKWbiIiIiIi0XE06dHM4HAwePJgvv/wyYPzLL7+ssSGCiIg0Dap0ExERERGR1qjRt5cWFhayadMm3/MtW7awcuVKkpKS6NKlCxMmTODKK69kyJAhnHTSSbz00kts376dm266qRFXLSIitRWu0m23GimIiIiIiEgL1uih248//siIESN8zydMmADA1VdfzRtvvMH48ePZu3cvjzzyCNnZ2aSnpzNnzhy6du3aWEsWEZE62B1iG6nX6w0bxomIiIiIiLQEjR66DR8+HK/XW+2cm2++mZtvvvkIrUhEROpTTohtpPklLspctW9yIyIiIiIi0tw06TPdRESk+QvVMEFbS0VEREREpKVT6CYiIg0qVMMEbS0VEREREZGWTqGbiIg0mMIyF0VOd9C4OpeKiIiIiEhLp9BNREQaTKjz3EDbS0VEREREpOVT6CYiIg0mJ8R5bqDtpSIiIiIi0vIpdBMRkQZTEbq1jXFUGVelm4iIiIiItGwK3UREpMFUbC9tFxcRML5blW4iIiIiItLCKXQTEZEGU1Hp1iE+ssq4GcY5rPq/IRERERERaZn0tx0REWkwFV1K21eqdPN6vb5Kt/bxESGvExERERERae4UuomISIPJCRGu5Ze4cLo8QPC2UxERERERkZZCoZuIiDSYim2klbeX7j441ibaToRN/zckIiIiIiItk/62IyIiDcZX6Vapoq1iy2mHuMiQ14iIiIiIiLQECt1ERKRBlJa7KShzAdCuUsAWasupiIiIiIhIS6PQTUREGkRF59Iou5W4SJtvvGJ7aXtVuomIiIiISAum0E1ERBqEr3NpfARGpfGKSrcOqnQTEREREZEWTKGbiIg0iIpKt6pnt+X4Kt0UuomIiIiISMul0E1ERBpERaVbuyoVbbt9lW7aXioiIiIiIi2XQjcREWkQBaVmE4WqFW2Vt52KiIiIiIi0VArdRESkQVWuaPN6vb5tp2qkICIiIiIiLZmt5ikiIiKHrnKlW0GpC5fHa46r0k1ERERERFowVbqJiEiDqlzpVhG4tYm2E2GzNtaSREREREREGpxCNxERaVChupRW7WgqIiIiIiLS0ih0ExGRBhXq7DZtLRURERERkZZOoZuIiDSYCJuF+Kjg40PVREFERERERFo6hW4iItJg2sdHYBhG0HgHVbqJiIiIiEgLp9BNREQaTLiKtlDnvImIiIiIiLQkCt1ERKTBhKtoq9zRVEREREREpCVS6CYiIg0mbKWbQjcREREREWnhFLqJiEiDCdelVNtLRURERESkpVPoJiIiDSZ8pZtCNxERERERadkUuomISIMJVdGWGG0nwmZthNWIiIiIiIgcOQrdRESkwYRqmBCu+k1ERERERKQlUegmIiINJlSlm7aWioiIiIhIa6DQTURE6lWPgh/40nEXw23raBNtD3pdlW4iIiIiItIaKHQTEZH6cWA7ZK3gvMyn6WXZyd/tb2NkrzTHgZMta/jScRfHe1c17jpFRERERESOAFtjL0BERFqI6f0AaH/w6VHebfDScABizn6Ze2zv0suyk8TM5yCrH0S3bZx1ioiIiIiIHAGqdBMRkXqx9+wZYV9LnfsH+lm2AJBcsMEM4w6GdCFtXgAzjjd/ioiIiIiINEMK3UREpF4M/iiRVZ4eAWOZnrb87EkLfcHoZ0gv+4kvHXeRnLPUHDuwHbYtgzl/g9xfYN5EyFrh26IqIiIiIiLSXBher9fb2ItoyvLz80lISCAvL4/4+PjGXo6ISJP10fItdPvoIgBmukcw3mpWqa04410u/OlqEvI2BMz32GI54HGQ5NlHYUwXYo8aCqveDf8GE/MabO0iIiIiIiK1UZecSKFbDRS6iYjUjtfrZdyMhfy0swgwAC+DOsWwZmc+sxwTAfiv+zT+aP2Urpacut08/WLIuBS+eABGTYWeI/yvbV4An90dPC4iIiIiIlLPFLrVI4VuIiK143R5uPgFc5vo+OM6M/OHHQBcdWJX7p31E+VYqQjjLrYs4knHS0H3KEy/AuvOH4na/3PwG1hs4HFBQmc47nqwRkBUIiz9J+RsgJQBMHq62aBh72YFcSIiIiIiUu8UutUjhW4iIrXndHmwWw0Mw8Dr9VLu9mK3Gox5dgmrM/3bQ4/tEMsH9r8TnbvaN5af1I/BWX9lluNhoGKL6nz6H2zAUCcpAyB7ZWAQ16bLYX02ERERERGRuuREtiO0JhERaQUcNn9/HsMwcNgMnC4PABlpCb4KuA2Ze9noKAR6+M9/yzW3pV7snOirinvHfTrjLN/wlOOF4DeLSoSS/aEXkr3S//Ol4ebjK2er+k1ERERERI4YVbrVQJVuIiKHr2oF3PvLM4O2nNpxkxAbTbnbS15JeaWrvXwV9zA9y3/1jRQnZ7Dvss9Ie/98s7tpBcMGXlfwAiLiAAuU5UFSDzhvGrTtaVa/6Uw4ERERERGppbrkRJZqXxUREakHDpsFwzAAswJu3OA0eqe1xQzcAAyOSU1iyd2n07VtNBlpCdw76liSYhzYcVNY5mKVpwf3lV/PKk8PNuYUctY/5pJX4uJXay/uK7+eX629KE7qjbPtscELKCswAzeAfb/Bv8bC9H7wxnkw+ybI/QXmPWQGeAe2m0HcjOPNnyIiIiIiIodAlW41UKWbiEj9C9d0YdZNQwECquJmLNjIM19sCKqKK8eGHVfAeDRl/MfxGD2SY3il+BRuiP6GOHce5G2v2wJT+kP2Kp0JJyIiIiIiAdRIoR4pdBMRaRihmi5UPhOugtfrZcyMJaze6W/EEOOwEmGzsK+4PGh+vw6RFJQbbN1XQnpqHFMuOJajP7sYx+5V/kkd+0OvkfDNU7Vf8MS8mueIiIiIiEiLpu2lIiLS5FXdchoqcAMod3vBMBsxPH5hOhlpCfRsH8uye88go1NC0Pw1u0vZuq8EgLVZBVz4wjI2ZBdQkNSPpyNvpiCpH1gscOrdkDow8GJ7dOjFnnwbeL2ht51qK6qIiIiIiISgSrcaqNJNRKTxhaqKAwK3qH6/g135peQUlAVdb8eF1W6ntNxLl8RIHji3F8d2iKfz7LEUlLr8W1EjrOD1wK7VwYtI6Gy+lr8T2veB0+4GWwQsnGJ2Sa28FXXvZjVnEBERERFpgbS9tB4pdBMRabqqhnFOl4dLXlzG6szabQVN7xBBVr6bfSUu0lPimDq6J73mXobDaoFBV8H3L0PRHvNXXbTpCge2QYd0uGAGxLRVp1QRERERkRZAoVs9UugmItJ8hGrQsL/YyY6D201rw46L168bysRP1jPx/D4M65EAq/4DH//l8BbX7VTY8zMU5UDHDLjgGTVoEBERERFpZhS61SOFbiIizUttqt/6dUrg3H4dmfr5LyHvERdpo6DURXpqPJMvyiAx2kbarPMga4V/UocM8LohZ51/LCIeyvLrtuArPoTP76ld9Zsq5UREREREGpUaKYiISKtVtUFDxePKjRgMA647uTsZacGNGAAKSl0ArM3KZ/SMxYx4Yp45HtCMwQCr3WzGcP7T5s+kHubZbpW17wMn3hp+we9cCrm/wOf3mqHege3Bcw5sN1+b95A5d97E8HNFRERERKRJUKVbDVTpJiLS/NWqEUM1W1GTYhxcfXwKX/y8j3XZBaSnxjFlTG/axNjZuq/cvxW1axS8eX5gc4ZIG1w7F14/O7BSrjr374Lt35pVbWc+DP/5Xfi5Dx2A3xaqAk5ERERE5AjQ9tJ6pNBNRKTlqs1WVKsB7mr+n7JHcgy/5RbRNzWeCSOPBo+Tp+dvYW1WYDiX9sFY84JBV8FPb0HxPrPZQij2WLBHQPFewACqWUBMO3+jhw7pMGaGOqiKiIiIiDQQhW71SKGbiEjrEaoRg9vjpVvbGD5dk31Y99762Jks/i3PXxXXPT64+i0uBQpq+T41nR/XsT/sWmVudx09XU0bRERERETqgUK3eqTQTUSkdQm1FdVuNRg9YzFrd/pDrii7hZJyT63uabPAMR3j2Z1fSm6hk/TUeKaMOZaj54zDYbX4q98AjrsBProl+CZtukF0YuDcwdfAx7fV/sNNzKt5joiIiIiIhKVGCiIiIoeoaiMGh81CuduLxTACmjEc1T6Wfp0CGzH0ah9D97bRAWMWA1weWJeVT26hEzAbNJz//Pf03XYni4e/x8ive7J4+Htw3Rcw4PdmU4bKUgfCrd8Hzx10dfBcR2zoD3bhi+E/9OYFMON486eIiIiIiNQLVbrVQJVuIiICwRVwRWVuLn/lW8C/FdXj9WJghnUVY3jh7PQO/GPuryHvG+2wUux00yclnqnjMkiM9Aad/+Z0e/j13Pe5538/s3ZnPumd4pl8Yei5eL3gdcOuNYFvlHYCDLwclj3nP+ftwHbz3LiPb4fslYFbUcOdCbd5gc6KExEREZFWS9tL65FCNxERCSdcV9RQ21PHPLskoEFDdaqe/3bta0spx1aruRUdVAGzam7x05CfRUAzhpj20Pk4+PnT8IuIT4P8TGjbE4bfC1aHGcbNvQ+yVymgExEREZFWSaFbPVLoJiIihytUg4b9xU527CsJOX9I1zbsKXSybW8xbaLtOF0eip3uoHmnHJXMZSd05rmFm4Mr4NomgGGYlW8Pt2nIjwfJR0Pur2b31LMngdsJ9kj4/D7YtVrNHERERESkxVDoVo8UuomISH2oWhXndHm45MVlAdVvcRE2Cspc9fJ+b19/AhM/XsfE0X0ZVjIfPvhD8KQBV8D2pbDvN/9YZBsoPRDijgYB1XKH6srZta9+U6WciIiIiDQxaqQgIiLSxFRt0FDxuHJzhu7tYrjnnGNCXp+WGOWbm54aT+ekKHqnxIWce1S7WP72/io25RQy5fMNrEk8C2eH/oGTUgfC+dMgMuHg46fNn4ndzMq0qnMf3Bc8Hq5pQzidT4TP74XcX2DeRMhaYZ4rV7WRw4HtsHMFfHF/8Nxw1AxCRERERJoYVbrVQJVuIiLSUEKdCRfq/LeMtATeu/EkX3BXee75zyxmXXZ+te9jx8Usx0T6pyX4Gy6A2QEVwGr3b0V1FvnPhKs89+pPgscr/hUie6X/zVIHwrVz4bWzAsdrIy4VOg2Gnz8OP6dqpVx1zSDqspVVVXUiIiIiUgvaXlqPFLqJiMiRFOr8N4BZNw3FYbNUO/fd73eQU1DK7vyyoPuO7ZfMBYO6MumzX8yGCz0SwOYAYPHGXP9W1F7J4HIGBnHucnNu1fHaBnTL3zKbMhTtObwvp01X8+eBbeY5chnjYf6j4edPrEXjigPb4cAOeONc83nHDLjgGZ0/JyIiIiIhKXQLobi4mN69e3PJJZfw5JNP1vo6hW4iInKkhaqAqxq4hZsb6qy4CnaLQbnHS/fkaP5wSg9cHi8RNgsvLvqN33KLSE+NZ/JFGSTG2NmaWxwYxB10yAGdywmvnWl2Pq0QEQ9lIar0Bl8P25fAnp8P/Uu86GXIuLTmeRMTqnmtdt1mRURERKT1qEtOZDtCa2p0jz/+OCeccEJjL0NERKRGlQM2wzBw2Ixaz618VlxF9duanWZ4VO4x/zvbltxi7vtwbdC91mblM3rG4oCxB2av4ZGx6UTarETarUz5fIPvrLjJUWZAl5boqFiAr3rO97Ni3DDAsJjbT2vanjpqMrx2duDcwhzI3xn8BXQZCvu3QEG2f8xqN6viatoyWrIf2vaCvRuDX7vo5eAxEREREZE6aBWVbhs3buSee+5h9OjRrF27VpVuIiLSolWtfnt/eSZ/nbU6aF5cpI2C0sPvlhrQKbUuVXHhtqeGOmsuVKVc6kC4bq4Z0AH0GA7LngW3EzAgrqMZxlU+523vZjOIO/Uu+OYp2LPBDAO9Hv997THwty1gjzjs70ZEREREWpZm1b3066+/ZvTo0aSmpmIYBrNnzw6a89xzz9G9e3ciIyMZPHgw33zzTZ3e469//SuTJ0+upxWLiIg0bVU7pY4bnEZGWuA2yoy0BFY9eGbQeLtYB3UR47ByxavfsSmnkDvfW8n0eb/ywsJN/PfHHTzyyTp/B9XMPDILXGaIZi4MImLNgO0PC2DIdebP674wq+RsjsC5lSvlKjqtmi/67zFyIvx108Fxr7/6LXslvDQcpveDeQ+ZHVFn32QGbtHJ5vlwqQNhxH1gWKG8CObeG/5Dq1OqiIiIiNRCo28vLSoqon///lx77bWMGzcu6PWZM2dy++2389xzz3HyySfz4osvMmrUKNavX0+XLuYBx4MHD6asLPjQ6C+++IIffviBo48+mqOPPpqlS5fWuJ6ysrKAe+XnV98RTkREpKkrd5tF7RVbTiuaMxQ7PUHjXq+XjglRvi2pAP06xfP0pf25/q3lbNtbHHDvIqfb93h3fhnT5wVv1Vy7079tNagqruo21ErPgyrlrvvCX/02+Fr/+XGVRSXAhS/Bh38M/WVUVMp5Dlb4FefChA3++3bIgHd/Bz++Co5Y+PXzwE6peTvhX2PNa798EKKeCaygq7qVVV1RRURERFqtJrW91DAMPvzwQ8aOHesbO+GEExg0aBDPP/+8b6x3796MHTu2VtVr9957L2+//TZWq5XCwkLKy8u58847efDBB0POnzhxIg8//HDQuLaXiohIcxauOUPV8aIyN5e/8i0Q2D31nRtODBrfV+gk80BJ0HslRdvZV1weNH5ct0TyS1z8sruA9E7xTL4wdNOGzP3F7C8q594PV7N2Z37A3LTE6Jo/rNcLL50WuBXV6ji47bSKUA0XPrsHvnseMAAvdOwHA34Pn98T/j1TBpgVdRVbWV1l5jpeP8d8XV1RRURERFqEZtu9tGro5nQ6iY6O5r333uPCCy/0zbvttttYuXIlixYtqtP933jjjRrPdAtV6da5c2eFbiIi0mrUNqAL1Sk1Iy2B2TcPZexzS0N2UA0lvVO8L1ybOLovF7+wLOzcrVPOCzkeUBXXPR5eO8t8oXLTBo8bdq/xX5Q60NyWWrGNtUJ1HU3rg7qiioiIiDRbzepMt+rk5ubidrvp0KFDwHiHDh3YtWtXg7xnREQE8fHxAb9ERERak6pnwlV0SK06XrlT6uMXpvvOh6u8bfXxC9Pp1ymBttWcFbd2Z77vZ3WB27RL+7N4Yy4jpy1i8cZcADL3F7MmMy+gq+qaXSVkXjQ78Ky4az4Fqy34TDi3WZEXcN9wnUu7DjO7olZmtYee2/+y0OMXvRz6TLhw58Tp/DgRERGRZqvRz3SrDaPKf4H2er1BY7VxzTXX1NOKRERExGGzMOumob7qt8uP7+Kriqs67nR5GPf8UtZm+c9KtRrgDlFvH2W3YDGMgPPiAJ75aiNeL2zbV8ykORu4blh3/vreqoA5lc+P81XFVW7aUOVMuMwCF/uLSgJCu8ljz+KYdhk49lTq+Jo6EK78wOyUGp0UWEHndkLO+sC5ofYRtO8L8Wnwxijz+acTYNgEcMTArGvNsYpz4lxlYIuAeRPNxg/zJkJUG21PFREREWlGDjl027FjB1u3bqW4uJh27drRt29fIiIi6nNtJCcnY7Vag6racnJygqrfRERE5MirqIKDiqo4I+S4YRhYLEZQ0wany8Mvuwt9c3unxPHhn4Zy6Uvm+XFjBqTy0qLf2F1QxtZKTRzWZ+cHBW6VTR8/IHgwRNOGYVO/DJiydmc+Fz27iFmOQvqnDfSHa+ZFwcGdswjePB8sNrM5Q7tjwVkMq//jv2nnE8FdBlkr/IEbwL7f4H+3Bq5x12qz02pVFR1YQdtTRURERJqJOoVu27Zt44UXXuA///kPO3aY/7JcweFwcMopp/DHP/6RcePGYbEc/s5Vh8PB4MGD+fLLLwPOdPvyyy8ZM2bMYd9fREREjoxQ1W8VTRuqdlW1WALnXndyd/61bBsP/m9d0H2Pah/D/qJy9hb5myTERdgYfky7Wq3rodF9ePjj9QFj5djYOuYD+g/qVn2n1MoVdE+nQ9Fus3PqnDsPvm4DrwvaHQ2jn4GV/4GP/lS7L2zI9WYH1arCbX0VERERkSan1snYbbfdRr9+/di4cSOPPPII69atIy8vD6fTya5du5gzZw7Dhg3jgQceICMjgx9++KFW9y0sLGTlypWsXLkSgC1btrBy5Uq2b98OwIQJE3jllVd47bXX2LBhA3fccQfbt2/npptuqvunFRERkUZT9Uy42Egbs24ayke3nMzvT+jKR7eczKybhuKwWYLmXnlSV9JTA89ZzUhL4NM/n0KnxCgy0hK4ZHAaBlBQ5mLMjMW8+/32gPPfqlq6KZdnvtoYNN47JY4LKgI3cwHBgVtlNod/7roPIPMHsEfD4Kv9cwwDBlwGHdIDr00ZAO16B46lDoTIxOD3SR0I/S4Jvw4RERERaVJqXenmcDjYvHkz7doF/5fj9u3bc/rpp3P66afz0EMPMWfOHLZt28Zxxx1X431//PFHRowY4Xs+YcIEAK6++mreeOMNxo8fz969e3nkkUfIzs4mPT2dOXPm0LVr13C3FBERkWYi3PbUqsrd3qDtqRXXVK6Ku+qkrlz/5o9s21fCPR+YnUof/ngdT13an6QYB1tzi5n48VqO65bEzB924PGaZ8h1SYrhQImT3fllbN9bTInTTXTEIZzC8e1z5s9hE6DqR3GXg1Hpv3emDAC85tltUYlQsh/iO0HRHlhcpdN6Sn//PaoLAEVERESkyTC8lfeISpC6tIIVERGRhuN0eXzhmtfr9TVtqKrbPZ/W+p6j0jsydVwGcZE2tuQWMfr/FlPkdHPz8J787ZxjQ16zeGMuEz9ex8TRfRnWK9kcnNINSvebjxO6wK3fw7IZMP8xiEyAS96EniPMQO0fR5nz7s/xV8i9PQ62fm02W8g5uI2211mw8QtI7AZ/WanATURERKQJqEtOdPgHr4mIiIgcAVW3nIYK3CBME4UwPlu7i/goO4Zh0KNdLFMvzgDguYWbWfBzTsDczP3FrMnMC+h0+vOGdez+eRmesgL/xOOug+xVkJ9tPi/No+DTv5uNFAor3dMwoHCXGbLtMqvyfIFbv0vh1LsC5ypwExEREWlWDqnSbeDAgb5/6a3JTz/9VOdFNSWqdBMREWlevF4vY55dwupMf5fPDvER7M4vC5o7ffwAxg7sFDD24EdreWvZNmIirLSLjeCxsf0Y1LUNfR6cG3T91sjLD32hl8+Cdy4O//r18+DVkWCxw+/fMyvlRERERKRRNXil2znnnMPmzZuJiIhg+PDhDB8+nMjISDZv3sxZZ53FmDFjfL9EREREjqRyt/nfEzPSEnj8wnQy0hJoHxdBv04JAfMy0hIYMyA16PprhnajR3IMRWVutu4t5oa3fqDfQ8GBG8BtzptDjr/lGhlyfIcn2f9k/qMw4v7QH+KcKZD7q/nYUw5z7zMr5Q5sh80LYMbx5s/KQo2HmysiIiIiDe6QKt1uuOEGUlJSePTRRwPGH3roIXbs2MFrr71WbwtsbKp0ExERaX6qnv9WVObm8le+BQhoxFDRLbWy6s6ES4iykVfiqjTi5SPHA/S3/OYbWeXpwRjnI3zkeDBgvNZSB5oBWzjxnSB/J7TvA6P/CV6P2Yzh49she6XZoGH4PebchVP8Y6OnQ3RbaNPFDOE+uxtGTVUFnYiIiEgd1CUnOqTQLSEhgR9//JFevXoFjG/cuJEhQ4aQl5cX5srmR6GbiIhIy1DbRgyzV+zk9pkrg8b/Ma4f//puO+AP7jZk7mWWYyIAM90jGG81K8ouc/6d/zgeCxhP5gCdLPuC7lsck8b2kkjeKjuNqyIW0SUpmpKB19H2y9vr54NXddlMWDjJPHeuchi3d3NwEBcunFNoJyIiIq1Ug4duHTt2ZPLkyVx77bUB46+//jr33HMPu3fvrustmyyFbiIiIq1LqDPhMtIS+OiWkyl3ewOCu5k/7OCBD1ZQjhUwAC923IzK6Mxnq3dUGXcxy/FwUFXcxc6HKMcWcH05VtZ2mkrs3tW+uc6o9jhKAps71Ks2XeHANkg+Bob+GRyxsOTpwHDOVRZcVVe5gi4UBXQiIiLSgtQlJ7Idyhvcfvvt/OlPf2L58uWceOKJAHz77be89tprPPjgg4dySxEREZEmofKZcJW3olatjDMMg/HHdebf321nzc6KgM7g2E5tmf67AWzOLWJdVr5v3PxlBm2Vq+IqvwYG5diw42LznkKg0twiLwbdybBs8S+2YwaMfQHeuwr2bvKPJ3YHwwL7NvvHItuALQoKs0N/8APbzJ+5v8D/bg18LXslvDQ8+JrK41fODgzXcn6GHd/D109A3g748kGIekZVdSIiItJqHFKlG8B///tf/vnPf7JhwwYAevfuzW233call15arwtsbKp0ExERaX1quxXV6fJw8QtLgcCz4t654UT/GXJDOvPG0q1kHSjG6XQGVcWVh/lvoHZcAXOjKeM/jseItFt4s/Q0ron8mrTEKPZd8j5pH11KQamLV4pP4Ybob4iLsAAGBWVu/1ikDa6dC6+dDdmVzoyLiIey/JBrqLWELuAqhaIciEo071kR4oWS1NMMBJOPgUFXmRV0K96G/b9B254w4gEzIIxtB59OCK6qCxXaiYiIiBwBDb69tDVR6CYiIiLVCRfQVR0vLHVx/KSvKCl3+66NOPh6qcvjG4uyW5lwZi8en/Nz0HtVDeIqQrsnL+rNq0t2sGF3IekpcUw4vTteA56ev4W1WQWkp8YxZUxv2sTYSftgbJWAzmrePHul/41SBgBec2tphaQecPIdsGiK2cihMcV2gMLd0CEdxsyofntrOKqgExERkUPQJEI3r9eLYRg1T2ziFLqJiIhIfXC6PFz0/BIM4HfHd+Hd77fj9noxAIth+Ma8wH9uOJHjJ82jpNwfxlkNcNfDv7V9dOMQ7v/4F18YN/X8nvSaexll5Z6gSjkMw6xE++kt8+KrP4E3zzcfV4wX7IaCrOA3GvUPWPVOYCfWqLZQsvfwP0QoDx2A3xbWvG31wHYo3hv6XLq6bHsNRUGeiIhIi9cgoVvv3r154IEHuPjii3E4HGHnbdy4kWnTptG1a1fuueeeuq28CVLoJiIiIvUlVFUcEHKsakDn8XqxGJZK58dBYrSdxGg7v+UWH9a67Lg4JjUxqCpu675yJn6ynonn92FYjwSwOcDlZPFvef7x7vHw+tmB4VrqQLjO3Mpaq6o6wwi+/tov4JXTYfca/3hUEpQEd4AFIKEzeNxmAJjUA/pfBobVDP72boL4NOhxKqx8J/wX0banGby17wPDJoAtEr55MnTTiMoBW9ueB4O824K7wladqzBORESkWWuQ0G3+/PncfffdbNq0ibPOOoshQ4aQmppKZGQk+/fvZ/369SxevJj169dz6623ct9997WIkEqhm4iIiDSGqgFdUZnbf05cpfPj3rvxJC55cVlAt9VjOsTiBX7dXegbi3FYcbk9lNWhXK5rUjTb9hVzVPsYJpx5NB4PREdYeeLzX/h5VwHpneKZcsGxHD1nHA6rJaAqLvOi2RwoKueejzYEVdUFzPV6qHVVXcW/tlYO7aLbmoHXkXTZu7BwshmwtT0qsIlFVdd8BnPvrV2314ZqJqHQT0REpN406PbSpUuXMnPmTL7++mu2bt1KSUkJycnJDBw4kLPPPpsrrriCNm3aHM76mxSFbiIiItJUhKuUq9rMwXNw22pFh9WKgG7mH05k7HNL+WV3ge+e5ulwh8eOi9evG+qrfjupezw9H5hXq7nDeiQABFbPVaqqw2o3AzmvF5xFwUEcwMArzYYLtdF3HGT9BPsrdYF1xIKzMPw1DaFyt9e2PaFoL8y+Cfb8DG26wrA7wB4DcR3gyweCK+jCNZMIWYF3e+1CPxEREalRo57ptnPnTjp16lSft2xUCt1ERESkqavLttWqAV3Faz/v8gdxMQ4rRU530PvYDHCF+DdHC2YFXGGZm2iHFY/HG9AcorJhRyWzK7+UTTmFpHeK544zjsZrwPR5v7J2Zz7pneKZfGEGiTF20hKjWbwxl4kfr2Pi6L4M65UcvL21R4IZzL08InCLarhtqwe3vQLVV9B1HAB4YNdq/1h0W7BHQ96O4A8WlQgl+ys9TzJ/htoO22cs7PvNvHfbo2HvryG/q2olHw25v0JKfxj9T7MDrC3CH7B1zAhce1UT88K/JiIiImE1Sui2a9cuHn/8cV555RVKSkrq45ZNgkI3ERERaUlqs23V/NdDI+D8uIy0BGbfPJSxzy0N2MpanWiHleIQ4V1t3TCsO3PX7WLH/hJ6tovh2pO7EWW38urirazPrhTQRXpJ+2CseVFN21av+8L8WVMFXbjrr50Lr51V5Vy6/mBYAq+vmPvKCNi97tC+AIsdPOWHdm11zpkKJ94UetuptqKKiIhUq8FCtwMHDnDLLbfwxRdfYLfbueeee7j11luZOHEiTz75JH379mXChAlcdtllh/0hmgqFbiIiItLS1fb8uHduODFofH+xkx37gv+D6xPj+vH2d9t9c/+1bBu780vZX9wAIRKw9bEzgyvgCLNtFUJW0AUEce6D6ww19tpZ5s+qZ9A5Yqqf+/3LkLcTykKEltHJUJzrf546EG6Yb1bwVQ746roV1uoAt7PKoAV6nWVusc39BZKPgYzxUJYPa2dBXia06w1jnoPY5NptZVVAJyIirUSDhW4333wzH3/8MePHj+fzzz9nw4YNnH322ZSWlvLQQw9x2mmnHfbimxqFbiIiItIahdqy6rBZgsadLk9QI4eMtAQ+uuVkyt3eGuf2ah8DGGzM8QdJHRMi6dU+hm821q5Bgs0Cx3Vry479xWTuL6FPSjx3nhl622ppuRu318uDH63l192FAdtZt+YWBwZx4YQK6A6GeTXOdTmDu72Gq5SrbTOJ9n1g9P/B7BsDmzpUvu/Aq+DbZ6FgV93Pr4ttD4U54bey1uasOAV0IiLSQjRY6Na1a1deffVVRo4cyW+//cZRRx3FX/7yF6ZPn364a26yFLqJiIiIhOd0eYLOiQOYddNQHDZLjXPDNX0I1ZW1X6d4DAxWV9r2arcavvPqDlfPdjFs3lN0aEEcIarnQnE5a18pV5dmEqECunD3Xfw0LJx0GN9UNSo3iOg5Ag5sVzMHERFpURosdLPb7Wzbto3U1FQAoqOj+f7770lPTz+8FTdhCt1EREREqheuKq62c6F2TR/CBXSnH9ue6fM2NshnS4y2s7+4nJ7tYnh6/ACSYhxBDR66JUezv6icez9cHbIZRJC6VMqFEu762t7X661d44m4FCjIDr5+yPXw46vB4yP+Dj9/EhiuvTQ8/OcI18xBVXEiItKENVjoZrVa2bVrF+3atQMgLi6O1atX071798NbcROm0E1ERESkcdQ2oLNbDcY8uyRo26rL7WXL3mLfWM92Mbx29RBu+c8K1u7M943HRdooKHXVel2PjU3n3e+3szYr31cdF87WKefVrgKOWlbK1YdQ1XahGkeE2sqaOhDG/wee7gOE7lBbKxe9DDHtQlfFfXQr7F6rqjgREWmSGix0s1gsjBo1ioiICAA+/vhjTj/9dGJiYgLmffDBB4ew7KZJoZuIiIhI01aXbauhmkGE6tbaLjaCPYVlh7UuhwWO75HM1r1FZO4vCVsBl7m/uNpKuQYJ42rTOKJiK+v+bVCyD+I7mee7tesNq94x50clQmI3KN4HB7YFv8+Jf4Zv/y9wrH1fGPMs/K9KuHYoVXEiIiJHWIOFbtdee22t5r3++uu1vWWTp9BNREREpOmrbVVcqGYQobq1hgriurWNpn1cBN9v3R/0/vGRNvIrVcvVdNbc29ef4AvSrnj1u7DzZlw2kOcXbmZddn7DnjUXjstpVp6tmQlnPgrdT4OXhx+sjMOsQLttNRTtgWl9wFOpO23KQDO42/ur+bxdb7BHBm5hrY2LXoaMS+t2jYiISANpsNCtNVLoJiIiItLy1SaIA7PBw8UvLAsI49I7xWMxjKC5w49uxzPzN1FVUowdh9XCrvwy2kTbcbo8FDvddVpveqf4Gqvi6nzWXDgf3Air34WzHoPfFsGmL6FNV7OyrU0XuH2NGcyt+FelD9kT3E7I2+EfO+pM+P175tbVj/8S/D4XvgRfPgCFu/1jqQPhDwvM6jsREZEmoC45ke0IrUlEREREpMmq3PjBMAxiI23MummoL4i7/Pguvso1w4CMtISgbasxEdaAuXarwcJf9wScNQewr8hfDXaguJxQEqLsxEXYyDxQEvL1ijPp1u7MZ/SMxQDceGoPPlmdzc4DJdz27gr2FjmDrqmYu3XKebX+bnw2L4DNX4HFBqf/HT74gzm+5Wt/4OaIA2cBjLjfPK8NoGMG7FptPjYMyPwx+N6pA6HvWDN0q8xVVvdGEyIiIk1E6LZSIiIiIiKtnMNmwThYYWUYBg6bBYfNwqybhvLRLSfz+xO68tEtJzPrpqHERtqC5laEdBlpCTx+YToZaQl0ahMV8r3SEqMC5nVtG81Xd55GRqeEgHl2a/iKrxe//o2dB0O6qoFbZX88pQder5fFG3MZOW0Rizfm+l4LNeaz+Svz53E3mJVsYIZi/ztYtTbkOkg+ynz87bNQvAcSu5vjFVa8DSveCrxvh74HHxjQ75LA1xK7KXATEZFmS5VuIiIiIiJ1ULUqzmELHYRVBHSVq+WcLg+XvLgsoPotIy2B9248yRfyVa6qo0pVndfrxeOFdVn+7qvJsQ56Jsfy3dZ9QWtIjnWQWxgYwL30zW98t2UvhWUuNu8pYvJnG7i19Cg8eHl2/iY25RQy5fMNTI4yt6LG5GwnseLiiAQ49W+wf6v5vGIraHwnGPkwvHWB+XzncvPnyInmuW4AezfBp3eaj0+7F5b8E1zFMP7f5vU2B1is5uu9zoJN8+CXObBtKXQdGu63Q0REpMlS6CYiIiIi0kCqBnQV1XBVt6dWfq1ykFc1tKs4a67q9W9df3xQmFdx1lxqmyjGH9eZd77bTk5+KXsKnayqNG9dVj5/+vdPAeteuzOfm2Z8SKJRwL8jVvvG9xx1Ed6ta2hfnh34QYf+GUoPQJk/DCTteOgzBlYe7HS6f4v5s9dZcNrfYOkzFV9McDVb8tEQnwrL34AvHoAb5ulcNxERaXbUSKEGaqQgIiIiIvUpVKfVyuHcoVwPcPELSwHCnjVXMfdfy7by6KcbanyfrZGX1/mzua2RWN2l5pMLZkByL/MsuEVTAPBY7FiueB+SusP/HQfuUrjwRej/O/OaLx80K+CiEuG8aWaDhvIiuORN88w3ERGRRqbupfVIoZuIiIiINAe1DfO8Xi9jnl0SUBXXJyUOi2GwttK21cujvmWS95mg699yjeQq27x6WLEF8EDbXjD6GbOS7ac3YNW7ABQkpRPXbYg5FtcRIuJh1BPQc0Q9vLeIiMihqUtOpEYKIiIiIiItQKjGD6GEavBgsRi+rqwVY8vjzuBny1EB167y9OBB1zWs8vQIGN/maRfyvd5yjaxmxR7zx96N8MYoeP0cX+AGELdvrRm4ARTsgtxfYd5EyFoBB7aHv+3mBTDjePOniIhII9KZbiIiIiIirUioBg8VQVzlsaPv/R9lDg+r6MFM9wjGW80QKxonVovBKk/lcS95nlgyLFt871MR0PW3/EZ/y2++8W2ednS17Dm0xWevhJeGm4+vnA2f3Q2jpprVbwe2Q/FeM5jL/cX8GdUGotvC3s2Bc0VERI4AbS+tgbaXioiIiEhrNHvFTu6a+SPlWAED8GLHTTk27LgCxqMp4z+OxwACArrLnH8PMe7FgICArrhtOh8fM5UBi2/kGEumb7zagM4eDeXFENsBupwE62eH/zAp/SF7FaQMgNHTzSCuTZdD+2JERKRV05lu9Uihm4iIiIi0RqHOfstIS+Daod2447+rguZXDeLsuLlgUFf+99O2Wgd0/418HLfH6xu3GF56JMcSs3dN/X/AiXnmFlRVwImISB0odKtHCt1EREREpDVyujwhO6K+d+NJXPLisoAwrl8n89+T1+zMrzSWwEe3DGXMs0tZszOPykIFdNVV0DmsFv7lPI1rIr+ma1QpEYU7ghecfjFkfh943pvVDu7y4Lm2SOhyorntNG8HdMyAC54JvxVV4ZyIiByk0K0eKXQTERERkdYqVEdUICiM83jNLaOGYQQEdO/ccCKXv/JtwNw9BaVk55UFvVeEzaDMFfxXk+CAzsUsx8MB58SROhCumwuvnU1BqYtXik/hhuhviIuwAl5za2kFwwped/gPHZ0MxbmQ1BOG3WEGcYummufJaXuqiEirp9CtHil0ExEREREJFC6MqzrmsFmC5haWujh+0leUlPuDryi7leX3n8EFzy5h054i33haYhSZ+0sC3tuOi/mJk0iIsvNqRbgWaSPzotkcKCrnno82sDargPTUOKae35Necy/DYbXAoKvgp7fACxx1Bnzz5OF9CeG2p6oqTkSkRVPoVo8UuomIiIiI1B+ny8NFzy/BAH53fBfe/X47XuDdP5wUVBVn/lXFCLk91WOx4fZA22g7QzrHMveX/SHfz46L168bysRP1jPx/D4M65Fgbjt9eQRkrfBPjO0Ihbtq9yGikqD7qWYF3f4t0CEdht8HFgssnBK6Kk5hnIhIi6DQrR4pdBMRERERqV+hKuVCVcUVlbkDgrh3vtvOuqz8Gu4eKNJuwWG1kF/q4pgOsTx5yQASI72kfTA2xFZUzMCsQscB4HFCzvrD+8DXfwlz7tIWVRGRFkChWz1S6CYiIiIi0niqBnHvL8/kr7NWB80bNzCV77fuZ0eV7ajh/POSPry8eAdrswO3opaVeyoFcRbAAMPwb0/1eKDLSfD9C4f/4a6creo3EZFmRqFbPVLoJiIiIiLSdHi9XsY8uySge2pGWoKvqyr4t6fuK3IGnQlXHTsuenZow8+7C0lPjWPKmN60ibGzdV95zdtTO/QzGzRUroqLiAOPG8qLg9+s0xAo2Q/7Nqv6TUSkGVHoVo8UuomIiIiINB1OlyeoeyrArJuGAoHNHJwuD5e8uCwgoEtLjCIx2s6anbXfpto7JY4N2QWkd4pn8oUZvu2pQKUGDR6CquIArv0cXjkddq+r3ZuFa9AgIiJNgkK3eqTQTURERESkaQl3JlyoeaECuvduPIlxLyxlbaXgLdJmodTlqfUatj52Jot/ywusgIOQY7x2lvlz0FWw/E3I3wlFe4JvGp0MfcbA9mVmxZwq4EREmhyFbvVIoZuIiIiISPMVKqADgsK4UJ1Sox1Wip3uoHu2i4tgVHpHvvttH7/sNivg7jjjaLwGTJ/3K2t35vur4mLspMXZzC2phgFeL7ic8PrZgdtTMYBq/mqm899ERJoEhW71SKGbiIiIiEjLU1On1HBBXA3RWEhbp5zH4o25TPx4HRNH92VY9/jA6reKBg0pGbDiX8E36DYMivcFV7/t3awgTkTkCFPoVo8UuomIiIiItA61CeLcHi+9U+KYtXxnre7ZMT6CoT2T+Wn7frbuLQ48F65tgr/6zV0eukFDddp0hQPbIKU/jP6ntqGKiBwBCt3qkUI3EREREZHWK9T2VLvVCOqg2iUpCo+XOnVLffv6E/zVb72SzW2nVc9/y8uE4tzaL1iNGEREGpRCt3qk0E1ERERERCoL1aDB4/ViAIZh+MbK3R4Gdm7DO9/vCHmfKLuFknIPvVPieGJc/9qf/xYRD2Uhuq9a7HD0OZD7K+T+okYMIiINQKFbPVLoJiIiIiIiVYVr0FCbqrjq1Or8t4q/wmWv9F9oiwRXafgbh2vEoKo4EZE6UehWjxS6iYiIiIjIoQpVFbe/2MmOfaG3oR7TMZaiMjeZ+0sCzn/buq+ciZ+sZ+L5fRjWNQrePN+8oCKIA+g+HJY8HXzT+DTAC/k7oWM/GP1/4C4DWwR8fLsZ3qkqTkSkVhS61SOFbiIiIiIicjiqVsU5XR4ueXFZQPVbmyg7B0rKw94jvVM8a3fmhw/ieiTUvRFDOBNrV5UnItIa1SUnsh2hNYmIiIiIiLRKDpvF99gwzPANICMtwVf9BnDbwE48/PH6kPdYuzPf93P0jMUAHNsxjk05hUyZ+/PBIK6YNIDUgf4KuMI9kJ9Z+8Ve9HLdP6CIiISkSrcaqNJNRERERETqW227otqthu+8uNrY+tiZNTdi6JAOY56D96+HvRv944YVLnkTImJ1zpuISBh1yYks1b4qIiIiIiIi9c5hs/gq3gzDwGGz+MK1jLQEHr8wnYy0BI7tGMcxHeICrrVbjZD3PKtvB/JdBos37WXktEUs3rTXDN/ArH47/2nzp9UObXua4VrqQDj1LrMRg9cN/70CZl1rdj+dN9EM6w5sb7DvQUSkJVOlWw1U6SYiIiIiIkdK1Qq4ojI3l7/yLeBvxGD+Fc5gzc7gs9diI23EOKzszi8LOP8trW2Cv/rNXQ42h1kFV1EVV14Kj3cIv7Bw3U9FRFoZNVKoRwrdRERERESkMdUYxH2/g7yScrbtKw57j7evP4GJH69j4ui+DOuVHHrS6v/CB38IHu/YH1ylZvWbupyKSCun0K0eKXQTEREREZGmJtSZcJ+szmLCf1cFzT26fSwuj5ffcov81W8xdtISo1m8Mdcfxh3Vtm7dT9XlVERaIZ3pJiIiIiIi0oKFOhPuwoGdyEhLCJr7a04hv+UWAf7up8OmLmDKZxu4/8M1ZgfUzzewdnsuTrcn8Py32I6hFzDiPvPn5gUw43jzZ4VQYyIirZAq3WqgSjcREREREWkOnC4PF7+wFPCf/5aTX8qu/LJa38OOi42TL6i++2mFbqdCUQ7s+dncdjr8HnN84RTIXqmtqCLSIml7aT1S6CYiIiIiIs1F1W2nTpeHS15cxupM/1bQDvERHNUuliWb9wZdP+3S/lw0KM0/4HLCa2eZjwddBT+8AvnZULKvbgvTVlQRaSHqkhPZjtCaREREREREpIE5bP4ThAzD8G1BzUhL8FW/Abx2zXFBYRzAez/uwG618M+vNvqbLlz3hb/L6eBrze6ny2bAVw/XblEXvVw/H05EpJlRpVsNVOkmIiIiIiLNWaimC4B/K+qQzjy/aDOZ+0sAsBrg9kJ6ajyTL/I3XQjg9QY3XUjoAl435O/0jxkWuGwmHH1Wg35GEZEjRdtL65FCNxERERERaYmqhnHd750Tdu7b15/g73LaKzl42+lPb4HXAxhmRVz6xbBwMjgLzbGzJ0H73vDZ3TBqKvQccUQ+o4hIfVPoVo8UuomIiIiISGswe8VObp+5Mmi8TZSdmAgbOw+UkN4pnskXHqx+i7P5t516vea2U/CPlZfBJ7fBqv+Y41FtoWSvGiyISLOm0K0eKXQTEREREZHWwOv1MubZJUHnvIWzdcp5tbkpPNwm/OtqsCAizUxdciJLta+KiIiIiIhIq1Bx1ltGWgKPX5hORloCndpEhZx7x8heACzemMvIaYtYvDE39E0NI3wjBTVYEJEWrlVUum3ZsoXrrruO3bt3Y7Va+fbbb4mJianVtap0ExERERGR1qLqOW9Olydkl1OAM3t3YMf+Yn7eVRCw7XRrbnHg+W+hmi4ADJsAXU+GufcFn/O2eYHOfxORJknbS6s47bTTeOyxxzjllFPYt28f8fHx2Gy2Wl2r0E1ERERERForp8vj73J6XGfe/nYb2QdKOVBSHvaa3ilxbMiuFMRFekn7YKz5Yv/LYdFUKD5YGWePgfIi6NgPznocnEVmx9OvHoGcdTr/TUSaHIVulaxbt47bbruNefPmHdL1Ct1ERERERKQ1q1r9Vu72MmP+Rp6Zv6nW99j62JmBTReqO+ctHJ3/JiJNQLM60+3rr79m9OjRpKamYhgGs2fPDprz3HPP0b17dyIjIxk8eDDffPNNre+/ceNGYmNjueCCCxg0aBCTJk2qx9WLiIiIiIi0bA6bBcMwADAMA4fNwh1nHk3f1MC/bNqtRsjrn760P9gcZuBm3qTu57np/DcRaYYaPXQrKiqif//+zJgxI+TrM2fO5Pbbb+f+++9nxYoVnHLKKYwaNYrt27f75gwePJj09PSgX1lZWZSXl/PNN9/w7LPPsmzZMr788ku+/PLLsOspKysjPz8/4JeIiIiIiIj4lbu9WC1GQNOFYzvG0a9TQtDcH7ftw+X2BA72uwRS+geOpQyEB/ZB6sDA8aSe5nwRkWamSW0vNQyDDz/8kLFjx/rGTjjhBAYNGsTzzz/vG+vduzdjx45l8uTJNd5z2bJlPPzww3z++ecA/OMf/wDgrrvuCjl/4sSJPPzww0Hj2l4qIiIiIiLiV3XbaVGZm8tf+RaA8UM6M2PBJrLzSgE4pVcyV57YlSfm/mI2WOgeD6+dZd5o0FXw01vm46s/gTfPNx9b7JD5vXnG243fQMf0I/0RRUSCNNsz3aqGbk6nk+joaN577z0uvPBC37zbbruNlStXsmjRohrv6XK5OO6445g/fz4JCQmMGTOGG2+8kfPPPz/k/LKyMsrKynzP8/Pz6dy5s0I3ERERERGRGlQN4j5bk80d/11Fmctf6dYnJZ6p4w42WGib4D/nzV1ubkN1Oc3z39xOePMC2PEtJHaDMx+F+Y+po6mINKpmdaZbdXJzc3G73XTo0CFgvEOHDuzatatW97DZbEyaNIlTTz2VjIwMevXqFTZwA4iIiCA+Pj7gl4iIiIiIiNSs6vlv52akBgRuAOuz8xk9YzHDnlwSeM6bzWE+rjj/zRYBv/s3xKfC/q0w+ybI/QXmTYSsFXBgO2xeADOON3+KiDQxtsZeQG1U/EO7gtfrDRqrzqhRoxg1alR9L0tERERERERqMH38AG6fuTJo/OlL+wdPriomGfKzzMfOIvNn9kp4aXjgvC8fhKhnILottOlyOMsVEak3TbrSLTk5GavVGlTVlpOTE1T9JiIiIiIiIk3PmAGpZKQFN1hY9Ose5v+8m5HTFrF4Y27Aa4s35vrHa9O5dNdqM4ib3q+eVi0icviadOjmcDgYPHhwULfRL7/8kqFDhzbSqkRERERERKS2yt3mMeIZaQk8Pjad1IRIAGavzOLmf//EppxCpny2gTWZefy4dR9rMvOY8vkGc/zzDaxJPAtnu4zAmxrW0G9Wm4BOROQIafTtpYWFhWzatMn3fMuWLaxcuZKkpCS6dOnChAkTuPLKKxkyZAgnnXQSL730Etu3b+emm25qxFWLiIiIiIhIbThsFmbdNNTXYOHyE7rQ/d45AJSWm+e9rc0yz3mrau3OfC56dhGzHIX0Txvo73Tq9YLHBbvXBl6wdTH0GWOeByci0sgavXvpwoULGTEiuPPM1VdfzRtvvAHAc889xxNPPEF2djbp6ek8/fTTnHrqqUdkfXXpSiEiIiIiIiI1m71iZ8hz3sL55yV9GDOom7/TqbMI3jzYIG/gVfD1P6Dg4NlvnQbDzuXm48vehWN0vreI1J+65ESNHro1dQrdRERERERE6pfX62X0/y1mbVa+b6xPShyvXTOEq1//kV92FfjGj+0Yx2e3nRLcTM/lBKvdH8T98hl8+Eco819Lxwy4QA0WRKT+1CUnatJnuomIiIiIiEjLU+72YrEY5jlvF6aTkZaAzWohNsJBhM1Ceqd4UtuYZ79t21tMQakr+CY2hxm4gfnz2HMDAzdQgwURaVSqdKuBKt1ERERERETqn9Pl8Z3z5vV6KXd7cdgsvvGdB0o4/5lvOFDiYvyQzky9OKPmm67+L3zwh+Dxi16GjEvr/0OISKujSjcRERERERFp0hw2i2/LqGEYOGyWgPG0xGj+7/JBWAyY+eMOHv1kPSOnLWLxxtzwN+13CaT0DxxLHWiOi4gcYQrdREREREREpEk6pVc7/nBqDwBeXbyFTTmFTJqzgTWZeWTuL2bxxtzAIM5dDobFPMvNerCDaWm+OS4icoRpe2kNtL1URERERESk8XS759Ma5/ROieOJcf1JjLGTFmczGyzMvQ++fQ66nwpXf3wEVioirYG2l4qIiIiIiEiLMH38gBrnbMguYPSMxQybusDfYOGEm8CwwpavIXt1wy9URKQKhW4iIiIiIiLSZI0ZkEp6amA1SdsYR8i5D5zX2/8ksSv0GWM+XvZsQy1PRCQshW4iIiIiIiLSZJW7vVgsBhlpCTx+YToZaQmktomkb2rwtq7nFm5i5Y4DvrPeVna+0nxh7SzI23mEVy4irZ3OdKuBznQTERERERFpXE6XB7vVwDAMvF4vRWVuLn/lWwDGH9eZt7/dxpY9RZS6PDhsBk6X+dfcPinx/DfiUWJ3fee/2X1Z4IhpjI8hIi1AXXIihW41UOgmIiIiIiLS9FQN4g4UlzPw0S+D5o20LOcVx1P+gcqhm7MIJqUGj4uIhKFGCiIiIiIiItKiOWwWDMMAwDAMEmMcPHVJRtC8rzwDKYjp6h/Y8nXoG4YbFxE5RLbGXoCIiIiIiIhIfbhoUBqvL9nK2qx839jpKeXEHjsals0wBz79K2StBK8HjEp1KAsmQVxHiG4Lbboc2YWLSIuk0E1ERERERERahHJ34OlJNqvBq/uvhWWVBvMzYdGU4It3rYaXhpuPJ+Y12BpFpPXQ9lIRERERERFpERw2Cx/cfDIbHjmbtjEOXG4vywdNDT05LjX0+EUvH94inEUwMcH85Sw6vHuJSLOm0E1ERERERERaDIfNQpTDxuUnmFtEJ2f2g5T+gZNSB8Id66BjiPF+lxyhlYpIS6fQTURERERERFqcK07sis1isGp7LsVOt/+FigCuvBiMShfEdTR/usvrbxFqziDSqil0ExERERERkRanQ3wk52ekUI6Nh9o+6X/hmjlw3RcQEQtXfeQfb9PdHLc5Dv1ND2yH7NX+5wsmQdYKc1xEWh2FbiIiIiIiItIiXXtydwBmr9tPjjfBHDQMf7BmrRSwZS0Hj+vw3nB6P3j9HP/ziuYM0/sd3n1FpFlS6CYiIiIiIiItUv/ObRjUpQ3lbi9vD5tndiV1xPgnOGLgoQNmUwW3E3Z8e3hvGK4Jw+E2ZxCRZkmhm4iIiIiIiLRY1w0zq92e+Woj3e75lGKnv5qt2Omi271zeP9AT3Pgt0WH92b9LgndtEHNGURaJYVuIiIiIiIi0mKd3bcjHeMjfc+Xbt4bNGeJu6/5YMthhm5VmzDYo0KPi0iroNBNREREREREWqzd+aWcfmw73/Mpn21g/s+7WbBhNz9t2w/AEk86AN6slezMzjr0N7M5ApszJB9z+M0ZRKTZsjX2AkREREREREQayrCpCwKeb8op4ro3fgwY200SmzypHGXJ4pH/e4kXHU+bL9yXFXgGXG1Ubs5QuWmDiLQ6qnQTERERERGRFmv6+AG1mrfEY24xvbvXzsN6v8pnxrk93sO6l4g0bwrdREREREREpMUaMyCV9NT4gLGMtAQ2PnYOx3aM840tPbjFtHvBcv/ELV8fkTWKSMuk0E1ERERERERarHJ3YLVZRQBX5vJisxi+8a2xA3FjYOzb7J88/zHIWgEHth+RtbYYziKYmGD++uWzxl6NSKNR6CYiIiIiIiItlsNm4e0bTvA9n3njicy6aSixkbaA8S5pqVipsh1091p4aThM73dI711Y5qp5UnNSmzDtwHbIXu1/vmCSgktptdRIQURERERERFo0h81fb2IYhu955fHvtuzD0+scLBs/D77BRS+bgdOkVPN5mAYLmfuLyc5zcdzB57vynWzPzCMxxk5aYnS9fZ5GcWA77Nvif75gEsR1hOi20KaLf7xqQLlrtRlcAkzMa/BlijQlCt1ERERERESkVYp22Nj0+CgGPvIl+SUuVqZcyqAqoVtxcgb70s4jrfLglq/hmFFB96volLo10nxeUu5m7IzF5tiU8xriIxw51YVpFS57F4bfBwsnBV9/0csNtjSRpkrbS0VERERERKRFi3bY2DrlPLZOOY9oR2Dtic1q4cSebQG46gsPlRuO7vXGsTGnkN8/8S5krfS/EGbLZLhOqbXtoNpk7dsCsR1Cv5Y2xP/4gz+GDtza9oJ+l4S+vvKWVWfR4a+1oe8rUgcK3URERERERKRVO6VXMgAp7dvznae3bzzHm8DFzoksipwAb5zrv6CiyqtK9Ve4TqljBqQ2rxCo8lqX/h+8MhIKd4PFHjw380f/47L80PfL2wHlJTW/b03dYg/1O1QXWmkkCt1ERERERESkVTv5KDN0c+ZuJTeqq2/8KCOL8zvuw3v25NAXVtkyWbVTqvVgd9Sq401a1UYIXzwAxbmQfAyetkf5ht0dMiCmfeh7nD/d/ziyDbhKYcW/an6v+mq6oGYO0kToTDcRERERERFp1Xokx5CSEMmisr+A0z9uNzw8feB2mAvOyLY4Svf6Xgt11puvU+oT5vMou5VZNw01GzZUum+TFtSp9WBgmPsLpXdn0+fh+YDBK6cNZmSvRHjtTMhe5Z+eOpDiflfQZ5YZyH02eBO9lz8I8x+HvhdBbLvw71X5nLj7sqpvXBHmXL0a76tmDnIEqdJNREREREREWjXDMDj5qGRuc94c8vUJzhvZX+L2PV/r6crGnEJGPDEvaG7ljqgujzfgebMQpuHB3rNn8N22AsCs3pv25UbWZuXjdFWq4kvpj9PtYX1mrm/ors39KWmbDmV58OXfA7eHjn2+9muoS/VauKYNF8xomlt8m9PWY6mTZva/fhEREREREZH6d0qvZD7ynMwv1qMCX2jbi1Mv/gsfuof5hia7LuNi50T+MX4I1XF5mtG20gr9LoGIwHPpSB3I4I8SufYN//lt67PzOf/57+m7468sdvdlZNkTzDvxLfpuu5OLX/7JN2/triJ+n3Wx+WTVu/575u+EH14LsQADSvPBW+m72/K1Wb32+jn+sTDn6gHQZ2zw+XPWCOh8QrUfXaS+KXQTERERERGRVm9oz2TsuCkt97DK04OfPZ3NF1yljMloh6fSX597GzvondbWbJBQDZfbg6e5BW+bFwQ0RHB3yKDY6aZNhBFyev/OSTzu+j2bvGk88OlGoiIjg+b85D2abV0uDBx8aQTs/AGMSrFERBzghTl3wn9+5x//8Mbw6w1V1bb2ffCU+59b7eAug7fH+ccOtbmCqtKkDhS6iYiIiIiISKvXLi6Cnh0Tudg5kTHOR/nak2G+cPQoygmsmhoasxOoXYOEAyXlNc6pd6GCodqERR43zHsIgCxPIiPLnuCfXZ/lxN13c6AMIkJslf1xRz4bvN0AyM53kl/qCprTLi6CLmf/BeyVzmVzFkLyMZSOe9s3VHzLahg50Xyy9Rv/3NIw57C1621W5lXm9cL3LwYMlVy7EOJSIa/SVtSm2lxBnVZbFIVuIiIiIiIiIphdTMuxAQabvWYVmyt3U9C5bMPjd/kbJFQR7QjsV5hbWNZg660XlcO4n96EXatx2eP4a/lNbPKm8czXO8gvNxjctQ092vlDs/TUeNrGOkLeslObwGq3PQVlGC+PgPIqYV/uL0TOupzF7nRGlj3B0q0FMOwOGH5vyPt6EjoHDhRkgbtKqLn9W8hagdcawbfuYxlZ9gRL9sWZcyurbnvqkaROqy2aQjcRERERERERYFivZN/jzZ4UADx7fg2aZ9m3CQfltaoeyy1o5NAtVOVUuGqqhVMAmFo8mqXewDBq+bYDfHTLMLZMPpetU87j4z8PY+ndp9M3NfD8t4y0BD75s//8u2FHtQXgxbb3hHzLzUOnMsX1OzZ505g2fwtrMvPIzPgz7vZ9AyemDqT02vksdvflOuedZk/V0jzIWh74e/DtcwDk9hjL78ofNO+7YDs7hv8z9GcO13ShNuqjKq0uZ9VJs2OreYqIiIiIiIhIy3d8tyRsFgOXx+urdHMUZgYHah4X7PkZ2h4V4i6B9hzpSrcD2yFvp//5gknmT1dZ4FhcR4hua/6qULiboujOvLnv7KDbTh8/IKCyzzAM81elOekHA7iYCDtbp5wHwG97Cjl7+tdM3tmP8R370ubAOt/8VZ4ejJmfRkVH1PW7ihg9YzF2XPzc1eqb5+6QgdvtYf2uIqa4LmOttwcL7KdxevkiSr6aSlTlz/7zJwD8fu0g3/Xrs/M5JTuZjxw96G/5zb/g1IHB21OrE+q7rfge23Sp/X0qu+hl+OAPocebE2cRTDp4xuF9WeCICT3WyqjSTURERERERASIibAxIC0OgP3Es9drPmbvpuDJu9bW6p65hc76Wl7thKqc+s/v4L2rA8fCVFNFnvsYVnvg9tCMtISQTSMcNgtv3+DvCDrzxhODtt32aBfLdcO6Y8dNdl4ZFX0l3B0yaBcXgR130H3vOjedsss/8D0vu+JjsyvqG+tZ6+0BwCOFF+DyWojatsB/4Y+vgddDTruh/OoN3Ipqx01alW2vuMuDt6dWpyGq0vpdAh3SA8fqGgZKk6XQTURERERERATI3F9Mz+Ro3/OKare920IEbLtrG7qZFWbFTn+DgcqP611dKqSik2HOXf7n9mjmZUeQVL7LN1RRvRauaUTV6rdQ59z9+fReJMbFcEHJg9xd/gdGlj3B7bFPctreew+eoRfoybm/8MYPe/janc4ZZf9g2vytpCQFbmPd6k3hA/cpgReufAeAxDNuw24N7LYaGRGJ5aoPA+cPuhJsoc+lCyncd3s4VWnucnBVCmYTu/vHpdlT6CYiIiIiIiICDJu6gJk/+QOnzR4zdHv7k3m+sQPeg1vkdq2p1T2P+Jlu/S7Ba48KHEs6Ck9SzyoTDSjOhZX/9g+VF3P2kt+xJPI231Co6rXKoh02tk45j61TzgtqIlEhNsLGTaf1pBwbszynscmbxsfrcinHRkKU/5pjOsQSG2HD6fbyxLwt/LH8TjZ7O/HKsp1s31ccdN8vkq/Ca6n0ns5CaNuL/xX2DggJDaCgzMWd/9tCude/bZVfvwi53rD6XQJRiYFjh1uVZnPA0Jv9z09/AK77om5hoDRZCt1EREREREREMM8tq6yi0m1cF3/gs8HT1Xywaw14Q1d/VXbEu5dm/oBRXuJ76u6QARExeO3RgWMdM2DQNSFv8VjEHb7H4arX6uqRT9YD4K0SQ+SV+Kv+Prh5KMv/PtL3vJSIau95oNSFp+eZAWPe3ufzyeIfA8ZeumowdqvB/F/3cZXzbq51/tV8YcvXUJpf+w/hLgd3paq0uFT/+GEoz93qe1zm9ihwa0EUuomIiIiIiIgAYwakkp4S63teEbp1cu/wjW30djKrq0oPQEFWjfds0DPdQnVP/elNAHI88Ywse4IFQ/8F18+j7PIPWezu6x+7YR6Mnm4GcJWs8vTglIturrF6ra6qBpoVpo7zn4dmGAYRdmvYuY9fGNjRdFbpjVg3fhYwZix+mtcPXEek3R93nHxUMo+NTcdqwDJvOgs8g8i0pIKnnL2r5tSqCy0ANgeeiDa+p+6Y9vVSlWbs33JY10vTpdBNREREREREBP+5ZRnGZh6xveYL3cjdhAUPAE5seCu2au5eF+o2AY5opVvJfryrZwEwyfV7NnnTeGzuZl5csp3XfsjhIdc1bPKmMW3BVtbsKiFzbx4ABd4oHiq/ilWeHsRH2jmtZ5t6X9qYAam+8+EqZKQlcOmQzkEBX7i5Ywd08j1vE23nNufNhHKb82YuGRx437vfX0PlY+k+cZrdTb/55M3afwi3C6PQv/3YumslFGSHDu1qG+QBxn5/R9UNu6qf2yxs+bp2Y62AQjcRERERERERDnbjvCaDjxwPcJVtHuUxqZR57RjuMtKMXN88T/s+5oPd62u8595CJ95abEOtFyv+jeEuZb2nK7M9wwDYuq+UyZ/9wpPztrLZa4ZW63cVMXrGYoY9uYTvT32T88oe5033OYx1Psrm0e+TWVD/jR6qNmKorkFDbeZed3I3PvKczAbjqIC5qzw9+J/3ZK4f1j1gvGr13JfuwQCcF7Gm9ttD83dieKt0W10/u3bXhpG5rwj2+kO3j9bsZk1mHpn7g8+wa7IObIfs1f7nXzwA6z+CjV/6xxZMgqwV5txWRKGbiIiIiIiIyEEOqwXjYOPLTkkx/ObtCEBPw7+V1NPuYOi2q1LQ4AwdkjjdHvJLGrBbaQWvB358FYC33Gditg+o2WX/2sB2zM/oxeCGf69m2NQF9b48h83C2zec4HteXYOGcHPbRDt81WvXD+tB+2gLTreHfV5zS/D2yGMAOLd3Mt2SYwLuWbV6boW3FweMBOyuAti+rHYf4mBgVOJ18HT5RebY2g9qd20YFz4xG6vLX922Y3+ZGYg2wO9Bg5neD14/x/9870b471Xw3tX+sV2r4aXh5txWRKGbiIiIiIiISAhpbSJ9W0x7GNm+cU+73ubP3Rt8Y8XO8MHaniOxxXTL17DvN7wRcXzsGRrwUr9O8QFn1QEkRtuJjbASSrgz1Q5X5YCtpgYNNc2NibBx3WnHcLFzImOdj5DtTeKMgoe42DmR6047Juh+VavnPFhYaj3OfPLr3Fqtf1/WJgDWe7vyL/dZuLFA9kqyt/1cq+tDmXZGbMjxhvo9aBAXvdwwc1sAhW4iIiIiIiIiIVQO3SIM/xbEiko3I692W+Ua6ly3ykGf64fXAVjf/nyKvJG+8fTUeLyA17AEjHVOiuaH+0bSo21UwD0z0hIYMyC1QdZb3646qStx0VFs93bktLJplLsho2s7BndNDJpbtXoOIL/rWeaDjbUL3d76zDyX7BdPGvuIZ4nbbOzwrzdeOOTPMCzpQNBYc/o9AKDfJZDSP3CsQzqexJ6BY6kDzbmtiEI3ERERERERkRDS2kSy2RMi/IhJhtiOGPirp4rLw1e6HYlmCtbfvgLgib3DAsZn3ngiH/zpZP4dYrum1Woh2mElw9jM47ZXfNVwoc5Za4r2FTk5t28yAE7MDqJn9ekQ9jy0qtVyb+zqBvZoyK+5Cy3ARd3N89wyve0B+MRzIgA3Jq2o/aKrNFjw5G4MeLldrPk5msvvAQDuctwe/3rd7fqCxYrXEe0fq+iSW9vz81qI+un9KyIiIiIiItLCdGoT4Ws+EKRjOmzaFfq1KnILygD/GWOWbd9A3/PrYYV+htfD/g5DWbQtkdgIG4VlZghYsTXT5Qm9XfPtazJImHYBhgFjr3sde1R8tds+D0e0w8bWKefV29xQ555N/uxnJn/2c43X2iwGP+91UdT7VGK2fF6rNXW27AEg09sOgLnu45hkf42Ego3VXVYt677fAp5fOKgTD48MfdZdk2VzUHbZLKKnHw1A2cVvE902jbKiPKKfNhtdlF3xMdFR0WBzNOZKj7hm9LsoIiIiIiIicuSktYn0NVII0rE2B8KbzQyce7dhVGq6YP96yuF3cjywPeCeAF+U9aETe7hwYO23JlZuHFHTOWtNTbhzz2pzHtqQbuYW1B8jTwoYr+5sPvabv1+ZXrO6zhKdyEr7wJoXWp29mwKeGjTw70GVSrt6Y60UphmGGa6FGmtlms//mkRERERERESOoHZxDty2aF/IEqBDeo3X26xmmvXHn8YQ9ba/ss26e83hd3Kc3i/gngDjD7zCksjbuPyELrW/jyOGbqXv0K30HXDE1Dy/CRkzIDWoQUR156FVVM9tnXIeZ/Uxw9R/7+sdcN6dZds35oOq4ZS7HArMbag7Dla6uT1e+p11TcB7VBvaVeVxw8FKt93eNrW/LpSGCtPksCh0ExEREREREQnBYhikJUbxmycl+MWOGTVeb7eaf+V+ud29oSccTifHMNe+2PYeurZtXuHZoao49+xQzqQ7o7d5LtvPO3bhanusb9xXhZiXGXhB/k4Mr4cyr51cEsyhUhc/RJ6Et1JFV0VoVzl8CxvE5WWCpxwndrK9bWtc8xGnIO+w6Uw3ERERERERkTA6J0azeX8qp7Im8IW2PfHaIjFcpWGvtVnMSrdPvMO4PjoZS3Gu/8XD7eTY7xLcS/4P627/FtNVnh4cc+Z1Ic9Dq8t5as2Fw2Y55DPpuraN4aj2sczLvxwq/bb4qhCrOuDfWuqtVL+0aPVmTkwZgC3ze+BgaJfYCcNbi62U+zYDsJ0UPAe3IkvLoko3ERERERERkTA6J0Wx2Rtiu6LFiivpqGqvtR2sdDtQEFgl5G5/cGtq6YFDryQ62AXS6bUCsMuTSITNwqk929TtPs3c4ZxJd8ax7bnNeXPoFy+YEfj8YOi2s8pW4/s3XuoL3MAf2kW9PNQ35tuyWtVec2vpRncH31BFdWRYzaD67Idteb7Hi93pjCx7gqW/HWi8BTUihW4iIiIiIiIiYXROjGZTmA6m7srbEjOXBb1uP3im264iD65e/iqzsvHvwXVfBB40X0eZBS5WjHiLrzzmQf7/dp/O7IGvkVXoPuR7tjZn9O7AR56TWUePwBdSB0LfCwPHDoZuO7ztfUPt4iLChnae+DTf47CNMw5Wum3xhti+3Ixk7i9mXXah7/mTX23h5a9/4/0Vu5jsuoxN3jSmzd/Cmsw8MvcXN+JKjzxtLxUREREREREJo3NSNJs9ISrdDmzHExHvexq97GlI6QXR/rO5bBazzqXM5aHEDRURW7HLTbTNAc7yQ17XsKkLAHjclnBwxOCFJZm8sCSzxW0jbSiDurQhOcrA5Qa3YWA1vHjadDOrkzxVfm/2bwMg82ATBYCz+3bg7W9PZkLEPLqW/Row3ZLvPxMuYMvqfVn+SQebKGzxduRE1tfXxzrihk1dgIGHLZHm88wDpTw+Z8PBV7sDsH5XEaNnLAZoVX8+VekmIiIiIiIiEkaXpGj2kEC+NxqAcdavze2C0/sRvfI13zx77vqgjqQWi0GMw9z+WeLy1Ou6po8fUKfxcCp39Ix2NMO6nMPovmqzWhh2TCoXOyeyztsVAPexF4SuQqx0ptukC/uxdcp5jB3QCTtu8ktdeA72bnB3yID40JWRXPRyQFMFz8HtpSEbdTQjdfkzV9c/n82dQjcRERERERGRMDonRtOJXLK8SQAkGkXmdsER94e+oEpX0eS4CABKy4NDt1p1uAxjzIBUX7fOChlpCYwZEKIqT8I6o3cHyrHxjcfsRmvs2QC2ENt+faFbO9ISowAY1CWR5IRYLiqbyJeewQCUXfEx/HmFGb5V1jEjqHGGpWAn0Py3l44ZkErfjoF/Fvt1iqdvSmAI2hr/fCp0ExEREREREQkjIdrOksjbONZSZbvggseDJ4foSJoca4ZuJZVCt++2FRz2usrdZmlVBE4AYiKsAeNSO6ce3Q6bxWCRuz8AriyzS21AIFpSjLfA3Baa6W1H5ySz6tFiMTivXwrl2PjEfZI52TDwdXaorDTP1/yiskIjhn3E1edHOuKq/pnr1S4GL4AXMozNPG57xRcQt7Y/nwrdRERERERERKrxRPSdIcdLo8yuky6vwS+Woyh2usncmxcwJznWrJraW+wPXP5v8S7WZOax80DpIa/JYbPw9jUZdDZyAEhPiWXWTUPr1L1ToKC0nGPbx7Dh4PbSyJJdrN+0NeD3xijIxvB6KPXa+f/27j866vre8/jrOzOZhCRkIATywwT5dYtgCNHURWn0hhZwIwXUlrW2VVDrXVZ7exRLt6231m21iPUHtjnY49UWr163rG5FBHeVs0JRKbuQSygCVwsGkkAwgPkd8mvmu39MMslkJpBMvpOZTJ6Pc+bI9/P99f7Od3LOzMvP9/M5K5eyxiX41i3J8/ZS+z+eq3XB7Ooh53Cq7dt/8j9R2t8F7UHnfbTUkD1YUBcGQ+ld2R+nw6ZXVvY8Vr3hGzP1p//yFf3rXXP1lvNn+o7jfW2+e+6o/HyOrqsFAAAAAGCQjmcU66DHf4bLg55pWlj7XyVJHtl0Y8t/09yqH6rwqY/8tuvu6Xb4zAVf27/XXNDSkg+1qGT/kOpy2nt+0huGMeoCDSsUrt+pj880qVGJOunxzkz62O83+90bo2tShFNmmtJTEhTvsPvW5eeMU5YrXi1K0IK2p7Xnszrvir5jwn32Z+lCXcD5j3kyZDM05Hs35DCtvVl61OV9tTcPeve4IJ9Fp93m6/Q3Wj+fo+KKn332WV155ZWaPXu2fvCDH8g0R1d3RgAAAABA6KaM9wYoBz3T9NOOe+ROz9Pk1ERJvXsnGeqQI2Cg+O7QLZj1y79kfbEYlN7360hXb7fZxkm/e2PUe0O3SnOScsYn+u1/qu6CvjzZO4vtGU3QM++XB/Ri9KTN9M6G+sk7Aecv92QoJzVRtmHq6TYY4egVN9rEfOh29uxZlZSUqLS0VIcOHVJpaan27t0b6bIAAAAAACPEZWkufbP9US1v/6Vec39Nbd99W+O+/74yx116oPjuiRT6Rip52S59PXdiOMvGAHgnAfDex8OeKZKkwuRTfvfGVl8pyTtzafckCt0K1+/U1kNnfctHzjT7ejF+6M7VwrYnVT7xa96Vh7cEnL/czNTUtN6fo4t3EiIIG1liPnSTpM7OTrW2tqqjo0MdHR2aNGlSpEsCAAAAAIwQOeMT1SGHfNGZYahDcX7bzE73hjF9B4qf2DWmW+/WGRO8QVyHh6ewhirR6dCJJ5boxBNLlOh0DHr/DrcpwzCUoDZfT7epneV+98Zo6A7deiZR6Na3Z2Nv/7njQR0zs/Vk1WxJknn8fe+ECr2Um5malpYcbHfEgIiHbrt379bSpUuVlZUlwzC0ZcuWgG02btyoqVOnKiEhQQUFBfrggw8GfPyJEyfqhz/8oSZPnqysrCwtXLhQ06dPt/AKAAAAAACxLCd1TECb02HTPxZm+JZfvn1G0IHigz1een9hhndbe8R/ko963RNS3GTs9fV0m+ypktPT7tum+/HSKnNiQE+35flZvpk5+2qWd9t3z6bqE0+2DE+H7Mfe89um3MzQ1IlJwXZHDIj4X3hzc7Pmzp2rkpKSoOs3b96sBx54QA8//LAOHDig66+/XsXFxaqoqPBtU1BQoNzc3IDX6dOnVVtbq23btunEiRM6deqU9uzZo927dw/X5QEAAAAARrjsPuN4des1nn6/A8UHC90q69pH5aDy0cppt+lK+0l9rvFqtLlkmG7Zzn3iW989kUKVOTFgTLfuno15xnE97nhRuZnJQUPa7e5rJUmOT7f72s4aqWpRgqanxUDoFtfrGuICr3+0GnzfS4sVFxeruLi43/XPPPOM7rnnHn3ve9+TJG3YsEHvvvuunn/+ea1bt06SVFpa2u/+r7/+umbMmKHU1FRJ0pIlS7R3717dcMMNQbdva2tTW1ubb7mhoWHQ1wQAAAAAiB0JcXa/5T2f1WlhnmtA+3aP6dZbZV17kC0RSVfaTkgydNScov+ggzJqPvatMxrPSOru6eYfunX3lHM9s0yGId189x/kSBirb2z8UB9XN/m2O5r6VanxDdlO9Dy5d9zt7Sk5dWKSSB5iU1RH6+3t7SotLdXixYv92hcvXqw9e/YM6Bg5OTnas2ePWltb5Xa7tWvXLs2cObPf7detWyeXy+V75eTkDOkaAAAAAAAjW1Vti99y9wyVX7RceiD7JKddCXH+P70ratv62RqRMsvwPk33bx3eDMBWc8S3zpCpC6ZTXxgpyhyXELCv026T4Rvuz5DRtZBnHNcUVUuSPm7PkGfiFTI8Hb79PvNkaEycXRkpgcdEbIjq0O3cuXNyu91KT0/3a09PT9eZM2cGdIxrr71WN910k6666irl5eVp+vTpWrZsWb/b/+QnP1F9fb3vVVlZOaRrAAAAAACMbIXrd/otd89Q+diOU5fc1zCMgEdMK+sI3aKNy2hRtlGjI13jutlqDvutrzInKtOVqLgBjMPX3fvtLefPtNLhHcMt05Ug25W3+G33WdfMpd0hXYD2ZulRl/fV3jz4i0LERXXo1q3vB9A0zf4/lEE8/vjjOnr0qA4fPqzf/OY3F903Pj5eKSkpfi8AAAAAwOjV3wyV37k6bUD79w3dzjR2qK3TPdSyYLHZxkkd7prB1Hb2qN+6KjNNl40f+Fhl3b3f/qN9nyTpQGWdzl3uP7RWgto0jUkUYlpUh25paWmy2+0BvdpqamoCer8BAAAAABAOy/OzNCEpzq8tL9ulq7MHFpj0Dd08plT5RUs/WyNSrrSdULmZqXYjXkbnBb91wSZRGIhM4wvlZ4+VaUp/Lm+WxzXZt26pfa+uTaiQ6ioucoTh0dLeGfTfoenq6ORM0pTW1zSl9TXJOTrDxagO3ZxOpwoKCrRjxw6/9h07dmj+/PkRqgoAAAAAMJp0uM3uGEHzjUPKzUyWJLk9A9t/YpDJFD47y+OC0Wa2cVIe2XTcNiVgnXcShdBm5Vx8hbdH5Dd2F8tW3xOwzbRV6bt/XSltmBPScRH9Ih66NTU1qaysTGVlZZKk8vJylZWVqaLC+0Fcs2aNXnzxRf3+97/X0aNH9eCDD6qiokKrV6+OYNUAAAAAgNHC6bBp2ZxJkqQrbJXafPdcvbF6vhz2S+zYZWKyM6DtxHlCt2gz23ZSkvRv7YETKg4ldFt0xQRJ0oMd9wXf4NZ/Dum4iH6OSBewf/9+LViwwLe8Zs0aSdLKlSu1adMm3XbbbTp//rx+8YtfqLq6Wrm5uXrnnXd0+eWXR6pkAAAAAMAoY7f1jA1uGIacjoH3YUkbG68LfdrKz/F4abTJ0nm5xjh0uP1yqU+gWmWmKSd18I+XStJl4xKUl+3Sm1Vf0U9Td2piY894cZ0ZV8kxZ4W09emhlI4oFfHQraioSKZpXnSb++67T/fd108iDAAAAABAFEtLjldln7YT5+jpFm0MQ7oiPUmHT/Z08jFNb3ulOSnknm6SVJybqaNV59VwoVNJplOPdd6h78Tt0pU2Se4OC6pHNIr446UAAAAAAMSyvhMpSDxeGq2uSE/SJ2aOPF1xiWFILWa8GmwpykhJCPm4xbkZ6pBDS5p/pv/h/nu95v6aHknbIN39nuQIfPwYsYHQDQAAAACAMEoLMqZbdX2rLnS4I1ANLmZWRrJaFa8qe7avrcpMU+a4MXLYQ49QpqQlaXZmilpNh150L5EkTU1LjJnALdHZ8yBlgjPiD1VGDUI3AAAAAADCKK3P7KUp8d4Bwyq+aB34QdqbpUdd3lc7veTCZVZ6kiTpr509kylUmROVMz608dx6K87N6Dqed1KOyyeE/rhqNDMuvcmoQegGAAAAAEAYjY13yG70RBE54729m05+0Xd6BUTalAlj5HTYdLCzZ1w3t2xDGs+tW/GcTL/ltk7PRbdvae8M+m+MHIRuAAAAAACEkWEYSojrCd0Wxx+RROgWjeLsNl0/8YJa1PPYZ65RrqscJ6W6iiEdOyHOpuxxPePC/a/DZ3Woql5VtcxkG6t40BYAAAAAgHCqq1CGrUHq6tj0nxo2aYexUg2fD/G4ziQdM7M1T59Ihn3IZcLrpdq7pLie5UxbrW4vu0Mqk/RofcjHLVy/02/5+LkLWlryoSTp3WRvm2maIR8/ooLUneh06MQTSyJQTPSgpxsAAAAAAN2cSZrS+pqmtL4mOZOsOeaGOVrY+WffYuaFv2lb/D/pZ59925rjw1IfzPlV8BW3/vOQjrvhtvxBtWPkI3QDAAAAAMAqvSc5MLu6tvUT1vyT7QcDPizjew2fxIJv6aBnml9be3q+NGfFkI67PD9LuZnJfm152S4tz88a0nERvQjdAAAAAAAIi65x3Oas0DnnZX5rDnqm6dWWeWo0hzZAv6ER+jhiFJs5yXtPDnqm6acd9+ivnmmKsxuSu2NIx+1we+9VnnFcjzte9AVwHW5TJnN+xiTGdAMAAAAAIIyqztcrodeyWzbF2UzFya2TZrpyjRNDPgfRm3WSExO1Jmmdjtd2SjL0UcpN+vPdN0gO5yX3vRinw6ZXV+XJ9cwyGYZ0891/UNyYFDkd1vaHsp38QLry65YeE6GhpxsAAAAAAGFU+NRHWtt0u2/ZLo/+Z8d8dcihcjMj5OMmOh2aMcnbW8puo6eUlf4uc7y6eypeNm7MkAO3bk67TUbXrTIMw5rAra5Cxpm/+hbjdj8hnT5wydlWbSc/GPq5uxD6BkfoBgAAAACARXqPt+bpmtFxw235cst/dtFV9vdkl1snhhC6SYQd4XJFes8kGpeNi49gJQOwYY7GvNrTs83++SHphSJpwxz/7UIM5xA6QjcAAAAAAAYolLG3ludnaXJXcHPck6nz5ljl2M7qRts+lXuGFrohPHqHbm5PlEeb/c2q2rd9oOEcLEPoBgAAAADAJRhDeHqzewB9SRpvNOp/JxRLkv7BsV0nzPShlgaLVdW2yNbrhv/fE/U6VFWvqtqWCFZ1EcFmVc26KrB9oOEcLEPoBgAAAAB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       "text/plain": [
        "<Figure size 1500x500 with 1 Axes>"
       ]
@@ -246,7 +285,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[8], line 39\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlen(ref) = \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m; len(new) = \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mlen\u001b[39m(_data[\u001b[38;5;241m1\u001b[39m]), \u001b[38;5;28mlen\u001b[39m(_refl[\u001b[38;5;241m1\u001b[39m])))\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m4\u001b[39m):\n\u001b[0;32m---> 39\u001b[0m     \u001b[38;5;28mprint\u001b[39m(np\u001b[38;5;241m.\u001b[39msum(\u001b[43m_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m_refl\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m))\n",
+      "Cell \u001b[0;32mIn[10], line 39\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlen(ref) = \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m; len(new) = \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mlen\u001b[39m(_data[\u001b[38;5;241m1\u001b[39m]), \u001b[38;5;28mlen\u001b[39m(_refl[\u001b[38;5;241m1\u001b[39m])))\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m4\u001b[39m):\n\u001b[0;32m---> 39\u001b[0m     \u001b[38;5;28mprint\u001b[39m(np\u001b[38;5;241m.\u001b[39msum(\u001b[43m_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m_refl\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m))\n",
       "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (160,) (217,) "
      ]
     }

From bf992eb077fb1baf469aa01a0f12aef85ab39018 Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Thu, 7 Mar 2024 16:07:27 -0500
Subject: [PATCH 08/15] test property

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

diff --git a/reduction/lr_reduction/DeadTimeCorrection.py b/reduction/lr_reduction/DeadTimeCorrection.py
index 8e25b28..fe10aee 100644
--- a/reduction/lr_reduction/DeadTimeCorrection.py
+++ b/reduction/lr_reduction/DeadTimeCorrection.py
@@ -41,7 +41,7 @@ def summary(self):
         return "Single read-out dead time correction calculation"
 
     def PyInit(self):
-        self.declareProperty(WorkspaceProperty("InputWorkspace", "", Direction.Input),
+        self.declareProperty(IEventWorkspaceProperty("InputWorkspace", "", Direction.Input),
                              "Input workspace use to compute dead time correction")
         self.declareProperty("DeadTime", 4.2, doc="Dead time in microseconds")
         self.declareProperty("TOFStep", 100,

From d971de20e41393edc3ec484454b6c770c61436d1 Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Thu, 7 Mar 2024 16:50:58 -0500
Subject: [PATCH 09/15] fix import

---
 reduction/lr_reduction/event_reduction.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/reduction/lr_reduction/event_reduction.py b/reduction/lr_reduction/event_reduction.py
index 156d7d6..243265c 100644
--- a/reduction/lr_reduction/event_reduction.py
+++ b/reduction/lr_reduction/event_reduction.py
@@ -6,8 +6,8 @@
 import mantid.simpleapi as api
 import numpy as np
 
-from . import background
-from .DeadTimeCorrection import call as DeadTimeCorrection
+import background
+import DeadTimeCorrection.call as DeadTimeCorrection
 
 
 def get_wl_range(ws):

From 47f362eb4d2f742728993f5c4a39cd390621baba Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Thu, 7 Mar 2024 18:42:45 -0500
Subject: [PATCH 10/15] fix import

---
 reduction/lr_reduction/event_reduction.py | 29 ++++++++++++-----------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/reduction/lr_reduction/event_reduction.py b/reduction/lr_reduction/event_reduction.py
index 243265c..2125adb 100644
--- a/reduction/lr_reduction/event_reduction.py
+++ b/reduction/lr_reduction/event_reduction.py
@@ -6,8 +6,9 @@
 import mantid.simpleapi as api
 import numpy as np
 
-import background
-import DeadTimeCorrection.call as DeadTimeCorrection
+from . import background
+from . import DeadTimeCorrection as _dtc
+import _dtc.call as SingleReadoutDeadTimeCorrection
 
 
 def get_wl_range(ws):
@@ -242,22 +243,22 @@ def get_dead_time_correction(self):
         tof_min = self._ws_sc.getTofMin()
         tof_max = self._ws_sc.getTofMax()
 
-        corr_ws = DeadTimeCorrection(InputWorkspace=self._ws_sc,
-                                     DeadTime=self.DEAD_TIME,
-                                     TOFStep=self.DEAD_TIME_TOF_STEP,
-                                     Paralyzable=self.paralyzable,
-                                     TOFRange=[tof_min, tof_max],
-                                     OutputWorkspace="corr")
+        corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=self._ws_sc,
+                                                  DeadTime=self.DEAD_TIME,
+                                                  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
-        corr_ws = DeadTimeCorrection(InputWorkspace=self._ws_db,
-                                     DeadTime=self.DEAD_TIME,
-                                     TOFStep=self.DEAD_TIME_TOF_STEP,
-                                     Paralyzable=self.paralyzable,
-                                     TOFRange=[tof_min, tof_max],
-                                     OutputWorkspace="corr")
+        corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=self._ws_db,
+                                                  DeadTime=self.DEAD_TIME,
+                                                  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

From 6546653230c018e2a4b411c61b2b94e256601258 Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Thu, 7 Mar 2024 18:50:11 -0500
Subject: [PATCH 11/15] fix import

---
 reduction/lr_reduction/event_reduction.py | 27 +++++++++++------------
 1 file changed, 13 insertions(+), 14 deletions(-)

diff --git a/reduction/lr_reduction/event_reduction.py b/reduction/lr_reduction/event_reduction.py
index 2125adb..b8d6dd7 100644
--- a/reduction/lr_reduction/event_reduction.py
+++ b/reduction/lr_reduction/event_reduction.py
@@ -7,8 +7,7 @@
 import numpy as np
 
 from . import background
-from . import DeadTimeCorrection as _dtc
-import _dtc.call as SingleReadoutDeadTimeCorrection
+from . import DeadTimeCorrection
 
 
 def get_wl_range(ws):
@@ -243,22 +242,22 @@ def get_dead_time_correction(self):
         tof_min = self._ws_sc.getTofMin()
         tof_max = self._ws_sc.getTofMax()
 
-        corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=self._ws_sc,
-                                                  DeadTime=self.DEAD_TIME,
-                                                  TOFStep=self.DEAD_TIME_TOF_STEP,
-                                                  Paralyzable=self.paralyzable,
-                                                  TOFRange=[tof_min, tof_max],
-                                                  OutputWorkspace="corr")
+        corr_ws = DeadTimeCorrection.call(InputWorkspace=self._ws_sc,
+                                          DeadTime=self.DEAD_TIME,
+                                          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
-        corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=self._ws_db,
-                                                  DeadTime=self.DEAD_TIME,
-                                                  TOFStep=self.DEAD_TIME_TOF_STEP,
-                                                  Paralyzable=self.paralyzable,
-                                                  TOFRange=[tof_min, tof_max],
-                                                  OutputWorkspace="corr")
+        corr_ws = DeadTimeCorrection.call(InputWorkspace=self._ws_db,
+                                          DeadTime=self.DEAD_TIME,
+                                          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

From 3534eff7e67f0f98a7a4d96c0e367f7e7c93d175 Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Fri, 8 Mar 2024 08:56:36 -0500
Subject: [PATCH 12/15] fix algo import

---
 reduction/lr_reduction/__init__.py        |  2 +
 reduction/lr_reduction/event_reduction.py | 25 +++++-----
 reduction/notebooks/workflow.ipynb        | 60 +++++++++++------------
 reduction/test/test_dead_time.py          | 19 ++-----
 4 files changed, 49 insertions(+), 57 deletions(-)

diff --git a/reduction/lr_reduction/__init__.py b/reduction/lr_reduction/__init__.py
index 2b6bf42..9efdbbe 100644
--- a/reduction/lr_reduction/__init__.py
+++ b/reduction/lr_reduction/__init__.py
@@ -1 +1,3 @@
 __version__ = '2.0.18'
+
+from . import DeadTimeCorrection
\ No newline at end of file
diff --git a/reduction/lr_reduction/event_reduction.py b/reduction/lr_reduction/event_reduction.py
index b8d6dd7..cecc5ed 100644
--- a/reduction/lr_reduction/event_reduction.py
+++ b/reduction/lr_reduction/event_reduction.py
@@ -7,7 +7,6 @@
 import numpy as np
 
 from . import background
-from . import DeadTimeCorrection
 
 
 def get_wl_range(ws):
@@ -242,22 +241,22 @@ def get_dead_time_correction(self):
         tof_min = self._ws_sc.getTofMin()
         tof_max = self._ws_sc.getTofMax()
 
-        corr_ws = DeadTimeCorrection.call(InputWorkspace=self._ws_sc,
-                                          DeadTime=self.DEAD_TIME,
-                                          TOFStep=self.DEAD_TIME_TOF_STEP,
-                                          Paralyzable=self.paralyzable,
-                                          TOFRange=[tof_min, tof_max],
-                                          OutputWorkspace="corr")
+        corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=self._ws_sc,
+                                                  DeadTime=self.DEAD_TIME,
+                                                  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
-        corr_ws = DeadTimeCorrection.call(InputWorkspace=self._ws_db,
-                                          DeadTime=self.DEAD_TIME,
-                                          TOFStep=self.DEAD_TIME_TOF_STEP,
-                                          Paralyzable=self.paralyzable,
-                                          TOFRange=[tof_min, tof_max],
-                                          OutputWorkspace="corr")
+        corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=self._ws_db,
+                                                  DeadTime=self.DEAD_TIME,
+                                                  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
diff --git a/reduction/notebooks/workflow.ipynb b/reduction/notebooks/workflow.ipynb
index 07e6f2a..d22818c 100644
--- a/reduction/notebooks/workflow.ipynb
+++ b/reduction/notebooks/workflow.ipynb
@@ -9,14 +9,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T18:47:15.346898Z",
-     "iopub.status.busy": "2024-03-07T18:47:15.346475Z",
-     "iopub.status.idle": "2024-03-07T18:47:15.997310Z",
-     "shell.execute_reply": "2024-03-07T18:47:15.996658Z",
-     "shell.execute_reply.started": "2024-03-07T18:47:15.346876Z"
+     "iopub.execute_input": "2024-03-08T13:52:42.623917Z",
+     "iopub.status.busy": "2024-03-08T13:52:42.623510Z",
+     "iopub.status.idle": "2024-03-08T13:52:43.265412Z",
+     "shell.execute_reply": "2024-03-08T13:52:43.264674Z",
+     "shell.execute_reply.started": "2024-03-08T13:52:42.623896Z"
     },
     "tags": []
    },
@@ -41,14 +41,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T18:47:16.748753Z",
-     "iopub.status.busy": "2024-03-07T18:47:16.748380Z",
-     "iopub.status.idle": "2024-03-07T18:47:17.489202Z",
-     "shell.execute_reply": "2024-03-07T18:47:17.488585Z",
-     "shell.execute_reply.started": "2024-03-07T18:47:16.748722Z"
+     "iopub.execute_input": "2024-03-08T13:52:43.892833Z",
+     "iopub.status.busy": "2024-03-08T13:52:43.892493Z",
+     "iopub.status.idle": "2024-03-08T13:52:44.714217Z",
+     "shell.execute_reply": "2024-03-08T13:52:44.713558Z",
+     "shell.execute_reply.started": "2024-03-08T13:52:43.892815Z"
     },
     "tags": []
    },
@@ -69,14 +69,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T18:47:17.616398Z",
-     "iopub.status.busy": "2024-03-07T18:47:17.615822Z",
-     "iopub.status.idle": "2024-03-07T18:47:17.619379Z",
-     "shell.execute_reply": "2024-03-07T18:47:17.618866Z",
-     "shell.execute_reply.started": "2024-03-07T18:47:17.616377Z"
+     "iopub.execute_input": "2024-03-08T13:52:45.257468Z",
+     "iopub.status.busy": "2024-03-08T13:52:45.257039Z",
+     "iopub.status.idle": "2024-03-08T13:52:45.260505Z",
+     "shell.execute_reply": "2024-03-08T13:52:45.259968Z",
+     "shell.execute_reply.started": "2024-03-08T13:52:45.257448Z"
     },
     "tags": []
    },
@@ -97,14 +97,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T18:47:18.651068Z",
-     "iopub.status.busy": "2024-03-07T18:47:18.650512Z",
-     "iopub.status.idle": "2024-03-07T18:47:18.972007Z",
-     "shell.execute_reply": "2024-03-07T18:47:18.971380Z",
-     "shell.execute_reply.started": "2024-03-07T18:47:18.651049Z"
+     "iopub.execute_input": "2024-03-08T13:52:46.129304Z",
+     "iopub.status.busy": "2024-03-08T13:52:46.129130Z",
+     "iopub.status.idle": "2024-03-08T13:52:46.449310Z",
+     "shell.execute_reply": "2024-03-08T13:52:46.448362Z",
+     "shell.execute_reply.started": "2024-03-08T13:52:46.129288Z"
     },
     "tags": []
    },
@@ -120,14 +120,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T18:47:19.256059Z",
-     "iopub.status.busy": "2024-03-07T18:47:19.255641Z",
-     "iopub.status.idle": "2024-03-07T18:47:34.587879Z",
-     "shell.execute_reply": "2024-03-07T18:47:34.587198Z",
-     "shell.execute_reply.started": "2024-03-07T18:47:19.256036Z"
+     "iopub.execute_input": "2024-03-08T13:52:48.067566Z",
+     "iopub.status.busy": "2024-03-08T13:52:48.067272Z",
+     "iopub.status.idle": "2024-03-08T13:53:03.315487Z",
+     "shell.execute_reply": "2024-03-08T13:53:03.314503Z",
+     "shell.execute_reply.started": "2024-03-08T13:52:48.067547Z"
     },
     "tags": []
    },
diff --git a/reduction/test/test_dead_time.py b/reduction/test/test_dead_time.py
index 86fd71d..462a17f 100644
--- a/reduction/test/test_dead_time.py
+++ b/reduction/test/test_dead_time.py
@@ -1,6 +1,6 @@
 import os
 import numpy as np
-from lr_reduction.DeadTimeCorrection import SingleReadoutDeadTimeCorrection
+#from lr_reduction.DeadTimeCorrection import SingleReadoutDeadTimeCorrection
 
 import mantid
 import mantid.simpleapi as mtd_api
@@ -18,12 +18,8 @@ def test_deadtime(nexus_dir):
     with amend_config(data_dir=nexus_dir):
         ws = mtd_api.Load("REF_L_198409")
 
-    algo = SingleReadoutDeadTimeCorrection()
-    algo.PyInit()
-    algo.setProperty("InputWorkspace", ws)
-    algo.setProperty("OutputWorkspace", "dead_time_corr")
-    algo.PyExec()
-    corr_ws = algo.getProperty('OutputWorkspace').value
+    corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=ws)
+
     corr = corr_ws.readY(0)
     for c in corr:
         assert(c>0)
@@ -37,13 +33,8 @@ def test_deadtime_paralyzable(nexus_dir):
     with amend_config(data_dir=nexus_dir):
         ws = mtd_api.Load("REF_L_198409")
 
-    algo = SingleReadoutDeadTimeCorrection()
-    algo.PyInit()
-    algo.setProperty("InputWorkspace", ws)
-    algo.setProperty("Paralyzable", True)
-    algo.setProperty("OutputWorkspace", "dead_time_corr")
-    algo.PyExec()
-    corr_ws = algo.getProperty('OutputWorkspace').value
+    corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=ws,
+                                              Paralyzable=True)
     corr = corr_ws.readY(0)
     with open("dc.txt", 'w') as fd:
         fd.write(str(corr))

From e6243f554e49ee9017b7f3be699aa7560ac9014e Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Fri, 8 Mar 2024 09:02:08 -0500
Subject: [PATCH 13/15] fix algo import

---
 reduction/lr_reduction/event_reduction.py | 25 +++---
 reduction/notebooks/workflow.ipynb        | 99 +++++++++++------------
 reduction/test/test_dead_time.py          | 19 +++--
 3 files changed, 75 insertions(+), 68 deletions(-)

diff --git a/reduction/lr_reduction/event_reduction.py b/reduction/lr_reduction/event_reduction.py
index cecc5ed..b8d6dd7 100644
--- a/reduction/lr_reduction/event_reduction.py
+++ b/reduction/lr_reduction/event_reduction.py
@@ -7,6 +7,7 @@
 import numpy as np
 
 from . import background
+from . import DeadTimeCorrection
 
 
 def get_wl_range(ws):
@@ -241,22 +242,22 @@ def get_dead_time_correction(self):
         tof_min = self._ws_sc.getTofMin()
         tof_max = self._ws_sc.getTofMax()
 
-        corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=self._ws_sc,
-                                                  DeadTime=self.DEAD_TIME,
-                                                  TOFStep=self.DEAD_TIME_TOF_STEP,
-                                                  Paralyzable=self.paralyzable,
-                                                  TOFRange=[tof_min, tof_max],
-                                                  OutputWorkspace="corr")
+        corr_ws = DeadTimeCorrection.call(InputWorkspace=self._ws_sc,
+                                          DeadTime=self.DEAD_TIME,
+                                          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
-        corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=self._ws_db,
-                                                  DeadTime=self.DEAD_TIME,
-                                                  TOFStep=self.DEAD_TIME_TOF_STEP,
-                                                  Paralyzable=self.paralyzable,
-                                                  TOFRange=[tof_min, tof_max],
-                                                  OutputWorkspace="corr")
+        corr_ws = DeadTimeCorrection.call(InputWorkspace=self._ws_db,
+                                          DeadTime=self.DEAD_TIME,
+                                          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
diff --git a/reduction/notebooks/workflow.ipynb b/reduction/notebooks/workflow.ipynb
index d22818c..ad7a840 100644
--- a/reduction/notebooks/workflow.ipynb
+++ b/reduction/notebooks/workflow.ipynb
@@ -12,11 +12,11 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-08T13:52:42.623917Z",
-     "iopub.status.busy": "2024-03-08T13:52:42.623510Z",
-     "iopub.status.idle": "2024-03-08T13:52:43.265412Z",
-     "shell.execute_reply": "2024-03-08T13:52:43.264674Z",
-     "shell.execute_reply.started": "2024-03-08T13:52:42.623896Z"
+     "iopub.execute_input": "2024-03-08T14:01:07.144441Z",
+     "iopub.status.busy": "2024-03-08T14:01:07.144070Z",
+     "iopub.status.idle": "2024-03-08T14:01:07.796688Z",
+     "shell.execute_reply": "2024-03-08T14:01:07.796054Z",
+     "shell.execute_reply.started": "2024-03-08T14:01:07.144422Z"
     },
     "tags": []
    },
@@ -44,11 +44,11 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-08T13:52:43.892833Z",
-     "iopub.status.busy": "2024-03-08T13:52:43.892493Z",
-     "iopub.status.idle": "2024-03-08T13:52:44.714217Z",
-     "shell.execute_reply": "2024-03-08T13:52:44.713558Z",
-     "shell.execute_reply.started": "2024-03-08T13:52:43.892815Z"
+     "iopub.execute_input": "2024-03-08T14:01:07.798312Z",
+     "iopub.status.busy": "2024-03-08T14:01:07.797810Z",
+     "iopub.status.idle": "2024-03-08T14:01:08.582215Z",
+     "shell.execute_reply": "2024-03-08T14:01:08.581745Z",
+     "shell.execute_reply.started": "2024-03-08T14:01:07.798293Z"
     },
     "tags": []
    },
@@ -72,11 +72,11 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-08T13:52:45.257468Z",
-     "iopub.status.busy": "2024-03-08T13:52:45.257039Z",
-     "iopub.status.idle": "2024-03-08T13:52:45.260505Z",
-     "shell.execute_reply": "2024-03-08T13:52:45.259968Z",
-     "shell.execute_reply.started": "2024-03-08T13:52:45.257448Z"
+     "iopub.execute_input": "2024-03-08T14:01:09.036645Z",
+     "iopub.status.busy": "2024-03-08T14:01:09.036400Z",
+     "iopub.status.idle": "2024-03-08T14:01:09.039159Z",
+     "shell.execute_reply": "2024-03-08T14:01:09.038760Z",
+     "shell.execute_reply.started": "2024-03-08T14:01:09.036625Z"
     },
     "tags": []
    },
@@ -100,17 +100,18 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-08T13:52:46.129304Z",
-     "iopub.status.busy": "2024-03-08T13:52:46.129130Z",
-     "iopub.status.idle": "2024-03-08T13:52:46.449310Z",
-     "shell.execute_reply": "2024-03-08T13:52:46.448362Z",
-     "shell.execute_reply.started": "2024-03-08T13:52:46.129288Z"
+     "iopub.execute_input": "2024-03-08T14:01:09.093272Z",
+     "iopub.status.busy": "2024-03-08T14:01:09.093063Z",
+     "iopub.status.idle": "2024-03-08T14:01:09.365008Z",
+     "shell.execute_reply": "2024-03-08T14:01:09.364410Z",
+     "shell.execute_reply.started": "2024-03-08T14:01:09.093256Z"
     },
     "tags": []
    },
    "outputs": [],
    "source": [
     "import importlib\n",
+    "import lr_reduction\n",
     "from lr_reduction import workflow\n",
     "from lr_reduction import template\n",
     "from lr_reduction import output\n",
@@ -123,11 +124,11 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-08T13:52:48.067566Z",
-     "iopub.status.busy": "2024-03-08T13:52:48.067272Z",
-     "iopub.status.idle": "2024-03-08T13:53:03.315487Z",
-     "shell.execute_reply": "2024-03-08T13:53:03.314503Z",
-     "shell.execute_reply.started": "2024-03-08T13:52:48.067547Z"
+     "iopub.execute_input": "2024-03-08T14:01:09.366446Z",
+     "iopub.status.busy": "2024-03-08T14:01:09.365789Z",
+     "iopub.status.idle": "2024-03-08T14:01:25.807895Z",
+     "shell.execute_reply": "2024-03-08T14:01:25.806756Z",
+     "shell.execute_reply.started": "2024-03-08T14:01:09.366424Z"
     },
     "tags": []
    },
@@ -185,14 +186,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-03-07T18:54:38.824105Z",
-     "iopub.status.busy": "2024-03-07T18:54:38.823533Z",
-     "iopub.status.idle": "2024-03-07T18:54:48.835710Z",
-     "shell.execute_reply": "2024-03-07T18:54:48.834905Z",
-     "shell.execute_reply.started": "2024-03-07T18:54:38.824083Z"
+     "iopub.execute_input": "2024-03-08T14:01:25.811641Z",
+     "iopub.status.busy": "2024-03-08T14:01:25.811469Z",
+     "iopub.status.idle": "2024-03-08T14:01:28.335294Z",
+     "shell.execute_reply": "2024-03-08T14:01:28.334217Z",
+     "shell.execute_reply.started": "2024-03-08T14:01:25.811622Z"
     },
     "tags": []
    },
@@ -201,26 +202,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "wl=4.25; ths=-0.599733; thi=-0.00812677; No offset\n",
-      "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [0.753782 -> 0.938569] at [0.0487517 -> 0.0220792]\n",
-      "Normalization options: True True\n",
-      "Template data was passed instead of a file path: template data not saved\n",
-      "wl=4.25; ths=-1.18271; thi=-0.00812677; No offset\n",
-      "Background on both sides: [133 135] [148 150]\n",
-      "Dead time correction: [0.749746 -> 0.92837] at [0.099448 -> 0.0441559]\n",
-      "Normalization options: True True\n",
-      "Template data was passed instead of a file path: template data not saved\n",
-      "wl=4.25; ths=-2.34284; thi=-0.00812677; No offset\n",
-      "Background on both sides: [131 133] [149 151]\n",
-      "Dead time correction: [0.753723 -> 0.937186] at [0.194985 -> 0.0865754]\n",
-      "Normalization options: True True\n",
-      "Template data was passed instead of a file path: template data not saved\n",
-      "wl=4.25; ths=-4.63906; thi=-0.00812677; No offset\n",
-      "Background on both sides: [131 133] [149 151]\n",
-      "Dead time correction: [0.7496 -> 0.928125] at [0.389949 -> 0.173141]\n",
-      "Normalization options: True True\n",
-      "Template data was passed instead of a file path: template data not saved\n"
+      "wl=4.25; ths=-0.599733; thi=-0.00812677; No offset\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'SingleReadoutDeadTimeCorrection' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 20\u001b[0m\n\u001b[1;32m     18\u001b[0m template_data \u001b[38;5;241m=\u001b[39m template\u001b[38;5;241m.\u001b[39mread_template(template_path, sequence_number)\n\u001b[1;32m     19\u001b[0m template_data\u001b[38;5;241m.\u001b[39mdead_time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m \u001b[43mworkflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mws\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemplate_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maverage_overlap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/git/LiquidsReflectometer/reduction/lr_reduction/workflow.py:29\u001b[0m, in \u001b[0;36mreduce\u001b[0;34m(ws, template_file, output_dir, average_overlap, q_summing, bck_in_q, is_live, functional_background)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;124;03m    Function called by reduce_REFL.py, which lives in /SNS/REF_L/shared/autoreduce\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;124;03m    and is called by the automated reduction workflow.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;124;03m                     along constant-Q lines rather than along TOF/pixel boundaries.\u001b[39;00m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m# Call the reduction using the template\u001b[39;00m\n\u001b[0;32m---> 29\u001b[0m qz_mid, refl, d_refl, meta_data \u001b[38;5;241m=\u001b[39m \u001b[43mtemplate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess_from_template_ws\u001b[49m\u001b[43m(\u001b[49m\u001b[43mws\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemplate_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     30\u001b[0m \u001b[43m                                                                    \u001b[49m\u001b[43mq_summing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq_summing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     31\u001b[0m \u001b[43m                                                                    \u001b[49m\u001b[43mtof_weighted\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq_summing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[43m                                                                    \u001b[49m\u001b[43mclean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq_summing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     33\u001b[0m \u001b[43m                                                                    \u001b[49m\u001b[43mbck_in_q\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbck_in_q\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     34\u001b[0m \u001b[43m                                                                    \u001b[49m\u001b[43mfunctional_background\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunctional_background\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     35\u001b[0m \u001b[43m                                                                    \u001b[49m\u001b[43minfo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;66;03m# Save partial results\u001b[39;00m\n\u001b[1;32m     38\u001b[0m coll \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mRunCollection()\n",
+      "File \u001b[0;32m~/git/LiquidsReflectometer/reduction/lr_reduction/template.py:232\u001b[0m, in \u001b[0;36mprocess_from_template_ws\u001b[0;34m(ws_sc, template_data, q_summing, tof_weighted, bck_in_q, clean, info, normalize, theta_value, ws_db, functional_background)\u001b[0m\n\u001b[1;32m    218\u001b[0m event_refl \u001b[38;5;241m=\u001b[39m event_reduction\u001b[38;5;241m.\u001b[39mEventReflectivity(ws_sc, ws_db,\n\u001b[1;32m    219\u001b[0m                                                signal_peak\u001b[38;5;241m=\u001b[39mpeak, signal_bck\u001b[38;5;241m=\u001b[39mpeak_bck,\n\u001b[1;32m    220\u001b[0m                                                norm_peak\u001b[38;5;241m=\u001b[39mnorm_peak, norm_bck\u001b[38;5;241m=\u001b[39mnorm_bck,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    228\u001b[0m                                                functional_background\u001b[38;5;241m=\u001b[39mfunctional_background,\n\u001b[1;32m    229\u001b[0m                                                instrument\u001b[38;5;241m=\u001b[39mevent_reduction\u001b[38;5;241m.\u001b[39mEventReflectivity\u001b[38;5;241m.\u001b[39mINSTRUMENT_4B)\n\u001b[1;32m    231\u001b[0m \u001b[38;5;66;03m# R(Q)\u001b[39;00m\n\u001b[0;32m--> 232\u001b[0m qz, refl, d_refl \u001b[38;5;241m=\u001b[39m \u001b[43mevent_refl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspecular\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq_summing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq_summing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtof_weighted\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtof_weighted\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    233\u001b[0m \u001b[43m                                       \u001b[49m\u001b[43mbck_in_q\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbck_in_q\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    234\u001b[0m qz_mid \u001b[38;5;241m=\u001b[39m (qz[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m qz[\u001b[38;5;241m1\u001b[39m:])\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2.0\u001b[39m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNormalization options: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (normalize, template_data\u001b[38;5;241m.\u001b[39mscaling_factor_flag))\n",
+      "File \u001b[0;32m~/git/LiquidsReflectometer/reduction/lr_reduction/event_reduction.py:293\u001b[0m, in \u001b[0;36mEventReflectivity.specular\u001b[0;34m(self, q_summing, tof_weighted, bck_in_q, clean, normalize)\u001b[0m\n\u001b[1;32m    290\u001b[0m \u001b[38;5;66;03m# First, let's compute the dead-time correction if we need it\u001b[39;00m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;66;03m# We do this first because the specular calls below may modify the input workspace\u001b[39;00m\n\u001b[1;32m    292\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdead_time:\n\u001b[0;32m--> 293\u001b[0m     dead_time_corr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_dead_time_correction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tof_weighted:\n\u001b[1;32m    296\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspecular_weighted(q_summing\u001b[38;5;241m=\u001b[39mq_summing, bck_in_q\u001b[38;5;241m=\u001b[39mbck_in_q)\n",
+      "File \u001b[0;32m~/git/LiquidsReflectometer/reduction/lr_reduction/event_reduction.py:244\u001b[0m, in \u001b[0;36mEventReflectivity.get_dead_time_correction\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    241\u001b[0m tof_min \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ws_sc\u001b[38;5;241m.\u001b[39mgetTofMin()\n\u001b[1;32m    242\u001b[0m tof_max \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ws_sc\u001b[38;5;241m.\u001b[39mgetTofMax()\n\u001b[0;32m--> 244\u001b[0m corr_ws \u001b[38;5;241m=\u001b[39m \u001b[43mSingleReadoutDeadTimeCorrection\u001b[49m(InputWorkspace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ws_sc,\n\u001b[1;32m    245\u001b[0m                                           DeadTime\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mDEAD_TIME,\n\u001b[1;32m    246\u001b[0m                                           TOFStep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mDEAD_TIME_TOF_STEP,\n\u001b[1;32m    247\u001b[0m                                           Paralyzable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparalyzable,\n\u001b[1;32m    248\u001b[0m                                           TOFRange\u001b[38;5;241m=\u001b[39m[tof_min, tof_max],\n\u001b[1;32m    249\u001b[0m                                           OutputWorkspace\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcorr\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    250\u001b[0m corr_sc \u001b[38;5;241m=\u001b[39m corr_ws\u001b[38;5;241m.\u001b[39mreadY(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    251\u001b[0m wl_bins \u001b[38;5;241m=\u001b[39m corr_ws\u001b[38;5;241m.\u001b[39mreadX(\u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconstant\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'SingleReadoutDeadTimeCorrection' is not defined"
      ]
     }
    ],
diff --git a/reduction/test/test_dead_time.py b/reduction/test/test_dead_time.py
index 462a17f..86fd71d 100644
--- a/reduction/test/test_dead_time.py
+++ b/reduction/test/test_dead_time.py
@@ -1,6 +1,6 @@
 import os
 import numpy as np
-#from lr_reduction.DeadTimeCorrection import SingleReadoutDeadTimeCorrection
+from lr_reduction.DeadTimeCorrection import SingleReadoutDeadTimeCorrection
 
 import mantid
 import mantid.simpleapi as mtd_api
@@ -18,8 +18,12 @@ def test_deadtime(nexus_dir):
     with amend_config(data_dir=nexus_dir):
         ws = mtd_api.Load("REF_L_198409")
 
-    corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=ws)
-
+    algo = SingleReadoutDeadTimeCorrection()
+    algo.PyInit()
+    algo.setProperty("InputWorkspace", ws)
+    algo.setProperty("OutputWorkspace", "dead_time_corr")
+    algo.PyExec()
+    corr_ws = algo.getProperty('OutputWorkspace').value
     corr = corr_ws.readY(0)
     for c in corr:
         assert(c>0)
@@ -33,8 +37,13 @@ def test_deadtime_paralyzable(nexus_dir):
     with amend_config(data_dir=nexus_dir):
         ws = mtd_api.Load("REF_L_198409")
 
-    corr_ws = SingleReadoutDeadTimeCorrection(InputWorkspace=ws,
-                                              Paralyzable=True)
+    algo = SingleReadoutDeadTimeCorrection()
+    algo.PyInit()
+    algo.setProperty("InputWorkspace", ws)
+    algo.setProperty("Paralyzable", True)
+    algo.setProperty("OutputWorkspace", "dead_time_corr")
+    algo.PyExec()
+    corr_ws = algo.getProperty('OutputWorkspace').value
     corr = corr_ws.readY(0)
     with open("dc.txt", 'w') as fd:
         fd.write(str(corr))

From 0249b1fb8e859bba1a0a72fed224473f8a8a3182 Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Fri, 8 Mar 2024 09:02:26 -0500
Subject: [PATCH 14/15] fix algo import

---
 reduction/test/test_dead_time.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/reduction/test/test_dead_time.py b/reduction/test/test_dead_time.py
index 86fd71d..2276814 100644
--- a/reduction/test/test_dead_time.py
+++ b/reduction/test/test_dead_time.py
@@ -1,5 +1,3 @@
-import os
-import numpy as np
 from lr_reduction.DeadTimeCorrection import SingleReadoutDeadTimeCorrection
 
 import mantid

From fdf844bfa655ce72099788c6b4b5b90d7763eb3b Mon Sep 17 00:00:00 2001
From: Mathieu Doucet <doucetm@ornl.gov>
Date: Fri, 8 Mar 2024 09:51:53 -0500
Subject: [PATCH 15/15] cleanup

---
 reduction/lr_reduction/__init__.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/reduction/lr_reduction/__init__.py b/reduction/lr_reduction/__init__.py
index 9efdbbe..2b6bf42 100644
--- a/reduction/lr_reduction/__init__.py
+++ b/reduction/lr_reduction/__init__.py
@@ -1,3 +1 @@
 __version__ = '2.0.18'
-
-from . import DeadTimeCorrection
\ No newline at end of file