From 0ae2f2f58b3a9180356a9bd46564f1ade5eacde8 Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Fri, 28 Feb 2025 14:10:19 +0100
Subject: [PATCH 01/18] Test tabular batch widget 'GUI'

This is a very preliminary attempt at having a table-based multiple run reduction using the LoKI workflows and locally stored data.
---
 src/ess/loki/.DS_Store                        | Bin 0 -> 6148 bytes
 src/ess/loki/batchwidget.py                   | 262 ++++++++++++++++++
 src/ess/loki/batchwidgets.ipynb               |  21 ++
 src/ess/loki/examplefiles/.DS_Store           | Bin 0 -> 6148 bytes
 .../loki/examplefiles/mask_new_July2022.xml   |   6 +
 5 files changed, 289 insertions(+)
 create mode 100644 src/ess/loki/.DS_Store
 create mode 100644 src/ess/loki/batchwidget.py
 create mode 100644 src/ess/loki/batchwidgets.ipynb
 create mode 100644 src/ess/loki/examplefiles/.DS_Store
 create mode 100644 src/ess/loki/examplefiles/mask_new_July2022.xml

diff --git a/src/ess/loki/.DS_Store b/src/ess/loki/.DS_Store
new file mode 100644
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diff --git a/src/ess/loki/batchwidget.py b/src/ess/loki/batchwidget.py
new file mode 100644
index 00000000..625dbe95
--- /dev/null
+++ b/src/ess/loki/batchwidget.py
@@ -0,0 +1,262 @@
+import os
+import glob
+import pandas as pd
+import scipp as sc
+import matplotlib.pyplot as plt
+import numpy as np
+import ipywidgets as widgets
+from ipydatagrid import DataGrid
+from IPython.display import display
+from ipyfilechooser import FileChooser
+from ess import sans
+from ess import loki
+from ess.sans.types import *
+
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101
+):
+    if mask_files is None:
+        mask_files = []
+    # Define wavelength and Q bins.
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    # Initialize the workflow.
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+
+def find_direct_beam(work_dir):
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+
+
+def find_mask_file(work_dir):
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+
+class SansBatchReductionWidget:
+    def __init__(self):
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.ebeam_sans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam SANS run number",
+            description="Ebeam SANS:"
+        )
+        self.ebeam_trans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam TRANS run number",
+            description="Ebeam TRANS:"
+        )
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        # New Clear Log button.
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(self.clear_log)
+        self.log_output = widgets.Output()
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button, self.clear_log_button]),
+            self.log_output
+        ])
+    
+    def clear_log(self, _):
+        self.log_output.clear_output()
+    
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+    
+    def run_reduction(self, _):
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
+            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            with self.log_output:
+                print("Using empty-beam files:")
+                print("  Background (Ebeam SANS):", background_run_file)
+                print("  Empty-beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Error finding empty-beam files:", e)
+            return
+        df = self.table.data
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            try:
+                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
+                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            mask_candidate = str(row.get("mask", "")).strip()
+            mask_file = None
+            if mask_candidate:
+                mask_file_candidate = os.path.join(input_dir, f"{mask_candidate}.xml")
+                if os.path.exists(mask_file_candidate):
+                    mask_file = mask_file_candidate
+            if mask_file is None:
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Using mask file: {mask_file} for sample {sample}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {sample}: {e}")
+                    continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file]
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            wavelength_bins = sc.linspace("wavelength", 1.0, 13.0, 201, unit="angstrom")
+            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+            plt.figure()
+            plt.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
+            plt.title(f"Transmission: {os.path.basename(sample_run_file)}")
+            plt.xlabel("Wavelength (angstrom)")
+            plt.ylabel("Transmission")
+            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            plt.savefig(trans_png)
+            plt.close()
+            q_bins = sc.linspace("Q", 0.01, 0.3, 101, unit="1/angstrom")
+            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+            plt.figure()
+            if res["IofQ"].variances is not None:
+                yerr = np.sqrt(res["IofQ"].variances)
+                plt.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+            else:
+                plt.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
+            plt.title(f"I(Q): {os.path.basename(sample_run_file)}")
+            plt.xlabel("Q (1/angstrom)")
+            plt.ylabel("I(Q)")
+            plt.xscale("log")
+            plt.yscale("log")
+            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            plt.savefig(iq_png)
+            plt.close()
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+    
+    @property
+    def widget(self):
+        return self.main
diff --git a/src/ess/loki/batchwidgets.ipynb b/src/ess/loki/batchwidgets.ipynb
new file mode 100644
index 00000000..14dc5195
--- /dev/null
+++ b/src/ess/loki/batchwidgets.ipynb
@@ -0,0 +1,21 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gui = SansBatchReductionWidget()\n",
+    "display(gui.widget)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/ess/loki/examplefiles/.DS_Store b/src/ess/loki/examplefiles/.DS_Store
new file mode 100644
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literal 0
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diff --git a/src/ess/loki/examplefiles/mask_new_July2022.xml b/src/ess/loki/examplefiles/mask_new_July2022.xml
new file mode 100644
index 00000000..8c9e06ac
--- /dev/null
+++ b/src/ess/loki/examplefiles/mask_new_July2022.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0"?>
+<detector-masking>
+	<group>
+		<detids>1-25158,25532-25670,26044-26182,26556-26694,27068-27206,27580-27718,28092-28230,28604-28742,29116-29254,29628-29766,30140-30278,30652-30790,31164-31302,31676-31814,32188-32326,32700-32838,33212-33350,33724-33862,34236-34374,34748-34886,35260-35398,35772-35910,36284-36422,36796-36934,37308-37446,37820-37958,38332-38470,38844-38982,39356-39494,39868-40006,40380-40518,40892-41030,41404-41542,41916-42054,42428-42566,42940-43078,43452-43590,43964-44102,44476-44614,44988-45126,45500-45638,46012-46150,46524-46662,47036-47174,47548-47686,48060-48198,48572-48710,49084-49222,49596-49734,50108-50246,50620-50758,51132-51270,51644-51782,52156-52294,52668-52806,53180-53318,53692-53830,53986-54020,54204-54854,55008-55045,55228-55366,55522-55555,55740-55878,56036-56066,56252-56390,56548-56578,56764-56902,57058-57091,57276-57414,57566-57607,57788-57926,58078-58119,58300-58438,58590-58631,58812-58950,59102-59143,59324-59462,59614-59655,59836-59974,60126-60167,60348-60486,60638-60679,60860-60998,61156-61186,61372-61510,61673-61692,61884-62022,62184-62205,62396-62534,62692-62722,62908-63046,63202-63236,63420-63558,63713-63748,63932-64070,64228-64258,64444-64582,64956-65094,65468-65606,65980-66118,66492-66630,67004-67142,67516-67654,68028-68166,68540-68678,69052-69190,69564-69702,70076-70214,70588-70726,71100-71238,71612-71750,72124-72262,72636-72774,73148-73286,73660-73798,74172-74310,74684-74822,75196-75334,75708-75846,76220-76358,76732-76870,77244-77382,77756-77894,78268-78406,78780-78918,79292-79430,79804-79942,80316-80454,80828-80966,81340-81478,81852-81990,82364-82502,82876-83014,83388-83526,83900-84038,84412-84550,84924-85062,85436-85574,85948-86086,86460-86598,86972-87110,87484-87622,87996-88134,88508-88646,89020-89158,89532-89670,90044-90182,90556-90694,91068-91206,91580-91718,92092-92230,92604-92742,93116-93254,93628-93766,94140-94278,94652-94790,95164-95302,95676-95814,96188-96326,96700-136262,136636-136774,137148-137286,137660-137798,138172-139334,139708-139846,140220-140358,140732-140870,141244-141382,141756-141894,142268-142406,142780-142918,143292-143430,143804-143942,144316-144454,144828-144966,145340-145478,145852-145990,146364-146502,146876-147014,147388-147526,147900-148038,148412-148550,148924-149062,149436-149574,149948-150086,150460-150598,150972-151110,151484-151622,151996-152134,152508-152646,153020-153158,153532-153670,154044-154182,154556-154694,155068-155206,155580-155718,156092-156230,156604-156742,157116-157254,157628-157766,158140-158278,158652-158790,159164-159302,159676-159814,160188-160326,160700-160838,161212-161350,161724-161862,162236-162374,162748-162886,163260-163398,163772-163910,164284-164422,164796-164934,165308-165446,165820-165958,166332-166470,166844-166982,167356-167494,167868-168006,168380-168518,168676-168706,168892-169030,169186-169220,169404-169542,169698-169731,169916-170054,170212-170242,170428-170566,170729-170748,170940-171078,171242-171259,171452-171590,171748-171778,171964-172102,172254-172295,172476-172614,172766-172807,172988-173126,173278-173319,173500-173638,173790-173831,174012-174150,174302-174343,174524-174662,174814-174855,175036-175174,175326-175367,175548-175686,175842-175875,176060-176198,176356-176386,176572-176710,176868-176898,177084-177222,177378-177412,177596-177734,177888-177925,178108-178246,178400-178438,178620-178758,178913-178948,179132-179270,179644-179782,180156-180294,180668-180806,181180-181318,181692-181830,182204-182342,182716-182854,183228-183366,183740-183878,184252-184390,184764-184902,185276-185414,185788-185926,186300-186438,186812-186950,187324-187462,187836-187974,188348-188486,188860-188998,189372-189510,189884-190022,190396-190534,190908-191046,191420-191558,191932-192070,192444-192582,192956-193094,193468-193606,193980-194118,194492-194630,195004-195142,195516-195654,196028-196166,196540-196678,197052-197190,197564-197702,198076-198214,198588-198726,199100-199238,199612-199750,200124-200262,200636-200774,201148-201286,201660-201798,202172-202310,202684-202822,203196-203334,203708-203846,204220-204358,204732-204870,205244-205382,205756-205894,206268-206406,206780-206918,207292-207430,207804-207942,208316-208454,208828-208966,209340-209478,209852-209990,210364-210502,210876-211014,211388-214086,214460-254534,254908-255046,255420-255558,255932-256070,256444-256582,256956-257094,257468-257606,257980-258118,258492-258630,259004-259142,259516-259654,260028-260166,260540-260678,261052-261190,261564-261702,262076-262214,262588-262726,263100-263238,263612-263750,264124-264262,264636-264774,265148-265286,265660-265798,266172-266310,266684-266822,267196-267334,267708-267846,268220-268358,268732-268870,269244-269382,269756-269894,270268-270406,270780-270918,271292-271430,271804-271942,272316-272454,272828-272966,273340-273478,273852-273990,274364-274502,274876-275014,275388-275526,275900-276038,276412-276550,276924-277062,277436-277574,277948-278086,278460-278598,278972-279110,279484-279622,279996-280134,280508-280646,281020-281158,281532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+	</group>
+</detector-masking>

From fdde1dfedd6a7b9c17e7ef5aac91a4f5beb5cc6c Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Fri, 28 Feb 2025 14:25:10 +0100
Subject: [PATCH 02/18] Update batchwidgets.ipynb

---
 src/ess/loki/batchwidgets.ipynb | 43 ++++++++++++++++++++++++++++++---
 1 file changed, 40 insertions(+), 3 deletions(-)

diff --git a/src/ess/loki/batchwidgets.ipynb b/src/ess/loki/batchwidgets.ipynb
index 14dc5195..14e517f0 100644
--- a/src/ess/loki/batchwidgets.ipynb
+++ b/src/ess/loki/batchwidgets.ipynb
@@ -4,16 +4,53 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36a6230223f14f42a36d72af759139ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(FileChooser(path='/Users/oliverhammond/esssans-gui/src/ess/loki', filename='', t…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "gui = SansBatchReductionWidget()\n",
+    "import batchwidget\n",
+    "gui = batchwidget.SansBatchReductionWidget()\n",
     "display(gui.widget)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,

From c5ba71cee13edf50a5910a466e1eb98487ff2e28 Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Fri, 28 Feb 2025 15:03:16 +0100
Subject: [PATCH 03/18] Improvements, improvements, improvements!

---
 .DS_Store                               | Bin 0 -> 6148 bytes
 src/.DS_Store                           | Bin 0 -> 6148 bytes
 src/ess/.DS_Store                       | Bin 0 -> 6148 bytes
 src/ess/loki/.DS_Store                  | Bin 6148 -> 6148 bytes
 src/ess/loki/batchwidget.py             |  84 ++++++++++++++++--------
 src/ess/loki/batchwidgets.ipynb         |   4 +-
 src/ess/loki/examplefiles/.DS_Store     | Bin 6148 -> 8196 bytes
 src/ess/loki/examplefiles/out/.DS_Store | Bin 0 -> 8196 bytes
 8 files changed, 59 insertions(+), 29 deletions(-)
 create mode 100644 .DS_Store
 create mode 100644 src/.DS_Store
 create mode 100644 src/ess/.DS_Store
 create mode 100644 src/ess/loki/examplefiles/out/.DS_Store

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
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GIT binary patch
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diff --git a/src/ess/.DS_Store b/src/ess/.DS_Store
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diff --git a/src/ess/loki/batchwidget.py b/src/ess/loki/batchwidget.py
index 625dbe95..b2d43336 100644
--- a/src/ess/loki/batchwidget.py
+++ b/src/ess/loki/batchwidget.py
@@ -12,6 +12,7 @@
 from ess import loki
 from ess.sans.types import *
 
+
 def reduce_loki_batch_preliminary(
     sample_run_file: str,
     transmission_run_file: str,
@@ -72,7 +73,7 @@ def find_direct_beam(work_dir):
     if files:
         return files[0]
     else:
-        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+        raise FileNotFoundError(f"Could not find direct beam file matching pattern {pattern}")
 
 
 def find_mask_file(work_dir):
@@ -123,22 +124,28 @@ def __init__(self):
         self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
         self.reduce_button = widgets.Button(description="Reduce")
         self.reduce_button.on_click(self.run_reduction)
-        # New Clear Log button.
         self.clear_log_button = widgets.Button(description="Clear Log")
         self.clear_log_button.on_click(self.clear_log)
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(self.clear_plots)
         self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
         self.main = widgets.VBox([
             widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
             widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
             self.load_csv_button,
             self.table,
-            widgets.HBox([self.reduce_button, self.clear_log_button]),
-            self.log_output
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
         ])
     
     def clear_log(self, _):
         self.log_output.clear_output()
     
+    def clear_plots(self, _):
+        self.plot_output.clear_output()
+    
     def load_csv(self, _):
         csv_path = self.csv_chooser.selected
         if not csv_path or not os.path.exists(csv_path):
@@ -164,21 +171,21 @@ def run_reduction(self, _):
         try:
             direct_beam_file = find_direct_beam(input_dir)
             with self.log_output:
-                print("Using direct-beam file:", direct_beam_file)
+                print("Using direct beam file:", direct_beam_file)
         except Exception as e:
             with self.log_output:
-                print("Direct-beam file not found:", e)
+                print("Direct beam file not found:", e)
             return
         try:
             background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
             empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
             with self.log_output:
-                print("Using empty-beam files:")
+                print("Using empty beam files:")
                 print("  Background (Ebeam SANS):", background_run_file)
-                print("  Empty-beam (Ebeam TRANS):", empty_beam_file)
+                print("  Empty beam (Ebeam TRANS):", empty_beam_file)
         except Exception as e:
             with self.log_output:
-                print("Error finding empty-beam files:", e)
+                print("Error finding empty beam files:", e)
             return
         df = self.table.data
         for idx, row in df.iterrows():
@@ -200,7 +207,7 @@ def run_reduction(self, _):
                 try:
                     mask_file = find_mask_file(input_dir)
                     with self.log_output:
-                        print(f"Using mask file: {mask_file} for sample {sample}")
+                        print(f"Using global mask file: {mask_file} for sample {sample}")
                 except Exception as e:
                     with self.log_output:
                         print(f"Mask file not found for sample {sample}: {e}")
@@ -228,35 +235,58 @@ def run_reduction(self, _):
             except Exception as e:
                 with self.log_output:
                     print(f"Failed to save reduced data for {sample}: {e}")
+            # Generate and display Transmission plot.
             wavelength_bins = sc.linspace("wavelength", 1.0, 13.0, 201, unit="angstrom")
             x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
-            plt.figure()
-            plt.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-            plt.title(f"Transmission: {os.path.basename(sample_run_file)}")
-            plt.xlabel("Wavelength (angstrom)")
-            plt.ylabel("Transmission")
+            fig_trans, ax_trans = plt.subplots()
+            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
+            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_xlabel("Wavelength (Å)")
+            ax_trans.set_ylabel("Transmission")
+            plt.tight_layout()
+            #with self.plot_output:
+                #display(fig_trans)
             trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
-            plt.savefig(trans_png)
-            plt.close()
+            fig_trans.savefig(trans_png, dpi=300)
+            plt.close(fig_trans)
+            # Generate and display I(Q) plot.
             q_bins = sc.linspace("Q", 0.01, 0.3, 101, unit="1/angstrom")
             x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
-            plt.figure()
+            fig_iq, ax_iq = plt.subplots()
             if res["IofQ"].variances is not None:
                 yerr = np.sqrt(res["IofQ"].variances)
-                plt.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
             else:
-                plt.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
-            plt.title(f"I(Q): {os.path.basename(sample_run_file)}")
-            plt.xlabel("Q (1/angstrom)")
-            plt.ylabel("I(Q)")
-            plt.xscale("log")
-            plt.yscale("log")
+                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
+            ax_iq.set_title(f"{os.path.basename(sample_run_file)} ({sample})")
+            ax_iq.set_xlabel("Q (Å$^{-1}$)")
+            ax_iq.set_ylabel("I(Q)")
+            ax_iq.set_xscale("log")
+            ax_iq.set_yscale("log")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_iq)
             iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
-            plt.savefig(iq_png)
-            plt.close()
+            fig_iq.savefig(iq_png, dpi=300)
+            plt.close(fig_iq)
             with self.log_output:
                 print(f"Reduced sample {sample} and saved outputs.")
     
     @property
     def widget(self):
         return self.main
+
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
\ No newline at end of file
diff --git a/src/ess/loki/batchwidgets.ipynb b/src/ess/loki/batchwidgets.ipynb
index 14e517f0..e97a9ff0 100644
--- a/src/ess/loki/batchwidgets.ipynb
+++ b/src/ess/loki/batchwidgets.ipynb
@@ -2,13 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "36a6230223f14f42a36d72af759139ac",
+       "model_id": "2f91f9ddaf6e4dfb98f67d743719bcee",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/src/ess/loki/examplefiles/.DS_Store b/src/ess/loki/examplefiles/.DS_Store
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From aa13d55376f2a36b536895f3ccb28780b0c2a30a Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Mon, 3 Mar 2025 18:00:59 +0100
Subject: [PATCH 04/18] Added tabs for different workflows

---
 src/ess/loki/batchwidget-tabs.py    | 295 +++++++++++++++++
 src/ess/loki/examplefiles/.DS_Store | Bin 8196 -> 10244 bytes
 src/ess/loki/tabwidget.ipynb        |  48 +++
 src/ess/loki/tabwidget.py           | 496 ++++++++++++++++++++++++++++
 4 files changed, 839 insertions(+)
 create mode 100644 src/ess/loki/batchwidget-tabs.py
 create mode 100644 src/ess/loki/tabwidget.ipynb
 create mode 100644 src/ess/loki/tabwidget.py

diff --git a/src/ess/loki/batchwidget-tabs.py b/src/ess/loki/batchwidget-tabs.py
new file mode 100644
index 00000000..21ded410
--- /dev/null
+++ b/src/ess/loki/batchwidget-tabs.py
@@ -0,0 +1,295 @@
+import os
+import glob
+import pandas as pd
+import scipp as sc
+import matplotlib.pyplot as plt
+import numpy as np
+import ipywidgets as widgets
+from ipydatagrid import DataGrid
+from IPython.display import display
+from ipyfilechooser import FileChooser
+from ess import sans
+from ess import loki
+from ess.sans.types import *
+
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101
+):
+    if mask_files is None:
+        mask_files = []
+    # Define wavelength and Q bins.
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    # Initialize the workflow.
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+def find_direct_beam(work_dir):
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+
+def find_mask_file(work_dir):
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+class SansBatchReductionWidget:
+    def __init__(self):
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.ebeam_sans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam SANS run number",
+            description="Ebeam SANS:"
+        )
+        self.ebeam_trans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam TRANS run number",
+            description="Ebeam TRANS:"
+        )
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(self.clear_log)
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(self.clear_plots)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def clear_log(self, _):
+        self.log_output.clear_output()
+    
+    def clear_plots(self, _):
+        self.plot_output.clear_output()
+    
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+    
+    def run_reduction(self, _):
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
+            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            with self.log_output:
+                print("Using empty-beam files:")
+                print("  Background (Ebeam SANS):", background_run_file)
+                print("  Empty-beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Error finding empty-beam files:", e)
+            return
+        df = self.table.data
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            try:
+                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
+                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            mask_candidate = str(row.get("mask", "")).strip()
+            mask_file = None
+            if mask_candidate:
+                mask_file_candidate = os.path.join(input_dir, f"{mask_candidate}.xml")
+                if os.path.exists(mask_file_candidate):
+                    mask_file = mask_file_candidate
+            if mask_file is None:
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Using global mask file: {mask_file} for sample {sample}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {sample}: {e}")
+                    continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file]
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            # Generate and display Transmission plot.
+            wavelength_bins = sc.linspace("wavelength", 1.0, 13.0, 201, unit="angstrom")
+            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+            fig_trans, ax_trans = plt.subplots()
+            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
+            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_xlabel("Wavelength (Å)")
+            ax_trans.set_ylabel("Transmission")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_trans)
+            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            fig_trans.savefig(trans_png, dpi=300)
+            plt.close(fig_trans)
+            # Generate and display I(Q) plot.
+            q_bins = sc.linspace("Q", 0.01, 0.3, 101, unit="1/angstrom")
+            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+            fig_iq, ax_iq = plt.subplots()
+            if res["IofQ"].variances is not None:
+                yerr = np.sqrt(res["IofQ"].variances)
+                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+            else:
+                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
+            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
+            ax_iq.set_xlabel("Q (Å$^{-1}$)")
+            ax_iq.set_ylabel("I(Q)")
+            ax_iq.set_xscale("log")
+            ax_iq.set_yscale("log")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_iq)
+            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            fig_iq.savefig(iq_png, dpi=300)
+            plt.close(fig_iq)
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+    
+        @property
+        def widget(self):
+            return self.main
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+# Build the main tabbed widget.
+reduction_widget = SansBatchReductionWidget().widget
+direct_beam_widget = widgets.HTML("<h3>Direct Beam</h3><p>Direct beam tab content goes here.</p>")
+tabs = widgets.Tab(children=[reduction_widget, direct_beam_widget])
+tabs.set_title(0, "Reduction")
+tabs.set_title(1, "Direct Beam")
+
+# Display the tab widget.
+display(tabs)
diff --git a/src/ess/loki/examplefiles/.DS_Store b/src/ess/loki/examplefiles/.DS_Store
index f05afb0823a78ff9b50b4141dc49918c6c157c57..7ca71aca2f4f16e462e1848c996fcbc14313d923 100644
GIT binary patch
delta 220
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diff --git a/src/ess/loki/tabwidget.ipynb b/src/ess/loki/tabwidget.ipynb
new file mode 100644
index 00000000..f281b0ec
--- /dev/null
+++ b/src/ess/loki/tabwidget.ipynb
@@ -0,0 +1,48 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'SansBatchReductionWidget' object has no attribute 'widget'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtabwidget\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tabs\n\u001b[1;32m      2\u001b[0m display(tabs)\n",
+      "File \u001b[0;32m~/esssans-gui/src/ess/loki/tabwidget.py:445\u001b[0m\n\u001b[1;32m      0\u001b[0m <Error retrieving source code with stack_data see ipython/ipython#13598>\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'SansBatchReductionWidget' object has no attribute 'widget'"
+     ]
+    }
+   ],
+   "source": [
+    "from tabwidget import tabs\n",
+    "display(tabs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/ess/loki/tabwidget.py b/src/ess/loki/tabwidget.py
new file mode 100644
index 00000000..2b37caff
--- /dev/null
+++ b/src/ess/loki/tabwidget.py
@@ -0,0 +1,496 @@
+import os
+import glob
+import pandas as pd
+import scipp as sc
+import matplotlib.pyplot as plt
+import numpy as np
+import ipywidgets as widgets
+from ipydatagrid import DataGrid
+from IPython.display import display
+from ipyfilechooser import FileChooser
+from ess import sans
+from ess import loki
+from ess.sans.types import *
+from scipp.scipy.interpolate import interp1d
+import plopp as pp  # used for plotting in direct beam section
+
+# ----------------------------
+# Reduction Functionality
+# ----------------------------
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101
+):
+    if mask_files is None:
+        mask_files = []
+    # Define wavelength and Q bins.
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    # Initialize the workflow.
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+def find_direct_beam(work_dir):
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+
+def find_mask_file(work_dir):
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+# ----------------------------
+# Direct Beam Functionality
+# ----------------------------
+def compute_direct_beam_local(
+    mask: str,
+    sample_sans: str,
+    background_sans: str,
+    sample_trans: str,
+    background_trans: str,
+    empty_beam: str,
+    local_Iq_theory: str,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    n_wavelength_bins: int = 50,
+    n_wavelength_bands: int = 50
+) -> dict:
+    """
+    Compute the direct beam function for the LoKI detectors using locally stored data.
+    """
+    workflow = loki.LokiAtLarmorWorkflow()
+    workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
+    workflow[NeXusDetectorName] = 'larmor_detector'
+    
+    wl_min = sc.scalar(wavelength_min, unit='angstrom')
+    wl_max = sc.scalar(wavelength_max, unit='angstrom')
+    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
+    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
+    workflow[CorrectForGravity] = True
+    workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
+    workflow[ReturnEvents] = False
+    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
+    
+    workflow[Filename[SampleRun]] = sample_sans
+    workflow[Filename[BackgroundRun]] = background_sans
+    workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
+    workflow[Filename[EmptyBeamRun]] = empty_beam
+    
+    center = sans.beam_center_from_center_of_mass(workflow)
+    print("Computed beam center:", center)
+    workflow[BeamCenter] = center
+    
+    Iq_theory = sc.io.load_hdf5(local_Iq_theory)
+    f = interp1d(Iq_theory, 'Q')
+    I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
+    print("Computed I0:", I0)
+    
+    results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
+    
+    iofq_full = results[-1]['iofq_full']
+    iofq_bands = results[-1]['iofq_bands']
+    direct_beam_function = results[-1]['direct_beam']
+    
+    pp.plot(
+        {'reference': Iq_theory, 'data': iofq_full},
+        color={'reference': 'darkgrey', 'data': 'C0'},
+        norm='log',
+    )
+    print("Plotted full-range result vs. theoretical reference.")
+    
+    return {
+        'direct_beam_function': direct_beam_function,
+        'iofq_full': iofq_full,
+        'Iq_theory': Iq_theory,
+    }
+
+# ----------------------------
+# Widgets for Reduction and Direct Beam
+# ----------------------------
+class SansBatchReductionWidget:
+    def __init__(self):
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.ebeam_sans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam SANS run number",
+            description="Ebeam SANS:"
+        )
+        self.ebeam_trans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam TRANS run number",
+            description="Ebeam TRANS:"
+        )
+        # Add GUI widgets for reduction parameters:
+        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(self.clear_log)
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(self.clear_plots)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+            # Reduction parameters:
+            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
+            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def clear_log(self, _):
+        self.log_output.clear_output()
+    
+    def clear_plots(self, _):
+        self.plot_output.clear_output()
+    
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+    
+    def run_reduction(self, _):
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
+            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            with self.log_output:
+                print("Using empty-beam files:")
+                print("  Background (Ebeam SANS):", background_run_file)
+                print("  Empty beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Error finding empty beam files:", e)
+            return
+        # Retrieve reduction parameters from widgets.
+        wl_min = self.wavelength_min_widget.value
+        wl_max = self.wavelength_max_widget.value
+        wl_n = self.wavelength_n_widget.value
+        q_start = self.q_start_widget.value
+        q_stop = self.q_stop_widget.value
+        q_n = self.q_n_widget.value
+        df = self.table.data
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            try:
+                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
+                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            mask_candidate = str(row.get("mask", "")).strip()
+            mask_file = None
+            if mask_candidate:
+                mask_file_candidate = os.path.join(input_dir, f"{mask_candidate}.xml")
+                if os.path.exists(mask_file_candidate):
+                    mask_file = mask_file_candidate
+            if mask_file is None:
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Identified mask file: {mask_file} for sample {sample}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {sample}: {e}")
+                    continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file],
+                    wavelength_min=wl_min,
+                    wavelength_max=wl_max,
+                    wavelength_n=wl_n,
+                    q_start=q_start,
+                    q_stop=q_stop,
+                    q_n=q_n
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            wavelength_bins = sc.linspace("wavelength", 1.0, 13.0, 201, unit="angstrom")
+            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+            fig_trans, ax_trans = plt.subplots()
+            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
+            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_xlabel("Wavelength (Å)")
+            ax_trans.set_ylabel("Transmission")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_trans)
+            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            fig_trans.savefig(trans_png, dpi=300)
+            plt.close(fig_trans)
+            q_bins = sc.linspace("Q", 0.01, 0.3, 101, unit="1/angstrom")
+            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+            fig_iq, ax_iq = plt.subplots()
+            if res["IofQ"].variances is not None:
+                yerr = np.sqrt(res["IofQ"].variances)
+                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+            else:
+                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
+            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
+            ax_iq.set_xlabel("Q (Å$^{-1}$)")
+            ax_iq.set_ylabel("I(Q)")
+            ax_iq.set_xscale("log")
+            ax_iq.set_yscale("log")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_iq)
+            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            fig_iq.savefig(iq_png, dpi=300)
+            plt.close(fig_iq)
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Direct Beam Widget
+# ----------------------------
+class DirectBeamWidget:
+    def __init__(self):
+        self.mask_text = widgets.Text(
+            value="",
+            placeholder="Enter mask file path",
+            description="Mask:"
+        )
+        self.sample_sans_text = widgets.Text(
+            value="",
+            placeholder="Enter sample SANS file path",
+            description="Sample SANS:"
+        )
+        self.background_sans_text = widgets.Text(
+            value="",
+            placeholder="Enter background SANS file path",
+            description="Background SANS:"
+        )
+        self.sample_trans_text = widgets.Text(
+            value="",
+            placeholder="Enter sample TRANS file path",
+            description="Sample TRANS:"
+        )
+        self.background_trans_text = widgets.Text(
+            value="",
+            placeholder="Enter background TRANS file path",
+            description="Background TRANS:"
+        )
+        self.empty_beam_text = widgets.Text(
+            value="",
+            placeholder="Enter empty beam file path",
+            description="Empty Beam:"
+        )
+        self.local_Iq_theory_text = widgets.Text(
+            value="",
+            placeholder="Enter I(q) theory file path",
+            description="I(q) Theory:"
+        )
+        # GUI widgets for direct beam parameters:
+        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
+        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
+        
+        self.compute_button = widgets.Button(description="Compute Direct Beam")
+        self.compute_button.on_click(self.compute_direct_beam)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            self.mask_text,
+            self.sample_sans_text,
+            self.background_sans_text,
+            self.sample_trans_text,
+            self.background_trans_text,
+            self.empty_beam_text,
+            self.local_Iq_theory_text,
+            widgets.HBox([
+                self.db_wavelength_min_widget,
+                self.db_wavelength_max_widget,
+                self.db_n_wavelength_bins_widget,
+                self.db_n_wavelength_bands_widget
+            ]),
+            self.compute_button,
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def compute_direct_beam(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        mask = self.mask_text.value
+        sample_sans = self.sample_sans_text.value
+        background_sans = self.background_sans_text.value
+        sample_trans = self.sample_trans_text.value
+        background_trans = self.background_trans_text.value
+        empty_beam = self.empty_beam_text.value
+        local_Iq_theory = self.local_Iq_theory_text.value
+        wl_min = self.db_wavelength_min_widget.value
+        wl_max = self.db_wavelength_max_widget.value
+        n_bins = self.db_n_wavelength_bins_widget.value
+        n_bands = self.db_n_wavelength_bands_widget.value
+        with self.log_output:
+            print("Computing direct beam with:")
+            print("  Mask:", mask)
+            print("  Sample SANS:", sample_sans)
+            print("  Background SANS:", background_sans)
+            print("  Sample TRANS:", sample_trans)
+            print("  Background TRANS:", background_trans)
+            print("  Empty Beam:", empty_beam)
+            print("  I(q) Theory:", local_Iq_theory)
+            print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
+        try:
+            results = compute_direct_beam_local(
+                mask,
+                sample_sans,
+                background_sans,
+                sample_trans,
+                background_trans,
+                empty_beam,
+                local_Iq_theory,
+                wavelength_min=wl_min,
+                wavelength_max=wl_max,
+                n_wavelength_bins=n_bins,
+                n_wavelength_bands=n_bands
+            )
+            with self.log_output:
+                print("Direct beam computation complete.")
+        except Exception as e:
+            with self.log_output:
+                print("Error computing direct beam:", e)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Build Tabbed Widget
+# ----------------------------
+reduction_widget = SansBatchReductionWidget().widget
+direct_beam_widget = DirectBeamWidget().widget
+tabs = widgets.Tab(children=[reduction_widget, direct_beam_widget])
+tabs.set_title(0, "Reduction")
+tabs.set_title(1, "Direct Beam")
+
+# Display the tab widget.
+#display(tabs)

From 7ebdedc02a46dff8ea9a534267b0eead9a9733b9 Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Tue, 4 Mar 2025 15:44:30 +0100
Subject: [PATCH 05/18] A lot more widgetry

---
 src/ess/.DS_Store                             | Bin 6148 -> 6148 bytes
 src/ess/loki/.DS_Store                        | Bin 6148 -> 8196 bytes
 src/ess/loki/examplefiles/.DS_Store           | Bin 10244 -> 10244 bytes
 src/ess/loki/examplefiles/nxsmod/.DS_Store    | Bin 0 -> 6148 bytes
 .../examplefiles/nxsmod/mask_new_July2022.xml |   6 +
 .../loki/examplefiles/nxsmod/out/.DS_Store    | Bin 0 -> 6148 bytes
 .../loki/examplefiles/nxsmodscript/.DS_Store  | Bin 0 -> 6148 bytes
 .../nxsmodscript/mask_new_July2022.xml        |   6 +
 .../examplefiles/nxsmodscript/out/.DS_Store   | Bin 0 -> 6148 bytes
 .../60339-2022-02-28_2215_mod_IofQ.png        | Bin 0 -> 94040 bytes
 ...60339-2022-02-28_2215_mod_transmission.png | Bin 0 -> 168524 bytes
 .../timed-test/mask_new_July2022.xml          |   6 +
 src/ess/loki/tabwidget.ipynb                  |  33 +-
 src/ess/loki/tabwidget.py                     | 290 +++++-
 src/ess/loki/tabwidgetauto.py                 | 969 ++++++++++++++++++
 15 files changed, 1293 insertions(+), 17 deletions(-)
 create mode 100644 src/ess/loki/examplefiles/nxsmod/.DS_Store
 create mode 100644 src/ess/loki/examplefiles/nxsmod/mask_new_July2022.xml
 create mode 100644 src/ess/loki/examplefiles/nxsmod/out/.DS_Store
 create mode 100644 src/ess/loki/examplefiles/nxsmodscript/.DS_Store
 create mode 100644 src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml
 create mode 100644 src/ess/loki/examplefiles/nxsmodscript/out/.DS_Store
 create mode 100644 src/ess/loki/examplefiles/nxsmodscript/timed-test/60339-2022-02-28_2215_mod_IofQ.png
 create mode 100644 src/ess/loki/examplefiles/nxsmodscript/timed-test/60339-2022-02-28_2215_mod_transmission.png
 create mode 100644 src/ess/loki/examplefiles/nxsmodscript/timed-test/mask_new_July2022.xml
 create mode 100644 src/ess/loki/tabwidgetauto.py

diff --git a/src/ess/.DS_Store b/src/ess/.DS_Store
index 6c8d8331870a31b21e310fbf904f077381f769b1..ff152f7e2baea4dfd43b46b80acf2af497e3c938 100644
GIT binary patch
delta 19
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delta 19
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diff --git a/src/ess/loki/.DS_Store b/src/ess/loki/.DS_Store
index b53770c25ab13a9b05834cdc0f0ec63776e45b51..6cf9604ead5a256c9a5a5cda6e7cca3f6329ad77 100644
GIT binary patch
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diff --git a/src/ess/loki/examplefiles/.DS_Store b/src/ess/loki/examplefiles/.DS_Store
index 7ca71aca2f4f16e462e1848c996fcbc14313d923..515095a69c7b7a8c2ce9bd7521a96b9044c8dd96 100644
GIT binary patch
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diff --git a/src/ess/loki/examplefiles/nxsmod/.DS_Store b/src/ess/loki/examplefiles/nxsmod/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..9021b034e75da73c7c3fa90d111d3665a6bd3ecb
GIT binary patch
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diff --git a/src/ess/loki/examplefiles/nxsmod/mask_new_July2022.xml b/src/ess/loki/examplefiles/nxsmod/mask_new_July2022.xml
new file mode 100644
index 00000000..8c9e06ac
--- /dev/null
+++ b/src/ess/loki/examplefiles/nxsmod/mask_new_July2022.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0"?>
+<detector-masking>
+	<group>
+		<detids>1-25158,25532-25670,26044-26182,26556-26694,27068-27206,27580-27718,28092-28230,28604-28742,29116-29254,29628-29766,30140-30278,30652-30790,31164-31302,31676-31814,32188-32326,32700-32838,33212-33350,33724-33862,34236-34374,34748-34886,35260-35398,35772-35910,36284-36422,36796-36934,37308-37446,37820-37958,38332-38470,38844-38982,39356-39494,39868-40006,40380-40518,40892-41030,41404-41542,41916-42054,42428-42566,42940-43078,43452-43590,43964-44102,44476-44614,44988-45126,45500-45638,46012-46150,46524-46662,47036-47174,47548-47686,48060-48198,48572-48710,49084-49222,49596-49734,50108-50246,50620-50758,51132-51270,51644-51782,52156-52294,52668-52806,53180-53318,53692-53830,53986-54020,54204-54854,55008-55045,55228-55366,55522-55555,55740-55878,56036-56066,56252-56390,56548-56578,56764-56902,57058-57091,57276-57414,57566-57607,57788-57926,58078-58119,58300-58438,58590-58631,58812-58950,59102-59143,59324-59462,59614-59655,59836-59974,60126-60167,60348-60486,60638-60679,60860-60998,61156-61186,61372-61510,61673-61692,61884-62022,62184-62205,62396-62534,62692-62722,62908-63046,63202-63236,63420-63558,63713-63748,63932-64070,64228-64258,64444-64582,64956-65094,65468-65606,65980-66118,66492-66630,67004-67142,67516-67654,68028-68166,68540-68678,69052-69190,69564-69702,70076-70214,70588-70726,71100-71238,71612-71750,72124-72262,72636-72774,73148-73286,73660-73798,74172-74310,74684-74822,75196-75334,75708-75846,76220-76358,76732-76870,77244-77382,77756-77894,78268-78406,78780-78918,79292-79430,79804-79942,80316-80454,80828-80966,81340-81478,81852-81990,82364-82502,82876-83014,83388-83526,83900-84038,84412-84550,84924-85062,85436-85574,85948-86086,86460-86598,86972-87110,87484-87622,87996-88134,88508-88646,89020-89158,89532-89670,90044-90182,90556-90694,91068-91206,91580-91718,92092-92230,92604-92742,93116-93254,93628-93766,94140-94278,94652-94790,95164-95302,95676-95814,96188-96326,96700-136262,136636-136774,137148-137286,137660-137798,138172-139334,139708-139846,140220-140358,140732-140870,141244-141382,141756-141894,142268-142406,142780-142918,143292-143430,143804-143942,144316-144454,144828-144966,145340-145478,145852-145990,146364-146502,146876-147014,147388-147526,147900-148038,148412-148550,148924-149062,149436-149574,149948-150086,150460-150598,150972-151110,151484-151622,151996-152134,152508-152646,153020-153158,153532-153670,154044-154182,154556-154694,155068-155206,155580-155718,156092-156230,156604-156742,157116-157254,157628-157766,158140-158278,158652-158790,159164-159302,159676-159814,160188-160326,160700-160838,161212-161350,161724-161862,162236-162374,162748-162886,163260-163398,163772-163910,164284-164422,164796-164934,165308-165446,165820-165958,166332-166470,166844-166982,167356-167494,167868-168006,168380-168518,168676-168706,168892-169030,169186-169220,169404-169542,169698-169731,169916-170054,170212-170242,170428-170566,170729-170748,170940-171078,171242-171259,171452-171590,171748-171778,171964-172102,172254-172295,172476-172614,172766-172807,172988-173126,173278-173319,173500-173638,173790-173831,174012-174150,174302-174343,174524-174662,174814-174855,175036-175174,175326-175367,175548-175686,175842-175875,176060-176198,176356-176386,176572-176710,176868-176898,177084-177222,177378-177412,177596-177734,177888-177925,178108-178246,178400-178438,178620-178758,178913-178948,179132-179270,179644-179782,180156-180294,180668-180806,181180-181318,181692-181830,182204-182342,182716-182854,183228-183366,183740-183878,184252-184390,184764-184902,185276-185414,185788-185926,186300-186438,186812-186950,187324-187462,187836-187974,188348-188486,188860-188998,189372-189510,189884-190022,190396-190534,190908-191046,191420-191558,191932-192070,192444-192582,192956-193094,193468-193606,193980-194118,194492-194630,195004-195142,195516-195654,196028-196166,196540-196678,197052-197190,197564-197702,198076-198214,198588-198726,199100-199238,199612-199750,200124-200262,200636-200774,201148-201286,201660-201798,202172-202310,202684-202822,203196-203334,203708-203846,204220-204358,204732-204870,205244-205382,205756-205894,206268-206406,206780-206918,207292-207430,207804-207942,208316-208454,208828-208966,209340-209478,209852-209990,210364-210502,210876-211014,211388-214086,214460-254534,254908-255046,255420-255558,255932-256070,256444-256582,256956-257094,257468-257606,257980-258118,258492-258630,259004-259142,259516-259654,260028-260166,260540-260678,261052-261190,261564-261702,262076-262214,262588-262726,263100-263238,263612-263750,264124-264262,264636-264774,265148-265286,265660-265798,266172-266310,266684-266822,267196-267334,267708-267846,268220-268358,268732-268870,269244-269382,269756-269894,270268-270406,270780-270918,271292-271430,271804-271942,272316-272454,272828-272966,273340-273478,273852-273990,274364-274502,274876-275014,275388-275526,275900-276038,276412-276550,276924-277062,277436-277574,277948-278086,278460-278598,278972-279110,279484-279622,279996-280134,280508-280646,281020-281158,281532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+	</group>
+</detector-masking>
diff --git a/src/ess/loki/examplefiles/nxsmod/out/.DS_Store b/src/ess/loki/examplefiles/nxsmod/out/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6
GIT binary patch
literal 6148
zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3
zem<@ulZcFPQ@L2!n>{z**<q8>++&mCkOWA81W14cNZ<zv;LbK1Poaz?KmsK2CSc!(
z0ynLxE!0092;Krf2c+FF_Fe*7ECH>lEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ
zLs35+`xjp>T0<F0fCPF1$Cyrb|F7^5{eNG?83~ZUUlGt@xh*qZDeu<Z%US-OSsOPv
j)R!Z4KLME7ReXlK;d!wEw5GODWMKRea10D2@KpjYNUI8I

literal 0
HcmV?d00001

diff --git a/src/ess/loki/examplefiles/nxsmodscript/.DS_Store b/src/ess/loki/examplefiles/nxsmodscript/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..8a603289b5ebedbed0526209fc6c3d7b0049e951
GIT binary patch
literal 6148
zcmeHKJ5Iwu5S>X<7-%BVP;N&>k)Fat<^;I_3XT9JmLri+?>VTs1wA+5CLDk_JEMeX
z9Vn1cgl44Kx1P`L`mN;g5D|~p^@wOlL<Ll^cZ8us#4nmj&n)6(Sz|Q0ZJT8l=Z3>&
zK<=(jEv;#_xXbR(JWX3w(?vT)Y!T+Cm(Q<nSKGX%t-tXu@3U2pX+;mz(2Q!jz<frJ
zox69&eLc(5`^Wi<o@Gz$_GCZBcAWEYZ_Q8U3^)VM!0#A9%@!$66n%6CoB?NG!+?Ar
z0#vXxjEd>gfg!a3zyZujFqd9Ja)M!L7!~1xu%-evm952KO@}>LTxl2;HJ#X+54N4z
zIuuU3WBriAiAzNvodIWHmw~=M4yFEI@9+P2ll;jUa0dPr13aiE)flg2wKaP=skH%m
q0~L|DsJKbNB$Z;sN+~{rCV@T31XvnIMOYyIBamqD!5R2d20j6Zq)NO1

literal 0
HcmV?d00001

diff --git a/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml b/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml
new file mode 100644
index 00000000..8c9e06ac
--- /dev/null
+++ b/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0"?>
+<detector-masking>
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diff --git a/src/ess/loki/examplefiles/nxsmodscript/out/.DS_Store b/src/ess/loki/examplefiles/nxsmodscript/out/.DS_Store
new file mode 100644
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diff --git a/src/ess/loki/examplefiles/nxsmodscript/timed-test/60339-2022-02-28_2215_mod_IofQ.png b/src/ess/loki/examplefiles/nxsmodscript/timed-test/60339-2022-02-28_2215_mod_IofQ.png
new file mode 100644
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diff --git a/src/ess/loki/examplefiles/nxsmodscript/timed-test/60339-2022-02-28_2215_mod_transmission.png b/src/ess/loki/examplefiles/nxsmodscript/timed-test/60339-2022-02-28_2215_mod_transmission.png
new file mode 100644
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diff --git a/src/ess/loki/examplefiles/nxsmodscript/timed-test/mask_new_July2022.xml b/src/ess/loki/examplefiles/nxsmodscript/timed-test/mask_new_July2022.xml
new file mode 100644
index 00000000..8c9e06ac
--- /dev/null
+++ b/src/ess/loki/examplefiles/nxsmodscript/timed-test/mask_new_July2022.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0"?>
+<detector-masking>
+	<group>
+		<detids>1-25158,25532-25670,26044-26182,26556-26694,27068-27206,27580-27718,28092-28230,28604-28742,29116-29254,29628-29766,30140-30278,30652-30790,31164-31302,31676-31814,32188-32326,32700-32838,33212-33350,33724-33862,34236-34374,34748-34886,35260-35398,35772-35910,36284-36422,36796-36934,37308-37446,37820-37958,38332-38470,38844-38982,39356-39494,39868-40006,40380-40518,40892-41030,41404-41542,41916-42054,42428-42566,42940-43078,43452-43590,43964-44102,44476-44614,44988-45126,45500-45638,46012-46150,46524-46662,47036-47174,47548-47686,48060-48198,48572-48710,49084-49222,49596-49734,50108-50246,50620-50758,51132-51270,51644-51782,52156-52294,52668-52806,53180-53318,53692-53830,53986-54020,54204-54854,55008-55045,55228-55366,55522-55555,55740-55878,56036-56066,56252-56390,56548-56578,56764-56902,57058-57091,57276-57414,57566-57607,57788-57926,58078-58119,58300-58438,58590-58631,58812-58950,59102-59143,59324-59462,59614-59655,59836-59974,60126-60167,60348-60486,60638-60679,60860-60998,61156-61186,61372-61510,61673-61692,61884-62022,62184-62205,62396-62534,62692-62722,62908-63046,63202-63236,63420-63558,63713-63748,63932-64070,64228-64258,64444-64582,64956-65094,65468-65606,65980-66118,66492-66630,67004-67142,67516-67654,68028-68166,68540-68678,69052-69190,69564-69702,70076-70214,70588-70726,71100-71238,71612-71750,72124-72262,72636-72774,73148-73286,73660-73798,74172-74310,74684-74822,75196-75334,75708-75846,76220-76358,76732-76870,77244-77382,77756-77894,78268-78406,78780-78918,79292-79430,79804-79942,80316-80454,80828-80966,81340-81478,81852-81990,82364-82502,82876-83014,83388-83526,83900-84038,84412-84550,84924-85062,85436-85574,85948-86086,86460-86598,86972-87110,87484-87622,87996-88134,88508-88646,89020-89158,89532-89670,90044-90182,90556-90694,91068-91206,91580-91718,92092-92230,92604-92742,93116-93254,93628-93766,94140-94278,94652-94790,95164-95302,95676-95814,96188-96326,96700-136262,136636-136774,137148-137286,137660-137798,138172-139334,139708-139846,140220-140358,140732-140870,141244-141382,141756-141894,142268-142406,142780-142918,143292-143430,143804-143942,144316-144454,144828-144966,145340-145478,145852-145990,146364-146502,146876-147014,147388-147526,147900-148038,148412-148550,148924-149062,149436-149574,149948-150086,150460-150598,150972-151110,151484-151622,151996-152134,152508-152646,153020-153158,153532-153670,154044-154182,154556-154694,155068-155206,155580-155718,156092-156230,156604-156742,157116-157254,157628-157766,158140-158278,158652-158790,159164-159302,159676-159814,160188-160326,160700-160838,161212-161350,161724-161862,162236-162374,162748-162886,163260-163398,163772-163910,164284-164422,164796-164934,165308-165446,165820-165958,166332-166470,166844-166982,167356-167494,167868-168006,168380-168518,168676-168706,168892-169030,169186-169220,169404-169542,169698-169731,169916-170054,170212-170242,170428-170566,170729-170748,170940-171078,171242-171259,171452-171590,171748-171778,171964-172102,172254-172295,172476-172614,172766-172807,172988-173126,173278-173319,173500-173638,173790-173831,174012-174150,174302-174343,174524-174662,174814-174855,175036-175174,175326-175367,175548-175686,175842-175875,176060-176198,176356-176386,176572-176710,176868-176898,177084-177222,177378-177412,177596-177734,177888-177925,178108-178246,178400-178438,178620-178758,178913-178948,179132-179270,179644-179782,180156-180294,180668-180806,181180-181318,181692-181830,182204-182342,182716-182854,183228-183366,183740-183878,184252-184390,184764-184902,185276-185414,185788-185926,186300-186438,186812-186950,187324-187462,187836-187974,188348-188486,188860-188998,189372-189510,189884-190022,190396-190534,190908-191046,191420-191558,191932-192070,192444-192582,192956-193094,193468-193606,193980-194118,194492-194630,195004-195142,195516-195654,196028-196166,196540-196678,197052-197190,197564-197702,198076-198214,198588-198726,199100-199238,199612-199750,200124-200262,200636-200774,201148-201286,201660-201798,202172-202310,202684-202822,203196-203334,203708-203846,204220-204358,204732-204870,205244-205382,205756-205894,206268-206406,206780-206918,207292-207430,207804-207942,208316-208454,208828-208966,209340-209478,209852-209990,210364-210502,210876-211014,211388-214086,214460-254534,254908-255046,255420-255558,255932-256070,256444-256582,256956-257094,257468-257606,257980-258118,258492-258630,259004-259142,259516-259654,260028-260166,260540-260678,261052-261190,261564-261702,262076-262214,262588-262726,263100-263238,263612-263750,264124-264262,264636-264774,265148-265286,265660-265798,266172-266310,266684-266822,267196-267334,267708-267846,268220-268358,268732-268870,269244-269382,269756-269894,270268-270406,270780-270918,271292-271430,271804-271942,272316-272454,272828-272966,273340-273478,273852-273990,274364-274502,274876-275014,275388-275526,275900-276038,276412-276550,276924-277062,277436-277574,277948-278086,278460-278598,278972-279110,279484-279622,279996-280134,280508-280646,281020-281158,281532-281670,282044-282182,282556-282694,283068-283206,283368-283389,283580-283718,283876-283906,284092-284230,284388-284418,284604-284742,285116-285254,285628-285766,286140-286278,286652-286790,286942-286983,287164-287302,287460-287490,287676-287814,287972-288002,288188-288326,288484-288514,288700-288838,288991-289030,289212-289350,289508-289538,289724-289862,290020-290050,290236-290374,290528-290565,290748-290886,291044-291074,291260-291398,291556-291586,291772-291910,292068-292098,292284-292422,292575-292615,292796-292934,293092-293122,293308-293446,293604-293634,293820-293958,294332-294470,294844-294982,295356-295494,295868-296006,296172-296186,296380-296518,296892-297030,297404-297542,297916-298054,298428-298566,298940-299078,299452-299590,299964-300102,300476-300614,300988-301126,301500-301638,302012-302150,302524-302662,303036-303174,303548-303686,304060-304198,304572-304710,305084-305222,305596-305734,306108-306246,306620-306758,307132-307270,307644-307782,308156-308294,308668-308806,309180-309318,309692-309830,310204-310342,310716-310854,311228-311366,311740-311878,312252-312390,312764-312902,313276-313414,313788-313926,314300-314438,314812-314950,315324-315462,315836-315974,316348-316486,316860-316998,317372-317510,317884-318022,318396-318534,318908-319046,319420-319558,319932-320070,320444-320582,320956-321094,321468-321606,321980-322118,322492-322630,323004-323142,323516-323654,324028-324166,324540-324678,325052-325190,325564-325702,326076-327750,328124-328262,328636-328774,329148-329286,329660-372806,373180-373318,373692-373830,374204-374342,374716-374854,375228-375366,375740-375878,376252-376390,376764-376902,377276-377414,377788-377926,378300-378438,378812-378950,379324-379462,379836-379974,380348-380486,380860-380998,381372-381510,381884-382022,382396-382534,382908-383046,383420-383558,383932-384070,384444-384582,384956-385094,385468-385606,385980-386118,386492-386630,387004-387142,387516-387654,388028-388166,388540-388678,389052-389190,389564-389702,390076-390214,390588-390726,391100-391238,391612-391750,392124-392262,392636-392774,393148-393286,393660-393798,394172-394310,394684-394822,395196-395334,395708-395846,396220-396358,396732-396870,397244-397382,397756-397894,398268-398406,398780-398918,399292-399430,399804-399942,400316-400454,400828-400966,401340-401478,401636-401666,401852-401990,402148-402178,402364-402502,402660-402690,402876-403014,403172-403202,403388-403526,403684-403714,403900-404038,404196-404226,404412-404550,404708-404738,404924-405062,405220-405250,405436-405574,405732-405762,405948-406086,406244-406274,406460-406598,406756-406786,406972-407110,407268-407298,407484-407622,407780-407810,407996-408134,408292-408322,408508-408646,409020-409158,409532-409670,410044-410182,410556-410694,410852-410882,411068-411206,411364-411394,411580-411718,412092-412230,412604-412742,413116-413254,413628-413766,414140-414278,414652-414790,415164-415302,415676-415814,416188-416326,416700-416838,417212-417350,417724-417862,418236-418374,418748-418886,419260-419398,419772-419910,420284-420422,420796-420934,421308-421446,421820-421958,422332-422470,422844-422982,423356-423494,423868-424006,424380-424518,424892-425030,425404-425542,425916-426054,426428-426566,426940-427078,427452-427590,427964-428102,428476-428614,428988-429126,429500-429638,430012-430150,430524-430662,431036-431174,431548-431686,432060-432198,432572-432710,433084-433222,433596-433734,434108-434246,434620-434758,435132-435270,435644-435782,436156-436294,436668-436806,437180-437318,437692-437830,438204-438342,438716-438854,439228-439366,439740-439878,440252-440390,440764-440902,441276-441414,441788-441926,442300-442438,442812-442950,443324-443462,443836-443974,444348-458752</detids>
+	</group>
+</detector-masking>
diff --git a/src/ess/loki/tabwidget.ipynb b/src/ess/loki/tabwidget.ipynb
index f281b0ec..17ff07bf 100644
--- a/src/ess/loki/tabwidget.ipynb
+++ b/src/ess/loki/tabwidget.ipynb
@@ -2,26 +2,35 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'SansBatchReductionWidget' object has no attribute 'widget'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtabwidget\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tabs\n\u001b[1;32m      2\u001b[0m display(tabs)\n",
-      "File \u001b[0;32m~/esssans-gui/src/ess/loki/tabwidget.py:445\u001b[0m\n\u001b[1;32m      0\u001b[0m <Error retrieving source code with stack_data see ipython/ipython#13598>\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'SansBatchReductionWidget' object has no attribute 'widget'"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa59bfe4261f4e4996bcf7fed79763cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(VBox(children=(Text(value='', description='Mask:', placeholder='Enter mask file path'), Text(val…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "from tabwidget import tabs\n",
+    "from tabwidgetauto import tabs\n",
     "display(tabs)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/src/ess/loki/tabwidget.py b/src/ess/loki/tabwidget.py
index 2b37caff..9f25070d 100644
--- a/src/ess/loki/tabwidget.py
+++ b/src/ess/loki/tabwidget.py
@@ -1,5 +1,7 @@
 import os
 import glob
+import re
+import h5py
 import pandas as pd
 import scipp as sc
 import matplotlib.pyplot as plt
@@ -99,6 +101,281 @@ def save_xye_pandas(data_array, filename):
     df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
     df.to_csv(filename, sep=" ", index=False, header=True)
 
+# ----------------------------
+# Helper Functions for Semi-Auto Reduction
+# ----------------------------
+def extract_run_number(filename):
+    m = re.search(r'(\d{4,})', filename)
+    if m:
+        return m.group(1)
+    return ""
+
+def parse_nx_details(filepath):
+    details = {}
+    with h5py.File(filepath, 'r') as f:
+        if 'nicos_details' in f['entry']:
+            grp = f['entry']['nicos_details']
+            if 'runlabel' in grp:
+                val = grp['runlabel'][()]
+                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+            if 'runtype' in grp:
+                val = grp['runtype'][()]
+                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+    return details
+
+# ----------------------------
+# Semi-Auto Reduction Widget
+# ----------------------------
+class SemiAutoReductionWidget:
+    def __init__(self):
+        # Only Input and Output Folder choosers are needed.
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        
+        self.scan_button = widgets.Button(description="Scan Directory")
+        self.scan_button.on_click(self.scan_directory)
+        
+        # DataGrid for auto-generated reduction table; now editable.
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        
+        # Buttons to add or delete rows from the table.
+        self.add_row_button = widgets.Button(description="Add Row")
+        self.add_row_button.on_click(self.add_row)
+        self.delete_row_button = widgets.Button(description="Delete Last Row")
+        self.delete_row_button.on_click(self.delete_last_row)
+        
+        # Parameter widgets for reduction (lambda and Q parameters)
+        self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
+        self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        
+        # Text fields to display the automatically identified empty-beam files.
+        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
+        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
+        
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        
+        # Build the layout.
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            self.scan_button,
+            self.table,
+            widgets.HBox([self.add_row_button, self.delete_row_button]),
+            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
+            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def add_row(self, _):
+        df = self.table.data
+        # Create a default new row if the DataFrame is empty, otherwise add blank cells.
+        if df.empty:
+            new_row = {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
+        else:
+            new_row = {col: "" for col in df.columns}
+        df = df.append(new_row, ignore_index=True)
+        self.table.data = df
+
+    def delete_last_row(self, _):
+        df = self.table.data
+        if not df.empty:
+            df = df.iloc[:-1]
+            self.table.data = df
+
+    def scan_directory(self, _):
+        self.log_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Invalid input folder.")
+            return
+        nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+        groups = {}
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runlabel' not in details or 'runtype' not in details:
+                continue
+            runlabel = details['runlabel']
+            runtype = details['runtype'].lower()
+            run_number = extract_run_number(os.path.basename(f))
+            if runlabel not in groups:
+                groups[runlabel] = {}
+            groups[runlabel][runtype] = run_number
+        table_rows = []
+        for runlabel, d in groups.items():
+            if 'sans' in d and 'trans' in d:
+                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+        df = pd.DataFrame(table_rows)
+        self.table.data = df
+        with self.log_output:
+            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+        # Identify empty beam files:
+        ebeam_sans_files = []
+        ebeam_trans_files = []
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runtype' in details:
+                if details['runtype'].lower() == 'ebeam_sans':
+                    ebeam_sans_files.append(f)
+                elif details['runtype'].lower() == 'ebeam_trans':
+                    ebeam_trans_files.append(f)
+        if ebeam_sans_files:
+            ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_sans_text.value = ebeam_sans_files[0]
+        else:
+            self.empty_beam_sans_text.value = ""
+        if ebeam_trans_files:
+            ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_trans_text.value = ebeam_trans_files[0]
+        else:
+            self.empty_beam_trans_text.value = ""
+    
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        background_run_file = self.empty_beam_sans_text.value
+        empty_beam_file = self.empty_beam_trans_text.value
+        if not background_run_file or not empty_beam_file:
+            with self.log_output:
+                print("Empty beam files not found.")
+            return
+        # Retrieve reduction parameters from widgets.
+        lam_min = self.lambda_min_widget.value
+        lam_max = self.lambda_max_widget.value
+        lam_n = self.lambda_n_widget.value
+        q_min = self.q_min_widget.value
+        q_max = self.q_max_widget.value
+        q_n = self.q_n_widget.value
+
+        df = self.table.data
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            sans_run = row["SANS"]
+            trans_run = row["TRANS"]
+            try:
+                sample_run_file = find_file(input_dir, sans_run, extension=".nxs")
+                transmission_run_file = find_file(input_dir, trans_run, extension=".nxs")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            try:
+                mask_file = find_mask_file(input_dir)
+                with self.log_output:
+                    print(f"Using mask file: {mask_file} for sample {sample}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Mask file not found for sample {sample}: {e}")
+                continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file],
+                    wavelength_min=lam_min,
+                    wavelength_max=lam_max,
+                    wavelength_n=lam_n,
+                    q_start=q_min,
+                    q_stop=q_max,
+                    q_n=q_n
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            wavelength_bins = sc.linspace("wavelength", lam_min, lam_max, lam_n, unit="angstrom")
+            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+            fig_trans, ax_trans = plt.subplots()
+            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
+            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_xlabel("Wavelength (Å)")
+            ax_trans.set_ylabel("Transmission")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_trans)
+            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            fig_trans.savefig(trans_png, dpi=300)
+            plt.close(fig_trans)
+            q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
+            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+            fig_iq, ax_iq = plt.subplots()
+            if res["IofQ"].variances is not None:
+                yerr = np.sqrt(res["IofQ"].variances)
+                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+            else:
+                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
+            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
+            ax_iq.set_xlabel("Q (Å$^{-1}$)")
+            ax_iq.set_ylabel("I(Q)")
+            ax_iq.set_xscale("log")
+            ax_iq.set_yscale("log")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_iq)
+            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            fig_iq.savefig(iq_png, dpi=300)
+            plt.close(fig_iq)
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+    
+    @property
+    def widget(self):
+        return self.main
+
 # ----------------------------
 # Direct Beam Functionality
 # ----------------------------
@@ -327,7 +604,7 @@ def run_reduction(self, _):
             except Exception as e:
                 with self.log_output:
                     print(f"Failed to save reduced data for {sample}: {e}")
-            wavelength_bins = sc.linspace("wavelength", 1.0, 13.0, 201, unit="angstrom")
+            wavelength_bins = sc.linspace("wavelength", wl_min, wl_max, wl_n, unit="angstrom")
             x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
             fig_trans, ax_trans = plt.subplots()
             ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
@@ -340,7 +617,7 @@ def run_reduction(self, _):
             trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
             fig_trans.savefig(trans_png, dpi=300)
             plt.close(fig_trans)
-            q_bins = sc.linspace("Q", 0.01, 0.3, 101, unit="1/angstrom")
+            q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
             x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
             fig_iq, ax_iq = plt.subplots()
             if res["IofQ"].variances is not None:
@@ -488,9 +765,12 @@ def widget(self):
 # ----------------------------
 reduction_widget = SansBatchReductionWidget().widget
 direct_beam_widget = DirectBeamWidget().widget
-tabs = widgets.Tab(children=[reduction_widget, direct_beam_widget])
-tabs.set_title(0, "Reduction")
-tabs.set_title(1, "Direct Beam")
+semi_auto_reduction_widget = SemiAutoReductionWidget().widget
+tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget])
+tabs.set_title(0, "Direct Beam")
+tabs.set_title(1, "Reduction (Manual)")
+tabs.set_title(2, "Reduction (Smart)")
+#tabs.set_title(3, "Reduction (Auto)")
 
 # Display the tab widget.
 #display(tabs)
diff --git a/src/ess/loki/tabwidgetauto.py b/src/ess/loki/tabwidgetauto.py
new file mode 100644
index 00000000..9cd5966f
--- /dev/null
+++ b/src/ess/loki/tabwidgetauto.py
@@ -0,0 +1,969 @@
+import os
+import glob
+import re
+import h5py
+import pandas as pd
+import scipp as sc
+import matplotlib.pyplot as plt
+import numpy as np
+import ipywidgets as widgets
+from ipydatagrid import DataGrid
+from IPython.display import display
+from ipyfilechooser import FileChooser
+from ess import sans
+from ess import loki
+from ess.sans.types import *
+from scipp.scipy.interpolate import interp1d
+import plopp as pp  # used for plotting in direct beam section
+import threading
+import time
+
+# ----------------------------
+# Reduction Functionality
+# ----------------------------
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101
+):
+    if mask_files is None:
+        mask_files = []
+    # Define wavelength and Q bins.
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    # Initialize the workflow.
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+def find_direct_beam(work_dir):
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+
+def find_mask_file(work_dir):
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+# ----------------------------
+# Helper Functions for Semi-Auto Reduction
+# ----------------------------
+def extract_run_number(filename):
+    m = re.search(r'(\d{4,})', filename)
+    if m:
+        return m.group(1)
+    return ""
+
+def parse_nx_details(filepath):
+    details = {}
+    with h5py.File(filepath, 'r') as f:
+        if 'nicos_details' in f['entry']:
+            grp = f['entry']['nicos_details']
+            if 'runlabel' in grp:
+                val = grp['runlabel'][()]
+                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+            if 'runtype' in grp:
+                val = grp['runtype'][()]
+                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+    return details
+
+# ----------------------------
+# Semi-Auto Reduction Widget (unchanged)
+# ----------------------------
+class SemiAutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        
+        self.scan_button = widgets.Button(description="Scan Directory")
+        self.scan_button.on_click(self.scan_directory)
+        
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        
+        self.add_row_button = widgets.Button(description="Add Row")
+        self.add_row_button.on_click(self.add_row)
+        self.delete_row_button = widgets.Button(description="Delete Last Row")
+        self.delete_row_button.on_click(self.delete_last_row)
+        
+        self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
+        self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        
+        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
+        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
+        
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            self.scan_button,
+            self.table,
+            widgets.HBox([self.add_row_button, self.delete_row_button]),
+            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
+            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def add_row(self, _):
+        df = self.table.data
+        if df.empty:
+            new_row = {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
+        else:
+            new_row = {col: "" for col in df.columns}
+        df = df.append(new_row, ignore_index=True)
+        self.table.data = df
+
+    def delete_last_row(self, _):
+        df = self.table.data
+        if not df.empty:
+            df = df.iloc[:-1]
+            self.table.data = df
+
+    def scan_directory(self, _):
+        self.log_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Invalid input folder.")
+            return
+        nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+        groups = {}
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runlabel' not in details or 'runtype' not in details:
+                continue
+            runlabel = details['runlabel']
+            runtype = details['runtype'].lower()
+            run_number = extract_run_number(os.path.basename(f))
+            if runlabel not in groups:
+                groups[runlabel] = {}
+            groups[runlabel][runtype] = run_number
+        table_rows = []
+        for runlabel, d in groups.items():
+            if 'sans' in d and 'trans' in d:
+                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+        df = pd.DataFrame(table_rows)
+        self.table.data = df
+        with self.log_output:
+            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+        ebeam_sans_files = []
+        ebeam_trans_files = []
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runtype' in details:
+                if details['runtype'].lower() == 'ebeam_sans':
+                    ebeam_sans_files.append(f)
+                elif details['runtype'].lower() == 'ebeam_trans':
+                    ebeam_trans_files.append(f)
+        if ebeam_sans_files:
+            ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_sans_text.value = ebeam_sans_files[0]
+        else:
+            self.empty_beam_sans_text.value = ""
+        if ebeam_trans_files:
+            ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_trans_text.value = ebeam_trans_files[0]
+        else:
+            self.empty_beam_trans_text.value = ""
+    
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        background_run_file = self.empty_beam_sans_text.value
+        empty_beam_file = self.empty_beam_trans_text.value
+        if not background_run_file or not empty_beam_file:
+            with self.log_output:
+                print("Empty beam files not found.")
+            return
+        lam_min = self.lambda_min_widget.value
+        lam_max = self.lambda_max_widget.value
+        lam_n = self.lambda_n_widget.value
+        q_min = self.q_min_widget.value
+        q_max = self.q_max_widget.value
+        q_n = self.q_n_widget.value
+
+        df = self.table.data
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            sans_run = row["SANS"]
+            trans_run = row["TRANS"]
+            try:
+                sample_run_file = find_file(input_dir, sans_run, extension=".nxs")
+                transmission_run_file = find_file(input_dir, trans_run, extension=".nxs")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            try:
+                mask_file = find_mask_file(input_dir)
+                with self.log_output:
+                    print(f"Using mask file: {mask_file} for sample {sample}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Mask file not found for sample {sample}: {e}")
+                continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file],
+                    wavelength_min=lam_min,
+                    wavelength_max=lam_max,
+                    wavelength_n=lam_n,
+                    q_start=q_min,
+                    q_stop=q_max,
+                    q_n=q_n
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            # --- Save Transmission Plot ---
+            wavelength_bins = sc.linspace("wavelength", lam_min, lam_max, lam_n, unit="angstrom")
+            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+            fig_trans, ax_trans = plt.subplots()
+            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
+            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_xlabel("Wavelength (Å)")
+            ax_trans.set_ylabel("Transmission")
+            plt.tight_layout()
+            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            fig_trans.savefig(trans_png, dpi=300)
+            plt.close(fig_trans)
+            # --- Save I(Q) Plot ---
+            q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
+            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+            fig_iq, ax_iq = plt.subplots()
+            if res["IofQ"].variances is not None:
+                yerr = np.sqrt(res["IofQ"].variances)
+                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+            else:
+                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
+            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
+            ax_iq.set_xlabel("Q (Å$^{-1}$)")
+            ax_iq.set_ylabel("I(Q)")
+            ax_iq.set_xscale("log")
+            ax_iq.set_yscale("log")
+            plt.tight_layout()
+            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            fig_iq.savefig(iq_png, dpi=300)
+            plt.close(fig_iq)
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Direct Beam Functionality and Widget (unchanged)
+# ----------------------------
+def compute_direct_beam_local(
+    mask: str,
+    sample_sans: str,
+    background_sans: str,
+    sample_trans: str,
+    background_trans: str,
+    empty_beam: str,
+    local_Iq_theory: str,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    n_wavelength_bins: int = 50,
+    n_wavelength_bands: int = 50
+) -> dict:
+    workflow = loki.LokiAtLarmorWorkflow()
+    workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
+    workflow[NeXusDetectorName] = 'larmor_detector'
+    
+    wl_min = sc.scalar(wavelength_min, unit='angstrom')
+    wl_max = sc.scalar(wavelength_max, unit='angstrom')
+    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
+    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
+    workflow[CorrectForGravity] = True
+    workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
+    workflow[ReturnEvents] = False
+    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
+    
+    workflow[Filename[SampleRun]] = sample_sans
+    workflow[Filename[BackgroundRun]] = background_sans
+    workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
+    workflow[Filename[EmptyBeamRun]] = empty_beam
+    
+    center = sans.beam_center_from_center_of_mass(workflow)
+    print("Computed beam center:", center)
+    workflow[BeamCenter] = center
+    
+    Iq_theory = sc.io.load_hdf5(local_Iq_theory)
+    f = interp1d(Iq_theory, 'Q')
+    I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
+    print("Computed I0:", I0)
+    
+    results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
+    
+    iofq_full = results[-1]['iofq_full']
+    iofq_bands = results[-1]['iofq_bands']
+    direct_beam_function = results[-1]['direct_beam']
+    
+    pp.plot(
+        {'reference': Iq_theory, 'data': iofq_full},
+        color={'reference': 'darkgrey', 'data': 'C0'},
+        norm='log',
+    )
+    print("Plotted full-range result vs. theoretical reference.")
+    
+    return {
+        'direct_beam_function': direct_beam_function,
+        'iofq_full': iofq_full,
+        'Iq_theory': Iq_theory,
+    }
+
+class DirectBeamWidget:
+    def __init__(self):
+        self.mask_text = widgets.Text(
+            value="",
+            placeholder="Enter mask file path",
+            description="Mask:"
+        )
+        self.sample_sans_text = widgets.Text(
+            value="",
+            placeholder="Enter sample SANS file path",
+            description="Sample SANS:"
+        )
+        self.background_sans_text = widgets.Text(
+            value="",
+            placeholder="Enter background SANS file path",
+            description="Background SANS:"
+        )
+        self.sample_trans_text = widgets.Text(
+            value="",
+            placeholder="Enter sample TRANS file path",
+            description="Sample TRANS:"
+        )
+        self.background_trans_text = widgets.Text(
+            value="",
+            placeholder="Enter background TRANS file path",
+            description="Background TRANS:"
+        )
+        self.empty_beam_text = widgets.Text(
+            value="",
+            placeholder="Enter empty beam file path",
+            description="Empty Beam:"
+        )
+        self.local_Iq_theory_text = widgets.Text(
+            value="",
+            placeholder="Enter I(q) Theory file path",
+            description="I(q) Theory:"
+        )
+        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
+        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
+        
+        self.compute_button = widgets.Button(description="Compute Direct Beam")
+        self.compute_button.on_click(self.compute_direct_beam)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            self.mask_text,
+            self.sample_sans_text,
+            self.background_sans_text,
+            self.sample_trans_text,
+            self.background_trans_text,
+            self.empty_beam_text,
+            self.local_Iq_theory_text,
+            widgets.HBox([
+                self.db_wavelength_min_widget,
+                self.db_wavelength_max_widget,
+                self.db_n_wavelength_bins_widget,
+                self.db_n_wavelength_bands_widget
+            ]),
+            self.compute_button,
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def compute_direct_beam(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        mask = self.mask_text.value
+        sample_sans = self.sample_sans_text.value
+        background_sans = self.background_sans_text.value
+        sample_trans = self.sample_trans_text.value
+        background_trans = self.background_trans_text.value
+        empty_beam = self.empty_beam_text.value
+        local_Iq_theory = self.local_Iq_theory_text.value
+        wl_min = self.db_wavelength_min_widget.value
+        wl_max = self.db_wavelength_max_widget.value
+        n_bins = self.db_n_wavelength_bins_widget.value
+        n_bands = self.db_n_wavelength_bands_widget.value
+        with self.log_output:
+            print("Computing direct beam with:")
+            print("  Mask:", mask)
+            print("  Sample SANS:", sample_sans)
+            print("  Background SANS:", background_sans)
+            print("  Sample TRANS:", sample_trans)
+            print("  Background TRANS:", background_trans)
+            print("  Empty Beam:", empty_beam)
+            print("  I(q) Theory:", local_Iq_theory)
+            print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
+        try:
+            results = compute_direct_beam_local(
+                mask,
+                sample_sans,
+                background_sans,
+                sample_trans,
+                background_trans,
+                empty_beam,
+                local_Iq_theory,
+                wavelength_min=wl_min,
+                wavelength_max=wl_max,
+                n_wavelength_bins=n_bins,
+                n_wavelength_bands=n_bands
+            )
+            with self.log_output:
+                print("Direct beam computation complete.")
+        except Exception as e:
+            with self.log_output:
+                print("Error computing direct beam:", e)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# New: Auto Reduction Widget (with plot saving)
+# ----------------------------
+class AutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        
+        self.start_stop_button = widgets.Button(description="Start")
+        self.start_stop_button.on_click(self.toggle_running)
+        self.status_label = widgets.Label(value="Stopped")
+        
+        self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
+        self.log_output = widgets.Output()
+        
+        self.running = False
+        self.thread = None
+        self.processed = set()  # Track already reduced entries.
+        self.empty_beam_sans = None
+        self.empty_beam_trans = None
+        
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.start_stop_button, self.status_label]),
+            self.table,
+            self.log_output
+        ])
+    
+    def toggle_running(self, _):
+        if not self.running:
+            self.running = True
+            self.start_stop_button.description = "Stop"
+            self.status_label.value = "Running"
+            self.thread = threading.Thread(target=self.background_loop, daemon=True)
+            self.thread.start()
+        else:
+            self.running = False
+            self.start_stop_button.description = "Start"
+            self.status_label.value = "Stopped"
+    
+    def background_loop(self):
+        while self.running:
+            input_dir = self.input_dir_chooser.selected
+            output_dir = self.output_dir_chooser.selected
+            if not input_dir or not os.path.isdir(input_dir):
+                with self.log_output:
+                    print("Invalid input folder. Waiting for valid selection...")
+                time.sleep(10)
+                continue
+            if not output_dir or not os.path.isdir(output_dir):
+                with self.log_output:
+                    print("Invalid output folder. Waiting for valid selection...")
+                time.sleep(10)
+                continue
+
+            # Scan for .nxs files and build the reduction table.
+            nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+            groups = {}
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runlabel' not in details or 'runtype' not in details:
+                    continue
+                runlabel = details['runlabel']
+                runtype = details['runtype'].lower()
+                run_number = extract_run_number(os.path.basename(f))
+                if runlabel not in groups:
+                    groups[runlabel] = {}
+                groups[runlabel][runtype] = run_number
+            table_rows = []
+            for runlabel, d in groups.items():
+                if 'sans' in d and 'trans' in d:
+                    table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+            df = pd.DataFrame(table_rows)
+            self.table.data = df
+            with self.log_output:
+                print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+            
+            # Identify empty beam files.
+            ebeam_sans_files = []
+            ebeam_trans_files = []
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runtype' in details:
+                    if details['runtype'].lower() == 'ebeam_sans':
+                        ebeam_sans_files.append(f)
+                    elif details['runtype'].lower() == 'ebeam_trans':
+                        ebeam_trans_files.append(f)
+            if ebeam_sans_files:
+                ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_sans = ebeam_sans_files[0]
+            else:
+                self.empty_beam_sans = None
+            if ebeam_trans_files:
+                ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_trans = ebeam_trans_files[0]
+            else:
+                self.empty_beam_trans = None
+
+            # Get the direct beam file.
+            try:
+                direct_beam_file = find_direct_beam(input_dir)
+            except Exception as e:
+                with self.log_output:
+                    print("Direct-beam file not found:", e)
+                time.sleep(10)
+                continue
+
+            # Process new reduction entries.
+            for index, row in df.iterrows():
+                key = (row["SAMPLE"], row["SANS"], row["TRANS"])
+                if key in self.processed:
+                    continue
+                try:
+                    sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs")
+                    transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Skipping sample {row['SAMPLE']}: {e}")
+                    continue
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
+                    continue
+                if not self.empty_beam_sans or not self.empty_beam_trans:
+                    with self.log_output:
+                        print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
+                    continue
+
+                with self.log_output:
+                    print(f"Reducing sample {row['SAMPLE']}...")
+                try:
+                    res = reduce_loki_batch_preliminary(
+                        sample_run_file=sample_run_file,
+                        transmission_run_file=transmission_run_file,
+                        background_run_file=self.empty_beam_sans,
+                        empty_beam_file=self.empty_beam_trans,
+                        direct_beam_file=direct_beam_file,
+                        mask_files=[mask_file],
+                        wavelength_min=1.0,
+                        wavelength_max=13.0,
+                        wavelength_n=201,
+                        q_start=0.01,
+                        q_stop=0.3,
+                        q_n=101
+                    )
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Reduction failed for sample {row['SAMPLE']}: {e}")
+                    continue
+                out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+                try:
+                    save_xye_pandas(res["IofQ"], out_xye)
+                    with self.log_output:
+                        print(f"Saved reduced data to {out_xye}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Failed to save reduced data for {row['SAMPLE']}: {e}")
+                # --- Save Transmission Plot ---
+                wavelength_bins = sc.linspace("wavelength", 1.0, 13.0, 201, unit="angstrom")
+                x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+                fig_trans, ax_trans = plt.subplots()
+                ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
+                ax_trans.set_title(f"Transmission: {row['SAMPLE']} {os.path.basename(sample_run_file)}")
+                ax_trans.set_xlabel("Wavelength (Å)")
+                ax_trans.set_ylabel("Transmission")
+                plt.tight_layout()
+                trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+                fig_trans.savefig(trans_png, dpi=300)
+                plt.close(fig_trans)
+                # --- Save I(Q) Plot ---
+                q_bins = sc.linspace("Q", 0.01, 0.3, 101, unit="1/angstrom")
+                x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+                fig_iq, ax_iq = plt.subplots()
+                if res["IofQ"].variances is not None:
+                    yerr = np.sqrt(res["IofQ"].variances)
+                    ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+                else:
+                    ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
+                ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({row['SAMPLE']})")
+                ax_iq.set_xlabel("Q (Å$^{-1}$)")
+                ax_iq.set_ylabel("I(Q)")
+                ax_iq.set_xscale("log")
+                ax_iq.set_yscale("log")
+                plt.tight_layout()
+                iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+                fig_iq.savefig(iq_png, dpi=300)
+                plt.close(fig_iq)
+                with self.log_output:
+                    print(f"Reduced sample {row['SAMPLE']} and saved outputs.")
+                self.processed.add(key)
+            time.sleep(10)
+    
+    @property
+    def widget(self):
+        return self.main
+    
+# ----------------------------
+# Widgets for Reduction and Direct Beam
+# ----------------------------
+class SansBatchReductionWidget:
+    def __init__(self):
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.ebeam_sans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam SANS run number",
+            description="Ebeam SANS:"
+        )
+        self.ebeam_trans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam TRANS run number",
+            description="Ebeam TRANS:"
+        )
+        # Add GUI widgets for reduction parameters:
+        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(self.clear_log)
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(self.clear_plots)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+            # Reduction parameters:
+            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
+            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def clear_log(self, _):
+        self.log_output.clear_output()
+    
+    def clear_plots(self, _):
+        self.plot_output.clear_output()
+    
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+    
+    def run_reduction(self, _):
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
+            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            with self.log_output:
+                print("Using empty-beam files:")
+                print("  Background (Ebeam SANS):", background_run_file)
+                print("  Empty beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Error finding empty beam files:", e)
+            return
+        # Retrieve reduction parameters from widgets.
+        wl_min = self.wavelength_min_widget.value
+        wl_max = self.wavelength_max_widget.value
+        wl_n = self.wavelength_n_widget.value
+        q_start = self.q_start_widget.value
+        q_stop = self.q_stop_widget.value
+        q_n = self.q_n_widget.value
+        df = self.table.data
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            try:
+                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
+                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            mask_candidate = str(row.get("mask", "")).strip()
+            mask_file = None
+            if mask_candidate:
+                mask_file_candidate = os.path.join(input_dir, f"{mask_candidate}.xml")
+                if os.path.exists(mask_file_candidate):
+                    mask_file = mask_file_candidate
+            if mask_file is None:
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Identified mask file: {mask_file} for sample {sample}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {sample}: {e}")
+                    continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file],
+                    wavelength_min=wl_min,
+                    wavelength_max=wl_max,
+                    wavelength_n=wl_n,
+                    q_start=q_start,
+                    q_stop=q_stop,
+                    q_n=q_n
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            wavelength_bins = sc.linspace("wavelength", wl_min, wl_max, wl_n, unit="angstrom")
+            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+            fig_trans, ax_trans = plt.subplots()
+            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
+            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_xlabel("Wavelength (Å)")
+            ax_trans.set_ylabel("Transmission")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_trans)
+            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            fig_trans.savefig(trans_png, dpi=300)
+            plt.close(fig_trans)
+            q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+            fig_iq, ax_iq = plt.subplots()
+            if res["IofQ"].variances is not None:
+                yerr = np.sqrt(res["IofQ"].variances)
+                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+            else:
+                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
+            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
+            ax_iq.set_xlabel("Q (Å$^{-1}$)")
+            ax_iq.set_ylabel("I(Q)")
+            ax_iq.set_xscale("log")
+            ax_iq.set_yscale("log")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_iq)
+            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            fig_iq.savefig(iq_png, dpi=300)
+            plt.close(fig_iq)
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Build the tabbed widget.
+# ----------------------------
+reduction_widget = SansBatchReductionWidget().widget
+direct_beam_widget = DirectBeamWidget().widget
+semi_auto_reduction_widget = SemiAutoReductionWidget().widget
+auto_reduction_widget = AutoReductionWidget().widget
+
+tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
+tabs.set_title(0, "Direct Beam")
+tabs.set_title(1, "Reduction (Manual)")
+tabs.set_title(2, "Reduction (Smart)")
+tabs.set_title(3, "Reduction (Auto)")
+
+#display(tabs)

From 9545a75addfb47949630631918d615972fa1ac6e Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Tue, 4 Mar 2025 16:07:59 +0100
Subject: [PATCH 06/18] yep

---
 src/ess/loki/tabwidgetauto.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/ess/loki/tabwidgetauto.py b/src/ess/loki/tabwidgetauto.py
index 9cd5966f..6ad7ce1a 100644
--- a/src/ess/loki/tabwidgetauto.py
+++ b/src/ess/loki/tabwidgetauto.py
@@ -17,6 +17,7 @@
 import plopp as pp  # used for plotting in direct beam section
 import threading
 import time
+from ipywidgets import Output, IntSlider
 
 # ----------------------------
 # Reduction Functionality
@@ -947,11 +948,13 @@ def run_reduction(self, _):
             plt.close(fig_iq)
             with self.log_output:
                 print(f"Reduced sample {sample} and saved outputs.")
+
     
     @property
     def widget(self):
         return self.main
 
+
 # ----------------------------
 # Build the tabbed widget.
 # ----------------------------

From 1802a3f4fc915b27ced6a7477b7dde1514c17dc6 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci-lite[bot]"
 <117423508+pre-commit-ci-lite[bot]@users.noreply.github.com>
Date: Tue, 4 Mar 2025 15:15:32 +0000
Subject: [PATCH 07/18] Apply automatic formatting

---
 src/ess/loki/batchwidget-tabs.py | 121 +++++---
 src/ess/loki/batchwidget.py      | 115 ++++---
 src/ess/loki/batchwidgets.ipynb  |  26 +-
 src/ess/loki/tabwidget.ipynb     |  26 +-
 src/ess/loki/tabwidget.py        | 392 +++++++++++++++---------
 src/ess/loki/tabwidgetauto.py    | 496 ++++++++++++++++++++-----------
 6 files changed, 746 insertions(+), 430 deletions(-)

diff --git a/src/ess/loki/batchwidget-tabs.py b/src/ess/loki/batchwidget-tabs.py
index 21ded410..fbe1dc46 100644
--- a/src/ess/loki/batchwidget-tabs.py
+++ b/src/ess/loki/batchwidget-tabs.py
@@ -1,17 +1,19 @@
-import os
 import glob
-import pandas as pd
-import scipp as sc
+import os
+
+import ipywidgets as widgets
 import matplotlib.pyplot as plt
 import numpy as np
-import ipywidgets as widgets
+import pandas as pd
+import scipp as sc
 from ipydatagrid import DataGrid
-from IPython.display import display
 from ipyfilechooser import FileChooser
-from ess import sans
-from ess import loki
+from IPython.display import display
+
+from ess import loki, sans
 from ess.sans.types import *
 
+
 def reduce_loki_batch_preliminary(
     sample_run_file: str,
     transmission_run_file: str,
@@ -20,19 +22,21 @@ def reduce_loki_batch_preliminary(
     direct_beam_file: str,
     mask_files: list = None,
     correct_for_gravity: bool = True,
-    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    uncertainty_mode=UncertaintyBroadcastMode.upper_bound,
     return_events: bool = False,
     wavelength_min: float = 1.0,
     wavelength_max: float = 13.0,
     wavelength_n: int = 201,
     q_start: float = 0.01,
     q_stop: float = 0.3,
-    q_n: int = 101
+    q_n: int = 101,
 ):
     if mask_files is None:
         mask_files = []
     # Define wavelength and Q bins.
-    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    wavelength_bins = sc.linspace(
+        "wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom"
+    )
     q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
     # Initialize the workflow.
     workflow = loki.LokiAtLarmorWorkflow()
@@ -56,6 +60,7 @@ def reduce_loki_batch_preliminary(
     da = workflow.compute(BackgroundSubtractedIofQ)
     return {"transmission": tf, "IofQ": da}
 
+
 def find_file(work_dir, run_number, extension=".nxs"):
     pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
     files = glob.glob(pattern)
@@ -64,13 +69,17 @@ def find_file(work_dir, run_number, extension=".nxs"):
     else:
         raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
 
+
 def find_direct_beam(work_dir):
     pattern = os.path.join(work_dir, "*direct-beam*.h5")
     files = glob.glob(pattern)
     if files:
         return files[0]
     else:
-        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+        raise FileNotFoundError(
+            f"Could not find direct-beam file matching pattern {pattern}"
+        )
+
 
 def find_mask_file(work_dir):
     pattern = os.path.join(work_dir, "*mask*.xml")
@@ -80,6 +89,7 @@ def find_mask_file(work_dir):
     else:
         raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
 
+
 def save_xye_pandas(data_array, filename):
     q_vals = data_array.coords["Q"].values
     i_vals = data_array.values
@@ -94,6 +104,7 @@ def save_xye_pandas(data_array, filename):
     df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
     df.to_csv(filename, sep=" ", index=False, header=True)
 
+
 class SansBatchReductionWidget:
     def __init__(self):
         self.csv_chooser = FileChooser(select_dir=False)
@@ -106,12 +117,12 @@ def __init__(self):
         self.ebeam_sans_widget = widgets.Text(
             value="",
             placeholder="Enter Ebeam SANS run number",
-            description="Ebeam SANS:"
+            description="Ebeam SANS:",
         )
         self.ebeam_trans_widget = widgets.Text(
             value="",
             placeholder="Enter Ebeam TRANS run number",
-            description="Ebeam TRANS:"
+            description="Ebeam TRANS:",
         )
         self.load_csv_button = widgets.Button(description="Load CSV")
         self.load_csv_button.on_click(self.load_csv)
@@ -124,22 +135,28 @@ def __init__(self):
         self.clear_plots_button.on_click(self.clear_plots)
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
-        self.main = widgets.VBox([
-            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
-            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
-            self.load_csv_button,
-            self.table,
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
-            self.log_output,
-            self.plot_output
-        ])
-    
+        self.main = widgets.VBox(
+            [
+                widgets.HBox(
+                    [self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]
+                ),
+                widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+                self.load_csv_button,
+                self.table,
+                widgets.HBox(
+                    [self.reduce_button, self.clear_log_button, self.clear_plots_button]
+                ),
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
     def clear_log(self, _):
         self.log_output.clear_output()
-    
+
     def clear_plots(self, _):
         self.plot_output.clear_output()
-    
+
     def load_csv(self, _):
         csv_path = self.csv_chooser.selected
         if not csv_path or not os.path.exists(csv_path):
@@ -150,7 +167,7 @@ def load_csv(self, _):
         self.table.data = df
         with self.log_output:
             print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
-    
+
     def run_reduction(self, _):
         input_dir = self.input_dir_chooser.selected
         output_dir = self.output_dir_chooser.selected
@@ -171,8 +188,12 @@ def run_reduction(self, _):
                 print("Direct-beam file not found:", e)
             return
         try:
-            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
-            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            background_run_file = find_file(
+                input_dir, self.ebeam_sans_widget.value, extension=".nxs"
+            )
+            empty_beam_file = find_file(
+                input_dir, self.ebeam_trans_widget.value, extension=".nxs"
+            )
             with self.log_output:
                 print("Using empty-beam files:")
                 print("  Background (Ebeam SANS):", background_run_file)
@@ -185,8 +206,12 @@ def run_reduction(self, _):
         for idx, row in df.iterrows():
             sample = row["SAMPLE"]
             try:
-                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
-                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+                sample_run_file = find_file(
+                    input_dir, str(row["SANS"]), extension=".nxs"
+                )
+                transmission_run_file = find_file(
+                    input_dir, str(row["TRANS"]), extension=".nxs"
+                )
             except Exception as e:
                 with self.log_output:
                     print(f"Skipping sample {sample}: {e}")
@@ -201,7 +226,9 @@ def run_reduction(self, _):
                 try:
                     mask_file = find_mask_file(input_dir)
                     with self.log_output:
-                        print(f"Using global mask file: {mask_file} for sample {sample}")
+                        print(
+                            f"Using global mask file: {mask_file} for sample {sample}"
+                        )
                 except Exception as e:
                     with self.log_output:
                         print(f"Mask file not found for sample {sample}: {e}")
@@ -215,13 +242,15 @@ def run_reduction(self, _):
                     background_run_file=background_run_file,
                     empty_beam_file=empty_beam_file,
                     direct_beam_file=direct_beam_file,
-                    mask_files=[mask_file]
+                    mask_files=[mask_file],
                 )
             except Exception as e:
                 with self.log_output:
                     print(f"Reduction failed for sample {sample}: {e}")
                 continue
-            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            out_xye = os.path.join(
+                output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")
+            )
             try:
                 save_xye_pandas(res["IofQ"], out_xye)
                 with self.log_output:
@@ -234,13 +263,18 @@ def run_reduction(self, _):
             x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
             fig_trans, ax_trans = plt.subplots()
             ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_title(
+                f"Transmission: {sample} {os.path.basename(sample_run_file)}"
+            )
             ax_trans.set_xlabel("Wavelength (Å)")
             ax_trans.set_ylabel("Transmission")
             plt.tight_layout()
             with self.plot_output:
                 display(fig_trans)
-            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            trans_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"),
+            )
             fig_trans.savefig(trans_png, dpi=300)
             plt.close(fig_trans)
             # Generate and display I(Q) plot.
@@ -249,7 +283,9 @@ def run_reduction(self, _):
             fig_iq, ax_iq = plt.subplots()
             if res["IofQ"].variances is not None:
                 yerr = np.sqrt(res["IofQ"].variances)
-                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+                ax_iq.errorbar(
+                    x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
+                )
             else:
                 ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
             ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
@@ -260,16 +296,20 @@ def run_reduction(self, _):
             plt.tight_layout()
             with self.plot_output:
                 display(fig_iq)
-            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            iq_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
+            )
             fig_iq.savefig(iq_png, dpi=300)
             plt.close(fig_iq)
             with self.log_output:
                 print(f"Reduced sample {sample} and saved outputs.")
-    
+
         @property
         def widget(self):
             return self.main
 
+
 def save_xye_pandas(data_array, filename):
     q_vals = data_array.coords["Q"].values
     i_vals = data_array.values
@@ -284,9 +324,12 @@ def save_xye_pandas(data_array, filename):
     df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
     df.to_csv(filename, sep=" ", index=False, header=True)
 
+
 # Build the main tabbed widget.
 reduction_widget = SansBatchReductionWidget().widget
-direct_beam_widget = widgets.HTML("<h3>Direct Beam</h3><p>Direct beam tab content goes here.</p>")
+direct_beam_widget = widgets.HTML(
+    "<h3>Direct Beam</h3><p>Direct beam tab content goes here.</p>"
+)
 tabs = widgets.Tab(children=[reduction_widget, direct_beam_widget])
 tabs.set_title(0, "Reduction")
 tabs.set_title(1, "Direct Beam")
diff --git a/src/ess/loki/batchwidget.py b/src/ess/loki/batchwidget.py
index b2d43336..0a85e961 100644
--- a/src/ess/loki/batchwidget.py
+++ b/src/ess/loki/batchwidget.py
@@ -1,15 +1,16 @@
-import os
 import glob
-import pandas as pd
-import scipp as sc
+import os
+
+import ipywidgets as widgets
 import matplotlib.pyplot as plt
 import numpy as np
-import ipywidgets as widgets
+import pandas as pd
+import scipp as sc
 from ipydatagrid import DataGrid
-from IPython.display import display
 from ipyfilechooser import FileChooser
-from ess import sans
-from ess import loki
+from IPython.display import display
+
+from ess import loki, sans
 from ess.sans.types import *
 
 
@@ -21,19 +22,21 @@ def reduce_loki_batch_preliminary(
     direct_beam_file: str,
     mask_files: list = None,
     correct_for_gravity: bool = True,
-    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    uncertainty_mode=UncertaintyBroadcastMode.upper_bound,
     return_events: bool = False,
     wavelength_min: float = 1.0,
     wavelength_max: float = 13.0,
     wavelength_n: int = 201,
     q_start: float = 0.01,
     q_stop: float = 0.3,
-    q_n: int = 101
+    q_n: int = 101,
 ):
     if mask_files is None:
         mask_files = []
     # Define wavelength and Q bins.
-    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    wavelength_bins = sc.linspace(
+        "wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom"
+    )
     q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
     # Initialize the workflow.
     workflow = loki.LokiAtLarmorWorkflow()
@@ -73,7 +76,9 @@ def find_direct_beam(work_dir):
     if files:
         return files[0]
     else:
-        raise FileNotFoundError(f"Could not find direct beam file matching pattern {pattern}")
+        raise FileNotFoundError(
+            f"Could not find direct beam file matching pattern {pattern}"
+        )
 
 
 def find_mask_file(work_dir):
@@ -112,12 +117,12 @@ def __init__(self):
         self.ebeam_sans_widget = widgets.Text(
             value="",
             placeholder="Enter Ebeam SANS run number",
-            description="Ebeam SANS:"
+            description="Ebeam SANS:",
         )
         self.ebeam_trans_widget = widgets.Text(
             value="",
             placeholder="Enter Ebeam TRANS run number",
-            description="Ebeam TRANS:"
+            description="Ebeam TRANS:",
         )
         self.load_csv_button = widgets.Button(description="Load CSV")
         self.load_csv_button.on_click(self.load_csv)
@@ -130,22 +135,28 @@ def __init__(self):
         self.clear_plots_button.on_click(self.clear_plots)
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
-        self.main = widgets.VBox([
-            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
-            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
-            self.load_csv_button,
-            self.table,
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
-            self.log_output,
-            self.plot_output
-        ])
-    
+        self.main = widgets.VBox(
+            [
+                widgets.HBox(
+                    [self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]
+                ),
+                widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+                self.load_csv_button,
+                self.table,
+                widgets.HBox(
+                    [self.reduce_button, self.clear_log_button, self.clear_plots_button]
+                ),
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
     def clear_log(self, _):
         self.log_output.clear_output()
-    
+
     def clear_plots(self, _):
         self.plot_output.clear_output()
-    
+
     def load_csv(self, _):
         csv_path = self.csv_chooser.selected
         if not csv_path or not os.path.exists(csv_path):
@@ -156,7 +167,7 @@ def load_csv(self, _):
         self.table.data = df
         with self.log_output:
             print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
-    
+
     def run_reduction(self, _):
         input_dir = self.input_dir_chooser.selected
         output_dir = self.output_dir_chooser.selected
@@ -177,8 +188,12 @@ def run_reduction(self, _):
                 print("Direct beam file not found:", e)
             return
         try:
-            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
-            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            background_run_file = find_file(
+                input_dir, self.ebeam_sans_widget.value, extension=".nxs"
+            )
+            empty_beam_file = find_file(
+                input_dir, self.ebeam_trans_widget.value, extension=".nxs"
+            )
             with self.log_output:
                 print("Using empty beam files:")
                 print("  Background (Ebeam SANS):", background_run_file)
@@ -191,8 +206,12 @@ def run_reduction(self, _):
         for idx, row in df.iterrows():
             sample = row["SAMPLE"]
             try:
-                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
-                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+                sample_run_file = find_file(
+                    input_dir, str(row["SANS"]), extension=".nxs"
+                )
+                transmission_run_file = find_file(
+                    input_dir, str(row["TRANS"]), extension=".nxs"
+                )
             except Exception as e:
                 with self.log_output:
                     print(f"Skipping sample {sample}: {e}")
@@ -207,7 +226,9 @@ def run_reduction(self, _):
                 try:
                     mask_file = find_mask_file(input_dir)
                     with self.log_output:
-                        print(f"Using global mask file: {mask_file} for sample {sample}")
+                        print(
+                            f"Using global mask file: {mask_file} for sample {sample}"
+                        )
                 except Exception as e:
                     with self.log_output:
                         print(f"Mask file not found for sample {sample}: {e}")
@@ -221,13 +242,15 @@ def run_reduction(self, _):
                     background_run_file=background_run_file,
                     empty_beam_file=empty_beam_file,
                     direct_beam_file=direct_beam_file,
-                    mask_files=[mask_file]
+                    mask_files=[mask_file],
                 )
             except Exception as e:
                 with self.log_output:
                     print(f"Reduction failed for sample {sample}: {e}")
                 continue
-            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            out_xye = os.path.join(
+                output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")
+            )
             try:
                 save_xye_pandas(res["IofQ"], out_xye)
                 with self.log_output:
@@ -240,13 +263,18 @@ def run_reduction(self, _):
             x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
             fig_trans, ax_trans = plt.subplots()
             ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_title(
+                f"Transmission: {sample} {os.path.basename(sample_run_file)}"
+            )
             ax_trans.set_xlabel("Wavelength (Å)")
             ax_trans.set_ylabel("Transmission")
             plt.tight_layout()
-            #with self.plot_output:
-                #display(fig_trans)
-            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            # with self.plot_output:
+            # display(fig_trans)
+            trans_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"),
+            )
             fig_trans.savefig(trans_png, dpi=300)
             plt.close(fig_trans)
             # Generate and display I(Q) plot.
@@ -255,7 +283,9 @@ def run_reduction(self, _):
             fig_iq, ax_iq = plt.subplots()
             if res["IofQ"].variances is not None:
                 yerr = np.sqrt(res["IofQ"].variances)
-                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+                ax_iq.errorbar(
+                    x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
+                )
             else:
                 ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
             ax_iq.set_title(f"{os.path.basename(sample_run_file)} ({sample})")
@@ -266,12 +296,15 @@ def run_reduction(self, _):
             plt.tight_layout()
             with self.plot_output:
                 display(fig_iq)
-            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            iq_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
+            )
             fig_iq.savefig(iq_png, dpi=300)
             plt.close(fig_iq)
             with self.log_output:
                 print(f"Reduced sample {sample} and saved outputs.")
-    
+
     @property
     def widget(self):
         return self.main
@@ -289,4 +322,4 @@ def save_xye_pandas(data_array, filename):
     else:
         err_vals = np.zeros_like(i_vals)
     df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
-    df.to_csv(filename, sep=" ", index=False, header=True)
\ No newline at end of file
+    df.to_csv(filename, sep=" ", index=False, header=True)
diff --git a/src/ess/loki/batchwidgets.ipynb b/src/ess/loki/batchwidgets.ipynb
index e97a9ff0..14da5e6b 100644
--- a/src/ess/loki/batchwidgets.ipynb
+++ b/src/ess/loki/batchwidgets.ipynb
@@ -2,36 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2f91f9ddaf6e4dfb98f67d743719bcee",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(FileChooser(path='/Users/oliverhammond/esssans-gui/src/ess/loki', filename='', t…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import batchwidget\n",
     "gui = batchwidget.SansBatchReductionWidget()\n",
     "display(gui.widget)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/src/ess/loki/tabwidget.ipynb b/src/ess/loki/tabwidget.ipynb
index 17ff07bf..8ec482fd 100644
--- a/src/ess/loki/tabwidget.ipynb
+++ b/src/ess/loki/tabwidget.ipynb
@@ -2,35 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fa59bfe4261f4e4996bcf7fed79763cc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Tab(children=(VBox(children=(Text(value='', description='Mask:', placeholder='Enter mask file path'), Text(val…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from tabwidgetauto import tabs\n",
     "display(tabs)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/src/ess/loki/tabwidget.py b/src/ess/loki/tabwidget.py
index 9f25070d..0c5f7c02 100644
--- a/src/ess/loki/tabwidget.py
+++ b/src/ess/loki/tabwidget.py
@@ -1,20 +1,22 @@
-import os
 import glob
+import os
 import re
+
 import h5py
-import pandas as pd
-import scipp as sc
+import ipywidgets as widgets
 import matplotlib.pyplot as plt
 import numpy as np
-import ipywidgets as widgets
+import pandas as pd
+import plopp as pp  # used for plotting in direct beam section
+import scipp as sc
 from ipydatagrid import DataGrid
-from IPython.display import display
 from ipyfilechooser import FileChooser
-from ess import sans
-from ess import loki
-from ess.sans.types import *
+from IPython.display import display
 from scipp.scipy.interpolate import interp1d
-import plopp as pp  # used for plotting in direct beam section
+
+from ess import loki, sans
+from ess.sans.types import *
+
 
 # ----------------------------
 # Reduction Functionality
@@ -27,19 +29,21 @@ def reduce_loki_batch_preliminary(
     direct_beam_file: str,
     mask_files: list = None,
     correct_for_gravity: bool = True,
-    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    uncertainty_mode=UncertaintyBroadcastMode.upper_bound,
     return_events: bool = False,
     wavelength_min: float = 1.0,
     wavelength_max: float = 13.0,
     wavelength_n: int = 201,
     q_start: float = 0.01,
     q_stop: float = 0.3,
-    q_n: int = 101
+    q_n: int = 101,
 ):
     if mask_files is None:
         mask_files = []
     # Define wavelength and Q bins.
-    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    wavelength_bins = sc.linspace(
+        "wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom"
+    )
     q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
     # Initialize the workflow.
     workflow = loki.LokiAtLarmorWorkflow()
@@ -63,6 +67,7 @@ def reduce_loki_batch_preliminary(
     da = workflow.compute(BackgroundSubtractedIofQ)
     return {"transmission": tf, "IofQ": da}
 
+
 def find_file(work_dir, run_number, extension=".nxs"):
     pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
     files = glob.glob(pattern)
@@ -71,13 +76,17 @@ def find_file(work_dir, run_number, extension=".nxs"):
     else:
         raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
 
+
 def find_direct_beam(work_dir):
     pattern = os.path.join(work_dir, "*direct-beam*.h5")
     files = glob.glob(pattern)
     if files:
         return files[0]
     else:
-        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+        raise FileNotFoundError(
+            f"Could not find direct-beam file matching pattern {pattern}"
+        )
+
 
 def find_mask_file(work_dir):
     pattern = os.path.join(work_dir, "*mask*.xml")
@@ -87,6 +96,7 @@ def find_mask_file(work_dir):
     else:
         raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
 
+
 def save_xye_pandas(data_array, filename):
     q_vals = data_array.coords["Q"].values
     i_vals = data_array.values
@@ -101,6 +111,7 @@ def save_xye_pandas(data_array, filename):
     df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
     df.to_csv(filename, sep=" ", index=False, header=True)
 
+
 # ----------------------------
 # Helper Functions for Semi-Auto Reduction
 # ----------------------------
@@ -110,6 +121,7 @@ def extract_run_number(filename):
         return m.group(1)
     return ""
 
+
 def parse_nx_details(filepath):
     details = {}
     with h5py.File(filepath, 'r') as f:
@@ -117,12 +129,17 @@ def parse_nx_details(filepath):
             grp = f['entry']['nicos_details']
             if 'runlabel' in grp:
                 val = grp['runlabel'][()]
-                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+                details['runlabel'] = (
+                    val.decode('utf8') if isinstance(val, bytes) else str(val)
+                )
             if 'runtype' in grp:
                 val = grp['runtype'][()]
-                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+                details['runtype'] = (
+                    val.decode('utf8') if isinstance(val, bytes) else str(val)
+                )
     return details
 
+
 # ----------------------------
 # Semi-Auto Reduction Widget
 # ----------------------------
@@ -133,19 +150,19 @@ def __init__(self):
         self.input_dir_chooser.title = "Select Input Folder"
         self.output_dir_chooser = FileChooser(select_dir=True)
         self.output_dir_chooser.title = "Select Output Folder"
-        
+
         self.scan_button = widgets.Button(description="Scan Directory")
         self.scan_button.on_click(self.scan_directory)
-        
+
         # DataGrid for auto-generated reduction table; now editable.
         self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
-        
+
         # Buttons to add or delete rows from the table.
         self.add_row_button = widgets.Button(description="Add Row")
         self.add_row_button.on_click(self.add_row)
         self.delete_row_button = widgets.Button(description="Delete Last Row")
         self.delete_row_button.on_click(self.delete_last_row)
-        
+
         # Parameter widgets for reduction (lambda and Q parameters)
         self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
         self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
@@ -153,36 +170,50 @@ def __init__(self):
         self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
         self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
         self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
-        
+
         # Text fields to display the automatically identified empty-beam files.
-        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
-        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
-        
+        self.empty_beam_sans_text = widgets.Text(
+            value="", description="Ebeam SANS:", disabled=True
+        )
+        self.empty_beam_trans_text = widgets.Text(
+            value="", description="Ebeam TRANS:", disabled=True
+        )
+
         self.reduce_button = widgets.Button(description="Reduce")
         self.reduce_button.on_click(self.run_reduction)
-        
+
         self.clear_log_button = widgets.Button(description="Clear Log")
         self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
         self.clear_plots_button = widgets.Button(description="Clear Plots")
         self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
-        
+
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
-        
+
         # Build the layout.
-        self.main = widgets.VBox([
-            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
-            self.scan_button,
-            self.table,
-            widgets.HBox([self.add_row_button, self.delete_row_button]),
-            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
-            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
-            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
-            self.log_output,
-            self.plot_output
-        ])
-    
+        self.main = widgets.VBox(
+            [
+                widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+                self.scan_button,
+                self.table,
+                widgets.HBox([self.add_row_button, self.delete_row_button]),
+                widgets.HBox(
+                    [
+                        self.lambda_min_widget,
+                        self.lambda_max_widget,
+                        self.lambda_n_widget,
+                    ]
+                ),
+                widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+                widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+                widgets.HBox(
+                    [self.reduce_button, self.clear_log_button, self.clear_plots_button]
+                ),
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
     def add_row(self, _):
         df = self.table.data
         # Create a default new row if the DataFrame is empty, otherwise add blank cells.
@@ -224,7 +255,9 @@ def scan_directory(self, _):
         table_rows = []
         for runlabel, d in groups.items():
             if 'sans' in d and 'trans' in d:
-                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+                table_rows.append(
+                    {'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}
+                )
         df = pd.DataFrame(table_rows)
         self.table.data = df
         with self.log_output:
@@ -252,7 +285,7 @@ def scan_directory(self, _):
             self.empty_beam_trans_text.value = ebeam_trans_files[0]
         else:
             self.empty_beam_trans_text.value = ""
-    
+
     def run_reduction(self, _):
         self.log_output.clear_output()
         self.plot_output.clear_output()
@@ -295,7 +328,9 @@ def run_reduction(self, _):
             trans_run = row["TRANS"]
             try:
                 sample_run_file = find_file(input_dir, sans_run, extension=".nxs")
-                transmission_run_file = find_file(input_dir, trans_run, extension=".nxs")
+                transmission_run_file = find_file(
+                    input_dir, trans_run, extension=".nxs"
+                )
             except Exception as e:
                 with self.log_output:
                     print(f"Skipping sample {sample}: {e}")
@@ -323,13 +358,15 @@ def run_reduction(self, _):
                     wavelength_n=lam_n,
                     q_start=q_min,
                     q_stop=q_max,
-                    q_n=q_n
+                    q_n=q_n,
                 )
             except Exception as e:
                 with self.log_output:
                     print(f"Reduction failed for sample {sample}: {e}")
                 continue
-            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            out_xye = os.path.join(
+                output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")
+            )
             try:
                 save_xye_pandas(res["IofQ"], out_xye)
                 with self.log_output:
@@ -337,17 +374,24 @@ def run_reduction(self, _):
             except Exception as e:
                 with self.log_output:
                     print(f"Failed to save reduced data for {sample}: {e}")
-            wavelength_bins = sc.linspace("wavelength", lam_min, lam_max, lam_n, unit="angstrom")
+            wavelength_bins = sc.linspace(
+                "wavelength", lam_min, lam_max, lam_n, unit="angstrom"
+            )
             x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
             fig_trans, ax_trans = plt.subplots()
             ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_title(
+                f"Transmission: {sample} {os.path.basename(sample_run_file)}"
+            )
             ax_trans.set_xlabel("Wavelength (Å)")
             ax_trans.set_ylabel("Transmission")
             plt.tight_layout()
             with self.plot_output:
                 display(fig_trans)
-            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            trans_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"),
+            )
             fig_trans.savefig(trans_png, dpi=300)
             plt.close(fig_trans)
             q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
@@ -355,7 +399,9 @@ def run_reduction(self, _):
             fig_iq, ax_iq = plt.subplots()
             if res["IofQ"].variances is not None:
                 yerr = np.sqrt(res["IofQ"].variances)
-                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+                ax_iq.errorbar(
+                    x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
+                )
             else:
                 ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
             ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
@@ -366,16 +412,20 @@ def run_reduction(self, _):
             plt.tight_layout()
             with self.plot_output:
                 display(fig_iq)
-            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            iq_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
+            )
             fig_iq.savefig(iq_png, dpi=300)
             plt.close(fig_iq)
             with self.log_output:
                 print(f"Reduced sample {sample} and saved outputs.")
-    
+
     @property
     def widget(self):
         return self.main
 
+
 # ----------------------------
 # Direct Beam Functionality
 # ----------------------------
@@ -390,7 +440,7 @@ def compute_direct_beam_local(
     wavelength_min: float = 1.0,
     wavelength_max: float = 13.0,
     n_wavelength_bins: int = 50,
-    n_wavelength_bands: int = 50
+    n_wavelength_bands: int = 50,
 ) -> dict:
     """
     Compute the direct beam function for the LoKI detectors using locally stored data.
@@ -398,50 +448,57 @@ def compute_direct_beam_local(
     workflow = loki.LokiAtLarmorWorkflow()
     workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
     workflow[NeXusDetectorName] = 'larmor_detector'
-    
+
     wl_min = sc.scalar(wavelength_min, unit='angstrom')
     wl_max = sc.scalar(wavelength_max, unit='angstrom')
-    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
-    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
+    workflow[WavelengthBins] = sc.linspace(
+        'wavelength', wl_min, wl_max, n_wavelength_bins + 1
+    )
+    workflow[WavelengthBands] = sc.linspace(
+        'wavelength', wl_min, wl_max, n_wavelength_bands + 1
+    )
     workflow[CorrectForGravity] = True
     workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
     workflow[ReturnEvents] = False
-    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
-    
+    workflow[QBins] = sc.linspace(
+        dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom'
+    )
+
     workflow[Filename[SampleRun]] = sample_sans
     workflow[Filename[BackgroundRun]] = background_sans
     workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
     workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
     workflow[Filename[EmptyBeamRun]] = empty_beam
-    
+
     center = sans.beam_center_from_center_of_mass(workflow)
     print("Computed beam center:", center)
     workflow[BeamCenter] = center
-    
+
     Iq_theory = sc.io.load_hdf5(local_Iq_theory)
     f = interp1d(Iq_theory, 'Q')
     I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
     print("Computed I0:", I0)
-    
+
     results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
-    
+
     iofq_full = results[-1]['iofq_full']
     iofq_bands = results[-1]['iofq_bands']
     direct_beam_function = results[-1]['direct_beam']
-    
+
     pp.plot(
         {'reference': Iq_theory, 'data': iofq_full},
         color={'reference': 'darkgrey', 'data': 'C0'},
         norm='log',
     )
     print("Plotted full-range result vs. theoretical reference.")
-    
+
     return {
         'direct_beam_function': direct_beam_function,
         'iofq_full': iofq_full,
         'Iq_theory': Iq_theory,
     }
 
+
 # ----------------------------
 # Widgets for Reduction and Direct Beam
 # ----------------------------
@@ -457,21 +514,27 @@ def __init__(self):
         self.ebeam_sans_widget = widgets.Text(
             value="",
             placeholder="Enter Ebeam SANS run number",
-            description="Ebeam SANS:"
+            description="Ebeam SANS:",
         )
         self.ebeam_trans_widget = widgets.Text(
             value="",
             placeholder="Enter Ebeam TRANS run number",
-            description="Ebeam TRANS:"
+            description="Ebeam TRANS:",
         )
         # Add GUI widgets for reduction parameters:
-        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
-        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_min_widget = widgets.FloatText(
+            value=1.0, description="λ min (Å):"
+        )
+        self.wavelength_max_widget = widgets.FloatText(
+            value=13.0, description="λ max (Å):"
+        )
         self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
-        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_start_widget = widgets.FloatText(
+            value=0.01, description="Q start (1/Å):"
+        )
         self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
         self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
-        
+
         self.load_csv_button = widgets.Button(description="Load CSV")
         self.load_csv_button.on_click(self.load_csv)
         self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
@@ -483,25 +546,39 @@ def __init__(self):
         self.clear_plots_button.on_click(self.clear_plots)
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
-        self.main = widgets.VBox([
-            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
-            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
-            # Reduction parameters:
-            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
-            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
-            self.load_csv_button,
-            self.table,
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
-            self.log_output,
-            self.plot_output
-        ])
-    
+        self.main = widgets.VBox(
+            [
+                widgets.HBox(
+                    [self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]
+                ),
+                widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+                # Reduction parameters:
+                widgets.HBox(
+                    [
+                        self.wavelength_min_widget,
+                        self.wavelength_max_widget,
+                        self.wavelength_n_widget,
+                    ]
+                ),
+                widgets.HBox(
+                    [self.q_start_widget, self.q_stop_widget, self.q_n_widget]
+                ),
+                self.load_csv_button,
+                self.table,
+                widgets.HBox(
+                    [self.reduce_button, self.clear_log_button, self.clear_plots_button]
+                ),
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
     def clear_log(self, _):
         self.log_output.clear_output()
-    
+
     def clear_plots(self, _):
         self.plot_output.clear_output()
-    
+
     def load_csv(self, _):
         csv_path = self.csv_chooser.selected
         if not csv_path or not os.path.exists(csv_path):
@@ -512,7 +589,7 @@ def load_csv(self, _):
         self.table.data = df
         with self.log_output:
             print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
-    
+
     def run_reduction(self, _):
         input_dir = self.input_dir_chooser.selected
         output_dir = self.output_dir_chooser.selected
@@ -533,8 +610,12 @@ def run_reduction(self, _):
                 print("Direct-beam file not found:", e)
             return
         try:
-            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
-            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            background_run_file = find_file(
+                input_dir, self.ebeam_sans_widget.value, extension=".nxs"
+            )
+            empty_beam_file = find_file(
+                input_dir, self.ebeam_trans_widget.value, extension=".nxs"
+            )
             with self.log_output:
                 print("Using empty-beam files:")
                 print("  Background (Ebeam SANS):", background_run_file)
@@ -554,8 +635,12 @@ def run_reduction(self, _):
         for idx, row in df.iterrows():
             sample = row["SAMPLE"]
             try:
-                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
-                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+                sample_run_file = find_file(
+                    input_dir, str(row["SANS"]), extension=".nxs"
+                )
+                transmission_run_file = find_file(
+                    input_dir, str(row["TRANS"]), extension=".nxs"
+                )
             except Exception as e:
                 with self.log_output:
                     print(f"Skipping sample {sample}: {e}")
@@ -590,13 +675,15 @@ def run_reduction(self, _):
                     wavelength_n=wl_n,
                     q_start=q_start,
                     q_stop=q_stop,
-                    q_n=q_n
+                    q_n=q_n,
                 )
             except Exception as e:
                 with self.log_output:
                     print(f"Reduction failed for sample {sample}: {e}")
                 continue
-            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            out_xye = os.path.join(
+                output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")
+            )
             try:
                 save_xye_pandas(res["IofQ"], out_xye)
                 with self.log_output:
@@ -604,17 +691,24 @@ def run_reduction(self, _):
             except Exception as e:
                 with self.log_output:
                     print(f"Failed to save reduced data for {sample}: {e}")
-            wavelength_bins = sc.linspace("wavelength", wl_min, wl_max, wl_n, unit="angstrom")
+            wavelength_bins = sc.linspace(
+                "wavelength", wl_min, wl_max, wl_n, unit="angstrom"
+            )
             x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
             fig_trans, ax_trans = plt.subplots()
             ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_title(
+                f"Transmission: {sample} {os.path.basename(sample_run_file)}"
+            )
             ax_trans.set_xlabel("Wavelength (Å)")
             ax_trans.set_ylabel("Transmission")
             plt.tight_layout()
             with self.plot_output:
                 display(fig_trans)
-            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            trans_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"),
+            )
             fig_trans.savefig(trans_png, dpi=300)
             plt.close(fig_trans)
             q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
@@ -622,7 +716,9 @@ def run_reduction(self, _):
             fig_iq, ax_iq = plt.subplots()
             if res["IofQ"].variances is not None:
                 yerr = np.sqrt(res["IofQ"].variances)
-                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+                ax_iq.errorbar(
+                    x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
+                )
             else:
                 ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
             ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
@@ -633,85 +729,99 @@ def run_reduction(self, _):
             plt.tight_layout()
             with self.plot_output:
                 display(fig_iq)
-            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            iq_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
+            )
             fig_iq.savefig(iq_png, dpi=300)
             plt.close(fig_iq)
             with self.log_output:
                 print(f"Reduced sample {sample} and saved outputs.")
-    
+
     @property
     def widget(self):
         return self.main
 
+
 # ----------------------------
 # Direct Beam Widget
 # ----------------------------
 class DirectBeamWidget:
     def __init__(self):
         self.mask_text = widgets.Text(
-            value="",
-            placeholder="Enter mask file path",
-            description="Mask:"
+            value="", placeholder="Enter mask file path", description="Mask:"
         )
         self.sample_sans_text = widgets.Text(
             value="",
             placeholder="Enter sample SANS file path",
-            description="Sample SANS:"
+            description="Sample SANS:",
         )
         self.background_sans_text = widgets.Text(
             value="",
             placeholder="Enter background SANS file path",
-            description="Background SANS:"
+            description="Background SANS:",
         )
         self.sample_trans_text = widgets.Text(
             value="",
             placeholder="Enter sample TRANS file path",
-            description="Sample TRANS:"
+            description="Sample TRANS:",
         )
         self.background_trans_text = widgets.Text(
             value="",
             placeholder="Enter background TRANS file path",
-            description="Background TRANS:"
+            description="Background TRANS:",
         )
         self.empty_beam_text = widgets.Text(
             value="",
             placeholder="Enter empty beam file path",
-            description="Empty Beam:"
+            description="Empty Beam:",
         )
         self.local_Iq_theory_text = widgets.Text(
             value="",
             placeholder="Enter I(q) theory file path",
-            description="I(q) Theory:"
+            description="I(q) Theory:",
         )
         # GUI widgets for direct beam parameters:
-        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
-        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
-        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
-        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
-        
+        self.db_wavelength_min_widget = widgets.FloatText(
+            value=1.0, description="λ min (Å):"
+        )
+        self.db_wavelength_max_widget = widgets.FloatText(
+            value=13.0, description="λ max (Å):"
+        )
+        self.db_n_wavelength_bins_widget = widgets.IntText(
+            value=50, description="λ n_bins:"
+        )
+        self.db_n_wavelength_bands_widget = widgets.IntText(
+            value=50, description="λ n_bands:"
+        )
+
         self.compute_button = widgets.Button(description="Compute Direct Beam")
         self.compute_button.on_click(self.compute_direct_beam)
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
-        self.main = widgets.VBox([
-            self.mask_text,
-            self.sample_sans_text,
-            self.background_sans_text,
-            self.sample_trans_text,
-            self.background_trans_text,
-            self.empty_beam_text,
-            self.local_Iq_theory_text,
-            widgets.HBox([
-                self.db_wavelength_min_widget,
-                self.db_wavelength_max_widget,
-                self.db_n_wavelength_bins_widget,
-                self.db_n_wavelength_bands_widget
-            ]),
-            self.compute_button,
-            self.log_output,
-            self.plot_output
-        ])
-    
+        self.main = widgets.VBox(
+            [
+                self.mask_text,
+                self.sample_sans_text,
+                self.background_sans_text,
+                self.sample_trans_text,
+                self.background_trans_text,
+                self.empty_beam_text,
+                self.local_Iq_theory_text,
+                widgets.HBox(
+                    [
+                        self.db_wavelength_min_widget,
+                        self.db_wavelength_max_widget,
+                        self.db_n_wavelength_bins_widget,
+                        self.db_n_wavelength_bands_widget,
+                    ]
+                ),
+                self.compute_button,
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
     def compute_direct_beam(self, _):
         self.log_output.clear_output()
         self.plot_output.clear_output()
@@ -735,7 +845,16 @@ def compute_direct_beam(self, _):
             print("  Background TRANS:", background_trans)
             print("  Empty Beam:", empty_beam)
             print("  I(q) Theory:", local_Iq_theory)
-            print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
+            print(
+                "  λ min:",
+                wl_min,
+                "λ max:",
+                wl_max,
+                "n_bins:",
+                n_bins,
+                "n_bands:",
+                n_bands,
+            )
         try:
             results = compute_direct_beam_local(
                 mask,
@@ -748,29 +867,32 @@ def compute_direct_beam(self, _):
                 wavelength_min=wl_min,
                 wavelength_max=wl_max,
                 n_wavelength_bins=n_bins,
-                n_wavelength_bands=n_bands
+                n_wavelength_bands=n_bands,
             )
             with self.log_output:
                 print("Direct beam computation complete.")
         except Exception as e:
             with self.log_output:
                 print("Error computing direct beam:", e)
-    
+
     @property
     def widget(self):
         return self.main
 
+
 # ----------------------------
 # Build Tabbed Widget
 # ----------------------------
 reduction_widget = SansBatchReductionWidget().widget
 direct_beam_widget = DirectBeamWidget().widget
 semi_auto_reduction_widget = SemiAutoReductionWidget().widget
-tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget])
+tabs = widgets.Tab(
+    children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget]
+)
 tabs.set_title(0, "Direct Beam")
 tabs.set_title(1, "Reduction (Manual)")
 tabs.set_title(2, "Reduction (Smart)")
-#tabs.set_title(3, "Reduction (Auto)")
+# tabs.set_title(3, "Reduction (Auto)")
 
 # Display the tab widget.
-#display(tabs)
+# display(tabs)
diff --git a/src/ess/loki/tabwidgetauto.py b/src/ess/loki/tabwidgetauto.py
index 6ad7ce1a..0ec590cb 100644
--- a/src/ess/loki/tabwidgetauto.py
+++ b/src/ess/loki/tabwidgetauto.py
@@ -1,23 +1,25 @@
-import os
 import glob
+import os
 import re
+import threading
+import time
+
 import h5py
-import pandas as pd
-import scipp as sc
+import ipywidgets as widgets
 import matplotlib.pyplot as plt
 import numpy as np
-import ipywidgets as widgets
+import pandas as pd
+import plopp as pp  # used for plotting in direct beam section
+import scipp as sc
 from ipydatagrid import DataGrid
-from IPython.display import display
 from ipyfilechooser import FileChooser
-from ess import sans
-from ess import loki
-from ess.sans.types import *
+from IPython.display import display
+from ipywidgets import IntSlider, Output
 from scipp.scipy.interpolate import interp1d
-import plopp as pp  # used for plotting in direct beam section
-import threading
-import time
-from ipywidgets import Output, IntSlider
+
+from ess import loki, sans
+from ess.sans.types import *
+
 
 # ----------------------------
 # Reduction Functionality
@@ -30,19 +32,21 @@ def reduce_loki_batch_preliminary(
     direct_beam_file: str,
     mask_files: list = None,
     correct_for_gravity: bool = True,
-    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    uncertainty_mode=UncertaintyBroadcastMode.upper_bound,
     return_events: bool = False,
     wavelength_min: float = 1.0,
     wavelength_max: float = 13.0,
     wavelength_n: int = 201,
     q_start: float = 0.01,
     q_stop: float = 0.3,
-    q_n: int = 101
+    q_n: int = 101,
 ):
     if mask_files is None:
         mask_files = []
     # Define wavelength and Q bins.
-    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    wavelength_bins = sc.linspace(
+        "wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom"
+    )
     q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
     # Initialize the workflow.
     workflow = loki.LokiAtLarmorWorkflow()
@@ -66,6 +70,7 @@ def reduce_loki_batch_preliminary(
     da = workflow.compute(BackgroundSubtractedIofQ)
     return {"transmission": tf, "IofQ": da}
 
+
 def find_file(work_dir, run_number, extension=".nxs"):
     pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
     files = glob.glob(pattern)
@@ -74,13 +79,17 @@ def find_file(work_dir, run_number, extension=".nxs"):
     else:
         raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
 
+
 def find_direct_beam(work_dir):
     pattern = os.path.join(work_dir, "*direct-beam*.h5")
     files = glob.glob(pattern)
     if files:
         return files[0]
     else:
-        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+        raise FileNotFoundError(
+            f"Could not find direct-beam file matching pattern {pattern}"
+        )
+
 
 def find_mask_file(work_dir):
     pattern = os.path.join(work_dir, "*mask*.xml")
@@ -90,6 +99,7 @@ def find_mask_file(work_dir):
     else:
         raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
 
+
 def save_xye_pandas(data_array, filename):
     q_vals = data_array.coords["Q"].values
     i_vals = data_array.values
@@ -104,6 +114,7 @@ def save_xye_pandas(data_array, filename):
     df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
     df.to_csv(filename, sep=" ", index=False, header=True)
 
+
 # ----------------------------
 # Helper Functions for Semi-Auto Reduction
 # ----------------------------
@@ -113,6 +124,7 @@ def extract_run_number(filename):
         return m.group(1)
     return ""
 
+
 def parse_nx_details(filepath):
     details = {}
     with h5py.File(filepath, 'r') as f:
@@ -120,12 +132,17 @@ def parse_nx_details(filepath):
             grp = f['entry']['nicos_details']
             if 'runlabel' in grp:
                 val = grp['runlabel'][()]
-                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+                details['runlabel'] = (
+                    val.decode('utf8') if isinstance(val, bytes) else str(val)
+                )
             if 'runtype' in grp:
                 val = grp['runtype'][()]
-                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+                details['runtype'] = (
+                    val.decode('utf8') if isinstance(val, bytes) else str(val)
+                )
     return details
 
+
 # ----------------------------
 # Semi-Auto Reduction Widget (unchanged)
 # ----------------------------
@@ -135,51 +152,65 @@ def __init__(self):
         self.input_dir_chooser.title = "Select Input Folder"
         self.output_dir_chooser = FileChooser(select_dir=True)
         self.output_dir_chooser.title = "Select Output Folder"
-        
+
         self.scan_button = widgets.Button(description="Scan Directory")
         self.scan_button.on_click(self.scan_directory)
-        
+
         self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
-        
+
         self.add_row_button = widgets.Button(description="Add Row")
         self.add_row_button.on_click(self.add_row)
         self.delete_row_button = widgets.Button(description="Delete Last Row")
         self.delete_row_button.on_click(self.delete_last_row)
-        
+
         self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
         self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
         self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
         self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
         self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
         self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
-        
-        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
-        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
-        
+
+        self.empty_beam_sans_text = widgets.Text(
+            value="", description="Ebeam SANS:", disabled=True
+        )
+        self.empty_beam_trans_text = widgets.Text(
+            value="", description="Ebeam TRANS:", disabled=True
+        )
+
         self.reduce_button = widgets.Button(description="Reduce")
         self.reduce_button.on_click(self.run_reduction)
-        
+
         self.clear_log_button = widgets.Button(description="Clear Log")
         self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
         self.clear_plots_button = widgets.Button(description="Clear Plots")
         self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
-        
+
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
-        
-        self.main = widgets.VBox([
-            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
-            self.scan_button,
-            self.table,
-            widgets.HBox([self.add_row_button, self.delete_row_button]),
-            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
-            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
-            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
-            self.log_output,
-            self.plot_output
-        ])
-    
+
+        self.main = widgets.VBox(
+            [
+                widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+                self.scan_button,
+                self.table,
+                widgets.HBox([self.add_row_button, self.delete_row_button]),
+                widgets.HBox(
+                    [
+                        self.lambda_min_widget,
+                        self.lambda_max_widget,
+                        self.lambda_n_widget,
+                    ]
+                ),
+                widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+                widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+                widgets.HBox(
+                    [self.reduce_button, self.clear_log_button, self.clear_plots_button]
+                ),
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
     def add_row(self, _):
         df = self.table.data
         if df.empty:
@@ -220,7 +251,9 @@ def scan_directory(self, _):
         table_rows = []
         for runlabel, d in groups.items():
             if 'sans' in d and 'trans' in d:
-                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+                table_rows.append(
+                    {'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}
+                )
         df = pd.DataFrame(table_rows)
         self.table.data = df
         with self.log_output:
@@ -247,7 +280,7 @@ def scan_directory(self, _):
             self.empty_beam_trans_text.value = ebeam_trans_files[0]
         else:
             self.empty_beam_trans_text.value = ""
-    
+
     def run_reduction(self, _):
         self.log_output.clear_output()
         self.plot_output.clear_output()
@@ -289,7 +322,9 @@ def run_reduction(self, _):
             trans_run = row["TRANS"]
             try:
                 sample_run_file = find_file(input_dir, sans_run, extension=".nxs")
-                transmission_run_file = find_file(input_dir, trans_run, extension=".nxs")
+                transmission_run_file = find_file(
+                    input_dir, trans_run, extension=".nxs"
+                )
             except Exception as e:
                 with self.log_output:
                     print(f"Skipping sample {sample}: {e}")
@@ -317,13 +352,15 @@ def run_reduction(self, _):
                     wavelength_n=lam_n,
                     q_start=q_min,
                     q_stop=q_max,
-                    q_n=q_n
+                    q_n=q_n,
                 )
             except Exception as e:
                 with self.log_output:
                     print(f"Reduction failed for sample {sample}: {e}")
                 continue
-            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            out_xye = os.path.join(
+                output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")
+            )
             try:
                 save_xye_pandas(res["IofQ"], out_xye)
                 with self.log_output:
@@ -332,15 +369,22 @@ def run_reduction(self, _):
                 with self.log_output:
                     print(f"Failed to save reduced data for {sample}: {e}")
             # --- Save Transmission Plot ---
-            wavelength_bins = sc.linspace("wavelength", lam_min, lam_max, lam_n, unit="angstrom")
+            wavelength_bins = sc.linspace(
+                "wavelength", lam_min, lam_max, lam_n, unit="angstrom"
+            )
             x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
             fig_trans, ax_trans = plt.subplots()
             ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_title(
+                f"Transmission: {sample} {os.path.basename(sample_run_file)}"
+            )
             ax_trans.set_xlabel("Wavelength (Å)")
             ax_trans.set_ylabel("Transmission")
             plt.tight_layout()
-            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            trans_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"),
+            )
             fig_trans.savefig(trans_png, dpi=300)
             plt.close(fig_trans)
             # --- Save I(Q) Plot ---
@@ -349,7 +393,9 @@ def run_reduction(self, _):
             fig_iq, ax_iq = plt.subplots()
             if res["IofQ"].variances is not None:
                 yerr = np.sqrt(res["IofQ"].variances)
-                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+                ax_iq.errorbar(
+                    x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
+                )
             else:
                 ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
             ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
@@ -358,16 +404,20 @@ def run_reduction(self, _):
             ax_iq.set_xscale("log")
             ax_iq.set_yscale("log")
             plt.tight_layout()
-            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            iq_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
+            )
             fig_iq.savefig(iq_png, dpi=300)
             plt.close(fig_iq)
             with self.log_output:
                 print(f"Reduced sample {sample} and saved outputs.")
-    
+
     @property
     def widget(self):
         return self.main
 
+
 # ----------------------------
 # Direct Beam Functionality and Widget (unchanged)
 # ----------------------------
@@ -382,120 +432,137 @@ def compute_direct_beam_local(
     wavelength_min: float = 1.0,
     wavelength_max: float = 13.0,
     n_wavelength_bins: int = 50,
-    n_wavelength_bands: int = 50
+    n_wavelength_bands: int = 50,
 ) -> dict:
     workflow = loki.LokiAtLarmorWorkflow()
     workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
     workflow[NeXusDetectorName] = 'larmor_detector'
-    
+
     wl_min = sc.scalar(wavelength_min, unit='angstrom')
     wl_max = sc.scalar(wavelength_max, unit='angstrom')
-    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
-    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
+    workflow[WavelengthBins] = sc.linspace(
+        'wavelength', wl_min, wl_max, n_wavelength_bins + 1
+    )
+    workflow[WavelengthBands] = sc.linspace(
+        'wavelength', wl_min, wl_max, n_wavelength_bands + 1
+    )
     workflow[CorrectForGravity] = True
     workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
     workflow[ReturnEvents] = False
-    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
-    
+    workflow[QBins] = sc.linspace(
+        dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom'
+    )
+
     workflow[Filename[SampleRun]] = sample_sans
     workflow[Filename[BackgroundRun]] = background_sans
     workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
     workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
     workflow[Filename[EmptyBeamRun]] = empty_beam
-    
+
     center = sans.beam_center_from_center_of_mass(workflow)
     print("Computed beam center:", center)
     workflow[BeamCenter] = center
-    
+
     Iq_theory = sc.io.load_hdf5(local_Iq_theory)
     f = interp1d(Iq_theory, 'Q')
     I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
     print("Computed I0:", I0)
-    
+
     results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
-    
+
     iofq_full = results[-1]['iofq_full']
     iofq_bands = results[-1]['iofq_bands']
     direct_beam_function = results[-1]['direct_beam']
-    
+
     pp.plot(
         {'reference': Iq_theory, 'data': iofq_full},
         color={'reference': 'darkgrey', 'data': 'C0'},
         norm='log',
     )
     print("Plotted full-range result vs. theoretical reference.")
-    
+
     return {
         'direct_beam_function': direct_beam_function,
         'iofq_full': iofq_full,
         'Iq_theory': Iq_theory,
     }
 
+
 class DirectBeamWidget:
     def __init__(self):
         self.mask_text = widgets.Text(
-            value="",
-            placeholder="Enter mask file path",
-            description="Mask:"
+            value="", placeholder="Enter mask file path", description="Mask:"
         )
         self.sample_sans_text = widgets.Text(
             value="",
             placeholder="Enter sample SANS file path",
-            description="Sample SANS:"
+            description="Sample SANS:",
         )
         self.background_sans_text = widgets.Text(
             value="",
             placeholder="Enter background SANS file path",
-            description="Background SANS:"
+            description="Background SANS:",
         )
         self.sample_trans_text = widgets.Text(
             value="",
             placeholder="Enter sample TRANS file path",
-            description="Sample TRANS:"
+            description="Sample TRANS:",
         )
         self.background_trans_text = widgets.Text(
             value="",
             placeholder="Enter background TRANS file path",
-            description="Background TRANS:"
+            description="Background TRANS:",
         )
         self.empty_beam_text = widgets.Text(
             value="",
             placeholder="Enter empty beam file path",
-            description="Empty Beam:"
+            description="Empty Beam:",
         )
         self.local_Iq_theory_text = widgets.Text(
             value="",
             placeholder="Enter I(q) Theory file path",
-            description="I(q) Theory:"
+            description="I(q) Theory:",
         )
-        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
-        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
-        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
-        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
-        
+        self.db_wavelength_min_widget = widgets.FloatText(
+            value=1.0, description="λ min (Å):"
+        )
+        self.db_wavelength_max_widget = widgets.FloatText(
+            value=13.0, description="λ max (Å):"
+        )
+        self.db_n_wavelength_bins_widget = widgets.IntText(
+            value=50, description="λ n_bins:"
+        )
+        self.db_n_wavelength_bands_widget = widgets.IntText(
+            value=50, description="λ n_bands:"
+        )
+
         self.compute_button = widgets.Button(description="Compute Direct Beam")
         self.compute_button.on_click(self.compute_direct_beam)
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
-        self.main = widgets.VBox([
-            self.mask_text,
-            self.sample_sans_text,
-            self.background_sans_text,
-            self.sample_trans_text,
-            self.background_trans_text,
-            self.empty_beam_text,
-            self.local_Iq_theory_text,
-            widgets.HBox([
-                self.db_wavelength_min_widget,
-                self.db_wavelength_max_widget,
-                self.db_n_wavelength_bins_widget,
-                self.db_n_wavelength_bands_widget
-            ]),
-            self.compute_button,
-            self.log_output,
-            self.plot_output
-        ])
-    
+        self.main = widgets.VBox(
+            [
+                self.mask_text,
+                self.sample_sans_text,
+                self.background_sans_text,
+                self.sample_trans_text,
+                self.background_trans_text,
+                self.empty_beam_text,
+                self.local_Iq_theory_text,
+                widgets.HBox(
+                    [
+                        self.db_wavelength_min_widget,
+                        self.db_wavelength_max_widget,
+                        self.db_n_wavelength_bins_widget,
+                        self.db_n_wavelength_bands_widget,
+                    ]
+                ),
+                self.compute_button,
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
     def compute_direct_beam(self, _):
         self.log_output.clear_output()
         self.plot_output.clear_output()
@@ -519,7 +586,16 @@ def compute_direct_beam(self, _):
             print("  Background TRANS:", background_trans)
             print("  Empty Beam:", empty_beam)
             print("  I(q) Theory:", local_Iq_theory)
-            print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
+            print(
+                "  λ min:",
+                wl_min,
+                "λ max:",
+                wl_max,
+                "n_bins:",
+                n_bins,
+                "n_bands:",
+                n_bands,
+            )
         try:
             results = compute_direct_beam_local(
                 mask,
@@ -532,18 +608,19 @@ def compute_direct_beam(self, _):
                 wavelength_min=wl_min,
                 wavelength_max=wl_max,
                 n_wavelength_bins=n_bins,
-                n_wavelength_bands=n_bands
+                n_wavelength_bands=n_bands,
             )
             with self.log_output:
                 print("Direct beam computation complete.")
         except Exception as e:
             with self.log_output:
                 print("Error computing direct beam:", e)
-    
+
     @property
     def widget(self):
         return self.main
 
+
 # ----------------------------
 # New: Auto Reduction Widget (with plot saving)
 # ----------------------------
@@ -553,27 +630,29 @@ def __init__(self):
         self.input_dir_chooser.title = "Select Input Folder"
         self.output_dir_chooser = FileChooser(select_dir=True)
         self.output_dir_chooser.title = "Select Output Folder"
-        
+
         self.start_stop_button = widgets.Button(description="Start")
         self.start_stop_button.on_click(self.toggle_running)
         self.status_label = widgets.Label(value="Stopped")
-        
+
         self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
         self.log_output = widgets.Output()
-        
+
         self.running = False
         self.thread = None
         self.processed = set()  # Track already reduced entries.
         self.empty_beam_sans = None
         self.empty_beam_trans = None
-        
-        self.main = widgets.VBox([
-            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
-            widgets.HBox([self.start_stop_button, self.status_label]),
-            self.table,
-            self.log_output
-        ])
-    
+
+        self.main = widgets.VBox(
+            [
+                widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+                widgets.HBox([self.start_stop_button, self.status_label]),
+                self.table,
+                self.log_output,
+            ]
+        )
+
     def toggle_running(self, _):
         if not self.running:
             self.running = True
@@ -585,7 +664,7 @@ def toggle_running(self, _):
             self.running = False
             self.start_stop_button.description = "Start"
             self.status_label.value = "Stopped"
-    
+
     def background_loop(self):
         while self.running:
             input_dir = self.input_dir_chooser.selected
@@ -620,12 +699,16 @@ def background_loop(self):
             table_rows = []
             for runlabel, d in groups.items():
                 if 'sans' in d and 'trans' in d:
-                    table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+                    table_rows.append(
+                        {'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}
+                    )
             df = pd.DataFrame(table_rows)
             self.table.data = df
             with self.log_output:
-                print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
-            
+                print(
+                    f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries."
+                )
+
             # Identify empty beam files.
             ebeam_sans_files = []
             ebeam_trans_files = []
@@ -665,8 +748,12 @@ def background_loop(self):
                 if key in self.processed:
                     continue
                 try:
-                    sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs")
-                    transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs")
+                    sample_run_file = find_file(
+                        input_dir, row["SANS"], extension=".nxs"
+                    )
+                    transmission_run_file = find_file(
+                        input_dir, row["TRANS"], extension=".nxs"
+                    )
                 except Exception as e:
                     with self.log_output:
                         print(f"Skipping sample {row['SAMPLE']}: {e}")
@@ -674,14 +761,19 @@ def background_loop(self):
                 try:
                     mask_file = find_mask_file(input_dir)
                     with self.log_output:
-                        print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
+                        print(
+                            f"Using mask file: {mask_file} for sample {row['SAMPLE']}"
+                        )
                 except Exception as e:
                     with self.log_output:
                         print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
                     continue
                 if not self.empty_beam_sans or not self.empty_beam_trans:
                     with self.log_output:
-                        print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
+                        print(
+                            "Empty beam files not found, skipping reduction for sample",
+                            row["SAMPLE"],
+                        )
                     continue
 
                 with self.log_output:
@@ -699,13 +791,16 @@ def background_loop(self):
                         wavelength_n=201,
                         q_start=0.01,
                         q_stop=0.3,
-                        q_n=101
+                        q_n=101,
                     )
                 except Exception as e:
                     with self.log_output:
                         print(f"Reduction failed for sample {row['SAMPLE']}: {e}")
                     continue
-                out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+                out_xye = os.path.join(
+                    output_dir,
+                    os.path.basename(sample_run_file).replace(".nxs", ".xye"),
+                )
                 try:
                     save_xye_pandas(res["IofQ"], out_xye)
                     with self.log_output:
@@ -714,15 +809,26 @@ def background_loop(self):
                     with self.log_output:
                         print(f"Failed to save reduced data for {row['SAMPLE']}: {e}")
                 # --- Save Transmission Plot ---
-                wavelength_bins = sc.linspace("wavelength", 1.0, 13.0, 201, unit="angstrom")
+                wavelength_bins = sc.linspace(
+                    "wavelength", 1.0, 13.0, 201, unit="angstrom"
+                )
                 x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
                 fig_trans, ax_trans = plt.subplots()
-                ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-                ax_trans.set_title(f"Transmission: {row['SAMPLE']} {os.path.basename(sample_run_file)}")
+                ax_trans.plot(
+                    x_wl, res["transmission"].values, marker='o', linestyle='-'
+                )
+                ax_trans.set_title(
+                    f"Transmission: {row['SAMPLE']} {os.path.basename(sample_run_file)}"
+                )
                 ax_trans.set_xlabel("Wavelength (Å)")
                 ax_trans.set_ylabel("Transmission")
                 plt.tight_layout()
-                trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+                trans_png = os.path.join(
+                    output_dir,
+                    os.path.basename(sample_run_file).replace(
+                        ".nxs", "_transmission.png"
+                    ),
+                )
                 fig_trans.savefig(trans_png, dpi=300)
                 plt.close(fig_trans)
                 # --- Save I(Q) Plot ---
@@ -731,27 +837,35 @@ def background_loop(self):
                 fig_iq, ax_iq = plt.subplots()
                 if res["IofQ"].variances is not None:
                     yerr = np.sqrt(res["IofQ"].variances)
-                    ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+                    ax_iq.errorbar(
+                        x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
+                    )
                 else:
                     ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
-                ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({row['SAMPLE']})")
+                ax_iq.set_title(
+                    f"I(Q): {os.path.basename(sample_run_file)} ({row['SAMPLE']})"
+                )
                 ax_iq.set_xlabel("Q (Å$^{-1}$)")
                 ax_iq.set_ylabel("I(Q)")
                 ax_iq.set_xscale("log")
                 ax_iq.set_yscale("log")
                 plt.tight_layout()
-                iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+                iq_png = os.path.join(
+                    output_dir,
+                    os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
+                )
                 fig_iq.savefig(iq_png, dpi=300)
                 plt.close(fig_iq)
                 with self.log_output:
                     print(f"Reduced sample {row['SAMPLE']} and saved outputs.")
                 self.processed.add(key)
             time.sleep(10)
-    
+
     @property
     def widget(self):
         return self.main
-    
+
+
 # ----------------------------
 # Widgets for Reduction and Direct Beam
 # ----------------------------
@@ -767,21 +881,27 @@ def __init__(self):
         self.ebeam_sans_widget = widgets.Text(
             value="",
             placeholder="Enter Ebeam SANS run number",
-            description="Ebeam SANS:"
+            description="Ebeam SANS:",
         )
         self.ebeam_trans_widget = widgets.Text(
             value="",
             placeholder="Enter Ebeam TRANS run number",
-            description="Ebeam TRANS:"
+            description="Ebeam TRANS:",
         )
         # Add GUI widgets for reduction parameters:
-        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
-        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_min_widget = widgets.FloatText(
+            value=1.0, description="λ min (Å):"
+        )
+        self.wavelength_max_widget = widgets.FloatText(
+            value=13.0, description="λ max (Å):"
+        )
         self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
-        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_start_widget = widgets.FloatText(
+            value=0.01, description="Q start (1/Å):"
+        )
         self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
         self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
-        
+
         self.load_csv_button = widgets.Button(description="Load CSV")
         self.load_csv_button.on_click(self.load_csv)
         self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
@@ -793,25 +913,39 @@ def __init__(self):
         self.clear_plots_button.on_click(self.clear_plots)
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
-        self.main = widgets.VBox([
-            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
-            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
-            # Reduction parameters:
-            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
-            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
-            self.load_csv_button,
-            self.table,
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
-            self.log_output,
-            self.plot_output
-        ])
-    
+        self.main = widgets.VBox(
+            [
+                widgets.HBox(
+                    [self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]
+                ),
+                widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+                # Reduction parameters:
+                widgets.HBox(
+                    [
+                        self.wavelength_min_widget,
+                        self.wavelength_max_widget,
+                        self.wavelength_n_widget,
+                    ]
+                ),
+                widgets.HBox(
+                    [self.q_start_widget, self.q_stop_widget, self.q_n_widget]
+                ),
+                self.load_csv_button,
+                self.table,
+                widgets.HBox(
+                    [self.reduce_button, self.clear_log_button, self.clear_plots_button]
+                ),
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
     def clear_log(self, _):
         self.log_output.clear_output()
-    
+
     def clear_plots(self, _):
         self.plot_output.clear_output()
-    
+
     def load_csv(self, _):
         csv_path = self.csv_chooser.selected
         if not csv_path or not os.path.exists(csv_path):
@@ -822,7 +956,7 @@ def load_csv(self, _):
         self.table.data = df
         with self.log_output:
             print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
-    
+
     def run_reduction(self, _):
         input_dir = self.input_dir_chooser.selected
         output_dir = self.output_dir_chooser.selected
@@ -843,8 +977,12 @@ def run_reduction(self, _):
                 print("Direct-beam file not found:", e)
             return
         try:
-            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
-            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            background_run_file = find_file(
+                input_dir, self.ebeam_sans_widget.value, extension=".nxs"
+            )
+            empty_beam_file = find_file(
+                input_dir, self.ebeam_trans_widget.value, extension=".nxs"
+            )
             with self.log_output:
                 print("Using empty-beam files:")
                 print("  Background (Ebeam SANS):", background_run_file)
@@ -864,8 +1002,12 @@ def run_reduction(self, _):
         for idx, row in df.iterrows():
             sample = row["SAMPLE"]
             try:
-                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
-                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+                sample_run_file = find_file(
+                    input_dir, str(row["SANS"]), extension=".nxs"
+                )
+                transmission_run_file = find_file(
+                    input_dir, str(row["TRANS"]), extension=".nxs"
+                )
             except Exception as e:
                 with self.log_output:
                     print(f"Skipping sample {sample}: {e}")
@@ -900,13 +1042,15 @@ def run_reduction(self, _):
                     wavelength_n=wl_n,
                     q_start=q_start,
                     q_stop=q_stop,
-                    q_n=q_n
+                    q_n=q_n,
                 )
             except Exception as e:
                 with self.log_output:
                     print(f"Reduction failed for sample {sample}: {e}")
                 continue
-            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            out_xye = os.path.join(
+                output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")
+            )
             try:
                 save_xye_pandas(res["IofQ"], out_xye)
                 with self.log_output:
@@ -914,17 +1058,24 @@ def run_reduction(self, _):
             except Exception as e:
                 with self.log_output:
                     print(f"Failed to save reduced data for {sample}: {e}")
-            wavelength_bins = sc.linspace("wavelength", wl_min, wl_max, wl_n, unit="angstrom")
+            wavelength_bins = sc.linspace(
+                "wavelength", wl_min, wl_max, wl_n, unit="angstrom"
+            )
             x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
             fig_trans, ax_trans = plt.subplots()
             ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
+            ax_trans.set_title(
+                f"Transmission: {sample} {os.path.basename(sample_run_file)}"
+            )
             ax_trans.set_xlabel("Wavelength (Å)")
             ax_trans.set_ylabel("Transmission")
             plt.tight_layout()
             with self.plot_output:
                 display(fig_trans)
-            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
+            trans_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"),
+            )
             fig_trans.savefig(trans_png, dpi=300)
             plt.close(fig_trans)
             q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
@@ -932,7 +1083,9 @@ def run_reduction(self, _):
             fig_iq, ax_iq = plt.subplots()
             if res["IofQ"].variances is not None:
                 yerr = np.sqrt(res["IofQ"].variances)
-                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
+                ax_iq.errorbar(
+                    x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
+                )
             else:
                 ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
             ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
@@ -943,13 +1096,15 @@ def run_reduction(self, _):
             plt.tight_layout()
             with self.plot_output:
                 display(fig_iq)
-            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
+            iq_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
+            )
             fig_iq.savefig(iq_png, dpi=300)
             plt.close(fig_iq)
             with self.log_output:
                 print(f"Reduced sample {sample} and saved outputs.")
 
-    
     @property
     def widget(self):
         return self.main
@@ -963,10 +1118,17 @@ def widget(self):
 semi_auto_reduction_widget = SemiAutoReductionWidget().widget
 auto_reduction_widget = AutoReductionWidget().widget
 
-tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
+tabs = widgets.Tab(
+    children=[
+        direct_beam_widget,
+        reduction_widget,
+        semi_auto_reduction_widget,
+        auto_reduction_widget,
+    ]
+)
 tabs.set_title(0, "Direct Beam")
 tabs.set_title(1, "Reduction (Manual)")
 tabs.set_title(2, "Reduction (Smart)")
 tabs.set_title(3, "Reduction (Auto)")
 
-#display(tabs)
+# display(tabs)

From df907c19cf5336f8ff83b760bcc66a4ab498b022 Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Tue, 4 Mar 2025 22:13:22 +0100
Subject: [PATCH 08/18] common plotting func

---
 src/ess/loki/tabwidgetauto.py | 592 +++++++++++++++-------------------
 1 file changed, 258 insertions(+), 334 deletions(-)

diff --git a/src/ess/loki/tabwidgetauto.py b/src/ess/loki/tabwidgetauto.py
index 6ad7ce1a..fa549882 100644
--- a/src/ess/loki/tabwidgetauto.py
+++ b/src/ess/loki/tabwidgetauto.py
@@ -17,7 +17,6 @@
 import plopp as pp  # used for plotting in direct beam section
 import threading
 import time
-from ipywidgets import Output, IntSlider
 
 # ----------------------------
 # Reduction Functionality
@@ -126,6 +125,230 @@ def parse_nx_details(filepath):
                 details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
     return details
 
+# ----------------------------
+# Common Plotting Function
+# ----------------------------
+def save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir):
+    """
+    Creates a figure with 1 row x 2 columns:
+      - Left subplot: I(Q) (scatter with errorbars, log-log).
+      - Right subplot: Transmission fraction vs. wavelength (scatter with errorbars).
+    A single centered title (filename and sample ID) is added above the subplots.
+    The figure is saved to output_dir with a filename based on sample_run_file.
+    """
+    # Create a figure with two subplots side by side.
+    fig, axs = plt.subplots(1, 2, figsize=(8, 4))
+    
+    # Force each axis to be square.
+    axs[0].set_box_aspect(1)
+    axs[1].set_box_aspect(1)
+    
+    # Create a common centered title containing the filename and sample ID.
+    title_str = f"{os.path.basename(sample_run_file)} - {sample}"
+    fig.suptitle(title_str, fontsize=10)
+    
+    # Subplot A: I(Q)
+    q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
+    x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+    if res["IofQ"].variances is not None:
+        yerr = np.sqrt(res["IofQ"].variances)
+        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', markerfacecolor='none', alpha=0.5)
+    else:
+        axs[0].scatter(x_q, res["IofQ"].values)
+    axs[0].set_xlabel("Q (Å$^{-1}$)")
+    axs[0].set_ylabel("I(Q)")
+    axs[0].set_xscale("log")
+    axs[0].set_yscale("log")
+    
+    # Subplot B: Transmission vs. Wavelength
+    wavelength_bins = sc.linspace("wavelength", lam_min, lam_max, lam_n, unit="angstrom")
+    x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+    if res["transmission"].variances is not None:
+        yerr_tr = np.sqrt(res["transmission"].variances)
+        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', markerfacecolor='none', alpha=0.5)
+    else:
+        axs[1].scatter(x_wl, res["transmission"].values)
+    axs[1].set_xlabel("Wavelength (Å)")
+    axs[1].set_ylabel("Transmission")
+    
+    # Adjust layout so that there is space 
+    plt.tight_layout()#rect=[0, 0, 1, 0.95])
+    out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png"))
+    fig.savefig(out_png, dpi=300)
+    plt.close(fig)
+
+
+
+# ----------------------------
+# SansBatchReductionWidget (Updated)
+# ----------------------------
+class SansBatchReductionWidget:
+    def __init__(self):
+        # CSV chooser for pre-loaded reduction table.
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        # Folder choosers.
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        # Empty-beam run number widgets.
+        self.ebeam_sans_widget = widgets.Text(value="", placeholder="Enter Ebeam SANS run number", description="Ebeam SANS:")
+        self.ebeam_trans_widget = widgets.Text(value="", placeholder="Enter Ebeam TRANS run number", description="Ebeam TRANS:")
+        # Reduction parameter widgets.
+        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        # Button to load CSV.
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        # DataGrid for the reduction table.
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        # Reduction and clear buttons.
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        # Output widgets.
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        # Build layout.
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
+            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+    
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
+            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            with self.log_output:
+                print("Using empty-beam files:")
+                print("  Background (Ebeam SANS):", background_run_file)
+                print("  Empty beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Error finding empty beam files:", e)
+            return
+        wl_min = self.wavelength_min_widget.value
+        wl_max = self.wavelength_max_widget.value
+        wl_n = self.wavelength_n_widget.value
+        q_start = self.q_start_widget.value
+        q_stop = self.q_stop_widget.value
+        q_n = self.q_n_widget.value
+        
+        df = self.table.data
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            try:
+                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
+                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            mask_candidate = str(row.get("mask", "")).strip()
+            mask_file = None
+            if mask_candidate:
+                mask_file_candidate = os.path.join(input_dir, f"{mask_candidate}.xml")
+                if os.path.exists(mask_file_candidate):
+                    mask_file = mask_file_candidate
+            if mask_file is None:
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Identified mask file: {mask_file} for sample {sample}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {sample}: {e}")
+                    continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file],
+                    wavelength_min=wl_min,
+                    wavelength_max=wl_max,
+                    wavelength_n=wl_n,
+                    q_start=q_start,
+                    q_stop=q_stop,
+                    q_n=q_n
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            try:
+                save_reduction_plots(res, sample, sample_run_file, wl_min, wl_max, wl_n, q_start, q_stop, q_n, output_dir)#, n_bands=5)
+                with self.log_output:
+                    print(f"Saved combined reduction plot for sample {sample}.")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduction plot for {sample}: {e}")
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+    
+    @property
+    def widget(self):
+        return self.main
+
 # ----------------------------
 # Semi-Auto Reduction Widget (unchanged)
 # ----------------------------
@@ -135,38 +358,33 @@ def __init__(self):
         self.input_dir_chooser.title = "Select Input Folder"
         self.output_dir_chooser = FileChooser(select_dir=True)
         self.output_dir_chooser.title = "Select Output Folder"
-        
         self.scan_button = widgets.Button(description="Scan Directory")
         self.scan_button.on_click(self.scan_directory)
-        
         self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
-        
         self.add_row_button = widgets.Button(description="Add Row")
         self.add_row_button.on_click(self.add_row)
         self.delete_row_button = widgets.Button(description="Delete Last Row")
         self.delete_row_button.on_click(self.delete_last_row)
-        
         self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
         self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
         self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
         self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
         self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
         self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
-        
         self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
         self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
-        
         self.reduce_button = widgets.Button(description="Reduce")
         self.reduce_button.on_click(self.run_reduction)
-        
         self.clear_log_button = widgets.Button(description="Clear Log")
         self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
         self.clear_plots_button = widgets.Button(description="Clear Plots")
         self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
-        
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
         
+        # Add the processed set here:
+        self.processed = set()
+        
         self.main = widgets.VBox([
             widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
             self.scan_button,
@@ -179,6 +397,7 @@ def __init__(self):
             self.log_output,
             self.plot_output
         ])
+
     
     def add_row(self, _):
         df = self.table.data
@@ -282,7 +501,8 @@ def run_reduction(self, _):
         q_max = self.q_max_widget.value
         q_n = self.q_n_widget.value
 
-        df = self.table.data
+        #df = self.table.data
+        df = self.table.data.copy()
         for idx, row in df.iterrows():
             sample = row["SAMPLE"]
             sans_run = row["SANS"]
@@ -331,38 +551,18 @@ def run_reduction(self, _):
             except Exception as e:
                 with self.log_output:
                     print(f"Failed to save reduced data for {sample}: {e}")
-            # --- Save Transmission Plot ---
-            wavelength_bins = sc.linspace("wavelength", lam_min, lam_max, lam_n, unit="angstrom")
-            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
-            fig_trans, ax_trans = plt.subplots()
-            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
-            ax_trans.set_xlabel("Wavelength (Å)")
-            ax_trans.set_ylabel("Transmission")
-            plt.tight_layout()
-            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
-            fig_trans.savefig(trans_png, dpi=300)
-            plt.close(fig_trans)
-            # --- Save I(Q) Plot ---
-            q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
-            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
-            fig_iq, ax_iq = plt.subplots()
-            if res["IofQ"].variances is not None:
-                yerr = np.sqrt(res["IofQ"].variances)
-                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
-            else:
-                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
-            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
-            ax_iq.set_xlabel("Q (Å$^{-1}$)")
-            ax_iq.set_ylabel("I(Q)")
-            ax_iq.set_xscale("log")
-            ax_iq.set_yscale("log")
-            plt.tight_layout()
-            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
-            fig_iq.savefig(iq_png, dpi=300)
-            plt.close(fig_iq)
+            try:
+                save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir)#, n_bands=5)
+                with self.log_output:
+                    print(f"Saved combined reduction plot for sample {sample}.")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduction plot for {sample}: {e}")
             with self.log_output:
                 print(f"Reduced sample {sample} and saved outputs.")
+            self.processed.add((row["SAMPLE"], row["SANS"], row["TRANS"]))
+            #time.sleep(1)  # small delay between rows
+        #time.sleep(60)
     
     @property
     def widget(self):
@@ -387,7 +587,6 @@ def compute_direct_beam_local(
     workflow = loki.LokiAtLarmorWorkflow()
     workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
     workflow[NeXusDetectorName] = 'larmor_detector'
-    
     wl_min = sc.scalar(wavelength_min, unit='angstrom')
     wl_max = sc.scalar(wavelength_max, unit='angstrom')
     workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
@@ -396,35 +595,28 @@ def compute_direct_beam_local(
     workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
     workflow[ReturnEvents] = False
     workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
-    
     workflow[Filename[SampleRun]] = sample_sans
     workflow[Filename[BackgroundRun]] = background_sans
     workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
     workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
     workflow[Filename[EmptyBeamRun]] = empty_beam
-    
     center = sans.beam_center_from_center_of_mass(workflow)
     print("Computed beam center:", center)
     workflow[BeamCenter] = center
-    
     Iq_theory = sc.io.load_hdf5(local_Iq_theory)
     f = interp1d(Iq_theory, 'Q')
     I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
     print("Computed I0:", I0)
-    
     results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
-    
     iofq_full = results[-1]['iofq_full']
     iofq_bands = results[-1]['iofq_bands']
     direct_beam_function = results[-1]['direct_beam']
-    
     pp.plot(
         {'reference': Iq_theory, 'data': iofq_full},
         color={'reference': 'darkgrey', 'data': 'C0'},
         norm='log',
     )
     print("Plotted full-range result vs. theoretical reference.")
-    
     return {
         'direct_beam_function': direct_beam_function,
         'iofq_full': iofq_full,
@@ -433,46 +625,17 @@ def compute_direct_beam_local(
 
 class DirectBeamWidget:
     def __init__(self):
-        self.mask_text = widgets.Text(
-            value="",
-            placeholder="Enter mask file path",
-            description="Mask:"
-        )
-        self.sample_sans_text = widgets.Text(
-            value="",
-            placeholder="Enter sample SANS file path",
-            description="Sample SANS:"
-        )
-        self.background_sans_text = widgets.Text(
-            value="",
-            placeholder="Enter background SANS file path",
-            description="Background SANS:"
-        )
-        self.sample_trans_text = widgets.Text(
-            value="",
-            placeholder="Enter sample TRANS file path",
-            description="Sample TRANS:"
-        )
-        self.background_trans_text = widgets.Text(
-            value="",
-            placeholder="Enter background TRANS file path",
-            description="Background TRANS:"
-        )
-        self.empty_beam_text = widgets.Text(
-            value="",
-            placeholder="Enter empty beam file path",
-            description="Empty Beam:"
-        )
-        self.local_Iq_theory_text = widgets.Text(
-            value="",
-            placeholder="Enter I(q) Theory file path",
-            description="I(q) Theory:"
-        )
+        self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:")
+        self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:")
+        self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:")
+        self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:")
+        self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:")
+        self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:")
+        self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:")
         self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
         self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
         self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
         self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
-        
         self.compute_button = widgets.Button(description="Compute Direct Beam")
         self.compute_button.on_click(self.compute_direct_beam)
         self.log_output = widgets.Output()
@@ -545,7 +708,7 @@ def widget(self):
         return self.main
 
 # ----------------------------
-# New: Auto Reduction Widget (with plot saving)
+# Auto Reduction Widget (unchanged, with common plotting call)
 # ----------------------------
 class AutoReductionWidget:
     def __init__(self):
@@ -553,20 +716,16 @@ def __init__(self):
         self.input_dir_chooser.title = "Select Input Folder"
         self.output_dir_chooser = FileChooser(select_dir=True)
         self.output_dir_chooser.title = "Select Output Folder"
-        
         self.start_stop_button = widgets.Button(description="Start")
         self.start_stop_button.on_click(self.toggle_running)
         self.status_label = widgets.Label(value="Stopped")
-        
         self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
         self.log_output = widgets.Output()
-        
         self.running = False
         self.thread = None
         self.processed = set()  # Track already reduced entries.
         self.empty_beam_sans = None
         self.empty_beam_trans = None
-        
         self.main = widgets.VBox([
             widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
             widgets.HBox([self.start_stop_button, self.status_label]),
@@ -593,15 +752,13 @@ def background_loop(self):
             if not input_dir or not os.path.isdir(input_dir):
                 with self.log_output:
                     print("Invalid input folder. Waiting for valid selection...")
-                time.sleep(10)
+                time.sleep(60)
                 continue
             if not output_dir or not os.path.isdir(output_dir):
                 with self.log_output:
                     print("Invalid output folder. Waiting for valid selection...")
-                time.sleep(10)
+                time.sleep(60)
                 continue
-
-            # Scan for .nxs files and build the reduction table.
             nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
             groups = {}
             for f in nxs_files:
@@ -625,8 +782,6 @@ def background_loop(self):
             self.table.data = df
             with self.log_output:
                 print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
-            
-            # Identify empty beam files.
             ebeam_sans_files = []
             ebeam_trans_files = []
             for f in nxs_files:
@@ -649,17 +804,13 @@ def background_loop(self):
                 self.empty_beam_trans = ebeam_trans_files[0]
             else:
                 self.empty_beam_trans = None
-
-            # Get the direct beam file.
             try:
                 direct_beam_file = find_direct_beam(input_dir)
             except Exception as e:
                 with self.log_output:
                     print("Direct-beam file not found:", e)
-                time.sleep(10)
+                time.sleep(60)
                 continue
-
-            # Process new reduction entries.
             for index, row in df.iterrows():
                 key = (row["SAMPLE"], row["SANS"], row["TRANS"])
                 if key in self.processed:
@@ -683,7 +834,6 @@ def background_loop(self):
                     with self.log_output:
                         print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
                     continue
-
                 with self.log_output:
                     print(f"Reducing sample {row['SAMPLE']}...")
                 try:
@@ -713,250 +863,24 @@ def background_loop(self):
                 except Exception as e:
                     with self.log_output:
                         print(f"Failed to save reduced data for {row['SAMPLE']}: {e}")
-                # --- Save Transmission Plot ---
-                wavelength_bins = sc.linspace("wavelength", 1.0, 13.0, 201, unit="angstrom")
-                x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
-                fig_trans, ax_trans = plt.subplots()
-                ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-                ax_trans.set_title(f"Transmission: {row['SAMPLE']} {os.path.basename(sample_run_file)}")
-                ax_trans.set_xlabel("Wavelength (Å)")
-                ax_trans.set_ylabel("Transmission")
-                plt.tight_layout()
-                trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
-                fig_trans.savefig(trans_png, dpi=300)
-                plt.close(fig_trans)
-                # --- Save I(Q) Plot ---
-                q_bins = sc.linspace("Q", 0.01, 0.3, 101, unit="1/angstrom")
-                x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
-                fig_iq, ax_iq = plt.subplots()
-                if res["IofQ"].variances is not None:
-                    yerr = np.sqrt(res["IofQ"].variances)
-                    ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
-                else:
-                    ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
-                ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({row['SAMPLE']})")
-                ax_iq.set_xlabel("Q (Å$^{-1}$)")
-                ax_iq.set_ylabel("I(Q)")
-                ax_iq.set_xscale("log")
-                ax_iq.set_yscale("log")
-                plt.tight_layout()
-                iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
-                fig_iq.savefig(iq_png, dpi=300)
-                plt.close(fig_iq)
-                with self.log_output:
-                    print(f"Reduced sample {row['SAMPLE']} and saved outputs.")
-                self.processed.add(key)
-            time.sleep(10)
-    
-    @property
-    def widget(self):
-        return self.main
-    
-# ----------------------------
-# Widgets for Reduction and Direct Beam
-# ----------------------------
-class SansBatchReductionWidget:
-    def __init__(self):
-        self.csv_chooser = FileChooser(select_dir=False)
-        self.csv_chooser.title = "Select CSV File"
-        self.csv_chooser.filter_pattern = "*.csv"
-        self.input_dir_chooser = FileChooser(select_dir=True)
-        self.input_dir_chooser.title = "Select Input Folder"
-        self.output_dir_chooser = FileChooser(select_dir=True)
-        self.output_dir_chooser.title = "Select Output Folder"
-        self.ebeam_sans_widget = widgets.Text(
-            value="",
-            placeholder="Enter Ebeam SANS run number",
-            description="Ebeam SANS:"
-        )
-        self.ebeam_trans_widget = widgets.Text(
-            value="",
-            placeholder="Enter Ebeam TRANS run number",
-            description="Ebeam TRANS:"
-        )
-        # Add GUI widgets for reduction parameters:
-        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
-        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
-        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
-        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
-        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
-        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
-        
-        self.load_csv_button = widgets.Button(description="Load CSV")
-        self.load_csv_button.on_click(self.load_csv)
-        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
-        self.reduce_button = widgets.Button(description="Reduce")
-        self.reduce_button.on_click(self.run_reduction)
-        self.clear_log_button = widgets.Button(description="Clear Log")
-        self.clear_log_button.on_click(self.clear_log)
-        self.clear_plots_button = widgets.Button(description="Clear Plots")
-        self.clear_plots_button.on_click(self.clear_plots)
-        self.log_output = widgets.Output()
-        self.plot_output = widgets.Output()
-        self.main = widgets.VBox([
-            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
-            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
-            # Reduction parameters:
-            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
-            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
-            self.load_csv_button,
-            self.table,
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
-            self.log_output,
-            self.plot_output
-        ])
-    
-    def clear_log(self, _):
-        self.log_output.clear_output()
-    
-    def clear_plots(self, _):
-        self.plot_output.clear_output()
-    
-    def load_csv(self, _):
-        csv_path = self.csv_chooser.selected
-        if not csv_path or not os.path.exists(csv_path):
-            with self.log_output:
-                print("CSV file not selected or does not exist.")
-            return
-        df = pd.read_csv(csv_path)
-        self.table.data = df
-        with self.log_output:
-            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
-    
-    def run_reduction(self, _):
-        input_dir = self.input_dir_chooser.selected
-        output_dir = self.output_dir_chooser.selected
-        if not input_dir or not os.path.isdir(input_dir):
-            with self.log_output:
-                print("Input folder is not valid.")
-            return
-        if not output_dir or not os.path.isdir(output_dir):
-            with self.log_output:
-                print("Output folder is not valid.")
-            return
-        try:
-            direct_beam_file = find_direct_beam(input_dir)
-            with self.log_output:
-                print("Using direct-beam file:", direct_beam_file)
-        except Exception as e:
-            with self.log_output:
-                print("Direct-beam file not found:", e)
-            return
-        try:
-            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
-            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
-            with self.log_output:
-                print("Using empty-beam files:")
-                print("  Background (Ebeam SANS):", background_run_file)
-                print("  Empty beam (Ebeam TRANS):", empty_beam_file)
-        except Exception as e:
-            with self.log_output:
-                print("Error finding empty beam files:", e)
-            return
-        # Retrieve reduction parameters from widgets.
-        wl_min = self.wavelength_min_widget.value
-        wl_max = self.wavelength_max_widget.value
-        wl_n = self.wavelength_n_widget.value
-        q_start = self.q_start_widget.value
-        q_stop = self.q_stop_widget.value
-        q_n = self.q_n_widget.value
-        df = self.table.data
-        for idx, row in df.iterrows():
-            sample = row["SAMPLE"]
-            try:
-                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
-                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
-            except Exception as e:
-                with self.log_output:
-                    print(f"Skipping sample {sample}: {e}")
-                continue
-            mask_candidate = str(row.get("mask", "")).strip()
-            mask_file = None
-            if mask_candidate:
-                mask_file_candidate = os.path.join(input_dir, f"{mask_candidate}.xml")
-                if os.path.exists(mask_file_candidate):
-                    mask_file = mask_file_candidate
-            if mask_file is None:
                 try:
-                    mask_file = find_mask_file(input_dir)
+                    save_reduction_plots(res, row["SAMPLE"], sample_run_file, 1.0, 13.0, 201, 0.01, 0.3, 101, output_dir)#, n_bands=5)
                     with self.log_output:
-                        print(f"Identified mask file: {mask_file} for sample {sample}")
+                        print(f"Saved combined reduction plot for sample {row['SAMPLE']}.")
                 except Exception as e:
                     with self.log_output:
-                        print(f"Mask file not found for sample {sample}: {e}")
-                    continue
-            with self.log_output:
-                print(f"Reducing sample {sample}...")
-            try:
-                res = reduce_loki_batch_preliminary(
-                    sample_run_file=sample_run_file,
-                    transmission_run_file=transmission_run_file,
-                    background_run_file=background_run_file,
-                    empty_beam_file=empty_beam_file,
-                    direct_beam_file=direct_beam_file,
-                    mask_files=[mask_file],
-                    wavelength_min=wl_min,
-                    wavelength_max=wl_max,
-                    wavelength_n=wl_n,
-                    q_start=q_start,
-                    q_stop=q_stop,
-                    q_n=q_n
-                )
-            except Exception as e:
-                with self.log_output:
-                    print(f"Reduction failed for sample {sample}: {e}")
-                continue
-            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
-            try:
-                save_xye_pandas(res["IofQ"], out_xye)
+                        print(f"Failed to save reduction plot for {row['SAMPLE']}: {e}")
                 with self.log_output:
-                    print(f"Saved reduced data to {out_xye}")
-            except Exception as e:
-                with self.log_output:
-                    print(f"Failed to save reduced data for {sample}: {e}")
-            wavelength_bins = sc.linspace("wavelength", wl_min, wl_max, wl_n, unit="angstrom")
-            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
-            fig_trans, ax_trans = plt.subplots()
-            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-            ax_trans.set_title(f"Transmission: {sample} {os.path.basename(sample_run_file)}")
-            ax_trans.set_xlabel("Wavelength (Å)")
-            ax_trans.set_ylabel("Transmission")
-            plt.tight_layout()
-            with self.plot_output:
-                display(fig_trans)
-            trans_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"))
-            fig_trans.savefig(trans_png, dpi=300)
-            plt.close(fig_trans)
-            q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
-            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
-            fig_iq, ax_iq = plt.subplots()
-            if res["IofQ"].variances is not None:
-                yerr = np.sqrt(res["IofQ"].variances)
-                ax_iq.errorbar(x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-')
-            else:
-                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
-            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
-            ax_iq.set_xlabel("Q (Å$^{-1}$)")
-            ax_iq.set_ylabel("I(Q)")
-            ax_iq.set_xscale("log")
-            ax_iq.set_yscale("log")
-            plt.tight_layout()
-            with self.plot_output:
-                display(fig_iq)
-            iq_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"))
-            fig_iq.savefig(iq_png, dpi=300)
-            plt.close(fig_iq)
-            with self.log_output:
-                print(f"Reduced sample {sample} and saved outputs.")
-
+                    print(f"Reduced sample {row['SAMPLE']} and saved outputs.")
+                self.processed.add(key)
+            time.sleep(60)
     
     @property
     def widget(self):
         return self.main
 
-
 # ----------------------------
-# Build the tabbed widget.
+# Build the Tabbed Widget
 # ----------------------------
 reduction_widget = SansBatchReductionWidget().widget
 direct_beam_widget = DirectBeamWidget().widget

From bee25a9d22a3ce4fac04685fbd67071e19be46e3 Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Thu, 6 Mar 2025 09:54:08 +0100
Subject: [PATCH 09/18] Minor updates here and there

---
 src/ess/loki/tabwidget-050325.py     | 826 ++++++++++++++++++++++++
 src/ess/loki/tabwidget050325.py      | 826 ++++++++++++++++++++++++
 src/ess/loki/tabwidgetauto-040325.py | 926 +++++++++++++++++++++++++++
 src/ess/loki/tabwidgetauto.py        |  64 +-
 4 files changed, 2625 insertions(+), 17 deletions(-)
 create mode 100644 src/ess/loki/tabwidget-050325.py
 create mode 100644 src/ess/loki/tabwidget050325.py
 create mode 100644 src/ess/loki/tabwidgetauto-040325.py

diff --git a/src/ess/loki/tabwidget-050325.py b/src/ess/loki/tabwidget-050325.py
new file mode 100644
index 00000000..1d52f3f0
--- /dev/null
+++ b/src/ess/loki/tabwidget-050325.py
@@ -0,0 +1,826 @@
+import os
+import glob
+import re
+import h5py
+import pandas as pd
+import scipp as sc
+import matplotlib.pyplot as plt
+import numpy as np
+import ipywidgets as widgets
+from ipydatagrid import DataGrid
+from IPython.display import display
+from ipyfilechooser import FileChooser
+from ess import sans
+from ess import loki
+from ess.sans.types import *
+from scipp.scipy.interpolate import interp1d
+import plopp as pp  # used for plotting in direct beam section
+import threading
+import time
+
+# ----------------------------
+# Common Utility Functions
+# ----------------------------
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+def find_direct_beam(work_dir):
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+
+def find_mask_file(work_dir):
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+def extract_run_number(filename):
+    m = re.search(r'(\d{4,})', filename)
+    if m:
+        return m.group(1)
+    return ""
+
+def parse_nx_details(filepath):
+    details = {}
+    with h5py.File(filepath, 'r') as f:
+        if 'nicos_details' in f['entry']:
+            grp = f['entry']['nicos_details']
+            if 'runlabel' in grp:
+                val = grp['runlabel'][()]
+                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+            if 'runtype' in grp:
+                val = grp['runtype'][()]
+                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+    return details
+
+# ----------------------------
+# Reduction and Plotting Functions
+# ----------------------------
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101
+):
+    if mask_files is None:
+        mask_files = []
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+def save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True):
+    fig, axs = plt.subplots(1, 2, figsize=(8, 4))
+    axs[0].set_box_aspect(1)
+    axs[1].set_box_aspect(1)
+    title_str = f"{sample} - {os.path.basename(sample_run_file)}"
+    fig.suptitle(title_str, fontsize=14)
+    q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
+    x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+    if res["IofQ"].variances is not None:
+        yerr = np.sqrt(res["IofQ"].variances)
+        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+    else:
+        axs[0].scatter(x_q, res["IofQ"].values)
+    axs[0].set_xlabel("Q (Å$^{-1}$)")
+    axs[0].set_ylabel("I(Q)")
+    axs[0].set_xscale("log")
+    axs[0].set_yscale("log")
+    wavelength_bins = sc.linspace("wavelength", lam_min, lam_max, lam_n, unit="angstrom")
+    x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+    if res["transmission"].variances is not None:
+        yerr_tr = np.sqrt(res["transmission"].variances)
+        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+    else:
+        axs[1].scatter(x_wl, res["transmission"].values)
+    axs[1].set_xlabel("Wavelength (Å)")
+    axs[1].set_ylabel("Transmission")
+    plt.tight_layout()
+    out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png"))
+    fig.savefig(out_png, dpi=300)
+    if show:
+        display(fig)
+    plt.close(fig)
+
+# ----------------------------
+# Unified Backend Function for Reduction
+# ----------------------------
+def perform_reduction_for_sample(
+    sample_info: dict,
+    input_dir: str,
+    output_dir: str,
+    reduction_params: dict,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    log_func: callable
+):
+    """
+    Processes a single sample reduction:
+      - Finds the necessary run files
+      - Optionally determines a mask (or finds one automatically)
+      - Calls the reduction and plotting routines
+      - Logs all steps via log_func(message)
+    """
+    sample = sample_info.get("SAMPLE", "Unknown")
+    try:
+        sample_run_file = find_file(input_dir, str(sample_info["SANS"]), extension=".nxs")
+        transmission_run_file = find_file(input_dir, str(sample_info["TRANS"]), extension=".nxs")
+    except Exception as e:
+        log_func(f"Skipping sample {sample}: {e}")
+        return None
+    # Determine mask file.
+    mask_file = None
+    mask_candidate = str(sample_info.get("mask", "")).strip()
+    if mask_candidate:
+        mask_candidate_file = os.path.join(input_dir, f"{mask_candidate}.xml")
+        if os.path.exists(mask_candidate_file):
+            mask_file = mask_candidate_file
+    if mask_file is None:
+        try:
+            mask_file = find_mask_file(input_dir)
+            log_func(f"Identified mask file: {mask_file} for sample {sample}")
+        except Exception as e:
+            log_func(f"Mask file not found for sample {sample}: {e}")
+            return None
+
+    log_func(f"Reducing sample {sample}...")
+    try:
+        res = reduce_loki_batch_preliminary(
+            sample_run_file=sample_run_file,
+            transmission_run_file=transmission_run_file,
+            background_run_file=background_run_file,
+            empty_beam_file=empty_beam_file,
+            direct_beam_file=direct_beam_file,
+            mask_files=[mask_file],
+            wavelength_min=reduction_params["wavelength_min"],
+            wavelength_max=reduction_params["wavelength_max"],
+            wavelength_n=reduction_params["wavelength_n"],
+            q_start=reduction_params["q_start"],
+            q_stop=reduction_params["q_stop"],
+            q_n=reduction_params["q_n"]
+        )
+    except Exception as e:
+        log_func(f"Reduction failed for sample {sample}: {e}")
+        return None
+    out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+    try:
+        save_xye_pandas(res["IofQ"], out_xye)
+        log_func(f"Saved reduced data to {out_xye}")
+    except Exception as e:
+        log_func(f"Failed to save reduced data for {sample}: {e}")
+    try:
+        save_reduction_plots(
+            res,
+            sample,
+            sample_run_file,
+            reduction_params["wavelength_min"],
+            reduction_params["wavelength_max"],
+            reduction_params["wavelength_n"],
+            reduction_params["q_start"],
+            reduction_params["q_stop"],
+            reduction_params["q_n"],
+            output_dir,
+            show=True
+        )
+        log_func(f"Saved reduction plot for sample {sample}.")
+    except Exception as e:
+        log_func(f"Failed to save reduction plot for {sample}: {e}")
+    log_func(f"Reduced sample {sample} and saved outputs.")
+    return res
+
+# ----------------------------
+# GUI Widgets (Refactored to use Unified Backend)
+# ----------------------------
+class SansBatchReductionWidget:
+    def __init__(self):
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.ebeam_sans_widget = widgets.Text(value="", placeholder="Enter Ebeam SANS run number", description="Ebeam SANS:")
+        self.ebeam_trans_widget = widgets.Text(value="", placeholder="Enter Ebeam TRANS run number", description="Ebeam TRANS:")
+        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
+            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+    
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
+            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            with self.log_output:
+                print("Using empty-beam files:")
+                print("  Background (Ebeam SANS):", background_run_file)
+                print("  Empty beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Error finding empty beam files:", e)
+            return
+        
+        reduction_params = {
+            "wavelength_min": self.wavelength_min_widget.value,
+            "wavelength_max": self.wavelength_max_widget.value,
+            "wavelength_n": self.wavelength_n_widget.value,
+            "q_start": self.q_start_widget.value,
+            "q_stop": self.q_stop_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+        
+        df = self.table.data
+        for idx, row in df.iterrows():
+            perform_reduction_for_sample(
+                sample_info=row,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file,
+                log_func=lambda msg: print(msg)
+            )
+    
+    @property
+    def widget(self):
+        return self.main
+
+class SemiAutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.scan_button = widgets.Button(description="Scan Directory")
+        self.scan_button.on_click(self.scan_directory)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.add_row_button = widgets.Button(description="Add Row")
+        self.add_row_button.on_click(self.add_row)
+        self.delete_row_button = widgets.Button(description="Delete Last Row")
+        self.delete_row_button.on_click(self.delete_last_row)
+        self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
+        self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
+        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.processed = set()
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            self.scan_button,
+            self.table,
+            widgets.HBox([self.add_row_button, self.delete_row_button]),
+            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
+            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def add_row(self, _):
+        df = self.table.data
+        new_row = {col: "" for col in df.columns} if not df.empty else {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
+        df = df.append(new_row, ignore_index=True)
+        self.table.data = df
+
+    def delete_last_row(self, _):
+        df = self.table.data
+        if not df.empty:
+            self.table.data = df.iloc[:-1]
+
+    def scan_directory(self, _):
+        self.log_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Invalid input folder.")
+            return
+        nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+        groups = {}
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runlabel' not in details or 'runtype' not in details:
+                continue
+            runlabel = details['runlabel']
+            runtype = details['runtype'].lower()
+            run_number = extract_run_number(os.path.basename(f))
+            if runlabel not in groups:
+                groups[runlabel] = {}
+            groups[runlabel][runtype] = run_number
+        table_rows = []
+        for runlabel, d in groups.items():
+            if 'sans' in d and 'trans' in d:
+                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+        df = pd.DataFrame(table_rows)
+        self.table.data = df
+        with self.log_output:
+            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+        ebeam_sans_files = []
+        ebeam_trans_files = []
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runtype' in details:
+                if details['runtype'].lower() == 'ebeam_sans':
+                    ebeam_sans_files.append(f)
+                elif details['runtype'].lower() == 'ebeam_trans':
+                    ebeam_trans_files.append(f)
+        if ebeam_sans_files:
+            ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_sans_text.value = ebeam_sans_files[0]
+        else:
+            self.empty_beam_sans_text.value = ""
+        if ebeam_trans_files:
+            ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_trans_text.value = ebeam_trans_files[0]
+        else:
+            self.empty_beam_trans_text.value = ""
+    
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        background_run_file = self.empty_beam_sans_text.value
+        empty_beam_file = self.empty_beam_trans_text.value
+        if not background_run_file or not empty_beam_file:
+            with self.log_output:
+                print("Empty beam files not found.")
+            return
+        
+        reduction_params = {
+            "wavelength_min": self.lambda_min_widget.value,
+            "wavelength_max": self.lambda_max_widget.value,
+            "wavelength_n": self.lambda_n_widget.value,
+            "q_start": self.q_min_widget.value,
+            "q_stop": self.q_max_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+        
+        df = self.table.data.copy()
+        for idx, row in df.iterrows():
+            perform_reduction_for_sample(
+                sample_info=row,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file,
+                log_func=lambda msg: print(msg)
+            )
+    
+    @property
+    def widget(self):
+        return self.main
+
+class AutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.start_stop_button = widgets.Button(description="Start")
+        self.start_stop_button.on_click(self.toggle_running)
+        self.status_label = widgets.Label(value="Stopped")
+        self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.running = False
+        self.thread = None
+        self.processed = set()
+        self.empty_beam_sans = None
+        self.empty_beam_trans = None
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.start_stop_button, self.status_label]),
+            self.table,
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def toggle_running(self, _):
+        if not self.running:
+            self.running = True
+            self.start_stop_button.description = "Stop"
+            self.status_label.value = "Running"
+            self.thread = threading.Thread(target=self.background_loop, daemon=True)
+            self.thread.start()
+        else:
+            self.running = False
+            self.start_stop_button.description = "Start"
+            self.status_label.value = "Stopped"
+    
+    def background_loop(self):
+        while self.running:
+            input_dir = self.input_dir_chooser.selected
+            output_dir = self.output_dir_chooser.selected
+            if not input_dir or not os.path.isdir(input_dir):
+                with self.log_output:
+                    print("Invalid input folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            if not output_dir or not os.path.isdir(output_dir):
+                with self.log_output:
+                    print("Invalid output folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+            groups = {}
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runlabel' not in details or 'runtype' not in details:
+                    continue
+                runlabel = details['runlabel']
+                runtype = details['runtype'].lower()
+                run_number = extract_run_number(os.path.basename(f))
+                if runlabel not in groups:
+                    groups[runlabel] = {}
+                groups[runlabel][runtype] = run_number
+            table_rows = []
+            for runlabel, d in groups.items():
+                if 'sans' in d and 'trans' in d:
+                    table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+            df = pd.DataFrame(table_rows)
+            self.table.data = df
+            with self.log_output:
+                print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+            ebeam_sans_files = []
+            ebeam_trans_files = []
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runtype' in details:
+                    if details['runtype'].lower() == 'ebeam_sans':
+                        ebeam_sans_files.append(f)
+                    elif details['runtype'].lower() == 'ebeam_trans':
+                        ebeam_trans_files.append(f)
+            if ebeam_sans_files:
+                ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_sans = ebeam_sans_files[0]
+            else:
+                self.empty_beam_sans = None
+            if ebeam_trans_files:
+                ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_trans = ebeam_trans_files[0]
+            else:
+                self.empty_beam_trans = None
+            try:
+                direct_beam_file = find_direct_beam(input_dir)
+            except Exception as e:
+                with self.log_output:
+                    print("Direct-beam file not found:", e)
+                time.sleep(60)
+                continue
+            for index, row in df.iterrows():
+                key = (row["SAMPLE"], row["SANS"], row["TRANS"])
+                if key in self.processed:
+                    continue
+                try:
+                    sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs")
+                    transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Skipping sample {row['SAMPLE']}: {e}")
+                    continue
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
+                    continue
+                if not self.empty_beam_sans or not self.empty_beam_trans:
+                    with self.log_output:
+                        print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
+                    continue
+                with self.log_output:
+                    print(f"Reducing sample {row['SAMPLE']}...")
+                reduction_params = {
+                    "wavelength_min": 1.0,
+                    "wavelength_max": 13.0,
+                    "wavelength_n": 201,
+                    "q_start": 0.01,
+                    "q_stop": 0.3,
+                    "q_n": 101
+                }
+                perform_reduction_for_sample(
+                    sample_info=row,
+                    input_dir=input_dir,
+                    output_dir=output_dir,
+                    reduction_params=reduction_params,
+                    background_run_file=self.empty_beam_sans,
+                    empty_beam_file=self.empty_beam_trans,
+                    direct_beam_file=direct_beam_file,
+                    log_func=lambda msg: print(msg)
+                )
+                self.processed.add(key)
+            time.sleep(60)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Direct Beam Functionality and Widget (unchanged)
+# ----------------------------
+def compute_direct_beam_local(
+    mask: str,
+    sample_sans: str,
+    background_sans: str,
+    sample_trans: str,
+    background_trans: str,
+    empty_beam: str,
+    local_Iq_theory: str,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    n_wavelength_bins: int = 50,
+    n_wavelength_bands: int = 50
+) -> dict:
+    workflow = loki.LokiAtLarmorWorkflow()
+    workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
+    workflow[NeXusDetectorName] = 'larmor_detector'
+    wl_min = sc.scalar(wavelength_min, unit='angstrom')
+    wl_max = sc.scalar(wavelength_max, unit='angstrom')
+    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
+    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
+    workflow[CorrectForGravity] = True
+    workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
+    workflow[ReturnEvents] = False
+    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
+    workflow[Filename[SampleRun]] = sample_sans
+    workflow[Filename[BackgroundRun]] = background_sans
+    workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
+    workflow[Filename[EmptyBeamRun]] = empty_beam
+    center = sans.beam_center_from_center_of_mass(workflow)
+    print("Computed beam center:", center)
+    workflow[BeamCenter] = center
+    Iq_theory = sc.io.load_hdf5(local_Iq_theory)
+    f = interp1d(Iq_theory, 'Q')
+    I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
+    print("Computed I0:", I0)
+    results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
+    iofq_full = results[-1]['iofq_full']
+    iofq_bands = results[-1]['iofq_bands']
+    direct_beam_function = results[-1]['direct_beam']
+    pp.plot(
+        {'reference': Iq_theory, 'data': iofq_full},
+        color={'reference': 'darkgrey', 'data': 'C0'},
+        norm='log',
+    )
+    print("Plotted full-range result vs. theoretical reference.")
+    return {
+        'direct_beam_function': direct_beam_function,
+        'iofq_full': iofq_full,
+        'Iq_theory': Iq_theory,
+    }
+
+class DirectBeamWidget:
+    def __init__(self):
+        self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:")
+        self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:")
+        self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:")
+        self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:")
+        self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:")
+        self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:")
+        self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:")
+        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
+        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
+        self.compute_button = widgets.Button(description="Compute Direct Beam")
+        self.compute_button.on_click(self.compute_direct_beam)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            self.mask_text,
+            self.sample_sans_text,
+            self.background_sans_text,
+            self.sample_trans_text,
+            self.background_trans_text,
+            self.empty_beam_text,
+            self.local_Iq_theory_text,
+            widgets.HBox([
+                self.db_wavelength_min_widget,
+                self.db_wavelength_max_widget,
+                self.db_n_wavelength_bins_widget,
+                self.db_n_wavelength_bands_widget
+            ]),
+            self.compute_button,
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def compute_direct_beam(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        mask = self.mask_text.value
+        sample_sans = self.sample_sans_text.value
+        background_sans = self.background_sans_text.value
+        sample_trans = self.sample_trans_text.value
+        background_trans = self.background_trans_text.value
+        empty_beam = self.empty_beam_text.value
+        local_Iq_theory = self.local_Iq_theory_text.value
+        wl_min = self.db_wavelength_min_widget.value
+        wl_max = self.db_wavelength_max_widget.value
+        n_bins = self.db_n_wavelength_bins_widget.value
+        n_bands = self.db_n_wavelength_bands_widget.value
+        with self.log_output:
+            print("Computing direct beam with:")
+            print("  Mask:", mask)
+            print("  Sample SANS:", sample_sans)
+            print("  Background SANS:", background_sans)
+            print("  Sample TRANS:", sample_trans)
+            print("  Background TRANS:", background_trans)
+            print("  Empty Beam:", empty_beam)
+            print("  I(q) Theory:", local_Iq_theory)
+            print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
+        try:
+            results = compute_direct_beam_local(
+                mask,
+                sample_sans,
+                background_sans,
+                sample_trans,
+                background_trans,
+                empty_beam,
+                local_Iq_theory,
+                wavelength_min=wl_min,
+                wavelength_max=wl_max,
+                n_wavelength_bins=n_bins,
+                n_wavelength_bands=n_bands
+            )
+            with self.log_output:
+                print("Direct beam computation complete.")
+        except Exception as e:
+            with self.log_output:
+                print("Error computing direct beam:", e)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Build the Tabbed Widget
+# ----------------------------
+reduction_widget = SansBatchReductionWidget().widget
+direct_beam_widget = DirectBeamWidget().widget
+semi_auto_reduction_widget = SemiAutoReductionWidget().widget
+auto_reduction_widget = AutoReductionWidget().widget
+
+tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
+tabs.set_title(0, "Direct Beam")
+tabs.set_title(1, "Reduction (Manual)")
+tabs.set_title(2, "Reduction (Smart)")
+tabs.set_title(3, "Reduction (Auto)")
+
+# display(tabs)
diff --git a/src/ess/loki/tabwidget050325.py b/src/ess/loki/tabwidget050325.py
new file mode 100644
index 00000000..2c723c51
--- /dev/null
+++ b/src/ess/loki/tabwidget050325.py
@@ -0,0 +1,826 @@
+import os
+import glob
+import re
+import h5py
+import pandas as pd
+import scipp as sc
+import matplotlib.pyplot as plt
+import numpy as np
+import ipywidgets as widgets
+from ipydatagrid import DataGrid
+from IPython.display import display
+from ipyfilechooser import FileChooser
+from ess import sans
+from ess import loki
+from ess.sans.types import *
+from scipp.scipy.interpolate import interp1d
+import plopp as pp  # used for plotting in direct beam section
+import threading
+import time
+
+# ----------------------------
+# Common Utility Functions
+# ----------------------------
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+def find_direct_beam(work_dir):
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+
+def find_mask_file(work_dir):
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+def extract_run_number(filename):
+    m = re.search(r'(\d{4,})', filename)
+    if m:
+        return m.group(1)
+    return ""
+
+def parse_nx_details(filepath):
+    details = {}
+    with h5py.File(filepath, 'r') as f:
+        if 'nicos_details' in f['entry']:
+            grp = f['entry']['nicos_details']
+            if 'runlabel' in grp:
+                val = grp['runlabel'][()]
+                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+            if 'runtype' in grp:
+                val = grp['runtype'][()]
+                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+    return details
+
+# ----------------------------
+# Reduction and Plotting Functions
+# ----------------------------
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101
+):
+    if mask_files is None:
+        mask_files = []
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelength_max, wavelength_n, q_min, q_max, q_n, output_dir, show=True):
+    fig, axs = plt.subplots(1, 2, figsize=(8, 4))
+    axs[0].set_box_aspect(1)
+    axs[1].set_box_aspect(1)
+    title_str = f"{sample} - {os.path.basename(sample_run_file)}"
+    fig.suptitle(title_str, fontsize=14)
+    q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
+    x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+    if res["IofQ"].variances is not None:
+        yerr = np.sqrt(res["IofQ"].variances)
+        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+    else:
+        axs[0].scatter(x_q, res["IofQ"].values)
+    axs[0].set_xlabel("Q (Å$^{-1}$)")
+    axs[0].set_ylabel("I(Q)")
+    axs[0].set_xscale("log")
+    axs[0].set_yscale("log")
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+    if res["transmission"].variances is not None:
+        yerr_tr = np.sqrt(res["transmission"].variances)
+        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+    else:
+        axs[1].scatter(x_wl, res["transmission"].values)
+    axs[1].set_xlabel("Wavelength (Å)")
+    axs[1].set_ylabel("Transmission")
+    plt.tight_layout()
+    out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png"))
+    fig.savefig(out_png, dpi=300)
+    if show:
+        display(fig)
+    plt.close(fig)
+
+# ----------------------------
+# Unified Backend Function for Reduction
+# ----------------------------
+def perform_reduction_for_sample(
+    sample_info: dict,
+    input_dir: str,
+    output_dir: str,
+    reduction_params: dict,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    log_func: callable
+):
+    """
+    Processes a single sample reduction:
+      - Finds the necessary run files
+      - Optionally determines a mask (or finds one automatically)
+      - Calls the reduction and plotting routines
+      - Logs all steps via log_func(message)
+    """
+    sample = sample_info.get("SAMPLE", "Unknown")
+    try:
+        sample_run_file = find_file(input_dir, str(sample_info["SANS"]), extension=".nxs")
+        transmission_run_file = find_file(input_dir, str(sample_info["TRANS"]), extension=".nxs")
+    except Exception as e:
+        log_func(f"Skipping sample {sample}: {e}")
+        return None
+    # Determine mask file.
+    mask_file = None
+    mask_candidate = str(sample_info.get("mask", "")).strip()
+    if mask_candidate:
+        mask_candidate_file = os.path.join(input_dir, f"{mask_candidate}.xml")
+        if os.path.exists(mask_candidate_file):
+            mask_file = mask_candidate_file
+    if mask_file is None:
+        try:
+            mask_file = find_mask_file(input_dir)
+            log_func(f"Identified mask file: {mask_file} for sample {sample}")
+        except Exception as e:
+            log_func(f"Mask file not found for sample {sample}: {e}")
+            return None
+
+    log_func(f"Reducing sample {sample}...")
+    try:
+        res = reduce_loki_batch_preliminary(
+            sample_run_file=sample_run_file,
+            transmission_run_file=transmission_run_file,
+            background_run_file=background_run_file,
+            empty_beam_file=empty_beam_file,
+            direct_beam_file=direct_beam_file,
+            mask_files=[mask_file],
+            wavelength_min=reduction_params["wavelength_min"],
+            wavelength_max=reduction_params["wavelength_max"],
+            wavelength_n=reduction_params["wavelength_n"],
+            q_start=reduction_params["q_start"],
+            q_stop=reduction_params["q_stop"],
+            q_n=reduction_params["q_n"]
+        )
+    except Exception as e:
+        log_func(f"Reduction failed for sample {sample}: {e}")
+        return None
+    out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+    try:
+        save_xye_pandas(res["IofQ"], out_xye)
+        log_func(f"Saved reduced data to {out_xye}")
+    except Exception as e:
+        log_func(f"Failed to save reduced data for {sample}: {e}")
+    try:
+        save_reduction_plots(
+            res,
+            sample,
+            sample_run_file,
+            reduction_params["wavelength_min"],
+            reduction_params["wavelength_max"],
+            reduction_params["wavelength_n"],
+            reduction_params["q_start"],
+            reduction_params["q_stop"],
+            reduction_params["q_n"],
+            output_dir,
+            show=True
+        )
+        log_func(f"Saved reduction plot for sample {sample}.")
+    except Exception as e:
+        log_func(f"Failed to save reduction plot for {sample}: {e}")
+    log_func(f"Reduced sample {sample} and saved outputs.")
+    return res
+
+# ----------------------------
+# GUI Widgets (Refactored to use Unified Backend)
+# ----------------------------
+class SansBatchReductionWidget:
+    def __init__(self):
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.ebeam_sans_widget = widgets.Text(value="", placeholder="Enter Ebeam SANS run number", description="Ebeam SANS:")
+        self.ebeam_trans_widget = widgets.Text(value="", placeholder="Enter Ebeam TRANS run number", description="Ebeam TRANS:")
+        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
+            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+    
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
+            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            with self.log_output:
+                print("Using empty-beam files:")
+                print("  Background (Ebeam SANS):", background_run_file)
+                print("  Empty beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Error finding empty beam files:", e)
+            return
+        
+        reduction_params = {
+            "wavelength_min": self.wavelength_min_widget.value,
+            "wavelength_max": self.wavelength_max_widget.value,
+            "wavelength_n": self.wavelength_n_widget.value,
+            "q_start": self.q_start_widget.value,
+            "q_stop": self.q_stop_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+        
+        df = self.table.data
+        for idx, row in df.iterrows():
+            perform_reduction_for_sample(
+                sample_info=row,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file,
+                log_func=lambda msg: print(msg)
+            )
+    
+    @property
+    def widget(self):
+        return self.main
+
+class SemiAutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.scan_button = widgets.Button(description="Scan Directory")
+        self.scan_button.on_click(self.scan_directory)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.add_row_button = widgets.Button(description="Add Row")
+        self.add_row_button.on_click(self.add_row)
+        self.delete_row_button = widgets.Button(description="Delete Last Row")
+        self.delete_row_button.on_click(self.delete_last_row)
+        self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
+        self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
+        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.processed = set()
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            self.scan_button,
+            self.table,
+            widgets.HBox([self.add_row_button, self.delete_row_button]),
+            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
+            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def add_row(self, _):
+        df = self.table.data
+        new_row = {col: "" for col in df.columns} if not df.empty else {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
+        df = df.append(new_row, ignore_index=True)
+        self.table.data = df
+
+    def delete_last_row(self, _):
+        df = self.table.data
+        if not df.empty:
+            self.table.data = df.iloc[:-1]
+
+    def scan_directory(self, _):
+        self.log_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Invalid input folder.")
+            return
+        nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+        groups = {}
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runlabel' not in details or 'runtype' not in details:
+                continue
+            runlabel = details['runlabel']
+            runtype = details['runtype'].lower()
+            run_number = extract_run_number(os.path.basename(f))
+            if runlabel not in groups:
+                groups[runlabel] = {}
+            groups[runlabel][runtype] = run_number
+        table_rows = []
+        for runlabel, d in groups.items():
+            if 'sans' in d and 'trans' in d:
+                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+        df = pd.DataFrame(table_rows)
+        self.table.data = df
+        with self.log_output:
+            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+        ebeam_sans_files = []
+        ebeam_trans_files = []
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runtype' in details:
+                if details['runtype'].lower() == 'ebeam_sans':
+                    ebeam_sans_files.append(f)
+                elif details['runtype'].lower() == 'ebeam_trans':
+                    ebeam_trans_files.append(f)
+        if ebeam_sans_files:
+            ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_sans_text.value = ebeam_sans_files[0]
+        else:
+            self.empty_beam_sans_text.value = ""
+        if ebeam_trans_files:
+            ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_trans_text.value = ebeam_trans_files[0]
+        else:
+            self.empty_beam_trans_text.value = ""
+    
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        background_run_file = self.empty_beam_sans_text.value
+        empty_beam_file = self.empty_beam_trans_text.value
+        if not background_run_file or not empty_beam_file:
+            with self.log_output:
+                print("Empty beam files not found.")
+            return
+        
+        reduction_params = {
+            "wavelength_min": self.lambda_min_widget.value,
+            "wavelength_max": self.lambda_max_widget.value,
+            "wavelength_n": self.lambda_n_widget.value,
+            "q_start": self.q_min_widget.value,
+            "q_stop": self.q_max_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+        
+        df = self.table.data.copy()
+        for idx, row in df.iterrows():
+            perform_reduction_for_sample(
+                sample_info=row,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file,
+                log_func=lambda msg: print(msg)
+            )
+    
+    @property
+    def widget(self):
+        return self.main
+
+class AutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.start_stop_button = widgets.Button(description="Start")
+        self.start_stop_button.on_click(self.toggle_running)
+        self.status_label = widgets.Label(value="Stopped")
+        self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.running = False
+        self.thread = None
+        self.processed = set()
+        self.empty_beam_sans = None
+        self.empty_beam_trans = None
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.start_stop_button, self.status_label]),
+            self.table,
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def toggle_running(self, _):
+        if not self.running:
+            self.running = True
+            self.start_stop_button.description = "Stop"
+            self.status_label.value = "Running"
+            self.thread = threading.Thread(target=self.background_loop, daemon=True)
+            self.thread.start()
+        else:
+            self.running = False
+            self.start_stop_button.description = "Start"
+            self.status_label.value = "Stopped"
+    
+    def background_loop(self):
+        while self.running:
+            input_dir = self.input_dir_chooser.selected
+            output_dir = self.output_dir_chooser.selected
+            if not input_dir or not os.path.isdir(input_dir):
+                with self.log_output:
+                    print("Invalid input folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            if not output_dir or not os.path.isdir(output_dir):
+                with self.log_output:
+                    print("Invalid output folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+            groups = {}
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runlabel' not in details or 'runtype' not in details:
+                    continue
+                runlabel = details['runlabel']
+                runtype = details['runtype'].lower()
+                run_number = extract_run_number(os.path.basename(f))
+                if runlabel not in groups:
+                    groups[runlabel] = {}
+                groups[runlabel][runtype] = run_number
+            table_rows = []
+            for runlabel, d in groups.items():
+                if 'sans' in d and 'trans' in d:
+                    table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+            df = pd.DataFrame(table_rows)
+            self.table.data = df
+            with self.log_output:
+                print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+            ebeam_sans_files = []
+            ebeam_trans_files = []
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runtype' in details:
+                    if details['runtype'].lower() == 'ebeam_sans':
+                        ebeam_sans_files.append(f)
+                    elif details['runtype'].lower() == 'ebeam_trans':
+                        ebeam_trans_files.append(f)
+            if ebeam_sans_files:
+                ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_sans = ebeam_sans_files[0]
+            else:
+                self.empty_beam_sans = None
+            if ebeam_trans_files:
+                ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_trans = ebeam_trans_files[0]
+            else:
+                self.empty_beam_trans = None
+            try:
+                direct_beam_file = find_direct_beam(input_dir)
+            except Exception as e:
+                with self.log_output:
+                    print("Direct-beam file not found:", e)
+                time.sleep(60)
+                continue
+            for index, row in df.iterrows():
+                key = (row["SAMPLE"], row["SANS"], row["TRANS"])
+                if key in self.processed:
+                    continue
+                try:
+                    sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs")
+                    transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Skipping sample {row['SAMPLE']}: {e}")
+                    continue
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
+                    continue
+                if not self.empty_beam_sans or not self.empty_beam_trans:
+                    with self.log_output:
+                        print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
+                    continue
+                with self.log_output:
+                    print(f"Reducing sample {row['SAMPLE']}...")
+                reduction_params = {
+                    "wavelength_min": 1.0,
+                    "wavelength_max": 13.0,
+                    "wavelength_n": 201,
+                    "q_start": 0.01,
+                    "q_stop": 0.3,
+                    "q_n": 101
+                }
+                perform_reduction_for_sample(
+                    sample_info=row,
+                    input_dir=input_dir,
+                    output_dir=output_dir,
+                    reduction_params=reduction_params,
+                    background_run_file=self.empty_beam_sans,
+                    empty_beam_file=self.empty_beam_trans,
+                    direct_beam_file=direct_beam_file,
+                    log_func=lambda msg: print(msg)
+                )
+                self.processed.add(key)
+            time.sleep(60)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Direct Beam Functionality and Widget (unchanged)
+# ----------------------------
+def compute_direct_beam_local(
+    mask: str,
+    sample_sans: str,
+    background_sans: str,
+    sample_trans: str,
+    background_trans: str,
+    empty_beam: str,
+    local_Iq_theory: str,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    n_wavelength_bins: int = 50,
+    n_wavelength_bands: int = 50
+) -> dict:
+    workflow = loki.LokiAtLarmorWorkflow()
+    workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
+    workflow[NeXusDetectorName] = 'larmor_detector'
+    wl_min = sc.scalar(wavelength_min, unit='angstrom')
+    wl_max = sc.scalar(wavelength_max, unit='angstrom')
+    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
+    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
+    workflow[CorrectForGravity] = True
+    workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
+    workflow[ReturnEvents] = False
+    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
+    workflow[Filename[SampleRun]] = sample_sans
+    workflow[Filename[BackgroundRun]] = background_sans
+    workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
+    workflow[Filename[EmptyBeamRun]] = empty_beam
+    center = sans.beam_center_from_center_of_mass(workflow)
+    print("Computed beam center:", center)
+    workflow[BeamCenter] = center
+    Iq_theory = sc.io.load_hdf5(local_Iq_theory)
+    f = interp1d(Iq_theory, 'Q')
+    I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
+    print("Computed I0:", I0)
+    results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
+    iofq_full = results[-1]['iofq_full']
+    iofq_bands = results[-1]['iofq_bands']
+    direct_beam_function = results[-1]['direct_beam']
+    pp.plot(
+        {'reference': Iq_theory, 'data': iofq_full},
+        color={'reference': 'darkgrey', 'data': 'C0'},
+        norm='log',
+    )
+    print("Plotted full-range result vs. theoretical reference.")
+    return {
+        'direct_beam_function': direct_beam_function,
+        'iofq_full': iofq_full,
+        'Iq_theory': Iq_theory,
+    }
+
+class DirectBeamWidget:
+    def __init__(self):
+        self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:")
+        self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:")
+        self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:")
+        self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:")
+        self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:")
+        self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:")
+        self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:")
+        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
+        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
+        self.compute_button = widgets.Button(description="Compute Direct Beam")
+        self.compute_button.on_click(self.compute_direct_beam)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            self.mask_text,
+            self.sample_sans_text,
+            self.background_sans_text,
+            self.sample_trans_text,
+            self.background_trans_text,
+            self.empty_beam_text,
+            self.local_Iq_theory_text,
+            widgets.HBox([
+                self.db_wavelength_min_widget,
+                self.db_wavelength_max_widget,
+                self.db_n_wavelength_bins_widget,
+                self.db_n_wavelength_bands_widget
+            ]),
+            self.compute_button,
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def compute_direct_beam(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        mask = self.mask_text.value
+        sample_sans = self.sample_sans_text.value
+        background_sans = self.background_sans_text.value
+        sample_trans = self.sample_trans_text.value
+        background_trans = self.background_trans_text.value
+        empty_beam = self.empty_beam_text.value
+        local_Iq_theory = self.local_Iq_theory_text.value
+        wl_min = self.db_wavelength_min_widget.value
+        wl_max = self.db_wavelength_max_widget.value
+        n_bins = self.db_n_wavelength_bins_widget.value
+        n_bands = self.db_n_wavelength_bands_widget.value
+        with self.log_output:
+            print("Computing direct beam with:")
+            print("  Mask:", mask)
+            print("  Sample SANS:", sample_sans)
+            print("  Background SANS:", background_sans)
+            print("  Sample TRANS:", sample_trans)
+            print("  Background TRANS:", background_trans)
+            print("  Empty Beam:", empty_beam)
+            print("  I(q) Theory:", local_Iq_theory)
+            print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
+        try:
+            results = compute_direct_beam_local(
+                mask,
+                sample_sans,
+                background_sans,
+                sample_trans,
+                background_trans,
+                empty_beam,
+                local_Iq_theory,
+                wavelength_min=wl_min,
+                wavelength_max=wl_max,
+                n_wavelength_bins=n_bins,
+                n_wavelength_bands=n_bands
+            )
+            with self.log_output:
+                print("Direct beam computation complete.")
+        except Exception as e:
+            with self.log_output:
+                print("Error computing direct beam:", e)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Build the Tabbed Widget
+# ----------------------------
+reduction_widget = SansBatchReductionWidget().widget
+direct_beam_widget = DirectBeamWidget().widget
+semi_auto_reduction_widget = SemiAutoReductionWidget().widget
+auto_reduction_widget = AutoReductionWidget().widget
+
+tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
+tabs.set_title(0, "Direct Beam")
+tabs.set_title(1, "Reduction (Manual)")
+tabs.set_title(2, "Reduction (Smart)")
+tabs.set_title(3, "Reduction (Auto)")
+
+# display(tabs)
diff --git a/src/ess/loki/tabwidgetauto-040325.py b/src/ess/loki/tabwidgetauto-040325.py
new file mode 100644
index 00000000..cad8b49f
--- /dev/null
+++ b/src/ess/loki/tabwidgetauto-040325.py
@@ -0,0 +1,926 @@
+import os
+import glob
+import re
+import h5py
+import pandas as pd
+import scipp as sc
+import matplotlib.pyplot as plt
+import numpy as np
+import ipywidgets as widgets
+from ipydatagrid import DataGrid
+from IPython.display import display
+from ipyfilechooser import FileChooser
+from ess import sans
+from ess import loki
+from ess.sans.types import *
+from scipp.scipy.interpolate import interp1d
+import plopp as pp  # used for plotting in direct beam section
+import threading
+import time
+
+
+# ----------------------------
+# Reduction Functionality
+# ----------------------------
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101
+):
+    if mask_files is None:
+        mask_files = []
+    # Define wavelength and Q bins.
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    # Initialize the workflow.
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+def find_direct_beam(work_dir):
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+
+def find_mask_file(work_dir):
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+# ----------------------------
+# Helper Functions for Semi-Auto Reduction
+# ----------------------------
+def extract_run_number(filename):
+    m = re.search(r'(\d{4,})', filename)
+    if m:
+        return m.group(1)
+    return ""
+
+def parse_nx_details(filepath):
+    details = {}
+    with h5py.File(filepath, 'r') as f:
+        if 'nicos_details' in f['entry']:
+            grp = f['entry']['nicos_details']
+            if 'runlabel' in grp:
+                val = grp['runlabel'][()]
+                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+            if 'runtype' in grp:
+                val = grp['runtype'][()]
+                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+    return details
+
+# ----------------------------
+# Common Plotting Function
+# ----------------------------
+
+def save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True):
+    """
+    Creates a figure with 1 row x 2 columns:
+      - Left subplot: I(Q) (scatter with errorbars, log-log).
+      - Right subplot: Transmission fraction vs. wavelength (scatter with errorbars).
+    A single centered title (filename and sample ID) is added above the subplots.
+    The figure is saved to output_dir with a filename based on sample_run_file.
+    If show is True, the figure is displayed in the current output.
+    """
+    # Create a figure with two subplots side by side.
+    fig, axs = plt.subplots(1, 2, figsize=(8, 4))
+    
+    # Force each axis to be square (requires Matplotlib>=3.3)
+    axs[0].set_box_aspect(1)
+    axs[1].set_box_aspect(1)
+    
+    # Set a centered overall title (filename and sample)
+    title_str = f"{sample} - {os.path.basename(sample_run_file)}"
+    fig.suptitle(title_str, fontsize=14)
+    
+    # Subplot A: I(Q)
+    q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
+    x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+    if res["IofQ"].variances is not None:
+        yerr = np.sqrt(res["IofQ"].variances)
+        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+    else:
+        axs[0].scatter(x_q, res["IofQ"].values)
+    axs[0].set_xlabel("Q (Å$^{-1}$)")
+    axs[0].set_ylabel("I(Q)")
+    axs[0].set_xscale("log")
+    axs[0].set_yscale("log")
+    
+    # Subplot B: Transmission vs. Wavelength
+    wavelength_bins = sc.linspace("wavelength", lam_min, lam_max, lam_n, unit="angstrom")
+    x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+    if res["transmission"].variances is not None:
+        yerr_tr = np.sqrt(res["transmission"].variances)
+        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+    else:
+        axs[1].scatter(x_wl, res["transmission"].values)
+    axs[1].set_xlabel("Wavelength (Å)")
+    axs[1].set_ylabel("Transmission")
+    
+    # Adjust layout to leave room for the suptitle.
+    plt.tight_layout()#rect=[0, 0, 1, 0.90])
+    
+    # Save the figure.
+    out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png"))
+    fig.savefig(out_png, dpi=300)
+    
+    # Display the figure in the GUI if requested.
+    if show:
+        display(fig)
+    
+    # Close the figure so memory is released.
+    plt.close(fig)
+
+
+
+
+# ----------------------------
+# SansBatchReductionWidget (Updated)
+# ----------------------------
+class SansBatchReductionWidget:
+    def __init__(self):
+        # CSV chooser for pre-loaded reduction table.
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        # Folder choosers.
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        # Empty-beam run number widgets.
+        self.ebeam_sans_widget = widgets.Text(value="", placeholder="Enter Ebeam SANS run number", description="Ebeam SANS:")
+        self.ebeam_trans_widget = widgets.Text(value="", placeholder="Enter Ebeam TRANS run number", description="Ebeam TRANS:")
+        # Reduction parameter widgets.
+        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        # Button to load CSV.
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        # DataGrid for the reduction table.
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        # Reduction and clear buttons.
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        # Output widgets.
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        # Build layout.
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
+            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+    
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
+            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            with self.log_output:
+                print("Using empty-beam files:")
+                print("  Background (Ebeam SANS):", background_run_file)
+                print("  Empty beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Error finding empty beam files:", e)
+            return
+        wl_min = self.wavelength_min_widget.value
+        wl_max = self.wavelength_max_widget.value
+        wl_n = self.wavelength_n_widget.value
+        q_start = self.q_start_widget.value
+        q_stop = self.q_stop_widget.value
+        q_n = self.q_n_widget.value
+        
+        df = self.table.data
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            try:
+                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
+                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            mask_candidate = str(row.get("mask", "")).strip()
+            mask_file = None
+            if mask_candidate:
+                mask_file_candidate = os.path.join(input_dir, f"{mask_candidate}.xml")
+                if os.path.exists(mask_file_candidate):
+                    mask_file = mask_file_candidate
+            if mask_file is None:
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Identified mask file: {mask_file} for sample {sample}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {sample}: {e}")
+                    continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file],
+                    wavelength_min=wl_min,
+                    wavelength_max=wl_max,
+                    wavelength_n=wl_n,
+                    q_start=q_start,
+                    q_stop=q_stop,
+                    q_n=q_n
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            try:
+                with self.plot_output:
+                    save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True)
+                with self.log_output:
+                    print(f"Saved combined reduction plot for sample {sample}.")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduction plot for {sample}: {e}")
+
+            #try:
+            #    save_reduction_plots(res, sample, sample_run_file, wl_min, wl_max, wl_n, q_start, q_stop, q_n, output_dir)#, n_bands=5)
+            #    with self.log_output:
+            #        print(f"Saved combined reduction plot for sample {sample}.")
+            #except Exception as e:
+            #    with self.log_output:
+            #        print(f"Failed to save reduction plot for {sample}: {e}")
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Semi-Auto Reduction Widget (unchanged)
+# ----------------------------
+class SemiAutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.scan_button = widgets.Button(description="Scan Directory")
+        self.scan_button.on_click(self.scan_directory)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.add_row_button = widgets.Button(description="Add Row")
+        self.add_row_button.on_click(self.add_row)
+        self.delete_row_button = widgets.Button(description="Delete Last Row")
+        self.delete_row_button.on_click(self.delete_last_row)
+        self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
+        self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
+        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        
+        # Add the processed set here:
+        self.processed = set()
+        
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            self.scan_button,
+            self.table,
+            widgets.HBox([self.add_row_button, self.delete_row_button]),
+            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
+            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+
+    
+    def add_row(self, _):
+        df = self.table.data
+        if df.empty:
+            new_row = {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
+        else:
+            new_row = {col: "" for col in df.columns}
+        df = df.append(new_row, ignore_index=True)
+        self.table.data = df
+
+    def delete_last_row(self, _):
+        df = self.table.data
+        if not df.empty:
+            df = df.iloc[:-1]
+            self.table.data = df
+
+    def scan_directory(self, _):
+        self.log_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Invalid input folder.")
+            return
+        nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+        groups = {}
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runlabel' not in details or 'runtype' not in details:
+                continue
+            runlabel = details['runlabel']
+            runtype = details['runtype'].lower()
+            run_number = extract_run_number(os.path.basename(f))
+            if runlabel not in groups:
+                groups[runlabel] = {}
+            groups[runlabel][runtype] = run_number
+        table_rows = []
+        for runlabel, d in groups.items():
+            if 'sans' in d and 'trans' in d:
+                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+        df = pd.DataFrame(table_rows)
+        self.table.data = df
+        with self.log_output:
+            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+        ebeam_sans_files = []
+        ebeam_trans_files = []
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runtype' in details:
+                if details['runtype'].lower() == 'ebeam_sans':
+                    ebeam_sans_files.append(f)
+                elif details['runtype'].lower() == 'ebeam_trans':
+                    ebeam_trans_files.append(f)
+        if ebeam_sans_files:
+            ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_sans_text.value = ebeam_sans_files[0]
+        else:
+            self.empty_beam_sans_text.value = ""
+        if ebeam_trans_files:
+            ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_trans_text.value = ebeam_trans_files[0]
+        else:
+            self.empty_beam_trans_text.value = ""
+    
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        background_run_file = self.empty_beam_sans_text.value
+        empty_beam_file = self.empty_beam_trans_text.value
+        if not background_run_file or not empty_beam_file:
+            with self.log_output:
+                print("Empty beam files not found.")
+            return
+        lam_min = self.lambda_min_widget.value
+        lam_max = self.lambda_max_widget.value
+        lam_n = self.lambda_n_widget.value
+        q_min = self.q_min_widget.value
+        q_max = self.q_max_widget.value
+        q_n = self.q_n_widget.value
+
+        #df = self.table.data
+        df = self.table.data.copy()
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            sans_run = row["SANS"]
+            trans_run = row["TRANS"]
+            try:
+                sample_run_file = find_file(input_dir, sans_run, extension=".nxs")
+                transmission_run_file = find_file(input_dir, trans_run, extension=".nxs")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            try:
+                mask_file = find_mask_file(input_dir)
+                with self.log_output:
+                    print(f"Using mask file: {mask_file} for sample {sample}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Mask file not found for sample {sample}: {e}")
+                continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file],
+                    wavelength_min=lam_min,
+                    wavelength_max=lam_max,
+                    wavelength_n=lam_n,
+                    q_start=q_min,
+                    q_stop=q_max,
+                    q_n=q_n
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            try:
+                save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir)#, n_bands=5)
+                with self.log_output:
+                    print(f"Saved combined reduction plot for sample {sample}.")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduction plot for {sample}: {e}")
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+            self.processed.add((row["SAMPLE"], row["SANS"], row["TRANS"]))
+            #time.sleep(1)  # small delay between rows
+        #time.sleep(60)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Direct Beam Functionality and Widget (unchanged)
+# ----------------------------
+def compute_direct_beam_local(
+    mask: str,
+    sample_sans: str,
+    background_sans: str,
+    sample_trans: str,
+    background_trans: str,
+    empty_beam: str,
+    local_Iq_theory: str,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    n_wavelength_bins: int = 50,
+    n_wavelength_bands: int = 50
+) -> dict:
+    workflow = loki.LokiAtLarmorWorkflow()
+    workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
+    workflow[NeXusDetectorName] = 'larmor_detector'
+    wl_min = sc.scalar(wavelength_min, unit='angstrom')
+    wl_max = sc.scalar(wavelength_max, unit='angstrom')
+    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
+    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
+    workflow[CorrectForGravity] = True
+    workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
+    workflow[ReturnEvents] = False
+    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
+    workflow[Filename[SampleRun]] = sample_sans
+    workflow[Filename[BackgroundRun]] = background_sans
+    workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
+    workflow[Filename[EmptyBeamRun]] = empty_beam
+    center = sans.beam_center_from_center_of_mass(workflow)
+    print("Computed beam center:", center)
+    workflow[BeamCenter] = center
+    Iq_theory = sc.io.load_hdf5(local_Iq_theory)
+    f = interp1d(Iq_theory, 'Q')
+    I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
+    print("Computed I0:", I0)
+    results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
+    iofq_full = results[-1]['iofq_full']
+    iofq_bands = results[-1]['iofq_bands']
+    direct_beam_function = results[-1]['direct_beam']
+    pp.plot(
+        {'reference': Iq_theory, 'data': iofq_full},
+        color={'reference': 'darkgrey', 'data': 'C0'},
+        norm='log',
+    )
+    print("Plotted full-range result vs. theoretical reference.")
+    return {
+        'direct_beam_function': direct_beam_function,
+        'iofq_full': iofq_full,
+        'Iq_theory': Iq_theory,
+    }
+
+class DirectBeamWidget:
+    def __init__(self):
+        self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:")
+        self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:")
+        self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:")
+        self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:")
+        self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:")
+        self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:")
+        self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:")
+        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
+        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
+        self.compute_button = widgets.Button(description="Compute Direct Beam")
+        self.compute_button.on_click(self.compute_direct_beam)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            self.mask_text,
+            self.sample_sans_text,
+            self.background_sans_text,
+            self.sample_trans_text,
+            self.background_trans_text,
+            self.empty_beam_text,
+            self.local_Iq_theory_text,
+            widgets.HBox([
+                self.db_wavelength_min_widget,
+                self.db_wavelength_max_widget,
+                self.db_n_wavelength_bins_widget,
+                self.db_n_wavelength_bands_widget
+            ]),
+            self.compute_button,
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def compute_direct_beam(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        mask = self.mask_text.value
+        sample_sans = self.sample_sans_text.value
+        background_sans = self.background_sans_text.value
+        sample_trans = self.sample_trans_text.value
+        background_trans = self.background_trans_text.value
+        empty_beam = self.empty_beam_text.value
+        local_Iq_theory = self.local_Iq_theory_text.value
+        wl_min = self.db_wavelength_min_widget.value
+        wl_max = self.db_wavelength_max_widget.value
+        n_bins = self.db_n_wavelength_bins_widget.value
+        n_bands = self.db_n_wavelength_bands_widget.value
+        with self.log_output:
+            print("Computing direct beam with:")
+            print("  Mask:", mask)
+            print("  Sample SANS:", sample_sans)
+            print("  Background SANS:", background_sans)
+            print("  Sample TRANS:", sample_trans)
+            print("  Background TRANS:", background_trans)
+            print("  Empty Beam:", empty_beam)
+            print("  I(q) Theory:", local_Iq_theory)
+            print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
+        try:
+            results = compute_direct_beam_local(
+                mask,
+                sample_sans,
+                background_sans,
+                sample_trans,
+                background_trans,
+                empty_beam,
+                local_Iq_theory,
+                wavelength_min=wl_min,
+                wavelength_max=wl_max,
+                n_wavelength_bins=n_bins,
+                n_wavelength_bands=n_bands
+            )
+            with self.log_output:
+                print("Direct beam computation complete.")
+        except Exception as e:
+            with self.log_output:
+                print("Error computing direct beam:", e)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Auto Reduction Widget (unchanged, with common plotting call)
+# ----------------------------
+class AutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.start_stop_button = widgets.Button(description="Start")
+        self.start_stop_button.on_click(self.toggle_running)
+        self.status_label = widgets.Label(value="Stopped")
+        self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
+        self.log_output = widgets.Output()
+        self.running = False
+        self.thread = None
+        self.processed = set()  # Track already reduced entries.
+        self.empty_beam_sans = None
+        self.empty_beam_trans = None
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.start_stop_button, self.status_label]),
+            self.table,
+            self.log_output
+        ])
+    
+    def toggle_running(self, _):
+        if not self.running:
+            self.running = True
+            self.start_stop_button.description = "Stop"
+            self.status_label.value = "Running"
+            self.thread = threading.Thread(target=self.background_loop, daemon=True)
+            self.thread.start()
+        else:
+            self.running = False
+            self.start_stop_button.description = "Start"
+            self.status_label.value = "Stopped"
+    
+    def background_loop(self):
+        while self.running:
+            input_dir = self.input_dir_chooser.selected
+            output_dir = self.output_dir_chooser.selected
+            if not input_dir or not os.path.isdir(input_dir):
+                with self.log_output:
+                    print("Invalid input folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            if not output_dir or not os.path.isdir(output_dir):
+                with self.log_output:
+                    print("Invalid output folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+            groups = {}
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runlabel' not in details or 'runtype' not in details:
+                    continue
+                runlabel = details['runlabel']
+                runtype = details['runtype'].lower()
+                run_number = extract_run_number(os.path.basename(f))
+                if runlabel not in groups:
+                    groups[runlabel] = {}
+                groups[runlabel][runtype] = run_number
+            table_rows = []
+            for runlabel, d in groups.items():
+                if 'sans' in d and 'trans' in d:
+                    table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+            df = pd.DataFrame(table_rows)
+            self.table.data = df
+            with self.log_output:
+                print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+            ebeam_sans_files = []
+            ebeam_trans_files = []
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runtype' in details:
+                    if details['runtype'].lower() == 'ebeam_sans':
+                        ebeam_sans_files.append(f)
+                    elif details['runtype'].lower() == 'ebeam_trans':
+                        ebeam_trans_files.append(f)
+            if ebeam_sans_files:
+                ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_sans = ebeam_sans_files[0]
+            else:
+                self.empty_beam_sans = None
+            if ebeam_trans_files:
+                ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_trans = ebeam_trans_files[0]
+            else:
+                self.empty_beam_trans = None
+            try:
+                direct_beam_file = find_direct_beam(input_dir)
+            except Exception as e:
+                with self.log_output:
+                    print("Direct-beam file not found:", e)
+                time.sleep(60)
+                continue
+            for index, row in df.iterrows():
+                key = (row["SAMPLE"], row["SANS"], row["TRANS"])
+                if key in self.processed:
+                    continue
+                try:
+                    sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs")
+                    transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Skipping sample {row['SAMPLE']}: {e}")
+                    continue
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
+                    continue
+                if not self.empty_beam_sans or not self.empty_beam_trans:
+                    with self.log_output:
+                        print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
+                    continue
+                with self.log_output:
+                    print(f"Reducing sample {row['SAMPLE']}...")
+                try:
+                    res = reduce_loki_batch_preliminary(
+                        sample_run_file=sample_run_file,
+                        transmission_run_file=transmission_run_file,
+                        background_run_file=self.empty_beam_sans,
+                        empty_beam_file=self.empty_beam_trans,
+                        direct_beam_file=direct_beam_file,
+                        mask_files=[mask_file],
+                        wavelength_min=1.0,
+                        wavelength_max=13.0,
+                        wavelength_n=201,
+                        q_start=0.01,
+                        q_stop=0.3,
+                        q_n=101
+                    )
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Reduction failed for sample {row['SAMPLE']}: {e}")
+                    continue
+                out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+                try:
+                    save_xye_pandas(res["IofQ"], out_xye)
+                    with self.log_output:
+                        print(f"Saved reduced data to {out_xye}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Failed to save reduced data for {row['SAMPLE']}: {e}")
+            try:
+                with self.plot_output:
+                    save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True)
+                with self.log_output:
+                    print(f"Saved combined reduction plot for sample {sample}.")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduction plot for {sample}: {e}")
+
+                #try:
+                #    save_reduction_plots(res, row["SAMPLE"], sample_run_file, 1.0, 13.0, 201, 0.01, 0.3, 101, output_dir)#, n_bands=5)
+                #    with self.log_output:
+                #        print(f"Saved combined reduction plot for sample {row['SAMPLE']}.")
+                #except Exception as e:
+                #    with self.log_output:
+                #        print(f"Failed to save reduction plot for {row['SAMPLE']}: {e}")
+                with self.log_output:
+                    print(f"Reduced sample {row['SAMPLE']} and saved outputs.")
+                self.processed.add(key)
+            time.sleep(60)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Build the Tabbed Widget
+# ----------------------------
+reduction_widget = SansBatchReductionWidget().widget
+direct_beam_widget = DirectBeamWidget().widget
+semi_auto_reduction_widget = SemiAutoReductionWidget().widget
+auto_reduction_widget = AutoReductionWidget().widget
+
+tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
+tabs.set_title(0, "Direct Beam")
+tabs.set_title(1, "Reduction (Manual)")
+tabs.set_title(2, "Reduction (Smart)")
+tabs.set_title(3, "Reduction (Auto)")
+
+#display(tabs)
diff --git a/src/ess/loki/tabwidgetauto.py b/src/ess/loki/tabwidgetauto.py
index fa549882..e9527b3b 100644
--- a/src/ess/loki/tabwidgetauto.py
+++ b/src/ess/loki/tabwidgetauto.py
@@ -18,6 +18,7 @@
 import threading
 import time
 
+
 # ----------------------------
 # Reduction Functionality
 # ----------------------------
@@ -128,31 +129,33 @@ def parse_nx_details(filepath):
 # ----------------------------
 # Common Plotting Function
 # ----------------------------
-def save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir):
+
+def save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True):
     """
     Creates a figure with 1 row x 2 columns:
       - Left subplot: I(Q) (scatter with errorbars, log-log).
       - Right subplot: Transmission fraction vs. wavelength (scatter with errorbars).
     A single centered title (filename and sample ID) is added above the subplots.
     The figure is saved to output_dir with a filename based on sample_run_file.
+    If show is True, the figure is displayed in the current output.
     """
     # Create a figure with two subplots side by side.
     fig, axs = plt.subplots(1, 2, figsize=(8, 4))
     
-    # Force each axis to be square.
+    # Force each axis to be square (requires Matplotlib>=3.3)
     axs[0].set_box_aspect(1)
     axs[1].set_box_aspect(1)
     
-    # Create a common centered title containing the filename and sample ID.
-    title_str = f"{os.path.basename(sample_run_file)} - {sample}"
-    fig.suptitle(title_str, fontsize=10)
+    # Set a centered overall title (filename and sample)
+    title_str = f"{sample} - {os.path.basename(sample_run_file)}"
+    fig.suptitle(title_str, fontsize=14)
     
     # Subplot A: I(Q)
     q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
     x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
     if res["IofQ"].variances is not None:
         yerr = np.sqrt(res["IofQ"].variances)
-        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', markerfacecolor='none', alpha=0.5)
+        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
     else:
         axs[0].scatter(x_q, res["IofQ"].values)
     axs[0].set_xlabel("Q (Å$^{-1}$)")
@@ -165,20 +168,29 @@ def save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n,
     x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
     if res["transmission"].variances is not None:
         yerr_tr = np.sqrt(res["transmission"].variances)
-        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', markerfacecolor='none', alpha=0.5)
+        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
     else:
         axs[1].scatter(x_wl, res["transmission"].values)
     axs[1].set_xlabel("Wavelength (Å)")
     axs[1].set_ylabel("Transmission")
     
-    # Adjust layout so that there is space 
-    plt.tight_layout()#rect=[0, 0, 1, 0.95])
+    # Adjust layout to leave room for the suptitle.
+    plt.tight_layout(rect=[0, 0, 1, 0.90])
+    
+    # Save the figure.
     out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png"))
     fig.savefig(out_png, dpi=300)
+    
+    # Display the figure in the GUI if requested.
+    if show:
+        display(fig)
+    
+    # Close the figure so memory is released.
     plt.close(fig)
 
 
 
+
 # ----------------------------
 # SansBatchReductionWidget (Updated)
 # ----------------------------
@@ -336,12 +348,21 @@ def run_reduction(self, _):
                 with self.log_output:
                     print(f"Failed to save reduced data for {sample}: {e}")
             try:
-                save_reduction_plots(res, sample, sample_run_file, wl_min, wl_max, wl_n, q_start, q_stop, q_n, output_dir)#, n_bands=5)
+                with self.plot_output:
+                    save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True)
                 with self.log_output:
                     print(f"Saved combined reduction plot for sample {sample}.")
             except Exception as e:
                 with self.log_output:
                     print(f"Failed to save reduction plot for {sample}: {e}")
+
+            #try:
+            #    save_reduction_plots(res, sample, sample_run_file, wl_min, wl_max, wl_n, q_start, q_stop, q_n, output_dir)#, n_bands=5)
+            #    with self.log_output:
+            #        print(f"Saved combined reduction plot for sample {sample}.")
+            #except Exception as e:
+            #    with self.log_output:
+            #        print(f"Failed to save reduction plot for {sample}: {e}")
             with self.log_output:
                 print(f"Reduced sample {sample} and saved outputs.")
     
@@ -863,13 +884,22 @@ def background_loop(self):
                 except Exception as e:
                     with self.log_output:
                         print(f"Failed to save reduced data for {row['SAMPLE']}: {e}")
-                try:
-                    save_reduction_plots(res, row["SAMPLE"], sample_run_file, 1.0, 13.0, 201, 0.01, 0.3, 101, output_dir)#, n_bands=5)
-                    with self.log_output:
-                        print(f"Saved combined reduction plot for sample {row['SAMPLE']}.")
-                except Exception as e:
-                    with self.log_output:
-                        print(f"Failed to save reduction plot for {row['SAMPLE']}: {e}")
+            try:
+                with self.plot_output:
+                    save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True)
+                with self.log_output:
+                    print(f"Saved combined reduction plot for sample {sample}.")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduction plot for {sample}: {e}")
+
+                #try:
+                #    save_reduction_plots(res, row["SAMPLE"], sample_run_file, 1.0, 13.0, 201, 0.01, 0.3, 101, output_dir)#, n_bands=5)
+                #    with self.log_output:
+                #        print(f"Saved combined reduction plot for sample {row['SAMPLE']}.")
+                #except Exception as e:
+                #    with self.log_output:
+                #        print(f"Failed to save reduction plot for {row['SAMPLE']}: {e}")
                 with self.log_output:
                     print(f"Reduced sample {row['SAMPLE']} and saved outputs.")
                 self.processed.add(key)

From 45656663881b4a2c9b84fb2e622669e334656a95 Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Thu, 6 Mar 2025 10:02:42 +0100
Subject: [PATCH 10/18] Cleaning

---
 src/ess/loki/.DS_Store                        |  Bin 8196 -> 8196 bytes
 .../batchwidget-tabs.py                       |    0
 .../{ => batch-gui-legacy}/batchwidget.py     |    0
 .../{ => batch-gui-legacy}/batchwidgets.ipynb |    0
 .../tabwidget-050325.py                       |    0
 src/ess/loki/batch-gui-legacy/tabwidget.py    |  898 +++++++++++++
 .../tabwidgetauto-040325.py                   |    0
 .../{ => batch-gui-legacy}/tabwidgetauto.py   |    0
 src/ess/loki/tabwidget.py                     | 1106 ++++++++---------
 9 files changed, 1415 insertions(+), 589 deletions(-)
 rename src/ess/loki/{ => batch-gui-legacy}/batchwidget-tabs.py (100%)
 rename src/ess/loki/{ => batch-gui-legacy}/batchwidget.py (100%)
 rename src/ess/loki/{ => batch-gui-legacy}/batchwidgets.ipynb (100%)
 rename src/ess/loki/{ => batch-gui-legacy}/tabwidget-050325.py (100%)
 create mode 100644 src/ess/loki/batch-gui-legacy/tabwidget.py
 rename src/ess/loki/{ => batch-gui-legacy}/tabwidgetauto-040325.py (100%)
 rename src/ess/loki/{ => batch-gui-legacy}/tabwidgetauto.py (100%)

diff --git a/src/ess/loki/.DS_Store b/src/ess/loki/.DS_Store
index 6cf9604ead5a256c9a5a5cda6e7cca3f6329ad77..51ecd829858ab2d70ee9708f46cc4dbfc634bccb 100644
GIT binary patch
delta 68
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diff --git a/src/ess/loki/batchwidget-tabs.py b/src/ess/loki/batch-gui-legacy/batchwidget-tabs.py
similarity index 100%
rename from src/ess/loki/batchwidget-tabs.py
rename to src/ess/loki/batch-gui-legacy/batchwidget-tabs.py
diff --git a/src/ess/loki/batchwidget.py b/src/ess/loki/batch-gui-legacy/batchwidget.py
similarity index 100%
rename from src/ess/loki/batchwidget.py
rename to src/ess/loki/batch-gui-legacy/batchwidget.py
diff --git a/src/ess/loki/batchwidgets.ipynb b/src/ess/loki/batch-gui-legacy/batchwidgets.ipynb
similarity index 100%
rename from src/ess/loki/batchwidgets.ipynb
rename to src/ess/loki/batch-gui-legacy/batchwidgets.ipynb
diff --git a/src/ess/loki/tabwidget-050325.py b/src/ess/loki/batch-gui-legacy/tabwidget-050325.py
similarity index 100%
rename from src/ess/loki/tabwidget-050325.py
rename to src/ess/loki/batch-gui-legacy/tabwidget-050325.py
diff --git a/src/ess/loki/batch-gui-legacy/tabwidget.py b/src/ess/loki/batch-gui-legacy/tabwidget.py
new file mode 100644
index 00000000..0c5f7c02
--- /dev/null
+++ b/src/ess/loki/batch-gui-legacy/tabwidget.py
@@ -0,0 +1,898 @@
+import glob
+import os
+import re
+
+import h5py
+import ipywidgets as widgets
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import plopp as pp  # used for plotting in direct beam section
+import scipp as sc
+from ipydatagrid import DataGrid
+from ipyfilechooser import FileChooser
+from IPython.display import display
+from scipp.scipy.interpolate import interp1d
+
+from ess import loki, sans
+from ess.sans.types import *
+
+
+# ----------------------------
+# Reduction Functionality
+# ----------------------------
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode=UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101,
+):
+    if mask_files is None:
+        mask_files = []
+    # Define wavelength and Q bins.
+    wavelength_bins = sc.linspace(
+        "wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom"
+    )
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    # Initialize the workflow.
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+
+def find_direct_beam(work_dir):
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(
+            f"Could not find direct-beam file matching pattern {pattern}"
+        )
+
+
+def find_mask_file(work_dir):
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+
+# ----------------------------
+# Helper Functions for Semi-Auto Reduction
+# ----------------------------
+def extract_run_number(filename):
+    m = re.search(r'(\d{4,})', filename)
+    if m:
+        return m.group(1)
+    return ""
+
+
+def parse_nx_details(filepath):
+    details = {}
+    with h5py.File(filepath, 'r') as f:
+        if 'nicos_details' in f['entry']:
+            grp = f['entry']['nicos_details']
+            if 'runlabel' in grp:
+                val = grp['runlabel'][()]
+                details['runlabel'] = (
+                    val.decode('utf8') if isinstance(val, bytes) else str(val)
+                )
+            if 'runtype' in grp:
+                val = grp['runtype'][()]
+                details['runtype'] = (
+                    val.decode('utf8') if isinstance(val, bytes) else str(val)
+                )
+    return details
+
+
+# ----------------------------
+# Semi-Auto Reduction Widget
+# ----------------------------
+class SemiAutoReductionWidget:
+    def __init__(self):
+        # Only Input and Output Folder choosers are needed.
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+
+        self.scan_button = widgets.Button(description="Scan Directory")
+        self.scan_button.on_click(self.scan_directory)
+
+        # DataGrid for auto-generated reduction table; now editable.
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+
+        # Buttons to add or delete rows from the table.
+        self.add_row_button = widgets.Button(description="Add Row")
+        self.add_row_button.on_click(self.add_row)
+        self.delete_row_button = widgets.Button(description="Delete Last Row")
+        self.delete_row_button.on_click(self.delete_last_row)
+
+        # Parameter widgets for reduction (lambda and Q parameters)
+        self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
+        self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+
+        # Text fields to display the automatically identified empty-beam files.
+        self.empty_beam_sans_text = widgets.Text(
+            value="", description="Ebeam SANS:", disabled=True
+        )
+        self.empty_beam_trans_text = widgets.Text(
+            value="", description="Ebeam TRANS:", disabled=True
+        )
+
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+
+        # Build the layout.
+        self.main = widgets.VBox(
+            [
+                widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+                self.scan_button,
+                self.table,
+                widgets.HBox([self.add_row_button, self.delete_row_button]),
+                widgets.HBox(
+                    [
+                        self.lambda_min_widget,
+                        self.lambda_max_widget,
+                        self.lambda_n_widget,
+                    ]
+                ),
+                widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+                widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+                widgets.HBox(
+                    [self.reduce_button, self.clear_log_button, self.clear_plots_button]
+                ),
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
+    def add_row(self, _):
+        df = self.table.data
+        # Create a default new row if the DataFrame is empty, otherwise add blank cells.
+        if df.empty:
+            new_row = {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
+        else:
+            new_row = {col: "" for col in df.columns}
+        df = df.append(new_row, ignore_index=True)
+        self.table.data = df
+
+    def delete_last_row(self, _):
+        df = self.table.data
+        if not df.empty:
+            df = df.iloc[:-1]
+            self.table.data = df
+
+    def scan_directory(self, _):
+        self.log_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Invalid input folder.")
+            return
+        nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+        groups = {}
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runlabel' not in details or 'runtype' not in details:
+                continue
+            runlabel = details['runlabel']
+            runtype = details['runtype'].lower()
+            run_number = extract_run_number(os.path.basename(f))
+            if runlabel not in groups:
+                groups[runlabel] = {}
+            groups[runlabel][runtype] = run_number
+        table_rows = []
+        for runlabel, d in groups.items():
+            if 'sans' in d and 'trans' in d:
+                table_rows.append(
+                    {'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}
+                )
+        df = pd.DataFrame(table_rows)
+        self.table.data = df
+        with self.log_output:
+            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+        # Identify empty beam files:
+        ebeam_sans_files = []
+        ebeam_trans_files = []
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runtype' in details:
+                if details['runtype'].lower() == 'ebeam_sans':
+                    ebeam_sans_files.append(f)
+                elif details['runtype'].lower() == 'ebeam_trans':
+                    ebeam_trans_files.append(f)
+        if ebeam_sans_files:
+            ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_sans_text.value = ebeam_sans_files[0]
+        else:
+            self.empty_beam_sans_text.value = ""
+        if ebeam_trans_files:
+            ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_trans_text.value = ebeam_trans_files[0]
+        else:
+            self.empty_beam_trans_text.value = ""
+
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        background_run_file = self.empty_beam_sans_text.value
+        empty_beam_file = self.empty_beam_trans_text.value
+        if not background_run_file or not empty_beam_file:
+            with self.log_output:
+                print("Empty beam files not found.")
+            return
+        # Retrieve reduction parameters from widgets.
+        lam_min = self.lambda_min_widget.value
+        lam_max = self.lambda_max_widget.value
+        lam_n = self.lambda_n_widget.value
+        q_min = self.q_min_widget.value
+        q_max = self.q_max_widget.value
+        q_n = self.q_n_widget.value
+
+        df = self.table.data
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            sans_run = row["SANS"]
+            trans_run = row["TRANS"]
+            try:
+                sample_run_file = find_file(input_dir, sans_run, extension=".nxs")
+                transmission_run_file = find_file(
+                    input_dir, trans_run, extension=".nxs"
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            try:
+                mask_file = find_mask_file(input_dir)
+                with self.log_output:
+                    print(f"Using mask file: {mask_file} for sample {sample}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Mask file not found for sample {sample}: {e}")
+                continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file],
+                    wavelength_min=lam_min,
+                    wavelength_max=lam_max,
+                    wavelength_n=lam_n,
+                    q_start=q_min,
+                    q_stop=q_max,
+                    q_n=q_n,
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(
+                output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")
+            )
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            wavelength_bins = sc.linspace(
+                "wavelength", lam_min, lam_max, lam_n, unit="angstrom"
+            )
+            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+            fig_trans, ax_trans = plt.subplots()
+            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
+            ax_trans.set_title(
+                f"Transmission: {sample} {os.path.basename(sample_run_file)}"
+            )
+            ax_trans.set_xlabel("Wavelength (Å)")
+            ax_trans.set_ylabel("Transmission")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_trans)
+            trans_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"),
+            )
+            fig_trans.savefig(trans_png, dpi=300)
+            plt.close(fig_trans)
+            q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
+            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+            fig_iq, ax_iq = plt.subplots()
+            if res["IofQ"].variances is not None:
+                yerr = np.sqrt(res["IofQ"].variances)
+                ax_iq.errorbar(
+                    x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
+                )
+            else:
+                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
+            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
+            ax_iq.set_xlabel("Q (Å$^{-1}$)")
+            ax_iq.set_ylabel("I(Q)")
+            ax_iq.set_xscale("log")
+            ax_iq.set_yscale("log")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_iq)
+            iq_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
+            )
+            fig_iq.savefig(iq_png, dpi=300)
+            plt.close(fig_iq)
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+
+    @property
+    def widget(self):
+        return self.main
+
+
+# ----------------------------
+# Direct Beam Functionality
+# ----------------------------
+def compute_direct_beam_local(
+    mask: str,
+    sample_sans: str,
+    background_sans: str,
+    sample_trans: str,
+    background_trans: str,
+    empty_beam: str,
+    local_Iq_theory: str,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    n_wavelength_bins: int = 50,
+    n_wavelength_bands: int = 50,
+) -> dict:
+    """
+    Compute the direct beam function for the LoKI detectors using locally stored data.
+    """
+    workflow = loki.LokiAtLarmorWorkflow()
+    workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
+    workflow[NeXusDetectorName] = 'larmor_detector'
+
+    wl_min = sc.scalar(wavelength_min, unit='angstrom')
+    wl_max = sc.scalar(wavelength_max, unit='angstrom')
+    workflow[WavelengthBins] = sc.linspace(
+        'wavelength', wl_min, wl_max, n_wavelength_bins + 1
+    )
+    workflow[WavelengthBands] = sc.linspace(
+        'wavelength', wl_min, wl_max, n_wavelength_bands + 1
+    )
+    workflow[CorrectForGravity] = True
+    workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
+    workflow[ReturnEvents] = False
+    workflow[QBins] = sc.linspace(
+        dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom'
+    )
+
+    workflow[Filename[SampleRun]] = sample_sans
+    workflow[Filename[BackgroundRun]] = background_sans
+    workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
+    workflow[Filename[EmptyBeamRun]] = empty_beam
+
+    center = sans.beam_center_from_center_of_mass(workflow)
+    print("Computed beam center:", center)
+    workflow[BeamCenter] = center
+
+    Iq_theory = sc.io.load_hdf5(local_Iq_theory)
+    f = interp1d(Iq_theory, 'Q')
+    I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
+    print("Computed I0:", I0)
+
+    results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
+
+    iofq_full = results[-1]['iofq_full']
+    iofq_bands = results[-1]['iofq_bands']
+    direct_beam_function = results[-1]['direct_beam']
+
+    pp.plot(
+        {'reference': Iq_theory, 'data': iofq_full},
+        color={'reference': 'darkgrey', 'data': 'C0'},
+        norm='log',
+    )
+    print("Plotted full-range result vs. theoretical reference.")
+
+    return {
+        'direct_beam_function': direct_beam_function,
+        'iofq_full': iofq_full,
+        'Iq_theory': Iq_theory,
+    }
+
+
+# ----------------------------
+# Widgets for Reduction and Direct Beam
+# ----------------------------
+class SansBatchReductionWidget:
+    def __init__(self):
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.ebeam_sans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam SANS run number",
+            description="Ebeam SANS:",
+        )
+        self.ebeam_trans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam TRANS run number",
+            description="Ebeam TRANS:",
+        )
+        # Add GUI widgets for reduction parameters:
+        self.wavelength_min_widget = widgets.FloatText(
+            value=1.0, description="λ min (Å):"
+        )
+        self.wavelength_max_widget = widgets.FloatText(
+            value=13.0, description="λ max (Å):"
+        )
+        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_start_widget = widgets.FloatText(
+            value=0.01, description="Q start (1/Å):"
+        )
+        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(self.clear_log)
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(self.clear_plots)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox(
+            [
+                widgets.HBox(
+                    [self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]
+                ),
+                widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+                # Reduction parameters:
+                widgets.HBox(
+                    [
+                        self.wavelength_min_widget,
+                        self.wavelength_max_widget,
+                        self.wavelength_n_widget,
+                    ]
+                ),
+                widgets.HBox(
+                    [self.q_start_widget, self.q_stop_widget, self.q_n_widget]
+                ),
+                self.load_csv_button,
+                self.table,
+                widgets.HBox(
+                    [self.reduce_button, self.clear_log_button, self.clear_plots_button]
+                ),
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
+    def clear_log(self, _):
+        self.log_output.clear_output()
+
+    def clear_plots(self, _):
+        self.plot_output.clear_output()
+
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+
+    def run_reduction(self, _):
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(
+                input_dir, self.ebeam_sans_widget.value, extension=".nxs"
+            )
+            empty_beam_file = find_file(
+                input_dir, self.ebeam_trans_widget.value, extension=".nxs"
+            )
+            with self.log_output:
+                print("Using empty-beam files:")
+                print("  Background (Ebeam SANS):", background_run_file)
+                print("  Empty beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Error finding empty beam files:", e)
+            return
+        # Retrieve reduction parameters from widgets.
+        wl_min = self.wavelength_min_widget.value
+        wl_max = self.wavelength_max_widget.value
+        wl_n = self.wavelength_n_widget.value
+        q_start = self.q_start_widget.value
+        q_stop = self.q_stop_widget.value
+        q_n = self.q_n_widget.value
+        df = self.table.data
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            try:
+                sample_run_file = find_file(
+                    input_dir, str(row["SANS"]), extension=".nxs"
+                )
+                transmission_run_file = find_file(
+                    input_dir, str(row["TRANS"]), extension=".nxs"
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            mask_candidate = str(row.get("mask", "")).strip()
+            mask_file = None
+            if mask_candidate:
+                mask_file_candidate = os.path.join(input_dir, f"{mask_candidate}.xml")
+                if os.path.exists(mask_file_candidate):
+                    mask_file = mask_file_candidate
+            if mask_file is None:
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Identified mask file: {mask_file} for sample {sample}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {sample}: {e}")
+                    continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file],
+                    wavelength_min=wl_min,
+                    wavelength_max=wl_max,
+                    wavelength_n=wl_n,
+                    q_start=q_start,
+                    q_stop=q_stop,
+                    q_n=q_n,
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(
+                output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")
+            )
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            wavelength_bins = sc.linspace(
+                "wavelength", wl_min, wl_max, wl_n, unit="angstrom"
+            )
+            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+            fig_trans, ax_trans = plt.subplots()
+            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
+            ax_trans.set_title(
+                f"Transmission: {sample} {os.path.basename(sample_run_file)}"
+            )
+            ax_trans.set_xlabel("Wavelength (Å)")
+            ax_trans.set_ylabel("Transmission")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_trans)
+            trans_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"),
+            )
+            fig_trans.savefig(trans_png, dpi=300)
+            plt.close(fig_trans)
+            q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+            fig_iq, ax_iq = plt.subplots()
+            if res["IofQ"].variances is not None:
+                yerr = np.sqrt(res["IofQ"].variances)
+                ax_iq.errorbar(
+                    x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
+                )
+            else:
+                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
+            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
+            ax_iq.set_xlabel("Q (Å$^{-1}$)")
+            ax_iq.set_ylabel("I(Q)")
+            ax_iq.set_xscale("log")
+            ax_iq.set_yscale("log")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_iq)
+            iq_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
+            )
+            fig_iq.savefig(iq_png, dpi=300)
+            plt.close(fig_iq)
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+
+    @property
+    def widget(self):
+        return self.main
+
+
+# ----------------------------
+# Direct Beam Widget
+# ----------------------------
+class DirectBeamWidget:
+    def __init__(self):
+        self.mask_text = widgets.Text(
+            value="", placeholder="Enter mask file path", description="Mask:"
+        )
+        self.sample_sans_text = widgets.Text(
+            value="",
+            placeholder="Enter sample SANS file path",
+            description="Sample SANS:",
+        )
+        self.background_sans_text = widgets.Text(
+            value="",
+            placeholder="Enter background SANS file path",
+            description="Background SANS:",
+        )
+        self.sample_trans_text = widgets.Text(
+            value="",
+            placeholder="Enter sample TRANS file path",
+            description="Sample TRANS:",
+        )
+        self.background_trans_text = widgets.Text(
+            value="",
+            placeholder="Enter background TRANS file path",
+            description="Background TRANS:",
+        )
+        self.empty_beam_text = widgets.Text(
+            value="",
+            placeholder="Enter empty beam file path",
+            description="Empty Beam:",
+        )
+        self.local_Iq_theory_text = widgets.Text(
+            value="",
+            placeholder="Enter I(q) theory file path",
+            description="I(q) Theory:",
+        )
+        # GUI widgets for direct beam parameters:
+        self.db_wavelength_min_widget = widgets.FloatText(
+            value=1.0, description="λ min (Å):"
+        )
+        self.db_wavelength_max_widget = widgets.FloatText(
+            value=13.0, description="λ max (Å):"
+        )
+        self.db_n_wavelength_bins_widget = widgets.IntText(
+            value=50, description="λ n_bins:"
+        )
+        self.db_n_wavelength_bands_widget = widgets.IntText(
+            value=50, description="λ n_bands:"
+        )
+
+        self.compute_button = widgets.Button(description="Compute Direct Beam")
+        self.compute_button.on_click(self.compute_direct_beam)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox(
+            [
+                self.mask_text,
+                self.sample_sans_text,
+                self.background_sans_text,
+                self.sample_trans_text,
+                self.background_trans_text,
+                self.empty_beam_text,
+                self.local_Iq_theory_text,
+                widgets.HBox(
+                    [
+                        self.db_wavelength_min_widget,
+                        self.db_wavelength_max_widget,
+                        self.db_n_wavelength_bins_widget,
+                        self.db_n_wavelength_bands_widget,
+                    ]
+                ),
+                self.compute_button,
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
+    def compute_direct_beam(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        mask = self.mask_text.value
+        sample_sans = self.sample_sans_text.value
+        background_sans = self.background_sans_text.value
+        sample_trans = self.sample_trans_text.value
+        background_trans = self.background_trans_text.value
+        empty_beam = self.empty_beam_text.value
+        local_Iq_theory = self.local_Iq_theory_text.value
+        wl_min = self.db_wavelength_min_widget.value
+        wl_max = self.db_wavelength_max_widget.value
+        n_bins = self.db_n_wavelength_bins_widget.value
+        n_bands = self.db_n_wavelength_bands_widget.value
+        with self.log_output:
+            print("Computing direct beam with:")
+            print("  Mask:", mask)
+            print("  Sample SANS:", sample_sans)
+            print("  Background SANS:", background_sans)
+            print("  Sample TRANS:", sample_trans)
+            print("  Background TRANS:", background_trans)
+            print("  Empty Beam:", empty_beam)
+            print("  I(q) Theory:", local_Iq_theory)
+            print(
+                "  λ min:",
+                wl_min,
+                "λ max:",
+                wl_max,
+                "n_bins:",
+                n_bins,
+                "n_bands:",
+                n_bands,
+            )
+        try:
+            results = compute_direct_beam_local(
+                mask,
+                sample_sans,
+                background_sans,
+                sample_trans,
+                background_trans,
+                empty_beam,
+                local_Iq_theory,
+                wavelength_min=wl_min,
+                wavelength_max=wl_max,
+                n_wavelength_bins=n_bins,
+                n_wavelength_bands=n_bands,
+            )
+            with self.log_output:
+                print("Direct beam computation complete.")
+        except Exception as e:
+            with self.log_output:
+                print("Error computing direct beam:", e)
+
+    @property
+    def widget(self):
+        return self.main
+
+
+# ----------------------------
+# Build Tabbed Widget
+# ----------------------------
+reduction_widget = SansBatchReductionWidget().widget
+direct_beam_widget = DirectBeamWidget().widget
+semi_auto_reduction_widget = SemiAutoReductionWidget().widget
+tabs = widgets.Tab(
+    children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget]
+)
+tabs.set_title(0, "Direct Beam")
+tabs.set_title(1, "Reduction (Manual)")
+tabs.set_title(2, "Reduction (Smart)")
+# tabs.set_title(3, "Reduction (Auto)")
+
+# Display the tab widget.
+# display(tabs)
diff --git a/src/ess/loki/tabwidgetauto-040325.py b/src/ess/loki/batch-gui-legacy/tabwidgetauto-040325.py
similarity index 100%
rename from src/ess/loki/tabwidgetauto-040325.py
rename to src/ess/loki/batch-gui-legacy/tabwidgetauto-040325.py
diff --git a/src/ess/loki/tabwidgetauto.py b/src/ess/loki/batch-gui-legacy/tabwidgetauto.py
similarity index 100%
rename from src/ess/loki/tabwidgetauto.py
rename to src/ess/loki/batch-gui-legacy/tabwidgetauto.py
diff --git a/src/ess/loki/tabwidget.py b/src/ess/loki/tabwidget.py
index 0c5f7c02..2c723c51 100644
--- a/src/ess/loki/tabwidget.py
+++ b/src/ess/loki/tabwidget.py
@@ -1,73 +1,26 @@
-import glob
 import os
+import glob
 import re
-
 import h5py
-import ipywidgets as widgets
-import matplotlib.pyplot as plt
-import numpy as np
 import pandas as pd
-import plopp as pp  # used for plotting in direct beam section
 import scipp as sc
+import matplotlib.pyplot as plt
+import numpy as np
+import ipywidgets as widgets
 from ipydatagrid import DataGrid
-from ipyfilechooser import FileChooser
 from IPython.display import display
-from scipp.scipy.interpolate import interp1d
-
-from ess import loki, sans
+from ipyfilechooser import FileChooser
+from ess import sans
+from ess import loki
 from ess.sans.types import *
-
+from scipp.scipy.interpolate import interp1d
+import plopp as pp  # used for plotting in direct beam section
+import threading
+import time
 
 # ----------------------------
-# Reduction Functionality
+# Common Utility Functions
 # ----------------------------
-def reduce_loki_batch_preliminary(
-    sample_run_file: str,
-    transmission_run_file: str,
-    background_run_file: str,
-    empty_beam_file: str,
-    direct_beam_file: str,
-    mask_files: list = None,
-    correct_for_gravity: bool = True,
-    uncertainty_mode=UncertaintyBroadcastMode.upper_bound,
-    return_events: bool = False,
-    wavelength_min: float = 1.0,
-    wavelength_max: float = 13.0,
-    wavelength_n: int = 201,
-    q_start: float = 0.01,
-    q_stop: float = 0.3,
-    q_n: int = 101,
-):
-    if mask_files is None:
-        mask_files = []
-    # Define wavelength and Q bins.
-    wavelength_bins = sc.linspace(
-        "wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom"
-    )
-    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
-    # Initialize the workflow.
-    workflow = loki.LokiAtLarmorWorkflow()
-    if mask_files:
-        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
-    workflow[NeXusDetectorName] = "larmor_detector"
-    workflow[WavelengthBins] = wavelength_bins
-    workflow[QBins] = q_bins
-    workflow[CorrectForGravity] = correct_for_gravity
-    workflow[UncertaintyBroadcastMode] = uncertainty_mode
-    workflow[ReturnEvents] = return_events
-    workflow[Filename[BackgroundRun]] = background_run_file
-    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
-    workflow[Filename[EmptyBeamRun]] = empty_beam_file
-    workflow[DirectBeamFilename] = direct_beam_file
-    workflow[Filename[SampleRun]] = sample_run_file
-    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
-    center = sans.beam_center_from_center_of_mass(workflow)
-    workflow[BeamCenter] = center
-    tf = workflow.compute(TransmissionFraction[SampleRun])
-    da = workflow.compute(BackgroundSubtractedIofQ)
-    return {"transmission": tf, "IofQ": da}
-
-
 def find_file(work_dir, run_number, extension=".nxs"):
     pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
     files = glob.glob(pattern)
@@ -76,17 +29,13 @@ def find_file(work_dir, run_number, extension=".nxs"):
     else:
         raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
 
-
 def find_direct_beam(work_dir):
     pattern = os.path.join(work_dir, "*direct-beam*.h5")
     files = glob.glob(pattern)
     if files:
         return files[0]
     else:
-        raise FileNotFoundError(
-            f"Could not find direct-beam file matching pattern {pattern}"
-        )
-
+        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
 
 def find_mask_file(work_dir):
     pattern = os.path.join(work_dir, "*mask*.xml")
@@ -96,7 +45,6 @@ def find_mask_file(work_dir):
     else:
         raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
 
-
 def save_xye_pandas(data_array, filename):
     q_vals = data_array.coords["Q"].values
     i_vals = data_array.values
@@ -111,17 +59,12 @@ def save_xye_pandas(data_array, filename):
     df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
     df.to_csv(filename, sep=" ", index=False, header=True)
 
-
-# ----------------------------
-# Helper Functions for Semi-Auto Reduction
-# ----------------------------
 def extract_run_number(filename):
     m = re.search(r'(\d{4,})', filename)
     if m:
         return m.group(1)
     return ""
 
-
 def parse_nx_details(filepath):
     details = {}
     with h5py.File(filepath, 'r') as f:
@@ -129,106 +72,343 @@ def parse_nx_details(filepath):
             grp = f['entry']['nicos_details']
             if 'runlabel' in grp:
                 val = grp['runlabel'][()]
-                details['runlabel'] = (
-                    val.decode('utf8') if isinstance(val, bytes) else str(val)
-                )
+                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
             if 'runtype' in grp:
                 val = grp['runtype'][()]
-                details['runtype'] = (
-                    val.decode('utf8') if isinstance(val, bytes) else str(val)
-                )
+                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
     return details
 
+# ----------------------------
+# Reduction and Plotting Functions
+# ----------------------------
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101
+):
+    if mask_files is None:
+        mask_files = []
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelength_max, wavelength_n, q_min, q_max, q_n, output_dir, show=True):
+    fig, axs = plt.subplots(1, 2, figsize=(8, 4))
+    axs[0].set_box_aspect(1)
+    axs[1].set_box_aspect(1)
+    title_str = f"{sample} - {os.path.basename(sample_run_file)}"
+    fig.suptitle(title_str, fontsize=14)
+    q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
+    x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+    if res["IofQ"].variances is not None:
+        yerr = np.sqrt(res["IofQ"].variances)
+        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+    else:
+        axs[0].scatter(x_q, res["IofQ"].values)
+    axs[0].set_xlabel("Q (Å$^{-1}$)")
+    axs[0].set_ylabel("I(Q)")
+    axs[0].set_xscale("log")
+    axs[0].set_yscale("log")
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+    if res["transmission"].variances is not None:
+        yerr_tr = np.sqrt(res["transmission"].variances)
+        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+    else:
+        axs[1].scatter(x_wl, res["transmission"].values)
+    axs[1].set_xlabel("Wavelength (Å)")
+    axs[1].set_ylabel("Transmission")
+    plt.tight_layout()
+    out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png"))
+    fig.savefig(out_png, dpi=300)
+    if show:
+        display(fig)
+    plt.close(fig)
 
 # ----------------------------
-# Semi-Auto Reduction Widget
+# Unified Backend Function for Reduction
 # ----------------------------
-class SemiAutoReductionWidget:
+def perform_reduction_for_sample(
+    sample_info: dict,
+    input_dir: str,
+    output_dir: str,
+    reduction_params: dict,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    log_func: callable
+):
+    """
+    Processes a single sample reduction:
+      - Finds the necessary run files
+      - Optionally determines a mask (or finds one automatically)
+      - Calls the reduction and plotting routines
+      - Logs all steps via log_func(message)
+    """
+    sample = sample_info.get("SAMPLE", "Unknown")
+    try:
+        sample_run_file = find_file(input_dir, str(sample_info["SANS"]), extension=".nxs")
+        transmission_run_file = find_file(input_dir, str(sample_info["TRANS"]), extension=".nxs")
+    except Exception as e:
+        log_func(f"Skipping sample {sample}: {e}")
+        return None
+    # Determine mask file.
+    mask_file = None
+    mask_candidate = str(sample_info.get("mask", "")).strip()
+    if mask_candidate:
+        mask_candidate_file = os.path.join(input_dir, f"{mask_candidate}.xml")
+        if os.path.exists(mask_candidate_file):
+            mask_file = mask_candidate_file
+    if mask_file is None:
+        try:
+            mask_file = find_mask_file(input_dir)
+            log_func(f"Identified mask file: {mask_file} for sample {sample}")
+        except Exception as e:
+            log_func(f"Mask file not found for sample {sample}: {e}")
+            return None
+
+    log_func(f"Reducing sample {sample}...")
+    try:
+        res = reduce_loki_batch_preliminary(
+            sample_run_file=sample_run_file,
+            transmission_run_file=transmission_run_file,
+            background_run_file=background_run_file,
+            empty_beam_file=empty_beam_file,
+            direct_beam_file=direct_beam_file,
+            mask_files=[mask_file],
+            wavelength_min=reduction_params["wavelength_min"],
+            wavelength_max=reduction_params["wavelength_max"],
+            wavelength_n=reduction_params["wavelength_n"],
+            q_start=reduction_params["q_start"],
+            q_stop=reduction_params["q_stop"],
+            q_n=reduction_params["q_n"]
+        )
+    except Exception as e:
+        log_func(f"Reduction failed for sample {sample}: {e}")
+        return None
+    out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+    try:
+        save_xye_pandas(res["IofQ"], out_xye)
+        log_func(f"Saved reduced data to {out_xye}")
+    except Exception as e:
+        log_func(f"Failed to save reduced data for {sample}: {e}")
+    try:
+        save_reduction_plots(
+            res,
+            sample,
+            sample_run_file,
+            reduction_params["wavelength_min"],
+            reduction_params["wavelength_max"],
+            reduction_params["wavelength_n"],
+            reduction_params["q_start"],
+            reduction_params["q_stop"],
+            reduction_params["q_n"],
+            output_dir,
+            show=True
+        )
+        log_func(f"Saved reduction plot for sample {sample}.")
+    except Exception as e:
+        log_func(f"Failed to save reduction plot for {sample}: {e}")
+    log_func(f"Reduced sample {sample} and saved outputs.")
+    return res
+
+# ----------------------------
+# GUI Widgets (Refactored to use Unified Backend)
+# ----------------------------
+class SansBatchReductionWidget:
     def __init__(self):
-        # Only Input and Output Folder choosers are needed.
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
         self.input_dir_chooser = FileChooser(select_dir=True)
         self.input_dir_chooser.title = "Select Input Folder"
         self.output_dir_chooser = FileChooser(select_dir=True)
         self.output_dir_chooser.title = "Select Output Folder"
+        self.ebeam_sans_widget = widgets.Text(value="", placeholder="Enter Ebeam SANS run number", description="Ebeam SANS:")
+        self.ebeam_trans_widget = widgets.Text(value="", placeholder="Enter Ebeam TRANS run number", description="Ebeam TRANS:")
+        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
+            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+    
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
+            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            with self.log_output:
+                print("Using empty-beam files:")
+                print("  Background (Ebeam SANS):", background_run_file)
+                print("  Empty beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Error finding empty beam files:", e)
+            return
+        
+        reduction_params = {
+            "wavelength_min": self.wavelength_min_widget.value,
+            "wavelength_max": self.wavelength_max_widget.value,
+            "wavelength_n": self.wavelength_n_widget.value,
+            "q_start": self.q_start_widget.value,
+            "q_stop": self.q_stop_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+        
+        df = self.table.data
+        for idx, row in df.iterrows():
+            perform_reduction_for_sample(
+                sample_info=row,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file,
+                log_func=lambda msg: print(msg)
+            )
+    
+    @property
+    def widget(self):
+        return self.main
 
+class SemiAutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
         self.scan_button = widgets.Button(description="Scan Directory")
         self.scan_button.on_click(self.scan_directory)
-
-        # DataGrid for auto-generated reduction table; now editable.
         self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
-
-        # Buttons to add or delete rows from the table.
         self.add_row_button = widgets.Button(description="Add Row")
         self.add_row_button.on_click(self.add_row)
         self.delete_row_button = widgets.Button(description="Delete Last Row")
         self.delete_row_button.on_click(self.delete_last_row)
-
-        # Parameter widgets for reduction (lambda and Q parameters)
         self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
         self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
         self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
         self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
         self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
         self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
-
-        # Text fields to display the automatically identified empty-beam files.
-        self.empty_beam_sans_text = widgets.Text(
-            value="", description="Ebeam SANS:", disabled=True
-        )
-        self.empty_beam_trans_text = widgets.Text(
-            value="", description="Ebeam TRANS:", disabled=True
-        )
-
+        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
+        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
         self.reduce_button = widgets.Button(description="Reduce")
         self.reduce_button.on_click(self.run_reduction)
-
         self.clear_log_button = widgets.Button(description="Clear Log")
         self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
         self.clear_plots_button = widgets.Button(description="Clear Plots")
         self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
-
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
-
-        # Build the layout.
-        self.main = widgets.VBox(
-            [
-                widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
-                self.scan_button,
-                self.table,
-                widgets.HBox([self.add_row_button, self.delete_row_button]),
-                widgets.HBox(
-                    [
-                        self.lambda_min_widget,
-                        self.lambda_max_widget,
-                        self.lambda_n_widget,
-                    ]
-                ),
-                widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
-                widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
-                widgets.HBox(
-                    [self.reduce_button, self.clear_log_button, self.clear_plots_button]
-                ),
-                self.log_output,
-                self.plot_output,
-            ]
-        )
-
+        self.processed = set()
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            self.scan_button,
+            self.table,
+            widgets.HBox([self.add_row_button, self.delete_row_button]),
+            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
+            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            self.log_output,
+            self.plot_output
+        ])
+    
     def add_row(self, _):
         df = self.table.data
-        # Create a default new row if the DataFrame is empty, otherwise add blank cells.
-        if df.empty:
-            new_row = {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
-        else:
-            new_row = {col: "" for col in df.columns}
+        new_row = {col: "" for col in df.columns} if not df.empty else {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
         df = df.append(new_row, ignore_index=True)
         self.table.data = df
 
     def delete_last_row(self, _):
         df = self.table.data
         if not df.empty:
-            df = df.iloc[:-1]
-            self.table.data = df
+            self.table.data = df.iloc[:-1]
 
     def scan_directory(self, _):
         self.log_output.clear_output()
@@ -255,14 +435,11 @@ def scan_directory(self, _):
         table_rows = []
         for runlabel, d in groups.items():
             if 'sans' in d and 'trans' in d:
-                table_rows.append(
-                    {'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}
-                )
+                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
         df = pd.DataFrame(table_rows)
         self.table.data = df
         with self.log_output:
             print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
-        # Identify empty beam files:
         ebeam_sans_files = []
         ebeam_trans_files = []
         for f in nxs_files:
@@ -285,7 +462,7 @@ def scan_directory(self, _):
             self.empty_beam_trans_text.value = ebeam_trans_files[0]
         else:
             self.empty_beam_trans_text.value = ""
-
+    
     def run_reduction(self, _):
         self.log_output.clear_output()
         self.plot_output.clear_output()
@@ -313,121 +490,188 @@ def run_reduction(self, _):
             with self.log_output:
                 print("Empty beam files not found.")
             return
-        # Retrieve reduction parameters from widgets.
-        lam_min = self.lambda_min_widget.value
-        lam_max = self.lambda_max_widget.value
-        lam_n = self.lambda_n_widget.value
-        q_min = self.q_min_widget.value
-        q_max = self.q_max_widget.value
-        q_n = self.q_n_widget.value
-
-        df = self.table.data
+        
+        reduction_params = {
+            "wavelength_min": self.lambda_min_widget.value,
+            "wavelength_max": self.lambda_max_widget.value,
+            "wavelength_n": self.lambda_n_widget.value,
+            "q_start": self.q_min_widget.value,
+            "q_stop": self.q_max_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+        
+        df = self.table.data.copy()
         for idx, row in df.iterrows():
-            sample = row["SAMPLE"]
-            sans_run = row["SANS"]
-            trans_run = row["TRANS"]
-            try:
-                sample_run_file = find_file(input_dir, sans_run, extension=".nxs")
-                transmission_run_file = find_file(
-                    input_dir, trans_run, extension=".nxs"
-                )
-            except Exception as e:
+            perform_reduction_for_sample(
+                sample_info=row,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file,
+                log_func=lambda msg: print(msg)
+            )
+    
+    @property
+    def widget(self):
+        return self.main
+
+class AutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.start_stop_button = widgets.Button(description="Start")
+        self.start_stop_button.on_click(self.toggle_running)
+        self.status_label = widgets.Label(value="Stopped")
+        self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.running = False
+        self.thread = None
+        self.processed = set()
+        self.empty_beam_sans = None
+        self.empty_beam_trans = None
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.start_stop_button, self.status_label]),
+            self.table,
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def toggle_running(self, _):
+        if not self.running:
+            self.running = True
+            self.start_stop_button.description = "Stop"
+            self.status_label.value = "Running"
+            self.thread = threading.Thread(target=self.background_loop, daemon=True)
+            self.thread.start()
+        else:
+            self.running = False
+            self.start_stop_button.description = "Start"
+            self.status_label.value = "Stopped"
+    
+    def background_loop(self):
+        while self.running:
+            input_dir = self.input_dir_chooser.selected
+            output_dir = self.output_dir_chooser.selected
+            if not input_dir or not os.path.isdir(input_dir):
                 with self.log_output:
-                    print(f"Skipping sample {sample}: {e}")
+                    print("Invalid input folder. Waiting for valid selection...")
+                time.sleep(60)
                 continue
-            try:
-                mask_file = find_mask_file(input_dir)
-                with self.log_output:
-                    print(f"Using mask file: {mask_file} for sample {sample}")
-            except Exception as e:
+            if not output_dir or not os.path.isdir(output_dir):
                 with self.log_output:
-                    print(f"Mask file not found for sample {sample}: {e}")
+                    print("Invalid output folder. Waiting for valid selection...")
+                time.sleep(60)
                 continue
+            nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+            groups = {}
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runlabel' not in details or 'runtype' not in details:
+                    continue
+                runlabel = details['runlabel']
+                runtype = details['runtype'].lower()
+                run_number = extract_run_number(os.path.basename(f))
+                if runlabel not in groups:
+                    groups[runlabel] = {}
+                groups[runlabel][runtype] = run_number
+            table_rows = []
+            for runlabel, d in groups.items():
+                if 'sans' in d and 'trans' in d:
+                    table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+            df = pd.DataFrame(table_rows)
+            self.table.data = df
             with self.log_output:
-                print(f"Reducing sample {sample}...")
+                print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+            ebeam_sans_files = []
+            ebeam_trans_files = []
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runtype' in details:
+                    if details['runtype'].lower() == 'ebeam_sans':
+                        ebeam_sans_files.append(f)
+                    elif details['runtype'].lower() == 'ebeam_trans':
+                        ebeam_trans_files.append(f)
+            if ebeam_sans_files:
+                ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_sans = ebeam_sans_files[0]
+            else:
+                self.empty_beam_sans = None
+            if ebeam_trans_files:
+                ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_trans = ebeam_trans_files[0]
+            else:
+                self.empty_beam_trans = None
             try:
-                res = reduce_loki_batch_preliminary(
-                    sample_run_file=sample_run_file,
-                    transmission_run_file=transmission_run_file,
-                    background_run_file=background_run_file,
-                    empty_beam_file=empty_beam_file,
-                    direct_beam_file=direct_beam_file,
-                    mask_files=[mask_file],
-                    wavelength_min=lam_min,
-                    wavelength_max=lam_max,
-                    wavelength_n=lam_n,
-                    q_start=q_min,
-                    q_stop=q_max,
-                    q_n=q_n,
-                )
+                direct_beam_file = find_direct_beam(input_dir)
             except Exception as e:
                 with self.log_output:
-                    print(f"Reduction failed for sample {sample}: {e}")
+                    print("Direct-beam file not found:", e)
+                time.sleep(60)
                 continue
-            out_xye = os.path.join(
-                output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")
-            )
-            try:
-                save_xye_pandas(res["IofQ"], out_xye)
-                with self.log_output:
-                    print(f"Saved reduced data to {out_xye}")
-            except Exception as e:
+            for index, row in df.iterrows():
+                key = (row["SAMPLE"], row["SANS"], row["TRANS"])
+                if key in self.processed:
+                    continue
+                try:
+                    sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs")
+                    transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Skipping sample {row['SAMPLE']}: {e}")
+                    continue
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
+                    continue
+                if not self.empty_beam_sans or not self.empty_beam_trans:
+                    with self.log_output:
+                        print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
+                    continue
                 with self.log_output:
-                    print(f"Failed to save reduced data for {sample}: {e}")
-            wavelength_bins = sc.linspace(
-                "wavelength", lam_min, lam_max, lam_n, unit="angstrom"
-            )
-            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
-            fig_trans, ax_trans = plt.subplots()
-            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-            ax_trans.set_title(
-                f"Transmission: {sample} {os.path.basename(sample_run_file)}"
-            )
-            ax_trans.set_xlabel("Wavelength (Å)")
-            ax_trans.set_ylabel("Transmission")
-            plt.tight_layout()
-            with self.plot_output:
-                display(fig_trans)
-            trans_png = os.path.join(
-                output_dir,
-                os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"),
-            )
-            fig_trans.savefig(trans_png, dpi=300)
-            plt.close(fig_trans)
-            q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
-            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
-            fig_iq, ax_iq = plt.subplots()
-            if res["IofQ"].variances is not None:
-                yerr = np.sqrt(res["IofQ"].variances)
-                ax_iq.errorbar(
-                    x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
+                    print(f"Reducing sample {row['SAMPLE']}...")
+                reduction_params = {
+                    "wavelength_min": 1.0,
+                    "wavelength_max": 13.0,
+                    "wavelength_n": 201,
+                    "q_start": 0.01,
+                    "q_stop": 0.3,
+                    "q_n": 101
+                }
+                perform_reduction_for_sample(
+                    sample_info=row,
+                    input_dir=input_dir,
+                    output_dir=output_dir,
+                    reduction_params=reduction_params,
+                    background_run_file=self.empty_beam_sans,
+                    empty_beam_file=self.empty_beam_trans,
+                    direct_beam_file=direct_beam_file,
+                    log_func=lambda msg: print(msg)
                 )
-            else:
-                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
-            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
-            ax_iq.set_xlabel("Q (Å$^{-1}$)")
-            ax_iq.set_ylabel("I(Q)")
-            ax_iq.set_xscale("log")
-            ax_iq.set_yscale("log")
-            plt.tight_layout()
-            with self.plot_output:
-                display(fig_iq)
-            iq_png = os.path.join(
-                output_dir,
-                os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
-            )
-            fig_iq.savefig(iq_png, dpi=300)
-            plt.close(fig_iq)
-            with self.log_output:
-                print(f"Reduced sample {sample} and saved outputs.")
-
+                self.processed.add(key)
+            time.sleep(60)
+    
     @property
     def widget(self):
         return self.main
 
-
 # ----------------------------
-# Direct Beam Functionality
+# Direct Beam Functionality and Widget (unchanged)
 # ----------------------------
 def compute_direct_beam_local(
     mask: str,
@@ -440,388 +684,83 @@ def compute_direct_beam_local(
     wavelength_min: float = 1.0,
     wavelength_max: float = 13.0,
     n_wavelength_bins: int = 50,
-    n_wavelength_bands: int = 50,
+    n_wavelength_bands: int = 50
 ) -> dict:
-    """
-    Compute the direct beam function for the LoKI detectors using locally stored data.
-    """
     workflow = loki.LokiAtLarmorWorkflow()
     workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
     workflow[NeXusDetectorName] = 'larmor_detector'
-
     wl_min = sc.scalar(wavelength_min, unit='angstrom')
     wl_max = sc.scalar(wavelength_max, unit='angstrom')
-    workflow[WavelengthBins] = sc.linspace(
-        'wavelength', wl_min, wl_max, n_wavelength_bins + 1
-    )
-    workflow[WavelengthBands] = sc.linspace(
-        'wavelength', wl_min, wl_max, n_wavelength_bands + 1
-    )
+    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
+    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
     workflow[CorrectForGravity] = True
     workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
     workflow[ReturnEvents] = False
-    workflow[QBins] = sc.linspace(
-        dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom'
-    )
-
+    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
     workflow[Filename[SampleRun]] = sample_sans
     workflow[Filename[BackgroundRun]] = background_sans
     workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
     workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
     workflow[Filename[EmptyBeamRun]] = empty_beam
-
     center = sans.beam_center_from_center_of_mass(workflow)
     print("Computed beam center:", center)
     workflow[BeamCenter] = center
-
     Iq_theory = sc.io.load_hdf5(local_Iq_theory)
     f = interp1d(Iq_theory, 'Q')
     I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
     print("Computed I0:", I0)
-
     results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
-
     iofq_full = results[-1]['iofq_full']
     iofq_bands = results[-1]['iofq_bands']
     direct_beam_function = results[-1]['direct_beam']
-
     pp.plot(
         {'reference': Iq_theory, 'data': iofq_full},
         color={'reference': 'darkgrey', 'data': 'C0'},
         norm='log',
     )
     print("Plotted full-range result vs. theoretical reference.")
-
     return {
         'direct_beam_function': direct_beam_function,
         'iofq_full': iofq_full,
         'Iq_theory': Iq_theory,
     }
 
-
-# ----------------------------
-# Widgets for Reduction and Direct Beam
-# ----------------------------
-class SansBatchReductionWidget:
-    def __init__(self):
-        self.csv_chooser = FileChooser(select_dir=False)
-        self.csv_chooser.title = "Select CSV File"
-        self.csv_chooser.filter_pattern = "*.csv"
-        self.input_dir_chooser = FileChooser(select_dir=True)
-        self.input_dir_chooser.title = "Select Input Folder"
-        self.output_dir_chooser = FileChooser(select_dir=True)
-        self.output_dir_chooser.title = "Select Output Folder"
-        self.ebeam_sans_widget = widgets.Text(
-            value="",
-            placeholder="Enter Ebeam SANS run number",
-            description="Ebeam SANS:",
-        )
-        self.ebeam_trans_widget = widgets.Text(
-            value="",
-            placeholder="Enter Ebeam TRANS run number",
-            description="Ebeam TRANS:",
-        )
-        # Add GUI widgets for reduction parameters:
-        self.wavelength_min_widget = widgets.FloatText(
-            value=1.0, description="λ min (Å):"
-        )
-        self.wavelength_max_widget = widgets.FloatText(
-            value=13.0, description="λ max (Å):"
-        )
-        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
-        self.q_start_widget = widgets.FloatText(
-            value=0.01, description="Q start (1/Å):"
-        )
-        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
-        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
-
-        self.load_csv_button = widgets.Button(description="Load CSV")
-        self.load_csv_button.on_click(self.load_csv)
-        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
-        self.reduce_button = widgets.Button(description="Reduce")
-        self.reduce_button.on_click(self.run_reduction)
-        self.clear_log_button = widgets.Button(description="Clear Log")
-        self.clear_log_button.on_click(self.clear_log)
-        self.clear_plots_button = widgets.Button(description="Clear Plots")
-        self.clear_plots_button.on_click(self.clear_plots)
-        self.log_output = widgets.Output()
-        self.plot_output = widgets.Output()
-        self.main = widgets.VBox(
-            [
-                widgets.HBox(
-                    [self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]
-                ),
-                widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
-                # Reduction parameters:
-                widgets.HBox(
-                    [
-                        self.wavelength_min_widget,
-                        self.wavelength_max_widget,
-                        self.wavelength_n_widget,
-                    ]
-                ),
-                widgets.HBox(
-                    [self.q_start_widget, self.q_stop_widget, self.q_n_widget]
-                ),
-                self.load_csv_button,
-                self.table,
-                widgets.HBox(
-                    [self.reduce_button, self.clear_log_button, self.clear_plots_button]
-                ),
-                self.log_output,
-                self.plot_output,
-            ]
-        )
-
-    def clear_log(self, _):
-        self.log_output.clear_output()
-
-    def clear_plots(self, _):
-        self.plot_output.clear_output()
-
-    def load_csv(self, _):
-        csv_path = self.csv_chooser.selected
-        if not csv_path or not os.path.exists(csv_path):
-            with self.log_output:
-                print("CSV file not selected or does not exist.")
-            return
-        df = pd.read_csv(csv_path)
-        self.table.data = df
-        with self.log_output:
-            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
-
-    def run_reduction(self, _):
-        input_dir = self.input_dir_chooser.selected
-        output_dir = self.output_dir_chooser.selected
-        if not input_dir or not os.path.isdir(input_dir):
-            with self.log_output:
-                print("Input folder is not valid.")
-            return
-        if not output_dir or not os.path.isdir(output_dir):
-            with self.log_output:
-                print("Output folder is not valid.")
-            return
-        try:
-            direct_beam_file = find_direct_beam(input_dir)
-            with self.log_output:
-                print("Using direct-beam file:", direct_beam_file)
-        except Exception as e:
-            with self.log_output:
-                print("Direct-beam file not found:", e)
-            return
-        try:
-            background_run_file = find_file(
-                input_dir, self.ebeam_sans_widget.value, extension=".nxs"
-            )
-            empty_beam_file = find_file(
-                input_dir, self.ebeam_trans_widget.value, extension=".nxs"
-            )
-            with self.log_output:
-                print("Using empty-beam files:")
-                print("  Background (Ebeam SANS):", background_run_file)
-                print("  Empty beam (Ebeam TRANS):", empty_beam_file)
-        except Exception as e:
-            with self.log_output:
-                print("Error finding empty beam files:", e)
-            return
-        # Retrieve reduction parameters from widgets.
-        wl_min = self.wavelength_min_widget.value
-        wl_max = self.wavelength_max_widget.value
-        wl_n = self.wavelength_n_widget.value
-        q_start = self.q_start_widget.value
-        q_stop = self.q_stop_widget.value
-        q_n = self.q_n_widget.value
-        df = self.table.data
-        for idx, row in df.iterrows():
-            sample = row["SAMPLE"]
-            try:
-                sample_run_file = find_file(
-                    input_dir, str(row["SANS"]), extension=".nxs"
-                )
-                transmission_run_file = find_file(
-                    input_dir, str(row["TRANS"]), extension=".nxs"
-                )
-            except Exception as e:
-                with self.log_output:
-                    print(f"Skipping sample {sample}: {e}")
-                continue
-            mask_candidate = str(row.get("mask", "")).strip()
-            mask_file = None
-            if mask_candidate:
-                mask_file_candidate = os.path.join(input_dir, f"{mask_candidate}.xml")
-                if os.path.exists(mask_file_candidate):
-                    mask_file = mask_file_candidate
-            if mask_file is None:
-                try:
-                    mask_file = find_mask_file(input_dir)
-                    with self.log_output:
-                        print(f"Identified mask file: {mask_file} for sample {sample}")
-                except Exception as e:
-                    with self.log_output:
-                        print(f"Mask file not found for sample {sample}: {e}")
-                    continue
-            with self.log_output:
-                print(f"Reducing sample {sample}...")
-            try:
-                res = reduce_loki_batch_preliminary(
-                    sample_run_file=sample_run_file,
-                    transmission_run_file=transmission_run_file,
-                    background_run_file=background_run_file,
-                    empty_beam_file=empty_beam_file,
-                    direct_beam_file=direct_beam_file,
-                    mask_files=[mask_file],
-                    wavelength_min=wl_min,
-                    wavelength_max=wl_max,
-                    wavelength_n=wl_n,
-                    q_start=q_start,
-                    q_stop=q_stop,
-                    q_n=q_n,
-                )
-            except Exception as e:
-                with self.log_output:
-                    print(f"Reduction failed for sample {sample}: {e}")
-                continue
-            out_xye = os.path.join(
-                output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")
-            )
-            try:
-                save_xye_pandas(res["IofQ"], out_xye)
-                with self.log_output:
-                    print(f"Saved reduced data to {out_xye}")
-            except Exception as e:
-                with self.log_output:
-                    print(f"Failed to save reduced data for {sample}: {e}")
-            wavelength_bins = sc.linspace(
-                "wavelength", wl_min, wl_max, wl_n, unit="angstrom"
-            )
-            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
-            fig_trans, ax_trans = plt.subplots()
-            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
-            ax_trans.set_title(
-                f"Transmission: {sample} {os.path.basename(sample_run_file)}"
-            )
-            ax_trans.set_xlabel("Wavelength (Å)")
-            ax_trans.set_ylabel("Transmission")
-            plt.tight_layout()
-            with self.plot_output:
-                display(fig_trans)
-            trans_png = os.path.join(
-                output_dir,
-                os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"),
-            )
-            fig_trans.savefig(trans_png, dpi=300)
-            plt.close(fig_trans)
-            q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
-            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
-            fig_iq, ax_iq = plt.subplots()
-            if res["IofQ"].variances is not None:
-                yerr = np.sqrt(res["IofQ"].variances)
-                ax_iq.errorbar(
-                    x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
-                )
-            else:
-                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
-            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
-            ax_iq.set_xlabel("Q (Å$^{-1}$)")
-            ax_iq.set_ylabel("I(Q)")
-            ax_iq.set_xscale("log")
-            ax_iq.set_yscale("log")
-            plt.tight_layout()
-            with self.plot_output:
-                display(fig_iq)
-            iq_png = os.path.join(
-                output_dir,
-                os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
-            )
-            fig_iq.savefig(iq_png, dpi=300)
-            plt.close(fig_iq)
-            with self.log_output:
-                print(f"Reduced sample {sample} and saved outputs.")
-
-    @property
-    def widget(self):
-        return self.main
-
-
-# ----------------------------
-# Direct Beam Widget
-# ----------------------------
 class DirectBeamWidget:
     def __init__(self):
-        self.mask_text = widgets.Text(
-            value="", placeholder="Enter mask file path", description="Mask:"
-        )
-        self.sample_sans_text = widgets.Text(
-            value="",
-            placeholder="Enter sample SANS file path",
-            description="Sample SANS:",
-        )
-        self.background_sans_text = widgets.Text(
-            value="",
-            placeholder="Enter background SANS file path",
-            description="Background SANS:",
-        )
-        self.sample_trans_text = widgets.Text(
-            value="",
-            placeholder="Enter sample TRANS file path",
-            description="Sample TRANS:",
-        )
-        self.background_trans_text = widgets.Text(
-            value="",
-            placeholder="Enter background TRANS file path",
-            description="Background TRANS:",
-        )
-        self.empty_beam_text = widgets.Text(
-            value="",
-            placeholder="Enter empty beam file path",
-            description="Empty Beam:",
-        )
-        self.local_Iq_theory_text = widgets.Text(
-            value="",
-            placeholder="Enter I(q) theory file path",
-            description="I(q) Theory:",
-        )
-        # GUI widgets for direct beam parameters:
-        self.db_wavelength_min_widget = widgets.FloatText(
-            value=1.0, description="λ min (Å):"
-        )
-        self.db_wavelength_max_widget = widgets.FloatText(
-            value=13.0, description="λ max (Å):"
-        )
-        self.db_n_wavelength_bins_widget = widgets.IntText(
-            value=50, description="λ n_bins:"
-        )
-        self.db_n_wavelength_bands_widget = widgets.IntText(
-            value=50, description="λ n_bands:"
-        )
-
+        self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:")
+        self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:")
+        self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:")
+        self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:")
+        self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:")
+        self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:")
+        self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:")
+        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
+        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
         self.compute_button = widgets.Button(description="Compute Direct Beam")
         self.compute_button.on_click(self.compute_direct_beam)
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
-        self.main = widgets.VBox(
-            [
-                self.mask_text,
-                self.sample_sans_text,
-                self.background_sans_text,
-                self.sample_trans_text,
-                self.background_trans_text,
-                self.empty_beam_text,
-                self.local_Iq_theory_text,
-                widgets.HBox(
-                    [
-                        self.db_wavelength_min_widget,
-                        self.db_wavelength_max_widget,
-                        self.db_n_wavelength_bins_widget,
-                        self.db_n_wavelength_bands_widget,
-                    ]
-                ),
-                self.compute_button,
-                self.log_output,
-                self.plot_output,
-            ]
-        )
-
+        self.main = widgets.VBox([
+            self.mask_text,
+            self.sample_sans_text,
+            self.background_sans_text,
+            self.sample_trans_text,
+            self.background_trans_text,
+            self.empty_beam_text,
+            self.local_Iq_theory_text,
+            widgets.HBox([
+                self.db_wavelength_min_widget,
+                self.db_wavelength_max_widget,
+                self.db_n_wavelength_bins_widget,
+                self.db_n_wavelength_bands_widget
+            ]),
+            self.compute_button,
+            self.log_output,
+            self.plot_output
+        ])
+    
     def compute_direct_beam(self, _):
         self.log_output.clear_output()
         self.plot_output.clear_output()
@@ -845,16 +784,7 @@ def compute_direct_beam(self, _):
             print("  Background TRANS:", background_trans)
             print("  Empty Beam:", empty_beam)
             print("  I(q) Theory:", local_Iq_theory)
-            print(
-                "  λ min:",
-                wl_min,
-                "λ max:",
-                wl_max,
-                "n_bins:",
-                n_bins,
-                "n_bands:",
-                n_bands,
-            )
+            print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
         try:
             results = compute_direct_beam_local(
                 mask,
@@ -867,32 +797,30 @@ def compute_direct_beam(self, _):
                 wavelength_min=wl_min,
                 wavelength_max=wl_max,
                 n_wavelength_bins=n_bins,
-                n_wavelength_bands=n_bands,
+                n_wavelength_bands=n_bands
             )
             with self.log_output:
                 print("Direct beam computation complete.")
         except Exception as e:
             with self.log_output:
                 print("Error computing direct beam:", e)
-
+    
     @property
     def widget(self):
         return self.main
 
-
 # ----------------------------
-# Build Tabbed Widget
+# Build the Tabbed Widget
 # ----------------------------
 reduction_widget = SansBatchReductionWidget().widget
 direct_beam_widget = DirectBeamWidget().widget
 semi_auto_reduction_widget = SemiAutoReductionWidget().widget
-tabs = widgets.Tab(
-    children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget]
-)
+auto_reduction_widget = AutoReductionWidget().widget
+
+tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
 tabs.set_title(0, "Direct Beam")
 tabs.set_title(1, "Reduction (Manual)")
 tabs.set_title(2, "Reduction (Smart)")
-# tabs.set_title(3, "Reduction (Auto)")
+tabs.set_title(3, "Reduction (Auto)")
 
-# Display the tab widget.
 # display(tabs)

From 2e38c55a80981e6b714dce95d98956c4e529d888 Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Thu, 6 Mar 2025 10:03:06 +0100
Subject: [PATCH 11/18] Spring cleaning

---
 src/ess/loki/.DS_Store                        | Bin 8196 -> 8196 bytes
 .../{ => batch-gui-legacy}/tabwidget050325.py |   0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename src/ess/loki/{ => batch-gui-legacy}/tabwidget050325.py (100%)

diff --git a/src/ess/loki/.DS_Store b/src/ess/loki/.DS_Store
index 51ecd829858ab2d70ee9708f46cc4dbfc634bccb..0902323893b043df9c45ad4cd6a9a6b6a2c88deb 100644
GIT binary patch
delta 246
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z(hY-?^K%Or5P+bb+<X_8q@4UDpcamUGo>5P?m6ZNWMk16f@)cAehQHqQS}vMAUkq%
TnZQoA&Fm82SvISQurUJw-u*c6

delta 39
vcmZp1XmOa}&&aniU^hP_-(((v{LR6FTi7Nx+}O-6@ttM!HBm#Ri49i)5Frk&

diff --git a/src/ess/loki/tabwidget050325.py b/src/ess/loki/batch-gui-legacy/tabwidget050325.py
similarity index 100%
rename from src/ess/loki/tabwidget050325.py
rename to src/ess/loki/batch-gui-legacy/tabwidget050325.py

From 25f53ae39e9d87ec38d2183637c10d51026ecfcb Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Thu, 6 Mar 2025 10:10:04 +0100
Subject: [PATCH 12/18] More cleaning

---
 .gitignore                   |   1 +
 src/ess/loki/tabwidget.ipynb | 286 ++++++++++++++++++++++++++++++++++-
 src/ess/loki/tabwidget.py    |  21 +--
 3 files changed, 295 insertions(+), 13 deletions(-)

diff --git a/.gitignore b/.gitignore
index 340e6499..5c204a51 100644
--- a/.gitignore
+++ b/.gitignore
@@ -45,3 +45,4 @@ docs/generated/
 *.zip
 *.sqw
 *.nxspe
+/src/ess/loki/examplefiles
diff --git a/src/ess/loki/tabwidget.ipynb b/src/ess/loki/tabwidget.ipynb
index 8ec482fd..42ab2576 100644
--- a/src/ess/loki/tabwidget.ipynb
+++ b/src/ess/loki/tabwidget.ipynb
@@ -2,13 +2,293 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f426308f9d1440abb6c436f2a6199c91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(VBox(children=(Text(value='', description='Mask:', placeholder='Enter mask file path'), Text(val…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample Polymer\n",
+      "Reducing sample Polymer...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60395-2022-02-28_2215.xye\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample Polymer.\n",
+      "Reduced sample Polymer and saved outputs.\n",
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample Carbon\n",
+      "Reducing sample Carbon...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60383-2022-02-28_2215.xye\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample Carbon.\n",
+      "Reduced sample Carbon and saved outputs.\n",
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample SiO2\n",
+      "Reducing sample SiO2...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60385-2022-02-28_2215.xye\n"
+     ]
+    },
+    {
+     "data": {
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IDR8+vMOhB4ydn/C1DBeIi4u74OOE8JSpU6f6vH/QoEEMAK960p3hLowxdv311zOxWOwV5nTq1Cnm5+fnM7TAYrGwuLg4FhAQ0OHF0YABA5hIJOILtKKiIp/hFb643W62ZMkSBoDdddddrarFMHbx70lDQwMbPXo0E4lE7MMPP+zQa1q9ejUDwK655hqfiefPPfdcq9d3zz33MMYuPgSoI++JrxCWthZZWVlZTKfTMY1G02bFq/Xr17e58Dt58iQD0KpyUVdc6H3tythbupgFQEFBAQPArr766g5d315yvPA7o63vy/vvv8+/d20lhNfW1rLly5ezyMhIfm1ISAh7/vnnaSFASDeiBQAhl4nb7eZlOA8fPsxv97UAEMIFLjR5+/7779vdeV2/fj0Ti8Vs6NCh7Z4QdKdPPvmEAWA33XSTz/vHjRvHALBz584xxhg7ffo0A8DmzJnj8/rf/e53DIDP8Clf3n777VZhRu+8806boQmM/VrBpqNVZ4RKQy1LN16Iy+ViixcvZgDYHXfc0eaE5mLek/r6ej6+C8WOC4RJ6owZM1hzc3PHX5CHyMhIFhAQ4PM1vfXWWwwA+/jjj1vd19H3pKOOHDnCgoKCmFqtZgcOHGjzut///vcMAMvNzfV5v/B6LkZn39eOjr2li1kANDU1MQBs5MiRHbq+qwuAs2fPMpVKxYKCgphIJGJTp071udATuN1udvLkSfbee+/xXKKXXnqpg6+KEHIhVAWIkMtEJBLB39+/Q9cuWrQIAPDmm2+22QmUMYaXX34ZALBkyZJW93/66adYtmwZhgwZgp9//tmrWdilNHPmTABAXl5eq/scDgfOnj2LgIAAPp7BgwcjKioKe/fuRXNzs9f1VqsVGRkZiIqKwqBBgzr09c+dOwfAu3KK3W4HANTW1vp8jHC7XC6/4PMbDAacOnUKWq3Wq9LQhbjdbixbtgwbNmzA7bffjn/+85+QSCQ+r+3qe9LQ0IBrr70WR48exbvvvouHHnroguNavXo1Vq9ejenTp+O7777r8M9oS9OnT0dzczP27t3b6j6hA+706dO9bu/Me9IRWVlZuPbaa+FwOPDDDz9gwoQJbV7b3s+Ey+WCXq/v0M9DWzr7vnZm7N1J6LocHx9/yb6G0+nEnXfeiebmZnz55Zd45JFHkJmZiZdeeqnNx4hEIqSkpGD58uX48ccfAQD/+9//LtkYCel3enoFQsiV5MMPP2zz2H7Lli1MJBIxrVbrFdvv6wSAsV93KG+66SavmvaMna+IIez6tWwExhhj69atYyKRiKWkpHhV2bhchFjfTz75xOt2IXb9rrvu8rq9s02v9uzZ47PHwNGjR5larWZSqdSr3vn+/ft5knBZWZnXY3766ScmkUhYeHi41+6zZ4UTgdlsZvPnz2cA2AMPPND+m+DB5XKxRYsWMeB8NZ6O9Efo7HtSX1/PRo0a1al8BuG5pk6d2qrfQGd5NgLzDHFrqxFYV96T9hw5coTpdDoWGBjo83vX0ssvv8wAsN/85jetcm2Enftbb721S2Pp7Pva2bG3dKETgMrKSp9N4srLy3li/ubNm73ua25uZnl5eaykpMTr9q6cAAh5Bk899RRj7Pzvr+HDhzOpVOpVMaiwsJCdOHGi1fMeOnSIAa2b9RFCuk7EGGOXbbVByBXu5ptvxn//+18MGjQIkydPRlRUFEwmE7Kzs5GZmQmxWIzPP/8cd9xxB3/MjBkzsHv3brT8KJpMJtx+++3Ytm0bgoKCcMMNN2DAgAGora3Ftm3bUFFRgQkTJuB///sfwsLC+ON+/vlnXHvttWCM4f7770dERESrcY4aNQo333zzJXsfCgoKMGnSJNTU1OCGG27AkCFDcPToUfz888+Ii4vDgQMHvMbV3NyMKVOmIDs7G7NmzcLYsWORk5OD77//HqNGjcKePXu86uGPGjUKtbW1mDx5MmJjY+F0OnH69Gns2LEDjDG8+eabeOyxx7zGtHDhQnz++edQqVS45ZZbEBERgdOnT+Obb74BAGzatAnz58/n14tEIqSmpmLs2LGIiopCTU0Ndu7cibKyMowcORK7du2CTqfr0PuxevVqPP/88wgMDMSjjz7qs677zTffjFGjRnX5PRF+joYMGYLbb7/d5zgee+wxaLVaAMDGjRuxePFiSKVSPProowgMDGx1/YwZMzBjxowOvUYAuPfee7Fu3TqkpqbihhtuQHV1Nb744gsoFArs27cPqampF/WetKWhoQGDBg2CXq/H3Llzfe6ea7Var58Jo9GIiRMn4uTJk4iPj8fcuXOhVCqxf/9+HDhwAEFBQdi/fz+SkpI6/PqBzr+vXRk7ALzyyis4deoUAGD//v3Iz8/HnDlz+Odq2bJlmDJlCoDzPRquueYaTJkyBUOGDEFQUBCKi4vx7bfform5Gffccw82bNgAkUjEnz89PR0zZ87E9OnTkZ6e3ur1bdiwgZ9UtnzMc889h9WrVwM43/vhmmuuwejRo7F//37e2+DEiRMYN24coqKicPToUajVanz99de45ZZbMH78eAwbNgwRERGoqKjA119/jebmZvz3v//FDTfc0OHvBSGkHT26/CDkCnPq1Cn22muvsVmzZrGBAwcyhULBFAoFS0xMZPfcc49X7L+grRMAxs7HwX755ZfshhtuYOHh4czPz48FBweza665hq1bt87nrmlb9es9/wnJm5dSaWkpW7RoEYuIiGB+fn5swIABbPny5W2WK21sbGSPP/44GzBgAL/+8ccfb3X6wRhjn332Gbv55ptZXFwcUyqVTCaTsbi4OPaHP/yhzcotLpeLffTRR2zSpElMpVIxiUTCwsLC2M033+xz13XFihVs0qRJLCwsjPn5+bHAwEA2fvx49uqrr3YomdNTWzXYPf/52lHtzHsi5Je098/zVMRXUm/Lf+1VMvLF5XKxd955hw0dOpTJ5XIWHBzM5s2b5zPhs6vviS9tJWF7/vOVwN7U1MSeeeYZNnToUKZQKJifnx+Lj49n9913HysuLu7Uaxd09n3t6tiF3xsdee9KS0vZsmXL2IgRI5hOp2NSqZQFBwezWbNmtdr5Fwi7+dOnT/e6vTMnAA0NDWzAgAEsICDA58/Ae++9xwCwO++8kzHGWFlZGfvLX/7CJk6cyMLCwphMJmOxsbFs3rx57Jdffmn3fSeEdA6dABBCCCGEENKPUBIwIYQQQggh/QgtAAghhBBCCOlHWmddEUIIIYR7++230djYeMHrFi1adEnLaRJCSHehHABCCCGkHfHx8SgpKbngdbt27epU1SRCCOkptAAghBBCCCGkH6EcAEIIIYQQQvoRWgAQQgghhBDSj9ACgBBCCCGEkH6EFgCEEEIIIYT0I7QAIIQQQgghpB+hBQAhhBBCCCH9CC0ACCGEEEII6UdoAUAIIYQQQkg/QgsAQgghhBBC+hFaABBCCCGEENKP0AKAEEIIIYSQfoQWAIQQQgghhPQjtAAghBBCCCGkH6EFACGEEEIIIf0ILQAIIYQQQgjpR2gBQAghhBBCSD9CCwBCCCGEEEL6EVoAEEIIIYQQ0o/QAoAQQgghhJB+hBYAhBBCCCGE9CPSnh5Ab+Z2u3Hu3DmoVCqIRKKeHg4hhAAAGGMwGo2IioqCWEz7OD2B/j4QQnqjjv59oAVAO86dO4cBAwb09DAIIcSnsrIyxMTE9PQw+iX6+0AI6c0u9PeBFgDtUKlUAM6/iWq1uodHQwgh5xkMBgwYMID/jiKXH/19IIT0Rh39+0ALgHYIx7pqtZp+wRNCeh0KPek59PeBENKbXejvAwWP+rB27VqkpqZi/PjxPT0UQgghhBBCuhUtAHxYvnw5Tp48iUOHDvX0UAghhBBCCOlWtAAghBBCCCGkH6EFACGEEEIIIf0ILQAIIYQQQgjpR2gBQAghhBBCSD9CZUC7mdvtRmlpKYxGI1QqFWJjY6lTJyGEEEII6TVoAeDD2rVrsXbtWrhcrk49Li8vD9u3b0djYyO/TavVYs6cOUhJSenmURJCCCGEENJ5IsYY6+lB9FYGgwEajQZNTU0XbPSSl5eHtLQ0JCUlYerUqQgLC0NNTQ0yMzORn5+P+fPn0yKAENItOvO7iVwa9D0ghPRGHf3dRLEp3cDtdmP79u1ISkrCggULEBMTA5lMhpiYGCxYsABJSUnYsWMH3G53Tw+VEEIIIYT0c7QA6AalpaVobGzE1KlT4XA4sHr1aqxevRp2ux0ikQhTpkyBXq9HaWlpTw+VEEIIIYS0w263e83lrkS0AOgGRqMRABAWFubzfuF24TpCCCGEEEJ6CiUBdwOVSgUAqKmpQUxMDFavXu11f01Njdd1hBBCCCGE9BQ6AegGsbGx0Gq1yMzMRMucasYY9uzZA51Oh9jY2B4aISGEEEIIIefRAqAbiMVizJkzB/n5+di8eTPKyspgs9lQVlaGzZs3Iz8/H7Nnz6Z+AIQQQgghpMdRCFA3SUlJwfz587F9+3asX7+e367T6agEKCGEEELIFcBut+Oll14CADz99NOQyWQ9PKKuoQVAN0pJSUFycjJ1AiaEEEIIIb0WLQB86GonYOB8OFB8fHz3D4oQQgghhJBuQFvTPixfvhwnT57EoUOHenoohBBCCCF9Qn+on3+loAUAIYQQQggh/QiFABFCCCGEEHIZ9JYkYjoBIIQQQgghpB+hBQAhhBBCCCH9CC0ACCGEEEII6UdoAUAIIYQQQkg/QgsAQgghhBBC+hFaABBCCCGEENKP0AKAEEIIIYSQC7Db7VizZg3S09Phcrl6ejgXhfoA9FJutxulpaUwGo1QqVSIjY2FWEzrNUIIIYQQcnFoAdAL5eXlYfv27WhsbOS3abVazJkzBykpKT03MEIIIYQQ0ufRAsCHtWvXYu3atT1yvJOXl4e0tDQkJSVh3rx5CAsLQ01NDTIzM5GWlob58+fTIoAQQgghhHQZxZT4sHz5cpw8eRKHDh26rF/X7XZj+/btSEpKwoIFCxATEwOZTIaYmBgsWLAASUlJ2LFjB9xu92UdFyGEEEIIuXLQAqAXKS0tRWNjI6ZOnQqRSOR1n0gkwpQpU6DX61FaWtpDIySEEEIIIX0dhQD1IkajEQAQFhYGu92Ol156CQDw9NNPQyaTISwszOs6QgghhJC+ytdch1wetADoRVQqFQCgpqYGMTExWL16tdf9NTU1XtcRQgghhBDSWRQC1IvExsZCq9UiMzMTjDGv+xhj2LNnD3Q6HWJjY3tohIQQQgghpK+jBUAvIhaLMWfOHOTn52Pz5s0oKyuDzWZDWVkZNm/ejPz8fMyePZv6ARBCCCGEkC6jEKBeJiUlBfPnz8f27duxfv16frtOp6MSoIQQQggh5KLRAqAXSklJQXJyMnUCJoQQQggh3Y4WAL2UWCxGfHx8Tw+DEEIIIYRcYWhLmRBCCCGEkH6ETgAIIYQQQki/5tmT4Iknnujh0Vx6dAJACCGEEEJIP0ILAEIIIYQQQvoRCgEihBBCCCFXJM/QnqeffhoymayHR9Q70AKAEEIIIYR0C71ej4KCAhQWFmLIkCE9Ng5h4u9yuQAAEomkx8bSG/WLEKBbbrkFOp0O8+bN6+mhEEII6YSMjAzcdNNNiIqKgkgkwtdff33Bx+zevRtjx46FQqFAQkICPvzww1bXbNmyBampqZDL5UhNTcXWrVsvwegJ6V8YYygqKoLJZEJ6ejoYYz09JNKGfrEAeOSRR/DZZ5/19DAIIYR0UnNzM0aOHIn33nuvQ9cXFRXh+uuvx9SpU3H06FE8/fTTeOSRR7BlyxZ+zf79+3H77bdj4cKFyMnJwcKFCzF//nz88ssvl+plENIvFBQUwGAwICYmBhUVFSgoKOjQ41wuF9asWYPVq1fDbrdf4lESoJ+EAM2cORPp6ek9PYzLyu12UydhQkifd9111+G6667r8PUffvghYmNj8fbbbwM431n98OHDeOONN3DrrbcCAN5++23MmjULK1euBACsXLkSu3fvxttvv41NmzZ1+2sgpD9gjCEjIwNqtRqJiYmIjIxEeno6EhMTIRKJenp4pIVePyPsyPHv+++/j4EDB0KhUGDs2LHIzMy8/APtRfLy8vDOO+9g48aN2LJlCzZu3Ih33nkHeXl5PT00Qgi5pPbv34/Zs2d73TZnzhwcPnwYDoej3Wv27dt32cZJyJWmoKAAFRUViI+Ph0gkwrRp01BeXt7hUwByefX6BcCFjn+/+OILPPbYY3jmmWdw9OhRTJ06Fddddx1KS0sv80h7h7y8PKSlpSE8PBzLli3D008/jWXLliE8PBxpaWm0CCCEXNGqqqoQHh7udVt4eDicTifq6uravaaqqqrN57XZbDAYDF7/CCHnMcaQnp6O6Oho6HQ6AEBiYiJiYmIoF6CX6vULgOuuuw4vvvgifv/73/u8/80338TSpUuxbNkypKSk4O2338aAAQPwwQcfdPpr9fVf8G63G9u3b0dSUhIWLFiAmJgYyGQyxMTEYMGCBUhKSsKOHTvgdrt7eqiEEHLJtAw3ECYfnrf7uqa9MIWXX34ZGo2G/xswYEA3jpiQvq2goADl5eWYNm0a/xyJRCLMmDGDTgF6qV6/AGiP3W7HkSNHWh3lzp49u0tHuX39F3xpaSkaGxsxdepUOBwOrF69mifUiEQiTJkyBXq9vt+ejhBCrnwRERGtdvJramoglUoRHBzc7jUtTwU8rVy5Ek1NTfxfWVlZ9w+ekD5I2P0PCgqCv78/jEYjjEYjKisr4e/vj6CgoD55CqDX63H48GEUFhb29FAuiT6dBFxXVweXy3XBo9w5c+YgKysLzc3NiImJwdatWzF+/PhWz7dy5UqsWLGC/7/BYOhTiwCj0QgACAsLg0wmw+rVq73uDwsL87qOEEKuNFdffTW++eYbr9t27NiBcePGwc/Pj1/z448/4vHHH/e6ZtKkSW0+r1wuh1wuvzSDJqQPc7lcPGpi/fr1OHLkCABg3bp1vPa+y+WCy+WCVNo3pp0ty5kmJyd3OZG5ZSOy3qJvfCcu4EJHudu3b+/Q8/T1X/AqlQrA+Z2smJiYVvfX1NR4XUcIIb2dyWTC2bNn+f8XFRUhOzsbQUFBiI2NxcqVK1FRUcFLPT/wwAN47733sGLFCtx7773Yv38/1q9f71Xd59FHH8W0adPw6quv4ne/+x3++9//YufOndizZ89lf32E9CW+uupKpVIsXboUZrMZdrsdZrMZALBs2TLedTcgIKDPTP4BoLCwsFU500GDBvX0sLpVnw4BCgkJgUQi6fRR7pUqNjYWWq0WmZmZrY7aGGPYs2cPdDodYmNje2iEhBDSOYcPH8bo0aMxevRoAMCKFSswevRoPPvsswCAyspKr7DGgQMHYtu2bUhPT8eoUaOwZs0avPPOO7wEKABMmjQJmzdvxoYNGzBixAhs3LgRX3zxBSZMmHB5XxwhVwiNRoPIyEhERkZCpVJBpVLx/4+MjIRare7pIXYYYwyZmZm8nGl0dHSfDGG6kL6zHPNBJpNh7Nix+PHHH3HLLbfw23/88Uf87ne/6/Lzrl27FmvXruXto/sKsViMOXPmIC0tDZs3b8aUKVMQFhaGmpoa7NmzB/n5+Zg/fz71AyCE9BkzZsxo9w/vxo0bW902ffp0ZGVltfu88+bNo+7whJBW9Ho9HA6HVznTtLQ0FBQUXFEbqL1+AXCh498VK1Zg4cKFGDduHK6++mp8/PHHKC0txQMPPNDlr7l8+XIsX74cBoMBGo2mO17GZZOSkoL58+dj+/btWL9+Pb9dp9Nh/vz5SElJ6cHREUIIIYT0TowxFBcXY/bs2aitrQXgXc504cKFPTzC7tPrFwCHDx/GzJkz+f8LSbr33HMPNm7ciNtvvx319fV44YUXUFlZiWHDhmHbtm2Ii4vrqSH3uJSUFCQnJ1MnYEIIIYSQDtLr9TAYDJg6dSq2bt0K4Ndypp9//jkvZ2qxWHDkyBEUFhZiyJAhPTnkLuv1C4ALHf8CwEMPPYSHHnroMo2obxCLxYiPj+/pYRBCCCGE9HrC7r9SqeTlTAF4lTPNyMiA2+1GY2Mj5HL5RVcIaouvZOvu1usXAD2hr+YAEEIIIYSQznO5XLDZbLDZbNiwYYPPcqYBAQFoaGiAzWZrs0JQby372RItAHzoyzkAhBBCCCF92eXYAW9JKpVi9OjRcDgcWLJkCWw2GwDvcqZSqRSLFy+GXC5HQkICrxCUmJjYoVMAu92OV199FZmZmZg6deolfT0XQgsAQgghhBDSJwiLAyFKQyKRdNsiQaFQQKFQICIigvdMioyM5M998uRJGAwGaLXaVhWC+lqfAFoAEEIIIYSQHqHX63HmzBkkJCQgJCSkp4fjk91ux1//+lccOXIEgYGBfLffs0JQR08BegsqC+PD2rVrkZqaivHjx/f0UAghhFwB7HY7Vq9ejdWrV8Nut/f0cAjpFRhjKCoqgslkQnFxca9utiVUCIqNjeUTfaFCUHl5Oa8Q1FfQAsCH5cuX4+TJkzh06FBPD+WycbvdKC4uxrFjx1BcXAy3293TQyKEEELIFaygoAAGgwExMTEwGAzQ6/U9PSS4XC689NJLSE9P52FGnhWC/Pz8YLPZYDQavSoE9bVuwRQCRJCXl4ft27ejsbGR36bVajFnzhxqHEYIIYSQbscYQ0ZGBtRqNRISEmAwGPgpQG8LpREqBFksFmRnZ6OyshJZWVleFYJcLlefqh5JC4B+Li8vD2lpaUhKSsK8efMQFhaGmpoaZGZmIi0tjboHXwJut5uatBFCCOnXCgoKUFFRgfj4eIhEIsTFxeHEiRO9MqFWqBBktVrhdrvhdDoxZswYrwpBAQEBkEqlfSbEjxYA/Zjb7cb27duRlJSEBQsW8BV3TEwMFixYgM2bN2PHjh1ITk6mCWo3odMWQggh/R1jDOnp6YiOjoZYLIbb7YZOp4NarUZGRgYSExN7eoitKBQK+Pn5weVyQS6XQ6VSeVUI6mtoVudDf0kCLi0tRWNjI6ZOnQqHw+GVoCYSiTBlyhTo9XqUlpb29FCvCMJpS3h4OJYtW4ann34ay5YtQ3h4ONLS0pCXl9fTQySEEEIuuYKCApSXl2PatGleCbXx8fG8uRa5tOgEwIf+0ghMaHMdFhYGmUyG1atXe90fFhbmdR3pOjptIYQQQn7d/Q8KCoK/vz+MRiOPnffz84NOp0NGRkavzAUAzlcDOnfu3EUlLFssFhw5cgSFhYUYMmRIN46u42im0Y8JTS5qamp83i/cLlxHus7ztKXlLzQ6bSGEENJfuFwuGAwGNDQ0YP369Thy5AiysrKQlZWFI0eOQK/Xw2g09sqKOowxlJSUwG63d7lsKWMMjY2NMJlMPVo5iE4A+rHY2FhotVpkZmZ67UoD539A9+zZA51Oh9jY2B4cZd/ndrtx6tQpVFdXw2w2w2q14pVXXgHwa4tzOm0hhBDSH0ilUixduhRmsxl2ux1ms9mrq++yZcsQEBCA9957r9u+psvlQmZmJlwuF1atWtXluH1hcaJWq2EwGFBQUIDU1NROP4fNZkNMTAwPd+qJpGdaAPRjYrEYc+bMQVpaGjZv3owpU6bwKkB79uxBfn4+5s+fTyEpF0FI+i0uLkZeXh4+/PBDxMbG4vbbb/dK+qXTFkIIIf2FRqOBRqOB3W6HSqXyWgBERkb28Oh8E3b/VSoVxGIxVCoVMjIykJKS0uFQJeE55HI5EhISEB0d3WNdhGlm18+lpKRg/vz5qK6uxvr16/Hyyy9j/fr1qKmpoRKgF8kz6XfFihW48cYbkZCQgLCwMK+kXzptIYQQQlpzuVxYs2ZNr+ig3bITcFxcXKcTlgsLC2EwGKDVaiESiTBt2rQe6yJMJwA+rF27FmvXru1TDR0uRkpKCpKTk6k2fTfylfR7ww03IC0tDUFBQQgODsa2bdvg7++Pffv20WkLIYQQ0kV2ux0vvfQSgF9Da7uT0AlYrVZDp9MBAHQ6Xad28BljyMzMhFqt5uG+iYmJiImJ6ZFTAJpt+LB8+XKcPHkShw4d6umhXDZisRjx8fEYPnw44uPjaSJ6kXwl/QqnLTU1NSgoKMA333yDt956i05bCCGE9Dt2ux1r1qxBenp6r99wLSgogMFgQFxcnFfZ0s7s4AvVg1o+x4wZM3rkFIBmeYRcAp4lVu12O++xkJiYiEceeQQPPPAAUlJSMHfuXCxfvhxKpRLHjh1DcXEx3G53D4+eEEIIubz0ej0OHz6MwsLCS/p1PP8mdySsiDGGjIwMKJVK+Pn5wWQywWazwWg0wt/fH0FBQRes5iOcIOh0Ovj5+fHHV1ZWdvg5uhuFAJFOc7vdFC50AZ4lVmNiYlr1WPD390d4eDikUinee+896gxMCCGkT9Dr9SgoKOjWGvbCBFkojTlw4MBued7uIJQttVgsyMrKAmMMlZWVyMrKwvr16yGRSOByueByuSCV+p5WM8Zgs9mg1+uRlZXFH79u3TpIJBL+ddp7ju5GCwDSKUJVG5qwtq8jJVZtNhv27NmD5ORkzJs3j1dgyszMRFpaGoUFEUII6VUYYygqKuIT9eTk5G6JW/dMsL0cnYA7UxZUKpVi8eLFqK6uhsvlgtvthtPpxJgxY7Bs2TLIZDIEBAS0O3EXi8UYPXo0lixZArPZDIfD4fV4ABd8ju5GCwDSYUJVm6SkJJqwXsCFSqyeOnUKAJCcnEydgQkhhPQJQix8WzXsZTJZqxPvC2GMobS0FGq1GomJiYiMjOSdgLvLxXbv1Wg0vFypy+WCXC6HSqVCZGRkhxOOFQoFIiIioFKpuvT47kYzC9IhLavaxMTEQCaT8QlrUlISduzYQfHrHtorsTp16lTI5XLqDEwIIaRPEGLhhYm6UAHnYifqVqsVRqORJ8dOmzYNFRUVXZ6sexLyCvLy8jrUvVev1/NuxFc6WgD4sHbtWqSmpmL8+PE9PZRew1dVGwFNWNuWkpKCRx55BIsWLcKtt96KRYsW4Y9//CPCw8MBtE4SFhKSqDMwIYSQ3qSgoAAVFRWIj4/vthr2jDE0NjZCpVLx8prC4uJCk/WOPHdRURHq6upQUVEBlUoFg8HQZpKxZ3hTSUlJl752X6psRCFAPixfvhzLly+HwWCARqPp6eH0Ci2r2rSst0sT1rYJJVY9XShJmDoDE0II6S0YY0hPT0d0dDQPS+1MDfu26vTr9XrYbDbeXAv4tbzm5s2bodfrERQU1KUxFxQUoKmpiY9XqVRCrVYjMzPT5+TeM7yprKysxxuPXWp0AkA6xHPCKsT4rV69mn+IacL6K7fbjeLi4nbLenomCbf8RUSdgQm5cl2os2lnSxQScjkUFBSgvLwc06ZN85qoT5o0CZ9//jkeffTRTv+8MsZQUlICqVQKPz8/GI1Gr9KYcrkcP/74Y5d204VwJalUCrlcjqioKDQ1NSE2NtZnLoBneFNCQgJUKhUaGxsva1nOy41OAEiHdKSqDU1YO14l6UJJwtQZmBBCSG8g7P4HBQXB39+fn/QLE3WlUtmlcB2XywWbzQan04ns7GyIRCKIxWKsW7cOAGCxWLocRiMsWABArVYjOjoaJSUlAICoqCjk5uZ6jbdleFNsbCxOnjx5RecC0AKAdAhNWC+ss1WShCTh7du3Y/369fx2nU5HFZUIIYT0CkIdfIPBgPXr1+PIkSMA4DVRd7vdnZ6sS6VSjBo1CiaTCaNHj4ZYLIZEIsGyZcsAnA8pdjqdnS4zKixYZDIZnE4n4uLioFarIZfLUVpaivvuuw9paWmw2Wxe1wvhTW63GzqdDnK5nOcCdHYMbrcbu3fvBgA89dRTnXrs5UILANJhNGFtW8sqSR0t65mSkoLk5GRqrEYIIaRXkkqlWLp0KcxmM+x2O8xmMwDwibrZbIZMJutSDXuFQgG5XI7AwEBIJBJIJBJERkYCOB9S3JXnLCgoQFlZGSQSCfz9/eHn54fm5mb4+/ujrq4OtbW1UCqVqK6uBmOMnxbMnz8faWlpAM6HN2m1WhgMhlalTq8UtAAgnUITVt+EKknz5s1rs0rS+vXrUVpa2ioh2FeSMCGEENJbaDQaaDQa2O12nuvnOVHvLYRYfq1Wi/r6ejQ2NuLAgQNgjKG2thZyuRxvvfUWDy9yu93IyMjwCm8SbheLxVAqlcjIyEBiYmJPv7RuRwsA0mk0YW2NqiQRQgghPYsxBoPBALPZzEN8PIWGhmLUqFFobm6G2+2GSCTi1wvhTW63G4wxVFZW8uTk3l7SsytoAUBIN6CynoQQQsjl4XK5eIz99OnT+e1isRiLFy+G0+mE3W7nuQlutxuHDh3ChAkT8PDDD+ODDz6AVCptdb3ZbObXO51OjBkzBosXL+5SKFJvd+W9om6wdu1arF279opc8V1Obre734QKUZUkQgghxJtwIi7MpyQSSYcfq9frUVlZCa1Wy8vnSiQSPPHEE+0+TqPRQCaT8XAll8sFl8sFuVwOlUoFtVp9UddfKWgB4AM1Art4HS2HeaWgKkmEkAvR6/U4c+YMEhISEBIS0tPDIf1UW025ehPGGIqLi2G329HU1HRF1+PvKbQAIN2us+UwrxRUJYkQ0hbGGIqKimAymVBcXIzg4OCeHhIhvZbQlVetVsNgMECv1yM0NLSnh3VFoQUA6VZdLYd5paAqSYQQX4QJTUxMDMrLy70aDHnuyF4ovIF0TV/Y9SbneXblZYzBZrOhtLSUTs26Gc1KSLcSymFOnTq1zXKYer0epaWlPTTCS0+okjR8+HDEx8fT5J+Qfs5zQpOQkAC1Wt2lzqmE9AdCV964uDiIRCJoNBoYjUavRbOQH9DY2IgjR46gqKioB0fcN9HMhHSrluUwV69ejdWrV8Nut/PbPa8jhJArnTChiY+Ph0gkQlxcHG8wRAj5lbBYjo6Ohk6nA3C+WZhKpeJdeYX8AJvNhurqaphMJuzevZsW1J1ECwDSrTzLYcpkMr4AEI5br7RymG63G8XFxTh27BiKi4vhdrt75XMSQnoGYwzp6eleExqdTge1Wo2MjAyatBDiQa/Xo6KiAtOmTeNRBCKRCLGxsTwXoLCwEAaDAQqFAlarFRqNBufOnfM6ISAXRjkApFv1p3KYl6LSUX+rnkTIla6goADl5eWYP38+0tLSAJyf0MTHx6OiogIFBQVXxO9DQi6WsLM/Y8YM3pVXiB7w8/ODUqlEUVERMjMzoVKpUFlZCYVCAbfbjaioKOzYsYMvssmF0QKAdKv+Ug7zUlQ66q/Vkwi5Ugm7/0FBQXxCI9RD9/Pzg06nQ3p6OhYuXNjDIyWkc4S6/ILO1Pdvi5Dwq9frsX79emRlZaGyshIAkJ2dDQA4deoU9Ho9BgwYALvdjrCwMBiNRh5WJ5wC2O12rFmzBpmZmZg0aVKXxuN2u3mzsaeeeqrdaz37HfjqIaXX61FQUNCrchVoAUC63ZVeDvNSVDrq79WTCLkSuVwuGAwGGAwGrF+/HkeOHOEhfWKxGP7+/hCJRB1qOtkfq9i0rI70xhtvAOg/r7+/EYvFGDFiBM8RHDVqFD8BGD16NBhjqKyshEqlQlNTE+RyOTQaDU+qV6lUPLm+ZRGSnuRZArg35SrQAoBcEh0th9kXuwULlY7mzZvXZqWj9evXo7S0FPHx8Z1+TofD0eoPfVeek5CelJ+fj/T0dNTU1LTKY3n22Wd7aFSXl1QqxdKlS2E2m2G322E2m706oi5btgw6nQ5SKf0pJv2TRCLBjBkz+AJPoVBAoVAAOL+AFhZ6gYGBaGhogNPphFarRXl5OTQaDcRiMeLi4lBZWQmdTofS0lIUFBRg0KBBFz2u6dOn8//uKpfLhSeffBI5OTlQq9X44osvMHTo0IsaW3eh3zrkkhHKYbalr8a7t6x01HKy3pVKR57PKSRPe6LqSaQv+eSTT/Dggw8iJCQEERERXgtlkUjUbxYAAKDRaKDRaGC326FSqbwWAJGRkZDJZHyXUyCECxQWFmLIkCE9MexeSwg9kUgknT4JoPfVt75wusQYQ2lpKSQSCaqrqyEWi+FyuWAymWC32yGRSFBVVQWFQoGff/4Z//73v5GRkdHTw+bjlsvl0Gq1UKlUKC0t7RWnALQAID2iL8e7e1Y6iomJaTVZ70qlo5bP2dKVVj2JXNlefPFF/PWvf71g3CxpzTNcID09HcnJyXA4HDyeeerUqV7X94XJW2/g633tTWEipH2MMdjtdjidThQXF8PpdKKpqQkAUF9fj9jYWJw7dw4xMTEwmUy9YoINnF90Go1GaDQaXs3oxIkTrRb9PaF3x1qQK1LLePeYmBjIZDIe756UlIQdO3b02vKXnpWOWv6S6Wqlo0vxnIT0FL1ej9tuu62nh9EnCSUOY2JieJUgcvE8OzHT+3qecJqyZs2aDuWh9BSLxYLs7GzExsYiKioKs2bNwqxZs5CYmIjExETMnj0br776Kq6//nqMGzcOd99990WHEuv1ehw5cqRV87GWt7U35nPnzuHUqVNQqVQ8rEmn00GlUqGxsbHHFym0APBh7dq1SE1Nxfjx43t6KFekvt4tWKh0lJ+fj82bN6OsrAw2mw1lZWXYvHkz8vPzMXv27E79AroUz0lIT7ntttuwY8eOnh5Gr+VyuZCeno41a9bwnUCXy4Vdu3Zh5cqVCAwMRGJiIqKjo5Gent6tEwVfDRr7CpfLhd27d2P37t2dmrB6dmK+VO8r6RyhSs/333+PgwcPtlkdhzGGpqYmNDc3o6qqCjKZDMHBwQgPD0dgYCACAwMRERGBESNGIDg4GCqVCmq1+qLGJpQjNZlM/LThr3/9K3744QcYjUbekKy9xzc1NcFsNqOiogKxsbGtehoI1Y56EoUA+bB8+XIsX74cBoMBGo2mp4dzxbkUMfSX26WodHSlV08i/cegQYOwatUqHDhwAMOHD4efn5/X/Y888kgPjax3s1qtcLvdGDFiBEQiEaZNm4a0tLRetVvdF0KOWo6xtLTUqxOz5/t6scmipOsYY2hsbIRcLm+zOo7VaoXNZkN0dDQqKiouy6JVr9fz06Ly8nLo9Xq4XK4Oj0Ov18NqtUIkEsHtdqO5uZlfbzKZ4OfnB6lUesGFxKVGCwBy2V2KGPqe0NFKRz39nIRcbh9//DECAwP5bq0nkUhECwAfhMlQYmIib2aUmJiImJiYS9YxuC9M5j3rp+v1elRWVkKr1Xb48Z6dmIXfo8L7mp6ejsTERMoF+P/aSrBuWY61u+j1ethsNsTExODcuXMwmUxem67CTrpcLsfAgQNhMBhQUFBwSct8MsZQUlICtVqNhIQEGAwGXlrU1zh8PV5I+nW5XBCLxThw4ABsNhtEIhGOHj0KAHA6nbDb7T0aekWzCnLZ9eV4d7fbjeLiYhw7dgzFxcUAgPj4eAwfPhzx8fHdMlEXqid153MScjkVFRW1+a+wsLDTz/f+++9j4MCBUCgUGDt2LDIzM9u9fu3atUhJSYFSqURycjI+++wzr/s3btwIkUjU6p/Vau302LpCr9fj3LlzreKLbTYbYmNj4Xa7kZ6ejhdffBGTJk1CRUVFj4cL9ISWibtFRUWw2+1oamrq8IJI6MQ8bdo0rzCMGTNmoLy8vFedrvRVer0ehw8f7tRnW5hoy+VyJCQkICoqik+0PZ/XZrN5JdBeTOiMxWLBkSNH2m3GJez+x8XFQSQSIS4uDjU1NaipqYFWq+XjsFqt2L59u1cYn/B4o9EIrVaLiIgITJw4EUFBQdDpdIiMjMTo0aORkJDA/873ZAlgOgEgl11f7RbcV8uWEtKThD/oXd2x++KLL/DYY4/h/fffx+TJk/HRRx/huuuuw8mTJ31uEnzwwQdYuXIlPvnkE4wfPx4HDx7EvffeC51Oh5tuuolfp1arcfr0aa/HCol6l5Iw8bHb7V4TnpKSEkilUvj5+cFkMsFms8FoNMLf3x86nQ5Hjx71mhx5dh4FuqcTa3fozlMFz8TdEydOoKamBmq1Gg0NDcjMzLxgOU8h9t+zEzMAVFZWwt/fH0FBQXQK0ILL5UJmZiZcLhdWrVp1we+fZ7z8haor6fV6FBcXo7CwEG63GwaDgU+qp06diq+++gp6vR5BQUFeO+meCbRyuRylpaX8lKzl87fVbbcj4UbCa1Gr1fz5tVotbDYbGGP8tpCQEAwfPhwAvOYpwmdbiF4QiUSIiYlBfX09zp49C7VajYCAAJw9exZutxvnzp1rdyF7qcvW0gKA9IiOxrv3lkZhfblsKSE94bPPPsPrr7+OM2fOAACSkpLw5JNPYuHChZ16njfffBNLly7FsmXLAABvv/02tm/fjg8++AAvv/xyq+v/+c9/4v7778ftt98OAEhISMCBAwfw6quvei0ARCIRIiIiuvryukzYIVSr1TyUID4+HjabDU6nE9nZ2QDOT1KzsrL478fujH32nCh1dsJ3uXgm7iYkJODs2bOw2WxQq9WoqalBZWUldu3a1e6EkzEGg8EAs9nMOzEDwLp16/iCyeVyweVy9YtmbC0XjV0h9KkRnqu2thYGgwGxsbG8upKvvArPhcKuXbsAnF+EC4uyhIQEqNVqlJSUQKfTtSqfCZz/zGq1WhiNxlanABfqttsy3EgoIepJWHAOHTqUf01h0cAYg81m4+OIi4vDsWPHWp3iGQwGpKam4uTJk/xaofSn1Wr1+flPTU31+X5d6rK1V/5PPOm1LhTv3lt23FuWLRU+hELZ0s2bN2PHjh1ITk7udacWhPSEN998E6tWrcLDDz+MyZMngzGGvXv34oEHHkBdXR0ef/zxDj2P3W7HkSNH8Je//MXr9tmzZ2Pfvn0+H2Oz2Vrt5CuVShw8eBAOh4MnJJtMJsTFxcHlcmHUqFFYs2YNRo8e3eZYbDYbnwAAgMFg6NBr8OS5QygWi6FSqZCRkYHExESMGjUKJpOJj8HpdGLMmDF84WOz2XDw4MFOf01fY2hvotQRl6OhVkFBAU/cbWxshFqthlwuh8FggEQigVwux/Hjx9tN5GWMoba2Fm63Gw8++CDMZjMAYNmyZXyhExAQ0COT/57Ov9Dr9SgsLERCQoLP3XSBUK3HVw8Kz3j5xMREREZGtnmiIkyOY2Njcfz4cQBAXFwcTpw4AeD8RDk+Ph7Z2dloaGhAaWkpFAoF7HY77HY7TCYTgPM77gqFgjfTEr6OZ/nclhN8z9OEhIQEREREIDc31+tnX1hwKpVK+Pn5wWg0gjGG06dPIyAgAG63GxUVFTAajZBIJPDz84NSqfQ6xSsuLuaP95X029jYiNLS0laf/5SUlFbvl6+ytd2dsE4LANKj2uoW3Jt23IWypfPmzYPD4Wj1S3vKlClYv349SktL2+18TEh/8e677+KDDz7A3XffzW/73e9+h6FDh2L16tUdXgDU1dXB5XIhPDzc6/bw8HBUVVX5fMycOXOwbt063HzzzRgzZgyOHDmCTz/9FA6HA3V1dYiMjMSQIUOwceNGDB8+HAaDAX//+98xefJk5OTkYPDgwT6f9+WXX8bzzz/fwXfAN88dwlOnTiEuLo7/cVcoFJDL5QgMDAQAyOVyqFQqREZG8v/viAtNLD0nFufOnUNpaWmrfIT2nuNy7Ex6Ju6KRCJkZ2dj9uzZKC0txcGDByESiRASEoKmpibs2rWr3RAehUIBiUSCiIgIHpohdGDuqzqzePCVwNuyzKUQhtNZws/z8OHD262u1DKxtrCwEHa7HZGRkTzUraqqymtS7XA4YLVa+edcSJ6trKzEgAEDWk3eMzMz+UIkNDSUT/BFIpFXXL4QbpSWlua1oHe5XDAYDLBYLMjKyuLPW1FRAZfLBcYYTCYTjh492mqjTzhRsdlsvGdBZWWl17idTic/kRo2bFirz3/L98uzbG17C6uLQQsA0uv0th13z7KlwvGnp75QtpSQy6myshKTJk1qdfukSZP4H8bOaPlHr70qIKtWrUJVVRUmTpwIxhjCw8OxaNEivPbaazzsY+LEiZg4cSJ/zOTJkzFmzBi8++67eOedd3w+78qVK7FixQr+/waDAQMGDOjwa/AVX6zT6RAdHY2MjAw0NDTwiXh7O7IXo+XEIiQkBN9++y1sNlurBMy2XI6dSSFxd/78+fjwww9hMBgwdepU7N+/H83NzXwiHxgYiHXr1uHYsWN44403+vSk/nLyVeYyKCioU8/R8ufZ5XLh3//+N3JzcxEREeE1WfVcKABAVFQUDhw4gKKiItTW1iIrKwuffvopjhw5ArfbDYVCgREjRsDhcMBgMMBgMGDgwIHQarVwOp0YPXo0/Pz8cOjQIf78brebl3kVJvhWq5WfFqhUKjQ3NwM4H24klUpx+vRpNDQ0AACkUikWL16M6upqrxCpYcOGwel0wu1249ixYxgzZozXvEOpVPITpNGjR/NSvg6Hg98GnF+I1dXV+fz8t5zce55+XcqytRSvQHodz0ZhDofDq2nN5WwUJlT8qaioQGNjY5s7jn2lbCkhl8ugQYOQlpbW6vYvvviizR12X0JCQiCRSFp99mpqalqdCgiUSiU+/fRTmM1mFBcX85M5lUqFkJAQn48Ri8UYP348z1fwRS6XQ61We/3rDGHiLFQXAcD/uJeXl+PkyZOw2+3t1gYXqpicPn0aa9asQXp6OlwuV4ersLScWMTHx6OpqQlisRinT59Genq6z8cJzcOee+45/Pzzz5e0oZaw+x8UFASlUolTp05BJBLBaDTypG2RSASz2Qy1Wg2r1YqioiKfMd8d7dran7TcjRfi7jvyPRTKhK5evRqnTp3y+fMcHx/v1Wm55UJBLBbjiSeewA033ACNRoOIiAiMGTMG999/PzZt2oR//vOfGDt2LPz9/REQEACLxQK32426ujoEBATwUzLhREx4PVFRUXxiLbyupqYmWCwWGI1Gr2ZcwuNsNhtOnjzJX7tGo4FKpfL6FxISgoiICISHh8Pf3x+BgYFe93uezCkUCqhUKgQGBkImk0Emk/FmZW63G06ns1VTMOHzn5eXxz9jO3fuRHR0dKtywN39WaMFAOl1PHfcfbkcO+55eXl45513sHHjRuzfvx+nTp3C//3f//HEHkFvL1tKSE94/vnn8eyzz2Lu3LlYs2YNXnzxRcydOxfPP/88XnjhhQ4/j0wmw9ixY/Hjjz963f7jjz/6PGHw5Ofnh5iYGEgkEmzevBk33nhjmyeGjDFkZ2fzcJvu1jK+uGWVHyG+WKVS+UxwFJ6jsbGxVex+yyosbU0QGGPYuXMnjh49ipycHDidTj4RFGLj//nPf8Ltdrf5OoTypZ47k75KaXalLKRACMVoaGjAunXreLLyo48+ihMnTsDtdsNms6G+vh5bt26FXq9HY2MjCgoK+ELlhRdewJkzZ3DixAns2LEDTqez0+O4Uvkqc2kwGDq1UBJCbjzj5YV/fn5+0Ol0/GfR18JXq9Xipptugt1uB2OMh7oJ/4RJtZC4KyQL+xqj1WpFU1MTjh8/zjtEC6/LarWirq4OCoUCfn5+/DN38OBBWCwWyGQynDhxos2Fb3cR+hm0VeUrKCiI9/rQ6/WoqKi4LGVrKQSI9Do93SjMV/7B3r17sXbtWjz77LN46KGHMHny5F5ftpSQnnLrrbfil19+wVtvvYWvv/4ajDGkpqbi4MGD7Sba+rJixQosXLgQ48aNw9VXX42PP/4YpaWleOCBBwCcD82pqKjgtf7z8/Nx8OBBTJgwAXq9Hm+++SaOHz+Of/zjH/w5n3/+eUycOBGDBw+GwWDAO++8g+zsbKxdu7b73gQPLeOLGWO8ys+6deuQm5sLqVQKnU6HwMBAnuDoqa0qJp7JlZ47ry0Ju/9C4qUQNpGQkICysjIMGDAApaWl2LVrV6tkT+DXhcbs2bNRW1sLwHdDLc8cgZ07d2LTpk0QiUQdTnSVSqVYunQpzGYzr/kvnP5WV1fD398fSqUSgwcP5rkT/v7+PJlaeE88K61c7ClATyfsdpe2wtA8q+909HmMRqNXvLxwe1lZGWJjY3HTTTfB6XS2SqyVSCS8DKtSqUR1dfUFG2pptVqoVKpWnwthYq3Var0m+EI+gVQq5eMU4vKPHDmC/Px81NfX80Zdn332GWbNmnWR727775fT6YTL5fJZ5UsikfBE4+LiYsyYMeOylK2lBQDpdTwbhXnmAADeO+4xMTEoLi7u1hKhbeUfzJw5E+Hh4XjllVfw2muvYcKECRCJRK3KlhJCzhs7diw+//zzi36e22+/HfX19XjhhRdQWVmJYcOGYdu2bYiLiwNw/o+jZzigy+XC3/72N5w+fRp+fn6YOXMm9u3b55Wg39jYiPvuuw9VVVXQaDQYPXo0MjIycNVVV130eH1pGV8shAOMGTMGs2bNgslkQmRkJM6cOcNLBnqW/WzZNEmoYiKUSfZMFhR2Elv+3kxPT4dOp4PRaITVasXp06cxc+ZMmEwmyGQyKBQKBAUF4bPPPsPkyZO9Kv0kJCTwhcbUqVOxdetWAL/uTH7++ed846S2thaNjY2IjY3l3V07G1+u0Wig0Whgt9sREhICl8uFEydOwN/fH1arFTabDVVVVTCZTGhoaOCvS0jW9KzFbrPZOhzicqUTKuV4lrl0u92orq7mG24d+V6JxWLcfffdPElf4PlzvXjxYohEIn6a891330Gn08Hf3x/r1q0DcD6kTfietdSyDKivzwVwPrnWarXyCb6QT5CVlQWXy4XAwECMGjUKYrEYTqcTiYmJUKvVUCgUOHjwIIYPH46ysrI2F77dQSwWIyIiAm6322eVL5lMBj8/P7zzzju80dnlKFtLCwDS63SkUdjYsWPx3nvvdXuJUM+KPy1X2KmpqVizZg3eeustXH311RgyZMgl70vQW/ogEHIhBoOBx8VfqERmZ+PnH3roITz00EM+79u4caPX/6ekpPDKG21566238NZbb3VqDBdLiC8W/oALscx5eXkYMmQIysvLcebMGeh0OqhUKhQUFPCJUUFBAWpqamCz2dDY2IjbbrsNaWlpvCPuiBEjeEjOpk2b0NTU5DWRE04g9Ho9srKyUFZWBqlUitDQUJw9exZ+fn5wOp2444478Prrr+Pnn3/2qvQTHx/PSxy2tTOZkZEBt9vtVRYyNDQUO3bsuOik5qamJsTHx0OtVuPUqVMAgDFjxkCv12PXrl1ISEjA4sWLIZVKW9Vi12g0MBgMXQpHupL4CtsBzv+NEYvFkEqlbTbY8sXz51kg/FyrVCqo1WpIpVIsWrQIO3fuhEgkQmBgIEaMGMFL2xqNRv71W47VcxEHoNXnQujeHRER0WpSvWTJEn6CJBaL+e8bmUyGuro6jB07FgUFBQgICMCYMWNQUlLCF76XijBh91XlSyaT8bGOHj2avz+XumwtLQBIr9Reo7CxY8fiyJEjl6REqGf+ga9j34iICGi1WkRHR1/ykp+9pQ8CIR2h0+lQWVmJsLCwNssKCn+4L6YR0ZVCOG1UKpV499138dVXXwH4tXHQyZMnodfrwRjD7t27YbVawRhDYWEhNm3ahMrKShgMhlbJgtHR0cjJyfGayAlhNY2NjTAYDKioqEBMTAzi4uJQXl6O5uZmDBo0CKmpqYiMjMRHH32EpqYmHhb0+OOPIzs7GzKZDHfccQcPATp06BCmT5/OQxhqa2tx7NgxhIWFwe12e3V37Syh/nxGRgZUKhWmT5/eKj9Bq9VCoVCgrq4OcrkcL7zwArZv347ExESv16/X6/G///0Pzz33XKdCJ3prt+WuaC9sR0iyF2Lyu1NdXR3sdjsSEhJQXl4Op9PJc21UKpXPyWx7DbWEz4WwwJVKpa0m1ULJ15ahWsJnKD4+npcFFYvFuP322/HHP/4Ry5Ytw8CBA7v19XdEy14Lnu8PcOnK1tICgPRavhqFxcTE4L333rtgidDBgwejvLy8Qzvnnrvswo5aT+UfCHpTHwRCOuLnn3/mf5SFTp+kbUL8clBQEN9Vt9lsXo2DSkpKUFBQgOPHj0Mul0Oj0cBoNKKxsREKhQJVVVU85CYkJISfAqxfvx7Hjh1DfX093nzzTchkMmg0GiiVSj5ZMpvN2Lt3L6xWKxoaGnD8+HF8+umnCAkJwU8//cRLOYaGhiInJwd+fn4IDg5GQEAAJBIJRCKRVwiDEOYkl8t5IzahGktHS4y2RQiLyMrKalVf3WKxoKCgAAUFBTxPQqi0IrzHGo0Gv/zyC86ePcurUF0pcf0d1V7YjlCycuTIkd16wtyym7PBYOA/C+11b75QQ63OnFR4Pm9jYyPi4uKQm5vLd92NRiOSk5MRGBiI3NxcxMXFtfseSCQSrFq1CgD4z09fdcUvAL799lv86U9/gtvtxlNPPcWPVkjf0LJRWHFxcZshOkKJ0FdeeQUvvPCC14e4rZ3zlrvsjDHk5uZi8+bN+NOf/tRm/sGlrPjT2/ogENIR06dP9/nfpG1OpxMWiwUbNmzgk1vPxkE2mw27du1CbW0tNBoNnwSdPn2adyqtq6tDXl4eRo4cicrKSiiVSpjNZhgMBl4e03Mnu6GhAYwxyGQyzJgxg8dNm0wmVFVVYe7cuXzX1eVy4fjx43xMAwcOREVFBdxuN5RKpVcIw8mTJ2EwGHjMNvBrWcjc3FwUFBQgNTW13fejZdMqiUSCmTNnYvny5TCbzTysAzhfX50xhnPnziEoKAiZmZkoLi72qrRiNBphNpsxdOhQmM1mfPnll1i5cmWHTgE8d2UnTZrUJ3b/L7SoaStsR7iuo83mOqpl2VkhAb29evYul6vNhlpWqxV6vZ5v1HWGkIir1+vxww8/wGq18h4EGzduREBAAOrq6tDQ0NBmueArzRW9AHA6nVixYgV27doFtVqNMWPG4Pe//32nE5JI73GhEJ2GhgacOHEC1113He68885WO+fz5s1DQEAAjEYjqqursWfPHiQnJ3vtsm/evBnfffcdgPMJiC3zDy51xZ/28hCERQ51Hia92Q8//IDAwEBMmTIFALB27Vp88sknSE1Nxdq1ay9Zo6u+RIhf9oxZdjgcXvHMI0eORElJCaqqqmCxWFBbW4uQkBBUV1dDr9fD4XDAYrHgzJkzMJlMCAgIQENDA0wmE+RyOZqamrBixQr+N0+IrRZ6BzidTl4RBTi/IZKeno6GhgZoNBqMHz8eWq2WTxjj4+NhMBhQXV3Nd/mF5xWSj4VmR0I1lubmZtTW1mLLli1ISUnhv9M6swOv0WgQGhqK1157jU/KAwMD+YbQ0KFDeb8Wi8WC77//HhqNBmVlZQDOh6FoNBpe/tTPz69bv5dXwmmCEBrUmRAYvV6PM2fOICEhodVn2rObs1gshtvt5hWHPCs2tSSVSn021Bo1ahROnjwJpVKJgIAAiEQiWCwWNDY2dqh5npCIq1QqcfXVV/PFdnJyMm688UbU19fj7NmzKCsrQ3BwcIdDxYTeCH3RFb0AOHjwIIYOHYro6GgAwPXXX4/t27fjjjvu6OGRka5qr0So2+3Gf/7zHwQHB2P+/PmIiYkB8OvO+d/+9jf83//9H0aMGAEA+OWXXxAWFtbq2j/96U8AgL1798JoNPJfBJer4s+FFjnUeZj0dk8++SReffVVAMCxY8ewYsUK/OlPf8LPP/+MFStWYMOGDT08wt5BKpV6xSy73W7k5+dj4MCBPJQmODgYN9xwA86dO4eDBw9i3LhxOH78OHJzc6HT6XDdddehsLAQYrEYS5YsQVpaGgYPHgyLxeJV3lEkEkGv18NkMvE447lz58JisfBd9YSEBDgcDgQEBMDf3x9isRgNDQ1891uo7nPixAlYrVb+OoQk4+rqajQ0NMBgMPASh5mZmaipqcGmTZuwYsUKBAQEdOo9EiZYEokETzzxBL9d6MwqXBMTE8MbRQkTOOE0YNCgQVi0aBF++OEHlJSUdHvn4t7E5XIhMzMTLpeLh6pciBAqpVAoOlwxybPca3FxMbRardf9p06dwueff46hQ4fyBaivRmG+CHX7PU8nHA4H7HY7goKCYLFY0NDQwEvElpSUtPr6vkilUp6DJCx8T58+jW+//RanT5+G2+2GRCJpN0TpStKr4wcyMjJw0003ISoqCiKRCF9//XWra95//30MHDgQCoUCY8eORWZmJr/v3LlzfPIPgLcuJ32XZ4nQlr+kSkpKcPz4cYwYMYKXCBScOnUK586dg1gsxty5c3HnnXciNTUVV111FTZv3ozly5d7dRu+/fbbMWLECMyaNQu33norFi1ahD/+8Y+XJe7ec5Ejk8l4J2ThFyF1Hia9XVFREQ/32LJlC2666Sa89NJLeP/99/H999/38Oh6JyFGubm5mU/C9Ho9GhoacNNNN0GtVnslOTqdTmi1WoSEhCA5ORl2ux2nTp2CyWRCcnIy/Pz8EBsby6v/eHaAjY+PR15eHl5//XUolUpehlDYvZfL5XC73TAajThz5gzEYjF/fuD8bnFdXR3/HSyVSjF9+nQEBQUhMjISSqUSCQkJuPbaa/ltGo0Gf/7zn/nv2a6SSCSYMWMGJk+eDIPBgJCQEBiNRsTFxfEcgPj4eNTV1cHpdEIsFqO2thYTJkxAYmJit3dTFUKFhK7MnXExDdO6i16vR2lpKUwmE6Kjo3mZ2AsRSorGxMS06rXQkUZhwolRRwg9AVQqFa8GdOrUKVit1nabhInFYkyfPp3/DZ05cybGjh2LJUuWYMyYMYiMjOQncGPHjsWYMWN4ydD+oFe/yubmZowcORLvvfeez/u/+OILPPbYY3jmmWdw9OhRTJ06Fddddx2vCe3rh6s/rOquZEKJ0Pz8fGzevBllZWWw2WwoKytDWloa6uvrcfPNN8PpdPIPvdVqxfbt2zFq1CgMGzYMgYGBvLvgvffei5SUFCQlJeHZZ5/lk+ywsDCIRCJoNBoMHz4c8fHxl+2XQnuLHOo8TPoCmUzGS9jt3LkTs2fPBgAEBQVdsERofyVMXoVJmF6v56U1PZOEhfBFmUzGq5oIk6J//etfiIqK8tnkSdiZ9+wAazKZsGDBAkyfPp1P8q1WK6qqqlBZWYn9+/cjLy8P9fX1vPZ+aWkp32DZu3cvCgsLwRjD0aNHERISApVKxXdl8/LyEBISgrCwMOh0uk7X49fr9Thy5EiryZ3b7camTZug0WgQHh4OtVqNoqIi2Gw2uFwuaLVaOBwOuFwu3gyssLCwVTdVl8uF9PR0rFmz5qIWJV3RsmHac889d9GLo66MQegi7XK5EB8fD5VKhcbGxna/T8IEX0juFX7GPLtTe1Yc8vwnfD+FHJaOsFqtMBqNPLl7wIABfEOvrSZhbVEoFPzEzc/Pj/cN8Pf3h0ql6pY8CL1ej8rKSlgslot+rkupV4cAXXfddbjuuuvavP/NN9/E0qVLeWLv22+/je3bt+ODDz7Ayy+/jOjoaK8d//LyckyYMKHN57PZbLDZbPz/6Q9V79RWiVC3242hQ4ciODiY75wDvyYOT5o0CWfOnOGPUalUqK2t9RlT35O77B3pg0Cdh0lvNmXKFKxYsQKTJ0/GwYMH8cUXXwA436VXCLcjv/Js9DVw4EA0NTUhPz8fAQEBOHr0KO6++26UlJQAAPbv34+amhqIxWIe/xwaGgqdTofi4mLceeedOHfuHADwiX5ubi7y8vJ8doAVNhqsVivvDVBeXs5rudfV1UEikUChUEAikeDcuXO8L0BxcTF27doFiUSCiooKxMbGIj8/H1KpFEVFRTh+/DhP/BRKONbV1bWKmfYV4tMyxCQ4OJjfV1xcjLKyMowYMQLl5eWIi4vDyZMnefLo0aNH4efnB5FIBIlEAplMhm+++QZ33XUX76a6cOHCTn2P9Ho9iouLUVhYiCFDhnglVnelrG1BQQHfQe9qw7SLpdfrUVtbC4lEAolEwkO8hFKb7T3O4XB4JfceO3aMP6atikPA+ROcZcuWISAgAH//+98vOEYhPEn4GQdab+S21SSsJwifZaGLdW8OJ+qzMwi73Y4jR47wnSXB7NmzsW/fPgDAVVddhePHj6OiogJGoxHbtm3DnDlz2nzOl19+mXcg1Gg0GDBgwCV9DaTrUlJS8Mgjj2DRokU8ROfZZ5/F4MGDW+2cCzsNp0+fRkhICN588028+eabCA4ORmZmJkJDQ/l1QO/YZRcWOdXV1Vi/fj1efvllrF+/HjU1NVQClPR67733HqRSKb766it88MEHPBTz+++/x9y5c3t4dL2DxWJBZWUl6urq8OSTT+LYsWO8edJDDz0EpVKJIUOGYNCgQUhISEBkZCQiIiKg0WgQHx8PuVwOPz8/5Ofno6mpCVVVVQgODkZOTg4MBgOampqwf/9+fhJTXFwMg8HAK54J8djV1dW44447EB4eDrvdjjNnzsBms6GyspLXYxeJRPwEoLKyEuXl5XC73XC73SgqKsIDDzyAo0ePwu12w2azQafTwWKxoKKigsdme55GdGSn1leIiUwmw7PPPguRSITIyEgEBwfDZrNBIpGgqqqKj1U4DWCMoby8HE1NTfjuu+/w6quvoqGhAXq9Hi+88AJ2797dqreAL5475d0RQuRZHjMxMRFRUVEXXSq1K2MoLi6GzWaDRqNBQEAAj56wWCzIy8vzOR7hcb5Omjx34YWKQ77+RUZGdrgZoND52bO0a2lpKaKiouB2u2G1WvkJ2IVOLoQTH8/eDt1NON1Qq9W8fG1LQhjbqlWrejRhvFefALRHWFmGh4d73R4eHs6bWkilUvztb3/DzJkz4Xa78ec//9lrF6GllStXYsWKFfz/DQYDLQJ6sZYlQgH43Dk3Go04fvw4jEYj7r33Xr5zLly7bt06NDU1QSaToaysrNfssvvqg0CdgElfEBsbi2+//bbV7Ze7+25vIpxK2u12vPDCCzyBsbi4GCKRiCf9CjuIGo0GEokESUlJ+OGHH3jlGpFIBLPZjKamJsjlcpw9e5ZPkKOjo/HTTz/BYrGgtLQU9fX1KCoqQnNzM9/lP3XqFIYOHQqDwYCysjJMmDABW7ZsQVNTE7RaLYqLi3kpxvr6emg0GqjVal4+VDhhTU5O5pVYysvLcc011/BTDKVSCaVS6RUb3nKnOCgoCHq9HoWFhUhISOClF/V6Pc6ePQsAPuvH5+fno6qqCoMGDcLRo0d5qJLFYuFlUt1uN6Kjo2GxWFBfX4/U1FSkpqZCIpHgD3/4A2QyWYd2nwXCQig2NpYnsF7M5lDL8phdaZim1+tRUFDATyQ6S6/Xo6amxqtvg8FgQF5eHqRSKcrLy5GXl4ctW7Zg9+7dXo8zGAyYOnUqtm7dCsD7pKk7d+GF3X/P0q4GgwFWqxVJSUl8cQegQycXFyKRSPD0008DQJcWCS07GNtsti71LLhc+uwCQNDyaKXlcctvf/tb/Pa3v+3Qc8nl8m6vg0suL1/hQYwxuN1uREVFef2iTElJwW233YZXXnkFNTU1+Pe//w2RSHTZqv10hK9FDiG9XVZWFvz8/DB8+HAAwH//+19s2LABqampXgnt/ZUQ769Wq3lXXaF+vl6vh9vtxsiRI3m8vTDpUSqVGD58OHJychAcHIzQ0FAEBARALBZjwoQJWLRoEZRKJd544w0UFxdj1KhROHbsGMRiMRwOB8xmM6qrq3lHYLvdzifsQhUipVKJ5uZmyOVy1NfXY+jQoZDJZHwiJnTe/c1vfoPy8nK+YKiurkZ9fT38/f2h1Wpx9dVXIzc3F9nZ2TCbzdi/fz/i4+OhVCp51RZhV10I8RHCfurq6nDw4EEkJiZ61Y8/e/Ys9u/fj2uuuQYzZ87EJ598ApPJBIVCAY1GA4vFArPZ7FXXXngdN910E9LS0mA2m/liw7OMpCchsXf37t0wm83Ys2cPwsLCkJiYiMjIyC6FEAlalscEOt8wzTM8Kj09nS/GOkKv1yM/P5/vrIeHh/NwZ6fTiYqKCqhUKjgcDmzdupWPx2Kx4MiRI3A4HFAqlTwvRZgk+/n5QaFQoKam5qJPMoTFzZo1a3D48GGcOXMG2dnZfPdfq9WCMcZPf4TkYqF53uU8SWk5bs8OxkLjvotZlFxKfXYBEBISwo/9PNXU1LQ6FeistWvXYu3atdSuvo/ytXPe3NyMr776qlVMfU5ODgYOHIi7774b4eHhtMtOSDe4//778Ze//AXDhw9HYWEhFixYgFtuuQVffvklzGYz3n777Z4eYo/xjPfXaDRoamqCy+XixQlOnz6Na665BjqdDvv27cP777+PwMBAVFVVQaFQwGKxwOFwIDg4GHK5HElJSTh58iQkEglGjhzJa/EHBAQgKioKJ0+ehNVqhUgkwujRo1FZWQmz2QydToeYmBieDGmz2VBUVAStVou6ujpERUWhsrISIpGI12SPiopCdXU1gF93ffPy8uB0OrF3716eACwsLqqqqvjJQUNDAyoqKhAeHg6lUsmTkmNiYlBeXs5DoXJychAREdHqfWtoaMCXX34JlUoFi8WCb775BqdPn0Z9fT1qa2shEongcDhgMBgQHBzMw5GExGm5XO4V/y/sLgunML4mjWazGUVFRZDL5TxUdNq0aUhLS2u3jGV7CgoKeFL2iRMnMHXq1E41TLPb7VixYgVyc3MxadIkfiLRXmlTz9MCYYHV2NiIsLAwnvTNGINYLIbBYIBUen5q+Msvv2DMmDH8vZLL5WhoaEBoaCg2bNiAI0eOeIVQMcbgcrkuGIYj5IC0nGMJp2M//PADwsPDsXfvXowcORImkwmjR4/mIWfCCRVwPvcvJycHwPkFjN1u75G5m2eVLc8df71ej1OnTvXKXIA+uwCQyWQYO3YsfvzxR9xyyy389h9//BG/+93vLuq5ly9fjuXLl/OuhqTv8bVzLhaLWyUO63Q6LFiwoFfs9hNypcjPz8eoUaMAAF9++SWmTZuGf//739i7dy8WLFjQrxcAQvKnsOOvUqlQXl7OY/OFuvUnT56E0+mEw+FAYmIiTpw4wUN7PIsTCPHXwiTW8/lLS0sRFBSE2tpaBAQEoL6+HoMHD8ZPP/2E6OhoDB06FIWFhXziLJVKwRiDUqlEZWUlhgwZgrKyMjgcDkilUiiVSjQ1NSE8PBz5+fkYMWIEpk+fjsbGRlRXV2PYsGGorq7GqFGjsGTJEpSVleG5555DbW0tRo8ejZKSEjidTsTFxaGsrMxniI9YLIZcLsf48eNx9OhRnjegVCpx+PBhvPDCC4iMjITdbofRaITdbodCocDEiRNRU1ODnTt3IjIyknd2Bc5PDDdu3AjgfH+goqIiXtlIqBLUcuLNGENtbS3MZjNUKhXvIpuYmIiIiAj8+c9/htFoxOTJkzv8vRd2/3U6nVdVp6qqKvj5+UGpVCIjI8OrYZqv5xAWkAkJCYiOjkZ6enqbjbU8Twu2bNmCxsZG3pgrLi6On/jYbDbIZDKEhISgpKSEx9g3Njby04KYmBi43W4MGjQIS5Ys4VWXBG63m38Pu0o4HROSoy0WC+RyOQIDA/nPZnBwMAICAqBQKPjCFjjfK8BkMuG1117r8te/mHEbDAYMHz6c5yo0NTVBrVajoqICGo0G/v7+l31c7enVCwCTycRjAYHztaWzs7MRFBSE2NhYrFixAgsXLsS4ceNw9dVX4+OPP0ZpaSkeeOCBHhw16a0opp6Qy0MIuwPOlwG98cYbAQADBgzgk7L+yDP5U9gRnDBhAhQKBYqKigAAcXFxWLhwITZu3Ijm5mYkJiZCJpNBKpWirq4OCoUCsbGxOHPmDLRaLaRSKV577TWkpaWhtLSUP7/VaoXBYIBSqYRCoYBUKoXBYPCqbqfX6/kpRHV1NQICAviJgJBcmZubC6vViqCgIOTn58NgMPDkRpPJhPHjx8NoNCIwMBAGgwGMMcjlcoSFhSEkJIT/HAwZMoRPvHNzc1FfX4+JEydCJBLxsscqlQqMMVRUVKCwsBA1NTXIy8tDXV0dYmJiYDKZ8MMPP2DlypVwOBz8erfbjTvuuANfffUVYmNjMW7cONx44434v//7P9hsNowfP55XC3Q6nRCJRCgtLYVcLodWq+XdaT0n3kKjKalUiuDgYEilUhQXFwMAX9B6Vg10u908Vr6t5E6hYZper0dWVhYqKyt5GcojR44AAA+rEXbhWxIWeFqtFiKRyOtEwldegnB9dHQ0Dh06BIlEArVazXsQhIWFoa6uDs3NzQgKCoJarYbT6YTT6URRURGsVisaGxt5hSqTyYTa2lp+Yu65AGg5bpfLxd+TKVOm8IZynj9/586d4yEyLRc3ERERyM3N5ScKQvfqgQMHoqKigoesBQYGAsBFhRZ6NlB76qmnOvVYYdxC3wOTyQSj0Qiz2YwhQ4agoaEBdXV1vS6ntFcvAA4fPoyZM2fy/xcSdO+55x5s3LgRt99+O+rr6/HCCy+gsrISw4YNw7Zt21o1gSJEQDH1hFx648aNw4svvohrr70Wu3fvxgcffADg/CbOxYZo9mVC8qeQDAsASqUSSUlJOHv2LJ/Mfvfddzy85eTJkwDO725aLBYoFApUVVXBYrHwCaW/vz+CgoKwadMm7Ny5E2azGQaDAZGRkXC5XAgLC0NTUxPEYjGOHTuGqKgoOBwOnDx5Etdffz327duHuro6OBwOJCQkwOl08h30gIAAWCwWngxstVohkUh4rxTGGCwWC5qbm9Hc3AyxWIysrCysW7cODQ0NMJlMkMvlaGxshEwmw7lz51BeXo6wsDDo9XrExcWhoaEBVqsVDocDcrmcTz6FJGaNRsOboOXk5MDpdAL4tXlafHx8q5j0uLg4aDQaFBcXIzAwEJGRkQDO5wXU1tbCaDTyU5i4uDivUBrGGE6ePMlDrTQaDaxWKywWCw4cOMB3zqurq/lCrq18Ak9SqRRLly5FY2MjjEYjHA4Hb0QlLCYWL14MqVTqswu85wJSqFqXmJiImJgYn3kJntcLSddisRgjR45EdHQ0Tp06xSf1brcbEydOxLlz5xAUFISgoCCIxWIUFxfDYrFAqVSisbGR52NcbPMyz3KZLU+vhMXN1KlTkZaWxis6CYu2gQMH8s7TQgKzL0LegpBofqkI+Qg2mw1ZWVlwu90oKysDAN5JuzN9Dy6XXr0AmDFjxgXfsIceeggPPfRQt35dygEgvZHb7abTC9InvP3227jzzjvx9ddf45lnnuHxyV999RUmTZrUw6PrGS3DP4RqKULlGqHKz+jRo7FkyRKYzWY4HA4e3mC323kCb01NDQ+B2bdvH9RqNcRiMU6cOAGFQoGmpiY0NzfDbrfz0wORSASj0QilUol7770Xn332GcrLy1FfX4/KykpUVVWhvr4eNTU1MBgMCAwMhNPpRGhoKCwWC6KiovD666/jo48+4rX1gV93fePi4njpwzFjxmDp0qX44osvMHjwYJjNZpSVlSEsLAznzp1DU1MTVCoVjEYjSkpKUFtbyxMmlUol1Go1xowZg/Lychw8eBDDhg3DwIED8dvf/hY//PADSkpKEBsbC8YYnE4nLBYLNmzY4LWrvmHDBr5I8pxHtKzUYrFYUFhYiIiICB5Kc/bsWVRUVPCiIAaDATU1NZBIJHj99dcxatSoVs99oXwCzwn9E088wZtOCZ2dhfG0VR7TbrfjiSeeQHZ2NlJTU3lXZpFIhBkzZuDzzz9vlZfgueAsKSnB+PHjkZ6eDovFgpiYGNTV1fHdf+G1mkwmngQ8aNAgHnoml8tRUlKC0aNHe/WP6CrPcpkGgwFnz57lzcWExY2QHH3mzBl+vbA4EGr/t9W1WFgcyuVypKenY+DAgV0e64WIxWKMGjUKDocDwPlJf3FxMUJCQjBmzBg0NjZi165dXidGvUGXFwDFxcXIzMxEcXExzGYzQkNDMXr0aFx99dXtrsj6AsoBIL1NXl4etm/fzuOEAUCr1WLOnDmUv0B6nREjRvAdbk+vv/56qzCA/sJX+AcAHD16FMD50BSpVIrAwEA+IRRin4HzJwXz5s2DTqfDrl27YDQa+cR56dKlkEgk2LhxI3bu3MknbBUVFXC5XHz30WQy8QROk8kEnU6Hm266CTNnzsS9994Lt9vNE2gjIyMxatQo5OTkIDAwEBKJhPcj8Nwcq62tBWMMycnJqKioAGMMKpUKZrMZJpMJycnJOHz4MIxGI1JSUvDLL7/wMBGHw4HDhw/zuv6MMdTU1PB4b+FvsM1mQ2BgICZOnIgzZ87w3W4/Pz/cdtttePDBBwGAL5o8d9VlMhmee+45yGQy2O12HqsdExOD/Px8OJ1OKJVKMMZQVlaGs2fPYsuWLfxkQMhHyMvLg9VqRWJiImbNmsV3zYVd8rq6OoSEhPjMJ+gOQv19IcxEyB+orKyEVCrFL7/8gsOHD/PKW8Luf3R0NA+7ioqKgkqlQklJCXQ6HeLi4pCTk8OTxA8cOADgfPlKIanas6Oy0WjkJy5C87KuzJE8F2FisRgqlQpbtmyB3W7nJwyAd+nYvLw8XtBDWIgJDe9WrlzZKu6/pKQEdXV1/Oeyq0nbHaVQKHgZ3/z8fN5ZODAwEAEBATx/pjedAnR6AfDvf/8b77zzDg4ePIiwsDBER0fzjP6CggIoFArceeedeOqppygUh5BukJeXh7S0NCQlJWHevHm8glFmZibS0tJ6TclSQi6kr28OXYyW4R/CCYBnAqNYLG73VM9isfAa/MeOHUNwcDDvOLp161Y0NzfzSkB2ux1OpxMulwt2u51PhrVarVet9m3btgEAYmJiIJFIMHz4cBw5cgQTJkzAww8/zMO3Vq5c2SqJ0TMsY9CgQZgyZQp27NgBkUiEjIwMhIaGYsmSJbj//vvhdrvR3NwMp9MJPz8/VFdXo6GhAUajEQEBAXzybjKZeKOx2tpahIWFweFw8FCblrvdCoWCh/e0tavuOV5hEl1VVQWTyQSbzYagoCA0NDRApVIhLS0Nv/zyC2+MBpyfiMbExOD06dMICAhAXl4eAgMDIZVK4Xa7cezYMR4OpVKpLpjI2xVC5RuLxYLs7Gx+0rFu3Tqvnw3PeHmRSIQFCxbg22+/5bX0Q0JCcOrUKZSXl0Or1UKpVEImkyEwMBAKhQLJycnIz88HYwwBAQHQ6XRobGzkj8/OzoZCocD48eNRXFyMESNGdPp1epbLPHXqFGJjY3Hw4EGMGDHCa3EjJEcDwLlz5/Cb3/zG6+RDqJzUMhxJ+J4I71tUVBQyMjIuy+RbOKkQwsuEsWo0GtTU1PDO3b1BpxYAY8aMgVgsxqJFi5CWltYq4cRms2H//v3YvHkzxo0bh/fffx+33XZbtw6YkP7E7XZj+/btSEpKwoIFC/gvlJiYGCxYsACbN2/Gjh07kJycTOFApEcJSaIhISHQ6XTtTgoaGhou48h6D41Gw2vuCyE0HU1gFHZN586dyz/rCoXCKxxD2IWcMGECD0dwuVz47rvv+CT9tddeg7+/PzZs2AAAPDnWbDZDIpFAKpW2GZri2czspZde4rH0njHbX331FRoaGhAYGAiz2Yw33niD/1yUlJRALBbD398fERERqK+v5+FEv/nNb2AwGLBr1y6IxWKo1WoEBgZi0KBBkMlkaG5uRmVlJc932LdvH5577jn+c9aRBlTCJLqhoQFVVVV8QVJeXs6Tg+VyOe+bUFFRwd97xhjOnj2L+vp6LF68mOc+7Ny5E/X19bwrsU6n61Bpzs6SSqUYPXo0L8nqdDoxZsyYVt8/sVjMf1YSExMhl8t5H4mDBw9CJBKhtrYWDQ0NiIiI4IvFyspKJCUlee3oGwwGvvgqLy/nidAKhYLnaniWt2yZ1OuLr3KZWq0WNTU1yM3NhcFgaJUcbTKZeOl3X5WTWoYjlZSUoKmpCSEhITAajYiPj8e+ffsueT1+4bUpFArY7XbY7XaYTCYA58OEpFIpSktLeR+KntapBcCaNWtwww03tHm/XC7HjBkzMGPGDLz44ou8qgEhpGtKS0vR2NiIefPmweFwtEoKmzJlCtavX4/S0lJKbiY96q233uKTxf5c5vNSsVqtYIzxSTbwa4hEy3AMYSEAnA/Rcblc0Ol0cLvdfFItfK88d89dLpdXeI8w4W9JCKv56KOPUFJSwmO2hwwZgrvuugsAMG/ePJjNZvzzn//EwIEDERoaijNnzkAmk0Eul2Pw4MHQ6/Xw9/cHYwx+fn4wm82Qy+UwGo08hjo/Px/A+QnUunXreAiZMFbPqjMSiQQzZszgvx9bjl0qlWLUqFE4cuQIJBIJD+HRarV44okn8N///pefwggN2gQikQj+/v6IjIzEnXfeiY8//phXCvLz80NoaChqa2uh1+sxefJknk/QnacACoUCfn5+cLlcfJHW8vtXV1eHc+fOITw8HHV1dViyZEmruVhoaCjkcjlGjRoFt9uNiooKmEwmVFdX8+7KjDFIpVJ+OiCVSpGSkgK9Xg+tVovExER+agX4Tur1pWW5TOD89+3JJ5/Epk2b0NjY6BXGZTKZUFZWhqCgIOTm5vqsnGQymfjXc7vdOH78ODQajVeJ3KioKBw9erTTpwBCt+qORLQIycBCbwXg1xA/4f/tdnuvCQPq1AKgvcl/SyEhIb1mldNZlARMegvhD2tYWJjP+4XbhesI6Sn33HOPz/8mbbNYLMjKyrpggqKvajcOhwMTJkzAY489hv/85z84fvx4q3AMzxAdz3KXLWvGe06WPZNV2yMkmGq1WuTn5/OQEyFEp66uDvHx8RCJRHC73cjOzobL5eJJm0LHY+D85DonJ4cvUIRJoEqlwrBhwyASiSCRSLBs2TJ+UhIQENBmqcyWhNfkcrlQUVHBOw8HBATwxOW0tDSUl5dj5syZuOWWW1BXV4fq6mqo1WpMmDABAHDo0CFMmDCBV9QRSqrqdDrY7XaeyBwfH49ffvmlU6cAns26EhISsGbNGmRmZmLq1Kkdejzw6yTc4XAgICAAixcvhtls5qcjV111FcRiMSQSCVauXAm1Wo3HH38cLpcLkZGRUKlUGDhwIJxOJ6xWK+RyOQ8JEolEaGhoQFxcHNxuN4KCgnD06FGEhITwDtaeSb2+qgT5Kpcp7OiHh4cjNjYWZ8+ehUwm4ydQGo0GMTExGDNmDADwkw/Pykl33303D1VrampCU1MTJk+ejMrKSr5AnjRpEi9h25n3U+hW3ZEOw0IysM1m4ydwniF+ADBy5Mhec1rf5STgiooKbNmyBfn5+Twmcf78+V4d0PoqSgImvYWwS1dTU4OYmJhWO1o1NTVe1xHSm9TU1KCmpsarWyhwPkm4v5LJZFi1ahUYY/jyyy+hUChQVlaGadOm8YluS0L54quuugqfffaZV7UbobGhUCrRcwEgTMp0Oh1mzJiBO+6444JdbNva9ffkWdFo//79MJvNOHXqFM6dO8dDdNLT07F06VIsXrwYlZWVOHLkCGw2G0pKSmCxWBAQEMArEwHnQ1g0Gg2am5u9eiTcddddSEtLA3D+tOJiar273W7k5ubC6XSivr6ex8CrVCqUlpZi4MCBaGhoQHNzM2pra+F2u73GKpfLIZfLeXMtoZpOaGgoSkpKEBISAplMhpycHKjVaqxYsaLVoqzl99flcuGvf/0r7357MRVrhO+3QqFAYWEh8vPzW4WbSSQS3gvAs+5+cHAwVCoV6uvrIZVKeXEXYQ5ksVjgdruRkpLCu0E3NTVBr9dDp9PxpF6hR8TWrVtbTZhblstkjLX6OfYVyiUkxwNot3KS2+1GTU0NgoODERwcjOLiYv7YnJycVqVbO/J+CgnjZWVlHQozE05p2grxE6ot9QZdWgC8//77WLFiBV/xCtn6K1aswLp163DHHXeAMYbs7Gy++iGEdF5sbCxvkuOZAwCc/2W6Z88e6HQ6nw1gCOkpR44cwT333IO8vLxWkwCRSESnq/i142l0dDSPmw4KCvJ5bVNTEwBg1qxZiIuL86oh79ngSiwW85CFgQMH8klZc3MzgF9rxreMn+8soaJRQUEBzpw5A4fDgTNnzuD111/nCY42mw3PPfccGGMIDAzEhAkTYLVaUVtbC6lUioSEBAwaNAjZ2dmw2WxQKBRITU3F3r17IZVKERMTA4VCwZM3uyOUpqSkBI2NjdDpdDCZTLzDcXBwMM6cOYOwsDAMHDgQW7ZsgcFg4LvZLb83hYWFvEOyUOO9vr4eVqsV0dHR2LZtG4YPH+5zUeaLZ/fbrlasESbzgYGBqK6uhkgkwr/+9a92FxMtu1J7ltYUTgGys7Nx7tw53iisoKAAEomE55+UlZVh+fLlEIlEqK+vR0FBAdRqNQ4ePNgqLLVluUzg/KnEI488wqs02Ww2HDx4sNOvHzgfJme32+Hv74+cnBxUVlbi6NGjcDqdOHbsmFfp1gt9TzxzFRISEtDY2IiCgoJ2TwE6Ey7UG3R6AfDdd9/hkUcewWOPPYY//elPPP6ssrISr7/+Ou655x4MGDAA77//PoYMGUILAEIuglgsxpw5c5CWlobNmzdjypQpvArQnj17kJ+fj/nz5/eaI0VCgPPNjJKSkrB+/XqEh4d3axz0lcBz51VoxCSUZvR1bXFxMWw2G06ePIkJEyZ4VbvxbHDldDp5ae7w8HDodDro9XqvsoqeVXS6mqQqlUqxZMkSfPTRRxg8eDBMJhMCAwMRGxuLhQsXQiQSwc/PD++99x5f7CkUCt6oS3idjz76KL788kt8+eWXvLNsTEwMhg0bhvLycgBAeXk5DAZDm4sjwYVOLoTd/8DAQD6xLy0thb+/P9xuN+rr67F3715ERkbi9OnTvMuwzWZDaWkpH7NQXlOv1/NJcFJSEj+NHTlyJGJiYvjJglgshsvlwp49ewAA06dP9xqXEKYlk8lQVlYGg8GA8PDwLsWqCx1/7XY7hg8fjrKysjb/NrTsSg2cD2VSqVSorq5GeHg4n78ZjUa43W5cffXVCAoKgkQiwT333IPKykrk5OTgq6++4uVGbTYbBg0ahKamJr4B4Pn598xPAc6fiAgnO3a7vdUOuZDX8cQTT+DVV19t8/ULG9FarZafupjNZiQnJ+Oee+7Btm3bsGfPHshksg79vWyZqxAbG4uTJ0+2mUjsK1yot//e6/QC4LXXXsNf/vIXvPjii163R0ZG4s0334S/vz9mzZqFiIgIvPzyy902UEL6q5SUFMyfPx/bt2/nx6TA+V/WVAKU9EZFRUX4z3/+061VUK4kLTueCrXOfZUIFCYisbGxF9wdFq4dMGAADh48iNGjR+PJJ5/Eu+++C7PZ7FVF52KTVOvq6nid/xMnTiA5ORkmkwlmsxmDBg3i4RISiQRPP/00GGO49dZbvfIRhF5CdXV1GDNmDEpLS3nXWqEbrkwmQ3Fx8UWHFwtdiYODg3ld+7q6Ol79JzQ0FAqFAtdccw0cDgeam5tx7NgxHtcvTPz0ej1qamogEon4pN1ms8HpdMJoNOLHH3/ETTfdhLi4OK+ynC0JFXOEJGrPrsRCs7SOEiafKpUKTU1NkMvlGDFiBIqKipCbm8sbxXny1ZXa8xTA6XTyfgxCcrZnSLTZbIafnx8YYzh48CBeffVVfPvtt5DL5UhMTIREIsGmTZugVqtblY+9FISmcG63G2fOnAEA1NfX4/Tp0/jmm28A/FqJpyPPVVxc7FWpSKfT8WZovib3LcOFrFYrlEplN7/K7tXpBcDRo0fx8ccft3n/woUL8dJLL2H37t19NiyBkoBJb5OSkoLk5GTqBEz6hN/85jfIycmhBYAPnjuvQvy7UK1EiCP3vFaYFCcmJiIyMrLNeuYtQxby8vKQk5ODdevW8WopF6qi05nXkJ6ejujoaK/d4+joaL6waKlluElcXBzKy8tx6NAhHlPPGENKSgrvjhwUFASr1YrRo0dj4cKFXY7/F97HwYMHY8iQITwMTXj/r7rqKkgkEvj5+eGtt94CAN5QS6FQQKPRYNiwYQgLC8PRo0f5JNdgMPBJXmhoKGw2G9/RrqmpwaFDh3x2vvY81Tl27BgGDBjA79PpdIiIiEBubm6HTwGE9zYmJgYlJSXQarUQi8W444478PDDD4Mx5rWAEn4G2+pKLZVKedMqYWLtcrmQnZ0NkUgEsViMTz/9FIcPH0Z1dTV0Oh2qqqpQV1cHf39/mEwm3HjjjTwZfMCAAZBIJFi1ahUA8ITs7iQWixEREQG3281PLlqWSu1oeFFhYSEMBgOGDh3KJ/qNjY28G3fL0zNf4ULV1dW9vu9Jpz/5brebN2bwRajL2lcn/wAlAZPeSUgEFLjdbhQXF9OCgPQ669atwz333IPjx49j2LBhrf5m/Pa3v+2hkfU8z51XXx1PPUMMGhsbUVFRgYkTJ0IkEmHatGnYtGmTzzCE4uJinD59GhMmTOBlFdPS0jBr1ixYLBYA6HIVHV+voby8HPPnz8emTZv4a5g2bRpPMvacAzDGsG/fPvzhD39AeXk5z10SKstIJBLk5+fzhEmRSMSTgLVaLbRa7UWdWAgT5KFDh/KEUZfLheDgYNTU1MDhcECr1aKhoaFViUrPXXmDwcC7sVutVjQ0NCAkJAQ1NTU8bl6r1cJoNMJsNrc5HmGCqVAo0NTUhGHDhvF68UIZy2PHjvEqb3a7Ha+++ioyMzNbLSiEybxCoUBVVRVkMhkcDgeMRiOSk5MRGBiImpoar7mMEC5jNBrb7EotXNdyYi1UEVqyZAksFguOHj2KpKQkvPXWW2hoaOAJvkqlEhKJBAaD4bKVvRR+nlsmDHuGyV0IYwyZmZm8UpHQRTs/Px8OhwNmsxm7d+/2WuT6ChcScimE+ysrK6FUKpGVlYVBgwb1iiqZnf70Dx06FP/973/x+OOP+7z/66+/xtChQy96YISQtuXl5WH79u38jxFwvpnKnDlzKCSI9Lh9+/Zhz549+P7771vd15+TgD2r5xiNRthsNphMJhiNRh5aINRQd7vdqK6uhkqlgl6vB2MMiYmJiI6ORk5ODqZPn45nnnkGMpmMJ2sK5RgZYxg1ahSOHTvGu9aKRKKLrqLj+RqCgoLg7+8Ps9mMsWPHYtmyZXzXPj09HQsXLuSlLdPT01stGIDzmxoajQZTpkxBTU0NTp8+3err2Ww2Hg7TlRMLYYLMGENeXh7i4uL4ZHjy5MnIy8tDeXk5goODW5WoFHbG/fz8oNPpcOzYMQQEBGDq1KmoqanBwYMHMW7cOBw/fhzHjx9HUFAQZsyYgdzcXJw9exbh4eE+x5OZmQmNRgONRgO5XI7a2lrYbDaIRKJWu/BOpxMvvfQSzyEAvBtuCQnZer0eVVVVCA0NRV1dHbKysrBx40b4+/vDbrfzyajwvgslQjvSldpzYi1UEYqIiEBgYCCcTidqa2uRkJCAkJAQnD59GmPGjMHSpUtRWVmJn376qVOlN3saYwxGo5GX6AXOV0Cqrq7mnaqLi4v57zBfjc2EcKGmpia43W6UlJTAZrPxxHKhDG1P6/QC4KGHHsKDDz4IuVyO++67j/9gOJ1OfPTRR/i///s/vP/++90+UELIeXl5eUhLS0NSUhLmzZvHk4IzMzORlpZGeQGkxz3yyCNYuHAhVq1a5XMS1F95TtY8S3nW1NTAZrNBLpdDqVTC5XKhpKQEVqsVY8eO5THowi775s2bvU4Bdu3ahaamJgwfPpyXZhQSfv/xj3+gqanpgkm0nX0NBoMB69evbzO8yOl0oqioCEajEf/85z8xfvx4rwXDrFmzsHXrVkRFRaGhoQErV67En//8ZyQnJ0Or1UIikfDqMEDXTyyE8VZVVcFqtaK8vBwRERFe1wgLrpYlKquqqhAbGwuVSsU71DY1NeGPf/wjDhw44FWSMisrC4GBgVAoFK12gAUWiwWZmZlITU1FXFwczpw5g+joaBw6dAgOhwN+fn5eu/AOhwNZWVlISEjwGqtnwy2Xy4WamhredyExMRE5OTlITk7GjTfeyO9ruRMvLEC60pXak0QiQV1dHYYOHco7JwtEIhGsVivq6uou+SmAsMuu1Wov6nnEYjHuvvtu1NXV8YpBJ06cgFqtRnNzMwIDAxEREcF/1n01NhOJRNBoNKipqeE5HsJpj0aj4b8DelqnP0333HMPjh07hocffhgrV67kxyAFBQUwmUx45JFHsGjRou4eJyEE54+Ht2/fjqSkJK+yoDExMViwYAE2b96MHTt2IDk5mcKBSI+pr6/H448/TpP/FqRSKZYuXYrGxkZeyjMhIQFOpxORkZGorKxEQkICxGIxcnNzIZPJMHDgQJw6dcqrzr5SqfQ6KfjXv/6FwMBAJCQk4MSJE17X6nQ65OTkdFuPHuE1CA2mhFCXluFFJSUlvCqNEDctLBgYY3y3XyQSwWazYdiwYXz3OiYmBlKptFtOLKRSKaZPn46tW7ciIiKCv8eeE0WZTMbzARITE/kJgWeJSqvViq+//pqXOBeaShmNRlRXV/NrhHh7YQdYSBgVmrk1NTUhOjoagwYNwujRo7F06VKcPXsWhYWFCA0N5bvwdrsddXV1MJvNXomnvhpuCaEtQgKskPz67bffIj8/H263m09mLwWh9n/L/hR79+6F3W6HyWRq1QukOwknZ3a73es97yphYeRyudDQ0AC73Y7U1FScOnUKycnJaGho4CVBPU+NhJ8hocO0RCLB8ePHERMTg6qqKigUCrjdbqhUqna7JV8uXQoAfOONNzBv3jxs2rSJZ1tPnToVd9xxByZOnNitAySE/Kq0tBSNjY2YN28eHA4H79j59NNPQyaTYcqUKVi/fj1KS0tb1WAm5HL5/e9/j127dvlMBu3vNBoNLxEpk8lQV1eHkJAQjBgxAnPmzIFUKkVFRQXCw8OhVCqRm5sLi8WC2tpavP766wgKCuJNmVwuF4qKilBdXY2AgAAcPXrU69rQ0FC4XC5ej747X4NGo4HdbufNmDwn656JzoMHD8b48ePhcrkwb948mM1m6PV6qNVq3HLLLfjf//4HmUwGPz8/xMfHIzs722c1pK5ijOHo0aMIDQ3F0KFD4XA4+CJDmCQyxnDy5EnYbDbU1tYiOjqaN2UTXldubi7MZjMCAgLwww8/8J4G+/fvR01NDfz8/OB2u3ncvLADLPQQsFqtaG5uRnBwMNRqNRobGxEUFITIyEgkJyfjzJkzvBMycH4S6XQ6ER0djYqKCr642LNnD0JCQnjJzszMTB7e1TL5dcmSJXyh5hnS0xkX2lkXiUQYNWoU/1pCf4prr70Wu3btglgsRnNzc6eqGnWWkFPRVt+GrmorvCc6OhoZGRm874FKpeLhQsLjqqqqeL8D4bMSFhYGk8nEE+ALCgqQmprq9TVdLhfWrFnD/99XY8Du0uVOwBMnTrxiJ/tUBYj0VsIOg5Ac1pJwu3AdIT0hKSkJK1euxJ49ezB8+PBWScCPPPJID42sd7FarXC73bxu+bRp0/DFF19g69atuP7661FWVsYnlXl5eYiNjcXtt98Os9kMmUwGiUSCPXv2tHmtUJP/ySef5Mmvl4OQ6BwfHw+RSMR7mZjNZgQGBuLMmTMYNmwYb5hkt9tRVVXFi4i0rIbUnWPxTLYWJomeJRzLy8tbTSCFJOakpCSkpKQgODiYT24DAwMREBAAo9EIsVjMT0SEkpOlpaXQarVobGyEy+VCVFQUBgwYgN27d2PYsGH8dXtW3gHAy3kOHDiQhzDZ7XYwxnj8uGfJ0Pa65V5M9SQh3MhzbC0pFAr+teRyOQIDA5GXl4eQkBC+MLpUtfGFnAqtVotp06YhOzsb5eXlF3XiJfSUOHnyJPbs2eNVDUj4nP773/9GXl4edDod/P39va5xu92w2+2ora3l1aKEMqpCTo9KpUJGRkaPhut2agFQWlraqeo+FRUViI6O7vSgehpVASK9lbDbVlNTg5iYmFaNb4RmNMJ1hPSEdevWITAwELt378bu3bu97hOJRLQAAHhISGJiIp+sJCYmQi6X48CBA3jllVeQlpbGN6KGDBnC6+wLn2/PajxtXXu5S7F6lggVdpyFDsQZGRk81l6v13vlEPzjH/+ASqXCVVddhYMHD3bqxMJut7c6DfU1FrfbDZ1OB61Wi+HDh+P+++8HAHz00UcoKSlBWVkZqqurERgY6DWBFBYRgwcPhlqtxo033ojt27fzCa3QSAz4tZJOVVUVH5vQ6Vcmk8FisaCsrAz5+floamqCQqFAVlaWV+WdxsZG2Gw2hIWF8aoywunO4MGD4XK54HA4ulQy1BexWIypU6fiqaee8mq21TLcqKMnM3q9Hm63my+2hLj3zjaf82zuJiQq+/paDofD5wKv5XOtWrWKlyC90AavcIrlmRQuhHwJzePKy8sxZswYVFRUwOl08kWjy+XiJ3QhISEoLy+HRqOBWCzmDcXi4+N5X4+eqprZqQXA+PHj8dvf/hb33nsvrrrqKp/XNDU1IS0tDX//+99x//33449//GO3DJQQAsTGxkKr1SIzM9MrBwAAPx7W6XR9ugwv6fuKiop6egi9njApjI2NbbUr6nA4UFNTwzvnAr9Wotm3bx+ee+45AMD69et5NR5f115ss6+uaLkoAdAqIXn06NG8NruvHILuOrFoayyeky8AXg2xPBt/hYaG+lzQJCQkQK1Wo7q6GpMnTwZjDGPHjuVf1+12w+Fw8LCd7OxsWK1WREdHY8yYMVi+fDkKCwsBgFfjEcJ0RCIRSktLIZfLeR15rVYLu93Ow2sqKiogFouxatUqFBQUIC0tDU1NTcjKysLAgQM7/P54TopbEnonCItNoSPyhU5mhFODuXPn8rCjOXPmICYmBvv27evWsEAh9n/27Nmora3lCzy1Wo3S0tKLWhQJyeOe1YCEk5UNGzYgNzcXUqkUAwcO5FW8hNK2jDE0NTVBIpGguroaEokEI0eO5BXQxGIxqqqqMGrUKF4xqyd0agGQl5eHl156CXPnzoWfnx/GjRuHqKgoKBQK6PV6nDx5EidOnMC4cePw+uuv47rrrrtU4yakXxJ+maalpWHz5s2YMmUKrwK0Z88e5OfnY/78+ZQATHoVl8uFY8eOIS4urtuSUfsyYZIklUp58qBEIkF5eTkvmfjGG2/wGuTA+c++v7+/VxnVltV4hETLltd2td7/hXju0Aqvy7NEqBCKKHQgFhKSR48ezWuz+8oh6A6+xtJygbRr1y4A8GpoBpxfnAkhLG0tIuLi4nD8+HFYLJZWu+Iulwt+fn68x0FZWRkkEgmam5uhVCqxYcMGHuJjNpvhdDpRX18PrVbLd93lcjmqqqr4brrwfTSbzbzc5oEDBxAXFweFQoH8/HyoVCoeanOxGhsbUVlZiauuugomk6nVwqgtwqJn6tSp+Oqrr/j75dkjorsIoVtTp07F1q1b+deKi4tDbm5um6cGHSGVSrF48WJUV1fznxuJRIJly5ahoqICTU1NiImJgUQiaRVWJjRPczqdvFJTy00RqVSK+vr6Hi2L3KnfCkFBQXjjjTfw4osvYtu2bbyNt8ViQUhICO68807MmTMHw4YNu1TjJaTfS0lJwfz587F9+3asX7+e367T6agEKOkVHnvsMQwfPhxLly6Fy+XCtGnTsH//fvj7++Pbb7/FjBkzenqIPUpIzHU6nV7dVTdu3AiHw4HU1FTeUVWYzAmTD51Oxyf0LavxtJyoeF57uV7XhUqEzpw5E3/84x8hlUovaoLWlbG0XCDV1dVBKpViwYIF2LRpE9+5VavVKC8vR319PTIyMlotaDzj9svLy/H3v/8dIpHIq8Ot1WqF1WqFQqGAw+FAYGAgzGYzj08Xi8VQKpXIyMhAUVER7HY7GhsbUVpaCoVCgebmZtjtdpw6dYonFjPGcODAAURERKChoQGvv/46Ro0aBb1eD7vdjqioKFRWVvp8X4Wd7I40oXK73aipqYFareZ9JRQKBV9ghISE+Dw9EMKX4uPj+fvlGTYTFBTUZifrzhJ2/5VKpc8FnkKhQE1NzUV9Lc9qQAB4/4Mff/wRQ4YMQWVlpdepg3AK4Nk8beTIkT4rICkUCtx3332X/TPqqUtfVaFQ4Pe//z1+//vfd/d4CCEdkJKSguTkZJSWlsJoNCIgIAAA0NzcjOLiYuoKTHrUV199hbvuugsA8M0336C4uBinTp3CZ599hmeeeQZ79+7t4RH2LKlUilGjRsFkMnl1V/UMg/Hz88N7773nNflouUveshpPe9dertfVkRKhl2PC09ZYhLKejDFs2bIFjDE+gRQ6+A4ZMgR6/f9r777Do6qzx4+/p2TSew/pgUBooUsLTQGxrI0FXEVFsKysDVfXsqyIBXVXlt0VCxbQr0rRFStKUSIo1UBoCS2kERImfTIJmUlm7u8PfnM3IQGCJJlAzut58jzkzp2ZM5OQuedTzinnwIEDREdHU1NT0yihef/999V1+1artcksi6IoFBcXU1NTg4uLizobUFdXx+bNm4mMjKSwsBAXFxe1XKqPjw+VlZVUVFRgtVopLS3F1dWVgwcPotFoMBgM6PV6wsLCGDBgAGazGW9vb6666iq2bdtGQEAACQkJmM1mjh49SmpqaqNmVY79Bi1pQlVZWUltbS2DBg3CZDJRW1uLu7u7un79bDXsHSPfp06dYunSpU3KggJs375drYB0MR1xHUm0xWJh6dKljRI8RywXUvq0YXO1cznXjJBjFsDX11f9fQgMDGy2ko/j/yjA888/32ivlM1mUxu/jR49ukXx/xYX/L+wpRf9n3/++QUHI4RoOa1WS2xsLJmZmXz11VfSFVh0GCUlJWqzpTVr1vD73/+exMREZs6cyb///W8nR9cxuLm5qRVTHN1VG160t+XoeFs6X4nQjhKLo9FWwxmC/Px84PTFYFJSEp6entx9993U1dU1SiLuvvtuLBYLgwcP5qmnnlK77To4lvHodDoURWHYsGFs2rQJNzc3NBoNcXFx2Gw2+vfvT1hYmFpsxGw2U1ZWhr+/P4GBgQQFBWEymbDb7Xh6eqLRaOjZsycajQY/Pz+1HKXVaiUwMFDdMNywCZnJZEKr1eLn50dMTAxxcXFMnz692Z+HwWDgmWee4csvv1QrEGVkZFBQUEBwcLBapclRw/7MvSVarZbw8PBGJUgd+xYcez5qa2v56quvcHV1vaiOuHq9nv79+1NXV6f+PBoupWlYkvV8zmyudrakwbEx+GzLyhwVrPr06fObXlN7u+AEoDNUxZEyoOJSIV2BRUcUGhpKRkYG4eHhfP/992p3+Jqamjata32pOLMiSWvR6XSNquB0dGfuIWhPZ84Q5Ofnk52dTVBQEAMHDuSmm27i+++/p6Kigq5duzZKIhwlL4Emm5UdZVgVRcHFxQWdToebmxtarRZXV1e1NKgjCSkrK1PXrDtG6h37ClxdXQkICOD48eOYzWZcXFzIyMhQl4y5u7tz8OBBvL291Yvxhk3I7HY72dnZFBYW4ubmRnx8PCEhIcyZM4f+/fvzzDPPNHlfDh8+rD7Xnj17OHXqFKWlpdTW1mIwGNTnOdveEr1e36gEqd1u5/Dhw5w6dYr4+HhqamqwWCx06dKlRSPu5+Lm5taoBGnD/0vn2vui0+nUikebN29uUu2oufr8cPpnazKZGs0Inbm8x5F0tOXIfWu54ARg6dKlbRFHhyJlQMWl4GxdgSMiIhg6dCh5eXksX76cv/3tb05bYyg6pxkzZjBlyhTCw8PRaDSMHz8eOD3936NHDydHJy5H5eXlZGVlcezYsRb/jjlmCCwWCyUlJXh4eODt7Y23tzdDhw7lyJEjaiWlliorKyM3N1cdgQ4PDycjI0O9GPb39+fEiRP4+fk1qpZTW1uLzWZTNwY7RtivuOIKdemOo1OwY8nY+PHjsVqtVFdXc+DAAeD0chRHE7Lc3FyMRiOurq64urpSUVHBLbfcwmeffdbshbej30G3bt3Uak2KolBWVgZAv3790Ol0uLu7t+gzxbEnwNXVldTUVGJjY8nNzVVnF8xmc5MR95YuxWktDasdabXac9bn12q1zJgxQ1361XDfjYPBYLhklt9eGlEKIZpwdAVOSUmhrq6OefPmMXv2bBYuXMgHH3xAUVERmzZtYv78+WRmZjo7XNGJzJs3j3fffZd7772XX375BVdXV+D0yNuTTz7p5OjE5UZRFLKzszGbzaSmpl7wxs+srCy1WZOj8o6jdKmjYyucHlVOTU0968yNo6Ow2WxW+x24u7tTXFyMXq9XR+5tNhuFhYVqBRtALRsZGBiIu7s7paWlKIqCu7s7iYmJWCwWtbust7e32mwrLCwMFxcXLBYLZrMZs9msJgj79u1TS82GhISQk5NDXFwcPj4+zS51ycrKoqioiH/+859cffXV6ibYwMBAtfeAo9lXSzhK3UZGRlJQUMDGjRsxmUz4+fmp6+YdI+6O9y8nJ+e8S3Fag2MWLjk5GbPZrJbjjYmJaVQi9ky+vr6Eh4cTHh6uJosNv1r63nQEMiwoxCXqzK7AxcXFHDhwgKFDhzJt2jR8fX159tln8fT0bLXlQHa7Xd147O3tLZuNxVlNnjy5ybE777zTCZEIZ2jP5T2OC3jHheaFNJxq2PDp+PHjauWdEydOqJVrzqzVrtPpmDt3bpOlVo7mUI5GUFVVVWzbto36+np1JNxRG99RBtTDw4Pjx49TXV2NVqvF09MTq9VKVlYWbm5umM1mtWJSbW0tubm5BAYGqstRqqqq1M22DZuQ1dXVUVdXR2hoKLGxsQAcOHCA7OxsYmNj2bt3b6P36Wzr2+12O3q9nh49etC7d2+Ki4tb/L46Rvvj4+OJiIjg448/brJcycfHRx1xd/wcm1uKc66eBb+VI+Hw8fFRyxP7+/vTpUsXp9Tnd1RqupBeDhdDEgAhLlENuwJHRETQrVs3Ro4cqS4Hys/Px8fHhylTprBt2zbWrVtH9+7df9MFu91u58cff2TdunVYrVb1D7TBYGDChAmMGzdOEgHRyA8//MAPP/yA0Whssk72/fffd1JU4nLjuHD18fEhISGB8PDwC2qA5igXWlZWplbeOXLkCH//+9/Vevct6RwLp2vnK4pC9+7dqaur48SJE+j1enr27MnevXvx9/cnJSWF6upqvv32WyoqKnj//ffVErk2m42cnBzMZjN1dXXk5+eza9cuKisrMZvNeHl5NerI62giVlVVRV1dHf379wdObyA3Go24u7sTGBiIi4sLiqJgMBj45ptv0Ov1aglSx/Kms61vVxRFrVjkKEvbkvf12LFjjUb7Y2NjWb58OdHR0VRXVwONm7IdPXpU/TlqNJpGS3Fas5Fdw6VidrudkydP4ubmphbRaKueBefTsFKTo5dDWzfwkwRAiEtUw67AQ4cOpaKigsmTJ1NXV8eLL77I/v37GT58ODExMej1et577z3y8vLU0aCWyszM5MMPP2Tr1q0EBgbi5eVFdnY2fn5+mM1mXnjhBX744QfuuOMO2XAsAHjuueeYP38+gwYNUvcBiLbjzM20zpaVlUVBQQGxsbFNLt4co9vnen/0ej133XUX69atIzAwkODgYHV2c/r06Wg0mhaVLnUs/wHo1q0bhw8fRqfTodVq8fLywmAwqBtpDQYDfn5++Pr6cuWVV5Kamkr37t3VpT9HjhyhrKyMsrIyAgICKC0tVevwO+rNBwUFqXsYHEtPvLy8ANTGYX5+fo062VosFr7//nusVivu7u6Nqtica337kCFDeOihh/D09OT1118/789EURQ2b96Mj4+P2swuJyeHsLAwioqKqK2tVZcqOZqyffrpp9TU1BATE8OBAwcaLcVpbjan4c/0zApMx44dIyYmptm4HEvFHE3gHMnIoUOHqK2t/U09CxzPGR8f/5sbHdbW1qqbowsKCtTSq21JEgAhLlENuwLn5eVRWVmJr68v+fn57N+/n9LSUq666iq0Wq26TMixbKilMjMzWbFiBQUFBUyePJmUlBQ+/PBDfHx8cHFx4eGHH+brr79m586d/P3vf+faa68lNDQUT09Pqqur8fb2xtfXV5YKdTJvvfUWy5Ytc1qL+8vJpVbZpz05uv126dJF/fuSkJBAZGTkBc0ClJSUqKU0XV1d6d69O2azmZqaGvXi83xlWbVarbp+Pzc3l8LCQqqrq9FoNGzcuBGbzYZWq2X37t1oNBp1hDcjIwM/Pz/8/PzUDbcBAQG4uLhQU1PD3r17sdvtBAcHY7FY8PX1paCgoNEmWcfGWZPJxIIFC9i7dy/l5eX07t270QWs47Ng+/bt9O7dmxkzZjRKbHx9fTEYDGftK9FSju7Fjot5RVGoqqoiODiY7du3U19fj4uLS6NqRkeOHGlUOafhUpyW/hwdiYbZbG62I3LDpWLHjx/HaDRiNpvRaDQUFhZis9kwGAy899576HQ6PD09z5sAKIrCsWPHOHDgAEePHuWWW245Z7J45v9nq9Wqjv47NkebTCZ1ZqItSQIgxCXM0RV4+fLl7N69m2effRYfHx9GjRrFhAkT6N69Ozk5ORw5coSKigq1Ydi5ONb5l5eXs3TpUtzd3fH39+e6667js88+Iycnh169euHh4cHzzz+Pt7c3mZmZ7N27l2+++YaQkBDq6+vVzWMJCQl069ZN+hJ0IlarleHDhzs7DHGZO1tTpjFjxvDRRx+1aC9AwyVEjou933LxmZubS3x8PJMmTWL16tVYrVbq6+uJiYnh2LFjWCwWPDw8GDBgAFqtliFDhvD444/z4YcfqqP0p06dwmg0EhwcTHFxMTqdjtLSUsLDw9USzxUVFXh7ezfaJNtw42x9fb3aZTozM7NR7I4kRaPR4OXl1aSEaWtwrP0fPXq0WvKzurqaadOmcerUKQ4cOEB+fr5aUchRzchmszFmzBiWL18ONF2K05I9HeXl5eoFfn5+fqOk7cylYmFhYRw5coRu3brRs2dP9u3bR25uLv3791cb153ZjO9sz9mwhKhjedaFKC8vV0uvVlRUNOnl0FYkARDiEpeUlMTf/vY35s+fj6enJ1OmTCEmJoZDhw7x73//m/Lycvbv34/ZbGbp0qUMGjSI5OTkZkflMzMzWbt2LUeOHGH//v3qVDOgfkCNGDGC4uJidu3aRUlJCSNHjiQuLg6DwUBeXh4ajYZ+/frh7e1NQkICRqOR+vp66UvQicyaNYtPPvmEuXPnOjsUcZlyjP433LQKUFhY2Gjz7vku4B2Vb1599VWWL1/O5s2bm734PNcmVEcsERERjZae6PV6IiIiMJlM5Obm4u7urq7jP3r0KBUVFcyYMYOTJ09SX1/PgQMH8PX1JSkpiZ07d1JRUYHFYlHX0fv6+mI2m4mJiWlUnajhxtm8vLwmXaYddDodd999d6MGWW2xfMxisVBeXt6oE7Bjz5rdbleXVbm4uKDVasnMzCQ8PFz9OVoslkZLcVpSitWRePj4+BAfH09FRQVZWVlqknTmUrHY2FiKioqIjY3Fx8eHxMREjh07Rn19vdos7nyzPo7ndLw2i8VCXl4eQUFBLV726Ji1cPQtyM3NpW/fvmovh7ashCQJQDOkEZi41Oj1em699VZWrVrFtm3byM7OZuPGjYSGhhIcHExISAhhYWFkZGSwefNmevXq1WhU3rHJ9/PPP8fPzw9XV1cmTpxIdnY2iYmJ/Pe//6WmpobS0lJuu+02Nm3aRGFhISUlJYSEhFBUVMTYsWPZsGEDAQEBDBkyhJCQEIqLixk4cCDFxcV07dr1ojYii0tHbW0tS5YsYcOGDfTt2xcXF5dGty9cuNBJkYnLhWPzbsNOvgDvvvuu2mzOsXn3bEsymksiznbxea4LujNjcVz4AqSnp6vnOJ7TsUwlNTWVGTNm4O3tTXFxMVarlT59+uDt7Y1er6e8vLxRxR83NzdcXFwoKirCzc2Nn376CaDRxtmtW7fywgsvsGDBgibXMDqdjrCwsDYtVekYADpbJ+Cqqirsdrv6GXDm5uP09HRcXV3x9vZm2bJl6nt3vusxx+h/nz591I7IGRkZlJeXN1kq5vgZREdHU15eTnR0tNpArbmlQ+d7zp49e5KRkYGvry9VVVWUl5cTEBDQosfIysrCaDSi0+nU+1dUVKi9HNqyH4IkAM2QRmDiUuRYDvTdd9/xwQcf4OnpiVarxWq1otPpGDFiBCNHjuTHH39k9+7d1NbW8vbbbzNu3DiOHj3KunXr8PDwICsri9DQUCZNmoTVamXKlCns27dPHXn65JNP8PX15a677mLhwoVs3ryZ4uJifHx8OHjwIEOGDGH16tXEx8eTnZ3NH//4Rw4dOkRUVBRlZWW/aSOyuLTs3buXfv36AbB///5Gt8mGYNEazuzkW1NTA6Au3wDOu3n3bBfuu3btUteBny+JaC6WqqoqdfTYUZlHURTGjBnD5MmT2bp1a6OSpY6RZHd3d7XaTlVVFbW1tYSGhlJaWkplZSWenp7qwEpYWBg5OTno9fpmN862hTNnC842Qt6wO6/jYt6xh8CR3DQspVpZWdmin+PZnu9s5TwdF/RnLhUrKyvDbrdz22238corr1BeXo6vry9+fn7n7AR8vud0bNTOzc3F39//vH/rFEXhp59+oqamBjc3Nzw8PHBxceHw4cNoNBp1RqCtZgEkARDiMpKUlISrqyt5eXlce+21REVF8fDDD+Pl5cXNN9/MsWPHOHbsGOnp6SQnJ3PgwAG+/vprxowZQ5cuXZg+fTqrV6/m8OHDPP/889hsNvbt28c999zDggULcHd359dffyU5OZktW7ZQUVGhVnEoKSlBr9cTGxvLyZMnMZvNFBQUsGHDBo4ePYrNZiMvL48BAwZIAnCZc1TYEGfnuJiyWq2tWtu8M3FUwXFsWgXU5Rst0dyFe8MRa4PB0KIKQM3F4ojBUZlHr9c3qo7jKFm6adMmtWmYxWJh165daj8BR88Ax+MGBgbSt29fjh07houLC0FBQfz0009UVlai0WjUvQstrV7Tmi6mTv/F/hwdm3t79eqlXnRrNBr8/PyorKzks88+U2d5TCYTBw8eZPjw4QQEBKDRaDh06BBJSUnq78B///vf8y5VPdtzNpx5ON8sgM1mU6sS1dXVUVRURHBwMEajEZvNRkJCAqNHj1ZntFqbzMMLcZmprq7Gz8+P0aNHo9VqsVgsxMTEsG/fPv74xz+yd+9ekpOTGTlyJHV1ddTW1rJjxw727t3L119/jcViYeDAgQQGBgJQWlrKiRMniI2NJSQkhKNHj/Lpp5/yzTff4O3tzV133UVMTAzx8fFcffXV/P73v6dXr14cO3YMg8HAxIkTSUlJ4ZprrsHT05PU1FTpTCwuyBtvvEFcXBxubm4MHDiQzZs3n/P8xYsXk5SUhLu7O927d+fDDz9scs5///tfevbsiaurKz179mT16tVtFf5v4kgQ5s2bJxWA2tCZnV0bjliHh4c32ShbXl7Or7/+yrFjxy74uRzVehqWLC0oKKCyspJ+/foxYMAABgwYQL9+/fDy8sLT0xNXV1cMBgMajYaysjKOHDmC3W6ntraW0tJSYmJiGl2AOh6zLZeOnPmafuv7caGa+z/RsJGbi4sLVVVVVFVVqWVG3dzc2Lt3L6WlpepSsaysLH788Ucef/xxsrOzyc7OZteuXeTl5VFdXc2ePXuor68/axxnPqfZbMZqtWK1WnFxcVErQZ0vCXNUV+rWrRtRUVGEh4czbNgwunbtipubG/37929Sqak1yQyAEJeZhg3CLBYLY8aM4cknn+T1118nMDCQqKgoLBYL3333HQEBAUyaNAlvb29Wr17Nr7/+CsC8efO47777eO+99xg5ciSbN29Wq1yEhIRQVVVFQEAAnp6ebNy4UW1+4+LiwocffkhhYaH6h8wxclVdXU1KSgrBwcGyF6AT2LlzJ59++il5eXlNpu4///zzFj/OypUreeSRR3jjjTcYMWIEb7/9NpMmTSIjI4Po6Ogm57/55ps89dRTvPPOOwwePJgdO3Zwzz334O/vz/XXXw/A1q1bmTp1Ks8//zw33XQTq1evZsqUKfz8889cccUVF/fCxSVLp9MxZsyYs5ZdbVhH3lG7/0I2e+bm5nL11Ver3XQTEhLo0qULe/bsoW/fvmrZR5vNRlRUFIMGDeJPf/oTr7/+Otu2bQNg4MCBPPLII3z99dfqAI7j/5dj74K/v7/6mG255O7M96O9Otg25FjG1bDfgSO2wsJCoqOj6du3LzNmzMBut1NTU0OfPn3UruSOPSOO0qEDBgwgPj6e3NzcZv++NPecjueC03s+HGVez9fMKysri7KyMrp3786+ffuA05/fDTckt0WlJgdJAIS4zJzZIAxg9+7dmM1mFi1axI8//siCBQuIjIykZ8+e+Pr6Mm7cOL7++mvc3NwICgpi6dKlvPXWWwCEhISQmJiIoih4eHgQGRnJLbfcwrJly9SW9gkJCRw5coSqqip1zbeHhwfV1dX85z//wcfHh6FDh3Lrrbfi5eX1m5uSiUvDihUruOOOO5gwYQLr169nwoQJHDlyhKKiIm666aYLeqyFCxcyc+ZMdRPhokWLWLt2LW+++SYLFixocv7//d//cd999zF16lQA4uPj2bZtG6+88oqaACxatIjx48fz1FNPAfDUU0/x008/sWjRIrUMoRBnalhH/lxNqppTW1uLoiikpKSos02OEfsVK1Y0WTKi1+vx9vZW19I7EhJvb29CQkKoq6trVGkHUPcuwOmKNG3dTba596O96fV6tZJSw43Cdrud+vp6BgwYwAMPPEBQUJC6xMjb25vk5GTg9FKj+vp6cnJy8PT0pFevXmofibP1MTnzOe12O3V1dQBq5SWDwXDOAS7HxmR/f/9Ge0Yc5UDbev0/SAIgxGWnYYMwRyv3H3/8kcrKSlJTU0lLS1M7Xebm5lJZWYndbmf8+PFoNBoCAgL44YcfWLBgAYWFhWzYsIHjx48TERFBdXU1t956K0lJSdx7772sXbuW2tpafvrpJ0pLS6mqqqK+vp6KigpcXV0pKSlRG+DccsstJCUlYbFYgAtvSiYuHS+99BL//Oc/mT17Nt7e3vzrX/8iLi6O++6774IaClmtVtLS0njyyScbHZ8wYQJbtmxp9j4Wi6VJAx13d3d27NhBXV0dLi4ubN26lUcffbTRORMnTmTRokVnjcWxRtvBZDK1+HWIS9+ZdeTDw8PPWSFIq9WSkpLC3LlzURSF/fv3U1dX12zJUseSkZZsHIX/7V2oqKhodPHYsNpOSUkJu3fvvqjutOfS3PuxZcsWnn32WTQazXlLaLYmRzfkhgmAzWZTl3OdbxTdUcvfUW61YQnYljyno4EYnN7z0ZI1+45ZhDOTuN27dwOoHZnbshqlJABCXIYcFYHWrl1LSUkJaWlp6kaxUaNGodPp6NevH5988gkGg4Hq6mqCgoIYPXo06enp1NfXs3LlSnUzWkJCAi4uLo3q+CclJdG9e3e1C3FVVRWenp4cPXqUDRs2MHHiRBISEpp0AjYajcD/liqJy09WVhbXXnstAK6urmpH1EcffZRx48bx3HPPtehxHAlkaGhoo+OhoaEUFRU1e5+JEyfy7rvvcuONNzJgwADS0tJ4//33qauro6SkhPDwcIqKii7oMQEWLFjQ4rgvRFvUYe9s2uM9PLOO/IU0qbLZbGoCuXTp0kYlSwFOnTqFwWA454h9w4TCYDBgMBhwd3dvMjsQHh6OoigUFBRgNpvJyclRL2xb08W8Hx1Jw1r+1dXVwP+6SbflZuqzJXGOqlH19fX069evzdb/gyQAQly2Gl6g7969m7feeovc3FxsNht79uzBz8+PBx98kF9//ZXS0lK1VKinpycbNmyge/fuXH311fTp06fJRbyDVqttsownOTmZgoICLBaLugbVbreTk5ODyWQiNTVVfTxxeQoICFBHObt06cL+/fvp06cPFRUVapm/C3Hmxcu5LpTmzp1LUVERQ4cORVEUQkNDueuuu3j11VcbjcxdyGPC6WVCc+bMUb83mUxERUVd8GsRl54z68jD/y4SW9InQK/X079/f+rq6rj77rvVmSTHaH1NTQ06na7V9kQ1XJpz/PjxC6pL3xIteT8uFQ1r+R88eBD4XzfpDz74gMrKyjYrB+/r69skiXNUjXJ1dW3Tfg0gCYAQlzXHBXpsbCyJiYm8++67hISEABAbG0vfvn05ePAg27Ztw2638/LLL7N//348PDx46qmn6NWr1296TscSpBUrVhAaGsquXbsoKCggLy+P0tJShg0bppZeE5eflJQU1q9fT58+fZgyZQoPP/wwP/74I+vXr+fKK69s8eMEBQWh0+majMwbjcYmI/gO7u7uvP/++7z99tucPHmS8PBwlixZgre3t9rVOiws7IIeE9rnA1l0TGfWkYf/XSR+9NFHTUa9m9tM/PLLLwM0W+rytddeU8vBXuySj4ZLc+Lj49UuxA2XAZWXl5OVlcWxY8fo0aPHBT9HS94PZw/wnG9DN/yvlr+jmo+jCZxjaVZ7baZ2FinBIUQn0atXL+69917c3d2pr69n6dKlPProo5jNZhYsWMBtt92Gh4cHUVFRPP/887/p4t/BsQRp7969zJ8/n61bt6LRaBg6dCj33Xcffn5+vP322xw4cKAVX6HoKF5//XWmTZsGnB45//Of/8zJkye5+eab1U2KLWEwGBg4cCDr169vdHz9+vUMHz78nPd1cXEhMjISnU7HihUruO6669TRymHDhjV5zHXr1p33McXl62xlV5vrFtzwItHRLbg1l4o4Ll4dy33OR6vVMnr0aObOnUt+fn6jpTkxMTHqWnPH62lYuedC43bG+9FWHEuzTp06RXp6utoE7t1332XJkiWUl5erm6lbytHgbO7cuW1Wv7+1yAyAEJ1Iw2VBe/bsIS0tDUVR+PLLL4HT3RPvu+++VhmZ7969OyEhIdx8882MGTMGo9HIvn37OHToEIqikJ+fz1//+ldeeOGFi0o2RMdSX1/P119/zcSJE4HTFydPPPEETzzxxG96vDlz5jB9+nQGDRrEsGHDWLJkCXl5edx///3A6QSjoKBArfV/+PBhduzYwRVXXEF5eTkLFy5k//79fPDBB+pjPvzww4waNYpXXnmFG264gS+//JINGzbw888/X+SrF5ebM7sFN1y/77jAa9gt2Jl7Os5cmmO32/H398fHx4fc3FyCgoI4duzYb65kBBf2fjiLTqc758i/g2NpVm1tbaOqQY4mcFarlfr6+su2XLUkAEJ0Mg2XBV1//fXk5eVRVVWlVgZqrT92js3Bs2bNorS0lBdeeIHAwED+/ve/ExkZSXp6Oq+88grvvvsu9957rywHukzo9Xr++Mc/tlqzt6lTp1JaWsr8+fMpLCykd+/erFmzhpiYGOB0JZW8vDz1fJvNxmuvvcahQ4dwcXFh7NixbNmypdFeleHDh7NixQr++te/MnfuXBISEli5cqX0ABBNnNkt2LGHxXGRCLS4W/C5tEZn6LMtzXE0giwrK2vSifhcexiau5Bur/ejvbi5ueHi4tKoapBjaZbVasXV1dWpyUxbujR+Qu1s8eLFLF68+LL9oQvh0Nwm3tbi2AQaFBTEihUrCAwMpHfv3kRGRmIwGEhOTqZ37954eHhIY7DLzBVXXMHu3bvVi/SL9cADD/DAAw80e9uyZcsafZ+UlKSW0juXyZMnM3ny5NYIT1zmfH198fX1bXb9fkfhWPvfcGmO4xrG0Z02IyMDLy+vi67c05L3oz3LgIrfRhKAZsyePZvZs2djMpnabPe3EJc7xweDownZv/71L0JCQnjppZdQFIVx48ZhNBq56qqr2Lt3rzQGu4w88MADPPbYYxw/fpyBAwfi6enZ6Pa+ffs6KTIhLk+KomAymaipqVGX5tjtduD0QM/IkSM5cuQIUVFRnDx5EriwSkYXymAwMHfu3N88myHaniQAQog24ehI7Kil7Kg+VFxczNGjR9m7d6/aSj0tLY0BAwZIAnCJu/vuu1m0aJHahfehhx5Sb9NoNGqpTZldFaJ1abVaZsyYoTaQcizNeeihhzAYDGRnZ/P1118zZsyY81Yyast9DOXl5Zw4cULdlCycRxIAIUSbcJQDffvtt8nPzyc9PV0tpZiYmIiLiwt33nknFouFgwcPkpqaSmJiouwFuIR98MEHvPzyy2RnZzs7FCEuSWdefFut1haVtITTS3Mca9cbLs1xcXHh22+/JTw8vNlOxI7KPa09C3AmR9lNq9VKTk7OJVEp6Lc6c09HRxz0kARACNFmkpKSuOeee/jrX//Kyy+/THV1NV5eXqSkpDB+/Hg8PDxYtWoVvXr1olu3brIX4BLn+EBvrbX/Qohza8mI+oVWMmorjgZlPj4+mEwmsrKy6NmzZ5s9nzg3SQCEEG2qV69evPDCC7z22muYTCamT59OREQEDzzwAEajkZCQEHr37o1Go6GkpET2AlziLseGOUI4y7mW47R0RL0jVO5p2KBMo9Hg7e3Npk2bSEpKkr8ZTiIJgBCizfXq1YsbbriBN998k7Vr15KRkUFxcTGhoaH84x//ICwsjA0bNrB27VpWr17N4MGD8fb2xtfXt1VLk4q2l5iYeN4P9LKysnaKRojL14WMqDu7klFWVhYFBQXExMRw4MABYmJiflMfgvbUGqVZOzJJAIQQ7SI5OZkhQ4ZgMpmYOHEiJ06cYN++fXz55ZdMnjyZ7OxsjEYjr7/+Ol26dEGj0RAeHk5iYiJTp06VZmGXiOeee06qpwlxFq21wfZcI+odjSPWLl26qLMU/v7+dOnSpVX2HlzuF+ptRRIAIUS7iI6OBiA/P5/HHnuM0NBQXnrpJYxGI++++y5ZWVn4+fnh7+9PbGwsZWVl5ObmUlZWxo4dO/jTn/7Edddd5+RXIc5n2rRpasUnIS4nzuzyeybHiHp8fDwBAQFq86+srCz1b21HUV5ejt1u59Zbb2X58uUAF9WH4HLQcHOwszYIy7y6EKJdaLVaBg4cSGlpKampqeTn51NYWMjatWvZunUrhw4dwtXVlZycHDIyMoiNjeXee+9l2LBhBAcH8/LLL7Nu3Tq1trXoeGQtrxBtT1EUUlNT6dKlC/7+/kDjmv4dqbqOY5+Cv7+/WoHIYrFQVVXVqAJRR4q5s5AEQAjRbpKTk+nVqxdZWVksXLiQPXv2qF2Bx40bh8Vi4dSpU9TV1fHzzz/zww8/sHXrVlxcXCgtLeXFF1/kX//6F5mZmc5+KaIZ8iEuRNvLysri+PHjjBo1Sk26HTX9jx8/TlZWlpMj/B9FUbBYLJSXl/Pee++p5aC9vb1ZtmwZZWVljboWi/YjS4CEEO0mOjqabt26ERgYiM1mY9iwYdjtdqZPn86yZcuoqamha9eu+Pv7s3v3blxdXfH09GTQoEHqVKlOp2PVqlVMmTJFXe9qt9vJy8ujqqoKb29v2TjsJDI7I0Tbcoz+BwQEnLWmv6P5orNn5AwGA/Pnz6eysrJFFYisVqszw70oWq2WlJQU5s6d224bqy+WJABCiHZzZnOw6dOns337dv7+97+zY8cO6urqGDVqFC4uLqSmplJYWEh4eDjvvPMOZWVlREdHM3nyZIKDg9WeAYcOHWLt2rVUVFQApz8gFUVh4MCBJCcnSzIghLhstKSmv6enZ4dIABycXYHoYnWkvR+tSRIAIUS7SkpKYuzYsbz55pv8+uuvpKWlkZ+fj1arpUePHsydO5fdu3fz8ccfo9frGTVqFJmZmbi6uhIYGMhXX33F8OHDOXbsGB999BHbtm2jR48e3H333ZSXl7N69Wr279/Ppk2b1AZjEydOlNkCIcQlryU1/V1cXHj99dedGaa4BEgCIIRod8nJyVxxxRVcffXV9O/fn7///e94eXlhtVp58cUXOXnyJB4eHgwZMoQdO3Zw5MgRwsPDMZvNmM1m9uzZQ3l5OV999RVdu3ZFq9Vyww03YLfbmTNnDjNnziQ1NZVjx44RHBzMihUrSElJobq6mrS0NDQajTo65ufnJwmCEOKScb4R9Ut1Kc3lOtLeUUkCIIRod9HR0fj7+5OTk8O0adPo2bMna9euZfv27Xz33XeUlJTg4uKCzWajrq6O2tpaDh8+zIABA/Dz8yMzMxONRoO7uzuVlZWUl5dz4sQJbDYbP/30E9999x0eHh5EREQQEhLC2rVr+eGHH9Dr9QQGBtK7d2+GDBmCi4sLmZmZrFixgmnTpgE0Wk4ETRMEIYQQ4lInw1pCiHbn2Atw+PBhVqxYgZeXF/fddx8PPvggo0aNwtfXl5iYGEpLSzGbzdhsNiZMmMCbb77J8ePHqaqqor6+nvz8fHJycsjJySEwMBA/Pz927tzJtm3b2Lt3Lz/++CPvv/8+/fr1Iz8/H7vdztixY1m5ciV//OMf+e9//0tpaSnZ2dksXLiQlStXEhgYiMlkwmazcccddxAaGsqqVauk8pAQQog25xjQKi8vb9Pn6RQJwE033YS/vz+TJ092dihCiP8vKSmJKVOmcPLkSd577z1eeeUV1q1bR1xcHDfeeCNPPvkksbGxVFRU4Ofnx9NPP83jjz/Onj170Gq1DB48mICAALp06UJ2djZZWVkEBQXh5uaGVqulvr4eo9HIsWPHOHnyJBUVFRQVFbFy5Up19qCuro6EhAQ8PT1Zt24dFouFKVOm4OPjg06nIzIykmnTppGYmCg9CIQQQlwQx7KmuXPnqpu0z0VRFHJzc7FareTk5LRpaeVOsQTooYce4u677+aDDz5wdihCiAaSkpLo3r17ozX3kZGR6ga2yZMns3//fux2O8uXLyctLQ273a52CtZoNJjNZhRFQa/Xo9VqsVqtnDp1ipKSEnQ6HVarlbfeeova2lpqa2u56qqrOHLkCN988w0ff/wxO3bswN3dnZKSEr7//numTp3aKEaNRsPIkSN57733yMvLIzY2Fmj5XoHa2lqefPJJLBYLjz76qLpnQQgh2oKspW97jvfYarXy/PPPt9rjlpeXU1VVhY+PDyaTiaysLHr27Nlqj99Qp0gAxo4dS2pqqrPDEEI0Q6vVqhfVDhMnTmTVqlXk5eXRvXt3XF1dCQoKQqPRYDAYiIiIoKqqitDQUAoKCqitrSU2NhZFUThx4gTe3t7Y7Xaio6Opq6ujoqICnU5HZWUln332mdpPQKfTUVJSwuDBgzEajZhMJv7xj3/g6+tLcHCwGk9ISAiAWnM7MzOTb7/9lm+++QaAlJQUAgMDm+wVcJyXnp4OwP/93/81e54QQojL15lJWXMbtRVFIS8vD29vb7RaLd7e3mzatImkpKQ2Kenq9GGoTZs2cf311xMREYFGo+GLL75ocs4bb7xBXFwcbm5uDBw4kM2bN7d/oEKIduNYHlRdXU1WVpZ6Ia3X6+nTpw9jx45Fq9XSv39/YmJi0Gq1uLi4sHfvXrXVvIuLC2azGZPJhJubGx4eHlgsFurr6ykvL8fX1xcfHx80Gg0VFRUkJCQQGRlJbm6uOuvgUFRUREVFBQUFBWzYsIGVK1cSGhrKgAEDGDlyJOPHj8disfD2229z4MAB4PTF/6pVq9TzUlJSmDFjhuwpEEKIDkSn0/H0008zZsyYFi3TaW3l5eUUFhZSUVFBVVUV0dHRaDQaYmJiKCgoaLPOzk6fAaiuriY5OZkZM2Zwyy23NLl95cqVPPLII7zxxhuMGDGCt99+m0mTJpGRkUF0dDQAAwcOxGKxNLnvunXriIiIaPPXIIRofUlJSfztb39j/vz5VFdXU15ejp+fH1OnTiU9PR2j0Uh2djZRUVFcd911GAwGCgoK8PHxobKyEg8PD3U61dvbm+TkZDZt2kRBQQHh4eGEhISQm5tLdXU1JSUl+Pn5YTQa8fT0pLS0FEVRGDp0KFarlWXLlrF37162bNmCXq8nLCyM3/3ud2zbto39+/eTmppKdnY2BoOBAwcO8I9//IONGzeSmJjIzTffrP4Bj4yMJC4ujhUrVqiNzGQ5kBDiUiRLjS6eY82/xWLBZDIRFRWFv78/AP7+/nTp0oXU1FQSEhJafRbA6QnApEmTmDRp0llvX7hwITNnzmTWrFkALFq0iLVr1/Lmm2+yYMECALUT3sWyWCyNEgmTydQqjyuE+G30ej233nqrOpL+ww8/sGLFCnWJj9lsVtfiV1ZWEhsby+9+9zu++eYb/Pz8yMnJobq6GqvVSkxMDN7e3pjNZvLy8igtLUWn01FXV6du+C0oKODUqVPo9XoKCwt58sknqa6uRlEU6urq1It8Hx8fHnzwQUwmE6GhoWg0GvR6PX5+fuTn53PvvfeSkJDAq6++2uSP9tn2FAghhOhcHINUbm5uVFZW4uvri0aj4dSpU+zatYuUlBS2bt1KVlYWXbt2bdXn7tBDT1arlbS0NCZMmNDo+IQJE9iyZUurP9+CBQvUBhu+vr5ERUW1+nMIIS6MYzmQwWAgISGBwsJCTpw4wcSJE3nppZfo168fdrudkpISysrKyMnJoU+fPlRWVuLt7a3+QXVUD3J89enTh969e6tTvj4+PnTr1o2KigpqamoIDQ3lxIkTmEwmhg4dSlhYGFqtlrCwMF577TUATpw4QUVFBQEBAURERDB+/HgmTJhAQEAAO3bsoLCwkOeff57U1FRsNpv6ms7cUyCEEO3BMWo/b948tXHY5cKZr+3UqVOkpaVx7NixFt/HMfrv5eWFoii4urpy8uRJTCaT+nm2e/du/P39SU1NbfWKQB06ASgpKcFmsxEaGtroeGhoKEVFRS1+nIkTJ/L73/+eNWvWEBkZyc6dO5s976mnnqKyslL9ys/Pv6j4hRCtIykpiYceeognnniCp556iuHDh+Pv709RUREhISGMHTuWJ554gq5du1JeXs6hQ4fIysrCw8ODYcOG4eXlRW1tLTfccANXXHEFXl5eHDx4kN27d1NTU4PJZKKiooI9e/ZQWlqK1WpVO2u6uLjQv39/JkyYgLe3NxUVFRQXFzNgwAC0Wi1ms5mkpCRcXV2pra3F19eXmTNn4uLiwgcffEB5eTlms5mKigp1X4HRaARQu3gKIYS4NCmKQkVFBWaz+YIu1MvLy9VlPzabDb1ez9GjR0lNTaW0tJTq6mrWrFlDVlYWVVVVjQaRWoPTlwC1xJlT6IqiXNBaqLVr17boPFdXV1xdXS8oNiFE+3BUC4qNjeX6669vtgTnlVdeybvvvouXlxfV1dXceeedJCYmMm/ePI4ePUp2djZms5mgoCBOnDiBv78/vXr1wt/fn927d6vlRKOiohgzZgy7du2ipKQEDw8PTCYTgYGBFBcX88knn1BRUYFWq0Wv12MymdTRnNraWmw2m9pbwNfXl5KSEvbs2cPrr7/ONddcw549e/D39ycsLExdQ/v0009fdiNyQgjRUTS3Z8HxfXNVeVqqvLwci8WiLiNtyXIdRVHIycnBx8eHwMBAwsLCsNlseHh4UFhYSEBAAOPGjSMiIgKdTsfdd9+NXt+6l+wdOgEICgpCp9M1Ge03Go1NZgVa0+LFi1m8eHGrZ1tCiNbRXOlQgF69enHvvfeyfPlyMjMz+b//+z/sdjsnTpwgODiYK664giFDhlBVVcXcuXPRaDTk5OSwb98+XF1d6dKlC8XFxcTHx5Ofn4+7uzsWi4UlS5YwfPhw6uvrsVqtrF69moqKCnUwIjs7m+LiYmw2GzExMezfv19NBAwGA6GhofTq1Yv169fz/vvvM3r0aB566CHZACyEEJcwx8CPq6sr8fHxLd60m5WVhclkolevXuoeMkeBiaysLPz9/fHx8eH6669n1apVFBcX4+vr26qxd+hPH4PBwMCBA1m/fn2j4+vXr2f48OFt9ryzZ88mIyPjrEuFhBAdl6N60KRJk7j66qt59tlnueqqqxg5ciRz587ld7/7HWVlZSQmJvL+++8zYsQIfHx8uPrqqxk+fDgGg4HAwEBKSkooLCzEZrNhNBrZvHkzWVlZKIqi9giw2+2Ul5ezZcsWNQHIy8tj+/bt1NfXExoaSm1tLSdPnmT//v3qBuO9e/eyYsWKixp1EkII4VyOC3k/Pz80Gg2jRo3i+PHj5yzdqSgKmzZtwt3dXS1XbbVasVgsnDx5Ei8vL06dOoWiKGp56rbYA+D0GQCz2czRo0fV77Ozs0lPTycgIIDo6GjmzJnD9OnTGTRoEMOGDWPJkiXk5eVx//33OzFqIURH1rB6kNFo5JlnniEkJASj0cjPP//MwYMH6dq1K8OGDaO4uJht27ZRUVGBl5cXOp2OgwcPUlRUhE6nw2AwoCgKiqKolYf8/Pzw9fWlvr6eiIgIampqKCoqQqPRoNVqcXV15corr6SyspLc3Fzq6upITExUZxCysrLUPQEVFRVYLBZycnKkS7AQosWkDKdzOS7kfXx81IIODS/YzzYLYLPZMJlMaqUfRVEoLCykrq4OvV5PSEhIoxnmMWPG8NFHH7V6JSCnJwC//vorY8eOVb+fM2cOAHfeeSfLli1j6tSplJaWMn/+fAoLC+nduzdr1qwhJibGWSELIS4BjupBa9eu5b333lOP+/v7c/PNN/Pzzz9jNBrp27cvwcHBVFZW0qtXL9zc3Ni9ezd2ux1PT08MBgPl5eVYrVa1UkNNTQ0+Pj54eHjg5eWF0WhUR/9DQ0NxcXHBz8+Pe+65h+effx673U5ubi5Hjx6lS5cunDx5kk2bNjFv3jzpEiyEEJegrKwsCgoKiImJURtAtuSCXa/XM2PGDE6ePInNZsNut2O1WikuLiYqKoqEhAT2799PdXU1hYWFeHh4EBAQ0Or9AJyeAIwZM+a80xoPPPAADzzwQDtFJHsAhLhcJCUl0b179yYbhgH279/P5s2bufHGGwkMDMTT05OhQ4fy9ddf4+7ujre3N1FRURQVFWG326mvr0ev1xMcHIzZbEar1ZKSksL111/Piy++yKFDh7BardTX16PRaBg9ejQ9e/bE3d2dAwcOEBAQQGhoKH369KG0tJTS0lL2799PdHQ0MTExzJgxg+3bt7Nq1SqmTJkiSYAQQnRQiqKQmpqKv7+/2n2+qqqqxRfsvr6+eHt7Y7PZ1L1icHpZ6ZEjRzAajezatYt3331XLVXtOLe1NgM7PQHoiGbPns3s2bMxmUytvulCCNG+zrZheOLEiaxatYrPPvuM0NBQjh07xnvvvYfFYsHLy4vhw4dTWlqKn58fvXv3prKyklOnTvHKK6/w+OOPY7PZCAoKIiwsjMGDB9O3b18qKioA2LFjB2+++SZXXHGFupTI1dWVqqoqfvzxR8rKyggODsbNzY0dO3bg5eUlXYKFEOIS4VjGYzKZ8Pb2xtXVlfT09N98wa7RaAgLC6N///4A1NfXM2DAAGbNmqUmB56enq1aCUgSACFEp9RwiVBcXBx2u52tW7cSFBREfX29WqJt8ODBhISEUFpayg8//IDRaMTNzU3dwOvp6Ul+fj4xMTEEBgZSWVlJQEAAZWVlLFmyhOPHj1NfX4/RaOTUqVMYDAY8PDzo378/BoOB0tJS0tLSyMzMJDk5WboECyFEB6fX65k5cyY1NTVYrVZqamoALuqCXa/X4+XlBZwuS+/t7a32o2kLkgAIITqthkuE0tLSCAgI4L777uP999/nwIEDarMvjUbDHXfcwXfffcdDDz2Eoih06dKFyspKtm3bRnV1NSdPnqR///4oioJOpyM0NJStW7eSl5eHzWbDxcUFd3d3hgwZQnZ2Nvn5+SQmJqr7CDZs2ECfPn2kS7AQQrSz37Kh2tfXF19fX6xWq9rUsS0v2FubJADNkD0AQnQeDZcI7du3Dx8fH6ZPn87DDz9MRkYGUVFR+Pj4cOjQIerr6ykvLwegurpa3Rzcp08fduzYwbFjxzhx4oRaMejkyZPU1dXh6uqKRqPB1dUVk8mEl5cXnp6eHD58GICoqCgqKirIy8tTp4+lS7AQQoi2IgtMmyF9AITofKKjo/Hz82Pz5s306NGDXr16cerUKXx9fSkrK2PBggUEBwdz1VVXERwcjLu7OxqNhoyMDFxcXJg8eTJGo5Hq6mpsNhtms5ny8nIMBgNarRYPDw/Cw8MpLi5Wa0cbjUZ17SeAyWTi559/xt/fX92sLIQQlwPHKPu8efMumVHyy5kkAEIIwemZgIkTJ3L48GFWrVqlNiIcN26cusZ/yZIlDBo0iDvvvJOpU6cyfvx4tYrQqVOnSEhIIDQ0FD8/PwwGA+Hh4XTr1g2dTofZbKauro7AwEC8vLw4evQoJSUlGAwGqqqqqKysJDU1lcOHDzNhwgTZACyEEJ2EVqtl9OjRPP300+oscFuTJUBCCPH/OTYGf/vtt+zevRs4PSpfV1fHX/7yF3r37s1XX32FXq/ntddew2AwkJ+fz9NPP01OTg5WqxWz2Ux9fT1arRZ/f39Gjx6N0Wjkiy++ID8/Hy8vL+x2O1qtFkVRqKio4Pvvvyc+Pp74+HgpASqEEKLNSQIghBANODYGT548maqqKnJycti7dy8//vgjgwcPbnSu1WrlxRdf5IcffqB79+4MGDAAT09PsrOzWbt2LSaTiW3btpGcnIyvry82mw2dToebmxsVFRVotVq1gkRcXBzjx4+Xi38hhOhEdDodY8aM4emnn27X55UEoBmyCViIzq3hxmCDwYDFYiEnJ4fc3Fy1PTucbtqSmZmJh4cHSUlJ6HQ6dfpWURQUReHw4cMUFxdz6tQpdamQY3Owj48P/v7+jBgxgpSUFD777DO0Wq0kAUIIIdqULDJthmwCFkIAZGZm8uWXX1JSUkJaWhofffQR27dvp7i4GICcnBwKCgrw9fXFy8uL3NxciouLyc/Px8XFhYCAAGJiYoDTjV1KSkowm83odDoCAgKIi4ujS5cuxMbGMmXKFBITE1m3bh12u92ZL1sIIcRlThIAIYRoRmZmJqtWrSI8PJwRI0YQHBxMUFAQGo2Gffv28cEHH/DYY49RUlICQEVFBenp6fz44494eHjQpUsX6urqMBqNuLu7ExMTg7u7O3V1dbi7u6udgB2zCRqNhpEjR1JeXk5eXp4zX7oQQojLnCQAQghxBrvdztq1a0lMTGTKlCnExcXRu3dv6uvrqamp4fjx47z88svo9XpiYmIYOXIkjz32mFrms7CwkLKyMurq6rBYLGg0GiorK9WLfTjd6KuwsFDtIAlIEzAhhBDtQhIAIYQ4Q15eHhUVFaSkpKgX7cHBwfzxj3+kX79+BAQE0LVrV1566SXCw8M5fvw4ERER9O7dm9DQULXRl6urK927d6d3794oikJ8fDwBAQGEhIQQGhqKwWDA09OTyZMnYzAYMBqNgDQBE0II0bYkARBCiDM4RuAdI/IOWq0WjUaDh4cHPXr0wGKxkJCQQGlpKZ999hlWqxW9Xk9ubi6FhYWYTCZqa2vZtWsXbm5u9OvXD3d3dyIjI7nqqqsIDQ0lICCADRs2YLPZpAmYEEKIdiEJQDMWL15Mz549m5T8E0J0Do4ReMeIvIPBYGDmzJkMHDgQHx8fvLy8CA4OplevXhiNRvbu3UtBQQEmkwlXV1cSEhLo3bs3Go0Gi8XCwYMHCQsL49SpU2RkZGCxWIiIiCA3N5fFixdLEzAhhBDtQj5lmiFVgITo3KKjo/Hz82Pz5s0oitLoNi8vL/Ly8tDr9epIfXBwMPfddx+enp7ExcWRlJRE3759MRgM6HQ6/Pz8CA8PR6fTkZycTM+ePSkpKSEvL4/t27eTnp5OQUGBNAETQgjRLiQBEEKIM2i1WiZOnMjhw4dZtWoVlZWV1NfXk5+fz5YtW6iursbT07PRpt6CggIAQkNDGTVqFABFRUXs37+foqIiXFxciIuLw2w2c+zYMTQaDVqtFovFgqIoXHnllXLxL4QQol1IIzAhhGhGUlISU6ZM4dtvv2X37t3A6VKdQUFB/OlPfyItLU1NDjw9PcnKysJoNBIcHMytt96Ku7s7JpOJvn37EhQURPfu3dm6dSuZmZlERkaSmJiIRqPB09OToKAgtmzZQlRUlCQBQggh2pwkAEIIcRZJSUnExcVx/PhxLBYL06dPp2vXrmi1WhISEholB46yn3FxcfTo0QONRoObmxuhoaFMnjyZZcuW8csvvxAYGEj37t0xm82Ulpai1Wr585//TGZmJuvWraN79+6yB0AIIUSbkgRACCHOQavV4ufnB0BsbKx6cX5mcvDHP/6RrKwszGZzk30DPXr0wN3dHavVSm1tLb/88gu5ubmYzWZGjhxJz549CQwM5L333iMvL4/Y2Nh2fpVCCCHaU3l5OUeOHCE+Pp6goKB2f34ZZhJCiN/IkRyEhobSvXt3Fi1aRN++ffniiy+orKzEbrdTWVnJqlWrqKioYMyYMQwePJju3bsTEhKCt7e3+odfmoCd3RtvvEFcXBxubm4MHDiQzZs3n/P8jz/+mOTkZDw8PAgPD2fGjBmUlpaqty9btgyNRtPkq7a2tq1fihBCoCgK2dnZmM1mcnJymgwatQdJAJohZUCFEA4Gg4F58+Yxb948DAbDOc917Bs4efIk6enp5OXlkZ6ejtFo5OabbyYkJAQXFxdCQkJwc3NrtIlYmoA1b+XKlTzyyCM888wz7N69m5SUFCZNmkReXl6z5//888/ccccdzJw5kwMHDvDpp5+yc+dOZs2a1eg8Hx8fCgsLG325ubm1x0sSQnRyWVlZmEwmIiMjMZlMlJeXt3sMkgA0Q8qACiF+q6SkJP70pz+RnJxMUFAQycnJzJ49m3HjxuHr60tubm6T0R5FUaQJ2FksXLiQmTNnMmvWLJKSkli0aBFRUVG8+eabzZ6/bds2YmNjeeihh4iLi2PkyJHcd999/Prrr43O02g0hIWFNfoSQoi2pigKmzZtwsfHh/j4eHx8fJwyCyAJgBBCtDKtVktgYCDXXXcdr732Gm5ubmi1WsaPH09paanaBExRFEwmE6tWrZImYM2wWq2kpaUxYcKERscnTJjAli1bmr3P8OHDOX78OGvWrEFRFE6ePMlnn33Gtdde2+g8s9lMTEwMkZGRXHfddepmbiGEaEtZWVkUFBQQGxuLRqMhJiYGk8lEVlZWu8Yhm4CFEOI3ciwPaqmkpCR69erFoUOHOHnyJCaTiT179hAXFydNwJpRUlKCzWYjNDS00fHQ0FCKioqavc/w4cP5+OOPmTp1KrW1tdTX1/O73/2O//znP+o5PXr0YNmyZfTp0weTycS//vUvRowYwZ49e+jWrVuzj2uxWLBYLOr3JpOpFV6hEKIzURSF1NRUunTpglarxW634+/vj4+PD5s2bSIhIaHdYpGhJiGEaEfBwcEMHjyYsLAwunbtyltvvcWjjz4qF//n0HCvBJz+ED3zmENGRgYPPfQQf/vb30hLS+P7778nOzub+++/Xz1n6NCh3H777SQnJ5OSksKqVatITExslCScacGCBfj6+qpfUVFRrfPihBCdRlZWFsePH2fUqFHq3zCNRkNsbCwFBQXtOgsgMwBCCNHOHD0CtFotMTExsuznLIKCgtDpdE1G+41GY5NZAYcFCxYwYsQIHn/8cQD69u2Lp6cnKSkpvPDCC4SHhze5j1arZfDgwRw5cuSssTz11FPMmTNH/d5kMkkSIIRoMcfof0BAAB4eHlRVVWGz2QBwcXHB39+fTZs2tdteAEkAhBBCdEgGg4GBAweyfv16brrpJvX4+vXrueGGG5q9T01NDXp94482nU4HcNYPVkVRSE9Pp0+fPmeNxdXVFVdX1wt9CUIIAYDNZsNkMmEymXjvvfdIS0vDbrcDpwchPDw8sNlskgAIIYQQc+bMYfr06QwaNIhhw4axZMkS8vLy1CU9Tz31FAUFBXz44YcAXH/99dxzzz28+eabTJw4kcLCQh555BGGDBlCREQEAM899xxDhw6lW7dumEwm/v3vf5Oens7ixYud9jqFEJc3vV7PzJkzqampwWq1UlNTo84A6HQ6Zs2ahaenJ//617/aJ552eRYhhBDqpmGz2cx1113n7HAuCVOnTqW0tJT58+dTWFhI7969WbNmDTExMQAUFhY26glw1113UVVVxeuvv85jjz2Gn58f48aN45VXXlHPqaio4N5776WoqAhfX1/69+/Ppk2bGDJkSLu/PiFE5+HYQ2S1WvH29m6UADS3PLEtSQIghBCt7HzVgex2u1qhJjc3l6SkJNkHcA4PPPAADzzwQLO3LVu2rMmxBx98kAcffPCsj/fPf/6Tf/7zn60VnhBCXHIkAWjG4sWLWbx4sZqZCSFEa8nMzOSrr75SN7Z+/PHHhISEMHHiRKkEJIQQol3IkFMzpBOwEKItZGZmsmrVKkJCQggPDyc6Opo777yT0NBQVq1aRWZmprNDFEII0QlIAiCEEO3Abrezdu1aEhMTmTx5Mq6urmi1Wrp06cK0adNITExk3bp1alUIIYQQoq1IAiCEEO0gLy+PiooKUlJSmjSx0mg0jBw5kvLy8kYbWoUQQoi2IAmAEEK0g6qqKgBCQkKavd1x3HGeEEII0VYkARBCiHbg7e0NnO5i2xzHccd5QgghRFuRBEAIIdpBdHQ0fn5+bN68uUmnR0VR+Pnnn/H39yc6OtpJEQohhOgsJAEQQoh2oNVqmThxIocPH+azzz6jtrYWu93O8ePHWbFiBYcPH2bChAnSD0AIIUSbkz4AQgjRTpKSkpgyZQrffvstbm5uwOk+AEFBQUyZMkX6AAghxCXmfI0fL/Sx5s6dy0svvdQqj3cukgAIIUQ7SkpKIi4ujuPHj2OxWJg+fTpdu3aVkX8hhBDtRhIAIYRoZ1qtFj8/PwBiY2Pl4l8IITohnU7H008/jcFgaPfnlk8dIYQQQgghOhFJAIQQQgghhOhEJAFoxuLFi+nZsyeDBw92dihCCCGEEEK0KtkD0IzZs2cze/ZsTCYTvr6+zg5HCCGEEEJ0Aq1ZVehcZAZACCGEEEKITkQSACGEEEIIIToRSQCEEEIIIYToRCQBEEIIIYQQohORBEAIIYQQQohORBIAIYQQQgghOhFJAIQQQgghhOhEJAEQQgghhBCiE5EEQAghhBBCiE5EEgAhhBBCCCE6EUkAhBBCCCGE6EQkARBCCCGEEKITkQRACCGEEEKITkQSACGEEEIIITqRyz4ByM/PZ8yYMfTs2ZO+ffvy6aefOjskIYQQQgghnEbv7ADaml6vZ9GiRfTr1w+j0ciAAQO45ppr8PT0dHZoQgghhBBCtLvLPgEIDw8nPDwcgJCQEAICAigrK5MEQAghhBBCdEpOXwK0adMmrr/+eiIiItBoNHzxxRdNznnjjTeIi4vDzc2NgQMHsnnz5t/0XL/++it2u52oqKiLjFoIIYQQQohLk9MTgOrqapKTk3n99debvX3lypU88sgjPPPMM+zevZuUlBQmTZpEXl6ees7AgQPp3bt3k68TJ06o55SWlnLHHXewZMmSNn9NQgghhBBCdFROXwI0adIkJk2adNbbFy5cyMyZM5k1axYAixYtYu3atbz55pssWLAAgLS0tHM+h8Vi4aabbuKpp55i+PDh5zzPYrGo35tMpgt5KUIIIYQQQnR4Tp8BOBer1UpaWhoTJkxodHzChAls2bKlRY+hKAp33XUX48aNY/r06ec8d8GCBfj6+qpfslRICCGEEEJcbjp0AlBSUoLNZiM0NLTR8dDQUIqKilr0GL/88gsrV67kiy++oF+/fvTr1499+/Y1e+5TTz1FZWWl+pWfn3/Rr0EIIYQQQoiOxOlLgFpCo9E0+l5RlCbHzmbkyJHY7fYWnevq6oqrq+sFxyeEEEIIIcSlokMnAEFBQeh0uiaj/UajscmsgBBCXCoMBgPz5s1zdhhCCCGcoCN8BnToJUAGg4GBAweyfv36RsfXr19/zs28F2vx4sX07NmTwYMHt9lzCCGEEEII4QxOnwEwm80cPXpU/T47O5v09HQCAgKIjo5mzpw5TJ8+nUGDBjFs2DCWLFlCXl4e999/f5vFNHv2bGbPno3JZMLX17fNnkcIIYQQQoj25vQE4Ndff2Xs2LHq93PmzAHgzjvvZNmyZUydOpXS0lLmz59PYWEhvXv3Zs2aNcTExDgrZCGEEEIIIS5ZGkVRFGcH0VE5ZgAqKyvx8fFxdjhCCAHI36aOQH4GQoiOqKV/mzr0HgBnkT0AQgghhBDiciUJQDNmz55NRkYGO3fudHYoQgghhBBCtCpJAIQQQgghhOhEJAEQQgghhBCiE5EEoBmyB0AIIYQQQlyuJAFohuwBEEIIIYQQlytJAIQQQgghhOhEnN4IrCNztEgwmUxOjkQIIf7H8TdJ2rg4j3w+CCE6opZ+PkgCcA5VVVUAREVFOTkSIYRoqqqqCl9fX2eH0SnJ54MQoiM73+eDdAI+B7vdzokTJ/D29qaqqoqoqCjy8/Mv666PgwcPdvreh7aOobUe/2Ie57fc90Lu05Jzz3eOyWSS3/kOGoOiKFRVVREREYFWKys5naHh54NGo3FqLJfi/1WJuX1IzO2jI8Xc0s8HmQE4B61WS2RkJID6B97Hx8fpP9y2pNPpnP762jqG1nr8i3mc33LfC7lPS85t6ePJ73zHjEFG/p2r4edDR3Ep/l+VmNuHxNw+OkrMLfl8kKEj0cjs2bOdHUKbx9Baj38xj/Nb7nsh92nJuR3hZ90RdIT3oSPEIIQQovOQJUAtZDKZ8PX1pbKyskNkd0K0NfmdF+LScCn+X5WY24fE3D4uxZhlBqCFXF1defbZZ3F1dXV2KEK0C/mdF+LScCn+X5WY24fE3D4uxZhlBkAIIYQQQohORGYAhBBCCCGE6EQkARBCCCGEEKITkQRACCGEEEKITkQSACGEEEIIIToRSQBaWX5+PmPGjKFnz5707duXTz/91NkhCdEubrrpJvz9/Zk8ebKzQxGiU1iwYAGDBw/G29ubkJAQbrzxRg4dOuTssFpswYIFaDQaHnnkEWeHck4FBQXcfvvtBAYG4uHhQb9+/UhLS3N2WGdVX1/PX//6V+Li4nB3dyc+Pp758+djt9udHZpq06ZNXH/99URERKDRaPjiiy8a3a4oCvPmzSMiIgJ3d3fGjBnDgQMHnBPs/3eumOvq6vjLX/5Cnz598PT0JCIigjvuuIMTJ044L+DzkASglen1ehYtWkRGRgYbNmzg0Ucfpbq62tlhCdHmHnroIT788ENnhyFEp/HTTz8xe/Zstm3bxvr166mvr2fChAmXxGfOzp07WbJkCX379nV2KOdUXl7OiBEjcHFx4bvvviMjI4PXXnsNPz8/Z4d2Vq+88gpvvfUWr7/+OpmZmbz66qv8/e9/5z//+Y+zQ1NVV1eTnJzM66+/3uztr776KgsXLuT1119n586dhIWFMX78eKqqqto50v85V8w1NTXs2rWLuXPnsmvXLj7//HMOHz7M7373OydE2kKKaFN9+vRR8vLynB2GEO1i48aNyi233OLsMITolIxGowIoP/30k7NDOaeqqiqlW7duyvr165XRo0crDz/8sLNDOqu//OUvysiRI50dxgW59tprlbvvvrvRsZtvvlm5/fbbnRTRuQHK6tWr1e/tdrsSFhamvPzyy+qx2tpaxdfXV3nrrbecEGFTZ8bcnB07diiAkpub2z5BXaBONwNwvmkngDfeeIO4uDjc3NwYOHAgmzdv/k3P9euvv2K324mKirrIqIW4OO35ey+EcI7KykoAAgICnBzJuc2ePZtrr72Wq666ytmhnNdXX33FoEGD+P3vf09ISAj9+/fnnXfecXZY5zRy5Eh++OEHDh8+DMCePXv4+eefueaaa5wcWctkZ2dTVFTEhAkT1GOurq6MHj2aLVu2ODGyC1NZWYlGo+mws0V6ZwfQ3hxTODNmzOCWW25pcvvKlSt55JFHeOONNxgxYgRvv/02kyZNIiMjg+joaAAGDhyIxWJpct9169YREREBQGlpKXfccQfvvvtu274gIVqgvX7vhRDOoSgKc+bMYeTIkfTu3dvZ4ZzVihUr2LVrFzt37nR2KC1y7Ngx3nzzTebMmcPTTz/Njh07eOihh3B1deWOO+5wdnjN+stf/kJlZSU9evRAp9Nhs9l48cUXufXWW50dWosUFRUBEBoa2uh4aGgoubm5zgjpgtXW1vLkk0/yhz/8AR8fH2eH0zxnT0E4E81M4QwZMkS5//77Gx3r0aOH8uSTT7b4cWtra5WUlBTlww8/bI0whWhVbfV7ryiyBEgIZ3nggQeUmJgYJT8/39mhnFVeXp4SEhKipKenq8c6+hIgFxcXZdiwYY2OPfjgg8rQoUOdFNH5LV++XImMjFSWL1+u7N27V/nwww+VgIAAZdmyZc4OrVlnfib98ssvCqCcOHGi0XmzZs1SJk6c2M7RNa+5z1EHq9Wq3HDDDUr//v2VysrK9g3sAnS6JUDnYrVaSUtLazTtBDBhwoQWTzspisJdd93FuHHjmD59eluEKUSrao3feyGE8zz44IN89dVXbNy4kcjISGeHc1ZpaWkYjUYGDhyIXq9Hr9fz008/8e9//xu9Xo/NZnN2iE2Eh4fTs2fPRseSkpLIy8tzUkTn9/jjj/Pkk08ybdo0+vTpw/Tp03n00UdZsGCBs0NrkbCwMOB/MwEORqOxyaxAR1NXV8eUKVPIzs5m/fr1HXf0H6kC1EhJSQk2m63ZaaczfxHP5pdffmHlypV88cUX9OvXj379+rFv3762CFeIVtEav/cAEydO5Pe//z1r1qwhMjLykpniF+JSpSgKf/rTn/j888/58ccfiYuLc3ZI53TllVeyb98+0tPT1a9BgwZx2223kZ6ejk6nc3aITYwYMaJJadXDhw8TExPjpIjOr6amBq228eWdTqfrUGVAzyUuLo6wsDDWr1+vHrNarfz0008MHz7ciZGdm+Pi/8iRI2zYsIHAwEBnh3ROnW4PQEtoNJpG3yuK0uTY2YwcOfKS+U8mREMX83sPsHbt2tYOSQhxDrNnz+aTTz7hyy+/xNvbW03YfX19cXd3d3J0TXl7ezfZn+Dp6UlgYGCH3bfw6KOPMnz4cF566SWmTJnCjh07WLJkCUuWLHF2aGd1/fXX8+KLLxIdHU2vXr3YvXs3Cxcu5O6773Z2aCqz2czRo0fV77Ozs0lPTycgIIDo6GgeeeQRXnrpJbp160a3bt146aWX8PDw4A9/+EOHjDkiIoLJkyeza9cuvvnmG2w2m/r/MSAgAIPB4Kywz865K5CcizPWcFksFkWn0ymff/55o/MeeughZdSoUe0cnRBtQ37vhbg8AM1+LV261NmhtVhH3wOgKIry9ddfK71791ZcXV2VHj16KEuWLHF2SOdkMpmUhx9+WImOjlbc3NyU+Ph45ZlnnlEsFouzQ1Nt3Lix2d/dO++8U1GU06VAn332WSUsLExxdXVVRo0apezbt6/DxpydnX3W/48bN250atxno1EURWm/dKNj0Wg0rF69mhtvvFE9dsUVVzBw4EDeeOMN9VjPnj254YYbLpn1c0Kci/zeCyGEEJ1bp1sCdL5ppzlz5jB9+nQGDRrEsGHDWLJkCXl5edx///1OjFqIiyO/90IIIYRw6HQzAKmpqYwdO7bJ8TvvvJNly5YBpxsivfrqqxQWFtK7d2/++c9/MmrUqHaOVIjWI7/3QgghhHDodAmAEEIIIYQQnZmUARVCCCGEEKITkQRACCGEEEKITkQSACGEEEIIIToRSQCEEEIIIYToRCQBEEIIIYQQ53Xo0CEGDx5MXFwcX375pbPDERdBqgAJIYQQQojzmjp1KoMHD6ZPnz7MmjWL/Px8Z4ckfiOZARBCCCGEaAXz5s2jX79+zg5DpdFo+OKLLy74focOHSIsLIyqqqpGx319fYmJiaFbt26EhoY2ud/gwYP5/PPPf2u4oh1JAiCEEEKIS8Zbb72Ft7c39fX16jGz2YyLiwspKSmNzt28eTMajYbDhw+3d5jtqrUTj2eeeYbZs2fj7e3d6Pj8+fOZNm0a3bp146mnnmpyv7lz5/Lkk09it9tbLRbRNiQBEEIIIcQlY+zYsZjNZn799Vf12ObNmwkLC2Pnzp3U1NSox1NTU4mIiCAxMdEZoV6Sjh8/zldffcWMGTOa3LZ9+3YiIyOZNm0av/zyS5Pbr732WiorK1m7dm17hCougiQAQgghhLhkdO/enYiICFJTU9Vjqamp3HDDDSQkJLBly5ZGx8eOHQvARx99xKBBg/D29iYsLIw//OEPGI1GAOx2O5GRkbz11luNnmvXrl1oNBqOHTsGQGVlJffeey8hISH4+Pgwbtw49uzZc854ly5dSlJSEm5ubvTo0YM33nhDvS0nJweNRsPnn3/O2LFj8fDwIDk5ma1btzZ6jHfeeYeoqCg8PDy46aabWLhwIX5+fgAsW7aM5557jj179qDRaNBoNCxbtky9b0lJCTfddBMeHh5069aNr7766pzxrlq1iuTkZCIjI5t9LX/4wx+YPn06H330EXV1dY1u1+l0XHPNNSxfvvyczyGcTxIAIdrB22+/TWRkJFdeeSUnT5684PvfdNNN+Pv7M3ny5DaITgghLi1jxoxh48aN6vcbN25kzJgxjB49Wj1utVrZunWrmgBYrVaef/559uzZwxdffEF2djZ33XUXAFqtlmnTpvHxxx83ep5PPvmEYcOGER8fj6IoXHvttRQVFbFmzRrS0tIYMGAAV155JWVlZc3G+c477/DMM8/w4osvkpmZyUsvvcTcuXP54IMPGp33zDPP8Oc//5n09HQSExO59dZb1SVOv/zyC/fffz8PP/ww6enpjB8/nhdffFG979SpU3nsscfo1asXhYWFFBYWMnXqVPX25557jilTprB3716uueYabrvttrPGC7Bp0yYGDRrU5LjRaGTNmjXcfvvtjB8/Hq1Wy7ffftvkvCFDhrB58+azPr7oIBQhRJsymUxKeHi4smXLFuXBBx9UnnjiiQt+jB9//FH56quvlFtuuaUNIhRCiEvLkiVLFE9PT6Wurk4xmUyKXq9XTp48qaxYsUIZPny4oiiK8tNPPymAkpWV1exj7NixQwGUqqoqRVEUZdeuXYpGo1FycnIURVEUm82mdOnSRVm8eLGiKIryww8/KD4+PkptbW2jx0lISFDefvttRVEU5dlnn1WSk5PV26KiopRPPvmk0fnPP/+8MmzYMEVRFCU7O1sBlHfffVe9/cCBAwqgZGZmKoqiKFOnTlWuvfbaRo9x2223Kb6+vur3Zz6vA6D89a9/Vb83m82KRqNRvvvuu2bfE0VRlOTkZGX+/PlNjr/22mtKv3791O8ffvhh5Xe/+12T87788ktFq9UqNpvtrM8hnE9mAIRoRaWlpYSEhJCTk6Mec3V1xc/Pj27duhEZGUlAQMAFP+7YsWObbMZymDx5MgsXLvytIQshxCVn7NixVFdXs3PnTjZv3kxiYiIhISGMHj2anTt3Ul1dTWpqKtHR0cTHxwOwe/dubrjhBmJiYvD29mbMmDEA5OXlAdC/f3969OihLl/56aefMBqNTJkyBYC0tDTMZjOBgYF4eXmpX9nZ2WRlZTWJsbi4mPz8fGbOnNno/BdeeKHJ+X379lX/HR4eDqAuTzp06BBDhgxpdP6Z359Lw8f29PTE29tbfezmnDp1Cjc3tybHly5dyu23365+f/vtt7NmzZoms9ru7u7Y7XYsFkuLYxTtT+/sAIToaPLz85k3bx7fffcdJSUlhIeHc+ONN/K3v/2NwMDAc953wYIFXH/99cTGxqrHDAYDM2bMIDQ0FH9/fwoKClo13r/97W+MHTuWWbNm4ePj06qPLYQQHVHXrl2JjIxk48aNlJeXM3r0aADCwsKIi4vjl19+YePGjYwbNw6A6upqJkyYwIQJE/joo48IDg4mLy+PiRMnYrVa1ce97bbb+OSTT3jyySf55JNPmDhxIkFBQcDpfQLh4eGN9h44ONbjN+SohPPOO+9wxRVXNLpNp9M1+t7FxUX9t0ajaXR/RVHUYw7KBbRwavjYjsc/V5WeoKAgysvLGx379ddf2b9/P0888QR/+ctf1OM2m42PPvqIxx57TD1WVlaGh4cH7u7uLY5RtD+ZARCigWPHjjFo0CAOHz7M8uXLOXr0KG+99RY//PADw4YNO+e6yVOnTvHee+8xa9asJrdt2bKFBx98kJqaGg4dOtTk9oEDB9K7d+8mXydOnDhvzH379iU2NrbJ2lUhhLicjR07ltTUVFJTU9XRfIDRo0ezdu1atm3bpq7/P3jwICUlJbz88sukpKTQo0ePZkfB//CHP7Bv3z7S0tL47LPPuO2229TbBgwYQFFREXq9nq5duzb6ciQJDYWGhtKlSxeOHTvW5Py4uLgWv84ePXqwY8eORscaVkCC0wNNNputxY95Lv379ycjI6PRsaVLlzJq1Cj27NlDenq6+vXEE0+wdOnSRufu37+fAQMGtEosog05ew2SEB3J1VdfrURGRio1NTWNjhcWFioeHh7K/ffff9b7/ve//1WCgoKaHDcajYqLi4ty8OBBZerUqcojjzzym2LbuHHjWfcAzJs3T0lJSflNjyuEEJei999/X3F3d1f0er1SVFSkHv/oo48Ub29vBVDy8vIURTn9d9hgMCiPP/64kpWVpXz55ZdKYmKiAii7d+9u9LjDhw9XkpOTFS8vr0afBXa7XRk5cqSSnJysfP/990p2drbyyy+/KM8884yyc+dORVGarsV/5513FHd3d2XRokXKoUOHlL179yrvv/++8tprrymK8r89AA1jKC8vVwBl48aNiqIoys8//6xotVrltddeUw4fPqy89dZbSmBgoOLn56fe5+OPP1Y8PT2V3bt3K8XFxeo+BUBZvXp1o9fn6+urLF269Kzv61dffaWEhIQo9fX1iqIoSm1treLv76+8+eabTc49fPiwAig7duxQj40ePbrZPQSiY5EZACH+v7KyMtauXcsDDzzQZOoyLCyM2267jZUrV5516vVslRM++ugjkpOT6d69O7fffjsff/xxk9JpF2vIkCHs2LFD1lwKITqNsWPHcurUKbp27dqoK+3o0aOpqqoiISGBqKgoAIKDg1m2bBmffvopPXv25OWXX+Yf//hHs4972223sWfPHm6++eZGnwUajYY1a9YwatQo7r77bhITE5k2bRo5OTnNdsUFmDVrFu+++y7Lli2jT58+jB49mmXLll3QDMCIESN46623WLhwIcnJyXz//fc8+uijjdbp33LLLVx99dWMHTuW4ODgiyrDec011+Di4sKGDRsA+OKLL6isrOSmm25qcm63bt3o06cP77//PgAFBQVs2bKl2R4ComPRKGe7mhGik9m+fTtDhw5l9erV3HjjjU1u/+c//8mcOXM4efIkISEhTW6/8cYbCQwM5L333mt0vG/fvsycOZOHH36Y+vp6wsPDWbJkSbN/TM9m4sSJ7Nq1i+rqagICAli9ejWDBw9Wb9+7dy/Jycnk5OQQExPT8hcthBDiknPPPfdw8ODBNiu3+cYbb/Dll19ecEOvxx9/nMrKSpYsWdImcYnWI5uAhWghR65sMBiavb25yglpaWlkZGQwbdo0APR6PVOnTmXp0qUXlACc74+wY5SqYQdMIYQQl4d//OMfjB8/Hk9PT7777js++OCDRg3FWtu9995LeXk5VVVVZ61A15yQkBD+/Oc/t1lcovVIAiDE/9e1a1c0Gg0ZGRnNzgAcPHiQ4ODgZqs9QPOVE5YuXYrNZqNLly7qMUVR0Gq1FBUVERYW1iqxOzYnBwcHt8rjCSGE6Dh27NjBq6++SlVVFfHx8fz73/9utuBEa9Hr9TzzzDMXfL/HH3+8DaIRbUH2AAjx/wUGBjJ+/HjeeOMNTp061ei2oqIiPv74Y7VrZHPOrJxgsVhYvnw5r732WqOqCXv27CE+Pp6PPvqo1WLfv38/kZGRzVaiEEIIcWlbtWoVRqORU6dOceDAAe6//35nhyQucbIHQIgGjhw5wvDhw0lKSuKFF14gLi6OAwcO8Pjjj6PX69m8eTNeXl7N3nffvn0MGDAAo9GIv78/q1atYvr06RiNRnx9fRud+8wzz/DFF19w4MCBVon7rrvuQqfTNdl/IIQQQghxJpkBEKKBbt26sXPnTuLj45kyZQoxMTFMmjSJxMREfvnll7Ne/AP06dOHQYMGsWrVKuD08p+rrrqqycU/nK7YkJGRwfbt2y865traWlavXs0999xz0Y8lhBBCiMufzAAIcR7PPvssCxcuZN26dQwbNuyc565Zs4Y///nP7N+/H622ffLrxYsX8+WXX7Ju3bp2eT4hhBBCXNpkE7AQ5/Hcc88RGxvL9u3bueKKK855YX/NNddw5MgRCgoK1PrTbc3FxYX//Oc/7fJcQgghhLj0yQyAEEIIIYQQnYjsARBCCCGEEKITkQRACCGEEEKITkQSACGEEEIIIToRSQCEEEIIIYToRCQBEEIIIYQQohORBEAIIYQQQohORBIAIYQQQgghOhFJAIQQQgghhOhEJAEQQgghhBCiE5EEQAghhBBCiE5EEgAhhBBCCCE6EUkAhBBCCCGE6ET+HxEXsY/gLHvRAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample SiO2.\n",
+      "Reduced sample SiO2 and saved outputs.\n",
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample AgBeh\n",
+      "Reducing sample AgBeh...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60387-2022-02-28_2215.xye\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample AgBeh.\n",
+      "Reduced sample AgBeh and saved outputs.\n",
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample dSDS\n",
+      "Reducing sample dSDS...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60389-2022-02-28_2215.xye\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample dSDS.\n",
+      "Reduced sample dSDS and saved outputs.\n",
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample VNb\n",
+      "Reducing sample VNb...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60391-2022-02-28_2215.xye\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample VNb.\n",
+      "Reduced sample VNb and saved outputs.\n",
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample sio2\n",
+      "Reducing sample sio2...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60385-2022-02-28_2215_mod.xye\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample sio2.\n",
+      "Reduced sample sio2 and saved outputs.\n",
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample glassy_carbon\n",
+      "Reducing sample glassy_carbon...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60383-2022-02-28_2215_mod.xye\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample glassy_carbon.\n",
+      "Reduced sample glassy_carbon and saved outputs.\n",
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample VNb\n",
+      "Reducing sample VNb...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60391-2022-02-28_2215_mod.xye\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample VNb.\n",
+      "Reduced sample VNb and saved outputs.\n",
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample dSDS\n",
+      "Reducing sample dSDS...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60389-2022-02-28_2215_mod.xye\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample dSDS.\n",
+      "Reduced sample dSDS and saved outputs.\n",
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample agbeh\n",
+      "Reducing sample agbeh...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60387-2022-02-28_2215_mod.xye\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample agbeh.\n",
+      "Reduced sample agbeh and saved outputs.\n",
+      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample isis_polymer\n",
+      "Reducing sample isis_polymer...\n",
+      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60339-2022-02-28_2215_mod.xye\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved reduction plot for sample isis_polymer.\n",
+      "Reduced sample isis_polymer and saved outputs.\n"
+     ]
+    }
+   ],
    "source": [
-    "from tabwidgetauto import tabs\n",
+    "from tabwidget import tabs\n",
     "display(tabs)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/src/ess/loki/tabwidget.py b/src/ess/loki/tabwidget.py
index 2c723c51..dc50e4a8 100644
--- a/src/ess/loki/tabwidget.py
+++ b/src/ess/loki/tabwidget.py
@@ -14,12 +14,12 @@
 from ess import loki
 from ess.sans.types import *
 from scipp.scipy.interpolate import interp1d
-import plopp as pp  # used for plotting in direct beam section
+import plopp as pp  
 import threading
 import time
 
 # ----------------------------
-# Common Utility Functions
+# Utility Functions
 # ----------------------------
 def find_file(work_dir, run_number, extension=".nxs"):
     pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
@@ -29,7 +29,7 @@ def find_file(work_dir, run_number, extension=".nxs"):
     else:
         raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
 
-def find_direct_beam(work_dir):
+def find_direct_beam(work_dir): #Find the direct beam automagically 
     pattern = os.path.join(work_dir, "*direct-beam*.h5")
     files = glob.glob(pattern)
     if files:
@@ -37,7 +37,7 @@ def find_direct_beam(work_dir):
     else:
         raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
 
-def find_mask_file(work_dir):
+def find_mask_file(work_dir): #Find the mask automagically 
     pattern = os.path.join(work_dir, "*mask*.xml")
     files = glob.glob(pattern)
     if files:
@@ -45,7 +45,7 @@ def find_mask_file(work_dir):
     else:
         raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
 
-def save_xye_pandas(data_array, filename):
+def save_xye_pandas(data_array, filename): ###Note here this needs to be 'fixed' / updated to use scipp io – ideally I want a nxcansas and xye saved for each file, but I struggled with the syntax and just did it in pandas as a first pass 
     q_vals = data_array.coords["Q"].values
     i_vals = data_array.values
     if len(q_vals) != len(i_vals):
@@ -65,7 +65,7 @@ def extract_run_number(filename):
         return m.group(1)
     return ""
 
-def parse_nx_details(filepath):
+def parse_nx_details(filepath): #For finding/grouping files by common title assigned by NICOS, e.g. 'runlabel' and 'runtype'
     details = {}
     with h5py.File(filepath, 'r') as f:
         if 'nicos_details' in f['entry']:
@@ -157,7 +157,7 @@ def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelengt
     plt.close(fig)
 
 # ----------------------------
-# Unified Backend Function for Reduction
+# Unified "Backend" Function for Reduction
 # ----------------------------
 def perform_reduction_for_sample(
     sample_info: dict,
@@ -244,7 +244,7 @@ def perform_reduction_for_sample(
     return res
 
 # ----------------------------
-# GUI Widgets (Refactored to use Unified Backend)
+# GUI Widgets 
 # ----------------------------
 class SansBatchReductionWidget:
     def __init__(self):
@@ -671,7 +671,7 @@ def widget(self):
         return self.main
 
 # ----------------------------
-# Direct Beam Functionality and Widget (unchanged)
+# Direct Beam stuff
 # ----------------------------
 def compute_direct_beam_local(
     mask: str,
@@ -810,7 +810,7 @@ def widget(self):
         return self.main
 
 # ----------------------------
-# Build the Tabbed Widget
+# Build it
 # ----------------------------
 reduction_widget = SansBatchReductionWidget().widget
 direct_beam_widget = DirectBeamWidget().widget
@@ -824,3 +824,4 @@ def widget(self):
 tabs.set_title(3, "Reduction (Auto)")
 
 # display(tabs)
+# voila /src/ess/loki/tabwidget.ipynb #--theme=dark
\ No newline at end of file

From 4d1b3bd2d1746e4ecc89ebff6fa97e5eb4e1b741 Mon Sep 17 00:00:00 2001
From: Oliver Hammond <56250478+olihammond@users.noreply.github.com>
Date: Thu, 6 Mar 2025 10:11:05 +0100
Subject: [PATCH 13/18] Update .gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 5c204a51..d15d62de 100644
--- a/.gitignore
+++ b/.gitignore
@@ -46,3 +46,4 @@ docs/generated/
 *.sqw
 *.nxspe
 /src/ess/loki/examplefiles
+/src/ess/loki/batch-gui-legacy

From 1e953ab2b0b8fa95e86a4845b5d616a125b6cece Mon Sep 17 00:00:00 2001
From: Oliver Hammond <56250478+olihammond@users.noreply.github.com>
Date: Thu, 6 Mar 2025 10:17:11 +0100
Subject: [PATCH 14/18] Update .gitignore

---
 .gitignore | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index d15d62de..3917992d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -32,7 +32,6 @@ docs/generated/
 # Data files
 *.data
 *.dat
-*.csv
 *.xye
 *.h5
 *.hdf5

From 6b6ff94a8c3267a2a47b156d5e644a99779908ff Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Tue, 25 Mar 2025 13:35:32 +0100
Subject: [PATCH 15/18] Messing around with plot colours (cute)

---
 .DS_Store                                     | Bin 6148 -> 6148 bytes
 src/.DS_Store                                 | Bin 6148 -> 6148 bytes
 src/ess/.DS_Store                             | Bin 6148 -> 6148 bytes
 src/ess/loki/.DS_Store                        | Bin 8196 -> 8196 bytes
 src/ess/loki/examplefiles/.DS_Store           | Bin 10244 -> 10244 bytes
 .../loki/examplefiles/nxsmodscript/.DS_Store  | Bin 6148 -> 10244 bytes
 src/ess/loki/tabwidget-i.py                   | 797 +++++++++++++++++
 src/ess/loki/tabwidget.ipynb                  | 264 +-----
 src/ess/loki/tabwidget.py                     |  88 +-
 src/ess/loki/tabwidgetii.py                   | 799 ++++++++++++++++++
 10 files changed, 1665 insertions(+), 283 deletions(-)
 create mode 100644 src/ess/loki/tabwidget-i.py
 create mode 100644 src/ess/loki/tabwidgetii.py

diff --git a/.DS_Store b/.DS_Store
index 018578b6996a039c9a7bc771e0278c33e37f432d..549fcc42dfb7737e581aa9bfa50667eff158f710 100644
GIT binary patch
delta 33
ocmZoMXffDuhE2rMTt~ss(6UxXq1w>M%v49g+{|+G12z>w0Ho*%C;$Ke

delta 33
ocmZoMXffDuhE2rO$Vf-Q*wUg_N1@u%$UsNI#Mo@}12z>w0HhQN9smFU

diff --git a/src/.DS_Store b/src/.DS_Store
index 76a0dc8250e279bd7f6215f1eac909b237b7d6ed..8072598055f02ba967955faa0eaeb4927aa177c6 100644
GIT binary patch
delta 33
ocmZoMXffEZhe^cJTt~ss(6UxXq1w>M%v49g+{|+G1tt?w0HTx$6aWAK

delta 33
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diff --git a/src/ess/loki/tabwidget-i.py b/src/ess/loki/tabwidget-i.py
new file mode 100644
index 00000000..e663c016
--- /dev/null
+++ b/src/ess/loki/tabwidget-i.py
@@ -0,0 +1,797 @@
+import os
+import glob
+import re
+import h5py
+import pandas as pd
+import scipp as sc
+import matplotlib.pyplot as plt
+import numpy as np
+import ipywidgets as widgets
+from ipydatagrid import DataGrid
+from IPython.display import display
+from ipyfilechooser import FileChooser
+from ess import sans
+from ess import loki
+from ess.sans.types import *
+from scipp.scipy.interpolate import interp1d
+import plopp as pp  
+import threading
+import time
+
+# ----------------------------
+# Utility Functions
+# ----------------------------
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+def find_direct_beam(work_dir):  # Find the direct beam automatically 
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+
+def find_mask_file(work_dir):  # Find the mask automatically 
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+def extract_run_number(filename):
+    m = re.search(r'(\d{4,})', filename)
+    if m:
+        return m.group(1)
+    return ""
+
+def parse_nx_details(filepath):
+    details = {}
+    with h5py.File(filepath, 'r') as f:
+        if 'nicos_details' in f['entry']:
+            grp = f['entry']['nicos_details']
+            if 'runlabel' in grp:
+                val = grp['runlabel'][()]
+                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+            if 'runtype' in grp:
+                val = grp['runtype'][()]
+                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+    return details
+
+# ----------------------------
+# Colour Mapping From Filename 
+# ----------------------------
+def string_to_colour(input_str):
+    if not input_str:
+        return "#000000"  # Empty input = black
+    total = 0
+    for ch in input_str:
+        if ch.isalpha():
+            total += ord(ch.lower()) - ord('a') + 1  # a=1, b=2, ..., z=26
+        elif ch.isdigit():
+            total += 1 + int(ch) * (25/9)  # Maps '0' to 1 and '9' to 26
+    avg = total / len(input_str)
+    norm = max(0, min(1, avg / 26))  # Average and normalise to [0,1]
+    rgba = plt.get_cmap('flag')(norm)
+    return '#{:02x}{:02x}{:02x}'.format(int(rgba[0]*255),
+                                        int(rgba[1]*255),
+                                        int(rgba[2]*255))
+
+# ----------------------------
+# Reduction and Plotting Functions
+# ----------------------------
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101
+):
+    if mask_files is None:
+        mask_files = []
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelength_max, wavelength_n, q_min, q_max, q_n, output_dir, show=True):
+    fig, axs = plt.subplots(1, 2, figsize=(8, 4))
+    axs[0].set_box_aspect(1)
+    axs[1].set_box_aspect(1)
+    title_str = f"{sample} - {os.path.basename(sample_run_file)}"
+    fig.suptitle(title_str, fontsize=14)
+    q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
+    x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+    if res["IofQ"].variances is not None:
+        yerr = np.sqrt(res["IofQ"].variances)
+        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none',
+                        color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+    else:
+        axs[0].scatter(x_q, res["IofQ"].values)
+    axs[0].set_xlabel("Q (Å$^{-1}$)")
+    axs[0].set_ylabel("I(Q)")
+    axs[0].set_xscale("log")
+    axs[0].set_yscale("log")
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+    if res["transmission"].variances is not None:
+        yerr_tr = np.sqrt(res["transmission"].variances)
+        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none',
+                        color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+    else:
+        axs[1].scatter(x_wl, res["transmission"].values)
+    axs[1].set_xlabel("Wavelength (Å)")
+    axs[1].set_ylabel("Transmission")
+    plt.tight_layout()
+    out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png"))
+    fig.savefig(out_png, dpi=300)
+    if show:
+        plt.show()
+    plt.close(fig)
+
+# ----------------------------
+# Unified "Backend" Function for Reduction
+# ----------------------------
+def perform_reduction_for_sample(
+    sample_info: dict,
+    input_dir: str,
+    output_dir: str,
+    reduction_params: dict,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+):
+    sample = sample_info.get("SAMPLE", "Unknown")
+    try:
+        sample_run_file = find_file(input_dir, str(sample_info["SANS"]), extension=".nxs")
+        transmission_run_file = find_file(input_dir, str(sample_info["TRANS"]), extension=".nxs")
+    except Exception as e:
+        print(f"Skipping sample {sample}: {e}")
+        return None
+
+    mask_file = None
+    mask_candidate = str(sample_info.get("mask", "")).strip()
+    if mask_candidate:
+        mask_candidate_file = os.path.join(input_dir, f"{mask_candidate}.xml")
+        if os.path.exists(mask_candidate_file):
+            mask_file = mask_candidate_file
+    if mask_file is None:
+        try:
+            mask_file = find_mask_file(input_dir)
+            print(f"Identified mask file: {mask_file} for sample {sample}")
+        except Exception as e:
+            print(f"Mask file not found for sample {sample}: {e}")
+            return None
+
+    print(f"Reducing sample {sample}...")
+    try:
+        res = reduce_loki_batch_preliminary(
+            sample_run_file=sample_run_file,
+            transmission_run_file=transmission_run_file,
+            background_run_file=background_run_file,
+            empty_beam_file=empty_beam_file,
+            direct_beam_file=direct_beam_file,
+            mask_files=[mask_file],
+            wavelength_min=reduction_params["wavelength_min"],
+            wavelength_max=reduction_params["wavelength_max"],
+            wavelength_n=reduction_params["wavelength_n"],
+            q_start=reduction_params["q_start"],
+            q_stop=reduction_params["q_stop"],
+            q_n=reduction_params["q_n"]
+        )
+    except Exception as e:
+        print(f"Reduction failed for sample {sample}: {e}")
+        return None
+
+    out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+    try:
+        save_xye_pandas(res["IofQ"], out_xye)
+        print(f"Saved reduced data to {out_xye}")
+    except Exception as e:
+        print(f"Failed to save reduced data for {sample}: {e}")
+
+    try:
+        save_reduction_plots(
+            res,
+            sample,
+            sample_run_file,
+            reduction_params["wavelength_min"],
+            reduction_params["wavelength_max"],
+            reduction_params["wavelength_n"],
+            reduction_params["q_start"],
+            reduction_params["q_stop"],
+            reduction_params["q_n"],
+            output_dir,
+            show=True
+        )
+        print(f"Saved reduction plot for sample {sample}.")
+    except Exception as e:
+        print(f"Failed to save reduction plot for {sample}: {e}")
+    print(f"Reduced sample {sample} and saved outputs.")
+    return res
+
+# ----------------------------
+# GUI Widgets 
+# ----------------------------
+class SansBatchReductionWidget:
+    def __init__(self):
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.ebeam_sans_widget = widgets.Text(value="", placeholder="Enter Ebeam SANS run number", description="Ebeam SANS:")
+        self.ebeam_trans_widget = widgets.Text(value="", placeholder="Enter Ebeam TRANS run number", description="Ebeam TRANS:")
+        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: plt.close('all'))
+        # Remove widget outputs from the layout since we use plain print and plt.show()
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
+            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button])
+        ])
+    
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+    
+    def run_reduction(self, _):
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
+            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            print("Using empty-beam files:")
+            print("  Background (Ebeam SANS):", background_run_file)
+            print("  Empty beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            print("Error finding empty beam files:", e)
+            return
+        
+        reduction_params = {
+            "wavelength_min": self.wavelength_min_widget.value,
+            "wavelength_max": self.wavelength_max_widget.value,
+            "wavelength_n": self.wavelength_n_widget.value,
+            "q_start": self.q_start_widget.value,
+            "q_stop": self.q_stop_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+        
+        df = self.table.data
+        for idx, row in df.iterrows():
+            perform_reduction_for_sample(
+                sample_info=row,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file
+            )
+    
+    @property
+    def widget(self):
+        return self.main
+
+class SemiAutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.scan_button = widgets.Button(description="Scan Directory")
+        self.scan_button.on_click(self.scan_directory)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.add_row_button = widgets.Button(description="Add Row")
+        self.add_row_button.on_click(self.add_row)
+        self.delete_row_button = widgets.Button(description="Delete Last Row")
+        self.delete_row_button.on_click(self.delete_last_row)
+        self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
+        self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
+        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: plt.close('all'))
+        self.processed = set()
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            self.scan_button,
+            self.table,
+            widgets.HBox([self.add_row_button, self.delete_row_button]),
+            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
+            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button])
+        ])
+    
+    def add_row(self, _):
+        df = self.table.data
+        new_row = {col: "" for col in df.columns} if not df.empty else {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
+        df = df.append(new_row, ignore_index=True)
+        self.table.data = df
+
+    def delete_last_row(self, _):
+        df = self.table.data
+        if not df.empty:
+            self.table.data = df.iloc[:-1]
+
+    def scan_directory(self, _):
+        print("Scanning directory...")
+        input_dir = self.input_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            print("Invalid input folder.")
+            return
+        nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+        groups = {}
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runlabel' not in details or 'runtype' not in details:
+                continue
+            runlabel = details['runlabel']
+            runtype = details['runtype'].lower()
+            run_number = extract_run_number(os.path.basename(f))
+            if runlabel not in groups:
+                groups[runlabel] = {}
+            groups[runlabel][runtype] = run_number
+        table_rows = []
+        for runlabel, d in groups.items():
+            if 'sans' in d and 'trans' in d:
+                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+        df = pd.DataFrame(table_rows)
+        self.table.data = df
+        print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+        ebeam_sans_files = []
+        ebeam_trans_files = []
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runtype' in details:
+                if details['runtype'].lower() == 'ebeam_sans':
+                    ebeam_sans_files.append(f)
+                elif details['runtype'].lower() == 'ebeam_trans':
+                    ebeam_trans_files.append(f)
+        if ebeam_sans_files:
+            ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_sans_text.value = ebeam_sans_files[0]
+        else:
+            self.empty_beam_sans_text.value = ""
+        if ebeam_trans_files:
+            ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_trans_text.value = ebeam_trans_files[0]
+        else:
+            self.empty_beam_trans_text.value = ""
+    
+    def run_reduction(self, _):
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            print("Direct-beam file not found:", e)
+            return
+        background_run_file = self.empty_beam_sans_text.value
+        empty_beam_file = self.empty_beam_trans_text.value
+        if not background_run_file or not empty_beam_file:
+            print("Empty beam files not found.")
+            return
+        
+        reduction_params = {
+            "wavelength_min": self.lambda_min_widget.value,
+            "wavelength_max": self.lambda_max_widget.value,
+            "wavelength_n": self.lambda_n_widget.value,
+            "q_start": self.q_min_widget.value,
+            "q_stop": self.q_max_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+        
+        df = self.table.data.copy()
+        for idx, row in df.iterrows():
+            perform_reduction_for_sample(
+                sample_info=row,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file
+            )
+    
+    @property
+    def widget(self):
+        return self.main
+
+class AutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.start_stop_button = widgets.Button(description="Start")
+        self.start_stop_button.on_click(self.toggle_running)
+        self.status_label = widgets.Label(value="Stopped")
+        self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
+        self.running = False
+        self.thread = None
+        self.processed = set()
+        self.empty_beam_sans = None
+        self.empty_beam_trans = None
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.start_stop_button, self.status_label]),
+            self.table
+        ])
+    
+    def toggle_running(self, _):
+        if not self.running:
+            self.running = True
+            self.start_stop_button.description = "Stop"
+            self.status_label.value = "Running"
+            self.thread = threading.Thread(target=self.background_loop, daemon=True)
+            self.thread.start()
+        else:
+            self.running = False
+            self.start_stop_button.description = "Start"
+            self.status_label.value = "Stopped"
+    
+    def background_loop(self):
+        while self.running:
+            input_dir = self.input_dir_chooser.selected
+            output_dir = self.output_dir_chooser.selected
+            if not input_dir or not os.path.isdir(input_dir):
+                print("Invalid input folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            if not output_dir or not os.path.isdir(output_dir):
+                print("Invalid output folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+            groups = {}
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runlabel' not in details or 'runtype' not in details:
+                    continue
+                runlabel = details['runlabel']
+                runtype = details['runtype'].lower()
+                run_number = extract_run_number(os.path.basename(f))
+                if runlabel not in groups:
+                    groups[runlabel] = {}
+                groups[runlabel][runtype] = run_number
+            table_rows = []
+            for runlabel, d in groups.items():
+                if 'sans' in d and 'trans' in d:
+                    table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+            df = pd.DataFrame(table_rows)
+            self.table.data = df
+            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+            ebeam_sans_files = []
+            ebeam_trans_files = []
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runtype' in details:
+                    if details['runtype'].lower() == 'ebeam_sans':
+                        ebeam_sans_files.append(f)
+                    elif details['runtype'].lower() == 'ebeam_trans':
+                        ebeam_trans_files.append(f)
+            if ebeam_sans_files:
+                ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_sans = ebeam_sans_files[0]
+            else:
+                self.empty_beam_sans = None
+            if ebeam_trans_files:
+                ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_trans = ebeam_trans_files[0]
+            else:
+                self.empty_beam_trans = None
+            try:
+                direct_beam_file = find_direct_beam(input_dir)
+            except Exception as e:
+                print("Direct-beam file not found:", e)
+                time.sleep(60)
+                continue
+            for index, row in df.iterrows():
+                key = (row["SAMPLE"], row["SANS"], row["TRANS"])
+                if key in self.processed:
+                    continue
+                try:
+                    sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs")
+                    transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs")
+                except Exception as e:
+                    print(f"Skipping sample {row['SAMPLE']}: {e}")
+                    continue
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
+                except Exception as e:
+                    print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
+                    continue
+                if not self.empty_beam_sans or not self.empty_beam_trans:
+                    print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
+                    continue
+                print(f"Reducing sample {row['SAMPLE']}...")
+                reduction_params = {
+                    "wavelength_min": 1.0,
+                    "wavelength_max": 13.0,
+                    "wavelength_n": 201,
+                    "q_start": 0.01,
+                    "q_stop": 0.3,
+                    "q_n": 101
+                }
+                perform_reduction_for_sample(
+                    sample_info=row,
+                    input_dir=input_dir,
+                    output_dir=output_dir,
+                    reduction_params=reduction_params,
+                    background_run_file=self.empty_beam_sans,
+                    empty_beam_file=self.empty_beam_trans,
+                    direct_beam_file=direct_beam_file
+                )
+                self.processed.add(key)
+            time.sleep(60)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Direct Beam stuff
+# ----------------------------
+def compute_direct_beam_local(
+    mask: str,
+    sample_sans: str,
+    background_sans: str,
+    sample_trans: str,
+    background_trans: str,
+    empty_beam: str,
+    local_Iq_theory: str,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    n_wavelength_bins: int = 50,
+    n_wavelength_bands: int = 50
+) -> dict:
+    workflow = loki.LokiAtLarmorWorkflow()
+    workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
+    workflow[NeXusDetectorName] = 'larmor_detector'
+    wl_min = sc.scalar(wavelength_min, unit='angstrom')
+    wl_max = sc.scalar(wavelength_max, unit='angstrom')
+    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
+    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
+    workflow[CorrectForGravity] = True
+    workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
+    workflow[ReturnEvents] = False
+    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
+    workflow[Filename[SampleRun]] = sample_sans
+    workflow[Filename[BackgroundRun]] = background_sans
+    workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
+    workflow[Filename[EmptyBeamRun]] = empty_beam
+    center = sans.beam_center_from_center_of_mass(workflow)
+    print("Computed beam center:", center)
+    workflow[BeamCenter] = center
+    Iq_theory = sc.io.load_hdf5(local_Iq_theory)
+    f = interp1d(Iq_theory, 'Q')
+    I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
+    print("Computed I0:", I0)
+    results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
+    iofq_full = results[-1]['iofq_full']
+    iofq_bands = results[-1]['iofq_bands']
+    direct_beam_function = results[-1]['direct_beam']
+    pp.plot(
+        {'reference': Iq_theory, 'data': iofq_full},
+        color={'reference': 'darkgrey', 'data': 'C0'},
+        norm='log',
+    )
+    print("Plotted full-range result vs. theoretical reference.")
+    return {
+        'direct_beam_function': direct_beam_function,
+        'iofq_full': iofq_full,
+        'Iq_theory': Iq_theory,
+    }
+
+class DirectBeamWidget:
+    def __init__(self):
+        self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:")
+        self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:")
+        self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:")
+        self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:")
+        self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:")
+        self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:")
+        self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:")
+        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
+        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
+        self.compute_button = widgets.Button(description="Compute Direct Beam")
+        self.compute_button.on_click(self.compute_direct_beam)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: plt.close('all'))
+        self.main = widgets.VBox([
+            self.mask_text,
+            self.sample_sans_text,
+            self.background_sans_text,
+            self.sample_trans_text,
+            self.background_trans_text,
+            self.empty_beam_text,
+            self.local_Iq_theory_text,
+            widgets.HBox([
+                self.db_wavelength_min_widget,
+                self.db_wavelength_max_widget,
+                self.db_n_wavelength_bins_widget,
+                self.db_n_wavelength_bands_widget
+            ]),
+            self.compute_button,
+            widgets.HBox([self.clear_log_button, self.clear_plots_button])
+        ])
+    
+    def compute_direct_beam(self, _):
+        # No longer clearing widget outputs; we simply print new info.
+        mask = self.mask_text.value
+        sample_sans = self.sample_sans_text.value
+        background_sans = self.background_sans_text.value
+        sample_trans = self.sample_trans_text.value
+        background_trans = self.background_trans_text.value
+        empty_beam = self.empty_beam_text.value
+        local_Iq_theory = self.local_Iq_theory_text.value
+        wl_min = self.db_wavelength_min_widget.value
+        wl_max = self.db_wavelength_max_widget.value
+        n_bins = self.db_n_wavelength_bins_widget.value
+        n_bands = self.db_n_wavelength_bands_widget.value
+        print("Computing direct beam with:")
+        print("  Mask:", mask)
+        print("  Sample SANS:", sample_sans)
+        print("  Background SANS:", background_sans)
+        print("  Sample TRANS:", sample_trans)
+        print("  Background TRANS:", background_trans)
+        print("  Empty Beam:", empty_beam)
+        print("  I(q) Theory:", local_Iq_theory)
+        print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
+        try:
+            results = compute_direct_beam_local(
+                mask,
+                sample_sans,
+                background_sans,
+                sample_trans,
+                background_trans,
+                empty_beam,
+                local_Iq_theory,
+                wavelength_min=wl_min,
+                wavelength_max=wl_max,
+                n_wavelength_bins=n_bins,
+                n_wavelength_bands=n_bands
+            )
+            print("Direct beam computation complete.")
+        except Exception as e:
+            print("Error computing direct beam:", e)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Build it
+# ----------------------------
+reduction_widget = SansBatchReductionWidget().widget
+direct_beam_widget = DirectBeamWidget().widget
+semi_auto_reduction_widget = SemiAutoReductionWidget().widget
+auto_reduction_widget = AutoReductionWidget().widget
+
+tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
+tabs.set_title(0, "Direct Beam")
+tabs.set_title(1, "Reduction (Manual)")
+tabs.set_title(2, "Reduction (Smart)")
+tabs.set_title(3, "Reduction (Auto)")
+
+# To display the tabs in a Voila deployment, simply display the tabs widget.
+#display(tabs)
diff --git a/src/ess/loki/tabwidget.ipynb b/src/ess/loki/tabwidget.ipynb
index 42ab2576..81508ea3 100644
--- a/src/ess/loki/tabwidget.ipynb
+++ b/src/ess/loki/tabwidget.ipynb
@@ -8,7 +8,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f426308f9d1440abb6c436f2a6199c91",
+       "model_id": "7024d6d63db745e98d2b39f691d4e559",
        "version_major": 2,
        "version_minor": 0
       },
@@ -18,264 +18,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample Polymer\n",
-      "Reducing sample Polymer...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60395-2022-02-28_2215.xye\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample Polymer.\n",
-      "Reduced sample Polymer and saved outputs.\n",
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample Carbon\n",
-      "Reducing sample Carbon...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60383-2022-02-28_2215.xye\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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nwIABPo+/9NJLnX5uZs6cedIqTR3vnixcuLDTc+69916Cg4O7vN6DDz7IihUruPjiiztVeYIjpS2nT59OYGAg2dnZpKen09TUxIoVK3jooYdYvXo1GzdulNNqTtTx7uuJtP1EPPfccz7fT5o0ie+//55hw4axcuVK5s+fL6c59cTxfr8YDAb5uFar5aOPPiIjI4N77rkHtVrNihUrfM4BuOqqq3jppZeYMWMG11xzDVOmTGH8+PHEx8f35q0KwrnjdPc4BOFM5E35+P7773v1vLvuuksCpLi4OOmmm26S/vGPf8hpHnSxEM47ynX04kUv7wh/VyN9Ho9HUiqVUkpKinwsOTlZUiqVXV6vq/M7Lto9WlFRUZdT6cdqZ8evrq55tLCwMAmQ3nnnnU6Pbdy4UQKk3//+9/Kxk5m+UlpaKul0Omn06NE+xxcsWNDtrMu3334rAdKNN97Yo9dYtWqVBEh///vf5WPen4WOX93d4+LiYikhIUHSarXSl19+2eU5v/SerF69WtLpdFJsbGynxZJdaWxslNLT0yWFQiG98cYbXZ7jrVzT8cubnvVLU3p6ck+Ofm3ovtLVwoUL5ZkNbyWbo2VmZkp+fn5SVVVVp8fmzp0rQefKQr3Vk/t6Im0/Wk9G+Lvj/b/x5ptv9uh87++3rhyrApnH45FnFMaNG9ft9bOysqRp06b5pAOOGDGiyxkFQTjXiYBfELrw0EMP+eSR9kRNTY2kUCikoUOHdprir6qqOmbA3x3vH8WOefpe1dXV8h84r/T0dAnoMlXFe35GRoZ87GQF/Cfq/PPPlwDpk08+6fTY3r175dQbL2+p1K7WFTQ2Nh43QDja8OHDJYVC4fN5ZWRkdJlqIElHKjIpFIoeV4VpamqSgE6dip4oKiqSEhMTJY1GI33++efdnvdL7sm3334r6fV6KSYmptuUoI4aGhrkn7Gjq6z0lLeq0O23397l48OGDZOUSmWXpU57ek96yhswT5w4sdu0HKvV2un/TUfe99PTykZdOZH72pO2d+WXBPzeqlMvvvhij84/0YDfuzYjNDRUAqS33nrrmK/T2toqrV+/XpozZ46k1+slPz+/LsudCsK5TFTpEYQuzJw5E5VKxVtvvUVdXd0xz3U4HMCRzXkkSeKiiy6SN2XyysnJ+UXt6er53mPDhw+Xj6WnpwN0ShmB/1Vq6Xj+6TZ58mQA8vLyOj3mPdYxDeSCCy4AYO3atZ3O9x7zntMTVVVVckUUL+9Or1197vX19UiS5FOZ6Vi8KVy9rZxSXFzMxIkTqaio4OOPP+b3v/99t+ee6D1ZvXo1M2bMwGQysX79elJSUo7ZpsbGRi666CJ27NjBK6+8wh133NGbtyQbO3YsOp2O7777DumofR+rqqrYs2cPY8aMQa/X+zzWm3vSEwsXLmThwoVccMEFfP31153+z3p5fx7q6+u7fNz7c9LTn4mjnch97WnbT7YtW7YAnNJNAHNzc5k/fz6pqans2bOHhIQE7r33Xg4cONDtc/z8/Jg4cSKLFy/mwQcfxG63s27dulPWRkH4TTrNHQ5BOGN5R05HjBghFRYWdnrcbrdLixcvlv7xj39IkvS/muBjx471qY9fVlYmJSUl/aIR/mNV6dmwYYN8fMOGDRIgDRo0yKcqhsVikQYNGtTp/NM9wl9YWCjpdDopIiJCKi8vl49brVZp+PDhEvhWSmpvb5eSkpIknU4n7dixw+d87yZTBw4ckI/X19dLe/fu7fS6Ho9HTq258MILfR67/fbb5bQdl8slH3e73fKC0vvvv18+np+f3+WMSktLi3TxxRdLgPT444/3+J4UFRVJCQkJklqt7nLm42i9vSeSdGRkX6fTSVFRUT1aGNrQ0CB/HkdvPnUiervxVm/vyfE8/PDDEiBlZmZKzc3Nxz1/wIABXS40tlgs0tChQyWg2/SiYzmR+9rbth/teCP827dv73LGYNWqVZJCoZDCwsI67SlSWVkp5efn+1QGk6Tej/A3NzdL/fv3l7RarfyznJOTI6lUKikjI8OnAlp2drbP7zivO++8U4LOm+MJwrlOLNoVhG488cQTtLW18eKLLzJgwAAmT57M4MGD0Wg0FBUVsW7dOhoaGnjiiSeAIzXEr7zySj755BNGjhzJhRdeSE1NDV999RWTJ0+msLDwhNsyatQohg0bxrXXXotOp+PTTz+luLiYW2+9lQkTJsjnTZgwgbvvvptXXnmFwYMHc+WVVyJJEp9++illZWXcc889Puefbn379uW5557jnnvuYdiwYVx++eXodDq+/vpriouLue2227jwwgvl89VqNUuXLmXatGlkZmZy/fXXExQUxKeffkpRURFPPPEE/fv3l88vKysjPT2d0aNHM2jQIKKioqivrycnJ4cDBw4QFRXFkiVLfNr00EMP8cUXX7B8+XJyc3OZPHkyCoWC9evXs2fPHhITE5k7d658/urVq5k7dy4TJ04kKSkJo9FIRUUF3377LQ0NDZx//vnd7n/QlYkTJ1JSUsLYsWPZvXs3u3fv7nROx0Wpvb0n+/fvZ8aMGTgcDiZOnMhHH33U6fqJiYnMnDlT/v6KK65g586dDBw4kMbGxl4vij3aM888w/r167nzzjtZt24d/fv3Jycnhx9//JFp06Zx0003/aJ7cizLli3jscceQ61WM3r06E4LVL2v13HB+UsvvcRll13GrbfeKi8obWpq4ssvv6Smpobp06fzf//3fz16/Y56e19PpO319fU88MAD8vft7e3U19f7fL7Lli2T//3Pf/6Tzz//nAsvvJD4+HgkSWL79u3k5OSg1+t5//33CQwM9HnNefPm8f777/Pee+/5XLe37rnnHg4ePMgLL7wgz0SOHz+eBx98kMcff5wHH3yQ559/HoDFixfz3XffMWnSJJKSktDr9Wzfvp3vv/+elJQULr/88hNuhyCclU53j0MQznQ///yzdPPNN0spKSmSn5+fpNPppMTEROn666/vVBLOZrNJ999/v5SYmCjvsvv4449LTqfzF43we3fa9ZZYTE5Olp599lmfEeiO3n33XWnUqFGSv7+/5O/vL40aNarLRYWne4Tf64svvpAyMzOlwMBASa/XSyNGjDhm3u6WLVukiy++WDIajZKfn580cuTITnscSNKRHPZ58+ZJ5513nhQRESGp1WopMDBQSk9Pl+bPn99tve6qqirp7rvvllJSUiStVivpdDqpf//+0pw5czo9Z8+ePdJf/vIXaciQIZLJZJLUarUUGhoqXXDBBdLrr7/e67KcdLHg9OivX3JPOu690N3X0T+nXS3CPfqrt7syV1ZWSjfffLMUGRkpabVaKSUlRXr00Ud9dq7+pfekK10tmj76q6v/D9u2bZOuueYaKTo6WlKr1VJAQIA0atQo6aWXXjrh0qu9va8n0nbv/+Oe3rtPP/1U+v3vfy8lJiZK/v7+klarlfr27SvdcsstUn5+fpfvw/t76r333vM53psRfm+Z4SlTpnQqOtDe3i6NHTtWUigU8u/c1atXSzfeeKM0YMAAyWAwSIGBgdKgQYOO+f9aEM5lCkk6KolSEARBEARBEISzhli0KwiCIAiCIAhnMRHwC4IgCIIgCMJZTCzaFQRBEISToLi42GcBbHeCg4O59957T3l7BEEQvEQOvyAIgiCcBFlZWUyaNOm45yUkJFBcXHzqGyQIgvD/iYBfEARBEARBEM5iIodfEARBEARBEM5iIuAXBEEQBEEQhLOYCPgFQRAEQRAE4SwmAn5BEARBEARBOIuJgF8QBEEQBEEQzmIi4BcEQRAEQRCEs5gI+AVBEARBEAThLCYCfkEQBEEQBEE4i4mAXxAEQRAEQRDOYiLgFwRBEARBEISzmAj4BUEQBEEQBOEsJgJ+QRAEQRAEQTiLiYBfEARBEARBEM5iIuAXBEEQBEEQhLOYCPgFQRAEQRAE4SwmAn5BEARBEARBOIuJgF8QBEEQBEEQzmIi4BcEQRAEQRCEs5gI+AVBEARBEAThLCYCfkEQBEEQBEE4i6lPdwNON4/HQ2VlJQaDAYVCcbqbIwiCAIAkSdhsNvr06YNSKcZmTjfxt0IQhDNRT/9WnPMBf2VlJXFxcae7GYIgCF0qKysjNjb2dDfjnCf+VgiCcCY73t+Kcz7gNxgMwJEbFRQUdJpbIwiCcITVaiUuLk7+HSWcXuJvhSAIZ6Ke/q045wN+79RsUFCQ+CUuCMIZR6SPnBnE3wpBEM5kx/tbIRJDBUEQBEEQBOEsJgJ+QRAEQRAEQTiLiYBfEARBEARBEM5iIuAXBEEQBEEQhLPYORvwL1myhEGDBjFq1KjT3RRBEARBEARBOGXO2YD/zjvvJC8vj59//vl0N0UQBEEQBEEQTplzNuAXBEEQBEEQhHPBOV+H/7fK4/FQWlqKzWbDYDAQHx9/zC2VBUEQBEEQhHOTCPh/g/Lz81mzZg1NTU3yseDgYKZNm0Zqaurpa5ggCIIgCIJwxhEBfy+dzJH1E7lWfn4+q1aton///lx11VVERERQW1tLTk4Oq1at4pprrjkpQX9P2iZmGQRBEARBEM58IuDvhZM5sn4i1/J4PKxZs4b+/ftzxRVX8PTTTwPw4IMPct1117Fy5UrWrl3LgAEDflFw3pO29bT9olMgCMK5yOl08tRTTwFHfkdrtdrT3CJBEM5lIuDvoZM5sn6i1yotLaWpqYmrrroKhULh85hCoWD8+PG88847lJaWkpiY6PN6Pe1c9KRtQI/a35PXFR0CQRAEQRCEU0sE/D3wS0bWT+a1bDYbABEREV1e23vcex70rnPRsW3XXXed3KmIjY2V27Z69WqA47bf4/Hwn//857gdh2N1CERnQBAEQRAE4ZcTAX8PnOjI+sm+lsFgAKC2trbLoL+2ttbnvJ4E8B07Fx3b1t7e3mk6evz48SxevBiAq6++utv2L126lI8//pjU1NRuX3f58uXodDoGDBjQZYdgxIgRHD58uNedAdFJEARBEARB8CUC/h44kZH1U3Gt+Ph4goODycnJ4brrrmPhwoXyY5IksXHjRkwmE/Hx8QA9CuA7di560jaHw3HccywWCw6Hg9tuu63L1x03bhzvv/8+l156aZcdgsWLF7NkyRL+9Kc/9aozkJKScsxOgiAIgiAIwrlIDH32QMeR9a4cPbJ+qq6lVCqZNm0aBw8eZOXKlZSVleFwOCgrK2PlypUcPHiQqVOnyiPave1c9KRtOp0OnU53zHMcDgc6na7b121ra6OtrY3U1FTa29tZuHAhCxcuxOl0IkkSNpsNf39/xo0bR2xsLFqtVu4MBAQEsGTJEsLDw5k9ezYPPvggs2fPxuVysXjxYlwul8/xyMhIVq5cybp169izZw/FxcV4PJ4u2yUIgiAIgnA2EiP8PdBxZP2KK67weayrkfVTea3U1FSuueYa1qxZwzvvvCMfN5lMnRb79jYFqCczCAkJCQDHPCc8PByFQtHt65aWlgLI1zr6MbfbTUJCAmazmffffx84MjugVqs7dQYA+vTpg0qlYvDgwajVavr06YNSqSQ2NpZhw4bx7bffsmXLFsaMGYNCoTjmqL9ICRIEQRAE4WwjAv4e8I6sr1q1ik8//ZRbbrmFiIgIampq2LhxIwcPHuSaa67pUWB4Mq6VmprKgAEDjhuY9jYFqGPbVq5cyfjx4+V0mo5tA455zrXXXst3333X7evm5+ej1+vR6/Wd3pvNZqOlpYXAwEACAwN9HuuuM/CHP/wBi8XCDTfcwJdffsmcOXMIDg7m8ssv5/PPP2f06NEUFhbyxz/+EX9/fzk16KqrriIgIEC+hy0tLXz33XciJUgQBEEQhLOKCPh7qDcj67/GtZRK5XEXCPc0gO/YUehp2453jlKp7PZ1GxoaSE9P58cff+zUIQgMDKS0tJShQ4d2muXorjPQ3Nwst/3LL7/E4XAgSRLfffcd/fv35/LLL+eZZ57B4XDIC5gXL17M/PnzGTp0KAqFgrq6OgoKCrjooouYPXu2z7qBlStXkpmZSWRkJAaDgdjYWMrLy+WOwtHfH935ErMGgiAIgiCcTiLg74Wejqz/2tc63uv0tnPRk7Yd75xjve51110HdD1LsGnTJlpaWggICECn0/WoM+AN/vPz8wHQ6XRYLBY8Hg/XXXcdFRUVZGVl0dTUxKJFiygoKKCyshKlUsnFF19Meno6zzzzDEqlkubmZmw2G7GxsV2mBNXX11NZWUmfPn0IDw+nrq7O53uAoKAghg4dSmRkJDU1NezZsweLxSK3t+PjAQEBALS0tHTqPBz9mOgoCIIgCIJwIkTA30s9GVk/Hdc6lhPpXPR0BuFY5xzvdbvrENx1113k5ub2uDPg8XgwGo188MEHJCcn8+ijj7Jv3z4++eQTwsPDWbVqFXq9HqPRKJcqHT58OAaDgcDAQKqrqwH4+9//TlZWFvfddx9jxozhiiuu8EkJGjt2LOvWrSM2NhaNRsOwYcPYuHGj/P3ll19OY2Mj7733Hl988QXR0dFUVVURHx/PrFmzOP/88/nxxx99HjebzQAkJycDyJ0HgIKCAvmx8PBwkV4knBOys7N57rnnyM3Npaqqis8++4wZM2Yc8zkbNmxgzpw57Nu3jz59+vD3v/+dP//5zz7nfPLJJyxYsICCggKSk5N58sknufzyy0/hOxEEQThziID/HPFrdS5687rH6hAkJyf3qjPgdrvZu3cvCQkJVFRUoNVqsVgsvP3229TW1pKcnIxCoZBLlY4bN45Dhw7xzjvv4HA4CAsLIzIyknHjxvHOO+/Q1NQkpwT93//9H5dddhlbtmzhz3/+M3/84x/56KOPWL58OVdddRXXX389H3/8MStXrkSn03HxxRdTX1/P999/z5VXXkloaCjZ2dm0tLSQm5vLxRdfTF5eHj///DPTp09HoVCwb98+7HY7sbGxWK1W/P39mT59OgA1NTVkZmayb98+XnnlFWbMmEFKSorc+QE6/VvMCAi/VS0tLQwbNoxZs2Zx5ZVXHvf8oqIiLr30Um699VY+/PBDfvzxR+644w7Cw8Pl52/evJlrr72Wxx9/nMsvv5zPPvuMa665ho0bNzJmzJhT/ZYEQRBOOxHwC6dVdx2CE+kM3H///Rw+fJh33nlHXhzc0NDAP/7xDwYNGgTAnj17kCSJAwcOEBwcjFKplNNtOlYVqq+vJygoiOuuu466ujp5/4Hzzz8fl8vF999/z44dO7jvvvtwuVxs2bKFr776ijvuuIPrr7+ezZs38+mnnzJy5EgyMjK4/vrr+eijj5g3bx6XX345b775JnV1dUydOpWUlBRmzZoFwOLFi5k9ezYtLS385S9/QalUsnjxYl599VWGDBnCoUOHuOeee0hISCAkJESeITCZTPK/k5KS0Gq1aLVaLrroIrlzIDoBwm/BJZdcwiWXXNLj89944w3i4+N56aWXgCO/O7Zt28bzzz8vB/wvvfQSU6ZMYd68eQDMmzePDRs28NJLL/HRRx+d9PcgCIJwphEBv3DGOpHOQMcFspmZmWzcuJFdu3ZhMBiIiIjAZrOxd+9ebDYbt956q7xr78svv0xOTg7nn38+EydOZOzYsfz0009ySpBKpUKtVssdArX6yH8dnU4HgMvlwuVyyXsLLFu2jOLiYpRKpVwKtK2tjb59+1JWVoYkSRiNRpqamrj//vs5ePAgY8aMkRdTKxQK9u/fz2uvvcb27duJiooiNTUVi8WCRqMhOTmZ3bt3k5mZCRwpk+r999atW4mOjqayspJPP/2UqKgohg4dKncCpk6dyuTJk0XgL5wVNm/ezNSpU32OTZs2jXfeeYf29nY0Gg2bN2/mvvvu63SOt5PQFYfDIXf0AaxW60lttyAIwq9JBPzCb1J3nYGOx4cMGUJMTIzPbIAkSXg8Hvr06cPAgQPl50ybNo2PP/6YTZs2ERoaSmJiImvWrOHtt9+moaGBRx99lI0bN9LU1ERsbCwzZ87k4MGD8iZe3lmCuLg44EgHAJADho4dhObmZpxOJ1qtlsDAQBwOB1qtloCAAOrq6tBqtcCRAKOgoICoqCgGDBjAq6++SkREBEOHDiU7OxuXy0VAQABZWVnY7Xbsdjvbtm3DbreTnJzMmDFjiImJoaKigsOHDxMdHU1zczOPP/44n3zyCRdffDHDhg0To/7Cb1p1dTWRkZE+xyIjI3G5XNTX1xMdHd3tOd71O115+umnefTRR09JmwVBEH5t4q+8cFZLTU3lnnvuYebMmVx55ZXMmjWLJ554gpaWFp/dir2lPg8dOkRdXR0fffQR+fn5bN26lauvvprJkyfLexp4PB6KioowmUwUFhaiVqsZNWoUAwcOJCgoCEmSaGpqQq/XU1RUhEajYebMmSQmJuLxeDCZTMTExHDhhReSmprK3LlzGTFiBG1tbYSHh+N0Omlvb8dms9HW1kZoaChWqxW32y3/2+PxMHToUMrKyrBYLAwZMoRdu3bh7+/P6NGjWbt2Lbm5uVx11VUcOnSImpoa4uPjmT9/PmFhYWzatIkXX3yR5557jpdfflmucCQIv0UKhcLne0mSOh3v6pyjj3U0b948LBaL/FVWVnYSW3xsTqfTZwdyQRCEX0qM8Atnva5mA5RKZZfrABYvXixvxnV0StCkSZN4//332bRpExqNhhtvvJGcnBx++ukn1Go1Q4YM4YsvviAkJIRhw4Zx8803k5uby0cffUR9fT1Go5Gff/6ZkJAQeZGtQqEgLi6OhoYGAMaPH0///v0BiIiI4IILLiAlJYVdu3ah0+loaGggMTERnU4nlwV1uVxotVocDgfR0dG43W5cLhehoaFyJ2DIkCHU19ezfPlyJk2aRHx8PDExMZSXl9PW1sabb77JrbfeSlpa2q/2uQjCyRAVFdVppL62tha1Wk1oaOgxzzl61L8jnU4np+wJgiD81omAXzgn9aRUaVcpQQ6HQy6duWvXLp/vnU4n//nPf4iPj2fmzJmcf/75+Pv7s2zZMkpLS4mOjubTTz8lPj6ea6+9lsLCQpYsWQJAQEAAdrud+++/H4VCgb+/P//973/5+eefaWpqYsyYMRQVFZGQkMCll16K0Wjk0ksvZcWKFYwfP57Ro0eTn5/PggULOHz4MN9++y0qlYqqqiqMRiMGg4G1a9fSt29fHn30UebNm0dBQQH5+fkMGzaM8vJy5s+fzxNPPCGCfuE35bzzzuPLL7/0ObZ27VpGjhyJRqORz/nuu+988vjXrl3LuHHjftW2CoIgnC4i4BfOWT0pVdpVx+BYO+16N9rKzs4mOzsbgPT0dG688UafjbhKSkpobGzk559/Bo7U2g8ICKC8vFyuw79p0yZKSkpwuVzyHgJ9+vRh0qRJ7Nmzhw8++IC+ffsCsGbNGvR6PTqdjsOHDzNw4ED+8Ic/8NZbb3HVVVeRkZHBt99+S0JCAjk5OXz99df079+fYcOGcdNNN8kpBE8//TQXX3wxQ4YMwWg0ivx+4VfX3NzM4cOH5e+LiorYuXMnISEhxMfHM2/ePCoqKli+fDkAf/7zn3n11VeZM2cOt956K5s3b+add97xqb7z17/+lQkTJvDss8/y+9//nv/+97+sW7eOjRs3/urvTxAE4XQ4ZwP+JUuWsGTJEtxu9+luinCG66pj0N33Q4YMYfLkyd3OHBz9+PF22j18+DCff/45YWFh3HXXXWRlZfH666/T0NDA7t27MRqNwJGgSK/X88gjjxATE0NycjIrVqygoaGB9PR0tm7dysCBA3n55Ze57bbbMBqNDBgwgB07dmA2mxk0aBANDQ0UFhby008/kZycTEpKCv369RObfQm/qm3btjFp0iT5+zlz5gBw0003sWzZMqqqqigtLZUf79u3L9988w333XcfS5YsoU+fPrz88ss+NfzHjRvHypUrmT9/PgsWLCA5OZmPP/5Y1OAXBOGcoZC8q5vOUVarFaPRiMViISgo6HQ3RxA6yc/PZ82aNTQ1NVFXVyfvwNux9r7JZKKsrIzq6mqSkpLkXX4B+vTpQ2BgIDabjaSkJH744QcSEhKw2WzU1tai0Wg477zzyMrKYsqUKVRVVZGamirPNrS0tHDNNdeIoP9XJn43nVl6+3k4nU7+9re/UVBQwPPPPy9XBesJp9PJU089BcCDDz4oV+4SBEE4Wk9/N52zI/yC8FtxdFpRd7vremcE1q1bh9PpJCAggN27d7Nx40b69u2L0+mkvr6ehIQEpkyZwsGDB1m/fj0hISEUFxfT2tqKy+UiKiqKyZMnk52dLQf9q1evZsCAASK9RxB6SJIkioqKaG5uJisriwEDBhyzKpAgCMKpJAJ+QfgN6Ml6Aziyy+5FF13k0znwdgKqq6s5ePAghYWFhIWFcdFFF9Hc3ExwcDA//PADw4cPp7m5mU2bNpGdnc2wYcPYtWsXkZGRVFRUEB4ezh/+8AcR9AtCDxQUFGC1WomNjaWiooKCggJSUlJ+1Tb82jMFYmZCEM5cIuAXhLPM0Z2Djp2AHTt28MYbb1BSUsIXX3zB/v37GTlyJKmpqYSFhfHTTz/R1NREQkIC06ZNY//+/Wg0GhoaGli0aBHr16/n+uuvFzv1CsIxSJJEdnY2QUFBJCcnEx0dTVZWFsnJyV2O8h8dKP9W/FbbLQjnIvEXWxDOAd5OwOWXX84LL7xAfHw8RqOR5ORk/u///g8/Pz8iIiJQqVQkJSURGRnJ2rVrMRgMjB07lvj4eEJDQ9m2bRtPPPEEDz30kNisSxC6UVBQQEVFBYmJiSgUCiZMmEB5ebm8/kboHbERmSD8ciLgF4RzTFpaGrfddpucqvPKK6+Ql5dHVlYWKSkpLF26lKSkJDZv3kxVVRWvvfYaNTU1PPbYY0yfPp309HT27t3Lm2++yb59+0732xGEM4okSWRlZRETE4PJZAKOlN2NjY0lKyuLntbJMJvNbNu2jcLCwl/Unt5eRwTXgnB2EgG/IJyDUlNTuffee3nggQeIiopixIgRBAQEUFlZyZYtW8jJyZEX+QKcf/75+Pn5kZubS319PVarlQMHDjB//nwR9AtCBwUFBZSXlzNhwgQ5fUehUDBx4kSfUf5jBdZHL/g90WJ6J+s6vXGyOirCmUF0AM8eIuAXhHOUUqnkoosu4u677yYiIgKAPXv28MQTT7B3715cLhdut5uRI0ciSRJ33nknpaWl3HTTTWRmZnLxxRdjs9lYvHixCPoFgf+N7oeEhODv74/NZsNms1FVVYW/vz8hISE9Cry7WvB7Ik7WdY6lY4B/OjoYgiD0jFi0KwjnOG/Zz+LiYl5//XXa29tpa2tj/PjxHDp0iHnz5vHqq6+Sm5tLbGwsy5Yt46effmLq1Km0t7dTWlrK/PnzeeKJJ0hLSzvdb0cQThu3243VasVqtfLOO++Qm5sLwNKlS1GpVPI5x9rwsbcLfnt7nbi4OJ5++mngf5V0Oi6+feCBBzCbzRQUFFBYWHjM/QOODvA9Hg9NTU00NTWxYsUKJkyYwKBBg3rcZkEQTh0R8AuCgFKpJCkpiZtvvpmVK1fS2NhIQUEBbreb1atX8+OPP6JSqTCZTOTm5hIQEMD111/P2rVrGTFiBB988AFLly7ltttuExt0CecstVrNLbfcQmtrK06nk9bWVgBmz54tl6gMCAhArVZ3mx7R1YLfVatWyWU9e1r60nuduLg4NmzYQFpaGiEhIccd5e8YxK9bt46PPvoIhULR5Wt1nEEoLy/nP//5D0FBQSgUCgwGA9nZ2aSmpnbbUemqyk/Hjsezzz5LTk4O48aNw2q1UlxcfNxOiCAIXRMpPYIgyFJTU7nuuuuIiYnhhx9+4Msvv+T555+npaWFhx9+GLVajUKhYOzYsbzwwgts3bqVpKQkwsPD0Wq1rF69Go/Hc7rfhiCcNkajkejoaKKjozEYDBgMBvn76OjoY+6E6R2V/yULfp1OJ4888ghz5swhMjJSvo7JZCImJobs7Owur+NNzcnOzpaD+MrKSnk37+7a6p1B0Gq1bN26lfj4eBQKBQkJCSctjUiSJIqLi0WqkCD8AiLgFwTBR2pqKk8++SQLFixg5MiRtLS0UFZWxrJly6irq+ORRx5h/PjxlJeX09LSwtKlS9m/fz/19fWsWbOGH3744XS/BUH4TTKbzVRUVBx3wW9PrmO1WsnMzEShUODxeMjOzmbPnj2UlpZSXFzss7DWO6pvs9n417/+hcFgIDk5mT59+lBcXNxlgJ2fn8+KFSuoqqrihx9+YP369XJn3263U1hYiEqlYs6cOTzyyCO/aMGn9/2cyrUIgnC2EwG/IAideBf0vv322zz77LNER0dTUVGBw+Fg79697NmzB4VCQWRkpFzL/09/+hMul4tly5aJRbyC0EveUWyTyfSLFvx6r+Pn5ydfx2q1UlZWhs1mY+vWraxZswar1UpWVhYOh4M5c+awe/dugoKCKCsrw2QyoVAoyMzMpKqqiuXLlzNnzhw5aO84uq/X62lra8PtdjNq1ChKSkowm820tLQgSRIWi6XbWYKe3pfS0lKCgoJITExkx44dzJkzB4fDccLXFIRzkQj4BUHollKpZOrUqbzyyiv0798fjUZDeno6JpOJ0NBQJEli//79NDY28sUXX9De3k5DQ4Mo1ykIvSRJEg6HA7PZLC/4zc3NZenSpbz11ls0NjZis9mOueDX6XTy2GOPsWPHDrZs2cL111/Ptm3bKCsro7Gxkc2bN2M2m3E6ncTExFBRUcHhw4cpKSlBq9Xi8XiIi4vDbDYjSRJ9+/bF4XBgtVopKiqSOxve9QEJCQkAWCwW/Pz8GD9+PBUVFVitVkJDQ2lsbJQ7IN11VI4u4+n9vqioCIC2tjZsNhsJCQlyqpDVahWj/ILQS2LRriAIx5WWlsb999/P/Pnzeeutt7Db7YwYMQK1Wi1X/pgwYQKxsbGcf/75PPfcc2IRryD0gsViAWDKlCkkJCQcc8HvsSgUCrRaLZIkERQURExMDMXFxWi1Wtrb27Hb7ahUKkJCQoiJieGTTz7BarXi5+eHzWbjz3/+M4sXL8ZsNlNUVIROp0OtVlNbW0tBQQGpqalkZWVhMpmw2Ww4nU5aWlo4ePAgy5cvp6KigtbWVurr63E4HNTU1KDX6+WOSseFuvfff79PlZ/ExET5+w0bNtDQ0EBBQQEhISHy+zOZTAQFBR13QfAv0dOF0YLwWyJG+AVB6JG0tDSeeOIJwsPD0Wg0XHbZZfJIv9vt5tNPP6WwsJC0tDQGDx5MVFQUa9euFYt4BeE4vDn0DoeDvLw8oqKiMBgM+Pv7s3TpUt58801CQ0PR6/U8/vjjZGVldTvSbzabcblcmEwmnE4npaWl+Pn5oVKpkCSJsrIyNBoNJSUlZGZmsnXrVhwOB1VVVUiSxIABA9BqteTn5/PVV19hNBrR6XRUVlayatUqHn74Yf79739TX1/Ppk2bOHjwIM3NzbS3t3Po0CG0Wi3BwcFkZGRw//334+/vT1tbG6WlpZ3aWlhY6JObv379ep/vc3NzaWlpweFwyLMECoWCxMRESktL+etf/9rthlBiwyhB8CUCfkEQeiwtLY2bbroJtVrNhx9+yLfffktTUxN2u51BgwYRHh5ObW2tnP9rNpu7/EMvCOcyp9PpE7h3DHxLS0uZM2eOT1Dvdrt5/PHHefzxx3G73djtdnJzczvtZitJEiUlJeh0OvR6vfx/cfTo0TidTvR6PS6XC51Oh9VqpbS0FEmSMJvNtLe3U11dzc0338y+ffvYt28fn332GU1NTbS0tKBSqdiyZQtNTU2kp6cza9YsgoKC0Ol0KJVK0tPTqampQalUEh8fT1BQEKNGjcLj8VBTU8P69et90nokSSInJ0eu8tOnTx+fBcMajYbDhw+jUqnQ6/WUl5fLX3l5eXg8nmOmCgmC4Euk9AiC0CuTJ09mz5491NTUoNVque+++4iPj+eZZ57B4/Hw3//+F7vdTkhICJIkYbPZTneTBeGMdXTgGx4ezu7du7sNZCVJoqmpCZ1OR1ZWFgMGDJDTWgoKCqitraWtrQ2Hw4FCoaC+vh44kurjdrsJCgqitbWVwMBAPvjgA2pqaqiqqiI0NJTAwEAiIyNpbW2lpaWFuLg4DAYD1dXVjB49mtbWVvLy8hg/fjytra04HA5UKhVKpVJON5IkSU47KioqQq/XI0kSe/fupaCggPj4eAC5k+HdbyA2NpacnByCg4MZPHgwGzduxGaz4e/vj9vtprq6mo0bN6JUKnE6nXKKknfDr7lz5/L8888D/6vpLwjC/4iAXxCEXlEqlVx88cW8+eab1NbW0tTURHR0NEVFRezcuVP+g/3mm2+Sn59PZmYmQ4YMOd3NFoQzjt1uZ8OGDXJevcfjITMzk1WrVnVbhcZsNuNwOHxKVKakpCBJEhs2bKCtrU3O1Y+NjcXf35+ioiLcbjcqlYohQ4awZcsW8vPzcbvdeDweeYdgjUbD7t27aWlpQaPRkJSUxP79+wkICGDgwIGoVCqWL1+O2+2W1wokJyejVCrJy8sjMjKS9vZ2kpOTaWlp4euvv8ZgMFBfX092djbFxcWkp6djNpvJycnhiiuuwGQyyTMTRqMRu92O2WymtbUVPz8/AgMDOe+889i9ezcOhwO9Xs+QIUOIiYnBbrdz6NAh+d643W5ycnJwu93MnTv31/oYBeE3QQT8giD0WmpqKrfeeivz58/n2WefJTIykpKSEoYNG8asWbMYO3YsixYtQqPR8NVXXxEdHU1aWtrpbrYgnDG8qTSSJKHX6+UNspKSkggKCuLQoUNIkoTb7Wbjxo0AnH/++ZSWlqLT6UhKSiImJoZ169bxwQcfyNV3tFotCoWC9vZ2jEYj/v7+FBQU4HK50Gg0WCwW3G43tbW1hISE0NzcTGBgIH5+fowdO1aeXfi///s/LrzwQn788Uf8/f2xWq00NjZSW1uL0+nEz89PXhgcGxvL2rVr0el0WCwWNm3aRHBwMDqdTi7ZaTAY5Gvk5+djsVhYv349AIMHD6a9vZ2hQ4eSk5NDXl4eTqcTk8mEx+PB39+fmJgYdu/ejVarJS0tjYiICFavXt3lTIjb7eapp55i06ZNZGZm/nofqiCcwUQOvyAIJ8S7iDcuLo6ysjImTZrEyy+/zLJlyxg7diw//PAD8fHxlJWViTKdwjnLm39/9OJRb616nU6HTqeTR/S9pSe9JTo7MpvN2Gw2goODUSgUTJgwgYqKChobGykqKqK+vl6uKKPRaCgtLaWmpgaHw0FbWxsGgwGlUonBYEChUMidDY1GQ2hoKDExMXg8HqxWK4cOHWLJkiU0NjbS2NjIjh075FkBu91Oc3Mzzc3NJCQkEBYWRr9+/dBqtYSEhKDX61Gr1Wi1WpRKJVFRUYwdOxaj0UheXh4VFRWEhYVht9uxWCwcOHBAztV3uVwUFhaiUCgwGo1oNBp5bwCPxyOnNGVmZlJTU0NpaWm3df67W+sgCOciEfALgnDC0tLSmD59Ou3t7VgsFubNm8eGDRsAeOihh3jxxReZO3cuSqWSpUuXkp+ff5pbLAinn7c2vdvtJjo6mqCgIOrr67FarVRXV6PRaFCr1fKiWu9zSktLMRgMaLVaNmzYwIoVK4iMjKSoqAiz2Ux1dTWVlZU4HA60Wi3Nzc1otVq0Wi0qlYqBAweSlJREeHg4KpWK5uZm3G43bW1tREdHc9111+Hv7094eDh6vZ7w8HDGjRtHnz59GD58OMHBwZhMJtrb2+WdttVqNc3NzURFRVFVVUVbWxt2u526ujr69euHTqfD7XaTl5eH0WikoqICj8dDQECAvKg/NzeX999/n6+//pqamhoaGxupr6+nqKgIq9VKQ0MD8fHxjBgxAo/HQ1FREYmJiV3uEdDxHjc1NcklP3u7uPfo/QEE4bdOBPyCIPwikZGRjBkzhttuu42AgABSU1OZPn06OTk5PPnkk3IAodVqWb16tSjTKZzzCgoKsFqtaLVa2tra5KB206ZNvPvuu+zcuZPY2FgmTJiAUnnkz7R3A6r4+Hh5ka63GpbNZkOlUjF69GgiIyOJi4sjMTGR8PBweTGwRqNh48aNbNq0iYaGBpxOJy6Xi7a2NgDq6upYvnw5RqOR2NhYKisraW1txWg0Akf2Cairq6OtrU1eb2CxWNiwYQM//fQTu3btwmw2U1tbS2Njo7xmwOFw0NDQgNlsJj8/H7PZTHBwMOnp6QwePBiPx0N4eDgXXXQRSUlJ+Pn5odPp5EXBNpuN0tJSVqxYQXV1tbwguaCgAJ1Oh0KhYO/evWRnZ2M2m6msrMRsNne51qGnvGVST7SzcLYRnZ+zg8jhFwThF/GmB5jNZgIDA3n77beJiIjgvvvuo6CgALPZzP79+0lOTmbXrl0MGTKEiy666HQ3WxBOC0mSyM7Oxmg0EhcXR3p6upzPbzQamTVrlpzec+ONN/L6668jSRIWi4Xg4GBaW1spKysjKChIrmIjSRLV1dUEBgYSHBxMU1MTCoWCkSNHEhAQQGZmJm+88QYqlYr+/ftz4MABKioqkCRJHuWvqanhyy+/xGQy4efnh7+/PzU1NXJnffPmzXIJXm+HQ6PRYLVa5TKfJpOJ2NhYAgMDcbvd7Nmzh7KyMlpaWtDr9ZSUlKDRaGhpacHf31+u8mOxWNBoNHJJT4VCgcfjwel00tzcTExMDBMnTmTy5Mn85z//wd/fn9WrVxMUFIRCocButzN37lx5diE7O5vw8HCftQ5ZWVkkJyf3aKMub4fs6IXR56KjOz8dq0IJvy0i4BcE4ReJj48nODiY7OxsJEkiIiKC/Px89u3bR2hoKH379sVgMHDeeefx6quv8sknnxATEyN24BXOSQUFBVRUVJCQkCAvmAUICwvD6XTS2tqKwWAAICgoCLPZTFVVFS0tLRQWFnLo0CHMZjNWq5Xt27fz7rvvUlZWRmtrKzU1NVgsFioqKuTXMxqN/Oc//2HKlCkEBgZSUlKCUqlk1KhRlJaWEhUVxc8//0xoaCgmk4mEhARMJhOzZ8/mr3/9KwcOHCAxMRGLxYK/vz+tra00NTURHh5OQkICFRUV9OnTB4VCwcCBA4mOjmbGjBl89NFHlJaWkpeXh8FgoH///lRVVREdHU1wcDBlZWXYbDZCQ0Oprq5mx44dhIWFyYMHKpWKqKgoampqaG1tRa/Xs3nzZgoLC7Hb7Wi1Wpqammhvb6dPnz40NDTgcrnQarU0NjbS1NSEWq2mqamJP/zhD6xatapHgbu3Q+adGYmOjpYXRisUCh544AGf8p9n+y68ovNz9hApPYIg/CJKpZJp06ZRVVXF3r172b59O99++y1+fn40NTXx6quvsnbtWtauXYvdbqempobly5eL1B7hnCNJEllZWZhMJjQaDQ6HQ178qlQq8fPzIycnxydvv7i4WE6/CQgIQKFQEBUVhZ+fH0lJSdx4441oNBra2tr47rvvAIiNjSU2NhaFQkFqaiojRoxg9uzZ3HbbbQwfPpzrr78eu91OWFgYAwcORKvVYrPZGD9+PPX19QQGBhIXF8fAgQNxOByUlJTQ3t4udwoAGhoaaGpqwmAwcPjwYUJDQ4mNjUWn0zF8+HAGDBjAvn37aG9vR6lUUlBQQFhYGIGBgUiSRG5uLlqtlksuuQSTyUR+fj4mk4nhw4dz/vnno9frcTgcBAcHExwczJQpU5g9ezYZGRkYjUYuvvhiIiIiCA0NZerUqSiVSnn2wTvj0N7eTnFxMUlJScTGxvYoPcfbIfPuD+BdGN3dwuDTqavdhE/mDsNHd368MyXHuodih+Mzlwj4BUH4xbxlOj0eDwsXLuTbb78lIiICtVpNcnIy48ePJz09ncmTJzNx4kQ2b97MDz/8cLqbLQi/GrfbzWOPPca///1v6uvr2b59O1VVVezYsYMdO3ZQVVUlV79pbGxk27ZtZGdnY7Va5eAXQK1WYzAY8Pf3p7a2ln/+85/Y7Xaio6MJDw9n4MCBjB07lrFjxzJu3Djuu+8+7rvvPuLi4qirq6O8vJzExERsNhsJCQmo1WqSkpJISkqiX79+8jnexcP+/v7o9XpCQ0Ox2WyUl5fT1tZGS0sLVquV6OhooqOjeeaZZ+QNt7yVhurr6+W1BE1NTXK6UWlpKZWVldTW1pKbmyunFZWVlVFUVMTgwYOJiooiOjqanJwcbr75Zg4dOkRUVBQulwun08nQoUNpb28nLCyMtrY2goODcblctLe3ExgYiFKplCsOFRYWMnHiREpKSvjrX//abTDq7ZDFxMTIHRtvoHs6d/U9XUF0V52f8vLyXq2HEM4cIuAXBOGk8JbpDA8Pl4OSa665hgEDBrBmzRqWLVvGjBkzuO222wgNDeW7774To/zCOUWpVJKens7NN99MRkYG0dHRpKenk56eTnR0NBkZGdxwww2UlJRgs9n417/+JY+IazQaWltb6dOnj5w3f+DAAXbs2IGfnx/h4eGEhoZSX1+P0Whk0aJFLF68mISEBIKCgnxmF3bt2oVCocDhcFBVVSUvkF22bBl1dXVs3LiRd955h40bN1JYWIjL5eKSSy7hvPPOQ6/XExYWxpAhQ4iLi8NutzN58mQMBgM2mw2bzUZlZSU//PADKpWK4OBg2tvbCQoKYty4cZx33nlotVrCwsKIiIiQKwIlJSXJZUQzMzPp27evT7BeXl7O4cOHKS4uRqvV8uKLL1JeXo4kSRw6dEheS+TtPISGhsqbkH355ZeoVCq2bt3KmjVrcLlcXX4+BQUFlJeXM2HCBJ+F0RMmTMBqtZ6Ro/ynSnedn57OlAhnHhHwC4Jw0qSlpXHTTTfhcrnIzc1l3759BAcHEx8fT1pampyyYDQaaWtro7S09HQ3WRBOCa1Wy8KFC1mwYAEqlUo+rtfriYqKwmAwoNPpCAwMJDAwEJ1OJ+9Ka7Va5Tx3jUYjB7Eul4vo6Gi5Yo/L5aKoqEgOduPj47sNTL276TY0NPD9999TVFTEd999x7p166ioqKCtrQ2n00lYWBjR0dHceOONGI1GAgMDUalU/P73v8ftduN2u+VUoMjISKqrq6moqOCdd94hNzeX3NxcFi1axBdffIGfnx+tra0oFAp50y+FQoFSqSQsLAyHw0FeXh7BwcFkZGTQ0tJCfX09NpsNh8OBQqHgyy+/xM/Pj5CQEDZu3MgFF1xAv379KCgooK2tjfLycg4ePEhZWRmSJNHe3k5TU5O8KLi0tJSvv/6a559/Hrvdjtvt7jJY9Qa4ISEh+Pv7y52Xqqoq/P390el0rFmzhieffBK3231Kf3ZOtZ7MGHTX+fF2vsQo/2+PWLQrCMJJNXnyZNasWcPevXv54x//iMFgoKGhgcLCQpYvX05+fj42mw2FQsGuXbtITEw83U0WhF9dxxKS3hFUSZLIycnBYDDg8XiIjY1l7969OJ1OPB6PvFBVoVDICym9wbFSqaSkpIQpU6YQHR2NRqPxeT21Ws0tt9xCa2srV199Na+++qo8w6ZUKpk9ezZGoxGDwUBAQADl5eU4HA50Oh0ANTU1HDp0iH79+pGWloZaraZPnz40NTURFxfH1VdfTWtrK5IkERYWRv/+/amvr6e+vh5JkpAkiQ0bNsgVeAAcDgf19fXExsZy4MABXC4XVVVV3HvvvdTX1wOwbt06WlpaUCqVGI1GbrrpJt555x369u2LxWKhoaGBkJAQamtr8ff3p62tDX9/f0aOHElTUxMajYYxY8YQFhaGWq3G4/HIpU478naIrFar3HkBWLp0KcAxOwtnm646P4Dc+QkJCelV1SPhzCACfkEQTirvIt6ff/6ZzZs3M2jQIEJDQxk0aJB8TmRkJP/973/573//S0pKCmlpaaexxYLw6+q4GLekpASAyspKiouL8Xg8mEwmSktLmT59OllZWTidTgwGA3q9nsrKSkJDQ/F4PCgUCjnYDQoKws/PD0mSKCsr67KaitFoxGg0EhoaSnR0NNB1pRnvYk3vLIRGo+Gxxx6jsLCQiIgIduzYgVKpxN/fn759+2Kz2eTqQo2NjdjtdhYsWMDKlSvJzc3F6XTS1tZGXV0dAHFxcSiVSg4fPgwg19NPSkrCbDYzefJkuWNz9913ExoaCkBAQAD+/v5yp6GhoUEO0tva2pAkCZfLhc1mIy8vT752QECAPCPiXWdwNG+HqKmpiRdffBGHw8GYMWOYPXs2ADabrdvOwm+Z0+nkqaeeAv73s3Cszo93tso729Pd/RTOPOfsJ7VkyRKWLFnym5+aE4Qz0eTJk/n+++/ZsmULX375pZyH7M399C7obWpqYv78+TzxxBMi6BfOGYWFhVitVoKCgrBareTl5eFwONi9ezdjxoxhz5496PV66urq5MDK4/FgNpux2WxIkiRvamUymbBYLHg8HoYMGUJjYyMGg+GYI7DedKPueBdrJiUl4e/vz8UXX8wTTzxBdHQ0gwcPltNyfve73xEUFMSaNWvYtGkTDz/8MO+++y6tra1ERkaiVCpJS0uTc+YPHDhAWVkZgwcPZurUqTz77LMMHDiQ8PBw+bXb2tpQq9Xo9XpCQkLo379/pw7JrFmzyM/Pp6ysjD59+mC323G5XHLKkEajwel04na7MRgMOJ1Otm3bRnFxMZGRkd2+b6PRiJ+fn9zRMRgMcsfI5XJRW1srlwwtLi6msLCQgQMH9uaj/03oOBvkLRULMHv2bPmzCAgIEMH+b8w5+2ndeeed3HnnnVitVnknQUEQTg6lUsmNN97Im2++SX19PVdeeSWBgYGsW7cOj8dDQkKCvMHQs88+y9KlS7nttttEbX7hrOdN2/FuGgVHRve1Wi0lJSVs3LhRzqf/5ptvsNvttLe3Y7PZCAgIoK2tTc79VygUtLW14fF4sNls1NXVyWUwTSbTCY3AdlysqVQqUSgUjBo1Sl5TcPDgQeDI//EvvvjCZ8TX6XR2GhnuuDDf21GJiIigtrZWXrTbUUBAACaTib1798qpTuA7En3//fdTV1eHx+OhtbWVqKgokpOT5XsH4PF4UKlUcseora0Nt9tNW1sbubm5vQrWO87IFBcXo1QqaWlpOas3ovLOBnlnlwCio6M7db6OniEQzlznbMAvCMKplZqayqRJk9i/fz95eXls3bpVzjt2OBx89tln3H///QwePBh/f3/Wrl3LgAEDzropc0HoyGw2097eTkJCAnv37pWPx8TEoNVqaWhoICUlBY/HQ0BAAG63m/LyclQqFaNGjeLnn3+Wq/vYbDb27t2L1WrF6XQSGRnJPffcwxdffMGkSZNOaATWu1jzmmuuYdWqVcCRHXUfeughPvzwQ373u9/xxRdfAJ1HfPV6fZcjw/fccw9arRan08kbb7yBWq3GZrNht9vZvn07cKQDkZmZiUqloqWlhUmTJnH33Xd3+R4KCwtpbm5m3Lhx5Obm4ufnR3x8PDExMbjdbgICAlAqlVx00UX89a9/BeDFF1/EZrNRUlKCQqEgKyuLvn378vTTTwPH3kTLu/lUUFAQdXV1KBQKBgwYIDaiEn5TRMAvCMIpM2zYMMaMGcOwYcOoq6sjLi5OniJvamoiJycHi8XClClTWLduHaWlpWIRr3DWcbvdbNiwAUmS5E2jlEolbW1taDQa+vTpQ21tLQEBARw6dAiLxYJSqUSr1eLxeFCr1ahUKlpbW0lISMDpdGK326mursbpdBIcHIzNZuPQoUOMHj2aQ4cOkZuby/Dhw3s1+nysxZqRkZEkJCSQn58vzy50NeJ7rJFhbyegoKCARx55hJkzZ+J0Onn55ZeBnqWMdJwh6TgDAEdSUdRqNf379ycvL0+uagQQGBiI2WyWq/90rDTjdrt5/PHHUalUPPDAA51ez7v5FBzpsHnXG/Tp0+eMX7zaVY7+meJMbtvZSAT8giCcMvHx8ZhMJnnTnIceeoj8/Hzmzp2L3W6nuLgYs9mMUqmkrq4Oi8VyupssnAFee+01nnvuOaqqqkhLS+Oll14iMzOz2/OXLFnCq6++SnFxMfHx8Tz00EPceOON8uPLli1j1qxZnZ5nt9vR6/Wn5D10pa2tDYVCQWZmJv/+979pamoiOTmZxMREdu7cSb9+/TAajUiShFKpZOzYsVRVVbF9+3a5xnx7ezv19fVs2LDBp6SiVqulpaWFw4cPM3HiRD788MNejz73ZLGm0WhkwYIFJzR7IEkSRUVFNDc3k5uby8iRI2lvbz9mysjROs6QlJSUcNVVV9Ha2kptbS0ZGRnyeX5+fvJmWQqFgsbGRmw2G4GBgWi1WrRaLdnZ2V1W3VGpVEycOJEHH3yQw4cPs2LFCux2O21tbfJIf0NDAw0NDezbt49x48b5FCUQ/kcE9WcOEfALgnDKeCv2vPnmm5SVlfHZZ5/Jm/54PB5aWlpITEyU034MBgN33323yOU/h3388cfce++9vPbaa5x//vm8+eabXHLJJeTl5REfH9/p/Ndff5158+bx9ttvM2rUKLZu3cqtt96KyWTisssuk88LCgriwIEDPs/9tYJ9bwlOl8vFgAED8Pf3p7y8nJaWFsLCwtBqtXL52v79+7Nnzx7gSAB+8OBBGhoaAOjTpw8AYWFhNDY2yhtuNTc3I0kSwcHBfPPNN/zpT386odKJJ3OxZlcLg72pMbGxsXI6TFefaXcaGxvJysriiiuuIDg4mIMHD8rVfryDChMmTACQdyZ2u92oVCq2b99OW1sb0dHR8rq98vJyeR2f2+0mJydHXodQXFxMQUGBPLrvdrspLS2VOyfFxcUMHz6cwMBA/v73vzNixAgeeuihXxTQngnBscjJP3uJgF8QhFMqNTWVW2+9lYceeoinnnqKlJQUEhISWLduHUlJSTz88MPk5OSwcuVKysrKePPNN7n11ltF1Z5z1AsvvMAtt9wil0N86aWXWLNmDa+//rqcb93RBx98wO233861114LQFJSEj/99BPPPvusT8CvUCiIior6dd5EBx0XfNrtdux2O++++y6bNm2itbWV/fv3y+tWdDodarUaf39/lEols2bNkoNvgFGjRqFUKmlqauLgwYNyIB8YGAhAa2srX3/9NRUVFYSEhJxQ6URvSg7A4sWLT+p98AbPycnJREdHk5WVxS233HLMikEdn79v3z6sVqs8A5Kens6tt95KcXEx//jHP0hJSZE7J3fccQcajYZFixZRU1NDYWEhGo0Gh8NBfHw8TqcTrVZLcXExQ4cO9Xmd4uJimpub+fe//43T6ZRnE9ra2hg5ciS1tbXU1tbS1NREQkICe/bsOeW78JrNZgoKCjotNu7qeMdjSUlJcmfG7XazYMGCU9pO4cwlVscJgnDKpaWlcccdd+Dn50f//v1Rq9UMHjyYAQMGcPvtt/PEE08QFhaG1WrlwIEDzJ8/n3379p3uZgu/MqfTSW5uLlOnTvU5PnXqVDZt2tTlcxwOR6eRej8/P7Zu3Up7e7t8rLm5mYSEBGJjY5k+fTo7duw4ZlscDoec3uL9OhHeUW2j0YherycpKYmbbrqJqKgouaa8l8vlYufOndjtdjweD+Hh4RgMBjkFxWg08uyzzzJ27FiMRiNDhw5lwIAB8tfw4cOZPHky8fHx3Hbbbdxyyy1nTOlEb6nPxMREFAoFEyZM6NWOrYcPH6a8vFye3SgvL5c/34SEBIKDg6mrqyMqKoro6Giio6MJCgpCkiS2b9+Oy+UiPDxc7jzFxsYCYLFYfIJ1s9mM1WolJiaGrVu3ytV+qqurUavVREREoNfrsVgs7N+/H7Va7ZM+dCp0TIXyljbu7nh3554perLLr3BqnBm/CQRBOOtFR0czYsQIFAoFu3fvpr29nfLycpqamkhKSmL+/Pn861//IjU1lU8++USU6jwH1dfX43a7O9VKj4yMpLq6usvnTJs2jaVLlzJjxgwyMjLIzc3l3XfflXPdo6OjGThwIMuWLWPIkCFYrVb++c9/cv7557Nr1y769evX5XWffvppHn300V/0fjqOakuShMPhoLa2lvfffx+Px8NVV13FXXfdxeuvv95pTxg/P78ug3Vvnr3dbu/UKVYqlURERMilL8+UYP/oUp8AycnJxMbG9ijtyPt8tVpNQEAABQUFFBQUEBUV5bMTrsfj6XQfGxoaKC4uJiQkBJPJRH19PQcPHuTyyy/nvffeo7y8nIaGBjIzM5EkidLSUoKCgkhKSiI/P5/du3dTXV2NzWZDo9Hw/fff43K5aGlpkcunett+qvb16SoVKiUlpcvj3pSkjsd6wrtw2cu7ZkM4e5wZvw0EQTjrGQwGwsPDGTduHK2trfzpT3/i008/5cCBAwwePBiNRoNKpWLEiBEcOHBAlOo8hx0d/HkXXnZlwYIFVFdXM3bsWCRJIjIykpkzZ7Jo0SI5aBk7dixjx46Vn3P++eeTkZHBK6+8IleIOdq8efOYM2eO/L3VaiUuLq5X78M7qu1N+9Dr9Rw8eJDg4GDUajUGg4GoqCgMBkOnYLFjwOUtWblgwQK0Wi2zZs2ipqamy+fMnj0bk8l0xgT70HWpT4VC0ePFxQUFBVRXVzN+/HgCAwO55JJLWLBgQacUHu9OvF7eNCCXy4W/vz+VlZW0t7ezf/9+li9fTl1dHU1NTWi1WvLy8iguLiY8PJwxY8agUqn429/+xooVK2hsbCQqKgqTyURGRgYKhYIDBw6gVqsZOnQoCoWi2w7aL9VdKlRSUlKn49999x1fffUVVVVVjB8/npiYmG4XJgvnHvFXVBCEX0V8fDzBwcHs378fo9FIe3s7KpWKhIQE3G43DzzwAD/99JO82U9mZiZms5nS0tLT3XThVxIWFianT3RUW1vb7Q6pfn5+8u6uxcXFcmlXg8FAWFhYl89RKpWMGjWKQ4cOddsWnU5HUFCQz1dvdBzVNplMSJKE3W5HoVCwZ88enw2puqPValmwYAETJ0706QCEh4ezePFiFi1aRHBwMMHBwSxatIjFixeTkJDQ67aeSl2V+rTZbFRVVeHv7y8vLu4uKO14H72dozFjxhAeHt5lCk9H3t1xg4OD5bQvtVqNVquloqKCoKAgNBoNw4YNo6KiAovFgsPhIDg4GIDhw4cTGhpKVVUVkiShVquJjIwkIiKCtrY2CgsLcTgc8s68p0J3qVDr16/vdHzv3r3y+/Ueq6ioOOXrC4TfBhHwC4Lwq/BW7GloaKCoqIgvv/wSSZK48847GTBgAGazWV5waTKZSE9PB5BrgQtnP61Wy4gRI/juu+98jn/33XeMGzfumM/VaDTExsaiUqlYuXIl06dP73ZmSJIkdu7cKddoPxW8o9oTJkyQd8R1Op30798fi8XSZQlas9lMbm7uWRWgeVOQGhsb5VKfubm5LF26lLfeeksul9ldOszR9xGOzA4kJiZitVqPmbJSUlKCXq8nPDycgQMHEhsbS1xcHBMmTCAtLQ1/f3/CwsKIioqivb0dl8uFxWKhpKREvoYkSXL6TkNDAzabjebmZpRKJZIksXHjRhobG+WUmK5y051Op9xxW7BgAc3NzT3KYz+60wgQFxdHbm4uc+bMITIyUj6elJQkd1i8nY/k5GRiYmIoKSnp1KEym81s27aNoqKibl9fOLucOXN+giCc9VJTU7nuuutYvnw5P/zwAxaLherqavr160dcXByFhYW43W6uvvpqamtrAeQyeMK5Yc6cOdxwww2MHDmS8847j7feeovS0lL+/Oc/A0dSbSoqKli+fDkABw8eZOvWrYwZMwaz2cwLL7zA3r17ef/99+VrPvroo4wdO5Z+/fphtVp5+eWX2blzJ0uWLDkl7+HoUW2r1UpDQwMqlYq+fftSVFRETU2Nzyh/x+owxcXFhIaGnpK2/dp+SanP7jYCa2ho4B//+Af/+c9/2LRpE6mpqV2mgW3YsAGHw4Faraa8vFxODQsODkan01FfX4/JZKKkpASdTockSbjdbnJzcwkPD6e8vJyamhq5M9DW1sbmzZvx8/OjqqqKlpYW1Go1xcXFmEymXm2+dXTlnK5KcHaXCmUymdizZw8JCQnU19cDR3YfNhqN6HQ6uSSpd5R/xYoV8jHvvfEu7PVuCHcyHV0lSDgziIBfEIRfVWpqKk8++STr1q3jmWeeoaKiAo1GQ11dHXAkQMjJyWH58uXExMT0qk638Nt37bXX0tDQwGOPPUZVVRWDBw/mm2++ISEhATiy62vHNC+3283ixYs5cOAAGo2GSZMmsWnTJp8dm5uamrjtttuorq7GaDSSnp5OdnY2o0ePPiXv4egNrDZt2kRDQwMBAQHs2rULf39/qquraWxslJ/jrQ4THx9P3759ueGGG+QgsCdlK89kx9p991h6shFYd6VH3W63HNQqFAqqqqpwuVxoNBpyc3MxGAzyc2w2GwEBAXg8Htrb26msrOSHH36QFwibTCZcLhcqlYqgoCDS0tKw2Wx4PB7S09MpLS0lJyeHIUOGdJtG1lvH2vW4uroaPz8/du/eLVeP+vLLLwkLC5P3eLBarXLalJ+fHzU1NXJg33Gxb2VlJRUVFdTV1ZGUlOSze7HT6eTZZ58lJyfnmBvfHd3ujlWC+vbte1LuR0+cCfsYnMlEwC8Iwq9OqVQydepUYmJiWLRoEYcPH2b8+PH84Q9/QKlU8u2337Jv3z5sNhsHDhwQlXrOMXfccQd33HFHl48tW7bM5/vU1NTjlth88cUXefHFF09W846r46i2w+Fg8+bNhIaGEh4eTnp6OpIk0dTURFlZmVxKsaSkpNPCzONVr+lqc6uzyS+ZHVCpVERHR9O/f39SU1PZs2cPJSUlTJ8+nSuuuILPPvuMwMBANmzYwMCBAwkLC5PTexobG2lvb+fCCy+kubmZ6OhoDh48SHt7O+3t7XJb/P396devH4cOHcJisRAYGHjSZma66ux4PB62bNkib1K4fv16uTSpVqvF4XBQUlKCzWZj+/btPhWM3G63XCnq73//u7ywNzIykq+//hqj0UhxcbGc/3+iuqocJJwZRMAvCMJpk5qaSp8+fZAkifb2dm699VYkSWL8+PH86U9/orS0lNWrV4tKPcJvjndUOy8vD6fTSWhoKDqdTt4kKzQ0FKvVSmFhoTy6P2TIEDkNY9WqVcetXvNbcyIdlBOdHSgoKKCxsZGBAwcSFBREamoqHo+H2267jc2bNxMfH8/WrVux2+1YLBa0Wi1hYWEEBwejVCqpqqri888/JzU1laqqKuLi4pAkid27dxMVFUVUVBQOh4OmpiY5Hai2tvYXr7/wpsOUlpZ26uy43W48Hg8Oh4Phw4dzxx138OGHHwIwc+ZM2traeOutt9i1axcjRoyQN6/zzkYolUoKCwuxWq1yYJ+YmIjFYiE+Pl7ekyAkJERuj0qlYuLEiT0aMe+qopC3StAv6UQIJ4f4CyoIwmlTWlqKTqfjiSee4MYbb5R3QlUoFOzcuZOioiLWrFnDDz/8cJpbKgi95w2A/Pz8UCqVOJ1Ompub5UWffn5+ZGdnU1RURFBQkJxO0bFGvSip2HtdLXY1mUwEBQWRlZWFxWLh8OHDHDhwAI/HQ3FxMZWVldTW1mK1WtFqteh0OtavX09mZqbPYuGEhAR+/vlnVCoVOp2O0tJSIiIiiI+Px+FwUFRUdMKf2dHpMEFBQXIFIoPBgMFgIDAwkICAAKKioujXr598fMCAAQwbNkze0M1gMMjP9VYramxsJCcnh6CgIPR6vTyzZDQa8Xg8GAyGLhf49lRXFYV6UyVIbMp1aomAXxCE08ablxoVFYXdbqe6upqwsDBuvvlmHnzwQe666y4CAgL49NNPyc/PP82tFYTe6bhJVnV1NVVVVezYsYMdO3ZQVVWF3W6npKQEq9VKQkKCT2A5ceLEXu1Ee7bzzg4sXLiwR6P73VX2qa6uZtKkScTGxhIaGirPuHjPgSOfm8vlwuFwyJtueb9aWlpob2+XP7va2loSExNJTExEp9NRW1t7wp/ZqUiH8S4Gdzqd5OXlyftCKBQKzGYzlZWVDBs2DJvNhslkwmq1ntAshbdz6+1kud1uVqxYwY4dOygsLPzNdlzPpk6ISOkRBOG08U7RV1dX89133xEaGkpqairvvfceKpWKG264gcGDBxMaGio24RJ+c9RqNbNmzaKyshKLxYLVaqVv376EhIQwevRo7r77br788kvi4+P53e9+xyuvvALQqUb98XL5hf/parGrt+SnRqPBZDKxevVqWltbyczMJCsri7i4OFwuF+3t7QwePFi+jr+/Py+++CI2m01ea1FbW0t8fDz19fVYrVb8/PxQqVQolUpMJhM1NTVs2LChy8pBx2t3VxtsJScn/6L74e1EGAwGKioq8Hg8aDQa2traOHDgAJMnTyYsLIyamhrMZrM8yn+8xcfeBbJutxuz2cy+fftIS0vjjjvu8Kko5N107tcuNSsW8HYmAn5BEE4b72Zcn3/+OTabjX/+859ERETw5JNPYjab+fjjj5Ekicsuu4z3339f3lRJEH4rjEYjgYGB2O12PB4P9fX1xMfHo1ariYiIoL29/YSq0Ahd666yT2ZmJiqVCovFwv79+7ngggvksqg6nY6mpiYaGxs5cOAASqUSlUrFkCFDMBgMcgrWHXfcwQcffEBxcTF79+7F6XRisVjkUp12u52Wlhb27NnT5foLu91Obm5up9r3TqeTBx54gJ07d3a5juNEK5V17ETY7Xbq6+vZsmULHo+HsrIy1Go1KSkp7N+/H4/Hg16vJzk5mX379vU4QPemIVVWVhIeHo6fn1+nTpafnx/FxcUil/80E79BBEE4bbybcb3yyivU1NTgcDjYvXs3WVlZ1NbWEhERweDBg/nkk0+oq6sTm3AJv0lmsxmHw0FQUBA2mw2z2Ux4ePgvqkIjdO1497SoqAin08nEiRP56KOPiI6OJiMjA7PZzPr160lJSeHRRx8F4OWXX8bhcBAYGIhKpSIuLo57772XN954gy1btqDT6YiJiUGpVNKvXz8OHjxISEgI9fX1rF+/3mdmprGxUQ56N2zYQGNjI5WVlZjNZjntxruOw5sO410gPGvWLJ/36PF42LBhA263G5VK5bMLc0fenPr4+HgOHTpEVFQUSqWS+Ph4KioqiIuL44YbbmD58uXyyL9Wq5VnSQoLC497v70LzgMDA6murub5559n3759Xe4kLTqup5e484IgnFapqalcccUVPP/88zz22GMUFxdTV1dHREQE999/P35+fmzatIm9e/dSU1PDkCFDTneTBaHHvAsjdTodwcHBGAwG8vPzKS4uprCwkIEDB55QFRqhe91V9tFoNHz99deEh4fz1ltv8dNPP8nPCQ4ORq/XU19fT1RUFAqFAoPB0GkH4Pr6emw2GyEhIVRUVOBwOKirq6OpqQmr1YrRaKS6upqioiI5wJUkiby8PBwOBwqFgvLycvLy8mhpaWHDhg1s2LABq9VKWlpapzUHpaWlzJkzh3379nW523THDbzmzp0r5+U3NjbKOfUNDQ04HA4yMjJ8dhL2eDx8/fXX8uJlOPLzWlNTg9vtlheNdzcq33HR77hx4+TqPjqdrlPA7+fnJ4L900wkwwqCcNpNnjyZKVOmYLPZuPTSS3n++efR6/W88MILbNiwgd27d9Pa2sr333/f5ciRIJypvDnURqMRhUJBXFwc5eXl1NfXiyo8vzJvuo/ZbGb79u1UV1ej1WoJDAxk165duFwunE5npyDfyzvyHRcXx3nnnUd0dDTnnXceKSkpuFwuUlJSmDRpElqtlp07d+J2u3E6ndx3333s3r2bgIAAtFotLS0tlJeXA2CxWHjzzTfR6/VoNBqfBcLeNQdHV86x2+1UVVV1Srs5eoFueXk5mZmZcjW05ORkRo0aRVVVFUajkYyMDG6++WZGjBhBRkYGycnJtLe34+/vz8iRI6murj5mao93dD8hIQE/Pz8uu+wympubcblccvUgg8GAy+Viz549PZoxEE4dEfALgnDaKZVKhg0bRmNjIzabjTVr1uDv709KSgrJyckkJCQwbdo0Nm/eLEp0Cr8ZHXOo9Xo9gM8IrqjC8+vypvvMnj2bjIwMOZ3n9ttv54MPPuDKK69kxIgR3Y5Ed6z+4+fnh06nk8tftra2Eh0dTVBQEAMGDKC1tZWCggIkSZIrjHkfLy4uljfMioqK4vDhwzQ2NrJ9+3afr9zcXMxms091GEmSsFgsOJ3OTh0Bb439jgt06+rqqK+vx9/fn+bmZjIzM+WFtoGBgURFRcnlPr2lOz0eDwMGDKBPnz5yGtLRjk5DgiPlZGNiYnye4z3PW2r06GuZzWa2bdsmOgO/AjG/IgjCGSEyMpJBgwaxZ88elEol9fX17Nixg7KyMhYtWsSAAQP44x//yHfffcfkyZNFtR7hjOfNofZWKpEkidLSUmJiYvB4PGi12pNSiUXoWlcbfRmNRvz8/DAYDD4BO/yvalhXvJ23jtV/HA4HNpuN6upqAgMDMZvNJCYmEhQURGVlJTNmzGDSpEmUl5djMBjw8/PDZDJRVFSEQqHA7XYzYsQIDh8+jN1uJz093Sd9RqVScfPNN+Nyudi6dSvQ9XoQb/u8NfbhSOrRrl272LVrF42NjTgcDrZv3y5XFfJuxuVlNpupra1Fp9PJi5ivvPJK/vOf/3Q5yu+duTo6DWnChAmsXLlS3sDLOwvgXTfQsYN79L4Dffv27c3HK/SSCPgFQTgjGAwGtFotSUlJXH755fzrX/+ivb2dUaNGkZqaSk1Njby5jajWI5zpvOkfJpMJm82G0+mkra0Np9Mp/zwDlJWV/aJKLMLJcXTnwDuqrlKpePDBB1Eqlbz88sty9Z/t27dTVVXF5s2b5TVH7e3tcs670WikpqaGHTt2AMhlLr2BcEVFBUqlEoVCwbhx41i5ciXt7e2EhobKbVCpVPJGWhMnTuT+++/n97//PXa7nfDwcIxGI4MHDyYyMhKz2YzH4yEhIUHO93e5XACEh4dz4MABMjIyuOWWW6iqquKHH37AYrEA/xuFdzgc8s9hcXExffv2lWckOo7Md9xQzpuGpFKp5HKyfn5+lJSUEBwcTElJSbc773pnJE7mvgNC90TALwjCGSE+Ph6tVktBQQFjxoxh7NixPPXUU/Io0Oeff46/v788OiUIZ7Kj88UrKytpaWkhJCSEgoICVCoVer0eo9FIVlYWN9xww+lu8jnFu7j1eDnqBQUF8uLqjtV/vJ24gIAAAgMDSUlJkfPzPR4PSqVSDqT79euHy+WSn+MdFVer1ZSWlnLrrbeycuVK8vPzGTt2LD/++CMAF1xwgU97vClFarUaq9XK6NGjqaysxGazUVJSwtSpU+WZz8jISEpKSnA6nfIMhldwcDBarVZOCeo4uu8dSNm3bx9FRUUkJiaye/duCgoKGDRoEOC7odz27duBI2mZS5cuBY6sMdBqtTQ2NmK1Wn1KjX700UeUlpZSX18P0G1n4Jdwu9089dRTbNq0iczMzF90rbOJCPgFQTgjKJVKpk6dyhNPPMHbb7/NJZdcQnV1Nfn5+ezatYvq6moiIiKwWCyMHz9eVOsRzmjefPGmpiZsNhtWqxWPx8N5551HSEiInK7R1NTEhx9+KEY3f0UdF7ceK0e9Y7rJgAEDfKr/BAcHM3XqVPz8/FizZg0HDx70eW5VVRUtLS2o1WqcTieVlZW0trbi7++Pw+HA4XBgt9uxWq3o9XoiIyOpqKjotgMiSRKfffYZACEhIfKgR58+ffjss89wuVxkZmby2WefMXHiRP7+97/zxBNPsHHjRoqKiqirq2P79u28++67/Pjjj5jNZpqamnC5XBQVFeFwOORKRpIkodVq+eqrr1Cr1fj5+ZGdnS1vJubdUM5bzQeOzEbMnj0bgNbWVpRKJfn5+Z1y/Pv06cMXX3yBVqvlX//6FyaTCY/HI3cGLBaLXO3naGIzrV9GBPyCIJwxJk+ezPfff8/WrVvJy8tj//79aLVa2traGD58OKWlpTQ1NfHCCy/gdDqZPn366W6yIHTLmy8eGBhIa2srOp0Oo9EoP97S0iLvqHuyRjeF4ysoKKC5uZnx48fTt29fysrKOm2S5c1R75hucvQ5SqWSG2+8kZqaGrnE5+jRo4Ejwal3kMI76u52u2ltbZVr73u/f/3112lvb0epVMqj7kf/HDQ2NlJQUEBMTIxcTcg7O/DKK6/IqT/ejkBdXR033ngjWq2WjRs3olKpyMjIYNasWWzdulX+uSwpKZEX+nYcsXc4HKxevRqn0+mzmZZ3QbPRaPQpW6pSqXzWQtTV1fmM7sP/So02NTURHBzs8/7i4uLYsWMHhw4d4uqrrz7xD1folgj4BUE4Y3j/gK5cuZI9e/bQv39/1Go1RUVFlJSUkJGRwQMPPMBnn33GkiVL6Nu3L2lpaae72YLQLa1Wy4MPPsiWLVs4dOgQO3fuRKFQyCkQ3k2TAgICRMD/K+hYOcmbSuJdOO299z05xys8PJxFixbx+OOPk5OTIy/ebWxsJCQkhPPOOw84Euy2t7ej0WhIS0tjx44d1NTUoFKp2LJlCyUlJfj7+2O1WjsVJPDW8VcqlSQkJLB//378/Pyor6+npqYGhUJBdXU17777bqfdmt1uN0qlUl6g7E1JCgkJoaWlhc8++wx/f38SExPlnH848rs4IiKC9evXo1KpmDRpUo/r6Hvr8x+d419ZWcmuXbvQarW0trbSp08fqqur5Z/7hIQE9u7d2+NdfoXeEQG/IAhnlNTUVDIzM/npp59obGzEZDLR3NxMVVUViYmJjB8/npSUFO677z5WrVrFI488Iir2CGc0tVrN8OHDaW5uJj09HaVSKadAdNxR11thRTh1vJWTEhMT5bzyVatW+Yzg9+Sc7niDXafTSUtLCwEBATQ1NdHQ0EBwcDCZmZncfvvtvPrqq2zZsoVRo0YhSRL19fWEhYUREBCA3W7HbreTm5tLUVERHo+H6upqEhISKCwspKqqCkmSsNvtvPzyy4SGhqLRaLjpppvkfQS8QfZf/vIXHA4HW7du9anko1AoCAgIYM+ePbS2trJv375O72Xfvn0cPHiQAQMGsG3bNkaMGNGjDqkkSXLaUscc/+eee47du3fjcrnQ6XTExcXJ6xKqq6vRaDSo1epO5UaFk0ME/IIgnHEiIyMZOHAgHo+HSy+9lG+++QalUilXuoiMjCQ+Pp7a2lpRsUf4TdDr9eh0OgIDA1GpVHIKhMhD/vV4KyfFxMTIgwTJycnExsb6lEftyTndMZvN2Gw2goKCsFqtNDY2UlZWhtPpxGKxoNPp5Nr33p+HxsZG3G43ffv2paKiQs7t9/PzY8OGDTQ1NWEymZgzZw5fffWVXPHJz8+P8PBwoqOjiYiIIDY2tlOaTVRUFDqdTm5bx0o+ffv2RafTYTKZuixJ2tjYSGlpKXFxcd2mNXVFqVQyfPhwea8B77HIyEhiYmJYs2YNLpeL4uJi7HY7mzZtkisfHW/zM+HEiWExQRDOOAaDAafTiUajITMzk+DgYJ+RpdraWgICAtDpdKJij/CboFKpmDhxIg8++KCcxiP8ujpunNUxr3zixInyJmg9OcdbwnPhwoU+HTbvPgsGgwG9Xk9zczNr1qyhtraWoKAgHA5Hl7vjenfC7du3LwaDgfr6etra2uT1A94NuDqmHFmtViRJoqWlhdra2uN2HL0zD3369JEX0ZpMJpKSkqirqyMwMNBnd9zAwEDq6+vx8/MjKSmJmJgYsrKycDgcLFy4kMcffxyn08mGDRvYsGFDpwBdr9d32m23ubmZGTNmcN555zFq1CgyMjKIj48nJCSECy+8UN4Mbfjw4T1OHxJ6TgT8giCcceLj4wkPD6ekpASz2cyCBQuYOHEiKpUKSZLYuHEjarVaXjgmCIJwLN7R/Y4bZ9lsNrl2fEhICOvXr2f9+vXHPKer3WK92trasNlsxMXFyRVwvItXjUYjOp2uU7qKd0YgODgYtVrNU089hVarRaVSkZSURGRkpLxT7eLFi9m+fTtlZWU0NDTQ0tJCTU0N1dXVbNu2jaKiom7ff1tbG1arlczMTNRqNRMnTuThhx9m8uTJcvlYt9stB/D19fVyu7xpTSe6M7S3KpLJZMLf399n5N9bmnb//v0EBgbKG38JJ5/oQgmCcMZRKpVce+213H///SxatIi//vWvuFwuWlpa5FzawMBANBqN2LBIEITj8taO926cdfTiVoDAwEB51Ly7c9xut0+1Gi9JkmhqapIX9npLcno8HpqbmwkKCsJoNGKxWPjb3/5GXl6ez4xAS0tLl+1OTk5GrVYzcOBAUlJSsFgslJeXo9frmTx5MhEREaxcuZLm5mY2bNjQbYnRpqYmEhMTfSr5HL1RlrfUcVft8qY1eatJwZHOSlVVVaeKO129vnd2w3tfPR6PXL5Uo9HQ3Nx81uTtn6nlQ0XALwjCGSktLY277rqLV199lblz5xIfH09AQIAc7Le0tHDNNdeIBbuCIByXd18Eb5Wa1tZWgE4LpyVJOu45XaWblJWVIUkSd9xxB6+//joul0tep2E2m6mvr5dz7EtLS5Ekiba2NuDI77r9+/fLs5chISEcPHiQoqIioqKiMJlMaLVa+vfvzyeffILL5SIyMpKgoCCGDh3K22+/TVJSEpWVlTQ3N/uUfoUjAbfL5cJut/Pee+/5dGTgfxtleQPuo9sF/0trev/997FYLAQFBcmLky0WS5fButlsprCwkKSkJNLT033q9LvdbjweDy6Xi4yMDG688UZef/31E/14hR4QAb8gCGes6dOn07dvXz7++GPq6urweDzAkT9QU6ZMYcCAAae5hYIg/FZ03DjLmwrY1cLpnpzTkcfjYefOnXg8Hn744QfKy8vxeDyo1WoGDRpEbm4uTU1NXHrppdx+++3cfffd8sLc4OBgNBoNDoeD8vJyLBaLPIixbds24uPjGTZsGKWlpcTHx1NRUYFSqUSv18t5+UajEY/HQ58+fVi7di1Dhw71WfOkVCqJjo4mIyODm2++GYfDAfyvI3PHHXeg0Wj45z//iSRJWCwWn3Z1TGsymUzs2rWL2NhYn8XJXa1N8KYiFRcXM2LECJ86/d6ZEo1Gw/bt23n11VcB5OpE3t2Ne8tsNnPo0CESEhJ6/dyznQj4BUE4o6WlpbFw4UJ++OEH1q5di8PhoK2tjU8++YTs7GyuvfZaUYtfEITT5uDBgzQ3N+Pv788XX3xBY2MjDocDrVZLRUUFarUanU5HbGwsQUFBBAQEEBgYiMlkwuFwsHPnTiorK2loaJDXLikUCurr67Hb7YSFhVFXV8e2bds65f97PB65Q5CQkCAH397darVaLQsWLJBr8ntr9WdmZvp0ZJxOp3xdl8tFW1sbO3fupKqqiu3bt/vU9W9ra6OkpETuEDkcjk4bhpnNZnnjsvLy8h7V1vemHul0Onl3497sS9Fxd2RR2rMzEfALgnDGO3DgAD/++CMRERE0NzfjdrtpaWlh37593H///dx1111i111BEH51kiSxadMm+vfvT3R0NHl5ebS1taHX61EqlSiVSoxGI21tbXz22Wdy2U2tVsvQoUNxu900NjZSXl6O0WiUy7e63W6cTic7duyQX2vfvn1ERkZSVFSExWLhwIEDTJ48GZPJxKZNm3jttdcICAigpKRErsTTWwqFgqioKNLT0wHklBvvbIDT6aSuro68vDwGDRpEXl4eRqMRq9XK/v37+eyzz5AkidTUVMrKykhKSsJqtVJcXHzcjeXMZjMOh6PT7sbenPiO5Ua70nF3ZG8pVOF/RMAvCMIZzePxsGbNGgICArBYLOTl5ZGQkMBjjz2G2Wxm0aJFYtddQRBOC+8mXSkpKRQXFxMcHExVVRUBAQFotVqSkpJQKpX4+/sTGhpKXFycXInm0UcfRZIkrrzySiRJQq/XM2jQIGJjY9m4cSMJCQkkJydjt9sJDAwkKCiIiooKWltbaWlpwWKxkJKSQl5eHi6Xi/b2dvr168e+ffswm82Eh4ef0HtSq9UEBgYCyDv0emcDHA4HlZWVBAUFyZ0KvV5PYGAg8+bNo6WlhcGDB9Pe3i5vXOat+3+sOv4dy5N2LAN6vH0POj7fuztyUlISTU1NFBQUiFH+DkTALwjCGa20tFSuQd2/f395Yduzzz7LggUL+Nvf/iZ23T0DHDx4kKysLGpra+W1Fl4PP/zwaWqVIHTmraP/S8/puJEXHFns6q3K462Nb7FYUCgU8s62CoWCwMBA+feUd1TaYDBQU1NDSEiInCqj0+morq6moKCAqKgoRowYAYC/vz9tbW0kJiZyww03sGzZMlpaWkhOTkan08lVd7wbFXrfi9Pp5PHHH/8Fd+5/7U1LS/PZpyA+Pp59+/bJ6T0XX3yxvO7KZDIRFBREdnZ2twF8x/KkR+9u3JNKbEfvjhwfH09eXl6PUom60tNZhd8SEfALgnBGs9lsWCwWDAYD559/PgcPHvR5XOy6e/q9/fbb/OUvfyEsLIyoqCifaXuFQnHOB/wdg0eRZnD28G7Sdc0117Bq1SpGjBjBtddey9NPP01OTg4mk4nzzjtPTu25++67iYqKkheodhyVbmtrkxfTdlxkO336dBYsWEBKSgp33HEHCoWC9vZ29uzZgyRJfP311xw4cICGhgby8/O54IILGD16NFu3bj3po9ve9vr5+cmlNL0/zxqNBr1eL5cMzczM5LPPPgOO/A5ITEyU03S6um53ZUCzsrK44YYbjtuujrsjezsZ3n0PHA7HGVkm89cmAn5BEM5oBoMBh8OBUqkkIiKi0+PeXXclSRK77p4mTzzxBE8++SRz58493U054/Vk5Fg483W1kRcc+X3Vp08f/P39cblcREREoFarUalU9O/f3+ca3lHp+Ph4Dh06RGRkJI2NjdhsNnlkOSEhgeDgYOrq6oiMjJQr3IwcORKPx8PNN99Ma2sr7e3tcq49QH19Pdu2bWPOnDmEhYXx4IMP9uh9KZVKMjMzmTt3Ls8++6zPY969DOx2O9u3b0eSJKqrq4mPj8dgMNDW1kZLSwt6vV6+J973odFoMJlMPnX8vY5VBvTDDz887mZfR3e8vM/X6/UcOHCA7OzsHr33s50I+AVBOKN5d9315qUuWLCAJ598EovFwp49e/jxxx9RqVRi193TyGw2c/XVV5/uZgjCr6a7jbzeffddtm/fjsvlAui2Pv3hw4dRqVTExMTgdrtxuVwoFAoKCgo4fPgwDoeD7du3895772G32/F4PHLwDEfy5lUqFVFRURgMBp9ce0mSqKiokEtihoaGdvkeOm4Q9cADDxz3PavVambNmkVNTU2nOvo333wzzc3NlJeX09bWJtf796b3edcxuN1un3tyvDKgISEhXXYSOj7/6I6Xt+Rnc3MzAP/6179ISUk559M9RcAvCMIZ7ehdd2fMmIHBYMBqtfLkk09SWVmJn58fAwcOJDY29nQ395x09dVXs3btWv785z+f7qYIwq+iu428vCPuTqdTTuXpyFs6sr6+ni1btrBo0SI++ugjnxSe+fPn43A4fOrma7Va/P395Vx8b6DelY7VanpaErMrZrOZyspKn+d7B1a8QbW3oxEVFYXRaCQmJsan3R1z4GfPnk1AQACvvvqqT+flWGVA4X8bonWlq46Xx+OhtbWVhoYG4uLiqK2tJSQkRF7TcK46ZwP+JUuWsGTJEp8fOkEQzkzeXXefeeYZvv/+exISEggNDaW5uRmtVktNTQ0tLS0sWLCAG2+8kdTU1NPd5HNKSkoKCxYs4KeffmLIkCFoNBqfx++5557T1DJBOHW62sgrPj6eRYsW8fjjj5OTk9PpOQUFBfJC3vb2dmprazul8BiNRoqLiwkMDJRH8Hvq6Go1PS2J6a3X7+1IeDfOcjqdFBcXy3nwx4uZ1Gq13AHwdgwAedfhox2vDCgcSQc6upPQ8fWO7ni5XC727t1LaGgokydPJi4ujuzs7G5nOo7HbDZTXFx8wpuBnSnO2YD/zjvv5M4778RqtXbahloQhDPPpZdeysaNGzl48CAqlYqsrCwMBgNXXXUVV155JRs3bmTr1q18/PHHXHvttSLo/xW99dZbBAYGsmHDBjZs2ODzmEKhEAG/cM5SqVQsWLAArVYrB+PeFByNRsPzzz+PzWaTR8qXL1+O3W7vlPrSU0dXq+lJScyuFBYWYrVa5Z10CwsLe90Wt9st/z4YP358pwpBZrOZqqoqgoODuy0DCsdf6H50x6uurg6n00lYWBhBQUFMmTKFr7766oRmOjruGHwim4GdSc7ZgF8QhN+W0tJS9Ho9L7zwAm+99RZms5mBAwciSRKff/45N9xwA3V1dYSEhLB27VoGDBhwzuds/lqKiopOdxME4YznDcaTkpLw9/fnkksu4bPPPpPLUXoX3NpsNjweT69/f3k7FEdXqzleScyOvOsLAIKCglAoFBgMBnJyck5q1R9JkigpKcHpdGKxWORddo9OITrR63as+JOUlERQUJA809GVjmlSHRc4m81mLBYLFouFFStWMGHCBAYNGnTC7TudxF9DQRB+E7xVMJxOJyqViqFDh2IymeTRloiICBQKBampqZjNZkpLS09nc89ZkiT94sDgtddeo2/fvuj1ekaMGNFlakRHS5YsITU1FT8/PwYMGMDy5cs7nfPJJ58waNAgdDodgwYNkksGCsLJptVqefzxx8nKyuLxxx+XR/e9pSNNJhN6vZ6xY8cycOBA6urqCAwMJDo6mujoaIKDg7nooovkmYGeMpvNVFRUMGHCBJ8a+ccqidlRx/UFW7duJT4+Xp4l6BiIm81mcnNzMZvN2O12cnNze93p99bdDwoKwuFw0NjYKHcAjhWYm81mtm3b1u2Mg9lsxmq1ym3veA+sVutx78HR98PbeejYcfqtbuYlAn5BEH4TvHmsJSUlwJGFXIA8MpSTk0NTU5O8cFeU6Px1LV++nCFDhuDn54efnx9Dhw7lgw8+6PV1Pv74Y+69914eeughduzYQWZmJpdcckm3HbjXX3+defPmsXDhQvbt28ejjz7KnXfeyZdffimfs3nzZq699lpuuOEGdu3axQ033MA111zDli1bTvj9CkJveEtHHh2MT5gwAavV+otGtQFaW1vJzs7G4/HI1Wq8Xx1LYlosFgIDA/njH//YqTPRcX1BRyaTiT59+lBcXIzH45FTXIqLizGbzTQ3N7Nhw4YuA2GlUskFF1zAgw8+KC/C7RhIBwcHo9Pp2L9/v08KUXf1+jum1xz9et7H/fz8aG1tpbS0lPLycqqrq9FoNPj5+fUqYD+685CQkNCjjtOJcjqdLFy4UF6YfbKJlB5BEH4T4uPjCQ4OJj8/H6VSya233kpDQwNz587FbrdTUlKC3W7no48+oqGhQZTo/BW98MILLFiwgLvuuovzzz8fSZL48ccf+fOf/0x9fT333Xdfr651yy23yOkNL730EmvWrOH111/n6aef7nT+Bx98wO233861114LHJm+/+mnn3j22We57LLL5GtMmTKFefPmATBv3jw2bNjASy+9xEcfffRL374gHFN3Nfu9pSf9/Py6XVjb1b4NR2/k5h30cLlc7Nq1i6VLl3ZZEtPlclFUVCQHzH379vVpY3Z2Nmr1kbBw1KhR5Obmym3KzMzkP//5DyUlJXIFoIMHD9LS0kJsbCyVlZVYLJYe3Q9vID1o0CDy8vIICgqisrKSPn36YDKZMBgMZGdnk5qa6nM/vM+LjY1lxYoV5OXl8fzzz8sdF7fbjcPhwG63s3nzZhobG/nxxx99yqZ6F0h73+exPrPi4mKCgoIwmUzAkY5PTEwMWVlZJCcn/+Zy+UXALwjCb4JSqWTatGmsXLmSoqIi3njjDRQKBdOnTwegpqaGSZMm8dlnn1FYWCjnbwqn3iuvvMLrr7/OjTfeKB/7/e9/T1paGgsXLuxxwO90OsnNzeUf//iHz/GpU6eyadOmLp/jcDjQ6/U+x/z8/Ni6dSvt7e1oNBo2b97cqQ3Tpk3jpZde6lG7BOFYjreZWnc1+5cuXQrgU2f/eIFoV8xmM06nk5EjRxIbG8uUKVPkhb/wv5KYNTU1fPPNN8TGxnYaqfbOQAD4+/szfvx41qxZI9fF9/f3R6/Xs3v3buLi4ujbty979uzB7XbTt29foqOj2b1793FHz72j+0FBQYSFhXHBBRfQ0NDAunXr5HM6jqR7Fxp3fN7R1Ye81Go16enpVFdX43A4UKvVGI1GLrzwQnnEfNasWT26x97SpmlpaZ1mZFatWtXrRdBnAhHwC4Lwm5Gamsp1113H+++/z8qVKwkJCWHMmDEcOHCAqKgoKioqCA0NJSEhgXXr1pGamioW7v4KqqqqGDduXKfj48aNo6qqqsfXqa+vx+12ExkZ6XM8MjKS6urqLp8zbdo0li5dyowZM8jIyCA3N5d3332X9vZ26uvriY6Oprq6ulfXhCMdCYfDIX9vtVp7/D4EoaPuavZ7Z7FaW1vRarWdAtGjF5J2lc+v0WgYMmQIZWVlpKWlER0dTX5+PoGBgfIIv3eDrm+//ZagoCCSk5OJjo72SW/Jzs4mOjr6/7V353FR1/kDx18zAwzXMBxyCQxXqHgDHnlrrdp9uKW2pXlttbpZ2XavrR2bu+2m7W5ZuV61HUZtZttaHiWKR2kgpuIJKIogosAM5zDD9/eHv/kuCCgoMCrv5+PBI+c73/nO+2s48/m+v+/P+4NOp6OyspLvv/9eLTPy9vbmgw8+oLi4mPLyckwmEyUlJej1enQ6HSUlJeoqt3X/zTTGkaXv1asXGo0GRVHIzc0lLCyMmpoaampqGmTSG3td3e5DdSfR6vV6ioqKCAgIwN3dHYPBwIEDB/D29kaj0eDj43PR/1+Oux0eHh64urpSVlZW78LH39//qszyyzehEOKqEh8fz0MPPcTgwYNJSkoCzi3NfujQIXbv3s3QoUO59957ZeJuO7ruuuvUJe3r+vTTT4mLi2vx8c7/Er1QD/G5c+dy8803c/311+Pq6sqdd97JlClTANSa4ZYeE2D+/Plquz+j0UhERESLz0MIB6PRqE7KNRgMautJx2O9Xg/8727BvHnzmjVh9/w2nMOHDycvL6/eBNuffvqJlJSUJvdzTPYdNWoUiYmJJCUlMX36dIYOHYq/vz+jR49m+vTpGI1G4uLi8Pf359ixYwQFBeHl5cWxY8eIjo7Gx8eHkpKSC66Ke/z4ce666y6eeuopKioqOHHiBEVFRURGRuLh4UFJSQkAw4cP58SJE2RlZaEoCtnZ2RQUFPDzzz9jt9ubnETbWN19Szv/OO7IVFZWkp6ezq5du9QFwZYuXcrZs2frrZ1wtZAMvxDiqlNeXk5MTAzPPvssGzduJD09HTi33PyWLVvIyMjg9OnTMnG3nbz00ktMmDCBzZs3M2TIEDQaDVu2bOG7775r9EKgKZ06dUKn0zXIvBcWFjbI0Dt4eHiwbNky3nvvPU6dOkVoaCiLFy/GYDCoK2uGhIS06Jhwrs5/zpw56mOz2SyDfnFFqdv5x3EnMzY2lrCwMHbv3k2vXr04evQoFouFf/3rXwwZMqTBfhkZGQCMGjUKT09Pampq1OP7+vqq86YCAwOxWq306NGDkpIStQb/zJkzmM1mcnJyiIyMZNeuXaSmpqpzas6Pt7q6muLiYpYuXcpPP/1Efn5+vTUHGsukDx48WF0zqanuQ9ddd12TdfchISGsW7dO3XYxLi4uTJ06lVOnTmG326mtrW2wIJirqyuvvvoq0PTdlyuNZPiFEFcdx4Tc7du3s337drUlZ21tLZMnT8bd3Z19+/Zx6tQpJ0faMfzyl7/kxx9/pFOnTnz55Zd88cUXdOrUiR07dnD33Xc3+zhubm4kJSWxfv36etvXr1/faMlQXa6uroSHh6PT6Vi5ciW33XabOrgZNGhQg2OuW7fugsfU6/X4+PjU+xHiSnKxzj+OCba+vr7k5uaq2f3z9yspKVEH4WlpaWpZXFpaGpWVlZjNZlJSUvDw8MDFxYWDBw+i1WqxWq3YbDa0Wi1ff/01ZWVlmM1mcnNzG+3ao9VqSUhIYMaMGcyYMYPExER1Rd7CwkKqqqqora1l165daia97ns73rOsrKxe9yFHxx5H3X1kZGS98xw2bFiLOyEZjUb1Toy3t3e9BcFCQ0Ovys8DyfALIa46JpMJHx8fli9fzk033cQLL7ygdnAJCwvD398fk8nE3r17ueGGG6SOvx0kJSXx4YcfXvZx5syZw6RJk+jXrx+DBg1i8eLF5Obm8sgjjwDnMu95eXlqr/1Dhw6xY8cOBg4cSHFxMQsWLGDv3r28//776jEfe+wxhg8fzp///GfuvPNOVq9ezYYNG9iyZctlxyvE5bjYhN+mXKzzj2OCbXh4OLW1tYSEhLB7927MZjMajUbdD+Ds2bOMHj2ayMhIdX7BtGnT1Hr8yZMnqysAp6enk5eXh91uV1fjNRqNfP/99xw5coSamhrsdjvHjx+nrKysQdzu7u6EhoYC5wbVAwYMICMjA3d3d7VjUd1Mul6vZ8mSJVRWVqp36Xbt2gXA8ePHMZlM3H777dhstgvW3dfthNRRyYBfCHHV0Wq19O7dm6+++oqzZ89y4sQJbDYbZWVlvP322xw5coSbbrqJgwcPqpkt0bocPbMdf76QlmTDJkyYwJkzZ3j55ZfJz8+nZ8+erFmzhsjISODcgKbu3Ay73c4bb7zBwYMHcXV1ZdSoUWzbtq3e//PBgwezcuVKfv/73zN37lxiY2P59NNPGThwYAvOWIgrx8U6/zgm2EZERHD06FFMJhNr1qyhuroaDw8PlixZgqIoFBQUYLPZ2Lt3LwMHDlTvnjoy7wD+/v71Slx69uyJzWajtraWjIwMEhMTufXWW5kzZw5+fn6Eh4erd1kvNsCurKzEarUSExPD8ePHURRFzaQ7ymSmTp3KyZMn1U47CQkJAGqZzdSpU9FoNPXq7hVFUevuly9fXq8TUkclA34hxFUpODiYHj16UFVVxYoVK1i7di3FxcXExMQQFxdHZmYmP/30E4mJiTLgbwN+fn7k5+cTFBSEr69voxNgHRNjW/olO3PmTGbOnNnocytWrKj3OD4+Xs34Xcg999zDPffc06I4hLhSXajzj6Io/Pjjj/j5+REYGIifnx+PPPIIn376Kd999x09e/ZkxowZHD16lO+++w6TyUR+fv4FF5RylLjY7XYMBgM6nY7f/e53/PWvf0VRFHbt2oWLiwsRERH4+/sD5xIBF+rac36rzZKSEnWS7vnvbbPZOHPmDL6+vnh7ewOoZTY+Pj4XrLt33K1orBNSR9Jxz1wIcVUzGAwEBgYyfvx49u3bx7fffktcXBz/+Mc/MJlMZGRksH//flJSUujSpQvx8fHODvma8v3336tf7Bs3bnRyNEJcHS61hKcxjg5SVqtVzcaHhoZy5MgRdYKtRqPB3d2dzp07c/vtt/PNN99gs9nUNp2BgYF069atXpvOlraaPHv2LFlZWYSFhVFbW0tkZCRWq1Vt2dlUlv/8Vpsmk4nMzMwGtfaOybhWq5XS0tImY6x7UWK329ULgrp3KzoyGfALIa5KjpV3U1NTKSwsZM6cOYwbN4758+ejKArXXXcdw4YNIzAwkHXr1tG1a1ep5W9FI0aMaPTPQgjnOb+HvMViQafTNVjVt7F2np988gmlpaXqhfz5iouLOXz4MDExMWoHLEVRyMzMRK/XEx8fT2ZmplrW4+HhwXfffddoyV9THXX0ej3Hjh2rd5HgmIzr4+OjTr5tKkbRNPn2E0JclRwr7+7cuZMtW7YQGxurZoD27t3L4cOHGTt2LMOHD5ee/G3s22+/rTcB9u2336Zv37786le/alFnDCHE5Tm/h3x6ejppaWksWbKEpUuXUllZSVVVldrO0zHYdrTpbGpiq6Io5OTkUFZWVm8fxxyqXr164ebmRnV1NWVlZcTHx3P27FmARo/ZVEcdX19fzGazWl7kuIDx8fHB19cXvV5Pbm5uh558e6lkwC+EuGrFx8czatQoysvL+c9//sPrr79Oeno6RUVF9O7dGw8PDzUTJT35285TTz2lZvH27NnDnDlzuOWWW8jOzq7Xy14I0TKOhbOys7Obtb+LiwsPP/wwn3zyCf/617/o378/SUlJaivMpKQkYmJiyM/Pb7KdZ2MX6Y4Benh4uLqP4yJAo9FQUVHBDz/8wPHjx/nhhx9YtWoV+/fvp6CggOrq6nrzeBq7C2GxWCgrK0Or1eLh4aGWFznuRDguDIxGIxaLRRIJl0BKeoQQV7U+ffowcOBAbrrpJk6ePMnmzZsB2L17N3v37kVRFIqKiqSGsw3l5OSoy9v/+9//5vbbb+e1114jPT2dW265xcnRCXF1qptVT0lJoWvXrs2qr2+qth/A29ubw4cP06VLl0bbeTbWvrJulj0mJgaz2czRo0ex2WzU1NTQqVOnRuOKj49Xa/iPHz9OcXExWVlZHD58uN5diLrvk5+fr14E2Gw29U6EIx53d3cMBkOrZvmtViuvvPIKqampDBs2rFWO6TjX7OxsunXr1irHvFwy4BdCXNVMJhN+fn5s3bpVrT8tKCjAbrczZcoU3nzzTbKzsykvL3d2qNcsNzc3tUvIhg0bmDx5MnCund/FWnYKIRpXN6ted0XZy3H+arfnt/NsrH3l+fX+kZGR7Nu3j9zcXBISEqipqeE3v/kNb731Fj/++CMDBw7kiSeeQFEUDh06REVFBSkpKWRnZ1NWVsaWLVuYMmWK2lGnrgEDBjB79mz8/Pw4duwYJ06cYPz48XzyyScA6uTeffv2qW06rzSXeqHW1mTAL4S4qmm1WkaPHs2TTz5JXFwcf/nLX1ixYgXl5eVs27aNgIAAIiMj2bBhA/Hx8TJxtw0MHTqUOXPmMGTIEHbs2MGnn34KnFsUKzw83MnRCXH1qZtVj42NJTQ0lJSUFGJjYy9r8Fh3tVugXjtPx+O67SsdcYSFhaHVaqmtrcXPzw8fHx9SU1PR6/W4u7urnXDqrkh75MgRKioqCA8PZ9++fRQWFhIXF0deXp561/X8Ab9OpyM0NBRXV1eSk5PrLSzmGOC7urri7u5OYWFhs7P87Zlxb4sLtdYg33xCiKuel5cXsbGxBAQE8P7775Oamsr27dvZvXs3Q4cO5d5775WJu23orbfewsXFhc8//5x33nmHsLAwAL755htuuukmJ0cnxNWnsS46J06cuGCv/OZyrHYbGhqKwWBQB+iOx3q9Xt23uLiYvLy8BvX+UVFRnDx5ssla+sbKgKqqqoiJiSEsLEyt0W+KY/Lx2bNnWbp0Kenp6eTn55Ofn09GRgZVVVXY7XYURcFqtTJv3jxeeeWVRtf8OD/jfrGLBEeJT0pKSovXEDn/Qi0sLKxZ79keJMMvhLjqWSwWAgMDefbZZ9m4cSPff/89cO6LbcuWLWRkZHD69GmZuNtGTCYTX3/9dYPtCxcudEI0QlzdFEVRa9cddyRjY2MJDw9vlSx/c7i5ufGHP/yBpUuXUlFRoWbZHQNgV1dX/Pz82Lt3r9rpp666FywlJSX4+Pjg7u5OSUkJEydOVFuAGo3GBq+1Wq389a9/paqqit/85jcA9TL8jpV2FUVp1h3b7OxsSkpKKCkp4eOPP2b48OHqnKMLccwxiI6Ovui+jZ2340ItOTn5isjyS4ZfCHHVc0xM2759O9u3bycoKAiNRkNtbS2TJ09Wl3k/deqUkyO9NqWnp7Nnzx718erVq7nrrrt4/vnnr9g6WyGuVFlZWZw4caJBVn3kyJGtluVvjvOz7Dt37mTVqlWsWrWKnTt3UlxcTHV1dYPsdd0yIF9fX44ePUrPnj0JCgri6NGjapa/qRagDq6urixZsoQlS5bg6emJm5sbbm5ueHt74+3tjVarZdOmTbz22mvY7XZ0Oh1z585l7ty56HQ6NZbU1FS137+Pj89F7y44XldSUkJ5eXmDdQEu9JrG2p06LtScneWXAb8Q4qpnMpnw8fFh+fLldOvWjZUrVzJ27Fjc3NwICwvD398fk8nE3r17qa2tdXa415yHH36YQ4cOAeeyaRMnTsTT05PPPvuMp59+2snRCXH1cAwa69auWywWtYuOv79/g8GjY/XeefPm4ebmdsnvff5xXFxcmD59Og8//DAzZswgMTFRLf1JTExk2rRpJCYmNsiy1y0DKikpwWw2M2zYMKKjozGbzWRnZ1+wBWhrKi4u5uTJk2pbz8jISLWu/mKvq66uJiwsDIvFQlVVVZP7OkqKHnvsMY4dO+b0C7WmyIBfCHHV02q19O7dm9zcXHUhGJvNRmlpKcnJyRw+fJgpU6ZQUlIidfxt4NChQ/Tt2xeAzz77jOHDh/Pxxx+zYsUK/v3vfzs3OCGuIudn1dPS0tSFsxYvXszZs2frlda0xKVcGBiNxnr1/Y4su8FgICQkpF69P5y7YDl27Bh+fn54eHhw4MABNBoNZWVlVFdXo9Fo+M9//oOHhwceHh7Nzp5fCkcsnTt3rrea78Xq6h2v0+v1REdHYzAY1PaiF3qvo0eP4ufn1+wLtfYmNfxCiGtCcHAwPXr0oKqqihUrVpCamkp1dTU6nY6bbrqJQYMGsXnzZqnjbwOKoqh3TjZs2MBtt90GQEREBEVFRc4MTYiriiOrXlFRgdVqrddFxzFI9/LyUrvotDetVsuwYcOYO3duk/s42n4uWbKErKws7HY7zz77LMePHwfOfUY4+vC7ubmhKEqL5yQUFxeTn5+Pr69vk/tUVVWhKArDhg3j888/B2hWXX12djZmsxlfX1+1DWhmZuYF70ZcqN2po7zIbrdjt9ud9v9OBvxCiGuCwWAgMDCQ8ePHs2/fvgYTd1NTU2UBrjbSr18/Xn31VX7xi1+wadMm3nnnHeDcglzBwcFOjk6Iq0tTC2ddTrlOe9FoNPTt21dt81laWkpNTQ0PPvgg77//PgCPPvooAQEB/Pa3v8XV1ZW33nqrRXcsHBl4q9VKaWlpo1lzRw1+VFSUmnGvrq7GYrHg6emJj48Pc+bMISEhgaeeego4N0n3p59+QqfT4ePjoyaH/Pz80Ov1F7wbcaF2p1fChRrIgF8IcY0wmUz4+vqSnJxMWVkZjzzyCLt27UKj0TBp0iRZgKsNvfnmm9x///18+eWXvPDCC2rW7PPPP2fw4MFOjk6IjslRwtMex687Od/R9hOgU6dOAPTu3Vvd1qVLF3UQ3JJJ/ZWVlZSUlHDs2DEsFgs+Pj7qPIDAwMB6+2q1WqKiohgwYAAffPCB2tYzPT2dpUuXAtSbcOy4QKitrWXHjh1ERUWRmZkJnLuI8fX1xWw2k5WV1WSHn7rnfSVeqF3ygP/o0aOkpqZy9OhRKioqCAwMJCEhgUGDBuHu7t6aMQohxEWdvwDXAw88wIEDB8jLy+PTTz9Fp9MxatQoWYCrDfTu3btelx6Hv/zlL+rtbCGEuFSKolBaWkp1dTV79uwhIiICODdoz83NVS8sHM7PuFssFmpqakhMTFS32Ww29XuguLiYqqoqfH19qampoaKigurqasrKyoBzFyYWi4V///vfxMfHq+9jt9t57bXX2LZtG8OGDWvzv4fL0eIB/8cff8zf//53duzYQVBQEGFhYXh4eHD27FmysrJwd3fn/vvv55lnniEyMrItYhZCiEbVXYDrjTfeYP369dhsNk6cOEFcXJzaYi03N5eoqChnh3vNk+SPEKI5dDodzz//PG5ubo1m/R1dc9zd3SktLaVnz56UlZVhNBqxWCyNZvnPz7jXXQUYUCcc152k6+Pjg6urK9u2baO6uppdu3ahKAq5ubl4e3uze/dubDZbG/9ttI0WpbgSExNZsGABDzzwAEePHqWgoIC0tDS2bNlCZmYmZrOZ1atXU1tbS79+/fjss8/aKm4hhGjAsQDXXXfdhaurK5GRkfj5+ZGUlMSTTz5JbGws+/btY/fu3c4O9arn7++vTsj18/PD39+/yR8hhLhUjgG3Y4Kv0WhUu+a4u7tjMBguq9uPY5Kun58fffv2Zc6cOepnV0JCAjExMXh4eJCYmEhMTAzHjh1r5TNsHy3K8L/yyivceuutTT6v1+sZOXIkI0eO5NVXXyUnJ+eyAxRCiOYyGAwoisKXX35J//798fHxYcuWLbi4uBAeHs7IkSNZvXo16enp3H777VLWcxkWLlyo1qm++eabzg1GCHFNsdvtbNq0CYDu3burk22NRiNPPfUUf/nLXygtLaWyspKIiAhOnDhxST396y7MZbFYcHd3Z+DAgXTq1InDhw/j6enJkSNH8PLyokePHuoiWpMmTWrtU25zLRrwX2iwf75OnTo1qKkSQoi2ZDKZUBSFvXv3Mn36dCZMmMBrr70GnPtg37p1K7169VIzRlLWc+kefPDBRv8shLg6tPWk3vPZ7XZSU1Ox2+3MnTu3WZNZ65bTnDp1ipCQELp27Yqrqyv5+fm4ublRUFCAu7u7unKvo8Wn3W7nlVdeUf/cmOLiYmpqaoiMjGTfvn0A6gJde/fuVScIO1p01m3pebW55Em7eXl5/Pvf/+bQoUO4ubnRtWtXxo8fry5uIIQQ7U2r1ZKUlMTmzZtJSUlh8ODB2Gw2ysrKePvttzly5Ai33XYbP/74o/TjbwOFhYUUFhY2WM24d+/eTopICNFedDodI0eOvGAtfks5VriNj4/n0KFDVFZWsmLFCgoLCzGbzXh7e3PkyBGCg4Px9/dvVp97q9XKn//8ZzZv3ozBYGDEiBHqJF2LxUJBQQGurq7odDr27t1LRESE2t0tNjaW8PBwNm/e7NRFtC7FJQ34Fy1axJw5c7BarRiNRhRFwWw2M2fOHJYsWcJ9992HoihkZGSQkJDQ2jELIUST+vTpQ48ePcjKyuLQoUOsXbuW4uJioqOjCQkJ4ZNPPiE3N5chQ4bQq1cvZ4d7TUhLS+PBBx9k//79Db4ENRrNJa0KKkRH194Z+MvRFrE6OvP4+vqi1+vx9/ena9eu3HrrraSmpuLt7Y2Pjw/R0dFqVx4XFxc1FqvVymuvvXbBzx/HYll123YuW7aM9PR0deGuiIgIDh48CJz7PBs5ciTvv/8+paWlGI3GVj3nttTiAtb//ve/zJ49m9/+9rfk5eVRXFxMSUkJeXl5PPzwwzz44INs2bKF+++/n//85z9tEbMQQjTJZDIRFxdHXFwcgwYNAs71Qh4wYACRkZGcOXOGkpISFixYwNdff+3kaK8NU6dOpUuXLmzbto3s7GxycnLUn+zsbGeHJ4S4StlsNqqqqsjIyOD06dMcPHiQf/3rX+Tm5qLX69FqtcTFxdVblbi5HIuETZs2jcTEREJDQ0lMTGTatGkkJCTg7u5OdHQ0v/vd70hKSqKiooL8/Hw8PT3x8/Nj3759/PTTT5c0d8AZWpzhf/3113n22Wd59dVX620PDQ1lwYIFeHp6Mnr0aEJCQpg/f36rBSqEEM2h1WoZO3YsK1euJCcnh7Fjx1JbW0t6ejpwrtvY7373O1atWsXbb79NdHQ0PXr0cHLUV7ecnBy++OKLRpepF0KIplzozoBGoyEkJEStFLHZbCQkJBAcHExcXBwWiwWdToe/vz8+Pj5qDX9LuLu7ExISUq9tZ0hICN7e3gDU1NSwfPly0tLSAFiyZAk6nQ6bzUZBQQH+/v6X1SHofMXFxWRlZZGdnU23bt1a5ZgOLc7w79q164KzkydNmkR1dTWbNm2SPvxCCKeIj49n2LBhnDp1ii1btrB//37Kyso4fPgwZ8+eZejQoTz11FN4enqSnJzcoOZctMyNN94orU6FuIa5ubkxd+5c+vTpw65du9rtzp2Liwve3t54e3uj1+vVOVldu3ZFr9fj4uKiTrJ1rITbGrRabb2Mf1JSEklJScyYMYOHH36Y0aNH4+fnR0REBBaLRZ1rcDkURSEnJ4eysjJSUlJafY5AizP8tbW1uLq6Nvm8q6srHh4emEymywpMCCEuR3BwMN26daO2tpZbbrmFNWvWoNVq1e5hwcHBmEwmCgsLpWPPZVqyZAkPPvgge/fupWfPng2+I+644w4nRSaEaC3nD0i7du2qdsRpr/c/duyYOsnWMSm4rKxMHXtu3ryZ+Pj4VonLxcVFzfg7WhCHhobi6urK6tWr8ff3JyYmhpKSErKyslo0QHfMLwDUSc5ZWVmYzWbCw8PJy8sjKyurVe+atnjA36NHD1avXs0TTzzR6PNffvml3B4XQjidwWDAarXi4eHBsGHD2LZtGwAlJSXs3buXqqoqPD09URRFOvZcpm3btrFlyxa++eabBs/JpF0hrg1tPSBtjvMn2cK5yhONRoNWq8VisTSrUw+cS2Bv2rSpxZ9PWVlZ5OXlERUVhUajwWQykZmZeVm1/IqisHnzZnx8fIiNjSU0NJSUlBRiY2Nb7aKqxQP+mTNn8pvf/Aa9Xs9DDz2k/qXabDbee+89fv/737No0aJWCU4IIS6VyWQiMDCQffv2cerUKYqKisjLy0NRFL744gv2799PeXk5MTExavZGXJrZs2czadIk5s6dS3BwsLPDEUK0svYYkNZVXFxMfn4+vr6+6ra6k2zrZvgTEhLQarXodDqmTp3arMH+pVIUhZSUFMLCwtBqtdTW1uLn54der1dr+S/l7+P8i4i6/f5b66KqxTX8Dz74IDNnzuS3v/0tAQEBJCYmkpiYSEBAALNnz+bhhx9mypQprRKcEEJcKq1Wy4QJEygvL+fFF1/Ezc2NHj16YLFY2LNnD+Hh4QQEBJCTk6P2WBaX5syZMzzxxBMy2BfiGtXYgPTEiRNtsgCVoigcPXoUq9VKUVERu3bt4r777mPkyJF4eXmpJTZubm64ubnh7e2NzWbj4MGDFBUVXdJ7FhcXc/LkyYtm6bOysjhx4gTDhw9XB/YajQZfX99LnkNQ9yLCsZaVo99/a9byX9K68n/961/Ztm0bU6ZMISQkhJCQEKZMmcLWrVtZuHBhqwQmhBCXq0ePHsycOZO0tDS2b9+OyWTCYDBQUlJCaGgocXFx3HjjjWzYsEEm7l6GcePGsXHjRmeHIYRoA60xIHVM+h05ciQ6ne6C+zpKhwwGA2azmaKiIjZt2tTk+zguEC51sqtjboDVar1gpx/HXQ5/f388PT2xWCxYLBbKysrQarXqHIKWvn9TFxEjR45s1YuqS77vcf3113P99de3ShBCCNFWevfuzQ033ICbmxuVlZVUV1cDUFRUxE033URcXBzLly+XibuXoUuXLjz33HNs2bKFXr16NZi0O3v2bCdFJoS4XI4B6fjx40lOTgb+NyD98MMPmyw7Ob/lZnNW3q1bOuToq6/VasnLy6O8vBx/f/96+1dWVrJlyxaqq6vp0qXLBecWVFZWkpaWRk5OTr3txcXFWCwWfHx8MJvNTXYgciwyW1FRwdKlS0lLS6O2thZFUTh58iQnT57kiy++YOLEiRc9z/PPt+5FBKD2+/f392+10qkWDfhzc3Nb1H0nLy+PsLCwFgclhBCtxWKxEBQUxLPPPsvGjRvZs2cPx44dY/PmzVgsFgICAigpKZGJu5dhyZIleHt7s2nTJjZt2lTvOY1GIwN+Ia5Sjux+ewxI4X+lQyaTiUOHDtUr3dm3b596h8ERW0lJCQAeHh7ExMTQuXNnNZ7zz6OkpAS9Xl/vboGiKOTm5mIwGNBqtRgMBlJTUxvN0mu1WqZOnYrNZlMX+rLb7dTW1lJTUwNA3759WzSHoLGLCPhfv38Au93e7InIF9KiV/fv35877riDX//61wwYMKDRfUpLS0lOTuZvf/sbDz/8MI8++uhlBSiEEJfDMSF3+/btbN++HU9PT1xdXdHr9bi7u5ORkcGhQ4dISkqiV69eTo726nR+xkwIcW2w2+2YzWbMZnObD0jrlg6dOXOG6upqAgMDMRqNwLnxZd0a+6qqKjXrr9frKSkpYeLEiepk17oJ6uLiYqqrqwkPD+fkyZOUlpaqx4Bz5Z8HDhwgMjKSkydPkpub22hNv9FoxM3NDavVisFgUM/dzc0NAL1ef9HztFqtvPLKK6SmpjJs2LAGFxEAM2bMUI/p5eXVKhORW3SE/fv389prr3HTTTfh6upKv3796Ny5M+7u7hQXF5OZmcm+ffvo168ff/nLX7j55psvO0AhhLgcJpMJHx8fli9fTvfu3bFarbi7u2O1WvH09GTMmDFYLBY+++wzrr/+emkr3Arsdjt79uwhMjKyXkZOCHF1cXFxYfr06WpXnLYckDpKh+69916+/vpr9Ho9Hh4eREZGYrVacXFxUWvsHRl7u92OyWRCo9Fw9OhRYmJi1LkFjkViHVl8vV5PTEwMISEh/Pzzz9TW1lJaWkpgYKD6OeXn50dwcDBff/011dXVl7R6b0udfxEB5/r9O/5+W0uLJu36+/vz17/+lZMnT/LOO+/QpUsXioqKOHz4MAD3338/aWlpbN26VQb7QogrglarpXfv3hw7doydO3cybtw47rrrLjQaDQcOHODs2bPMmzcPLy8vWXX3Ej3++OMsXboUODfYHz58OImJiURERJCSktLi4y1atIjo6Gjc3d1JSkoiNTX1gvt/9NFH9OnTB09PT0JDQ5k6dSpnzpxRn1+xYgUajabBT2usjinEtc5oNBIaGkpoaCgGgwGDwaA+Dg0NxcfH57Lfo27p0OnTpykqKsLT05OamhpcXV3V2v3S0lKys7MpLi6mvLwcnU6HyWQiKipKrb8/f7Kro0bf19cXjUbDsGHDMJvNlJaWUl1drV4wwLkSxKioKEpLS3F3d79gTf/V5pIuydzd3Rk3bhzjxo1r7XiEEKLVOVbVPXXqFDk5OezatYv8/Hzc3Nzo3bs34eHhREREyKq7l+jzzz/ngQceAOA///kPR48e5cCBA3zwwQe88MILbN26tdnH+vTTT3n88cdZtGgRQ4YM4b333uPmm28mMzOz0TlkW7ZsYfLkySxcuJDbb7+dvLw8HnnkEWbMmMGqVavU/Xx8fDh48GC917q7u1/iGQshWpOjdKi0tJS//vWvnD59mqqqKjQaDT/88AMDBgzAZrOpnXBycnKw2+14e3vj6uqKVqvFzc2N//znPzzwwAP4+/uzefNmamtrOXbsGAaDQW2/7Fh75eDBg+rry8rKqK6uxmw28/PPP+Pt7U1NTU29mv72XFW4LbR4wN/cQf4XX3zR4mCEEKItGAwGvL296dSpE9OnT+fll1/m2LFjAOzevZutW7eSmZlJSEiITN69BEVFRYSEhACwZs0a7r33Xrp06cL06dP5+9//3qJjLViwgOnTpzNjxgwA3nzzTdauXcs777zD/PnzG+z/ww8/EBUVpU4Mjo6O5uGHH+b111+vt59Go1FjFEI4R3FxMVlZWWRnZ9OtWzd1u6N0yGw2U1tbS1FREUePHqWqqkq9E2e326mqquLYsWOYzWbc3Nyw2WxkZGQAkJ2dzcGDB8nNzSUwMBAvLy/Onj2L2Wyme/fuHDhwAEBdHXfbtm1UVlaqr8/Pz2fbtm24u7vj6elJcXExJpOJkydPUlZW1qBD0NWmxX34jUZjs36EEOJK4Vh1Nzc3l7y8PE6dOoWHhwfV1dXYbDaioqIwGAzk5uZy6tQpZ4d71QkODiYzMxO73c63337LL37xCwAqKiou2nO7LqvVSlpaGmPGjKm3fcyYMWzbtq3R1wwePJgTJ06wZs0aFEXh1KlTfP7559x666319isrKyMyMpLw8HBuu+02du3adcFYHNm+uj9CiEunKAo5OTlN9ss3Go1EREQwe/ZsBg4ciKenp5qoefTRRxk4cCCJiYmEhITg5eVFUFAQAQEBdOnShS5duhASEkJ0dDQmk4mHHnqIKVOmkJubi5eXF08++SRJSUlUVFRQUFCAXq/Hy8uL6upqoqOjSUhIICQkBKPRyM0338zgwYMJCQkhICCAzp07t0stf1trcYZ/+fLlbRGHEEK0Gcequ3PmzOHVV19l3LhxnDp1ipSUFA4cOICLiwvXX389e/bsYe/evdxwww1otZe0LmGHNHXqVMaPH09oaCgajYbRo0cD8OOPP9bL4l1MUVERdru9wYq9wcHBFBQUNPqawYMH89FHHzFhwgSqqqqw2Wzccccd/OMf/1D36datGytWrKBXr16YzWb+9re/MWTIEHbv3k1cXFyjx50/fz4vvfRSs2MXQlyYY0Gt8PDwC/bLNxqN2Gw27HY7fn5+6mRhg8GgtsCsrKxUV9U9dOgQcG7Fby8vL7RaLUFBQVitVqqrq6murmb58uVqh6Fly5aRlpZGZWUltbW1nD59mrCwMBRFwWq1cuutt/L555/j4uKi1vx//vnnF1yFt7KykpKSkouu1OtM8o0mhOgQevTowfjx48nPz2fdunVUVFTg5ubG2bNn8fLyoqKigilTplBSUkJubq6zw72qzJs3jyVLlvDQQw+xdetWtTWdTqfj2WefbfHxzq+VvVD9bGZmJrNnz+bFF18kLS2Nb7/9lpycHB555BF1n+uvv54HHniAPn36MGzYMJKTk+nSpUu9i4LzPffcc5SWlqo/x48fb/F5CCHOqbugVmxsLGFhYU2uiutY+Vav1+Pr64uPj49aR6/Vapk8eTKJiYnqpOGEhAQSEhIIDQ0lMTGRqVOn4uLigouLCwkJCSQlJTFt2jSSkpLUP8fGxuLh4UFoaChlZWWcPXuWkpISPDw81PUGqqursVgseHp64uHh0WSWX1EUSktLL7pSr7Ndfh8lIYS4SgwYMIAxY8bg5+dHUVERVqsVvV6Pq6srd955JzExMeqCXKJl7rnnngbbHnzwwRYdo1OnTuh0ugbZ/MLCwgZZf4f58+czZMgQnnrqKeDcyspeXl4MGzaMV199ldDQ0Aav0Wq19O/fX+0w1xi9Xt+sntpCiItzLKgVFRWFRqNh+PDhar/887P8jjsBRqMRjUaj9sZ31NEbjUZ1QS4Ab29v4Ny/WYPBUK9rkLu7O+7u7oSEhKgtL4ODg9UuQAEBARgMBo4dO4bNZqOyspLly5eTnp5Ofn4+6enpLF++XL0bYLfbG5ybo8f/xVbqdTYZ8AshOgyDwUBQUBDTpk2jqqqKhQsXotfrmTVrFu7u7moW1/HFIJrvu+++47vvvqOwsLBBa9Nly5Y16xhubm4kJSWxfv167r77bnX7+vXrufPOOxt9TUVFRYMe4I55A01l2hRFISMjQxZaE6Id1F1Qy1EqGRsbq/bLr7tKb907AY5/v35+foSEhLBu3Tr8/Pxwc3Nj7ty5ABdt2euYJFx3ccDs7Ox6FxQmk4l9+/bh6+tLYmIi06ZNo6KigpqaGvVxdXU1bm5uDT5r6vb4P/9uxJXW1UdKeoQQHYbJZMLX15etW7cSGRlJcHAwvr6+aLVaFEVhy5Yt+Pn5Ndr+UTTtpZdeYsyYMXz33XcUFRVRXFxc76cl5syZw5IlS1i2bBn79+/niSeeIDc3Vy3Ree6555g8ebK6/+23384XX3zBO++8Q3Z2Nlu3bmX27NkMGDCAzp07q/GtXbuW7OxsMjIymD59OhkZGfXKfoQQbcOxoNbw4cPr9bs/v1++Y9+8vDwiIyPr7XvDDTeQmJjIpEmTmr0gVd1Jwps2bVIX7EpNTcXHx0dty+vn54fBYKCsrAxvb29CQkLw9fVl7NixvP7665hMJgwGQ6N3/IqLiyksLKS6upqqqirCw8NJTk6muLiY559/HjhX8jhv3jysVutl/T1eLsnwCyE6DK1Wy9ixY0lOTiY5OZmSkhJsNhsbNmzgyJEjnDlzhokTJ8qE3RZ69913WbFihbqy5eWYMGECZ86c4eWXXyY/P5+ePXuyZs0aIiMjgXOt8+rOsZgyZQoWi4W33nqLJ598El9fX2644Qb+/Oc/q/uUlJTw0EMPUVBQgNFoJCEhgc2bNzNgwIDLjlcI0bS6C2o5auPh3L9jT09P/P391Sw/QEpKCn5+flgsFnWA7KijP3/fi6k7SdhREgRQU1ODr68vBw8eVBfjMplMZGZmUlRUxGuvvca2bdsYNmzYRc/t6NGjVFdXq3X8jiy/o5b/Ssryy4BfCNGhxMfHM378eD744AMOHDhAVVUVe/fuxd3dnYSEBGeHd1WyWq0MHjy41Y43c+ZMZs6c2ehzK1asaLDt0Ucf5dFHH23yeAsXLmThwoWtFZ4QopkcC2qZzWaWLl2qdspZsmSJWnpnt9vV2niz2UxxcbFaQw+Qnp7O0qVL0el09fa9kPMnCQcGBrJ27VoAhg0bxo4dO6isrMRut2OxWHB1dcXFxYXc3Fz8/PyadW6O7L5er8doNGI2mykpKSEyMpJ9+/Y12YXIWWTAL4TokPR6PWPGjGHXrl34+vry2GOPsXPnTpKTkxk/fjzx8fHODvGqMWPGDD7++GO1rlYIIeB/C2pVVFSo7TXh3GeGozTHy8tLrY2fPn06JSUl9TL8iYmJ6v6OfS9WHnP+JOFhw4bx2WefUVtbS3Z2NocPH8ZqtWKxWNi+fTseHh7YbDasVmuzuuw4yoUqKioIDg6mvLyc8vJyDh06RK9evdQVgZt7N6I9yIBfCNGh1NbWsnbtWrp27cq4cePU1VtNJhOxsbGsXLmSdevW0bVrVyntaaaqqioWL17Mhg0b6N27N66urvWeX7BggZMiE0K0Fjc3N+bNm9fi1zkWZLVarWpDhNDQ0EZr8Y1GIx4eHvW68BgMhib3b0xjk4RjYmLUGCIiIoiLi8NisXD69Gl8fHzo1asXAwYM4Mknn+Sdd9656HvY7XZKSkooKyvDw8OD06dPU1NTw5EjR7BYLHh5eWGxWJp1N6K9yLeZEKJDyc3NpaSkhGHDhjWor9RoNAwdOpTi4mLpxd8CP//8M3379kWr1bJ371527dql/jiWrRdCiNbi6NQzcuTIBqt5NzVJOCoqiuLiYo4fP07Xrl3R6/UEBARQU1ODzWZr0NLzQnQ6Hd7e3sTFxTFo0CBCQ0OJiIjguuuuw2g01lsPwG63k5KSwiuvvOLUibuS4RdCdCiOSWNBQUHqtrqTr9zc3FAURXrxt8DGjRudHYIQQqi1++dPEi4oKMDFxYXy8nLMZjPR0dHAuT79jj78nTp1avb7ZGVlUVlZSY8ePerdjejSpQuZmZnYbLZmXzy0FxnwCyE6FMct5cLCQoKCgigsLGTPnj18/fXXuLq6qgsyDRs2TPq0CyHEVURRFMxmMxUVFfUmCS9btozU1FROnDiBj4+P2nq5qqoKjUZDZWVlky2Ezy9lqq6u5umnn+bEiRP06tWLsrIyNXPv6upab1Ve6dIjhBBO4ujFn5qairu7O2vXrqW0tBS9Xk90dDQnTpxAr9ezZcsWwsLCZPJuM+3cuZPPPvuM3NzcBretv/jiCydFJYToSLRaLVOnTlUn4DomCU+dOpUdO3bg6+tLr169qKiooLq6mjNnztCpUycqKirIyclp1oRdu91OdXU1NptNLVl0dBTKyMhQB/l2u73BQl3OdOVEIoQQ7cDRi/+tt95iy5YtDBkyBG9vb3766Se8vb3RaDSEh4fj7e0tk3ebaeXKlUyePJkxY8awfv16xowZw+HDhykoKKi3Yq4QQrQ1o9GIm5tbvUnCgYGB6kA8MzMTgOPHj1NeXo6npydlZWWUlJQ0a5Kti4sLffv2paysTG3lXFNTA0BCQgJarRYPD48rarAPMuAXQnRAXbt2pba2FgBfX18yMjIoLCzEz8+PF198kf3793Pw4EF12fSoqCjnBnyFe+2111i4cCGzZs3CYDDwt7/9jejoaB5++GFCQ0OdHZ4QooM7f5CuKAp5eXm4u7szatQo9u/fj5ubW4MJwE1xd3dHr9fj7e0NoNbwe3t7o9Ppmn2c9iRpKyFEh+PowHPDDTfw0EMPER8fT0hICP3796d79+4MHToUm81GaWmpTN5thqysLG699Vbg3PoG5eXlaDQannjiCRYvXuzk6IQQVxNHB54RI0a06t3VuoP0mpoa7HY7AQEBGAwGunbtSkVFBdnZ2a32flcaGfALIToci8WifvB7enoSFBSEu7u7ess3KCiI8vJyqqur1VvComn+/v7qhVFYWBh79+4FoKSkRK2hFUKIK4Hjzq1er8fd3R0APz8/fHx8SE1NbVYd/9VIBvxCiA7HYDBgNBrR6XRs3bq1wQf8qVOnyM3NJSgoSO3mIJo2bNgw1q9fD8D48eN57LHH+PWvf819993HjTfe6OTohBDif4qLi7FYLBiNxgZ9+k+ePNlkt57WVllZSVpaWrvdVZAafiFEh2MymfDz88Nms3Ho0CH2799PQkICTz/9NAUFBfzlL3+hoqKC8ePHy4TdZnjrrbeoqqoC4LnnnsPV1ZUtW7Ywbtw45s6d6+TohBBXI51Ox8iRI3n++eebvcruxTiy++7u7litVqxWK2VlZWi1WlxdXfHz8+Pnn3++7Cy/3W7nlVdeUf/cWBwlJSXo9XpSUlLo2rXrZb1fc8g3mRCiw3F06ikvL8doNNKzZ088PT157LHHmDZtGj///DO/+c1v6NGjh7NDveLZbDb+85//qBdGWq2Wp59+mq+++ooFCxbg5+fn5AiFENcynU7H888/T58+fdi1a9dFM+ZWq5WqqioKCgrIz89n165dpKenk5aWRnFxcbNWw617MeKYoFtZWUl6enqz7hAUFxdTXV1NeHg4eXl5ZGVlNe9kL8M1MeD/+uuv6dq1K3FxcSxZssTZ4QghrgLx8fGMHz8eFxcXTp8+zebNm/nmm2/Yv38/QUFB/PTTT+zbt8/ZYV7xXFxc+M1vfkN1dbWzQxFCdFCKopCTk0NZWRkpKSlNZug1Gg29e/cmISGB0NBQQkNDSUhIIDExkaSkJKZNm0bfvn2bXDDLarXyyiuvkJKSUi9zrygKpaWllJeXq4tuXSjWY8eOodfriYmJISws7IIxt5arvqTHZrMxZ84cNm7ciI+PD4mJiYwbNw5/f39nhyaEuMLFx8dTW1vLsWPHSEpKorq6mvz8fA4fPsyZM2fYtGkTTz75JHfccYezQ72iDRw4kF27dhEZGensUIQQHUTdFXAzMzMxm831MuZNzb9yTNRtrJVmSEgIer2+xbFUVVVRXV1NWFiYOg+gqXFoVlYWZrMZX19fNBoNw4cPJzk5uc2z/Ff9gH/Hjh306NGDsLAwAG655RbWrl3Lfffd5+TIhBBXutraWtavX09YWBilpaX4+PhQWFiIwWCgZ8+e/PDDDzzzzDMAMui/gJkzZ/Lkk09y4sQJkpKS8PLyqvd87969nRSZEOJapygKmzdvxsfHh9jYWEJDQ0lJSWHSpEnNer1Op2Pu3LnqYl2X8v51V2svKyvj2LFj+Pn5NbhTUDdWR2ez2NhYwsPD2bx5M4qiNHl34XI5vaRn8+bN3H777XTu3BmNRsOXX37ZYJ9FixYRHR2Nu7s7SUlJpKamqs+dPHlSHewD6tWdEEJcTG5uLsXFxZSVleHl5YXValUnc3l7e/PGG29gMBhYsGCBlPc0Ytq0aZjNZiZMmEBOTg6zZ89myJAh9O3bl4SEBPW/QgjhyMjPmzev1SbhwrmMeV5eHlFRUWrG/MSJE/Uy5o7e/iNHjmz1RbEc9fiOrj+RkZGYzeZGa/kdsfr6+pKfn09xcTEajYaRI0eSl5fXph2CnD7gLy8vp0+fPrz11luNPv/pp5/y+OOP88ILL7Br1y6GDRvGzTffrC6c01jNU1tdHQkhri0Wi4XS0lJsNhvl5eUEBAQQGBiIXq9Hp9PRt29fBg8ejIuLC8nJyerqvOKc999/n6qqKnJychr8ZGdnq/8VQoi2oCgKKSkphIWFqQ0Czs+Yt/X7O+rxz+/pf+zYsXrv78ju+/n5UVBQQEVFBQcOHODkyZN4enri5+d30fr/y+H0kp6bb76Zm2++ucnnFyxYwPTp05kxYwYAb775JmvXruWdd95h/vz5hIWF1cvonzhxgoEDBzZ5vOrq6nqTy8xmcyuchRDiamQwGKiursZisRAcHIzJZOLMmTPq84WFhXh7exMZGUlhYSG5ublERUU5L+ArjOOLSWr3hRCtqW59/oVkZWVx4sQJxo8fT3JyMoCaMX///fcpLS1t0zmd2dnZmM3mBj39IyMj2bNnD8XFxRiNRuDc56XZbObYsWMcPnyYmpoaDh8+zF/+8hcCAwMBGDVqFI8++iguLq0/PHd6hv9CrFYraWlpjBkzpt72MWPGsG3bNgAGDBjA3r17ycvLw2KxsGbNGsaOHdvkMefPn4/RaFR/IiIi2vQchBBXLpPJRGBgIMeOHVPLehwURWHbtm24uLioE7kcNZfif+SOqhDCGRzZfX9/fzw9PbFYLFgsFvLz89slY64oCqmpqXh4eKDVatWe/haLBVdXVzw8POpl+bVaLVOmTMFkMhEXF0d0dDRxcXGYTCYeeughHn74YaZPn94mg324AjL8F1JUVITdbic4OLje9uDgYAoKCoBzLeHeeOMNRo0aRW1tLU8//TQBAQFNHvO5555jzpw56mOz2SyDfiE6KK1Wy4QJE9i0aRPbt2+nZ8+e1NbWYrVa2bFjB6dPnyYgIABXV1eMRiMGg8HZIV9xunTpctFB/9mzZ9spGiFER2G32zGbzZjNZpYuXUpaWhoAS5YsQafTYbfbqa6ubvGAv7kLfimKgsViobKyUh2T7tq1q97noaIo9d6/qKiIsrIyunbtyr59++jatStlZWVUVFRw3XXXtSjOlrqiB/wOjc1yrrvtjjvuaHYHDb1ef0ktl4QQ16YePXrw5JNP8vTTT7N3716qqqo4ceIEtbW1HDlyhPDwcPz8/OjevXuTbd46spdeekm9ZS2EEO3FxcWF6dOnU1FRgdVqpaKiAoAZM2aoHXdsNlurrJZutVp57bXXsNvtav99rVbL5MmTOXXqlNrdJyEhod77ubm5qY8dNfxhYWHqRYCfn5/ahz82NrZN75he0QP+Tp06odPp1Csnh8LCwgZZfyGEuFSOhMErr7xCeXk5BoMBm81GWFgYQ4YM4fDhw1gsFg4ePEh8fLyTo72yTJw4kaCgIGeHIYTogBzl2VarVb0DGxoaqg74LyXBW1xcTFZWFtnZ2XTr1u2i728wGBr09K/LcYHg6Mhz33338cknnwA06MPflln+K7qG383NjaSkJNavX19v+/r16xk8eLCTohJCXItuu+02brzxRvr27ctNN92EyWRCq9XSqVMnXn75ZYYMGcK6deukU08dUr8vhLiWNHfF3ks5rqM3v2O+gaNhhKenJ/7+/m2+2q7TM/xlZWUcOXJEfZyTk0NGRgb+/v6YTCbmzJnDpEmT6NevH4MGDWLx4sXk5ubyyCOPODFqIcS1Jjc3F3d3d958802qqqpYuHAher2eWbNm4e7ujsFgYOnSpdKpp462bnknhBBtQafTMWLECPXPDo5VcOuu2NvSrHtxcTHZ2dnExMSorULhXJfI4uJili5dSnp6Ovn5+aSnp7N06VJ1zoHdbr92J+3+9NNPjBo1Sn3smFD74IMPsmLFCiZMmMCZM2d4+eWXyc/Pp2fPnqxZs0bawAkhWpWjA09ISAgAQUFBlJaWkpmZib+/v1pGKJ16/kfudgghribFxcUcPny4wWAcml6xNzY2ttnHVxSFo0ePUlZWxtGjR/H19aW4uJj8/HxGjBjBgw8+yFtvvYXVaiUkJITExER1zoGXl1ebDfbhChjwjxw58qJZopkzZzJz5sx2ikgI0RE56j8LCws5c+YM27dv59ChQ6SnpzNixAi0Wi1FRUXSqUcIIa4C5/fyr66uVst1HIPxumWJja3Y66itb27DhuLiYvUOwYkTJzh79izHjh3DarWSn59PcHBwvZp/g8Ggzjloa1d0Db8QQrQXk8mEr68vK1eu5PPPP8fLy4vQ0FCGDh3KlClTOHv2LNnZ2ZSXlzs7VCGEEC1Ut1zHbDZTXFysPnehFXubW1uvKAq5ubn4+PgQExODj48P+/fvx2w24+Pjg9lsdurK4zLgF0IIzrVYGz16NBs2bODMmTOEh4fj6uqKxWJh9erV2Gw2EhISZOKuEEJcZeqW6zgG43UXxXKs2Dt8+PB6K+aOHDmSEydOkJWVddH3qKqqwmKxEBkZiUajwWQykZeXh06nw8/PDx8fH1JTU50298npJT1CCHGl8PLyIjY2Fj8/PzZv3qx+CWg0Gvz9/Rk0aBDp6en06tWLX/ziF84OVwghRDPULddxcXHh9ddf55NPPqG0tFT9vK+7Yi+grtjr7+/P5s2b6w3UKysrSUtLIycnB51Ox/Dhw8nIyABoMDfAsT/Avn37WmVdgEshA34hhPh/FouFwMBAnn32WTZu3MiLL76Iv78/Xbt2xWg0cv/99/PCCy/wxRdfEBYWJj35hRDiCle3XMcx2I6NjSUsLIzdu3djNBoxm81UVFQ0umIvnEsGOQb8iqJQUlKCXq9n06ZNKIqi1u736tULjUajlveEhYVhtVo5ffo0ERERlJaWUlpa2mAB2fbQYQf8b7/9Nm+//ba6IIIQQtSduLtv3z5MJhOhoaFqv2SdTkfPnj0JCAhg3bp1dO3a1WnZGiGEEBfnuFM7fvx4kpOTgf8teLVy5UpKS0uZPXs2Nput0RV7AVxdXXnrrbew2+0UFxdTXV1NeHg4J0+exGKxcOzYMTw8PNQy0JKSEoqKiujWrRuHDx/GbDYTFhaGj48Phw8fBsDDw6Nd/x467IB/1qxZzJo1C7PZLMvCCyGA/03c/fLLLzly5AinT5/mp59+AiAyMpJ58+ZhMpm48847Wb58ufTkF0KIK5gju99UuY6HhwdHjx7Fx8cHvV7f6Iq9AFarVT1ebm4uer2emJgYwsPD0Wg0REREsGbNGtLT01EUhVOnTmG326mtreX06dMABAYGEhwcTGpqKlarFXd393b9u5DUlBBC/D+tVsvYsWPZvn07mzZtwt3dnaCgIIKCgvD09KSyslLN3oD05G8rixYtIjo6Gnd3d5KSkkhNTb3g/h999BF9+vTB09OT0NBQpk6dypkzZ+rt8+9//5vu3buj1+vp3r07q1atastTEEJcAex2O2azmbNnz6rlOmlpaSxZsoSlS5dSWVlJdXV1s6s9iouLsVgsakvP4cOHqz32k5KSSExMJCEhgZCQEAIDA7FarVitVtzc3MjIyOD48eOUlZVRXV2txvfKK68wb9489aKirXTYDL8QQjSma9eu+Pr64ubmRrdu3cjIyCA3Nxd/f39efPFF9u/fz6pVq1AURXryt4FPP/2Uxx9/nEWLFjFkyBDee+89br75ZjIzMxvthb1lyxYmT57MwoULuf3228nLy+ORRx5hxowZ6qB++/btTJgwgVdeeYW7776bVatWMX78eLZs2cLAgQPb+xSFEO3ExcWF6dOnU1FR0aBcB6CiogI3N7dmLXilKArHjh3DYDCo7ZkdrTt37dqFt7e32sFt4MCBWK1W9u3bR0BAAOXl5SQmJjJr1iyOHDlCTk6Oely73U5qaip2u525c+e2WU9+yfALIUQdubm5BAUFcddddxEVFUW3bt0IDAzk7NmzvPPOO4SGhvLzzz8DNHsxFtF8CxYsYPr06cyYMYP4+HjefPNNIiIieOeddxrd/4cffiAqKorZs2cTHR3N0KFDefjhh9VSLIA333yT0aNH89xzz9GtWzeee+45brzxRt588812OishhLMYjUZCQ0MJDQ3FYDCoi11FRkbyxhtvMH/+/GYNsh0Tc00mU4PWnXl5efX6+ru7u6tzAuLi4nB3d1fft2vXrthsNqqqqtrsnBsjA34hhKjDYrGg0WgYN24cJSUlZGdnU1hYyKlTp8jIyGDhwoX8/PPPBAYGyoTdVma1WklLS2PMmDH1to8ZM4Zt27Y1+prBgwdz4sQJ1qxZo9bOfv7559x6663qPtu3b29wzLFjxzZ5TCGEqEtRFI4ePapOzHU0cnDMBfDz8+Po0aP1OvnUncjr2L+goABXV1dcXFzUbj3tRUp6hBCiDkeZTkBAAAkJCXz00UeUl5ej1+txdXUlKiqKyspKsrKy2L9/v7TmbEVFRUXY7XaCg4PrbQ8ODqagoKDR1wwePJiPPvqICRMmUFVVhc1m44477uAf//iHuk9BQUGLjglQXV2t1tkCmM3mSzklIcQ1wG63U11dTWVlJdu3bycnJwe73Y7BYECn06nPO9ptKoqifoZkZGSQn59Peno6y5YtIz09HZvNBtCu7TllwC+EEHU4OvVs2rSJ/Px8+vbtS2hoKDU1Nbi7u5OYmEh0dDSBgYHSmrONnP8FeKEvxczMTGbPns2LL77I2LFjyc/P56mnnuKRRx5h6dKll3RMgPnz5/PSSy9dxlkIIa4VLi4uJCQkUFlZyZ49e/Dw8MDHx4fp06er3X1sNpv6XaDVaomKiiIrK4vw8HBsNhuJiYlMmzZNnU+g1Wrb9btDvqWEEKIOR6eenTt3snXrVvz8/DAYDOj1enWJ9bFjxzJ8+HCKi4vJzc11dsjXjE6dOqHT6Rpk3gsLCxtk6B3mz5/PkCFDeOqpp+jduzdjx45l0aJFLFu2jPz8fABCQkJadEyA5557Tl0kp7S0lOPHj1/m2QkhrmaOuvyamhr8/f3VScCO+QF6vV7dV1EU8vPzsdlsFBUV4ebmhsFgICQkBIPB0OyJwq1JBvxCCHGe+Ph4Ro0aRWVlJaGhobi4uPDzzz9TXl7OL3/5S+Lj4wkKCgKkNWdrcnNzIykpifXr19fbvn79egYPHtzoayoqKhpkyRyrYzrqYwcNGtTgmOvWrWvymAB6vR4fH596P0KIjqtulx4/Pz98fHzYvHlzo3X4jgm+4eHhWCyWdp+g2xgp6RFCiEb06dOHgQMHctNNN6HX67FYLBiNRrVmv7CwEEBac7ayOXPmMGnSJPr168egQYNYvHgxubm5PPLII8C5zHteXh4ffPABALfffju//vWveeedd9SSnscff5wBAwbQuXNnAB577DGGDx/On//8Z+68805Wr17Nhg0b2LJli9POUwjRetzc3Jg3b16bvodjEN+9e3cOHDhAZGQkeXl5ZGVl1evY5rgw8PHxISYmhpKSErKystp1gm5jJMMvhBCNMJlMaueFHj16qAutwLkP9C1btuDn5yetOVvZhAkTePPNN3n55Zfp27cvmzdvZs2aNURGRgLnVsisW0Y1ZcoUFixYwFtvvUXPnj2599576dq1K1988YW6z+DBg1m5ciXLly+nd+/erFixgk8//VR68AshmsXRpcfHxwc/Pz8A/Pz8CAsLIyUlpd5g3nFhEBkZiUajwWQyUV1dXa9tpzNIhl8IIRrhqOVPTk4mOTmZM2fO8OOPP3Lo0CH69etHaWkpEydOlAm7bWDmzJnMnDmz0edWrFjRYNujjz7Ko48+esFj3nPPPdxzzz2tEZ4QooPJysrCbDbTo0ePej34hw8fTnJyMllZWUD97H7dCwO9Xs+xY8ecmuXvsN9Ub7/9Nt27d6d///7ODkUIcYWKj49n/Pjx/Pzzz6xZs4ZDhw6xdetWvv322zZfBl0IIYTzKYrC5s2b1Z76ZWVlal99T09P/P391Vr+87P7cO7CwNfXF7PZTHZ2ttPOo8Nm+GfNmsWsWbMwm80YjUZnhyOEuIJ5eXkxa9Ysdu3aha+vL4899hg7d+4kOTmZ8ePHSy9+IYS4RtntdsxmM2fPnuW///0vRqORoqIi0tPTWbp0KTqdDi8vL2pra+sttuVo6FBbW4tWq8XDw4PU1FSnZfk77IBfCCEupra2lrVr19K1a1fGjRvH/PnzgXP1/bGxsaxcuVJ68QshxDXMxcWFKVOmsGHDBjQaDZ6enri4uJCYmMiMGTNwc3PD1dWVv//97+piW+np6errHS06HXcHZMAvhBBXmNzcXEpKSrjnnnvU27M2m40nn3wSm83GxIkTOXDgALm5uURFRTk3WCGEEG2iqKgIq9VKTEwMx48fR1EUDAYDoaGhuLm5AfDyyy9z+vRp/vSnP2G329XX1tbWqgtvTZ48mXfeeccp5yADfiGEaILjlqyj535BQQFr1qyhpqaGiIgIjEYjGRkZJCYmyoBfCCGuQY4a/ua02TQajRgMhnoDfrvdjl6vx2AwqOt5aLVahg0bxjPPPMOf//zndjkPuQcthBBNcPTYLyws5L///S/r169XF3pyc3OjoqKC2tpaUlJS2L9/v5OjFUII0dqysrLIy8sjKirqimqz2VKS4RdCiCaYTCZ8fX35+OOP2bBhA/7+/gQEBODm5kZiYiI2mw2tVkvnzp2lll8IIa4xiqKQkpJCWFgYWq2W2traK6bNZkvJN5MQQjRBq9UyevRo/vvf/1JcXEzPnj1xc3OjpqaG48ePExAQwC9+8QvKy8s5e/ZsvQWhhBBCXN2ysrI4ceIEw4cPb7TNpqP//tVABvxCCHEBXl5ehIaG4uHhweHDhzl+/DgFBQVUVFRwzz33MGHCBGw2G6WlpWrNvxBCiKubI7vv7++Pp6cnFosFi8VCWVmZ2mbT0X+/JYqLizl58mS7lwRJSY8QQlyAxWIhJCSE6667jjFjxvDRRx+xe/du+vfvT3x8PIqiUF5eTnV1tVrzL4QQ4urj5ubGvHnzgHMd2cxmM2azmaVLl5KWlkZtbW29NpsWiwW73Y6LS/OG04qikJubS+fOnenZsyeurq5teDb1yYBfCCEuwGAwYDQa0Wg0HDt2jMDAQNzd3dXbu6dOnSI3N5eePXtiMpmcHK0QQojW4OLiwvTp06moqMBqtVJRUYHdbq/XZnPq1KnNGuzX1tayadMmTp8+TUlJCSaTiby8vHYtCZIBvxBCXIDJZMLPzw+bzcahQ4fYt28fVVVV2Gw2cnNz+dvf/kZZWRmnT5/m5Zdf5vnnn1f7MgshhLgy1M3eN5fRaMRoNGK1WtV2m4212WwOR3bfx8eH2NhYQkNDL6kk6FJJDb8QQlyAVqtl7NixlJeX4+XlxZkzZzh58iQffPABt912G4cOHeKhhx7Czc2NU6dOcfToUWpra50dthBCiCtIVVUVFouFyMhINBoNw4cPJy8vr91q+WXAL4QQFxEfH8/48eNxcXFBo9EQHByMn58f3t7e3H333WRnZ5ORkcH+/fv517/+xd///nfpyy+EEAI4l90vLS3FYDDg5+cHQGxsLGFhYe3W3lMG/EII0Qzx8fH89re/JTIyEldXVzw9PdFoNHzyySdkZGQQGRnJsGHDmDp1KsHBwSQnJ8ugXwghBFVVVVRXV2Mymeq19xw+fDhms5mqqqo2j6HDDvjffvttunfvTv/+/Z0dihDiKpGTk0NsbCy/+c1vGDJkCEajEYCTJ09y4MABzpw5Q3h4OBMnTqRLly6sW7dOynuEEKKDcMwTmDt3LjqdDvhfdt/FxUXt7GOxWMjPz8fT0xMPDw9KSkraPMvfYQf8s2bNIjMzk507dzo7FCHEVaC2tpa1a9fSpUsXxo8fT1FRETk5OVRVVWG32yktLeX7778nMzMTjUbD0KFDKS4ulsW4hBCiA1MUBZvNhs1mIyMjg/T0dNLS0liyZAlLly6lsrISu93e5gN+6dIjhBDNkJubS0lJCffccw8HDhwgMzOTs2fP4u7uTt++fYmNjeXHH39k2bJl/OY3vyEmJgZAFuMSQogOSqfT8cwzz2C329myZQsJCQlotVp0Oh0zZswAzn1H1NbWotW2bQ6+w2b4hRCiJRwD906dOvHhhx9SVlZGbW0tJSUlZGZmcvz4cTw9PQkJCWHdunUUFBQAyGJcQghxDdHpdIwcOZK5c+c2uwWzu7s7er0eb29vDAYDBoOB0NBQQkNDsdls5OXlkZqaSnZ2dpvFLQN+IYRoBsfAfdWqVezYsYOoqCh8fX3x9vamd+/euLi4UFRURHh4OGfPnmX16tX4+fnJYlxCCCEapSgKOTk5mM1mTp48ycaNG9ustEcG/EII0QwmkwkfHx8+/vhj/P39SUxMJDAwkKqqKk6cOIGiKBgMBjIzM9m7dy8HDhxgzJgxbX6bVgghxNUpKyuLwsJCXFxc0Ov17N27t81W35VvIiGEaAatVkvv3r05ffo0lZWVnDp1Cnd3d4xGI1lZWRw4cACtVsvGjRspKipi3LhxxMfHOztsIYQQTuTm5sbcuXMZOXKk2rkHzmX3N23aRHV1NUajEZPJRGlpaZtl+WXAL4QQzRQcHExiYiIuLi5s27ZNnchbXl6Oj48P/fr1w2g0MnDgQG644QZnhyuEEOIKlZWVxd69e9Hr9fj5+REVFYXRaGTPnj1tkuWXAb8QQjSTwWAgKCiIJ554gjFjxhAXF4evry/+/v706tWLsLAwqqurpZRHCCFEkxzZ/dLSUoKCgnB3d8fPz4+ePXu2WZZfvpGEEKKZTCYTvr6+FBYWMn36dCorKzl+/DhHjhxh69atfPnll7i6uhIaGursUIUQQlyhiouL2bt3L0ajkaioKDQaDRqNhhEjRrRZll8G/EII0UxarZaxY8dy6NAhvvnmG+x2O35+foSGhhIXF8eoUaO46aab+Pzzz9m/f7+zwxVCCHGFcXTmOX36NJ06daK6uhqLxUJBQQFlZWUEBgZy+vTpVs/yy8JbQgjRAvHx8dxzzz08//zzWCwWwsLC0Gg0uLi4MG3aNHr37s3KlStZt24dXbt2ldIeIYS4Ruh0Op5//vlm999vjKIoVFVVUVVVxcaNGzl27BilpaUUFRVx8OBBtFoter2e0tJS7HY7Li6tM1SXAb8QQrSQl5cXvXr1olOnTri4uKDT6di3bx+ff/458fHxDB06lKVLl5Kbm0tUVJSzwxVCCHGF0Gq1JCUlcf/991NVVcXixYtJT08nMTGRhx56CFdXV3URx9Ya7IMM+IUQosUsFgsajYbOnTsDYLPZqK6uprCwkKNHjxIREaHuJ4QQQtTl7u5O165dAQgJCcFgMBASEkKfPn0u6+7BhciAXwghWshgMKDT6ZgxYwZnzpzhqaeeoqCgADc3N/71r3+h1WopKipSV+cVQgjRcbi5uTFv3jz1sdVqdV4w/0+KS4UQooUc3XpWrlzJ559/jpeXF6GhoQwdOpQpU6Zw9uxZsrOzKS8vd3aoQgghhAz4hRCipbRaLaNHj2bDhg2cOXOG8PBwXF1dKSsrY9u2bQQEBHDjjTeyYcMGamtrnR2uEEKIDq7DDvjffvttunfvTv/+/Z0dihDiKuTl5UVsbCz+/v7s3r2b3Nxcdu3axYcffsjp06e5++67KS4uJjc319mhCiGE6OA6bA3/rFmzmDVrFmazGaPR6OxwhBBXGYvFQmBgIHPmzKG0tJSKigp69eqFv78/Li4uBAUFqfsJIYS4ep1fk3816rAZfiGEuByOCblms5k33niD2267DX9/f0pLSzl16hTp6ekoiiITdy/BokWLiI6Oxt3dnaSkJFJTU5vcd8qUKeoqlXV/evTooe6zYsWKRvepqqpqj9MRQgin67AZfiGEuByOibupqamMGzeO06dPc/jwYSorK8nNzeX7779Ho9Fw+vRp3nzzzTZrtXat+fTTT3n88cdZtGgRQ4YM4b333uPmm28mMzMTk8nUYP+//e1v/OlPf1If22w2+vTpw7333ltvPx8fHw4ePFhvm7u7e9uchBBCXGEkwy+EEJdAq9UyduxYDh06xMKFC0lPT8fDw4PY2Fg8PT0xGo2Eh4eTmZnJ/v37nR3uVWPBggVMnz6dGTNmEB8fz5tvvklERATvvPNOo/sbjUZCQkLUn59++oni4mKmTp1abz+NRlNvv5CQkPY4HSGEAKC4uJi0tDSKi4ud8v4y4BdCiEsUHx/PPffcw/bt2yktLeXs2bMcOXKEiooKIiIi6Ny5MzqdjuTkZGw2m7PDveJZrVbS0tIYM2ZMve1jxoxh27ZtzTrG0qVL+cUvfkFkZGS97WVlZURGRhIeHs5tt93Grl27Lnic6upqzGZzvR8hhLgUiqKQk5NDWVkZR48eRVGUdo9BSnqEEOIyeHl5qZN1f/zxRywWCzU1NXz//ffU1tbi6+tLaWkpOp2O++67j/j4eGeHfMUqKirCbrcTHBxcb3twcDAFBQUXfX1+fj7ffPMNH3/8cb3t3bp1Y8WKFfTq1Quz2czf/vY3hgwZwu7du4mLi2v0WPPnz+ell1669JMRQoj/l5WVhdlsJjw8nBMnTjglyy8ZfiGEuAwWiwWNRoOrqyvl5eXodDp0Oh2BgYHExMTQu3dvXFxcKC8vJzk5Wcp7mkGj0dR7rChKg22NWbFiBb6+vtx11131tl9//fU88MAD9OnTh2HDhpGcnEyXLl34xz/+0eSxnnvuOUpLS9Wf48ePX9K5CCE6NkVR2Lx5Mz4+PsTExODj4+OULL8M+IUQ4jIYDAa0Wi2BgYF07doVm82GXq+nU6dOGAwGoqKiCA0NxcvLi+uuu45169bJYlxN6NSpEzqdrkE2v7CwsEHW/3yKorBs2TImTZp00QnSWq2W/v37c/jw4Sb30ev1+Pj41PsRQoiWysrKIi8vj6ioKDQaDZGRkZjNZrKysto1DhnwCyHEZTCZTBQWFvLVV19x6tQpLBYLlZWVHD9+nKKiIg4ePEiPHj3QaDRER0fLYlwX4ObmRlJSEuvXr6+3ff369QwePPiCr920aRNHjhxh+vTpF30fRVHIyMggNDT0suIVQogLURSFlJQUwsLC8PPzA8DPzw8fHx82b97crll+GfALIcRlOHjwICUlJdhsNjw9PdHr9eh0OnVy1t69e6moqKCoqAi9Xg/IYlwXMmfOHJYsWcKyZcvYv38/TzzxBLm5uTzyyCPAuVKbyZMnN3jd0qVLGThwID179mzw3EsvvcTatWvJzs4mIyOD6dOnk5GRoR5TCCHaQlZWFidOnGD48OFqWaJGoyEqKoq8vLx2zfLLpF0hhLhEtbW1rF27lkGDBmE0Gtm6dStmsxkPDw/c3d1xdXWlc+fOREREsHr1arUPvCzG1bQJEyZw5swZXn75ZfLz8+nZsydr1qxRu+7k5+c3uENSWlrKv//9b/72t781esySkhIeeughCgoKMBqNJCQksHnzZgYMGNDm5yOE6Jgc2X1/f388PT3VRM/s2bNxdXXl888/b9csvwz4hRDiEuXm5lJSUsK0adOoqqpiy5YteHl54e3tjb+/P6dOnaKwsJCamhoiIiL49ttvSUxMbHQBKfE/M2fOZObMmY0+t2LFigbbjEYjFRUVTR5v4cKFLFy4sLXCE0KIi7Lb7WpL36VLl5KWlgbAkiVL0Ol0wLkubzLgF0KIK5wjYxMSEkLPnj2xWCx4enpis9mwWCzY7Xaqq6vZtWsXsbGxbNmyhcmTJ6PVSjWlEEJcy1xcXJg+fToVFRVYrVY1KTFjxgy1sYCrq2uTdyZbPZ52eRchhLgGOUpzCgsLCQkJITAwkLNnz1JZWUlRUREVFRXq4N7Pz48ePXpctNuMEEKIa4PRaMRoNGK1WtXvi9DQUHXAb7Va2y0WSTMJIcQlMplM+Pr6kpqaqpby3Hbbbdx9992YTCbc3NwwGo08+OCDjBs3jsDAQKnfF0II0e4kwy+EEJdIq9UyduxYkpOTsVqtKIrCsWPH8PT0pKCgAJvNhk6nY9OmTXz88ceEhYVJ/b4QQoh2Jxl+IYS4DPHx8YwfP57Tp09TUVHBTz/9xK5du/D398doNBIWFkanTp2oqKjAYrGonXqEEEKI9iIZfiGEuEzx8fFER0dz/PhxNm/eTEREBGfOnKGgoIDCwkLsdjsvv/wyu3fvZt26dXTt2lUm7gohhGg38o0jhBCtQKvVotFoMBqN/PnPfyYxMZHrrruOO+64g9mzZ9O9e3eGDh0qK+0KIYRodx12wP/222/TvXt3+vfv7+xQhBDXADc3N6ZPn87IkSOJiIjA19cXT09Pdu/ezR//+EesVitBQUGArLQrhBCifXXYAf+sWbPIzMxk586dzg5FCHGNqNumszGO7dKpRwghRHvqsAN+IYRobY42nT/++CO///3vGTFihFqrrygKW7Zswc/PTzr1CCGEwM3Njblz5zJy5Eh19d22IpN2hRCildRt05mcnExJSQkVFRVkZWXx9ttvU1paysSJE2XCrhBCCODcoH/evHlt/j4y4BdCiFbkaNO5bNky1qxZw5kzZzh16hRFRUUkJSU5OzwhhBAdkKSZhBCiDbi7u3PddddhMpm49dZbWbRoEb179yY5OZn9+/c7OzwhhBAdiAz4hRCiFdXW1rJ27Vq6dOlC//798ff3JyAgAJPJxMSJE+nSpQvr1q2jtrbW2aEKIYToIGTAL4QQrSg3N5eSkhKGDBmCRqOp95xGo5Fe/EIIIdqdDPiFEKIVOXrsh4eHN9p9QXrxCyGEaG8y4BdCiFYkvfiFEEJcaWTAL4QQrcjRiz81NRVFUeo9J734hRBCOIMM+IUQohU5evEfOnSI5ORkSktLsdlsHD9+nJUrV3Lo0CHGjBkjvfiFEEK0G+nDL4QQrczRi/+///0vu3btAs5N2O3UqRPjx48nPj7eyREKIYToSGTAL4QQbSA+Pp7o6GhOnDhBdXU1kyZN4rrrrpPMvhBCiHYnA34hhGgjWq0WX19fAKKiomSwL4QQwink20cIIYQQQohrmAz4hRBCCCGEuIbJgF8IIYQQQohrmEY5v1F0B2M2mzEajZSWluLj4+PscIQQApDPpiuN/P8QQlyJmvvZJBl+IYQQQgghrmEy4BdCCCGEEOIaJgN+IYQQQgghrmEy4BdCCCGEEOIaJgN+IYQQQgghrmEy4BdCCCGEEOIa1mEH/G+//Tbdu3enf//+zg5FCCGEEEKINtNhB/yzZs0iMzOTnTt3OjsUIYQQQggh2kyHHfALIYQQQgjREciAXwghhBBCiGuYDPiFEEIIIYS4hrk4OwBnUxQFALPZ7ORIhBDifxyfSY7PKOFc8l0hhLgSNfe7osMP+C0WCwARERFOjkQIIRqyWCwYjUZnh9HhyXeFEOJKdrHvCo3SwdNHtbW1nDx5EkVRMJlMHD9+HB8fH2eH1Wb69+/v9M5EbR1Dax3/co5zKa9tyWuau++F9jObzURERMjv/BUah6IoWCwWOnfujFYr1ZfO5viuMBgMaDQap8ZyNf7blZjbh8TcPq6kmJv7XdHhM/xarZbw8HD1loiPj4/T/+e1JZ1O5/Tza+sYWuv4l3OcS3ltS17T3H2bs5/8zl+5cUhm/8rh+K64klyN/3Yl5vYhMbePKyXm5nxXSNqog5k1a5azQ2jzGFrr+JdznEt5bUte09x9r4T/3852pfwdXClxCCGE6Hg6fEmPg9lsxmg0UlpaekVcrQnR1uR3Xoir09X4b1dibh8Sc/u4GmOWDP//0+v1/OEPf0Cv1zs7FCHahfzOC3F1uhr/7UrM7UNibh9XY8yS4RdCCCGEEOIaJhl+IYQQQgghrmEy4BdCCCGEEOIaJgN+IYQQQgghrmEy4BdCCCGEEOIaJgP+Fjp+/DgjR46ke/fu9O7dm88++8zZIQnRLu6++278/Py45557nB2KEB3S/Pnz6d+/PwaDgaCgIO666y4OHjzo7LCabf78+Wg0Gh5//HFnh3JReXl5PPDAAwQEBODp6Unfvn1JS0tzdliNstls/P73vyc6OhoPDw9iYmJ4+eWXqa2tdXZo9WzevJnbb7+dzp07o9Fo+PLLL+s9rygK8+bNo3Pnznh4eDBy5Ej27dvnnGC5cLw1NTU888wz9OrVCy8vLzp37szkyZM5efKk0+K9GBnwt5CLiwtvvvkmmZmZbNiwgSeeeILy8nJnhyVEm5s9ezYffPCBs8MQosPatGkTs2bN4ocffmD9+vXYbDbGjBlzVXwH7dy5k8WLF9O7d29nh3JRxcXFDBkyBFdXV7755hsyMzN544038PX1dXZojfrzn//Mu+++y1tvvcX+/ft5/fXX+ctf/sI//vEPZ4dWT3l5OX369OGtt95q9PnXX3+dBQsW8NZbb7Fz505CQkIYPXo0FoulnSM950LxVlRUkJ6ezty5c0lPT+eLL77g0KFD3HHHHU6ItJkUcVl69eql5ObmOjsMIdrFxo0blV/+8pfODkMIoShKYWGhAiibNm1ydigXZLFYlLi4OGX9+vXKiBEjlMcee8zZIV3QM888owwdOtTZYTTbrbfeqkybNq3etnHjxikPPPCAkyK6OEBZtWqV+ri2tlYJCQlR/vSnP6nbqqqqFKPRqLz77rtOiLC+8+NtzI4dOxRAOXbsWPsE1ULXXIb/YreMABYtWkR0dDTu7u4kJSWRmpp6Se/1008/UVtbS0RExGVGLcTlac/feyHElaG0tBQAf39/J0dyYbNmzeLWW2/lF7/4hbNDaZavvvqKfv36ce+99xIUFERCQgL//Oc/nR1Wk4YOHcp3333HoUOHANi9ezdbtmzhlltucXJkzZeTk0NBQQFjxoxRt+n1ekaMGMG2bducGFnzlZaWotFortg7QS7ODqC1OW7BTJ06lV/+8pcNnv/00095/PHHWbRoEUOGDOG9997j5ptvJjMzE5PJBEBSUhLV1dUNXrtu3To6d+4MwJkzZ5g8eTJLlixp2xMSohna6/deCHFlUBSFOXPmMHToUHr27OnscJq0cuVK0tPT2blzp7NDabbs7Gzeeecd5syZw/PPP8+OHTuYPXs2er2eyZMnOzu8Bp555hlKS0vp1q0bOp0Ou93OH//4R+677z5nh9ZsBQUFAAQHB9fbHhwczLFjx5wRUotUVVXx7LPP8qtf/QofHx9nh9M4Z99iaEs0cgtmwIAByiOPPFJvW7du3ZRnn3222cetqqpShg0bpnzwwQetEaYQraqtfu8VRUp6hLhSzJw5U4mMjFSOHz/u7FCalJubqwQFBSkZGRnqtquhpMfV1VUZNGhQvW2PPvqocv311zspogv75JNPlPDwcOWTTz5Rfv75Z+WDDz5Q/P39lRUrVjg7tCad/z21detWBVBOnjxZb78ZM2YoY8eObefoGmrse9XBarUqd955p5KQkKCUlpa2b2AtcM2V9FyI1WolLS2t3i0jgDFjxjT7lpGiKEyZMoUbbriBSZMmtUWYQrSq1vi9F0JcOR599FG++uorNm7cSHh4uLPDaVJaWhqFhYUkJSXh4uKCi4sLmzZt4u9//zsuLi7Y7XZnh9io0NBQunfvXm9bfHw8ubm5Torowp566imeffZZJk6cSK9evZg0aRJPPPEE8+fPd3ZozRYSEgL8L9PvUFhY2CDrfyWpqalh/Pjx5OTksH79+is3u08H69JTVFSE3W5v9JbR+b9kTdm6dSuffvopX375JX379qVv377s2bOnLcIVolW0xu89wNixY7n33ntZs2YN4eHhV9UteiGuBYqi8Nvf/pYvvviC77//nujoaGeHdEE33ngje/bsISMjQ/3p168f999/PxkZGeh0OmeH2KghQ4Y0aHd66NAhIiMjnRTRhVVUVKDV1h/O6XS6K64t54VER0cTEhLC+vXr1W1Wq5VNmzYxePBgJ0bWNMdg//Dhw2zYsIGAgABnh3RB11wNf3NoNJp6jxVFabCtKUOHDr2q/hEJ4XA5v/cAa9eube2QhBAtMGvWLD7++GNWr16NwWBQL9iNRiMeHh5Ojq4hg8HQYH6Bl5cXAQEBV/S8gyeeeILBgwfz2muvMX78eHbs2MHixYtZvHixs0Nr1O23384f//hHTCYTPXr0YNeuXSxYsIBp06Y5O7R6ysrKOHLkiPo4JyeHjIwM/P39MZlMPP7447z22mvExcURFxfHa6+9hqenJ7/61a+uuHg7d+7MPffcQ3p6Ol9//TV2u1399+jv74+bm5tTYr4g51YUtS3Oq7mqrq5WdDqd8sUXX9Tbb/bs2crw4cPbOToh2ob83gtxbQIa/Vm+fLmzQ2u2q6GGX1EU5T//+Y/Ss2dPRa/XK926dVMWL17s7JCaZDablccee0wxmUyKu7u7EhMTo7zwwgtKdXW1s0OrZ+PGjY3+/j744IOKopxrzfmHP/xBCQkJUfR6vTJ8+HBlz549V2S8OTk5Tf573Lhxo9NivhCNoihK+11etC+NRsOqVau466671G0DBw4kKSmJRYsWqdu6d+/OnXfeeVXVuwnRFPm9F0IIIURd11xJz8VuGc2ZM4dJkybRr18/Bg0axOLFi8nNzeWRRx5xYtRCXB75vRdCCCFEU665DH9KSgqjRo1qsP3BBx9kxYoVwLkFiF5//XXy8/Pp2bMnCxcuZPjw4e0cqRCtR37vhRBCCNGUa27AL4QQQgghhPifDtWWUwghhBBCiI5GBvxCCCGEEEJcw2TAL4QQQgghxDVMBvxCCCGEEEJcw2TAL4QQQgghGjh48CD9+/cnOjqa1atXOzsccRmkS48QQgghhGhgwoQJ9O/fn169ejFjxgyOHz/u7JDEJZIMvxBCCCHEJZg3bx59+/Z1dhgqjUbDl19+2eLXHTx4kJCQECwWS73tRqORyMhI4uLiCA4ObvC6/v3788UXX1xquKIdyYBfCCGEEFesd999F4PBgM1mU7eVlZXh6urKsGHD6u2bmpqKRqPh0KFD7R1mu2rtC40XXniBWbNmYTAY6m1/+eWXmThxInFxcTz33HMNXjd37lyeffZZamtrWy0W0TZkwC+EEEKIK9aoUaMoKyvjp59+UrelpqYSEhLCzp07qaioULenpKTQuXNnunTp4oxQr0onTpzgq6++YurUqQ2e+/HHHwkPD2fixIls3bq1wfO33norpaWlrF27tj1CFZdBBvxCCCGEuGJ17dqVzp07k5KSom5LSUnhzjvvJDY2lm3bttXbPmrUKAA+/PBD+vXrh8FgICQkhF/96lcUFhYCUFtbS3h4OO+++26990pPT0ej0ZCdnQ1AaWkpDz30EEFBQfj4+HDDDTewe/fuC8a7fPly4uPjcXd3p1u3bixatEh97ujRo2g0Gr744gtGjRqFp6cnffr0Yfv27fWO8c9//pOIiAg8PT25++67WbBgAb6+vgCsWLGCl156id27d6PRaNBoNKxYsUJ9bVFREXfffTeenp7ExcXx1VdfXTDe5ORk+vTpQ3h4eKPn8qtf/YpJkybx4YcfUlNTU+95nU7HLbfcwieffHLB9xDOJwN+IVrBe++9R3h4ODfeeCOnTp1q8evvvvtu/Pz8uOeee9ogOiGEuLqNHDmSjRs3qo83btzIyJEjGTFihLrdarWyfft2dcBvtVp55ZVX2L17N19++SU5OTlMmTIFAK1Wy8SJE/noo4/qvc/HH3/MoEGDiImJQVEUbr31VgoKClizZg1paWkkJiZy4403cvbs2Ubj/Oc//8kLL7zAH//4R/bv389rr73G3Llzef/99+vt98ILL/C73/2OjIwMunTpwn333aeWLG3dupVHHnmExx57jIyMDEaPHs0f//hH9bUTJkzgySefpEePHuTn55Ofn8+ECRPU51966SXGjx/Pzz//zC233ML999/fZLwAmzdvpl+/fg22FxYWsmbNGh544AFGjx6NVqvlv//9b4P9BgwYQGpqapPHF1cIRQhxWcxmsxIaGqps27ZNefTRR5Wnn366xcf4/vvvla+++kr55S9/2QYRCiHE1W3x4sWKl5eXUlNTo5jNZsXFxUU5deqUsnLlSmXw4MGKoijKpk2bFEDJyspq9Bg7duxQAMVisSiKoijp6emKRqNRjh49qiiKotjtdiUsLEx5++23FUVRlO+++07x8fFRqqqq6h0nNjZWee+99xRFUZQ//OEPSp8+fdTnIiIilI8//rje/q+88ooyaNAgRVEUJScnRwGUJUuWqM/v27dPAZT9+/criqIoEyZMUG699dZ6x7j//vsVo9GoPj7/fR0A5fe//736uKysTNFoNMo333zT6N+JoihKnz59lJdffrnB9jfeeEPp27ev+vixxx5T7rjjjgb7rV69WtFqtYrdbm/yPYTzSYZfiBY4c+YMQUFBHD16VN2m1+vx9fUlLi6O8PBw/P39W3zcUaNGNZgs5XDPPfewYMGCSw1ZCCGueqNGjaK8vJydO3eSmppKly5dCAoKYsSIEezcuZPy8nJSUlIwmUzExMQAsGvXLu68804iIyMxGAyMHDkSgNzcXAASEhLo1q2bWo6yadMmCgsLGT9+PABpaWmUlZUREBCAt7e3+pOTk0NWVlaDGE+fPs3x48eZPn16vf1fffXVBvv37t1b/XNoaCiAWm508OBBBgwYUG//8x9fSN1je3l5YTAY1GM3prKyEnd39wbbly9fzgMPPKA+fuCBB1izZk2Du9geHh7U1tZSXV3d7BhF+3NxdgBCtLfjx48zb948vvnmG4qKiggNDeWuu+7ixRdfJCAg4IKvnT9/PrfffjtRUVHqNjc3N6ZOnUpwcDB+fn7k5eW1arwvvvgio0aNYsaMGfj4+LTqsYUQ4mpw3XXXER4ezsaNGykuLmbEiBEAhISEEB0dzdatW9m4cSM33HADAOXl5YwZM4YxY8bw4YcfEhgYSG5uLmPHjsVqtarHvf/++/n444959tln+fjjjxk7diydOnUCztX5h4aG1ps74OCop6/L0anmn//8JwMHDqz3nE6nq/fY1dVV/bNGo6n3ekVR1G0OSguWTKp7bMfxL9RFp1OnThQXF9fb9tNPP7F3716efvppnnnmGXW73W7nww8/5Mknn1S3nT17Fk9PTzw8PJodo2h/kuEXHUp2djb9+vXj0KFDfPLJJxw5coR3332X7777jkGDBl2wzrGyspKlS5cyY8aMBs9t27aNRx99lIqKCg4ePNjg+aSkJHr27Nng5+TJkxeNuXfv3kRFRTWoNRVCiI5k1KhRpKSkkJKSombrAUaMGMHatWv54Ycf1Pr9AwcOUFRUxJ/+9CeGDRtGt27dGs1y/+pXv2LPnj2kpaXx+eefc//996vPJSYmUlBQgIuLC9ddd129H8dFQV3BwcGEhYWRnZ3dYP/o6Ohmn2e3bt3YsWNHvW11OxTBuUST3W5v9jEvJCEhgczMzHrbli9fzvDhw9m9ezcZGRnqz9NPP83y5cvr7bt3714SExNbJRbRhpxdUyREe7rpppuU8PBwpaKiot72/Px8xdPTU3nkkUeafO2///1vpVOnTg22FxYWKq6ursqBAweUCRMmKI8//vglxbZx48Yma/jnzZunDBs27JKOK4QQ14Jly5YpHh4eiouLi1JQUKBu//DDDxWDwaAASm5urqIo5z6X3dzclKeeekrJyspSVq9erXTp0kUBlF27dtU77uDBg5U+ffoo3t7e9b4bamtrlaFDhyp9+vRRvv32WyUnJ0fZunWr8sILLyg7d+5UFKVhLf0///lPxcPDQ3nzzTeVgwcPKj///LOybNky5Y033lAU5X81/HVjKC4uVgBl48aNiqIoypYtWxStVqu88cYbyqFDh5R3331XCQgIUHx9fdXXfPTRR4qXl5eya9cu5fTp0+o8A0BZtWpVvfMzGo3K8uXLm/x7/eqrr5SgoCDFZrMpiqIoVVVVip+fn/LOO+802PfQoUMKoOzYsUPdNmLEiEbnAIgri2T4RYdx9uxZ1q5dy8yZMxvcegwJCeH+++/n008/bfLWaVOdDD788EP69OlD165deeCBB/joo48atC67XAMGDGDHjh1SIymE6LBGjRpFZWUl1113Xb1VX0eMGIHFYiE2NpaIiAgAAgMDWbFiBZ999hndu3fnT3/6E3/9618bPe7999/P7t27GTduXL3vBo1Gw5o1axg+fDjTpk2jS5cuTJw4kaNHjza66izAjBkzWLJkCStWrKBXr16MGDGCFStWtCjDP2TIEN59910WLFhAnz59+Pbbb3niiSfq1dn/8pe/5KabbmLUqFEEBgZeVlvMW265BVdXVzZs2ADAl19+SWlpKXfffXeDfePi4ujVqxfLli0DIC8vj23btjXaw19cWTRKU6MbIa4xP/74I9dffz2rVq3irrvuavD8woULmTNnDqdOnSIoKKjB83fddRcBAQEsXbq03vbevXszffp0HnvsMWw2G6GhoSxevLjRD8umjB07lvT0dMrLy/H392fVqlX0799fff7nn3+mT58+HD16lMjIyOaftBBCiKver3/9aw4cONBm7S8XLVrE6tWrW7yA1lNPPUVpaSmLFy9uk7hE65FJu0L8P8e1r5ubW6PPN9bJIC0tjczMTCZOnAiAi4sLEyZMYPny5S0a8F/sQ9aRdaq7oqQQQohr01//+ldGjx6Nl5cX33zzDe+//369Bbxa20MPPURxcTEWi6XJjnGNCQoK4ne/+12bxSVajwz4RYdx3XXXodFoyMzMbDTDf+DAAQIDAxvtvgCNdzJYvnw5drudsLAwdZuiKGi1WgoKCggJCWmV2B2TiQMDA1vleEIIIa5cO3bs4PXXX8disRATE8Pf//73RhtGtBYXFxdeeOGFFr/uqaeeaoNoRFuQGn7RYQQEBDB69GgWLVpEZWVlvecKCgr46KOP1FUYG3N+J4Pq6mo++eQT3njjjXpdDHbv3k1MTAwffvhhq8W+d+9ewsPDG+0MIYQQ4tqSnJxMYWEhlZWV7Nu3j0ceecTZIYmrnNTwiw7l8OHDDB48mPj4eF599VWio6PZt28fTz31FC4uLqSmpuLt7d3oa/fs2UNiYiKFhYX4+fmRnJzMpEmTKCwsxGg01tv3hRde4Msvv2Tfvn2tEveUKVPQ6XQN5g8IIYQQQlyMZPhFhxIXF8fOnTuJiYlh/PjxREZGcvPNN9OlSxe2bt3a5GAfoFevXvTr14/k5GTgXDnPL37xiwaDfTjXQSEzM5Mff/zxsmOuqqpi1apV/PrXv77sYwkhhBCi45EMv+jw/vCHP7BgwQLWrVvHoEGDLrjvmjVr+N3vfsfevXvRatvnevntt99m9erVrFu3rl3eTwghhBDXFpm0Kzq8l156iaioKH788UcGDhx4wYH8LbfcwuHDh8nLy1P7Pbc1V1dX/vGPf7TLewkhhBDi2iMZfiGEEEIIIa5hUsMvhBBCCCHENUwG/EIIIYQQQlzDZMAvhBBCCCHENUwG/EIIIYQQQlzDZMAvhBBCCCHENUwG/EIIIYQQQlzDZMAvhBBCCCHENUwG/EIIIYQQQlzDZMAvhBBCCCHENUwG/EIIIYQQQlzDZMAvhBBCCCHENUwG/EIIIYQQQlzD/g8+Cs7V+cH2VQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample Carbon.\n",
-      "Reduced sample Carbon and saved outputs.\n",
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample SiO2\n",
-      "Reducing sample SiO2...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60385-2022-02-28_2215.xye\n"
-     ]
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample SiO2.\n",
-      "Reduced sample SiO2 and saved outputs.\n",
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample AgBeh\n",
-      "Reducing sample AgBeh...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60387-2022-02-28_2215.xye\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample AgBeh.\n",
-      "Reduced sample AgBeh and saved outputs.\n",
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample dSDS\n",
-      "Reducing sample dSDS...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60389-2022-02-28_2215.xye\n"
-     ]
-    },
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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample dSDS.\n",
-      "Reduced sample dSDS and saved outputs.\n",
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/mask_new_July2022.xml for sample VNb\n",
-      "Reducing sample VNb...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/out/60391-2022-02-28_2215.xye\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample VNb.\n",
-      "Reduced sample VNb and saved outputs.\n",
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample sio2\n",
-      "Reducing sample sio2...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60385-2022-02-28_2215_mod.xye\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample sio2.\n",
-      "Reduced sample sio2 and saved outputs.\n",
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample glassy_carbon\n",
-      "Reducing sample glassy_carbon...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60383-2022-02-28_2215_mod.xye\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample glassy_carbon.\n",
-      "Reduced sample glassy_carbon and saved outputs.\n",
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample VNb\n",
-      "Reducing sample VNb...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60391-2022-02-28_2215_mod.xye\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample VNb.\n",
-      "Reduced sample VNb and saved outputs.\n",
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample dSDS\n",
-      "Reducing sample dSDS...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60389-2022-02-28_2215_mod.xye\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample dSDS.\n",
-      "Reduced sample dSDS and saved outputs.\n",
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample agbeh\n",
-      "Reducing sample agbeh...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60387-2022-02-28_2215_mod.xye\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample agbeh.\n",
-      "Reduced sample agbeh and saved outputs.\n",
-      "Identified mask file: /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/mask_new_July2022.xml for sample isis_polymer\n",
-      "Reducing sample isis_polymer...\n",
-      "Saved reduced data to /Users/oliverhammond/esssans-gui/src/ess/loki/examplefiles/nxsmodscript/out/60339-2022-02-28_2215_mod.xye\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved reduction plot for sample isis_polymer.\n",
-      "Reduced sample isis_polymer and saved outputs.\n"
-     ]
     }
    ],
    "source": [
@@ -293,7 +35,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -311,5 +53,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/src/ess/loki/tabwidget.py b/src/ess/loki/tabwidget.py
index dc50e4a8..f789a19c 100644
--- a/src/ess/loki/tabwidget.py
+++ b/src/ess/loki/tabwidget.py
@@ -17,6 +17,7 @@
 import plopp as pp  
 import threading
 import time
+from ipywidgets import Layout
 
 # ----------------------------
 # Utility Functions
@@ -78,6 +79,27 @@ def parse_nx_details(filepath): #For finding/grouping files by common title assi
                 details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
     return details
 
+# ----------------------------
+# Colour Mapping From Filename 
+
+def string_to_colour(input_str):
+    if not input_str:
+        return "#000000"  # Empty input = black
+    total = 0
+    for ch in input_str:
+        if ch.isalpha():
+            total += ord(ch.lower()) - ord('a') + 1  # a=1, b=2, ..., z=26
+        elif ch.isdigit():
+            total += 1 + int(ch) * (25/9)  # Maps '0' to 1 and '9' to 26
+        # Special characters equal 0
+    avg = total / len(input_str)
+    norm = max(0, min(1, avg / 26))  # Average and normalise to [0,1]
+    rgba = plt.get_cmap('flag')(norm)  #prism 
+    return '#{:02x}{:02x}{:02x}'.format(int(rgba[0]*255),
+                                        int(rgba[1]*255),
+                                        int(rgba[2]*255))
+
+
 # ----------------------------
 # Reduction and Plotting Functions
 # ----------------------------
@@ -133,7 +155,9 @@ def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelengt
     x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
     if res["IofQ"].variances is not None:
         yerr = np.sqrt(res["IofQ"].variances)
-        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+        #axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+
     else:
         axs[0].scatter(x_q, res["IofQ"].values)
     axs[0].set_xlabel("Q (Å$^{-1}$)")
@@ -144,7 +168,10 @@ def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelengt
     x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
     if res["transmission"].variances is not None:
         yerr_tr = np.sqrt(res["transmission"].variances)
-        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+        #axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+
+        
     else:
         axs[1].scatter(x_wl, res["transmission"].values)
     axs[1].set_xlabel("Wavelength (Å)")
@@ -167,21 +194,23 @@ def perform_reduction_for_sample(
     background_run_file: str,
     empty_beam_file: str,
     direct_beam_file: str,
-    log_func: callable
+    #log_func: callable
 ):
     """
     Processes a single sample reduction:
       - Finds the necessary run files
       - Optionally determines a mask (or finds one automatically)
       - Calls the reduction and plotting routines
-      - Logs all steps via log_func(message)
+      - Logs all steps via log_func(message) ### edited to just print statements - does logfunc work correctly with voila???
     """
     sample = sample_info.get("SAMPLE", "Unknown")
     try:
         sample_run_file = find_file(input_dir, str(sample_info["SANS"]), extension=".nxs")
         transmission_run_file = find_file(input_dir, str(sample_info["TRANS"]), extension=".nxs")
     except Exception as e:
-        log_func(f"Skipping sample {sample}: {e}")
+        #log_func(f"Skipping sample {sample}: {e}")
+        print(f"Skipping sample {sample}: {e}")
+
         return None
     # Determine mask file.
     mask_file = None
@@ -193,12 +222,18 @@ def perform_reduction_for_sample(
     if mask_file is None:
         try:
             mask_file = find_mask_file(input_dir)
-            log_func(f"Identified mask file: {mask_file} for sample {sample}")
+            #log_func(f"Identified mask file: {mask_file} for sample {sample}")
+            print(f"Identified mask file: {mask_file} for sample {sample}")
+
         except Exception as e:
-            log_func(f"Mask file not found for sample {sample}: {e}")
+            #log_func(f"Mask file not found for sample {sample}: {e}")
+            print(f"Mask file not found for sample {sample}: {e}")
+
             return None
 
-    log_func(f"Reducing sample {sample}...")
+    #log_func(f"Reducing sample {sample}...")
+    print(f"Reducing sample {sample}...")
+
     try:
         res = reduce_loki_batch_preliminary(
             sample_run_file=sample_run_file,
@@ -215,14 +250,18 @@ def perform_reduction_for_sample(
             q_n=reduction_params["q_n"]
         )
     except Exception as e:
-        log_func(f"Reduction failed for sample {sample}: {e}")
+        #log_func(f"Reduction failed for sample {sample}: {e}")
+        print(f"Reduction failed for sample {sample}: {e}")
         return None
     out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
     try:
         save_xye_pandas(res["IofQ"], out_xye)
-        log_func(f"Saved reduced data to {out_xye}")
+        #log_func(f"Saved reduced data to {out_xye}")
+        print(f"Saved reduced data to {out_xye}")
+
     except Exception as e:
-        log_func(f"Failed to save reduced data for {sample}: {e}")
+        #log_func(f"Failed to save reduced data for {sample}: {e}")
+        print(f"Failed to save reduced data for {sample}: {e}")
     try:
         save_reduction_plots(
             res,
@@ -237,10 +276,15 @@ def perform_reduction_for_sample(
             output_dir,
             show=True
         )
-        log_func(f"Saved reduction plot for sample {sample}.")
+#        log_func(f"Saved reduction plot for sample {sample}.")
+#    except Exception as e:
+#        log_func(f"Failed to save reduction plot for {sample}: {e}")
+#    log_func(f"Reduced sample {sample} and saved outputs.")
+#    return res
+        print(f"Saved reduction plot for sample {sample}.")
     except Exception as e:
-        log_func(f"Failed to save reduction plot for {sample}: {e}")
-    log_func(f"Reduced sample {sample} and saved outputs.")
+        print(f"Failed to save reduction plot for {sample}: {e}")
+    print(f"Reduced sample {sample} and saved outputs.")
     return res
 
 # ----------------------------
@@ -349,7 +393,7 @@ def run_reduction(self, _):
                 background_run_file=background_run_file,
                 empty_beam_file=empty_beam_file,
                 direct_beam_file=direct_beam_file,
-                log_func=lambda msg: print(msg)
+                #log_func=lambda msg: print(msg)
             )
     
     @property
@@ -379,10 +423,10 @@ def __init__(self):
         self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
         self.reduce_button = widgets.Button(description="Reduce")
         self.reduce_button.on_click(self.run_reduction)
-        self.clear_log_button = widgets.Button(description="Clear Log")
-        self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
-        self.clear_plots_button = widgets.Button(description="Clear Plots")
-        self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        #self.clear_log_button = widgets.Button(description="Clear Log")
+        #self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        #self.clear_plots_button = widgets.Button(description="Clear Plots")
+        #self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
         self.log_output = widgets.Output()
         self.plot_output = widgets.Output()
         self.processed = set()
@@ -394,7 +438,7 @@ def __init__(self):
             widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
             widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
             widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]),
+            widgets.HBox([self.reduce_button]),# self.clear_log_button, self.clear_plots_button]),
             self.log_output,
             self.plot_output
         ])
@@ -510,7 +554,7 @@ def run_reduction(self, _):
                 background_run_file=background_run_file,
                 empty_beam_file=empty_beam_file,
                 direct_beam_file=direct_beam_file,
-                log_func=lambda msg: print(msg)
+                #log_func=lambda msg: print(msg)
             )
     
     @property
@@ -661,7 +705,7 @@ def background_loop(self):
                     background_run_file=self.empty_beam_sans,
                     empty_beam_file=self.empty_beam_trans,
                     direct_beam_file=direct_beam_file,
-                    log_func=lambda msg: print(msg)
+                    #log_func=lambda msg: print(msg)
                 )
                 self.processed.add(key)
             time.sleep(60)
diff --git a/src/ess/loki/tabwidgetii.py b/src/ess/loki/tabwidgetii.py
new file mode 100644
index 00000000..e60a778e
--- /dev/null
+++ b/src/ess/loki/tabwidgetii.py
@@ -0,0 +1,799 @@
+### attempt at altering log output commands to basic print/plot – but still doesnt work with voila??? 
+
+import os
+import glob
+import re
+import h5py
+import pandas as pd
+import scipp as sc
+import matplotlib.pyplot as plt
+import numpy as np
+import ipywidgets as widgets
+from ipydatagrid import DataGrid
+from IPython.display import display
+from ipyfilechooser import FileChooser
+from ess import sans
+from ess import loki
+from ess.sans.types import *
+from scipp.scipy.interpolate import interp1d
+import plopp as pp  
+import threading
+import time
+
+# ----------------------------
+# Utility Functions
+# ----------------------------
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+def find_direct_beam(work_dir):  # Find the direct beam automatically 
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+
+def find_mask_file(work_dir):  # Find the mask automatically 
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+def extract_run_number(filename):
+    m = re.search(r'(\d{4,})', filename)
+    if m:
+        return m.group(1)
+    return ""
+
+def parse_nx_details(filepath):
+    details = {}
+    with h5py.File(filepath, 'r') as f:
+        if 'nicos_details' in f['entry']:
+            grp = f['entry']['nicos_details']
+            if 'runlabel' in grp:
+                val = grp['runlabel'][()]
+                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+            if 'runtype' in grp:
+                val = grp['runtype'][()]
+                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+    return details
+
+# ----------------------------
+# Colour Mapping From Filename 
+# ----------------------------
+def string_to_colour(input_str):
+    if not input_str:
+        return "#000000"  # Empty input = black
+    total = 0
+    for ch in input_str:
+        if ch.isalpha():
+            total += ord(ch.lower()) - ord('a') + 1  # a=1, b=2, ..., z=26
+        elif ch.isdigit():
+            total += 1 + int(ch) * (25/9)  # Maps '0' to 1 and '9' to 26
+    avg = total / len(input_str)
+    norm = max(0, min(1, avg / 26))  # Average and normalise to [0,1]
+    rgba = plt.get_cmap('flag')(norm)
+    return '#{:02x}{:02x}{:02x}'.format(int(rgba[0]*255),
+                                        int(rgba[1]*255),
+                                        int(rgba[2]*255))
+
+# ----------------------------
+# Reduction and Plotting Functions
+# ----------------------------
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101
+):
+    if mask_files is None:
+        mask_files = []
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelength_max, wavelength_n, q_min, q_max, q_n, output_dir, show=True):
+    fig, axs = plt.subplots(1, 2, figsize=(8, 4))
+    axs[0].set_box_aspect(1)
+    axs[1].set_box_aspect(1)
+    title_str = f"{sample} - {os.path.basename(sample_run_file)}"
+    fig.suptitle(title_str, fontsize=14)
+    q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
+    x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+    if res["IofQ"].variances is not None:
+        yerr = np.sqrt(res["IofQ"].variances)
+        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none',
+                        color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+    else:
+        axs[0].scatter(x_q, res["IofQ"].values)
+    axs[0].set_xlabel("Q (Å$^{-1}$)")
+    axs[0].set_ylabel("I(Q)")
+    axs[0].set_xscale("log")
+    axs[0].set_yscale("log")
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+    if res["transmission"].variances is not None:
+        yerr_tr = np.sqrt(res["transmission"].variances)
+        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none',
+                        color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+    else:
+        axs[1].scatter(x_wl, res["transmission"].values)
+    axs[1].set_xlabel("Wavelength (Å)")
+    axs[1].set_ylabel("Transmission")
+    plt.tight_layout()
+    out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png"))
+    fig.savefig(out_png, dpi=300)
+    if show:
+        plt.show()
+    plt.close(fig)
+
+# ----------------------------
+# Unified "Backend" Function for Reduction
+# ----------------------------
+def perform_reduction_for_sample(
+    sample_info: dict,
+    input_dir: str,
+    output_dir: str,
+    reduction_params: dict,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+):
+    sample = sample_info.get("SAMPLE", "Unknown")
+    try:
+        sample_run_file = find_file(input_dir, str(sample_info["SANS"]), extension=".nxs")
+        transmission_run_file = find_file(input_dir, str(sample_info["TRANS"]), extension=".nxs")
+    except Exception as e:
+        print(f"Skipping sample {sample}: {e}")
+        return None
+
+    mask_file = None
+    mask_candidate = str(sample_info.get("mask", "")).strip()
+    if mask_candidate:
+        mask_candidate_file = os.path.join(input_dir, f"{mask_candidate}.xml")
+        if os.path.exists(mask_candidate_file):
+            mask_file = mask_candidate_file
+    if mask_file is None:
+        try:
+            mask_file = find_mask_file(input_dir)
+            print(f"Identified mask file: {mask_file} for sample {sample}")
+        except Exception as e:
+            print(f"Mask file not found for sample {sample}: {e}")
+            return None
+
+    print(f"Reducing sample {sample}...")
+    try:
+        res = reduce_loki_batch_preliminary(
+            sample_run_file=sample_run_file,
+            transmission_run_file=transmission_run_file,
+            background_run_file=background_run_file,
+            empty_beam_file=empty_beam_file,
+            direct_beam_file=direct_beam_file,
+            mask_files=[mask_file],
+            wavelength_min=reduction_params["wavelength_min"],
+            wavelength_max=reduction_params["wavelength_max"],
+            wavelength_n=reduction_params["wavelength_n"],
+            q_start=reduction_params["q_start"],
+            q_stop=reduction_params["q_stop"],
+            q_n=reduction_params["q_n"]
+        )
+    except Exception as e:
+        print(f"Reduction failed for sample {sample}: {e}")
+        return None
+
+    out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+    try:
+        save_xye_pandas(res["IofQ"], out_xye)
+        print(f"Saved reduced data to {out_xye}")
+    except Exception as e:
+        print(f"Failed to save reduced data for {sample}: {e}")
+
+    try:
+        save_reduction_plots(
+            res,
+            sample,
+            sample_run_file,
+            reduction_params["wavelength_min"],
+            reduction_params["wavelength_max"],
+            reduction_params["wavelength_n"],
+            reduction_params["q_start"],
+            reduction_params["q_stop"],
+            reduction_params["q_n"],
+            output_dir,
+            show=True
+        )
+        print(f"Saved reduction plot for sample {sample}.")
+    except Exception as e:
+        print(f"Failed to save reduction plot for {sample}: {e}")
+    print(f"Reduced sample {sample} and saved outputs.")
+    return res
+
+# ----------------------------
+# GUI Widgets 
+# ----------------------------
+class SansBatchReductionWidget:
+    def __init__(self):
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.ebeam_sans_widget = widgets.Text(value="", placeholder="Enter Ebeam SANS run number", description="Ebeam SANS:")
+        self.ebeam_trans_widget = widgets.Text(value="", placeholder="Enter Ebeam TRANS run number", description="Ebeam TRANS:")
+        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: plt.close('all'))
+        # Remove widget outputs from the layout since we use plain print and plt.show()
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
+            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button])
+        ])
+    
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+    
+    def run_reduction(self, _):
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
+            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
+            print("Using empty-beam files:")
+            print("  Background (Ebeam SANS):", background_run_file)
+            print("  Empty beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            print("Error finding empty beam files:", e)
+            return
+        
+        reduction_params = {
+            "wavelength_min": self.wavelength_min_widget.value,
+            "wavelength_max": self.wavelength_max_widget.value,
+            "wavelength_n": self.wavelength_n_widget.value,
+            "q_start": self.q_start_widget.value,
+            "q_stop": self.q_stop_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+        
+        df = self.table.data
+        for idx, row in df.iterrows():
+            perform_reduction_for_sample(
+                sample_info=row,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file
+            )
+    
+    @property
+    def widget(self):
+        return self.main
+
+class SemiAutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.scan_button = widgets.Button(description="Scan Directory")
+        self.scan_button.on_click(self.scan_directory)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.add_row_button = widgets.Button(description="Add Row")
+        self.add_row_button.on_click(self.add_row)
+        self.delete_row_button = widgets.Button(description="Delete Last Row")
+        self.delete_row_button.on_click(self.delete_last_row)
+        self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
+        self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
+        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: plt.close('all'))
+        self.processed = set()
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            self.scan_button,
+            self.table,
+            widgets.HBox([self.add_row_button, self.delete_row_button]),
+            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
+            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button])
+        ])
+    
+    def add_row(self, _):
+        df = self.table.data
+        new_row = {col: "" for col in df.columns} if not df.empty else {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
+        df = df.append(new_row, ignore_index=True)
+        self.table.data = df
+
+    def delete_last_row(self, _):
+        df = self.table.data
+        if not df.empty:
+            self.table.data = df.iloc[:-1]
+
+    def scan_directory(self, _):
+        print("Scanning directory...")
+        input_dir = self.input_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            print("Invalid input folder.")
+            return
+        nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+        groups = {}
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runlabel' not in details or 'runtype' not in details:
+                continue
+            runlabel = details['runlabel']
+            runtype = details['runtype'].lower()
+            run_number = extract_run_number(os.path.basename(f))
+            if runlabel not in groups:
+                groups[runlabel] = {}
+            groups[runlabel][runtype] = run_number
+        table_rows = []
+        for runlabel, d in groups.items():
+            if 'sans' in d and 'trans' in d:
+                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+        df = pd.DataFrame(table_rows)
+        self.table.data = df
+        print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+        ebeam_sans_files = []
+        ebeam_trans_files = []
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runtype' in details:
+                if details['runtype'].lower() == 'ebeam_sans':
+                    ebeam_sans_files.append(f)
+                elif details['runtype'].lower() == 'ebeam_trans':
+                    ebeam_trans_files.append(f)
+        if ebeam_sans_files:
+            ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_sans_text.value = ebeam_sans_files[0]
+        else:
+            self.empty_beam_sans_text.value = ""
+        if ebeam_trans_files:
+            ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_trans_text.value = ebeam_trans_files[0]
+        else:
+            self.empty_beam_trans_text.value = ""
+    
+    def run_reduction(self, _):
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            print("Direct-beam file not found:", e)
+            return
+        background_run_file = self.empty_beam_sans_text.value
+        empty_beam_file = self.empty_beam_trans_text.value
+        if not background_run_file or not empty_beam_file:
+            print("Empty beam files not found.")
+            return
+        
+        reduction_params = {
+            "wavelength_min": self.lambda_min_widget.value,
+            "wavelength_max": self.lambda_max_widget.value,
+            "wavelength_n": self.lambda_n_widget.value,
+            "q_start": self.q_min_widget.value,
+            "q_stop": self.q_max_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+        
+        df = self.table.data.copy()
+        for idx, row in df.iterrows():
+            perform_reduction_for_sample(
+                sample_info=row,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file
+            )
+    
+    @property
+    def widget(self):
+        return self.main
+
+class AutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.start_stop_button = widgets.Button(description="Start")
+        self.start_stop_button.on_click(self.toggle_running)
+        self.status_label = widgets.Label(value="Stopped")
+        self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
+        self.running = False
+        self.thread = None
+        self.processed = set()
+        self.empty_beam_sans = None
+        self.empty_beam_trans = None
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.start_stop_button, self.status_label]),
+            self.table
+        ])
+    
+    def toggle_running(self, _):
+        if not self.running:
+            self.running = True
+            self.start_stop_button.description = "Stop"
+            self.status_label.value = "Running"
+            self.thread = threading.Thread(target=self.background_loop, daemon=True)
+            self.thread.start()
+        else:
+            self.running = False
+            self.start_stop_button.description = "Start"
+            self.status_label.value = "Stopped"
+    
+    def background_loop(self):
+        while self.running:
+            input_dir = self.input_dir_chooser.selected
+            output_dir = self.output_dir_chooser.selected
+            if not input_dir or not os.path.isdir(input_dir):
+                print("Invalid input folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            if not output_dir or not os.path.isdir(output_dir):
+                print("Invalid output folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+            groups = {}
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runlabel' not in details or 'runtype' not in details:
+                    continue
+                runlabel = details['runlabel']
+                runtype = details['runtype'].lower()
+                run_number = extract_run_number(os.path.basename(f))
+                if runlabel not in groups:
+                    groups[runlabel] = {}
+                groups[runlabel][runtype] = run_number
+            table_rows = []
+            for runlabel, d in groups.items():
+                if 'sans' in d and 'trans' in d:
+                    table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+            df = pd.DataFrame(table_rows)
+            self.table.data = df
+            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+            ebeam_sans_files = []
+            ebeam_trans_files = []
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runtype' in details:
+                    if details['runtype'].lower() == 'ebeam_sans':
+                        ebeam_sans_files.append(f)
+                    elif details['runtype'].lower() == 'ebeam_trans':
+                        ebeam_trans_files.append(f)
+            if ebeam_sans_files:
+                ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_sans = ebeam_sans_files[0]
+            else:
+                self.empty_beam_sans = None
+            if ebeam_trans_files:
+                ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_trans = ebeam_trans_files[0]
+            else:
+                self.empty_beam_trans = None
+            try:
+                direct_beam_file = find_direct_beam(input_dir)
+            except Exception as e:
+                print("Direct-beam file not found:", e)
+                time.sleep(60)
+                continue
+            for index, row in df.iterrows():
+                key = (row["SAMPLE"], row["SANS"], row["TRANS"])
+                if key in self.processed:
+                    continue
+                try:
+                    sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs")
+                    transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs")
+                except Exception as e:
+                    print(f"Skipping sample {row['SAMPLE']}: {e}")
+                    continue
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
+                except Exception as e:
+                    print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
+                    continue
+                if not self.empty_beam_sans or not self.empty_beam_trans:
+                    print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
+                    continue
+                print(f"Reducing sample {row['SAMPLE']}...")
+                reduction_params = {
+                    "wavelength_min": 1.0,
+                    "wavelength_max": 13.0,
+                    "wavelength_n": 201,
+                    "q_start": 0.01,
+                    "q_stop": 0.3,
+                    "q_n": 101
+                }
+                perform_reduction_for_sample(
+                    sample_info=row,
+                    input_dir=input_dir,
+                    output_dir=output_dir,
+                    reduction_params=reduction_params,
+                    background_run_file=self.empty_beam_sans,
+                    empty_beam_file=self.empty_beam_trans,
+                    direct_beam_file=direct_beam_file
+                )
+                self.processed.add(key)
+            time.sleep(60)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Direct Beam stuff
+# ----------------------------
+def compute_direct_beam_local(
+    mask: str,
+    sample_sans: str,
+    background_sans: str,
+    sample_trans: str,
+    background_trans: str,
+    empty_beam: str,
+    local_Iq_theory: str,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    n_wavelength_bins: int = 50,
+    n_wavelength_bands: int = 50
+) -> dict:
+    workflow = loki.LokiAtLarmorWorkflow()
+    workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
+    workflow[NeXusDetectorName] = 'larmor_detector'
+    wl_min = sc.scalar(wavelength_min, unit='angstrom')
+    wl_max = sc.scalar(wavelength_max, unit='angstrom')
+    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
+    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
+    workflow[CorrectForGravity] = True
+    workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
+    workflow[ReturnEvents] = False
+    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
+    workflow[Filename[SampleRun]] = sample_sans
+    workflow[Filename[BackgroundRun]] = background_sans
+    workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
+    workflow[Filename[EmptyBeamRun]] = empty_beam
+    center = sans.beam_center_from_center_of_mass(workflow)
+    print("Computed beam center:", center)
+    workflow[BeamCenter] = center
+    Iq_theory = sc.io.load_hdf5(local_Iq_theory)
+    f = interp1d(Iq_theory, 'Q')
+    I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
+    print("Computed I0:", I0)
+    results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
+    iofq_full = results[-1]['iofq_full']
+    iofq_bands = results[-1]['iofq_bands']
+    direct_beam_function = results[-1]['direct_beam']
+    pp.plot(
+        {'reference': Iq_theory, 'data': iofq_full},
+        color={'reference': 'darkgrey', 'data': 'C0'},
+        norm='log',
+    )
+    print("Plotted full-range result vs. theoretical reference.")
+    return {
+        'direct_beam_function': direct_beam_function,
+        'iofq_full': iofq_full,
+        'Iq_theory': Iq_theory,
+    }
+
+class DirectBeamWidget:
+    def __init__(self):
+        self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:")
+        self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:")
+        self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:")
+        self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:")
+        self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:")
+        self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:")
+        self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:")
+        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
+        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
+        self.compute_button = widgets.Button(description="Compute Direct Beam")
+        self.compute_button.on_click(self.compute_direct_beam)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(lambda _: plt.close('all'))
+        self.main = widgets.VBox([
+            self.mask_text,
+            self.sample_sans_text,
+            self.background_sans_text,
+            self.sample_trans_text,
+            self.background_trans_text,
+            self.empty_beam_text,
+            self.local_Iq_theory_text,
+            widgets.HBox([
+                self.db_wavelength_min_widget,
+                self.db_wavelength_max_widget,
+                self.db_n_wavelength_bins_widget,
+                self.db_n_wavelength_bands_widget
+            ]),
+            self.compute_button,
+            widgets.HBox([self.clear_log_button, self.clear_plots_button])
+        ])
+    
+    def compute_direct_beam(self, _):
+        # No longer clearing widget outputs; we simply print new info.
+        mask = self.mask_text.value
+        sample_sans = self.sample_sans_text.value
+        background_sans = self.background_sans_text.value
+        sample_trans = self.sample_trans_text.value
+        background_trans = self.background_trans_text.value
+        empty_beam = self.empty_beam_text.value
+        local_Iq_theory = self.local_Iq_theory_text.value
+        wl_min = self.db_wavelength_min_widget.value
+        wl_max = self.db_wavelength_max_widget.value
+        n_bins = self.db_n_wavelength_bins_widget.value
+        n_bands = self.db_n_wavelength_bands_widget.value
+        print("Computing direct beam with:")
+        print("  Mask:", mask)
+        print("  Sample SANS:", sample_sans)
+        print("  Background SANS:", background_sans)
+        print("  Sample TRANS:", sample_trans)
+        print("  Background TRANS:", background_trans)
+        print("  Empty Beam:", empty_beam)
+        print("  I(q) Theory:", local_Iq_theory)
+        print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
+        try:
+            results = compute_direct_beam_local(
+                mask,
+                sample_sans,
+                background_sans,
+                sample_trans,
+                background_trans,
+                empty_beam,
+                local_Iq_theory,
+                wavelength_min=wl_min,
+                wavelength_max=wl_max,
+                n_wavelength_bins=n_bins,
+                n_wavelength_bands=n_bands
+            )
+            print("Direct beam computation complete.")
+        except Exception as e:
+            print("Error computing direct beam:", e)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Build it
+# ----------------------------
+reduction_widget = SansBatchReductionWidget().widget
+direct_beam_widget = DirectBeamWidget().widget
+semi_auto_reduction_widget = SemiAutoReductionWidget().widget
+auto_reduction_widget = AutoReductionWidget().widget
+
+tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
+tabs.set_title(0, "Direct Beam")
+tabs.set_title(1, "Reduction (Manual)")
+tabs.set_title(2, "Reduction (Smart)")
+tabs.set_title(3, "Reduction (Auto)")
+
+# To display the tabs in a Voila deployment, simply display the tabs widget.
+#display(tabs)

From a53ad84a35ae585a57c7a4121e5924666f448ee3 Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Tue, 25 Mar 2025 13:35:54 +0100
Subject: [PATCH 16/18] typos

---
 src/ess/loki/tabwidget.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/ess/loki/tabwidget.py b/src/ess/loki/tabwidget.py
index f789a19c..3dd03558 100644
--- a/src/ess/loki/tabwidget.py
+++ b/src/ess/loki/tabwidget.py
@@ -357,16 +357,16 @@ def run_reduction(self, _):
         try:
             direct_beam_file = find_direct_beam(input_dir)
             with self.log_output:
-                print("Using direct-beam file:", direct_beam_file)
+                print("Using direct beam file:", direct_beam_file)
         except Exception as e:
             with self.log_output:
-                print("Direct-beam file not found:", e)
+                print("Direct beam file not found:", e)
             return
         try:
             background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
             empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
             with self.log_output:
-                print("Using empty-beam files:")
+                print("Using empty beam files:")
                 print("  Background (Ebeam SANS):", background_run_file)
                 print("  Empty beam (Ebeam TRANS):", empty_beam_file)
         except Exception as e:
@@ -523,10 +523,10 @@ def run_reduction(self, _):
         try:
             direct_beam_file = find_direct_beam(input_dir)
             with self.log_output:
-                print("Using direct-beam file:", direct_beam_file)
+                print("Using direct beam file:", direct_beam_file)
         except Exception as e:
             with self.log_output:
-                print("Direct-beam file not found:", e)
+                print("Direct beam file not found:", e)
             return
         background_run_file = self.empty_beam_sans_text.value
         empty_beam_file = self.empty_beam_trans_text.value

From 43b3fb8586f74394351bd0fb823aa52f0334e3a2 Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Thu, 27 Mar 2025 10:11:06 +0100
Subject: [PATCH 17/18] Added scitacean "auto" upload

Hacky implementation in lieu of 'proper' UOS metadata
---
 .DS_Store                                     | Bin 6148 -> 6148 bytes
 src/.DS_Store                                 | Bin 6148 -> 6148 bytes
 src/ess/.DS_Store                             | Bin 6148 -> 6148 bytes
 src/ess/loki/.DS_Store                        | Bin 8196 -> 10244 bytes
 src/ess/loki/examplefiles/.DS_Store           | Bin 10244 -> 10244 bytes
 src/ess/loki/tabwidget-i.py                   | 797 ------------------
 ...abwidgetii.py => tabwidget-visa-scicat.py} | 457 +++++++---
 src/ess/loki/tabwidget.py                     |   2 +-
 8 files changed, 344 insertions(+), 912 deletions(-)
 delete mode 100644 src/ess/loki/tabwidget-i.py
 rename src/ess/loki/{tabwidgetii.py => tabwidget-visa-scicat.py} (65%)

diff --git a/.DS_Store b/.DS_Store
index 549fcc42dfb7737e581aa9bfa50667eff158f710..f7cb7e68583f202211745f9e15a5d8f797e388a7 100644
GIT binary patch
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diff --git a/src/.DS_Store b/src/.DS_Store
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diff --git a/src/ess/loki/tabwidget-i.py b/src/ess/loki/tabwidget-i.py
deleted file mode 100644
index e663c016..00000000
--- a/src/ess/loki/tabwidget-i.py
+++ /dev/null
@@ -1,797 +0,0 @@
-import os
-import glob
-import re
-import h5py
-import pandas as pd
-import scipp as sc
-import matplotlib.pyplot as plt
-import numpy as np
-import ipywidgets as widgets
-from ipydatagrid import DataGrid
-from IPython.display import display
-from ipyfilechooser import FileChooser
-from ess import sans
-from ess import loki
-from ess.sans.types import *
-from scipp.scipy.interpolate import interp1d
-import plopp as pp  
-import threading
-import time
-
-# ----------------------------
-# Utility Functions
-# ----------------------------
-def find_file(work_dir, run_number, extension=".nxs"):
-    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
-    files = glob.glob(pattern)
-    if files:
-        return files[0]
-    else:
-        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
-
-def find_direct_beam(work_dir):  # Find the direct beam automatically 
-    pattern = os.path.join(work_dir, "*direct-beam*.h5")
-    files = glob.glob(pattern)
-    if files:
-        return files[0]
-    else:
-        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
-
-def find_mask_file(work_dir):  # Find the mask automatically 
-    pattern = os.path.join(work_dir, "*mask*.xml")
-    files = glob.glob(pattern)
-    if files:
-        return files[0]
-    else:
-        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
-
-def save_xye_pandas(data_array, filename):
-    q_vals = data_array.coords["Q"].values
-    i_vals = data_array.values
-    if len(q_vals) != len(i_vals):
-        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
-    if data_array.variances is not None:
-        err_vals = np.sqrt(data_array.variances)
-        if len(err_vals) != len(i_vals):
-            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
-    else:
-        err_vals = np.zeros_like(i_vals)
-    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
-    df.to_csv(filename, sep=" ", index=False, header=True)
-
-def extract_run_number(filename):
-    m = re.search(r'(\d{4,})', filename)
-    if m:
-        return m.group(1)
-    return ""
-
-def parse_nx_details(filepath):
-    details = {}
-    with h5py.File(filepath, 'r') as f:
-        if 'nicos_details' in f['entry']:
-            grp = f['entry']['nicos_details']
-            if 'runlabel' in grp:
-                val = grp['runlabel'][()]
-                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
-            if 'runtype' in grp:
-                val = grp['runtype'][()]
-                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
-    return details
-
-# ----------------------------
-# Colour Mapping From Filename 
-# ----------------------------
-def string_to_colour(input_str):
-    if not input_str:
-        return "#000000"  # Empty input = black
-    total = 0
-    for ch in input_str:
-        if ch.isalpha():
-            total += ord(ch.lower()) - ord('a') + 1  # a=1, b=2, ..., z=26
-        elif ch.isdigit():
-            total += 1 + int(ch) * (25/9)  # Maps '0' to 1 and '9' to 26
-    avg = total / len(input_str)
-    norm = max(0, min(1, avg / 26))  # Average and normalise to [0,1]
-    rgba = plt.get_cmap('flag')(norm)
-    return '#{:02x}{:02x}{:02x}'.format(int(rgba[0]*255),
-                                        int(rgba[1]*255),
-                                        int(rgba[2]*255))
-
-# ----------------------------
-# Reduction and Plotting Functions
-# ----------------------------
-def reduce_loki_batch_preliminary(
-    sample_run_file: str,
-    transmission_run_file: str,
-    background_run_file: str,
-    empty_beam_file: str,
-    direct_beam_file: str,
-    mask_files: list = None,
-    correct_for_gravity: bool = True,
-    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
-    return_events: bool = False,
-    wavelength_min: float = 1.0,
-    wavelength_max: float = 13.0,
-    wavelength_n: int = 201,
-    q_start: float = 0.01,
-    q_stop: float = 0.3,
-    q_n: int = 101
-):
-    if mask_files is None:
-        mask_files = []
-    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
-    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
-    workflow = loki.LokiAtLarmorWorkflow()
-    if mask_files:
-        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
-    workflow[NeXusDetectorName] = "larmor_detector"
-    workflow[WavelengthBins] = wavelength_bins
-    workflow[QBins] = q_bins
-    workflow[CorrectForGravity] = correct_for_gravity
-    workflow[UncertaintyBroadcastMode] = uncertainty_mode
-    workflow[ReturnEvents] = return_events
-    workflow[Filename[BackgroundRun]] = background_run_file
-    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
-    workflow[Filename[EmptyBeamRun]] = empty_beam_file
-    workflow[DirectBeamFilename] = direct_beam_file
-    workflow[Filename[SampleRun]] = sample_run_file
-    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
-    center = sans.beam_center_from_center_of_mass(workflow)
-    workflow[BeamCenter] = center
-    tf = workflow.compute(TransmissionFraction[SampleRun])
-    da = workflow.compute(BackgroundSubtractedIofQ)
-    return {"transmission": tf, "IofQ": da}
-
-def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelength_max, wavelength_n, q_min, q_max, q_n, output_dir, show=True):
-    fig, axs = plt.subplots(1, 2, figsize=(8, 4))
-    axs[0].set_box_aspect(1)
-    axs[1].set_box_aspect(1)
-    title_str = f"{sample} - {os.path.basename(sample_run_file)}"
-    fig.suptitle(title_str, fontsize=14)
-    q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
-    x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
-    if res["IofQ"].variances is not None:
-        yerr = np.sqrt(res["IofQ"].variances)
-        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none',
-                        color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
-    else:
-        axs[0].scatter(x_q, res["IofQ"].values)
-    axs[0].set_xlabel("Q (Å$^{-1}$)")
-    axs[0].set_ylabel("I(Q)")
-    axs[0].set_xscale("log")
-    axs[0].set_yscale("log")
-    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
-    x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
-    if res["transmission"].variances is not None:
-        yerr_tr = np.sqrt(res["transmission"].variances)
-        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none',
-                        color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
-    else:
-        axs[1].scatter(x_wl, res["transmission"].values)
-    axs[1].set_xlabel("Wavelength (Å)")
-    axs[1].set_ylabel("Transmission")
-    plt.tight_layout()
-    out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png"))
-    fig.savefig(out_png, dpi=300)
-    if show:
-        plt.show()
-    plt.close(fig)
-
-# ----------------------------
-# Unified "Backend" Function for Reduction
-# ----------------------------
-def perform_reduction_for_sample(
-    sample_info: dict,
-    input_dir: str,
-    output_dir: str,
-    reduction_params: dict,
-    background_run_file: str,
-    empty_beam_file: str,
-    direct_beam_file: str,
-):
-    sample = sample_info.get("SAMPLE", "Unknown")
-    try:
-        sample_run_file = find_file(input_dir, str(sample_info["SANS"]), extension=".nxs")
-        transmission_run_file = find_file(input_dir, str(sample_info["TRANS"]), extension=".nxs")
-    except Exception as e:
-        print(f"Skipping sample {sample}: {e}")
-        return None
-
-    mask_file = None
-    mask_candidate = str(sample_info.get("mask", "")).strip()
-    if mask_candidate:
-        mask_candidate_file = os.path.join(input_dir, f"{mask_candidate}.xml")
-        if os.path.exists(mask_candidate_file):
-            mask_file = mask_candidate_file
-    if mask_file is None:
-        try:
-            mask_file = find_mask_file(input_dir)
-            print(f"Identified mask file: {mask_file} for sample {sample}")
-        except Exception as e:
-            print(f"Mask file not found for sample {sample}: {e}")
-            return None
-
-    print(f"Reducing sample {sample}...")
-    try:
-        res = reduce_loki_batch_preliminary(
-            sample_run_file=sample_run_file,
-            transmission_run_file=transmission_run_file,
-            background_run_file=background_run_file,
-            empty_beam_file=empty_beam_file,
-            direct_beam_file=direct_beam_file,
-            mask_files=[mask_file],
-            wavelength_min=reduction_params["wavelength_min"],
-            wavelength_max=reduction_params["wavelength_max"],
-            wavelength_n=reduction_params["wavelength_n"],
-            q_start=reduction_params["q_start"],
-            q_stop=reduction_params["q_stop"],
-            q_n=reduction_params["q_n"]
-        )
-    except Exception as e:
-        print(f"Reduction failed for sample {sample}: {e}")
-        return None
-
-    out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
-    try:
-        save_xye_pandas(res["IofQ"], out_xye)
-        print(f"Saved reduced data to {out_xye}")
-    except Exception as e:
-        print(f"Failed to save reduced data for {sample}: {e}")
-
-    try:
-        save_reduction_plots(
-            res,
-            sample,
-            sample_run_file,
-            reduction_params["wavelength_min"],
-            reduction_params["wavelength_max"],
-            reduction_params["wavelength_n"],
-            reduction_params["q_start"],
-            reduction_params["q_stop"],
-            reduction_params["q_n"],
-            output_dir,
-            show=True
-        )
-        print(f"Saved reduction plot for sample {sample}.")
-    except Exception as e:
-        print(f"Failed to save reduction plot for {sample}: {e}")
-    print(f"Reduced sample {sample} and saved outputs.")
-    return res
-
-# ----------------------------
-# GUI Widgets 
-# ----------------------------
-class SansBatchReductionWidget:
-    def __init__(self):
-        self.csv_chooser = FileChooser(select_dir=False)
-        self.csv_chooser.title = "Select CSV File"
-        self.csv_chooser.filter_pattern = "*.csv"
-        self.input_dir_chooser = FileChooser(select_dir=True)
-        self.input_dir_chooser.title = "Select Input Folder"
-        self.output_dir_chooser = FileChooser(select_dir=True)
-        self.output_dir_chooser.title = "Select Output Folder"
-        self.ebeam_sans_widget = widgets.Text(value="", placeholder="Enter Ebeam SANS run number", description="Ebeam SANS:")
-        self.ebeam_trans_widget = widgets.Text(value="", placeholder="Enter Ebeam TRANS run number", description="Ebeam TRANS:")
-        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
-        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
-        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
-        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
-        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
-        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
-        self.load_csv_button = widgets.Button(description="Load CSV")
-        self.load_csv_button.on_click(self.load_csv)
-        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
-        self.reduce_button = widgets.Button(description="Reduce")
-        self.reduce_button.on_click(self.run_reduction)
-        self.clear_log_button = widgets.Button(description="Clear Log")
-        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
-        self.clear_plots_button = widgets.Button(description="Clear Plots")
-        self.clear_plots_button.on_click(lambda _: plt.close('all'))
-        # Remove widget outputs from the layout since we use plain print and plt.show()
-        self.main = widgets.VBox([
-            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
-            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
-            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
-            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
-            self.load_csv_button,
-            self.table,
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button])
-        ])
-    
-    def load_csv(self, _):
-        csv_path = self.csv_chooser.selected
-        if not csv_path or not os.path.exists(csv_path):
-            print("CSV file not selected or does not exist.")
-            return
-        df = pd.read_csv(csv_path)
-        self.table.data = df
-        print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
-    
-    def run_reduction(self, _):
-        input_dir = self.input_dir_chooser.selected
-        output_dir = self.output_dir_chooser.selected
-        if not input_dir or not os.path.isdir(input_dir):
-            print("Input folder is not valid.")
-            return
-        if not output_dir or not os.path.isdir(output_dir):
-            print("Output folder is not valid.")
-            return
-        try:
-            direct_beam_file = find_direct_beam(input_dir)
-            print("Using direct-beam file:", direct_beam_file)
-        except Exception as e:
-            print("Direct-beam file not found:", e)
-            return
-        try:
-            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
-            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
-            print("Using empty-beam files:")
-            print("  Background (Ebeam SANS):", background_run_file)
-            print("  Empty beam (Ebeam TRANS):", empty_beam_file)
-        except Exception as e:
-            print("Error finding empty beam files:", e)
-            return
-        
-        reduction_params = {
-            "wavelength_min": self.wavelength_min_widget.value,
-            "wavelength_max": self.wavelength_max_widget.value,
-            "wavelength_n": self.wavelength_n_widget.value,
-            "q_start": self.q_start_widget.value,
-            "q_stop": self.q_stop_widget.value,
-            "q_n": self.q_n_widget.value
-        }
-        
-        df = self.table.data
-        for idx, row in df.iterrows():
-            perform_reduction_for_sample(
-                sample_info=row,
-                input_dir=input_dir,
-                output_dir=output_dir,
-                reduction_params=reduction_params,
-                background_run_file=background_run_file,
-                empty_beam_file=empty_beam_file,
-                direct_beam_file=direct_beam_file
-            )
-    
-    @property
-    def widget(self):
-        return self.main
-
-class SemiAutoReductionWidget:
-    def __init__(self):
-        self.input_dir_chooser = FileChooser(select_dir=True)
-        self.input_dir_chooser.title = "Select Input Folder"
-        self.output_dir_chooser = FileChooser(select_dir=True)
-        self.output_dir_chooser.title = "Select Output Folder"
-        self.scan_button = widgets.Button(description="Scan Directory")
-        self.scan_button.on_click(self.scan_directory)
-        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
-        self.add_row_button = widgets.Button(description="Add Row")
-        self.add_row_button.on_click(self.add_row)
-        self.delete_row_button = widgets.Button(description="Delete Last Row")
-        self.delete_row_button.on_click(self.delete_last_row)
-        self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
-        self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
-        self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
-        self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
-        self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
-        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
-        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
-        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
-        self.reduce_button = widgets.Button(description="Reduce")
-        self.reduce_button.on_click(self.run_reduction)
-        self.clear_log_button = widgets.Button(description="Clear Log")
-        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
-        self.clear_plots_button = widgets.Button(description="Clear Plots")
-        self.clear_plots_button.on_click(lambda _: plt.close('all'))
-        self.processed = set()
-        self.main = widgets.VBox([
-            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
-            self.scan_button,
-            self.table,
-            widgets.HBox([self.add_row_button, self.delete_row_button]),
-            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
-            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
-            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button])
-        ])
-    
-    def add_row(self, _):
-        df = self.table.data
-        new_row = {col: "" for col in df.columns} if not df.empty else {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
-        df = df.append(new_row, ignore_index=True)
-        self.table.data = df
-
-    def delete_last_row(self, _):
-        df = self.table.data
-        if not df.empty:
-            self.table.data = df.iloc[:-1]
-
-    def scan_directory(self, _):
-        print("Scanning directory...")
-        input_dir = self.input_dir_chooser.selected
-        if not input_dir or not os.path.isdir(input_dir):
-            print("Invalid input folder.")
-            return
-        nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
-        groups = {}
-        for f in nxs_files:
-            try:
-                details = parse_nx_details(f)
-            except Exception:
-                continue
-            if 'runlabel' not in details or 'runtype' not in details:
-                continue
-            runlabel = details['runlabel']
-            runtype = details['runtype'].lower()
-            run_number = extract_run_number(os.path.basename(f))
-            if runlabel not in groups:
-                groups[runlabel] = {}
-            groups[runlabel][runtype] = run_number
-        table_rows = []
-        for runlabel, d in groups.items():
-            if 'sans' in d and 'trans' in d:
-                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
-        df = pd.DataFrame(table_rows)
-        self.table.data = df
-        print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
-        ebeam_sans_files = []
-        ebeam_trans_files = []
-        for f in nxs_files:
-            try:
-                details = parse_nx_details(f)
-            except Exception:
-                continue
-            if 'runtype' in details:
-                if details['runtype'].lower() == 'ebeam_sans':
-                    ebeam_sans_files.append(f)
-                elif details['runtype'].lower() == 'ebeam_trans':
-                    ebeam_trans_files.append(f)
-        if ebeam_sans_files:
-            ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
-            self.empty_beam_sans_text.value = ebeam_sans_files[0]
-        else:
-            self.empty_beam_sans_text.value = ""
-        if ebeam_trans_files:
-            ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
-            self.empty_beam_trans_text.value = ebeam_trans_files[0]
-        else:
-            self.empty_beam_trans_text.value = ""
-    
-    def run_reduction(self, _):
-        input_dir = self.input_dir_chooser.selected
-        output_dir = self.output_dir_chooser.selected
-        if not input_dir or not os.path.isdir(input_dir):
-            print("Input folder is not valid.")
-            return
-        if not output_dir or not os.path.isdir(output_dir):
-            print("Output folder is not valid.")
-            return
-        try:
-            direct_beam_file = find_direct_beam(input_dir)
-            print("Using direct-beam file:", direct_beam_file)
-        except Exception as e:
-            print("Direct-beam file not found:", e)
-            return
-        background_run_file = self.empty_beam_sans_text.value
-        empty_beam_file = self.empty_beam_trans_text.value
-        if not background_run_file or not empty_beam_file:
-            print("Empty beam files not found.")
-            return
-        
-        reduction_params = {
-            "wavelength_min": self.lambda_min_widget.value,
-            "wavelength_max": self.lambda_max_widget.value,
-            "wavelength_n": self.lambda_n_widget.value,
-            "q_start": self.q_min_widget.value,
-            "q_stop": self.q_max_widget.value,
-            "q_n": self.q_n_widget.value
-        }
-        
-        df = self.table.data.copy()
-        for idx, row in df.iterrows():
-            perform_reduction_for_sample(
-                sample_info=row,
-                input_dir=input_dir,
-                output_dir=output_dir,
-                reduction_params=reduction_params,
-                background_run_file=background_run_file,
-                empty_beam_file=empty_beam_file,
-                direct_beam_file=direct_beam_file
-            )
-    
-    @property
-    def widget(self):
-        return self.main
-
-class AutoReductionWidget:
-    def __init__(self):
-        self.input_dir_chooser = FileChooser(select_dir=True)
-        self.input_dir_chooser.title = "Select Input Folder"
-        self.output_dir_chooser = FileChooser(select_dir=True)
-        self.output_dir_chooser.title = "Select Output Folder"
-        self.start_stop_button = widgets.Button(description="Start")
-        self.start_stop_button.on_click(self.toggle_running)
-        self.status_label = widgets.Label(value="Stopped")
-        self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
-        self.running = False
-        self.thread = None
-        self.processed = set()
-        self.empty_beam_sans = None
-        self.empty_beam_trans = None
-        self.main = widgets.VBox([
-            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
-            widgets.HBox([self.start_stop_button, self.status_label]),
-            self.table
-        ])
-    
-    def toggle_running(self, _):
-        if not self.running:
-            self.running = True
-            self.start_stop_button.description = "Stop"
-            self.status_label.value = "Running"
-            self.thread = threading.Thread(target=self.background_loop, daemon=True)
-            self.thread.start()
-        else:
-            self.running = False
-            self.start_stop_button.description = "Start"
-            self.status_label.value = "Stopped"
-    
-    def background_loop(self):
-        while self.running:
-            input_dir = self.input_dir_chooser.selected
-            output_dir = self.output_dir_chooser.selected
-            if not input_dir or not os.path.isdir(input_dir):
-                print("Invalid input folder. Waiting for valid selection...")
-                time.sleep(60)
-                continue
-            if not output_dir or not os.path.isdir(output_dir):
-                print("Invalid output folder. Waiting for valid selection...")
-                time.sleep(60)
-                continue
-            nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
-            groups = {}
-            for f in nxs_files:
-                try:
-                    details = parse_nx_details(f)
-                except Exception:
-                    continue
-                if 'runlabel' not in details or 'runtype' not in details:
-                    continue
-                runlabel = details['runlabel']
-                runtype = details['runtype'].lower()
-                run_number = extract_run_number(os.path.basename(f))
-                if runlabel not in groups:
-                    groups[runlabel] = {}
-                groups[runlabel][runtype] = run_number
-            table_rows = []
-            for runlabel, d in groups.items():
-                if 'sans' in d and 'trans' in d:
-                    table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
-            df = pd.DataFrame(table_rows)
-            self.table.data = df
-            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
-            ebeam_sans_files = []
-            ebeam_trans_files = []
-            for f in nxs_files:
-                try:
-                    details = parse_nx_details(f)
-                except Exception:
-                    continue
-                if 'runtype' in details:
-                    if details['runtype'].lower() == 'ebeam_sans':
-                        ebeam_sans_files.append(f)
-                    elif details['runtype'].lower() == 'ebeam_trans':
-                        ebeam_trans_files.append(f)
-            if ebeam_sans_files:
-                ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
-                self.empty_beam_sans = ebeam_sans_files[0]
-            else:
-                self.empty_beam_sans = None
-            if ebeam_trans_files:
-                ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
-                self.empty_beam_trans = ebeam_trans_files[0]
-            else:
-                self.empty_beam_trans = None
-            try:
-                direct_beam_file = find_direct_beam(input_dir)
-            except Exception as e:
-                print("Direct-beam file not found:", e)
-                time.sleep(60)
-                continue
-            for index, row in df.iterrows():
-                key = (row["SAMPLE"], row["SANS"], row["TRANS"])
-                if key in self.processed:
-                    continue
-                try:
-                    sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs")
-                    transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs")
-                except Exception as e:
-                    print(f"Skipping sample {row['SAMPLE']}: {e}")
-                    continue
-                try:
-                    mask_file = find_mask_file(input_dir)
-                    print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
-                except Exception as e:
-                    print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
-                    continue
-                if not self.empty_beam_sans or not self.empty_beam_trans:
-                    print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
-                    continue
-                print(f"Reducing sample {row['SAMPLE']}...")
-                reduction_params = {
-                    "wavelength_min": 1.0,
-                    "wavelength_max": 13.0,
-                    "wavelength_n": 201,
-                    "q_start": 0.01,
-                    "q_stop": 0.3,
-                    "q_n": 101
-                }
-                perform_reduction_for_sample(
-                    sample_info=row,
-                    input_dir=input_dir,
-                    output_dir=output_dir,
-                    reduction_params=reduction_params,
-                    background_run_file=self.empty_beam_sans,
-                    empty_beam_file=self.empty_beam_trans,
-                    direct_beam_file=direct_beam_file
-                )
-                self.processed.add(key)
-            time.sleep(60)
-    
-    @property
-    def widget(self):
-        return self.main
-
-# ----------------------------
-# Direct Beam stuff
-# ----------------------------
-def compute_direct_beam_local(
-    mask: str,
-    sample_sans: str,
-    background_sans: str,
-    sample_trans: str,
-    background_trans: str,
-    empty_beam: str,
-    local_Iq_theory: str,
-    wavelength_min: float = 1.0,
-    wavelength_max: float = 13.0,
-    n_wavelength_bins: int = 50,
-    n_wavelength_bands: int = 50
-) -> dict:
-    workflow = loki.LokiAtLarmorWorkflow()
-    workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
-    workflow[NeXusDetectorName] = 'larmor_detector'
-    wl_min = sc.scalar(wavelength_min, unit='angstrom')
-    wl_max = sc.scalar(wavelength_max, unit='angstrom')
-    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
-    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
-    workflow[CorrectForGravity] = True
-    workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
-    workflow[ReturnEvents] = False
-    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
-    workflow[Filename[SampleRun]] = sample_sans
-    workflow[Filename[BackgroundRun]] = background_sans
-    workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
-    workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
-    workflow[Filename[EmptyBeamRun]] = empty_beam
-    center = sans.beam_center_from_center_of_mass(workflow)
-    print("Computed beam center:", center)
-    workflow[BeamCenter] = center
-    Iq_theory = sc.io.load_hdf5(local_Iq_theory)
-    f = interp1d(Iq_theory, 'Q')
-    I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
-    print("Computed I0:", I0)
-    results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
-    iofq_full = results[-1]['iofq_full']
-    iofq_bands = results[-1]['iofq_bands']
-    direct_beam_function = results[-1]['direct_beam']
-    pp.plot(
-        {'reference': Iq_theory, 'data': iofq_full},
-        color={'reference': 'darkgrey', 'data': 'C0'},
-        norm='log',
-    )
-    print("Plotted full-range result vs. theoretical reference.")
-    return {
-        'direct_beam_function': direct_beam_function,
-        'iofq_full': iofq_full,
-        'Iq_theory': Iq_theory,
-    }
-
-class DirectBeamWidget:
-    def __init__(self):
-        self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:")
-        self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:")
-        self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:")
-        self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:")
-        self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:")
-        self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:")
-        self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:")
-        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
-        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
-        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
-        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
-        self.compute_button = widgets.Button(description="Compute Direct Beam")
-        self.compute_button.on_click(self.compute_direct_beam)
-        self.clear_log_button = widgets.Button(description="Clear Log")
-        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
-        self.clear_plots_button = widgets.Button(description="Clear Plots")
-        self.clear_plots_button.on_click(lambda _: plt.close('all'))
-        self.main = widgets.VBox([
-            self.mask_text,
-            self.sample_sans_text,
-            self.background_sans_text,
-            self.sample_trans_text,
-            self.background_trans_text,
-            self.empty_beam_text,
-            self.local_Iq_theory_text,
-            widgets.HBox([
-                self.db_wavelength_min_widget,
-                self.db_wavelength_max_widget,
-                self.db_n_wavelength_bins_widget,
-                self.db_n_wavelength_bands_widget
-            ]),
-            self.compute_button,
-            widgets.HBox([self.clear_log_button, self.clear_plots_button])
-        ])
-    
-    def compute_direct_beam(self, _):
-        # No longer clearing widget outputs; we simply print new info.
-        mask = self.mask_text.value
-        sample_sans = self.sample_sans_text.value
-        background_sans = self.background_sans_text.value
-        sample_trans = self.sample_trans_text.value
-        background_trans = self.background_trans_text.value
-        empty_beam = self.empty_beam_text.value
-        local_Iq_theory = self.local_Iq_theory_text.value
-        wl_min = self.db_wavelength_min_widget.value
-        wl_max = self.db_wavelength_max_widget.value
-        n_bins = self.db_n_wavelength_bins_widget.value
-        n_bands = self.db_n_wavelength_bands_widget.value
-        print("Computing direct beam with:")
-        print("  Mask:", mask)
-        print("  Sample SANS:", sample_sans)
-        print("  Background SANS:", background_sans)
-        print("  Sample TRANS:", sample_trans)
-        print("  Background TRANS:", background_trans)
-        print("  Empty Beam:", empty_beam)
-        print("  I(q) Theory:", local_Iq_theory)
-        print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
-        try:
-            results = compute_direct_beam_local(
-                mask,
-                sample_sans,
-                background_sans,
-                sample_trans,
-                background_trans,
-                empty_beam,
-                local_Iq_theory,
-                wavelength_min=wl_min,
-                wavelength_max=wl_max,
-                n_wavelength_bins=n_bins,
-                n_wavelength_bands=n_bands
-            )
-            print("Direct beam computation complete.")
-        except Exception as e:
-            print("Error computing direct beam:", e)
-    
-    @property
-    def widget(self):
-        return self.main
-
-# ----------------------------
-# Build it
-# ----------------------------
-reduction_widget = SansBatchReductionWidget().widget
-direct_beam_widget = DirectBeamWidget().widget
-semi_auto_reduction_widget = SemiAutoReductionWidget().widget
-auto_reduction_widget = AutoReductionWidget().widget
-
-tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
-tabs.set_title(0, "Direct Beam")
-tabs.set_title(1, "Reduction (Manual)")
-tabs.set_title(2, "Reduction (Smart)")
-tabs.set_title(3, "Reduction (Auto)")
-
-# To display the tabs in a Voila deployment, simply display the tabs widget.
-#display(tabs)
diff --git a/src/ess/loki/tabwidgetii.py b/src/ess/loki/tabwidget-visa-scicat.py
similarity index 65%
rename from src/ess/loki/tabwidgetii.py
rename to src/ess/loki/tabwidget-visa-scicat.py
index e60a778e..05907c3d 100644
--- a/src/ess/loki/tabwidgetii.py
+++ b/src/ess/loki/tabwidget-visa-scicat.py
@@ -1,5 +1,3 @@
-### attempt at altering log output commands to basic print/plot – but still doesnt work with voila??? 
-
 import os
 import glob
 import re
@@ -19,6 +17,11 @@
 import plopp as pp  
 import threading
 import time
+from ipywidgets import Layout
+import csv
+from scitacean import Client, Dataset, Attachment, Thumbnail
+from scitacean.transfer.copy import CopyFileTransfer
+from scitacean.transfer.select import SelectFileTransfer
 
 # ----------------------------
 # Utility Functions
@@ -31,7 +34,7 @@ def find_file(work_dir, run_number, extension=".nxs"):
     else:
         raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
 
-def find_direct_beam(work_dir):  # Find the direct beam automatically 
+def find_direct_beam(work_dir): #Find the direct beam automagically 
     pattern = os.path.join(work_dir, "*direct-beam*.h5")
     files = glob.glob(pattern)
     if files:
@@ -39,7 +42,7 @@ def find_direct_beam(work_dir):  # Find the direct beam automatically
     else:
         raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
 
-def find_mask_file(work_dir):  # Find the mask automatically 
+def find_mask_file(work_dir): #Find the mask automagically 
     pattern = os.path.join(work_dir, "*mask*.xml")
     files = glob.glob(pattern)
     if files:
@@ -47,7 +50,7 @@ def find_mask_file(work_dir):  # Find the mask automatically
     else:
         raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
 
-def save_xye_pandas(data_array, filename):
+def save_xye_pandas(data_array, filename): ###Note here this needs to be 'fixed' / updated to use scipp io – ideally I want a nxcansas and xye saved for each file, but I struggled with the syntax and just did it in pandas as a first pass 
     q_vals = data_array.coords["Q"].values
     i_vals = data_array.values
     if len(q_vals) != len(i_vals):
@@ -67,7 +70,7 @@ def extract_run_number(filename):
         return m.group(1)
     return ""
 
-def parse_nx_details(filepath):
+def parse_nx_details(filepath): #For finding/grouping files by common title assigned by NICOS, e.g. 'runlabel' and 'runtype'
     details = {}
     with h5py.File(filepath, 'r') as f:
         if 'nicos_details' in f['entry']:
@@ -82,7 +85,7 @@ def parse_nx_details(filepath):
 
 # ----------------------------
 # Colour Mapping From Filename 
-# ----------------------------
+
 def string_to_colour(input_str):
     if not input_str:
         return "#000000"  # Empty input = black
@@ -92,13 +95,15 @@ def string_to_colour(input_str):
             total += ord(ch.lower()) - ord('a') + 1  # a=1, b=2, ..., z=26
         elif ch.isdigit():
             total += 1 + int(ch) * (25/9)  # Maps '0' to 1 and '9' to 26
+        # Special characters equal 0
     avg = total / len(input_str)
     norm = max(0, min(1, avg / 26))  # Average and normalise to [0,1]
-    rgba = plt.get_cmap('flag')(norm)
+    rgba = plt.get_cmap('flag')(norm)  #prism 
     return '#{:02x}{:02x}{:02x}'.format(int(rgba[0]*255),
                                         int(rgba[1]*255),
                                         int(rgba[2]*255))
 
+
 # ----------------------------
 # Reduction and Plotting Functions
 # ----------------------------
@@ -145,17 +150,18 @@ def reduce_loki_batch_preliminary(
     return {"transmission": tf, "IofQ": da}
 
 def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelength_max, wavelength_n, q_min, q_max, q_n, output_dir, show=True):
-    fig, axs = plt.subplots(1, 2, figsize=(8, 4))
+    fig, axs = plt.subplots(1, 2, figsize=(6, 3))
     axs[0].set_box_aspect(1)
     axs[1].set_box_aspect(1)
     title_str = f"{sample} - {os.path.basename(sample_run_file)}"
-    fig.suptitle(title_str, fontsize=14)
+    fig.suptitle(title_str, fontsize=12)
     q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
     x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
     if res["IofQ"].variances is not None:
         yerr = np.sqrt(res["IofQ"].variances)
-        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none',
-                        color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+        #axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+
     else:
         axs[0].scatter(x_q, res["IofQ"].values)
     axs[0].set_xlabel("Q (Å$^{-1}$)")
@@ -166,8 +172,10 @@ def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelengt
     x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
     if res["transmission"].variances is not None:
         yerr_tr = np.sqrt(res["transmission"].variances)
-        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none',
-                        color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+        #axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+
+        
     else:
         axs[1].scatter(x_wl, res["transmission"].values)
     axs[1].set_xlabel("Wavelength (Å)")
@@ -176,7 +184,7 @@ def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelengt
     out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png"))
     fig.savefig(out_png, dpi=300)
     if show:
-        plt.show()
+        display(fig)
     plt.close(fig)
 
 # ----------------------------
@@ -190,15 +198,25 @@ def perform_reduction_for_sample(
     background_run_file: str,
     empty_beam_file: str,
     direct_beam_file: str,
+    log_func: callable
 ):
+    """
+    Processes a single sample reduction:
+      - Finds the necessary run files
+      - Optionally determines a mask (or finds one automatically)
+      - Calls the reduction and plotting routines
+      - Logs all steps via log_func(message) ### edited to just print statements - does logfunc work correctly with voila???
+    """
     sample = sample_info.get("SAMPLE", "Unknown")
     try:
         sample_run_file = find_file(input_dir, str(sample_info["SANS"]), extension=".nxs")
         transmission_run_file = find_file(input_dir, str(sample_info["TRANS"]), extension=".nxs")
     except Exception as e:
-        print(f"Skipping sample {sample}: {e}")
-        return None
+        log_func(f"Skipping sample {sample}: {e}")
+        #print(f"Skipping sample {sample}: {e}")
 
+        return None
+    # Determine mask file.
     mask_file = None
     mask_candidate = str(sample_info.get("mask", "")).strip()
     if mask_candidate:
@@ -208,12 +226,18 @@ def perform_reduction_for_sample(
     if mask_file is None:
         try:
             mask_file = find_mask_file(input_dir)
-            print(f"Identified mask file: {mask_file} for sample {sample}")
+            log_func(f"Using mask: {mask_file} for sample {sample}")
+            #print(f"Identified mask file: {mask_file} for sample {sample}")
+
         except Exception as e:
-            print(f"Mask file not found for sample {sample}: {e}")
+            log_func(f"Mask file not found for sample {sample}: {e}")
+            #print(f"Mask file not found for sample {sample}: {e}")
+
             return None
 
-    print(f"Reducing sample {sample}...")
+    log_func(f"Reducing sample {sample}...")
+    #print(f"Reducing sample {sample}...")
+
     try:
         res = reduce_loki_batch_preliminary(
             sample_run_file=sample_run_file,
@@ -230,16 +254,18 @@ def perform_reduction_for_sample(
             q_n=reduction_params["q_n"]
         )
     except Exception as e:
-        print(f"Reduction failed for sample {sample}: {e}")
+        log_func(f"Reduction failed for sample {sample}: {e}")
+        #print(f"Reduction failed for sample {sample}: {e}")
         return None
-
     out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
     try:
         save_xye_pandas(res["IofQ"], out_xye)
-        print(f"Saved reduced data to {out_xye}")
-    except Exception as e:
-        print(f"Failed to save reduced data for {sample}: {e}")
+        log_func(f"Saved reduced data to {out_xye}")
+        #print(f"Saved reduced data to {out_xye}")
 
+    except Exception as e:
+        log_func(f"Failed to save reduced data for {sample}: {e}")
+        #print(f"Failed to save reduced data for {sample}: {e}")
     try:
         save_reduction_plots(
             res,
@@ -254,86 +280,110 @@ def perform_reduction_for_sample(
             output_dir,
             show=True
         )
-        print(f"Saved reduction plot for sample {sample}.")
+        log_func(f"Saved reduction plot for sample {sample}.")
     except Exception as e:
-        print(f"Failed to save reduction plot for {sample}: {e}")
-    print(f"Reduced sample {sample} and saved outputs.")
+        log_func(f"Failed to save reduction plot for {sample}: {e}")
+    #log_func(f"Reduced sample {sample} and saved outputs.")
     return res
+#        print(f"Saved reduction plot for sample {sample}.")
+#    except Exception as e:
+#        print(f"Failed to save reduction plot for {sample}: {e}")
+#    print(f"Reduced sample {sample} and saved outputs.")
+#    return res
+
+
+###########################################################################################
+
+##############################################################################################
 
 # ----------------------------
 # GUI Widgets 
 # ----------------------------
 class SansBatchReductionWidget:
     def __init__(self):
+        # File Choosers for CSV, input dir, output dir
         self.csv_chooser = FileChooser(select_dir=False)
         self.csv_chooser.title = "Select CSV File"
         self.csv_chooser.filter_pattern = "*.csv"
+        
         self.input_dir_chooser = FileChooser(select_dir=True)
         self.input_dir_chooser.title = "Select Input Folder"
+        
         self.output_dir_chooser = FileChooser(select_dir=True)
         self.output_dir_chooser.title = "Select Output Folder"
-        self.ebeam_sans_widget = widgets.Text(value="", placeholder="Enter Ebeam SANS run number", description="Ebeam SANS:")
-        self.ebeam_trans_widget = widgets.Text(value="", placeholder="Enter Ebeam TRANS run number", description="Ebeam TRANS:")
+        
+        # Remove references to Ebeam SANS/TRANS widgets 
+        # (since these are now specified per row in the CSV).
+        
+        # Reduction parameter widgets
         self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
         self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
         self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
         self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
         self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
         self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        
+        # Button to load CSV
         self.load_csv_button = widgets.Button(description="Load CSV")
         self.load_csv_button.on_click(self.load_csv)
+        
+        # Table to display/edit CSV data
         self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        
+        # Button to run reduction
         self.reduce_button = widgets.Button(description="Reduce")
         self.reduce_button.on_click(self.run_reduction)
-        self.clear_log_button = widgets.Button(description="Clear Log")
-        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
-        self.clear_plots_button = widgets.Button(description="Clear Plots")
-        self.clear_plots_button.on_click(lambda _: plt.close('all'))
-        # Remove widget outputs from the layout since we use plain print and plt.show()
+        
+        # (Optional) log/plot outputs
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        
+        # Main layout
         self.main = widgets.VBox([
             widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
-            widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
             widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
             widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
             self.load_csv_button,
             self.table,
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button])
+            widgets.HBox([self.reduce_button]),
+            self.log_output,
+            self.plot_output
         ])
-    
+
     def load_csv(self, _):
+        """Loads the CSV file into the DataGrid."""
         csv_path = self.csv_chooser.selected
         if not csv_path or not os.path.exists(csv_path):
-            print("CSV file not selected or does not exist.")
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
             return
         df = pd.read_csv(csv_path)
         self.table.data = df
-        print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
-    
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+
     def run_reduction(self, _):
+        """Loops over each row of the CSV table, finds input files, and performs the reduction."""
+        # Clear old log/plot outputs
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        
         input_dir = self.input_dir_chooser.selected
         output_dir = self.output_dir_chooser.selected
+        
         if not input_dir or not os.path.isdir(input_dir):
-            print("Input folder is not valid.")
+            with self.log_output:
+                print("Input folder is not valid.")
             return
         if not output_dir or not os.path.isdir(output_dir):
-            print("Output folder is not valid.")
-            return
-        try:
-            direct_beam_file = find_direct_beam(input_dir)
-            print("Using direct-beam file:", direct_beam_file)
-        except Exception as e:
-            print("Direct-beam file not found:", e)
-            return
-        try:
-            background_run_file = find_file(input_dir, self.ebeam_sans_widget.value, extension=".nxs")
-            empty_beam_file = find_file(input_dir, self.ebeam_trans_widget.value, extension=".nxs")
-            print("Using empty-beam files:")
-            print("  Background (Ebeam SANS):", background_run_file)
-            print("  Empty beam (Ebeam TRANS):", empty_beam_file)
-        except Exception as e:
-            print("Error finding empty beam files:", e)
+            with self.log_output:
+                print("Output folder is not valid.")
             return
         
+        # Read current table data
+        df = self.table.data
+        
+        # Reduction parameters
         reduction_params = {
             "wavelength_min": self.wavelength_min_widget.value,
             "wavelength_max": self.wavelength_max_widget.value,
@@ -342,21 +392,50 @@ def run_reduction(self, _):
             "q_stop": self.q_stop_widget.value,
             "q_n": self.q_n_widget.value
         }
-        
-        df = self.table.data
+
+        # Loop over each row of the CSV table
         for idx, row in df.iterrows():
+            try:
+                # Use the CSV columns to find file paths
+                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
+                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+                background_run_file = find_file(input_dir, str(row["Ebeam_SANS"]), extension=".nxs")
+                empty_beam_file = find_file(input_dir, str(row["Ebeam_TRANS"]), extension=".nxs")
+                
+                # For mask and direct beam, we assume the CSV filename is relative to input_dir
+                mask_file = os.path.join(input_dir, str(row["mask"]))
+                direct_beam_file = os.path.join(input_dir, str(row["direct_beam"]))
+                
+            except Exception as e:
+                # If something fails, log and skip this row
+                with self.log_output:
+                    print(f"Error finding input files for row {idx} ({row['SAMPLE']}): {e}")
+                continue
+            
+            # Create a mini dict for the sample info
+            sample_info = {
+                "SAMPLE": row["SAMPLE"],
+                "SANS": row["SANS"],
+                "TRANS": row["TRANS"],
+                # We store the mask as a column, so pass it along
+                "mask": mask_file
+            }
+            
+            # Call the actual reduction
             perform_reduction_for_sample(
-                sample_info=row,
+                sample_info=sample_info,
                 input_dir=input_dir,
                 output_dir=output_dir,
                 reduction_params=reduction_params,
                 background_run_file=background_run_file,
                 empty_beam_file=empty_beam_file,
-                direct_beam_file=direct_beam_file
+                direct_beam_file=direct_beam_file,
+                log_func=lambda msg: print(msg)
             )
-    
+
     @property
     def widget(self):
+        """Return the main widget layout."""
         return self.main
 
 class SemiAutoReductionWidget:
@@ -382,10 +461,12 @@ def __init__(self):
         self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
         self.reduce_button = widgets.Button(description="Reduce")
         self.reduce_button.on_click(self.run_reduction)
-        self.clear_log_button = widgets.Button(description="Clear Log")
-        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
-        self.clear_plots_button = widgets.Button(description="Clear Plots")
-        self.clear_plots_button.on_click(lambda _: plt.close('all'))
+        #self.clear_log_button = widgets.Button(description="Clear Log")
+        #self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        #self.clear_plots_button = widgets.Button(description="Clear Plots")
+        #self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
         self.processed = set()
         self.main = widgets.VBox([
             widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
@@ -395,7 +476,9 @@ def __init__(self):
             widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
             widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
             widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
-            widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button])
+            widgets.HBox([self.reduce_button]),# self.clear_log_button, self.clear_plots_button]),
+            #self.log_output,
+            #self.plot_output
         ])
     
     def add_row(self, _):
@@ -410,10 +493,11 @@ def delete_last_row(self, _):
             self.table.data = df.iloc[:-1]
 
     def scan_directory(self, _):
-        print("Scanning directory...")
+        self.log_output.clear_output()
         input_dir = self.input_dir_chooser.selected
         if not input_dir or not os.path.isdir(input_dir):
-            print("Invalid input folder.")
+            with self.log_output:
+                print("Invalid input folder.")
             return
         nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
         groups = {}
@@ -436,7 +520,8 @@ def scan_directory(self, _):
                 table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
         df = pd.DataFrame(table_rows)
         self.table.data = df
-        print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+        with self.log_output:
+            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
         ebeam_sans_files = []
         ebeam_trans_files = []
         for f in nxs_files:
@@ -461,24 +546,31 @@ def scan_directory(self, _):
             self.empty_beam_trans_text.value = ""
     
     def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
         input_dir = self.input_dir_chooser.selected
         output_dir = self.output_dir_chooser.selected
         if not input_dir or not os.path.isdir(input_dir):
-            print("Input folder is not valid.")
+            with self.log_output:
+                print("Input folder is not valid.")
             return
         if not output_dir or not os.path.isdir(output_dir):
-            print("Output folder is not valid.")
+            with self.log_output:
+                print("Output folder is not valid.")
             return
         try:
             direct_beam_file = find_direct_beam(input_dir)
-            print("Using direct-beam file:", direct_beam_file)
+            with self.log_output:
+                print("Using direct beam:", direct_beam_file)
         except Exception as e:
-            print("Direct-beam file not found:", e)
+            with self.log_output:
+                print("Direct beam file not found:", e)
             return
         background_run_file = self.empty_beam_sans_text.value
         empty_beam_file = self.empty_beam_trans_text.value
         if not background_run_file or not empty_beam_file:
-            print("Empty beam files not found.")
+            with self.log_output:
+                print("Empty beam files not found.")
             return
         
         reduction_params = {
@@ -490,7 +582,8 @@ def run_reduction(self, _):
             "q_n": self.q_n_widget.value
         }
         
-        df = self.table.data.copy()
+        #df = self.table.data.copy()
+        df = self.table.data.drop_duplicates(subset=['SAMPLE', 'SANS', 'TRANS'])
         for idx, row in df.iterrows():
             perform_reduction_for_sample(
                 sample_info=row,
@@ -499,7 +592,8 @@ def run_reduction(self, _):
                 reduction_params=reduction_params,
                 background_run_file=background_run_file,
                 empty_beam_file=empty_beam_file,
-                direct_beam_file=direct_beam_file
+                direct_beam_file=direct_beam_file,
+                log_func=lambda msg: print(msg)
             )
     
     @property
@@ -516,6 +610,8 @@ def __init__(self):
         self.start_stop_button.on_click(self.toggle_running)
         self.status_label = widgets.Label(value="Stopped")
         self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
         self.running = False
         self.thread = None
         self.processed = set()
@@ -524,8 +620,14 @@ def __init__(self):
         self.main = widgets.VBox([
             widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
             widgets.HBox([self.start_stop_button, self.status_label]),
-            self.table
+            self.table,
+            self.log_output,
+            self.plot_output
         ])
+        # SciCat settings – adjust these as needed.
+        self.token = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJfaWQiOiI2NmRhYjhmYzFiNThkNDFlYTM4OTc5MzIiLCJ1c2VybmFtZSI6Imh0dHBzOi8vbG9naW4uZXNzLmV1X29saXZlcmhhbW1vbmQiLCJlbWFpbCI6Im9saXZlci5oYW1tb25kQGVzcy5ldSIsImF1dGhTdHJhdGVneSI6Im9pZGMiLCJfX3YiOjAsImlkIjoiNjZkYWI4ZmMxYjU4ZDQxZWEzODk3OTMyIiwidXNlcklkIjoiNjZkYWI4ZmMxYjU4ZDQxZWEzODk3OTMyIiwiaWF0IjoxNzQyOTk2Mzc4LCJleHAiOjE3NDI5OTk5Nzh9.YytBMfX0p971InDFs0cSkfoVP92RvpgE_Vu9K_OLbiY'
+        self.scicat_url = 'https://staging.scicat.ess.eu/api/v3'
+        self.scicat_source_folder = '/scratch/oliverhammond/LARMOR/nxs/out/scicat'
     
     def toggle_running(self, _):
         if not self.running:
@@ -544,11 +646,13 @@ def background_loop(self):
             input_dir = self.input_dir_chooser.selected
             output_dir = self.output_dir_chooser.selected
             if not input_dir or not os.path.isdir(input_dir):
-                print("Invalid input folder. Waiting for valid selection...")
+                with self.log_output:
+                    print("Invalid input folder. Waiting for valid selection...")
                 time.sleep(60)
                 continue
             if not output_dir or not os.path.isdir(output_dir):
-                print("Invalid output folder. Waiting for valid selection...")
+                with self.log_output:
+                    print("Invalid output folder. Waiting for valid selection...")
                 time.sleep(60)
                 continue
             nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
@@ -572,7 +676,8 @@ def background_loop(self):
                     table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
             df = pd.DataFrame(table_rows)
             self.table.data = df
-            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+            with self.log_output:
+                print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
             ebeam_sans_files = []
             ebeam_trans_files = []
             for f in nxs_files:
@@ -598,7 +703,8 @@ def background_loop(self):
             try:
                 direct_beam_file = find_direct_beam(input_dir)
             except Exception as e:
-                print("Direct-beam file not found:", e)
+                with self.log_output:
+                    print("Direct-beam file not found:", e)
                 time.sleep(60)
                 continue
             for index, row in df.iterrows():
@@ -609,18 +715,23 @@ def background_loop(self):
                     sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs")
                     transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs")
                 except Exception as e:
-                    print(f"Skipping sample {row['SAMPLE']}: {e}")
+                    with self.log_output:
+                        print(f"Skipping sample {row['SAMPLE']}: {e}")
                     continue
                 try:
                     mask_file = find_mask_file(input_dir)
-                    print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
+                    with self.log_output:
+                        print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
                 except Exception as e:
-                    print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
+                    with self.log_output:
+                        print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
                     continue
                 if not self.empty_beam_sans or not self.empty_beam_trans:
-                    print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
+                    with self.log_output:
+                        print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
                     continue
-                print(f"Reducing sample {row['SAMPLE']}...")
+                with self.log_output:
+                    print(f"Reducing sample {row['SAMPLE']}...")
                 reduction_params = {
                     "wavelength_min": 1.0,
                     "wavelength_max": 13.0,
@@ -636,11 +747,112 @@ def background_loop(self):
                     reduction_params=reduction_params,
                     background_run_file=self.empty_beam_sans,
                     empty_beam_file=self.empty_beam_trans,
-                    direct_beam_file=direct_beam_file
+                    direct_beam_file=direct_beam_file,
+                    log_func=lambda msg: print(msg)
                 )
                 self.processed.add(key)
+                # Call the uploader function using the run number/name from the reduction table.
+                self.upload_dataset(run=row["SANS"], sample_name=row["SAMPLE"], metadata_file='uos_metadata.csv')
             time.sleep(60)
     
+    def upload_dataset(self, run, sample_name, metadata_file='uos_metadata.csv'):
+        """
+        Uploads a reduced dataset to SciCat using files in the widget's output directory.
+        Metadata is combined from the reduction process (run, sample_name) and additional 
+        arguments provided in a CSV file as a proxy for metadata provided by the user office.
+
+        The CSV file should have a header with these columns:
+          contact_email, owner_email, investigator, owner, owner_group, description
+
+        Parameters:
+          run (str): The run number to search for and use in the dataset (same as tabular input).
+          sample_name (str): The sample name to include in the dataset metadata (from nxs file).
+          metadata_file (str): Path to the CSV file containing additional metadata.
+        """
+        # Read metadata from the CSV file.
+        try:
+            with open(metadata_file, newline='') as csvfile:
+                reader = csv.DictReader(csvfile)
+                row = next(reader)
+        except Exception as e:
+            with self.log_output:
+                print(f"Error reading metadata CSV file '{metadata_file}': {e}")
+            return
+
+        contact_email = row.get('contact_email', 'default@example.com')
+        owner_email = row.get('owner_email', contact_email)
+        investigator = row.get('investigator', 'Unknown')
+        owner = row.get('owner', 'Unknown')
+        owner_group = row.get('owner_group', 'ess')
+        description = row.get('description', '')
+
+        # Use the output directory from the widget to search for the reduced files.
+        file_folder = self.output_dir_chooser.selected
+        if not file_folder or not os.path.isdir(file_folder):
+            with self.log_output:
+                print("Invalid output folder selected for uploading.")
+            return
+
+        # Initialize the SciCat client.
+        client = Client.from_token(
+            url=self.scicat_url,
+            token=self.token,
+            file_transfer=SelectFileTransfer([CopyFileTransfer()])
+        )
+
+        # Use glob to find the .xye and .png files based on the run number.
+        xye_pattern = os.path.join(file_folder, f"*{run}*.xye")
+        png_pattern = os.path.join(file_folder, f"*{run}*_reduced.png")
+        xye_files = glob.glob(xye_pattern)
+        png_files = glob.glob(png_pattern)
+
+        if not xye_files:
+            with self.log_output:
+                print(f"No .xye file found for run {run}.")
+            return
+        if not png_files:
+            with self.log_output:
+                print(f"No .png file found for run {run}.")
+            return
+
+        xye_file = xye_files[0]  # Use first matching file.
+        png_file = png_files[0]  # Use first matching file.
+
+        # Construct the dataset object with combined metadata.
+        dataset = Dataset(
+            type='derived',
+            contact_email=contact_email,
+            owner_email=owner_email,
+            input_datasets=[],
+            investigator=investigator,
+            owner=owner,
+            owner_group=owner_group,
+            access_groups=[owner_group],
+            source_folder=self.scicat_source_folder,
+            used_software=['esssans'],
+            name=f"{run}.xye",  # Derived from run number.
+            description=description,
+            run_number=run,
+            meta={'sample_name': {'value': sample_name, 'unit': ''}}
+        )
+
+        # Add the primary .xye file.
+        dataset.add_local_files(xye_file)
+
+        # Add the attachment (thumbnail).
+        dataset.attachments.append(
+            Attachment(
+                caption=f"Reduced I(Q) and transmission for {dataset.name}",
+                owner_group=owner_group,
+                thumbnail=Thumbnail.load_file(png_file)
+            )
+        )
+
+        # Upload the dataset.
+        client.upload_new_dataset_now(dataset)
+        with self.log_output:
+            print(f"Uploaded dataset for run {run} using files:\n  - {xye_file}\n  - {png_file}")
+
     @property
     def widget(self):
         return self.main
@@ -715,10 +927,8 @@ def __init__(self):
         self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
         self.compute_button = widgets.Button(description="Compute Direct Beam")
         self.compute_button.on_click(self.compute_direct_beam)
-        self.clear_log_button = widgets.Button(description="Clear Log")
-        self.clear_log_button.on_click(lambda _: print("\n--- Log Cleared ---\n"))
-        self.clear_plots_button = widgets.Button(description="Clear Plots")
-        self.clear_plots_button.on_click(lambda _: plt.close('all'))
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
         self.main = widgets.VBox([
             self.mask_text,
             self.sample_sans_text,
@@ -734,11 +944,13 @@ def __init__(self):
                 self.db_n_wavelength_bands_widget
             ]),
             self.compute_button,
-            widgets.HBox([self.clear_log_button, self.clear_plots_button])
+            self.log_output,
+            self.plot_output
         ])
     
     def compute_direct_beam(self, _):
-        # No longer clearing widget outputs; we simply print new info.
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
         mask = self.mask_text.value
         sample_sans = self.sample_sans_text.value
         background_sans = self.background_sans_text.value
@@ -750,15 +962,16 @@ def compute_direct_beam(self, _):
         wl_max = self.db_wavelength_max_widget.value
         n_bins = self.db_n_wavelength_bins_widget.value
         n_bands = self.db_n_wavelength_bands_widget.value
-        print("Computing direct beam with:")
-        print("  Mask:", mask)
-        print("  Sample SANS:", sample_sans)
-        print("  Background SANS:", background_sans)
-        print("  Sample TRANS:", sample_trans)
-        print("  Background TRANS:", background_trans)
-        print("  Empty Beam:", empty_beam)
-        print("  I(q) Theory:", local_Iq_theory)
-        print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
+        with self.log_output:
+            print("Computing direct beam with:")
+            print("  Mask:", mask)
+            print("  Sample SANS:", sample_sans)
+            print("  Background SANS:", background_sans)
+            print("  Sample TRANS:", sample_trans)
+            print("  Background TRANS:", background_trans)
+            print("  Empty Beam:", empty_beam)
+            print("  I(q) Theory:", local_Iq_theory)
+            print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
         try:
             results = compute_direct_beam_local(
                 mask,
@@ -773,9 +986,11 @@ def compute_direct_beam(self, _):
                 n_wavelength_bins=n_bins,
                 n_wavelength_bands=n_bands
             )
-            print("Direct beam computation complete.")
+            with self.log_output:
+                print("Direct beam computation complete.")
         except Exception as e:
-            print("Error computing direct beam:", e)
+            with self.log_output:
+                print("Error computing direct beam:", e)
     
     @property
     def widget(self):
@@ -784,16 +999,30 @@ def widget(self):
 # ----------------------------
 # Build it
 # ----------------------------
+#reduction_widget = SansBatchReductionWidget().widget
+#direct_beam_widget = DirectBeamWidget().widget
+#semi_auto_reduction_widget = SemiAutoReductionWidget().widget
+#auto_reduction_widget = AutoReductionWidget().widget
+
+#tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
+#tabs.set_title(0, "Direct Beam")
+#tabs.set_title(1, "Reduction (Manual)")
+#tabs.set_title(2, "Reduction (Smart)")
+#tabs.set_title(3, "Reduction (Auto)")
+
 reduction_widget = SansBatchReductionWidget().widget
-direct_beam_widget = DirectBeamWidget().widget
+#direct_beam_widget = DirectBeamWidget().widget
 semi_auto_reduction_widget = SemiAutoReductionWidget().widget
 auto_reduction_widget = AutoReductionWidget().widget
 
-tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
-tabs.set_title(0, "Direct Beam")
-tabs.set_title(1, "Reduction (Manual)")
-tabs.set_title(2, "Reduction (Smart)")
-tabs.set_title(3, "Reduction (Auto)")
+tabs = widgets.Tab(children=[reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
+#tabs.set_title(0, "Direct Beam")
+tabs.set_title(0, "Reduction (Manual)")
+#tabs.set_title(2, "Reduction (Smart)")
+tabs.set_title(1, "Reduction (Smart)")
+tabs.set_title(2, "Reduction (Auto)")
+
+
+# display(tabs)
+# voila /src/ess/loki/tabwidget.ipynb #--theme=dark
 
-# To display the tabs in a Voila deployment, simply display the tabs widget.
-#display(tabs)
diff --git a/src/ess/loki/tabwidget.py b/src/ess/loki/tabwidget.py
index 3dd03558..8346cc3a 100644
--- a/src/ess/loki/tabwidget.py
+++ b/src/ess/loki/tabwidget.py
@@ -36,7 +36,7 @@ def find_direct_beam(work_dir): #Find the direct beam automagically
     if files:
         return files[0]
     else:
-        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+        raise FileNotFoundError(f"Could not find direct beam file matching pattern {pattern}")
 
 def find_mask_file(work_dir): #Find the mask automagically 
     pattern = os.path.join(work_dir, "*mask*.xml")

From 209c57f69e54a878be845ba12e6c7eed4d7492c2 Mon Sep 17 00:00:00 2001
From: Oliver Hammond <olihammond@hotmail.com>
Date: Thu, 27 Mar 2025 10:54:25 +0100
Subject: [PATCH 18/18] =?UTF-8?q?updat=C3=A9?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 src/ess/loki/.DS_Store              |  Bin 10244 -> 10244 bytes
 src/ess/loki/tabwidgetvisascicat.py | 1028 +++++++++++++++++++++++++++
 2 files changed, 1028 insertions(+)
 create mode 100644 src/ess/loki/tabwidgetvisascicat.py

diff --git a/src/ess/loki/.DS_Store b/src/ess/loki/.DS_Store
index 24eb20abb76db728f1101fb28fe9858c257246b6..3a87832fd5bc0b7e9b832210c4a745efd51e6adb 100644
GIT binary patch
delta 118
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gWDjwL$r9p%o3$nGuup8z+03r+jA%`)+(0Aq0fRLjM*si-

delta 154
zcmZn(XbG6$&nUSuU^hRb<Ypd$hiu|}3?&SS3`q>-44Dim4CxH1K)x<R=41r{k;(7G
j1t-51JBC$FCPWPrv-9Q;;;-2^vnxC!$}mxE7G?qfG$to&

diff --git a/src/ess/loki/tabwidgetvisascicat.py b/src/ess/loki/tabwidgetvisascicat.py
new file mode 100644
index 00000000..05907c3d
--- /dev/null
+++ b/src/ess/loki/tabwidgetvisascicat.py
@@ -0,0 +1,1028 @@
+import os
+import glob
+import re
+import h5py
+import pandas as pd
+import scipp as sc
+import matplotlib.pyplot as plt
+import numpy as np
+import ipywidgets as widgets
+from ipydatagrid import DataGrid
+from IPython.display import display
+from ipyfilechooser import FileChooser
+from ess import sans
+from ess import loki
+from ess.sans.types import *
+from scipp.scipy.interpolate import interp1d
+import plopp as pp  
+import threading
+import time
+from ipywidgets import Layout
+import csv
+from scitacean import Client, Dataset, Attachment, Thumbnail
+from scitacean.transfer.copy import CopyFileTransfer
+from scitacean.transfer.select import SelectFileTransfer
+
+# ----------------------------
+# Utility Functions
+# ----------------------------
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+def find_direct_beam(work_dir): #Find the direct beam automagically 
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find direct-beam file matching pattern {pattern}")
+
+def find_mask_file(work_dir): #Find the mask automagically 
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+def save_xye_pandas(data_array, filename): ###Note here this needs to be 'fixed' / updated to use scipp io – ideally I want a nxcansas and xye saved for each file, but I struggled with the syntax and just did it in pandas as a first pass 
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+def extract_run_number(filename):
+    m = re.search(r'(\d{4,})', filename)
+    if m:
+        return m.group(1)
+    return ""
+
+def parse_nx_details(filepath): #For finding/grouping files by common title assigned by NICOS, e.g. 'runlabel' and 'runtype'
+    details = {}
+    with h5py.File(filepath, 'r') as f:
+        if 'nicos_details' in f['entry']:
+            grp = f['entry']['nicos_details']
+            if 'runlabel' in grp:
+                val = grp['runlabel'][()]
+                details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+            if 'runtype' in grp:
+                val = grp['runtype'][()]
+                details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val)
+    return details
+
+# ----------------------------
+# Colour Mapping From Filename 
+
+def string_to_colour(input_str):
+    if not input_str:
+        return "#000000"  # Empty input = black
+    total = 0
+    for ch in input_str:
+        if ch.isalpha():
+            total += ord(ch.lower()) - ord('a') + 1  # a=1, b=2, ..., z=26
+        elif ch.isdigit():
+            total += 1 + int(ch) * (25/9)  # Maps '0' to 1 and '9' to 26
+        # Special characters equal 0
+    avg = total / len(input_str)
+    norm = max(0, min(1, avg / 26))  # Average and normalise to [0,1]
+    rgba = plt.get_cmap('flag')(norm)  #prism 
+    return '#{:02x}{:02x}{:02x}'.format(int(rgba[0]*255),
+                                        int(rgba[1]*255),
+                                        int(rgba[2]*255))
+
+
+# ----------------------------
+# Reduction and Plotting Functions
+# ----------------------------
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode = UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101
+):
+    if mask_files is None:
+        mask_files = []
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+def save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelength_max, wavelength_n, q_min, q_max, q_n, output_dir, show=True):
+    fig, axs = plt.subplots(1, 2, figsize=(6, 3))
+    axs[0].set_box_aspect(1)
+    axs[1].set_box_aspect(1)
+    title_str = f"{sample} - {os.path.basename(sample_run_file)}"
+    fig.suptitle(title_str, fontsize=12)
+    q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom")
+    x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+    if res["IofQ"].variances is not None:
+        yerr = np.sqrt(res["IofQ"].variances)
+        #axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+        axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+
+    else:
+        axs[0].scatter(x_q, res["IofQ"].values)
+    axs[0].set_xlabel("Q (Å$^{-1}$)")
+    axs[0].set_ylabel("I(Q)")
+    axs[0].set_xscale("log")
+    axs[0].set_yscale("log")
+    wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom")
+    x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+    if res["transmission"].variances is not None:
+        yerr_tr = np.sqrt(res["transmission"].variances)
+        #axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none')
+        axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor=string_to_colour(sample), ecolor='k', markersize=6)
+
+        
+    else:
+        axs[1].scatter(x_wl, res["transmission"].values)
+    axs[1].set_xlabel("Wavelength (Å)")
+    axs[1].set_ylabel("Transmission")
+    plt.tight_layout()
+    out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png"))
+    fig.savefig(out_png, dpi=300)
+    if show:
+        display(fig)
+    plt.close(fig)
+
+# ----------------------------
+# Unified "Backend" Function for Reduction
+# ----------------------------
+def perform_reduction_for_sample(
+    sample_info: dict,
+    input_dir: str,
+    output_dir: str,
+    reduction_params: dict,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    log_func: callable
+):
+    """
+    Processes a single sample reduction:
+      - Finds the necessary run files
+      - Optionally determines a mask (or finds one automatically)
+      - Calls the reduction and plotting routines
+      - Logs all steps via log_func(message) ### edited to just print statements - does logfunc work correctly with voila???
+    """
+    sample = sample_info.get("SAMPLE", "Unknown")
+    try:
+        sample_run_file = find_file(input_dir, str(sample_info["SANS"]), extension=".nxs")
+        transmission_run_file = find_file(input_dir, str(sample_info["TRANS"]), extension=".nxs")
+    except Exception as e:
+        log_func(f"Skipping sample {sample}: {e}")
+        #print(f"Skipping sample {sample}: {e}")
+
+        return None
+    # Determine mask file.
+    mask_file = None
+    mask_candidate = str(sample_info.get("mask", "")).strip()
+    if mask_candidate:
+        mask_candidate_file = os.path.join(input_dir, f"{mask_candidate}.xml")
+        if os.path.exists(mask_candidate_file):
+            mask_file = mask_candidate_file
+    if mask_file is None:
+        try:
+            mask_file = find_mask_file(input_dir)
+            log_func(f"Using mask: {mask_file} for sample {sample}")
+            #print(f"Identified mask file: {mask_file} for sample {sample}")
+
+        except Exception as e:
+            log_func(f"Mask file not found for sample {sample}: {e}")
+            #print(f"Mask file not found for sample {sample}: {e}")
+
+            return None
+
+    log_func(f"Reducing sample {sample}...")
+    #print(f"Reducing sample {sample}...")
+
+    try:
+        res = reduce_loki_batch_preliminary(
+            sample_run_file=sample_run_file,
+            transmission_run_file=transmission_run_file,
+            background_run_file=background_run_file,
+            empty_beam_file=empty_beam_file,
+            direct_beam_file=direct_beam_file,
+            mask_files=[mask_file],
+            wavelength_min=reduction_params["wavelength_min"],
+            wavelength_max=reduction_params["wavelength_max"],
+            wavelength_n=reduction_params["wavelength_n"],
+            q_start=reduction_params["q_start"],
+            q_stop=reduction_params["q_stop"],
+            q_n=reduction_params["q_n"]
+        )
+    except Exception as e:
+        log_func(f"Reduction failed for sample {sample}: {e}")
+        #print(f"Reduction failed for sample {sample}: {e}")
+        return None
+    out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye"))
+    try:
+        save_xye_pandas(res["IofQ"], out_xye)
+        log_func(f"Saved reduced data to {out_xye}")
+        #print(f"Saved reduced data to {out_xye}")
+
+    except Exception as e:
+        log_func(f"Failed to save reduced data for {sample}: {e}")
+        #print(f"Failed to save reduced data for {sample}: {e}")
+    try:
+        save_reduction_plots(
+            res,
+            sample,
+            sample_run_file,
+            reduction_params["wavelength_min"],
+            reduction_params["wavelength_max"],
+            reduction_params["wavelength_n"],
+            reduction_params["q_start"],
+            reduction_params["q_stop"],
+            reduction_params["q_n"],
+            output_dir,
+            show=True
+        )
+        log_func(f"Saved reduction plot for sample {sample}.")
+    except Exception as e:
+        log_func(f"Failed to save reduction plot for {sample}: {e}")
+    #log_func(f"Reduced sample {sample} and saved outputs.")
+    return res
+#        print(f"Saved reduction plot for sample {sample}.")
+#    except Exception as e:
+#        print(f"Failed to save reduction plot for {sample}: {e}")
+#    print(f"Reduced sample {sample} and saved outputs.")
+#    return res
+
+
+###########################################################################################
+
+##############################################################################################
+
+# ----------------------------
+# GUI Widgets 
+# ----------------------------
+class SansBatchReductionWidget:
+    def __init__(self):
+        # File Choosers for CSV, input dir, output dir
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        
+        # Remove references to Ebeam SANS/TRANS widgets 
+        # (since these are now specified per row in the CSV).
+        
+        # Reduction parameter widgets
+        self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):")
+        self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        
+        # Button to load CSV
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        
+        # Table to display/edit CSV data
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        
+        # Button to run reduction
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        
+        # (Optional) log/plot outputs
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        
+        # Main layout
+        self.main = widgets.VBox([
+            widgets.HBox([self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]),
+            widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_widget]),
+            self.load_csv_button,
+            self.table,
+            widgets.HBox([self.reduce_button]),
+            self.log_output,
+            self.plot_output
+        ])
+
+    def load_csv(self, _):
+        """Loads the CSV file into the DataGrid."""
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+
+    def run_reduction(self, _):
+        """Loops over each row of the CSV table, finds input files, and performs the reduction."""
+        # Clear old log/plot outputs
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        
+        # Read current table data
+        df = self.table.data
+        
+        # Reduction parameters
+        reduction_params = {
+            "wavelength_min": self.wavelength_min_widget.value,
+            "wavelength_max": self.wavelength_max_widget.value,
+            "wavelength_n": self.wavelength_n_widget.value,
+            "q_start": self.q_start_widget.value,
+            "q_stop": self.q_stop_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+
+        # Loop over each row of the CSV table
+        for idx, row in df.iterrows():
+            try:
+                # Use the CSV columns to find file paths
+                sample_run_file = find_file(input_dir, str(row["SANS"]), extension=".nxs")
+                transmission_run_file = find_file(input_dir, str(row["TRANS"]), extension=".nxs")
+                background_run_file = find_file(input_dir, str(row["Ebeam_SANS"]), extension=".nxs")
+                empty_beam_file = find_file(input_dir, str(row["Ebeam_TRANS"]), extension=".nxs")
+                
+                # For mask and direct beam, we assume the CSV filename is relative to input_dir
+                mask_file = os.path.join(input_dir, str(row["mask"]))
+                direct_beam_file = os.path.join(input_dir, str(row["direct_beam"]))
+                
+            except Exception as e:
+                # If something fails, log and skip this row
+                with self.log_output:
+                    print(f"Error finding input files for row {idx} ({row['SAMPLE']}): {e}")
+                continue
+            
+            # Create a mini dict for the sample info
+            sample_info = {
+                "SAMPLE": row["SAMPLE"],
+                "SANS": row["SANS"],
+                "TRANS": row["TRANS"],
+                # We store the mask as a column, so pass it along
+                "mask": mask_file
+            }
+            
+            # Call the actual reduction
+            perform_reduction_for_sample(
+                sample_info=sample_info,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file,
+                log_func=lambda msg: print(msg)
+            )
+
+    @property
+    def widget(self):
+        """Return the main widget layout."""
+        return self.main
+
+class SemiAutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.scan_button = widgets.Button(description="Scan Directory")
+        self.scan_button.on_click(self.scan_directory)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.add_row_button = widgets.Button(description="Add Row")
+        self.add_row_button.on_click(self.add_row)
+        self.delete_row_button = widgets.Button(description="Delete Last Row")
+        self.delete_row_button.on_click(self.delete_last_row)
+        self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:")
+        self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):")
+        self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):")
+        self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:")
+        self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True)
+        self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        #self.clear_log_button = widgets.Button(description="Clear Log")
+        #self.clear_log_button.on_click(lambda _: self.log_output.clear_output())
+        #self.clear_plots_button = widgets.Button(description="Clear Plots")
+        #self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output())
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.processed = set()
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            self.scan_button,
+            self.table,
+            widgets.HBox([self.add_row_button, self.delete_row_button]),
+            widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]),
+            widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]),
+            widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]),
+            widgets.HBox([self.reduce_button]),# self.clear_log_button, self.clear_plots_button]),
+            #self.log_output,
+            #self.plot_output
+        ])
+    
+    def add_row(self, _):
+        df = self.table.data
+        new_row = {col: "" for col in df.columns} if not df.empty else {'SAMPLE': '', 'SANS': '', 'TRANS': ''}
+        df = df.append(new_row, ignore_index=True)
+        self.table.data = df
+
+    def delete_last_row(self, _):
+        df = self.table.data
+        if not df.empty:
+            self.table.data = df.iloc[:-1]
+
+    def scan_directory(self, _):
+        self.log_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Invalid input folder.")
+            return
+        nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+        groups = {}
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runlabel' not in details or 'runtype' not in details:
+                continue
+            runlabel = details['runlabel']
+            runtype = details['runtype'].lower()
+            run_number = extract_run_number(os.path.basename(f))
+            if runlabel not in groups:
+                groups[runlabel] = {}
+            groups[runlabel][runtype] = run_number
+        table_rows = []
+        for runlabel, d in groups.items():
+            if 'sans' in d and 'trans' in d:
+                table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+        df = pd.DataFrame(table_rows)
+        self.table.data = df
+        with self.log_output:
+            print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+        ebeam_sans_files = []
+        ebeam_trans_files = []
+        for f in nxs_files:
+            try:
+                details = parse_nx_details(f)
+            except Exception:
+                continue
+            if 'runtype' in details:
+                if details['runtype'].lower() == 'ebeam_sans':
+                    ebeam_sans_files.append(f)
+                elif details['runtype'].lower() == 'ebeam_trans':
+                    ebeam_trans_files.append(f)
+        if ebeam_sans_files:
+            ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_sans_text.value = ebeam_sans_files[0]
+        else:
+            self.empty_beam_sans_text.value = ""
+        if ebeam_trans_files:
+            ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+            self.empty_beam_trans_text.value = ebeam_trans_files[0]
+        else:
+            self.empty_beam_trans_text.value = ""
+    
+    def run_reduction(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct beam:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct beam file not found:", e)
+            return
+        background_run_file = self.empty_beam_sans_text.value
+        empty_beam_file = self.empty_beam_trans_text.value
+        if not background_run_file or not empty_beam_file:
+            with self.log_output:
+                print("Empty beam files not found.")
+            return
+        
+        reduction_params = {
+            "wavelength_min": self.lambda_min_widget.value,
+            "wavelength_max": self.lambda_max_widget.value,
+            "wavelength_n": self.lambda_n_widget.value,
+            "q_start": self.q_min_widget.value,
+            "q_stop": self.q_max_widget.value,
+            "q_n": self.q_n_widget.value
+        }
+        
+        #df = self.table.data.copy()
+        df = self.table.data.drop_duplicates(subset=['SAMPLE', 'SANS', 'TRANS'])
+        for idx, row in df.iterrows():
+            perform_reduction_for_sample(
+                sample_info=row,
+                input_dir=input_dir,
+                output_dir=output_dir,
+                reduction_params=reduction_params,
+                background_run_file=background_run_file,
+                empty_beam_file=empty_beam_file,
+                direct_beam_file=direct_beam_file,
+                log_func=lambda msg: print(msg)
+            )
+    
+    @property
+    def widget(self):
+        return self.main
+
+class AutoReductionWidget:
+    def __init__(self):
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.start_stop_button = widgets.Button(description="Start")
+        self.start_stop_button.on_click(self.toggle_running)
+        self.status_label = widgets.Label(value="Stopped")
+        self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.running = False
+        self.thread = None
+        self.processed = set()
+        self.empty_beam_sans = None
+        self.empty_beam_trans = None
+        self.main = widgets.VBox([
+            widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]),
+            widgets.HBox([self.start_stop_button, self.status_label]),
+            self.table,
+            self.log_output,
+            self.plot_output
+        ])
+        # SciCat settings – adjust these as needed.
+        self.token = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJfaWQiOiI2NmRhYjhmYzFiNThkNDFlYTM4OTc5MzIiLCJ1c2VybmFtZSI6Imh0dHBzOi8vbG9naW4uZXNzLmV1X29saXZlcmhhbW1vbmQiLCJlbWFpbCI6Im9saXZlci5oYW1tb25kQGVzcy5ldSIsImF1dGhTdHJhdGVneSI6Im9pZGMiLCJfX3YiOjAsImlkIjoiNjZkYWI4ZmMxYjU4ZDQxZWEzODk3OTMyIiwidXNlcklkIjoiNjZkYWI4ZmMxYjU4ZDQxZWEzODk3OTMyIiwiaWF0IjoxNzQyOTk2Mzc4LCJleHAiOjE3NDI5OTk5Nzh9.YytBMfX0p971InDFs0cSkfoVP92RvpgE_Vu9K_OLbiY'
+        self.scicat_url = 'https://staging.scicat.ess.eu/api/v3'
+        self.scicat_source_folder = '/scratch/oliverhammond/LARMOR/nxs/out/scicat'
+    
+    def toggle_running(self, _):
+        if not self.running:
+            self.running = True
+            self.start_stop_button.description = "Stop"
+            self.status_label.value = "Running"
+            self.thread = threading.Thread(target=self.background_loop, daemon=True)
+            self.thread.start()
+        else:
+            self.running = False
+            self.start_stop_button.description = "Start"
+            self.status_label.value = "Stopped"
+    
+    def background_loop(self):
+        while self.running:
+            input_dir = self.input_dir_chooser.selected
+            output_dir = self.output_dir_chooser.selected
+            if not input_dir or not os.path.isdir(input_dir):
+                with self.log_output:
+                    print("Invalid input folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            if not output_dir or not os.path.isdir(output_dir):
+                with self.log_output:
+                    print("Invalid output folder. Waiting for valid selection...")
+                time.sleep(60)
+                continue
+            nxs_files = glob.glob(os.path.join(input_dir, "*.nxs"))
+            groups = {}
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runlabel' not in details or 'runtype' not in details:
+                    continue
+                runlabel = details['runlabel']
+                runtype = details['runtype'].lower()
+                run_number = extract_run_number(os.path.basename(f))
+                if runlabel not in groups:
+                    groups[runlabel] = {}
+                groups[runlabel][runtype] = run_number
+            table_rows = []
+            for runlabel, d in groups.items():
+                if 'sans' in d and 'trans' in d:
+                    table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']})
+            df = pd.DataFrame(table_rows)
+            self.table.data = df
+            with self.log_output:
+                print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.")
+            ebeam_sans_files = []
+            ebeam_trans_files = []
+            for f in nxs_files:
+                try:
+                    details = parse_nx_details(f)
+                except Exception:
+                    continue
+                if 'runtype' in details:
+                    if details['runtype'].lower() == 'ebeam_sans':
+                        ebeam_sans_files.append(f)
+                    elif details['runtype'].lower() == 'ebeam_trans':
+                        ebeam_trans_files.append(f)
+            if ebeam_sans_files:
+                ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_sans = ebeam_sans_files[0]
+            else:
+                self.empty_beam_sans = None
+            if ebeam_trans_files:
+                ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
+                self.empty_beam_trans = ebeam_trans_files[0]
+            else:
+                self.empty_beam_trans = None
+            try:
+                direct_beam_file = find_direct_beam(input_dir)
+            except Exception as e:
+                with self.log_output:
+                    print("Direct-beam file not found:", e)
+                time.sleep(60)
+                continue
+            for index, row in df.iterrows():
+                key = (row["SAMPLE"], row["SANS"], row["TRANS"])
+                if key in self.processed:
+                    continue
+                try:
+                    sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs")
+                    transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Skipping sample {row['SAMPLE']}: {e}")
+                    continue
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}")
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {row['SAMPLE']}: {e}")
+                    continue
+                if not self.empty_beam_sans or not self.empty_beam_trans:
+                    with self.log_output:
+                        print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"])
+                    continue
+                with self.log_output:
+                    print(f"Reducing sample {row['SAMPLE']}...")
+                reduction_params = {
+                    "wavelength_min": 1.0,
+                    "wavelength_max": 13.0,
+                    "wavelength_n": 201,
+                    "q_start": 0.01,
+                    "q_stop": 0.3,
+                    "q_n": 101
+                }
+                perform_reduction_for_sample(
+                    sample_info=row,
+                    input_dir=input_dir,
+                    output_dir=output_dir,
+                    reduction_params=reduction_params,
+                    background_run_file=self.empty_beam_sans,
+                    empty_beam_file=self.empty_beam_trans,
+                    direct_beam_file=direct_beam_file,
+                    log_func=lambda msg: print(msg)
+                )
+                self.processed.add(key)
+                # Call the uploader function using the run number/name from the reduction table.
+                self.upload_dataset(run=row["SANS"], sample_name=row["SAMPLE"], metadata_file='uos_metadata.csv')
+            time.sleep(60)
+    
+    def upload_dataset(self, run, sample_name, metadata_file='uos_metadata.csv'):
+        """
+        Uploads a reduced dataset to SciCat using files in the widget's output directory.
+        Metadata is combined from the reduction process (run, sample_name) and additional 
+        arguments provided in a CSV file as a proxy for metadata provided by the user office.
+
+        The CSV file should have a header with these columns:
+          contact_email, owner_email, investigator, owner, owner_group, description
+
+        Parameters:
+          run (str): The run number to search for and use in the dataset (same as tabular input).
+          sample_name (str): The sample name to include in the dataset metadata (from nxs file).
+          metadata_file (str): Path to the CSV file containing additional metadata.
+        """
+        # Read metadata from the CSV file.
+        try:
+            with open(metadata_file, newline='') as csvfile:
+                reader = csv.DictReader(csvfile)
+                row = next(reader)
+        except Exception as e:
+            with self.log_output:
+                print(f"Error reading metadata CSV file '{metadata_file}': {e}")
+            return
+
+        contact_email = row.get('contact_email', 'default@example.com')
+        owner_email = row.get('owner_email', contact_email)
+        investigator = row.get('investigator', 'Unknown')
+        owner = row.get('owner', 'Unknown')
+        owner_group = row.get('owner_group', 'ess')
+        description = row.get('description', '')
+
+        # Use the output directory from the widget to search for the reduced files.
+        file_folder = self.output_dir_chooser.selected
+        if not file_folder or not os.path.isdir(file_folder):
+            with self.log_output:
+                print("Invalid output folder selected for uploading.")
+            return
+
+        # Initialize the SciCat client.
+        client = Client.from_token(
+            url=self.scicat_url,
+            token=self.token,
+            file_transfer=SelectFileTransfer([CopyFileTransfer()])
+        )
+
+        # Use glob to find the .xye and .png files based on the run number.
+        xye_pattern = os.path.join(file_folder, f"*{run}*.xye")
+        png_pattern = os.path.join(file_folder, f"*{run}*_reduced.png")
+        xye_files = glob.glob(xye_pattern)
+        png_files = glob.glob(png_pattern)
+
+        if not xye_files:
+            with self.log_output:
+                print(f"No .xye file found for run {run}.")
+            return
+        if not png_files:
+            with self.log_output:
+                print(f"No .png file found for run {run}.")
+            return
+
+        xye_file = xye_files[0]  # Use first matching file.
+        png_file = png_files[0]  # Use first matching file.
+
+        # Construct the dataset object with combined metadata.
+        dataset = Dataset(
+            type='derived',
+            contact_email=contact_email,
+            owner_email=owner_email,
+            input_datasets=[],
+            investigator=investigator,
+            owner=owner,
+            owner_group=owner_group,
+            access_groups=[owner_group],
+            source_folder=self.scicat_source_folder,
+            used_software=['esssans'],
+            name=f"{run}.xye",  # Derived from run number.
+            description=description,
+            run_number=run,
+            meta={'sample_name': {'value': sample_name, 'unit': ''}}
+        )
+
+        # Add the primary .xye file.
+        dataset.add_local_files(xye_file)
+
+        # Add the attachment (thumbnail).
+        dataset.attachments.append(
+            Attachment(
+                caption=f"Reduced I(Q) and transmission for {dataset.name}",
+                owner_group=owner_group,
+                thumbnail=Thumbnail.load_file(png_file)
+            )
+        )
+
+        # Upload the dataset.
+        client.upload_new_dataset_now(dataset)
+        with self.log_output:
+            print(f"Uploaded dataset for run {run} using files:\n  - {xye_file}\n  - {png_file}")
+
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Direct Beam stuff
+# ----------------------------
+def compute_direct_beam_local(
+    mask: str,
+    sample_sans: str,
+    background_sans: str,
+    sample_trans: str,
+    background_trans: str,
+    empty_beam: str,
+    local_Iq_theory: str,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    n_wavelength_bins: int = 50,
+    n_wavelength_bands: int = 50
+) -> dict:
+    workflow = loki.LokiAtLarmorWorkflow()
+    workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask])
+    workflow[NeXusDetectorName] = 'larmor_detector'
+    wl_min = sc.scalar(wavelength_min, unit='angstrom')
+    wl_max = sc.scalar(wavelength_max, unit='angstrom')
+    workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1)
+    workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1)
+    workflow[CorrectForGravity] = True
+    workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound
+    workflow[ReturnEvents] = False
+    workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom')
+    workflow[Filename[SampleRun]] = sample_sans
+    workflow[Filename[BackgroundRun]] = background_sans
+    workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans
+    workflow[Filename[EmptyBeamRun]] = empty_beam
+    center = sans.beam_center_from_center_of_mass(workflow)
+    print("Computed beam center:", center)
+    workflow[BeamCenter] = center
+    Iq_theory = sc.io.load_hdf5(local_Iq_theory)
+    f = interp1d(Iq_theory, 'Q')
+    I0 = f(sc.midpoints(workflow.compute(QBins))).data[0]
+    print("Computed I0:", I0)
+    results = sans.direct_beam(workflow=workflow, I0=I0, niter=6)
+    iofq_full = results[-1]['iofq_full']
+    iofq_bands = results[-1]['iofq_bands']
+    direct_beam_function = results[-1]['direct_beam']
+    pp.plot(
+        {'reference': Iq_theory, 'data': iofq_full},
+        color={'reference': 'darkgrey', 'data': 'C0'},
+        norm='log',
+    )
+    print("Plotted full-range result vs. theoretical reference.")
+    return {
+        'direct_beam_function': direct_beam_function,
+        'iofq_full': iofq_full,
+        'Iq_theory': Iq_theory,
+    }
+
+class DirectBeamWidget:
+    def __init__(self):
+        self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:")
+        self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:")
+        self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:")
+        self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:")
+        self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:")
+        self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:")
+        self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:")
+        self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):")
+        self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):")
+        self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:")
+        self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:")
+        self.compute_button = widgets.Button(description="Compute Direct Beam")
+        self.compute_button.on_click(self.compute_direct_beam)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox([
+            self.mask_text,
+            self.sample_sans_text,
+            self.background_sans_text,
+            self.sample_trans_text,
+            self.background_trans_text,
+            self.empty_beam_text,
+            self.local_Iq_theory_text,
+            widgets.HBox([
+                self.db_wavelength_min_widget,
+                self.db_wavelength_max_widget,
+                self.db_n_wavelength_bins_widget,
+                self.db_n_wavelength_bands_widget
+            ]),
+            self.compute_button,
+            self.log_output,
+            self.plot_output
+        ])
+    
+    def compute_direct_beam(self, _):
+        self.log_output.clear_output()
+        self.plot_output.clear_output()
+        mask = self.mask_text.value
+        sample_sans = self.sample_sans_text.value
+        background_sans = self.background_sans_text.value
+        sample_trans = self.sample_trans_text.value
+        background_trans = self.background_trans_text.value
+        empty_beam = self.empty_beam_text.value
+        local_Iq_theory = self.local_Iq_theory_text.value
+        wl_min = self.db_wavelength_min_widget.value
+        wl_max = self.db_wavelength_max_widget.value
+        n_bins = self.db_n_wavelength_bins_widget.value
+        n_bands = self.db_n_wavelength_bands_widget.value
+        with self.log_output:
+            print("Computing direct beam with:")
+            print("  Mask:", mask)
+            print("  Sample SANS:", sample_sans)
+            print("  Background SANS:", background_sans)
+            print("  Sample TRANS:", sample_trans)
+            print("  Background TRANS:", background_trans)
+            print("  Empty Beam:", empty_beam)
+            print("  I(q) Theory:", local_Iq_theory)
+            print("  λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands)
+        try:
+            results = compute_direct_beam_local(
+                mask,
+                sample_sans,
+                background_sans,
+                sample_trans,
+                background_trans,
+                empty_beam,
+                local_Iq_theory,
+                wavelength_min=wl_min,
+                wavelength_max=wl_max,
+                n_wavelength_bins=n_bins,
+                n_wavelength_bands=n_bands
+            )
+            with self.log_output:
+                print("Direct beam computation complete.")
+        except Exception as e:
+            with self.log_output:
+                print("Error computing direct beam:", e)
+    
+    @property
+    def widget(self):
+        return self.main
+
+# ----------------------------
+# Build it
+# ----------------------------
+#reduction_widget = SansBatchReductionWidget().widget
+#direct_beam_widget = DirectBeamWidget().widget
+#semi_auto_reduction_widget = SemiAutoReductionWidget().widget
+#auto_reduction_widget = AutoReductionWidget().widget
+
+#tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
+#tabs.set_title(0, "Direct Beam")
+#tabs.set_title(1, "Reduction (Manual)")
+#tabs.set_title(2, "Reduction (Smart)")
+#tabs.set_title(3, "Reduction (Auto)")
+
+reduction_widget = SansBatchReductionWidget().widget
+#direct_beam_widget = DirectBeamWidget().widget
+semi_auto_reduction_widget = SemiAutoReductionWidget().widget
+auto_reduction_widget = AutoReductionWidget().widget
+
+tabs = widgets.Tab(children=[reduction_widget, semi_auto_reduction_widget, auto_reduction_widget])
+#tabs.set_title(0, "Direct Beam")
+tabs.set_title(0, "Reduction (Manual)")
+#tabs.set_title(2, "Reduction (Smart)")
+tabs.set_title(1, "Reduction (Smart)")
+tabs.set_title(2, "Reduction (Auto)")
+
+
+# display(tabs)
+# voila /src/ess/loki/tabwidget.ipynb #--theme=dark
+