From 8d40caeb9240a1438e7dad99591bce3a023a9bda Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Sun, 5 Jul 2026 22:39:38 +0100
Subject: [PATCH 01/15] apply to more MIDI pieces

---
 notebooks/Alignment_on_ASAPdataset.ipynb | 264 +++++++++++++----------
 1 file changed, 151 insertions(+), 113 deletions(-)

diff --git a/notebooks/Alignment_on_ASAPdataset.ipynb b/notebooks/Alignment_on_ASAPdataset.ipynb
index d7b5b34..37bde62 100644
--- a/notebooks/Alignment_on_ASAPdataset.ipynb
+++ b/notebooks/Alignment_on_ASAPdataset.ipynb
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -232,58 +232,70 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded 169 score (reference) /performance (response) pairs.\n",
-      "1. Bach(Fugue_bwv_846) | ref notes: 755 | response notes: 754\n",
-      "2. Bach(Fugue_bwv_848) | ref notes: 1429 | response notes: 1435\n",
-      "3. Bach(Fugue_bwv_848) | ref notes: 1429 | response notes: 1438\n",
-      "4. Bach(Fugue_bwv_848) | ref notes: 1429 | response notes: 1429\n",
-      "5. Bach(Fugue_bwv_848) | ref notes: 1429 | response notes: 1431\n",
-      "6. Bach(Fugue_bwv_848) | ref notes: 1429 | response notes: 1433\n",
-      "7. Bach(Fugue_bwv_848) | ref notes: 1429 | response notes: 1438\n",
-      "8. Bach(Fugue_bwv_848) | ref notes: 1429 | response notes: 1440\n",
-      "9. Bach(Fugue_bwv_848) | ref notes: 1429 | response notes: 1431\n",
-      "10. Bach(Fugue_bwv_848) | ref notes: 1429 | response notes: 1430\n"
+      "Skipped 424 pairs due to exceeding max_notes limit of 3500\n",
+      "Loaded 642 score (reference) /performance (response) pairs.\n"
      ]
     }
    ],
    "source": [
-    "def load_samples(asap_path, composer):\n",
+    "def load_samples(asap_path, composer=None, max_notes=3500):\n",
     "    \"\"\"\n",
     "    Read the ASAP metadata CSV and return a list of sample dicts\n",
     "    for a specific composer only.\n",
     "\n",
     "    Args:\n",
-    "        asap_path: str, path to the cloned ASAP repo\n",
-    "        composer: str, e.g. \"Bach\"\n",
+    "        asap_path:  str, path to the cloned ASAP repo root\n",
+    "        composer:   str or None.\n",
+    "                    If a string (e.g. \"Bach\"), only that composer is loaded.\n",
+    "                    If None, all composers in the dataset are loaded.\n",
+    "        max_notes:  int, skip any pair where either the score or performance\n",
+    "                    has more than this many notes. Default 3000.\n",
+    "                    Set to None to disable the limit.\n",
     "\n",
     "    Returns:\n",
     "        list of sample dicts (see build_sample)\n",
     "    \"\"\"\n",
     "    metadata_path = os.path.join(asap_path, \"metadata.csv\")\n",
     "    samples = []\n",
+    "    skipped = 0\n",
     "\n",
     "    with open(metadata_path, \"r\", encoding=\"utf-8\") as csv_file:\n",
     "        reader = csv.DictReader(csv_file)\n",
+    "\n",
     "        for row in reader:\n",
-    "            if row.get(\"composer\", \"\").strip() == composer:\n",
-    "                ref_path = os.path.join(asap_path, row.get(\"midi_score\", \"\").strip())\n",
-    "                response_path = os.path.join(asap_path, row.get(\"midi_performance\", \"\").strip())\n",
-    "\n",
-    "                if os.path.isfile(ref_path) and os.path.isfile(response_path):\n",
-    "                    sample = build_sample(\n",
-    "                        ref_path, response_path,\n",
-    "                        row.get(\"composer\", \"Unknown\"),\n",
-    "                        row.get(\"title\", \"Unknown\"),\n",
-    "                        dict(row),\n",
+    "            row_composer = row.get(\"composer\", \"\").strip()\n",
+    "            # Skip rows that do not match the requested composer (if one is given)\n",
+    "            if composer is not None and row_composer != composer:\n",
+    "                continue\n",
+    "\n",
+    "            ref_path = os.path.join(asap_path, row.get(\"midi_score\", \"\").strip())\n",
+    "            response_path = os.path.join(asap_path, row.get(\"midi_performance\", \"\").strip())\n",
+    "\n",
+    "            if os.path.isfile(ref_path) and os.path.isfile(response_path):\n",
+    "                sample = build_sample(\n",
+    "                    ref_path, response_path,\n",
+    "                    row_composer,     \n",
+    "                    row.get(\"title\", \"Unknown\"),\n",
+    "                    dict(row),\n",
+    "                    )\n",
+    "\n",
+    "                if sample is not None:\n",
+    "                    ref_count  = len(sample[\"reference\"][\"notes\"])\n",
+    "                    perf_count = len(sample[\"response\"][\"notes\"])\n",
+    "                    exceeds_limit = (\n",
+    "                        max_notes is not None\n",
+    "                        and (ref_count > max_notes or perf_count > max_notes)\n",
     "                    )\n",
-    "                    if sample is not None:\n",
+    "                    if exceeds_limit:                            \n",
+    "                        skipped = skipped + 1\n",
+    "                    else:\n",
     "                        samples.append(sample)\n",
     "\n",
+    "    print(f\"Skipped {skipped} pairs due to exceeding max_notes limit of {max_notes}\")\n",
+    "    print(f\"Loaded {len(samples)} score (reference) /performance (response) pairs.\")\n",
     "    return samples\n",
     "\n",
-    "samples = load_samples(ASAP_PATH, \"Bach\")\n",
-    "\n",
-    "print(\"Loaded\", len(samples), \"score (reference) /performance (response) pairs.\")"
+    "samples = load_samples(ASAP_PATH, composer=None, max_notes=3500)"
    ]
   },
   {
@@ -301,14 +313,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Alignment complete for 169 pieces.\n"
+      "Alignment complete for 642 pieces.\n"
      ]
     }
    ],
@@ -319,7 +331,6 @@
     "    group_notes_into_events,\n",
     ")\n",
     "\n",
-    "\n",
     "def run_alignment_on_samples(samples):\n",
     "    \"\"\"\n",
     "    Run compare_performance_ED on every sample and collect results.\n",
@@ -365,6 +376,7 @@
     "            \"is_correct\": result.is_correct,\n",
     "            \"response_events_normalized\": response_events_norm,\n",
     "            \"response_onset_offset\": response_onset_offset,\n",
+    "            \"metadata_row\": sample[\"metadata_row\"],\n",
     "        })\n",
     "\n",
     "    return results\n",
@@ -414,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -517,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -625,7 +637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -695,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -725,16 +737,42 @@
     "\n",
     "    ground_truth = load_ground_truth(tsv_path)\n",
     "    my_pairs = convert_pipeline_output(event_details, response_events_normalized)\n",
-    "    metrics = compute_metrics(ground_truth, my_pairs, onset_offset=response_onset_offset)\n",
+    "    metrics = compute_metrics_label_level(ground_truth, my_pairs, onset_offset=response_onset_offset)\n",
     "\n",
     "    return metrics"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TSV not found: asap-dataset/Beethoven/Piano_Sonatas/17-2/KaszoS10_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
+      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -769,119 +807,119 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Bach (Fugue_bwv_846)</td>\n",
-       "      <td>0.9444</td>\n",
-       "      <td>0.8740</td>\n",
-       "      <td>0.9078</td>\n",
-       "      <td>645</td>\n",
-       "      <td>38</td>\n",
-       "      <td>93</td>\n",
+       "      <td>0.9463</td>\n",
+       "      <td>0.9136</td>\n",
+       "      <td>0.9297</td>\n",
+       "      <td>423</td>\n",
+       "      <td>24</td>\n",
+       "      <td>40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9614</td>\n",
-       "      <td>0.9322</td>\n",
-       "      <td>0.9465</td>\n",
-       "      <td>1319</td>\n",
-       "      <td>53</td>\n",
-       "      <td>96</td>\n",
+       "      <td>0.9850</td>\n",
+       "      <td>0.9500</td>\n",
+       "      <td>0.9672</td>\n",
+       "      <td>855</td>\n",
+       "      <td>13</td>\n",
+       "      <td>45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9376</td>\n",
-       "      <td>0.9031</td>\n",
-       "      <td>0.9200</td>\n",
-       "      <td>1277</td>\n",
-       "      <td>85</td>\n",
-       "      <td>137</td>\n",
+       "      <td>0.9621</td>\n",
+       "      <td>0.9162</td>\n",
+       "      <td>0.9386</td>\n",
+       "      <td>864</td>\n",
+       "      <td>34</td>\n",
+       "      <td>79</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9494</td>\n",
-       "      <td>0.9171</td>\n",
-       "      <td>0.9329</td>\n",
-       "      <td>1294</td>\n",
+       "      <td>0.9675</td>\n",
+       "      <td>0.9260</td>\n",
+       "      <td>0.9463</td>\n",
+       "      <td>864</td>\n",
+       "      <td>29</td>\n",
        "      <td>69</td>\n",
-       "      <td>117</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9370</td>\n",
+       "      <td>0.9809</td>\n",
        "      <td>0.9083</td>\n",
-       "      <td>0.9224</td>\n",
-       "      <td>1278</td>\n",
-       "      <td>86</td>\n",
-       "      <td>129</td>\n",
+       "      <td>0.9432</td>\n",
+       "      <td>872</td>\n",
+       "      <td>17</td>\n",
+       "      <td>88</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9470</td>\n",
-       "      <td>0.9190</td>\n",
-       "      <td>0.9328</td>\n",
-       "      <td>1304</td>\n",
-       "      <td>73</td>\n",
-       "      <td>115</td>\n",
+       "      <td>0.9861</td>\n",
+       "      <td>0.9193</td>\n",
+       "      <td>0.9515</td>\n",
+       "      <td>854</td>\n",
+       "      <td>12</td>\n",
+       "      <td>75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9585</td>\n",
-       "      <td>0.9294</td>\n",
-       "      <td>0.9437</td>\n",
-       "      <td>1316</td>\n",
+       "      <td>0.9840</td>\n",
+       "      <td>0.9378</td>\n",
+       "      <td>0.9604</td>\n",
+       "      <td>860</td>\n",
+       "      <td>14</td>\n",
        "      <td>57</td>\n",
-       "      <td>100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9453</td>\n",
-       "      <td>0.9071</td>\n",
-       "      <td>0.9258</td>\n",
-       "      <td>1279</td>\n",
-       "      <td>74</td>\n",
-       "      <td>131</td>\n",
+       "      <td>0.9522</td>\n",
+       "      <td>0.9232</td>\n",
+       "      <td>0.9375</td>\n",
+       "      <td>877</td>\n",
+       "      <td>44</td>\n",
+       "      <td>73</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9563</td>\n",
-       "      <td>0.9273</td>\n",
-       "      <td>0.9416</td>\n",
-       "      <td>1313</td>\n",
-       "      <td>60</td>\n",
-       "      <td>103</td>\n",
+       "      <td>0.9873</td>\n",
+       "      <td>0.9326</td>\n",
+       "      <td>0.9592</td>\n",
+       "      <td>858</td>\n",
+       "      <td>11</td>\n",
+       "      <td>62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9541</td>\n",
-       "      <td>0.9251</td>\n",
-       "      <td>0.9394</td>\n",
-       "      <td>1310</td>\n",
-       "      <td>63</td>\n",
-       "      <td>106</td>\n",
+       "      <td>0.9874</td>\n",
+       "      <td>0.9330</td>\n",
+       "      <td>0.9594</td>\n",
+       "      <td>863</td>\n",
+       "      <td>11</td>\n",
+       "      <td>62</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                  Piece  Precision  Recall      F1    TP  FP   FN\n",
-       "0  Bach (Fugue_bwv_846)     0.9444  0.8740  0.9078   645  38   93\n",
-       "1  Bach (Fugue_bwv_848)     0.9614  0.9322  0.9465  1319  53   96\n",
-       "2  Bach (Fugue_bwv_848)     0.9376  0.9031  0.9200  1277  85  137\n",
-       "3  Bach (Fugue_bwv_848)     0.9494  0.9171  0.9329  1294  69  117\n",
-       "4  Bach (Fugue_bwv_848)     0.9370  0.9083  0.9224  1278  86  129\n",
-       "5  Bach (Fugue_bwv_848)     0.9470  0.9190  0.9328  1304  73  115\n",
-       "6  Bach (Fugue_bwv_848)     0.9585  0.9294  0.9437  1316  57  100\n",
-       "7  Bach (Fugue_bwv_848)     0.9453  0.9071  0.9258  1279  74  131\n",
-       "8  Bach (Fugue_bwv_848)     0.9563  0.9273  0.9416  1313  60  103\n",
-       "9  Bach (Fugue_bwv_848)     0.9541  0.9251  0.9394  1310  63  106"
+       "                  Piece  Precision  Recall      F1   TP  FP  FN\n",
+       "0  Bach (Fugue_bwv_846)     0.9463  0.9136  0.9297  423  24  40\n",
+       "1  Bach (Fugue_bwv_848)     0.9850  0.9500  0.9672  855  13  45\n",
+       "2  Bach (Fugue_bwv_848)     0.9621  0.9162  0.9386  864  34  79\n",
+       "3  Bach (Fugue_bwv_848)     0.9675  0.9260  0.9463  864  29  69\n",
+       "4  Bach (Fugue_bwv_848)     0.9809  0.9083  0.9432  872  17  88\n",
+       "5  Bach (Fugue_bwv_848)     0.9861  0.9193  0.9515  854  12  75\n",
+       "6  Bach (Fugue_bwv_848)     0.9840  0.9378  0.9604  860  14  57\n",
+       "7  Bach (Fugue_bwv_848)     0.9522  0.9232  0.9375  877  44  73\n",
+       "8  Bach (Fugue_bwv_848)     0.9873  0.9326  0.9592  858  11  62\n",
+       "9  Bach (Fugue_bwv_848)     0.9874  0.9330  0.9594  863  11  62"
       ]
      },
      "metadata": {},
@@ -891,9 +929,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean Precision: 0.941\n",
-      "Mean Recall : 0.9044\n",
-      "Mean F1 : 0.9221\n"
+      "Mean Precision: 0.9038\n",
+      "Mean Recall : 0.8915\n",
+      "Mean F1 : 0.8964\n"
      ]
     }
    ],
@@ -901,10 +939,10 @@
     "import pandas as pd\n",
     "\n",
     "eval_rows = []\n",
-    "for sample, result in zip(samples, all_results):\n",
+    "for result in all_results:\n",
     "    metrics = evaluate_one_piece(\n",
     "        ASAP_PATH,\n",
-    "        sample[\"metadata_row\"],\n",
+    "        result[\"metadata_row\"],\n",
     "        result[\"event_details\"],\n",
     "        result[\"response_events_normalized\"],\n",
     "        result[\"response_onset_offset\"],\n",

From 249e61b659c73cc6c34246d890e0e240d285cbc8 Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Wed, 8 Jul 2026 22:29:02 +0100
Subject: [PATCH 02/15] plan for optimisation

---
 notebooks/optimise_alignment.ipynb | 362 +++++++++++++++++++++++++++++
 1 file changed, 362 insertions(+)
 create mode 100644 notebooks/optimise_alignment.ipynb

diff --git a/notebooks/optimise_alignment.ipynb b/notebooks/optimise_alignment.ipynb
new file mode 100644
index 0000000..22b8fb5
--- /dev/null
+++ b/notebooks/optimise_alignment.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "67c86ddb",
+   "metadata": {},
+   "source": [
+    "The `event_alignment_ED` is basically an O(N×M) dynamic programming procedure, and is implemented as a nested for-loop. For a Bach fugue with roughly 1,400 events on each side, this already amounts to close to 2 million cell computations. For a substantially longer piece such as a Liszt piano sonata (~16,000 notes), the same recursion would require on the order of 2.5×10⁸ cell computations. Consider that sutudents' practice is usually not that long as the sonata, `max_notes = 3500` was introduced as a hard cut-off to skip such pieces during evaluation.\n",
+    "\n",
+    "In order to make the evaluation more comprehensive and improve the algorithm, can consider optimisation methods such as vectorised NumPy operation and Numba to reduce the computational cost. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11c82556",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-08T19:50:26.720009Z",
+     "iopub.status.busy": "2026-07-08T19:50:26.719339Z",
+     "iopub.status.idle": "2026-07-08T19:50:28.604012Z",
+     "shell.execute_reply": "2026-07-08T19:50:28.602549Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from evaluation_function.compare_MIDI import (\n",
+    "    compute_event_cost,\n",
+    "    group_notes_into_events,\n",
+    "    normalize_start_times,\n",
+    "    DEFAULT_GAP_PENALTY,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d54cad29",
+   "metadata": {},
+   "source": [
+    "**Precomputes the whole cost matrix:**\n",
+    "- using numpy broadcasting for note-vs-note pairs, instead of calling compute_event_cost() once per cell in a pure Python loop. \n",
+    "- chord vs chord: encode each chord as a 12-dimensional pitch-class binary vector, then compute all pairwise Hamming distances using broadcasting\n",
+    "- note vs chord (type mismatch): fill directly with `gap_penalty`\n",
+    "\n",
+    "\n",
+    "**Add a band around the diagonal**\n",
+    "- See Muller's book section 3.2.2.3 (Sakoe-Chiba band used in DTW), instead of filling the whole (N+1) x (M+1) matrix, this constrained version only fills a band around the diagonal. This is safe as long as the student performance is not wildly different in length from the reference (which is true in almost all practice scenarios). Cells outside the band are treated as \"too expensive\", so the aligner will never choose a path through them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccba850e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-08T19:50:28.607168Z",
+     "iopub.status.busy": "2026-07-08T19:50:28.606866Z",
+     "iopub.status.idle": "2026-07-08T19:50:28.619428Z",
+     "shell.execute_reply": "2026-07-08T19:50:28.618085Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# A very large number used to represent \"outside the search band, do not use this cell\"\n",
+    "LARGE_PENALTY = 1000000\n",
+    "\n",
+    "def build_cost_matrix(response_events, ref_events, gap_penalty):\n",
+    "    \"\"\"\n",
+    "    Precompute the full cost matrix between response_events\n",
+    "    and ref_events, using vectorized numpy operations where possible.\n",
+    "\n",
+    "    Args:\n",
+    "        response_events: list of event dicts\n",
+    "        ref_events: list of event dicts\n",
+    "        gap_penalty: cost used for note-vs-chord type mismatches\n",
+    "\n",
+    "    Returns:\n",
+    "        cost_matrix: numpy array of shape (N, M), where\n",
+    "            cost_matrix[i, j] is the cost of aligning response_events[i]\n",
+    "            with ref_events[j]\n",
+    "    \"\"\"\n",
+    "\n",
+    "    return cost_matrix\n",
+    "\n",
+    "\n",
+    "def event_alignment_ED_banded(response_events, ref_events, gap_penalty, band_width=20):\n",
+    "    \"\"\"\n",
+    "    Banded version of event_alignment_ED(). Only cells within band_width of\n",
+    "    the diagonal are considered valid alignment candidates, which reduces\n",
+    "    the amount of work needed for long MIDI sequences.\n",
+    "\n",
+    "    Args:\n",
+    "        response_events: list of event dicts\n",
+    "        ref_events: list of event dicts\n",
+    "        gap_penalty: cost of leaving an event unaligned\n",
+    "        band_width: how far (in event-index terms) the search area extends\n",
+    "            on either side of the diagonal. Increase this if the response\n",
+    "            and reference are expected to differ a lot in length. If the\n",
+    "            true optimal path falls outside the band, the result will not\n",
+    "            be optimal, so choose band_width generously relative to how much\n",
+    "            students are expected to add or drop notes.\n",
+    "\n",
+    "    Returns:\n",
+    "        operations: same format as event_alignment_ED()\n",
+    "        D: accumulated cost matrix, shape (N+1, M+1)\n",
+    "    \"\"\"\n",
+    "    # if a raw note dict with \"pitch\"/\"start\"/\"duration\" but no \"event_type\" is\n",
+    "    # passed in, group them into events first. \n",
+    "    if \"event_type\" not in response_events[0]:\n",
+    "        raise ValueError(\"response_events must already be grouped into events\")\n",
+    "    if \"event_type\" not in ref_events[0]:\n",
+    "        raise ValueError(\"ref_events must already be grouped into events\")\n",
+    "\n",
+    "    # the rows of D correspond to response events\n",
+    "    N = len(response_events)\n",
+    "    # the columns of D correspond to reference events\n",
+    "    M = len(ref_events)\n",
+    "\n",
+    "    # cost matrix for all event pairs is precomputed \n",
+    "    cost_matrix = build_cost_matrix(response_events, ref_events, gap_penalty)\n",
+    "\n",
+    "    # Build the accumulated cost matrix D of size (N+1 x M+1)\n",
+    "    D = np.full((N + 1, M + 1), LARGE_PENALTY, dtype=int)\n",
+    "    D[0, 0] = 0\n",
+    "\n",
+    "    # Boundary conditions: aligning against an empty sequence means every event\n",
+    "    # is unaligned, so the cost is n (or m) times the gap penalty.\n",
+    "    for n in range(1, N + 1):\n",
+    "        if n <= band_width:\n",
+    "            D[n, 0] = n * gap_penalty # n extra response events\n",
+    "    for m in range(1, M + 1):\n",
+    "        if m <= band_width:\n",
+    "            D[0, m] = m * gap_penalty # m missing ref events\n",
+    "\n",
+    "    # Recursion (accumulated cost / score matrix D):\n",
+    "    for n in range(1, N + 1):\n",
+    "        # Only look at the columns inside the band for this row:\n",
+    "        # instead of looping over all M columns,\n",
+    "        # only loop over roughly 2 * band_width columns.\n",
+    "        m_low = max(1, n - band_width)\n",
+    "        m_high = min(M, n + band_width)\n",
+    "        for m in range(m_low, m_high + 1):\n",
+    "            replace_cost = cost_matrix[n - 1, m - 1]\n",
+    "            D[n, m] = min(\n",
+    "                D[n - 1, m - 1] + replace_cost,\n",
+    "                D[n - 1, m] + gap_penalty,\n",
+    "                D[n, m - 1] + gap_penalty,\n",
+    "            )\n",
+    "\n",
+    "    # Backtrack and classify each transformation op based on movement direction in D\n",
+    "    # (it will naturally stay inside the band, since cells outside the band \n",
+    "    # all carry the LARGE_PENALTY value and will never be chosen as the minimum).\n",
+    "    operations = []\n",
+    "    n, m = N, M\n",
+    "    while n > 0 or m > 0:\n",
+    "        # Boundary conditions: at the top row, only horizontal moves possible\n",
+    "        if n == 0:\n",
+    "            # Missing response for ref[m-1] (deletion)\n",
+    "            operations.append({\n",
+    "                \"type\": \"missing\",\n",
+    "                \"response_idx\": None,\n",
+    "                \"reference_idx\": m - 1,\n",
+    "                \"cost\": gap_penalty,\n",
+    "            })\n",
+    "            m -= 1\n",
+    "        # At the leftmost column, only vertical moves possible\n",
+    "        elif m == 0:\n",
+    "            # Extra event response[n-1] (insertion)\n",
+    "            operations.append({\n",
+    "                \"type\": \"extra\",\n",
+    "                \"response_idx\": n - 1,\n",
+    "                \"reference_idx\": None,\n",
+    "                \"cost\": gap_penalty,\n",
+    "            })\n",
+    "            n -= 1\n",
+    "        # For all other cases, we can move in any direction (diagonal, vertical, horizontal)\n",
+    "        else:\n",
+    "            replace_cost = cost_matrix[n - 1, m - 1]\n",
+    "            diag = D[n - 1, m - 1] + replace_cost # # diagonal: match or replacement\n",
+    "            up = D[n - 1, m] + gap_penalty # vertical: extra event in response[n-1]\n",
+    "            left = D[n, m - 1] + gap_penalty # horizontal: missing event in reference[m-1]\n",
+    "            min_cost = min(diag, up, left) # find the minimum cost step\n",
+    "            \n",
+    "            # classify the transformation ops based on the minimum cost step\n",
+    "            # rule: always prefer diagonal > insertion or deletion\n",
+    "            if min_cost == diag: # Diagonal -> two events are aligned (match or replacement)\n",
+    "                operations.append({\n",
+    "                    \"type\": \"match\" if replace_cost == 0 else \"replacement\",\n",
+    "                    \"response_idx\": n - 1,\n",
+    "                    \"reference_idx\": m - 1,\n",
+    "                    \"cost\": replace_cost,\n",
+    "                })\n",
+    "                n, m = n - 1, m - 1\n",
+    "            elif min_cost == up: # Vertical -> response[n-1] is extra (insertion)\n",
+    "                operations.append({\n",
+    "                    \"type\": \"extra\",\n",
+    "                    \"response_idx\": n - 1,\n",
+    "                    \"reference_idx\": None,\n",
+    "                    \"cost\": gap_penalty,\n",
+    "                })\n",
+    "                n -= 1\n",
+    "            else: # Horizontal -> response is missing for ref[m-1] (deletion)\n",
+    "                operations.append({\n",
+    "                    \"type\": \"missing\",\n",
+    "                    \"response_idx\": None,\n",
+    "                    \"reference_idx\": m - 1,\n",
+    "                    \"cost\": gap_penalty,\n",
+    "                })\n",
+    "                m -= 1\n",
+    "\n",
+    "    operations.reverse() # Reverse to get ops in order from first note to last\n",
+    "    return operations, D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1ff03f8",
+   "metadata": {},
+   "source": [
+    "**Verify Equivalence with test cases**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95a6044d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-08T19:50:28.632709Z",
+     "iopub.status.busy": "2026-07-08T19:50:28.632444Z",
+     "iopub.status.idle": "2026-07-08T19:50:29.078084Z",
+     "shell.execute_reply": "2026-07-08T19:50:29.076689Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "note_count~50  events=41x43  match=True\n",
+      "note_count~300  events=246x240  match=True\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "note_count~800  events=657x651  match=True\n",
+      "\n",
+      "If all lines above say match=True, the vectorised version is verified correct.\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aab57c9",
+   "metadata": {},
+   "source": [
+    "**Timing Comparison**\n",
+    "\n",
+    "Measure the runtime of both implementations at several data sizes, and plot \"time vs. piece size\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d310e8fb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-08T19:50:29.081318Z",
+     "iopub.status.busy": "2026-07-08T19:50:29.079824Z",
+     "iopub.status.idle": "2026-07-08T19:50:30.631757Z",
+     "shell.execute_reply": "2026-07-08T19:50:30.630381Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N=M~50: old=0.0009s  vectorised=0.0003s  speedup=2.8x\n",
+      "N=M~100: old=0.0028s  vectorised=0.0004s  speedup=6.4x\n",
+      "N=M~200: old=0.0111s  vectorised=0.0015s  speedup=7.6x\n",
+      "N=M~400: old=0.0466s  vectorised=0.0056s  speedup=8.3x\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N=M~800: old=0.1977s  vectorised=0.0695s  speedup=2.8x\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N=M~1500: old=0.7535s  vectorised=0.4387s  speedup=1.7x\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0335fb99",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-08T19:50:30.633793Z",
+     "iopub.status.busy": "2026-07-08T19:50:30.633523Z",
+     "iopub.status.idle": "2026-07-08T19:50:30.803542Z",
+     "shell.execute_reply": "2026-07-08T19:50:30.802265Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HPXr0wNXVVb9+s2bNnnoOCvOSYlY8V2lFVUxMjFHLX79+HQsLC8qUKWPQXqRIEdzc3PS/VBo1akSHDh2YMGEC7u7utG3blkWLFpGYmPjMMaf9AkmT9qHm7e2drv3JD8/169dTp04d7OzsKFiwoL67Q1RUVLZiePLr96x06dKFN954g0OHDtG3b1+aNGny1HWuXLkCQOXKlTNdJi3X5cuXTzevQoUKBr/gAezs7NL94nF1daV48eLpftFllDuAsmXLGkw7OTnh5eWV43cQnz17lrfeegtXV1dcXFzw8PDg3XffBcj2sYKsc1WxYkUiIyN59OiRQfuT51mBAgUAMv2FbKpnOS6XLl1i06ZNeHh4GPw0bdoUSO3uk13Xr1+naNGi6f7ATesC8uR5Zcx7ITw8nPj4+HSfG0CGbZkx5pjk9LnTuXNnbt68yf79+4HU9+bRo0fp3LmzwTL169enT58+FC5cmC5durB69WqjCtvOnTuzd+9ebt26BcCuXbsIDw832P6lS5eIiorC09Mz3bGOjY3VH2djPjeycv36dcqWLYuFhWHp8SzH3tbWlqlTp/LXX39RuHBhfXeasLAwg/0DeO2119Lt35YtW/T7l/b6T34OQcbvbZF7SJ9Z8Vy5uLhQtGhRzpw5k631nvZXvkaj4ZdffuHAgQP8+eefbN68mV69evH1119z4MCBp/YjzEpmw71k1P741ZTdu3fz5ptv0rBhQ77//nu8vLywtrZm0aJF/PTTT9mK4fGrP09z7949jhw5AqSOrarT6dL9sngRspM3INO+wKbSarVGLffw4UMaNWqEi4sLEydOpHTp0tjZ2XHs2DFGjhyZrStfz+JF5eVZjotOp6NZs2Z8/PHHGS5brly5Zw/wKbLzXnhWT8vJ8zh32rRpg4ODA6tXr6ZevXqsXr0aCwsLOnbsqF/G3t6ef/75h507d7JhwwY2bdrEqlWreO2119iyZUuWQ1R17tyZ0aNHs2bNGoYNG8bq1atxdXWlRYsW+mV0Oh2enp6sWLEiw21kp99xTjL22A8bNow2bdrw+++/s3nzZv73v/8xZcoUduzYQfXq1fXHZdmyZQZ9h9PIKDF5nxxB8dy1bt2aH3/8kf3791O3bt0sl/Xx8UGn03Hp0iX9X+sAd+/e5eHDh/j4+BgsX6dOHerUqcOkSZP46aef6NatGytXrqRPnz4v/GlZa9euxc7Ojs2bNxsM/bNo0aJ0y+ZkbAMHDiQmJoYpU6YwevRoZs6cma6bxpPSbjg7c+ZMpleu0nIdEhLCa6+9ZjAvJCQk3bHICZcuXeLVV1/VT8fGxnLnzh2Dm9kKFCjAw4cPDdZLSkrizp07Bm2Z5XjXrl3cu3ePX3/9lYYNG+rbQ0ND0y1r7HF6PFdPunDhAu7u7jg6Ohq1rad5ked16dKliY2N1V+JzQk+Pj5s27aNmJgYg6uzaV/Rm3JeeXp6Ymdnx+XLl9PNy6jNVNk5d4zl6OhI69atWbNmDdOnT2fVqlU0aNDAoLsEgIWFBU2aNKFJkyZMnz6dyZMnM2bMGHbu3Jnl8SlZsiS1a9dm1apVDBo0iF9//ZV27doZfEaVLl2abdu2Ub9+/SwLSGM+NyDzc9THx4dTp06l+4P7WY7947F9+OGHfPjhh1y6dAl/f3++/vprli9fro/b09Mzy1ylvX7aldzHZfTeFrmHdDMQz93HH3+Mo6Mjffr04e7du+nmX7lyhVmzZgHoi5Ynnx4zffp0AP2IAA8ePEh3Bcvf3x9A39XAwcEBIF3h87xYWlqi0WgMrhBeu3Ytwyd9OTo65khcv/zyC6tWreKLL75g1KhRdOnShU8//ZSLFy9mud7rr7+Os7MzU6ZMISEhwWBeWl4DAgLw9PRk7ty5Bt03/vrrL86fP68/Fjnpxx9/NOgDPWfOHFJSUgz6PZYuXdqgr2baek9emU0rHp/Mc9pVrMfPn6SkJL7//vt08Tg6Ohr11bGXlxf+/v4sWbLE4PXOnDnDli1bnjqyRHYYG1NO6NSpE/v372fz5s3p5j18+JCUlBT99J07d7hw4cJT+7C3atUKrVbL7NmzDdpnzJiBRqPJVh/XNJaWljRt2pTff/+d27dv69svX76crg/us8jOuZMdnTt35vbt28yfP5+TJ08adAEAuH//frp1nvy8e9r2Dxw4wMKFC4mMjEy3/U6dOqHVavnss8/SrZuSkqI/p4353IDMz9FWrVoRFhZm0Ic3JSWFb7/9FicnJxo1avTUfXlSXFxculhKly6Ns7OzPjfNmzfHxcWFyZMnZ3h+RkREAIbv48fj37p1K+fOnct2bOLFkSuz4rkrXbo0P/30E507d6ZixYoGTwDbt2+ffmgWgGrVqtGjRw9+/PFH/Vd6hw4dYsmSJbRr105/1W7JkiV8//33vPXWW5QuXZqYmBjmzZuHi4uLvnCwt7fHz8+PVatWUa5cOQoWLEjlypVN7u/1NG+88QbTp0+nRYsWdO3alfDwcL777jvKlCnDqVOnDJatWbMm27ZtY/r06RQtWpSSJUsSGBiYrdcLDw9nwIABvPrqqwwaNAiA2bNns3PnToKDg9mzZ0+m3Q1cXFyYMWMGffr0oVatWnTt2pUCBQpw8uRJ4uLiWLJkCdbW1kydOpWePXvSqFEj3nnnHf3QXL6+vnzwwQemJSoLSUlJNGnShE6dOhESEsL333/PK6+8wptvvqlfpk+fPvTv358OHTrQrFkzTp48yebNm3F3dzfYlr+/P5aWlkydOpWoqChsbW157bXXqFevHgUKFKBHjx4MGTIEjUbDsmXLMvx6v2bNmqxatYrhw4dTq1YtnJycaNOmTYaxT5s2jZYtW1K3bl169+6tH5rL1dU1R59Al52YntVHH33EH3/8QevWrQkODqZmzZo8evSI06dP88svv3Dt2jV93o0dmqtNmza8+uqrjBkzhmvXrlGtWjW2bNnCunXrGDZsWLph6ow1fvx4tmzZQv369RkwYIC+YK5cuTInTpwwaZtPys65kx2tWrXC2dmZESNGYGlpSYcOHQzmT5w4kX/++Yc33ngDHx8fwsPD+f777ylevDivvPLKU7ffqVMnRowYwYgRIyhYsGC6q5ONGjXivffeY8qUKZw4cYLXX38da2trLl26xJo1a5g1axZvv/22UZ8bkPk52q9fP3744QeCg4M5evQovr6+/PLLL+zdu5eZM2cafaPw4y5evKj/zPDz88PKyorffvuNu3fv6ocec3FxYc6cOXTv3p0aNWrQpUsXPDw8uHHjBhs2bKB+/fr6P66mTJnCG2+8wSuvvEKvXr24f/8+3377LZUqVSI2Njbb8YkX5MUPoCDyq4sXL6q+ffsqX19fZWNjo5ydnVX9+vXVt99+azCkTXJyspowYYIqWbKksra2Vt7e3ukemnDs2DH1zjvvqBIlSugH+G7durU6cuSIwWvu27dP1axZU9nY2Bj90IQnh/tKG0rpyWFx0gakf9yCBQtU2bJlla2trapQoYJatGhRhkNqXbhwQTVs2FDZ29tn+NCEjIbgeXI77du3V87OzuratWsGy61bt04BaurUqZnua5o//vhD1atXT9nb2ysXFxdVu3Zt9fPPPxsss2rVKlW9enVla2urChYsmOVDE57UqFEjValSpXTtPj4+6o033tBPP/nQhAIFCignJyfVrVs3g6GulEod3H3kyJH6ge2bN2+uLl++nG64JaWUmjdvnipVqpSytLQ0GKZr7969qk6dOsre3l4VLVpUffzxx2rz5s3phvKKjY1VXbt2VW5ubkY9NGHbtm2qfv36+ny2adMm04cmPHmM03LwtKGtshPTsx4XpVKHNRo9erQqU6aMsrGxUe7u7qpevXrqq6++MhhGzdihudK2+cEHH6iiRYsqa2trVbZs2SwfmpCRjN7P27dvV9WrV9c/3GH+/Pnqww8/VHZ2dun2M6OhuZ587+/cuTPdOWHsuZPdQfa7deumHx7sSdu3b1dt27ZVRYsWVTY2Nqpo0aLqnXfeSTdkWlbq16+f4bCHj/vxxx9VzZo1lb29vXJ2dlZVqlRRH3/8sbp9+7bBck/73MjsHFUq9aEJPXv2VO7u7srGxkZVqVIl3fvo8YcmPOnJ8zwyMlINHDhQVahQQTk6OipXV1cVGBioVq9enW7dnTt3qubNmytXV1dlZ2enSpcurYKDg9P93li7dq2qWLGisrW1VX5+fvLQhDxAo1QO320ghBDZlDZY++HDhwkICDB3OOIl0q5dO86ePZthP0ghxMtB+swKIYR4KcTHxxtMX7p0iY0bN9K4cWPzBCSEeCGkz6wQQoiXQqlSpQgODqZUqVJcv36dOXPmYGNjk+nQYkKIl4MUs0IIIV4KLVq04OeffyYsLAxbW1vq1q3L5MmTMxwEXwjx8pA+s0IIIYQQIs+SPrNCCCGEECLPkmJWCCGEEELkWfmuz6xOp+P27ds4Ozu/8MedCiGEEEKIp1NKERMTQ9GiRTN9AFCafFfM3r59G29vb3OHIYQQQgghnuLmzZsUL148y2XyXTGb9ri8mzdv4uLikuEyOp2OiIgIPDw8nvrXgPiP5M00krfsk5yZRvJmGslb9knOTCN5+090dDTe3t5GPeY43xWzaV0LXFxcsixmExIScHFxyfcnU3ZI3kwjecs+yZlpJG+mkbxln+TMNJK39IzpEiqZEkIIIYQQeZYUs0IIIYQQIs+SYlYIIYQQQuRZ+a7PrDGUUmi1WhISEqTPSjbodDqSk5Mlb9kkecs+yZlpcnverK2tsbS0NHcYQog8RorZJyQlJXH79m1iYmKIioqSsWizQSmFTqcjJiZG8pYNkrfsk5yZJrfnTaPRULx4cZycnMwdihAiD5Fi9jE6nY7Q0FAsLS0pVqwYdnZ2ufLqRW6llCIlJQUrK6tc+Ysyt5K8ZZ/kzDS5OW9KKSIiIvj3338pW7asXKEVQhhNitnHJCUlodPpKF68ODY2NrnyAz83y82/KHMzyVv2Sc5Mk9vz5uHhwbVr10hOTpZiVghhNLnsmAG5GiuEEC9ebiywhRC5n1RtQgghhBAiz5JuBkIIIYQQIlNaneJQ6H3CYxLwdLajdsmCWFrknm9S5Mrsc6LVKfZfuce6E7fYf+UeWp0yd0hZunbtGhqNhhMnThi9zuLFi3Fzc3vhcfj6+jJz5swcfV1T3bt3D09PT65du2buUIQwu8aNGzNs2DD99OPv1aSkJHx9fTly5Ih5ghNCmGTTmTu8MnUH78w7wNCVJ3hn3gFembqDTWfumDs0PSlmnwNzHfibN2/Sq1cvihYtio2NDT4+PgwdOpR79+49dV1vb2/u3LlD5cqVjX69zp07c/HixWcJOc+bNGkSbdu2xdfX19yh5AiNRsPvv/9u7jCei9y0b7t27UKj0fDw4UNzh/LC2NjYMGLECEaOHGnuUIQQRtp05g4Dlh/jTlSCQXtYVAIDlh/LNQWtFLM5zFwH/urVqwQEBHDp0iV+/vlnLl++zNy5c9m+fTt169bl/v37ma6blJSEpaUlRYoUwcrK+J4n9vb2eHp65kT4eVJcXBwLFiygd+/e5g5FiGxJTk42y+t269aNPXv2cPbsWbO8vhDCeFqdYsKf58joe+W0tgl/nssV3zxLMfsUSiniklKM+olJSGbcH2ezPPDj/zhHTEKyUdtTyvgTZODAgdjY2LBlyxYaNWpEiRIlaNmyJdu2bePWrVuMGTNGv6yvry+fffYZQUFBuLi40K9fvwy/3v/jjz8oW7YsdnZ2vPrqqyxZssTgatKT3QzGjx9PQEAAy5Ytw9fXF1dXV7p06UJMTIx+mU2bNvHKK6/g5uZGoUKFaN26NVeuXDF6PzNy48YN2rZti5OTEy4uLnTq1Im7d+8aLDNnzhxKly6NjY0N5cuXZ9myZQbzNRoNc+bMoWXLltjb21OqVCl++eWXLF9348aN2NraUqdOHYP2s2fP0rp1a1xcXHB2dqZBgwb6fdTpdEycOJHixYtja2uLv78/mzZt0q+bdhxWr15NgwYNsLe3p1atWly8eJHDhw8TEBCAk5MTLVu2JCIiQr9ecHAw7dq1Y8KECXh4eODi4kL//v1JSkrSL5NR9wx/f3/Gjx+vnw/w1ltvodFoDK42r1u3jho1amBnZ0epUqWYMGECKSkpWeYnzcOHD+nTp48+rtdee42TJ08CcPHiRTQaDRcuXDBYZ8aMGZQuXVo/febMGVq2bImTkxOFCxcmKCiIyMhI/fzGjRszZMgQPv74YwoWLEiRIkX0+/W0fcvKn3/+Sa1atbCzs8Pd3Z233npLP+/BgwcEBQVRoEABHBwcaNmyJZcuXdLPv379Om3atKFAgQI4OjpSqVIlNm7cyLVr13j11VcBKFCgABqNhuDg4AxfP+099vvvv+vfi82bN+fmzZsGyz3t+KSd323btsXNzY1JkyZl+HqJiYmMHDkSb29vbG1tKVOmDAsWLNDPf/I4dO/e3eA4PE2BAgWoX78+K1euNHodIYR5HAq9n+7C3OMUcCcqgUOhmV8se1HkBrCniE/W4jd2c45sSwFh0QlUGb/FqOXPTWyOg83TD9H9+/fZvHkzkyZNwt7e3mBekSJF6NatG6tWreL777/XD33z1VdfMXbsWMaNG5fhNkNDQ3n77bcZOnQoffr04fjx44wYMeKpsVy9epV169axfv16Hjx4QKdOnfjiiy/0vzwfPXrE8OHDqVq1KrGxsYwdO5a33nqLEydOmDQkmk6n0xeyf//9NykpKQwcOJDOnTuza9cuAH777TeGDh3KzJkzadq0KevXr6dnz54UL15cX1QA/O9//+OLL75g1qxZLFu2jC5dunD69GkqVqyY4Wvv3r2bmjVrGrTdunWLhg0b0rhxY3bs2IGLiwt79+7VFxazZs3i66+/5ocffqB69eosXLiQtm3bcuLECYPXGTduHDNnzqREiRL06tWLrl274uzszKxZs3BwcKBTp06MHTuWOXPm6NfZvn07dnZ27Nq1i2vXrtGzZ08KFSqUaeHypMOHD+Pp6cmiRYto0aKFfpzP3bt3ExQUxDfffKMvzPv166eP82k6duyIvb09f/31F66urvzwww80adKEixcvUq5cOQICAlixYgWfffaZfp0VK1bQtWtXILUYfu211+jTpw8zZswgPj6ekSNH0rVrV3bs2KFfZ8mSJQwfPpyDBw+yf/9+goODqV+/Ps2aNct037KyYcMG3nrrLcaMGcPSpUtJSkpi48aN+vnBwcFcunSJP/74AxcXF0aOHEmrVq04d+4c1tbWDBw4kKSkJP755x8cHR05d+4cTk5OeHt7s3btWjp06EBISAguLi7p3rePi4uLY9KkSSxduhQbGxvef/99unTpwt69e7N1fMaPH8+UKVOYNm0adnZ2Gb5WUFAQ+/fv55tvvqFatWqEhobqi9XMjkOnTp0MjsPT1K5dm927dxu9vBDCPMJjMi9kTVnuuVL5TFRUlAJUVFRUunnx8fHq3LlzKi4uTiUlJSmdTqceJSYrn5HrzfLzKDHZqH06cOCAAtRvv/2W4fzp06crQN29e1cppZSPj49q166dwTKhoaEKUMePH1dKKTVy5EhVuXJlg2XGjBmjAPXgwQOllFKLFi1Srq6u+vljx45VDg4OBrn96KOPVGBgYKaxR0REKECdPn06wzgy4uPjo2bMmKGUUmrLli3K0tJS3bhxQz//7NmzClCHDh1SSilVr1491bdvX4NtdOzYUbVq1Uo/Daj+/fsbLBMYGKgGDBiQaRxt27ZVvXr1MmgbPXq0KlmypEpKSspwnaJFi6pJkyYZtNWqVUv1799f6XQ6/f7Pnz9fP//nn39WgNq+fbu+bcqUKap8+fL66R49eqiCBQuqR48e6dvmzJmjnJyclFarVUoZ5i1NtWrV1Lhx4wzy8OR51KRJEzV58mSDtmXLlikvL68M9/Fxu3fvVi4uLiohIcGgvXTp0uqHH35QSik1Y8YMVbp0af28kJAQBajz588rpZT67LPP1Ouvv26w/o0bNxSgLly4oJRSqlGjRuqVV14xWKZWrVpq5MiRWe5bVurWrau6deuW4byLFy8qQO3du1ffFhkZqezt7dXq1auVUkpVqVJFjR8/PsP1d+7cafBeysyiRYsUoA4cOKBvO3/+vALUwYMHlVLGHR9ADRs2TOl0Ov1n25PS8r5169YMY8noONy8eVMBKiQkRCmVehyGDh2qn5/ROTdr1izl6+ub4WukfQbHx8dnON9ctFqtunPnjv69JJ5Ocmaa3JS3fZcjjapV9l2OfC6vn1W99iS5MvsU9taWnJvY3KhlD4XeJ3jR4acut7hnLWqXLGjUa2eHyka3hICAgCznh4SEUKtWLYO22rVrP3W7Pj4+ODs766e9vLwIDw/XT1+6dImxY8dy8OBBIiMj0el0QGpXgezcfJbm/PnzeHt74+3trW/z8/PDzc2N8+fPU6tWLc6fP6+/UpWmfv36zJo1y6Ctbt266aazGlUhPj4+3RWuEydO0KBBA6ytrdMtHx0dze3bt6lfv75Be7169fRfu6epWrWq/v+FCxcGoEqVKgZtj+cVoFq1ajg4OBjEHxsby82bN/Hx8cl0P57m5MmT7N271+AKr1arJSEhgbi4OIPXzGjd2NhYChUqZNAeHx+v73rRpUsXRowYwYEDB6hTpw4rVqygRo0aVKhQQb+NnTt34uTklG77V65coXz58oBhziD9uZddJ06coG/fvhnOO3/+PFZWVgQGBurbChUqRPny5Tl//jwAQ4YMYcCAAWzZsoWmTZvSoUOHdDEaw8rKyuC9WKFCBf35Xbt2baOPz9Pe8ydOnMDS0pJGjRplOP9px6FcuXJG7Y+9vT1xcXFGLSuEMJ8KXs5YW2pI1mZcW2iAIq52RtUzz5sUs0+h0WiM+qofoEFZD7xc7QiLSsiw32zagW9Q1iNHx2crU6YMGo2G8+fPG/TpS3P+/HkKFCiAh4eHvs3R0THHXv9xTxZxGo1GX7ACtGnTBh8fH+bNm0fRokXR6XRUrlzZoG9nXuHu7s6DBw8M2rL6ujg7Hs9jWteQJ9sez6sxLCws0v3BY8yNQLGxsUyYMIH27dunm5fZ19WPr+vl5aXv8vG4tP7WRYoU4bXXXuOnn36iTp06/PTTTwwYMMBgG23atGHq1Kn6NvX/j2V9/I+Yp5172fWsx7JPnz40b96cDRs2sGXLFqZMmcLXX3/N4MGDn2m7TzL2+DztPf+0/c3oOKTx8vIyMtrUblGPfxYJIXKf+4+SCFp4MMtCFmBcG79cMd6s3ACWgywtNIxr4wf8d6DTPM8DX6hQIZo1a8b3339PfHy8wbywsDBWrFhB586ds/WoyPLly6cbD/Lw4adfdc7KvXv3CAkJ4dNPP6VJkyZUrFgxXTGYXRUrVuTmzZsGN8ScO3eOhw8f4ufnp18mrX9hmr179+rnpzlw4EC66cz6ywJUr16dc+fOGbRVrVqV3bt3Z1gkuri4ULRo0XSx7Nu3L8vXMdbJkycNjv+BAwf0fTQh9bn3d+78N5pGdHQ0oaGhBtuwtrZGq9UatNWoUYOQkBDKlCmT7udp/Zxr1KhBWFgYVlZW6dZ1d3fXL5fWr3v//v1cvXqVLl26GGzj7Nmz+Pr6pttGdv4oy2jfslK1alW2b9+e4byKFSuSkpLCwYMH9W1p5/fj55W3tzf9+/fn119/5cMPP2TevHlA6jBVgFHxpKSkGLwXQ0JCePjwof6ceZbj87gqVaqg0+n4+++/M5yfU8fhzJkzVK9e3ejlhRAvVnh0Ap1/2M+ZW9EUdLThk5YV8HI1vHBRxNWOOe/WoEVl4/+QfZ6kmM1hLSp7MefdGhR5wQd+9uzZJCYm0rx5c/755x9u3rzJpk2baNasGcWKFTP6JqA07733HhcuXGDkyJFcvHiR1atXs3jxYsD056cXKFCAQoUK8eOPP3L58mV27NjB8OHDTdpWmqZNm1KlShW6devGsWPHOHToEEFBQTRq1Ej/tepHH33E4sWLmTNnDpcuXWL69On8+uuv6W5oW7NmDQsXLuTixYuMGzeOQ4cOMWjQoExfu3nz5pw9e9agIB80aBDR0dF06dKFI0eOcOnSJZYtW0ZISIg+lqlTp7Jq1SpCQkIYNWoUJ06cyPJ1jJWUlETv3r05d+4cGzduZNy4cQwaNEhf0Lz22mssW7aM3bt3c/r0aXr06JHuRihfX1+2b99OWFiYfr/Gjh3L0qVLmTBhAmfPnuX8+fOsXLmSTz/99KkxNW3alLp169KuXTu2bNnCtWvX2LdvH2PGjDEo0Nq3b09MTAwDBgzg1VdfpWjRovp5AwcO5P79+7zzzjscPnyYK1eusHnzZvr06ZOt4jSjfcvKuHHj+Pnnnxk3bhznz5/n9OnT+quSZcuWpW3btvTt25c9e/Zw8uRJ3n33XYoVK0bbtm0BGDZsGJs3byY0NJRjx46xc+dOfQHq4+ODRqNh/fr1REREEBsbm2kc1tbWDB48mIMHD3L06FGCg4OpU6eOvtvPsxyfJ/PTo0cPevXqxe+//05oaCi7du1i9erVQObHoWfPntk6Drt37+b111/PVmxCiBfj5v04Ov6wn0vhsRR2sWX1e3Xo16g0e0a+xs996zCriz8/963DnpGv5ZpCFpAbwB6X0Q1gpkrR6tS+y5Hq9+P/qn2XI1WK1vRtGevatWuqR48eqnDhwsra2lp5e3urwYMHq8hIw87ZGd2UkdGNV+vWrVNlypRRtra2qnHjxmrOnDkK0N+ckdENYFWrVjXI24wZM5SPj49+euvWrapixYrK1tZWVa1aVe3atcvgxpzs3gCmlFLXr19Xb775pnJ0dFTOzs6qY8eOKiwszGCd77//XpUqVUpZW1urcuXKqaVLlxrMB9R3332nmjVrpmxtbZWvr69atWpVpjGkqV27tpo7d65B28mTJ9Xrr7+uHBwclLOzs2rQoIG6cuWKUiq1c//48eNVsWLFlLW1tapWrZrauHGj/nzLaP8zulnoydz36NFDtW3bVo0dO1YVKlRIOTk5qb59+xrceBUVFaU6d+6sXFxclLe3t1q8eHG6G8D++OMPVaZMGWVlZWVw3DZt2qTq1aun7O3tlYuLi6pdu7b68ccfn5ofpZSKjo5WgwcPVkWLFtWfl926dTO4aU8ppTp16qQAtXDhwnTbuHjxonrrrbeUm5ubsre3VxUqVFBDhgzR3yTx5I1HSqXeoNejR4+n7ltW1q5dq/z9/ZWNjY1yd3dX7du318+7f/++6t69u3J1dVX29vaqefPm6uLFi/r5gwYNUqVLl1a2trbKw8NDde/e3eC9OHHiRFWkSBGl0WgM4nxc2nFeu3atKlWqlLK1tVVNmzZV169fN1juaccn7T2W1Q1gSqV+Bn7wwQfKy8tL2djYqDJlyhgcj4yOQ9qNZUo9/Qawffv2KTc3NxUXF5fp68sNYC8HyZlpzJm3y+Exqs7kbcpn5Hr1ytTt6nrko6ev9Bxl5wYwjVLZuGvoJRAdHY2rqytRUVG4uLgYzEtISCA0NBRfX1+srKywsrIy+Srky2jSpEnMnTs33RiXadT/92PMi3nTaDT89ttvtGvXLlvrbdiwgY8++ogzZ86YNLQY5EzegoODefjwYa55wtXzlpfPtexYvHgxw4YNy7EnhZk7b507d6ZatWp88sknGc5P+wwuWbLkU/tjv0g6nY7w8HA8PT1Nfp/nN5Iz05grb+fvRNN9wUEiY5Mo7eHIij510n3D/KJlVa89SW4AE5n6/vvvqVWrFoUKFWLv3r1MmzYtR74Of5m88cYbXLp0iVu3bhncjCSEMJSUlESVKlX44IMPzB2KEOIxx288oMfCQ0QnpODn5cKy3rUp5GRr7rCyRf5cEpm6dOkSbdu2xc/Pj88++4wPP/zQ4KlKItWwYcPybSG7YsUKnJycMvypVKmSucPLUqVKlTKNfcWKFeYO76VjY2PDp59+mmMjfgghnt3+K/d4d/5BohNSqFHCjZ/71clzhSyAdDN4jHQzeDbm/gozr8rLeYuJiUn36OA01tbWzzS+bVZyImfXr1/PdGiywoULG4yX/LLI7eeadDN4eUjOTPMi87bzQjj9lx8lMUVHvdKFmBcUgKNt7vnCXroZCCFeCGdn5zxb9D2vQlsIIXK7jafvMHTlcZK1iiYVPPmuWw3ssvmgptxEilkhhBBCiHzil6P/8vEvJ9EpaF3Vixmd/bG2zNtXz6WYFUIIIYTIB5buv8bYdWcB6BRQnCntq+aKJ3g9KylmhRBCCCFect/vusyXm1If4NOzvi//e8MPi5egkAUpZoUQQgghXlpKKb7aEsJ3O68AMPi1MgxvVi5X3gRqKilmhRBCCCFeQjqdYuL6cyzedw2AkS0qMKBxafMG9Rzk7R6/uZlOC6G74fQvqf/qjH92eX4wfvx4/P39n+traDSaLJ+I1bhxY4YNG/ZMr3Ht2jU0Gg0nTpwAYNeuXWg0miyf2LR48WLc3Nye6XXzs8WLF1OgQAFzhyGEELmaVqcYufaUvpD9rG2ll7KQBSlmn49zf8DMyrCkNaztnfrvzMqp7c9BmzZtaNGiRYbzdu/ejUaj4dSpU8/0GsYUadkxYsQItm/fniPbMidvb2/u3LlD5cqVzR1KtuX0MX0WwcHBRj9KuHPnzoSEhDzfgIQQIg9LStExZOVx1hz9FwsNfNWxGt3r+po7rOdGitmcdu4PWB0E0bcN26PvpLY/h4K2d+/ebN26lX///TfdvEWLFhEQEEDVqlVz/HVNkTZou5OTE4UKFTJ3OM/M0tKSIkWKYGUlPXZehOTkZOzt7fH09DR3KEIIkSslJGvpv/woG07dwdpSw+yuNXi7ZnFzh/VcSTH7NEpB0iPjfhKi4a+PgYweqvb/bZtGpi5nzPaMfDhb69at8fDwYPHixQbtsbGxrFmzht69ewOwZ88eGjRogL29Pd7e3gwZMoRHjx7pl09MTGTkyJF4e3tja2tLmTJlWLBgAdeuXePVV18FoECBAmg0GoKDg/XrDBkyBE9PT+zt7WncuDGHDx/WbzPt6t9ff/1FzZo1sbW1Zc+ePem6GezatYvatWvj6OiIm5sb9evX5/r16/r569ato0aNGtjZ2VGqVCkmTJhASkqKfv6lS5do2LAhdnZ2+Pn5sXXrVqNyl5KSwqBBg3B1dcXd3Z3//e9/PP5QvIy6Kri5uelz/WQ3g4wsXryYEiVK4ODgwFtvvcW9e/eMii3Nn3/+Sa1atbCzs8Pd3Z233npLP+/BgwcEBQVRoEABHBwcaNmyJZcuXdLPv379Om3atKFAgQI4OjpSqVIlNm7cmOUxzUrjxo0ZPHgww4YNo0CBAhQuXJh58+bx6NEjevbsibOzM2XKlOGvv/7Sr6PVaunduzclS5bE3t6e8uXLM2vWLP388ePHs2TJEtatW4dGo0Gj0bBr1y59bletWkWjRo2ws7NjxYoVBt0MlFI0bdqU5s2b64/b/fv3KV68OGPHjs1WnoUQIq97lJhCz0WH2XEhHFsrC34MCqBVFS9zh/Xc5YrLSd999x3Tpk0jLCyMatWq8e2331K7du0Ml23cuDF///13uvZWrVqxYcOGnA8uOQ4mF82hjanUK7ZfeBu3+Ce3wcbxqYtZWVkRFBTE4sWLGTNmjP4OxTVr1qDVannnnXe4cuUKLVq04PPPP2fhwoVEREQwaNAgBg0axKJFiwAICgpi//79fPPNN1SrVo3Q0FAiIyPx9vZm7dq1dOjQgZCQEFxcXPTPV//4449Zu3YtS5YsoUSJEkydOpUWLVpw+fJlChYsqI9x1KhRfPXVV5QqVYoCBQqwa9cu/byUlBTatWtH3759+fnnn0lKSuLQoUP6/di9ezdBQUF88803NGjQgCtXrtCvXz8Axo0bh06no3379hQuXJiDBw8SFRVldF/YJUuW0Lt3bw4dOsSRI0fo168fJUqUoG/fvkat/zQHDx6kd+/eTJkyhXbt2rFp0ybGjRtn9PobNmzgrbfeYsyYMSxdupSkpCQ2btyonx8cHMylS5f4448/cHFxYeTIkbRq1Ypz585hbW3NwIEDSUpK4p9//sHR0ZFz587h5OSU5TF9miVLlvDxxx9z6NAhVq1axYABA/jtt9946623+OSTT5gxYwbdu3fnxo0bODg4oNPpKF68OGvWrKFQoULs27ePfv364eXlRadOnRgxYgTnz58nOjpafy4WLFiQ27dTv90YNWoUX3/9NdWrV8fOzo7NmzfrY9FoNCxZsoQqVarwzTffMHToUPr370+xYsWkmBVC5CtRcckELz7E8RsPcbSxZEFwLeqUyvvfgBpFmdnKlSuVjY2NWrhwoTp79qzq27evcnNzU3fv3s1w+Xv37qk7d+7of86cOaMsLS3VokWLjHq9qKgoBaioqKh08+Lj49W5c+dUXFycSkpKUjqdTqnEWKXGuZjnJzHW6DyeP39eAWrnzp36tgYNGqh3331XKaVU7969Vb9+/QzW2b17t7KwsFDx8fEqJCREAWrr1q0Zbn/nzp0KUA8ePNC3xcbGKmtra7VixQqllFI6nU49evRIFS1aVH355ZcG6/3+++8G2xs3bpyqVq2aUir1mAJq165dGb52kyZN1OTJkw3ali1bpry8vJRSSm3evFlZWVmpW7du6ef/9ddfClC//fZbhttUSqlGjRqpihUrph7n/zdy5EhVsWJF/XRG23B1ddWfb6GhoQpQx48fzzBP77zzjmrVqpXB+p07d1aurq76aZ1O99/59oS6deuqbt26ZRj/xYsXFaD27t2rb4uMjFT29vZq9erVSimlqlSposaPH5/h+hkd06dp1KiReuWVV/TTKSkpytHRUXXv3l3fdufOHQWo/fv3Z7qdgQMHqg4dOuine/Toodq2bWuwTFpuZ86cadC+aNEi5erqapCz1atXKzs7OzVq1Cjl6OioLl68aPQ+5SdZnWu5QdpncHx8vLlDMaDVatWdO3eUVqs1dyh5huTMNKbmLSImQbWc+Y/yGbleVR2/WR2/8eD5BPgCZVWvPcnsV2anT59O37596dmzJwBz585lw4YNLFy4kFGjRqVb/vGrfQArV67EwcGBjh07Pp8ArR1Sr5Aa4/o+WPH205fr9gv41DPutY1UoUIF6tWrx8KFC2ncuDGXL19m9+7dTJw4EYCTJ09y6tQpVqxYoV9HKYVOpyM0NJTTp09jaWlJo0aNjH7NK1eukJycTP369f8L2dqa2rVrc/78eYNlAwICMt1OwYIFCQ4Opnnz5jRr1oymTZvSqVMnvLy89LHv3buXSZMm6dfRarUkJCQQFxfH+fPn8fb2pmjR/66g161b16h9qFOnjsFYe3Xr1uXrr79Gq9Viafnsz6k+f/68QbeAtNfYtGmTUeufOHEi06vE58+fx8rKisDAQH1boUKFKF++vD7/Q4YMYcCAAWzZsoWmTZvSoUOHZ+4//fj6lpaWFCpUiCpVqujbChcuDEB4eLi+7bvvvmPhwoXcuHGD+Ph4kpKSjB7NIqtzJ03Hjh357bff+OKLL5gzZw5ly5Y1cm+EECJvuxMVz7vzD3Il4hHuTjYs6x1IRS8Xc4f1Qpm1mE1KSuLo0aOMHj1a32ZhYUHTpk3Zv3+/UdtYsGABXbp0wdEx46/jExMTSUxM1E9HR0cDoNPp0Ol0BsvqdDp9vzv9v2B8UVnqVXApCtF30GTQb1ahSZ1f6lWwMLJQMrLfLECvXr0YMmQIs2fPZuHChZQuXZqGDRuilCI2NpZ+/foxZMiQdOuVKFFC389SKWXQZ/S/MFS6+ca0pU07ODgYbPfJ5RcuXMjgwYPZtGkTq1at4tNPP2XLli3UqVOH2NhYxo8fT/v27dPFZWtrm25bmcWWkSfnP7meRqMxOC8g9SakJ/cvs+mnvUZWbQD29vbZOiZP7lfv3r15/fXX2bBhA1u3bmXKlCl89dVXDB482OgcPcnKyipdv+In2yD1Dw6lFCtXrmTEiBF89dVX1K1bF2dnZ6ZNm8ahQ4cyjPvJ/z/t3FFKERcXx9GjR7G0tOTixYvZ2p/8JrNzLTdIOxcz+nw2p7TPgNwUU24nOTNNdvN2434c7y44xL8P4vFytWNZ79qUcnd8KfKenX0wazEbGRmJVqvVX8lJU7hwYS5cuPDU9Q8dOsSZM2dYsGBBpstMmTKFCRMmpGuPiIggISHBoC05ORmdTkdycrK+LbtPyNA0m4zl2p4oNAYFrSJ1O9pmk1A6BbqUzDZhsvbt2zNs2DCWL1/OsmXL6NevH1pt6vi2/v7+nDt3Dl9f3wzXrVixIjqdjh07dtCkSZN089OuUiYmJupvvPLx8cHGxoZ//vmHd955B6UUCQkJHD58mMGDB5OSkqJ//ZSUFIMbttLesI+3ValShSpVqvDRRx/RoEEDVqxYQUBAANWrV+fChQsZxq7T6ShXrhw3b97k5s2b+qu5e/fuBVILqsdf43FKKQ4ePGgwf9++fZQpU0Yfm4eHB7du3dIvc+nSJeLi4tDpdAb7lPb/J/e3fPny6V4j7Q+1tDallH69J8+3KlWqsG3bNrp3754u/rJly5KSksK+ffv0V6Lv3btHSEgI5cuX12/fy8uLPn360KdPH8aMGcO8efMYMGBAhsf0adKKjSeXT8vH49Jyv2fPHurWravv5wypV/Uf346VlRXJyckG23gyt4+/Vtr203I2fPhwNBoNf/75J2+++SYtWrTQ3+Am/pPVuZYbpKSkoNPpuHfvHtbW1uYOR0+n0xEVFYVSCgsLuW/aGJIz02Qnb6H34xmy9hIRj5Ip7mrLtx3K4KR7RHj4oyzXyytiYmKMXtbs3QyexYIFC6hSpUqmN4sBjB49muHDh+uno6Oj8fb2xsPDAxcXw8vwCQkJxMTEYG1tjaWlpWkfppXbgaUFbBplODyXS1FoMQXLim9mf5tGcnNzo1OnTnz66adER0fTq1cv/ZBRo0aNom7dugwbNow+ffrobwbaunUrs2fPpkyZMvTo0YN+/foxa9YsqlWrxvXr1wkPD6dTp06UKlUKjUbDpk2baNWqFfb29ri6utK/f39Gjx6Nh4eH/gawuLg4+vbti5WVlb5gsrKyMhi+ysLCQn9FLzQ0lB9//JE333yTokWLEhISwuXLlwkKCsLKyoqxY8fSpk0bfHx8ePvtt7GwsODkyZOcOXOGzz//nObNm1OuXDn69OnDl19+SXR0tP4mK0tLy0yHzdJoNNy8eZOPP/6Y9957j2PHjvH999/z1Vdf6dd57bXXmDNnDvXr10er1TJq1Cisra2xsLAw2Ke0/z+5v0OHDuWVV15h5syZtG3bls2bN7Nlyxb9Mo/L6HwbN24cTZs2pUyZMnTp0oWUlBQ2btzIyJEjqVixIm3btmXAgAHMnTsXZ2dnRo8eTbFixWjfvj1WVlYMGzaMli1bUq5cOR48eMA///yDn58fVlZWGR5TJyenLM+xtNEGnow9LR+PS8t9uXLlWL58Odu3b6dkyZIsW7aMI0eOULJkSf06JUuWZOvWrVy5coVChQrh6uqaLrePv1ba9q2trdmwYQOLFy9m37591KhRgxEjRtC7d29OnjwpD1fIRG4qFB9nZWWFhYUFhQoVws7Oztzh6Ol0OjQaDR4eHlKYGUlyZhpj83b2dhQDfznF/bhkynk6sbRXLTxdcs97Jidk6zPgqb1qn6PExERlaWmZ7gaboKAg9eabb2a5bmxsrHJxcUl3c8jTZPsGMFNpU5S6+o9Sp9ak/qtNMX1b2bBv3z4FpLvpSCmlDh06pJo1a6acnJyUo6Ojqlq1qpo0aZJ+fnx8vPrggw+Ul5eXsrGxUWXKlFELFy7Uz584caIqUqSI0mg0qkePHvp1Bg8erNzd3ZWtra2qV6+eOnjwoH6dzG4yevwGsLCwMNWuXTv96/r4+KixY8cadIDftGmTqlevnrK3t1cuLi6qdu3a6scff9TPDwkJUa+88oqysbFR5cqVU5s2bTLqBrD3339f9e/fX7m4uKgCBQqoTz75xOC437p1S73++uvK0dFRlS1bVm3cuDFbN4AppdSCBQtU8eLFlb29vWrTpo366quvjL4BTCml1q5dq/z9/ZWNjY1yd3dX7du318+7f/++6t69u3J1dVX29vaqefPmBjc/DRo0SJUuXVrZ2toqDw8P1b17dxUZGamfn9ExzUqjRo3U0KFDDdp8fHzUjBkzDNoez31CQoIKDg5Wrq6uys3NTQ0YMECNGjVKf/yVUio8PFx/bvL/NzI+mds0j98AdvfuXVW4cGGDGwSTkpJUzZo1VadOnZ66P/mN3ABmGrmZKfskZ6YxJm9Hrt1TlcdtUj4j16vW3+xW92ITX2CEL052bgDTKGXejlOBgYHUrl2bb7/9Fkj9q6REiRIMGjQowxvA0ixevJj+/ftz69atbA2+Hx0djaurK1FRURlemQ0NDcXX11d/NSg3fhWXW6n//9pY8pY9krfsk5yZJrfnLe0zuGTJkrnuymx4eDienp5yldFIkjPTPC1vey9H0nfpEeKStNTyLcCC4Fq42OXOb1qeVVb12pPM3s1g+PDh9OjRg4CAAGrXrs3MmTP1A7BD6tinxYoVY8qUKQbrLViwgHbt2r0UT5ESQgghhMjKtnN3ef+nYySl6GhQ1p0futfEwcbsZVyuYPYsdO7cmYiICMaOHUtYWBj+/v5s2rRJf1PYjRs30v11EhISwp49e/R9D4XIqypVqmTwpLPH/fDDD3Tr1u2FxXLjxg38/PwynX/u3DlKlCjxwuIRQgiR6o+Ttxm+6gQpOsXrfoX5tmt1bK2effjIl4XZi1lA/ySqjDz+pKg05cuXz5XDygiRXRs3biQpKSnDr36fHOXjeStatGiWj+V9fBxfIYQQL8aqwzcY9etplIJ2/kWZ1rEa1pbSdeNxuaKYFSK/8vHxyTX9GK2srChTpozZXl8IIYShhXtCmbj+HADv1C7BpHaVsbDIff3dzU2K2QzIVV8hhHjx5LNXiFRKKb7dfomvt14EoG+DknzSqmKuvHEzN5Bi9jFpYy/GxcXh6upq5miEECJ/SUpKAsiRR0kLkVcppZi6OYQf/wkFYFjTsgxtUlYK2SxIMfsYS0tL3NzciIiIQKfT4eTkJEOKZENu+bo8r5G8ZZ/kzDS5OW86nY6IiAgcHBwyfdCJEC87nU7x1c6brD0VAcCnb1SkT4NSZo4q95NPjCcUKVIEpRR3794lMjIy133g52bq/58nnfZ0L2EcyVv2Sc5Mk9vzZmFhQYkSJXJlbEI8bylaHR+vPc2vpyLQaGBSuyp0DZQRZIwhxewTNBoNRYoUAVIfDytXZo2X9kz1QoUKSd6yQfKWfZIz0+T2vNnY2OTKuIR43pJSdAxdeZy/zoRhqYFpHavSvoa3ucPKM6SYzYSFhQV2dnbywZoNOp0Oa2tryVs2Sd6yT3JmGsmbELlPfJKW/suP8vfFCGwsNXzWshTt/IuZO6w8RYpZIYQQQggziElIpveSIxwKvY+dtQU/vFuT8q46c4eV58if5kIIIYQQL9jDuCTenX+QQ6H3cba1YlnvQBqUdTd3WHmSXJkVQgghhHiBImIS6b7gIBfCYijgYM3SXoFUKe6KTidXZU0hxawQQgghxAty62E8784/SGjkIzycbVnRJ5ByhZ3NHVaeJsWsEEIIIcQLcC3yEd3mH+TWw3iKudmzok8gvu6O5g4rz5NiVgghhBDiOQsJi+HdBQeJiEmklLsjy/sEUtTN3txhvRSkmBVCCCGEeI5O/fuQoIWHeBiXTIUizizrHYiHs625w3ppSDErhBBCCPGcHAq9T6/Fh4lNTKGatxtLetbCzcHG3GG9VKSYFUIIIYR4Dv65GEG/ZUdISNYRWLIgC4Jr4WQrpVdOk4wKIYQQQuSwzWfDGPzTcZK0OhqX92BOt5rY21iaO6yXkhSzQgghhBA56Pfjt/hwzUm0OkXLykWY1aU6NlbynKrnRYpZIYQQQogcsuLgdT79/QxKQfsaxfiyQ1WsLKWQfZ6kmBVCCCGEyAHz/rnKpI3nAehex4cJb1bCwkJj5qheflLMCiGEEEI8A6UUM7ddYtb2SwD0b1SakS3Ko9FIIfsiSDErhBBCCGEipRSTNpxn/p5QAD5qXp6Br5Yxc1T5ixSzQgghhBAm0OoUn/5+hp8P3QBgbGs/er1S0sxR5T9SzAohhBBCZFOyVseINSdZd+I2Gg1MbV+VTrW8zR1WviTFrBBCCCFENiSmaBn003G2nruLlYWGGZ39aVOtqLnDyrekmBVCCCGEMFJcUgrvLTvK7kuR2FhZ8H3XGjT1K2zusPI1KWaFEEIIIYwQnZBMr0WHOXL9AQ42lswLCqB+GXdzh5XvSTErhBBCCPEU9x8l0WPhIU7fisLZzorFPWtT06eAucMSSDErhBBCCJGl8OgEus0/yKXwWAo62rC0V20qF3M1d1ji/0kxK4QQQgiRiX8fxNFt/kGu34ujsIstK/oEUsbT2dxhicdIMSuEEEIIkYGrEbF0m3+QO1EJeBe0Z0XvOpQo5GDusMQTpJgVQgghhHjC+TvRdF9wkMjYJEp7OLKiTx2KuNqZOyyRASlmhRBCCCEec/zGA3osPER0Qgp+Xi4s7V0bdydbc4clMiHFrBBCCCHE/9t/5R59lhzmUZKWGiXcWNSzNq721uYOS2RBilkhhBBCCGBnSDj9lx0lMUVHvdKFmBcUgKOtlEq5nRwhIYQQQuR7G0/fYejK4yRrFU0qePJdtxrYWVuaOyxhBAtzB/Ddd9/h6+uLnZ0dgYGBHDp0KMvlHz58yMCBA/Hy8sLW1pZy5cqxcePGFxStEEIIIV42vxz9l0E/HSNZq2hd1Yu53WtKIZuHmPXK7KpVqxg+fDhz584lMDCQmTNn0rx5c0JCQvD09Ey3fFJSEs2aNcPT05NffvmFYsWKcf36ddzc3F588EIIIYTI85btv8b/1p0FoFNAcaa0r4qlhcbMUYnsMGsxO336dPr27UvPnj0BmDt3Lhs2bGDhwoWMGjUq3fILFy7k/v377Nu3D2vr1M7Yvr6+LzJkIYQQQrwk5uy6wtRNFwAIrufL2NZ+WEghm+eYrZhNSkri6NGjjB49Wt9mYWFB06ZN2b9/f4br/PHHH9StW5eBAweybt06PDw86Nq1KyNHjsTSMuOvAxITE0lMTNRPR0dHA6DT6dDpdBmuo9PpUEplOl9kTPJmGslb9knOTCN5M43kLftye86UUkzfeonvdl0BYGDj0gxvVhZQ6HTKbHHl9ry9SNnJgdmK2cjISLRaLYULFzZoL1y4MBcuXMhwnatXr7Jjxw66devGxo0buXz5Mu+//z7JycmMGzcuw3WmTJnChAkT0rVHRESQkJCQ4To6nY6oqCiUUlhYmL1bcZ4heTON5C37JGemkbyZRvKWfbk5ZzqlmPn3v6w+EQ7A+/WL0d3fjYiICDNHlrvz9qLFxMQYvWyeGs1Ap9Ph6enJjz/+iKWlJTVr1uTWrVtMmzYt02J29OjRDB8+XD8dHR2Nt7c3Hh4euLi4ZPo6Go0GDw+PfH8yZYfkzTSSt+yTnJlG8mYayVv25dacaXWKT347w5r/L2QnvOlH9zo+Zo7qP7k1b+ZgZ2f809bMVsy6u7tjaWnJ3bt3Ddrv3r1LkSJFMlzHy8sLa2trgy4FFStWJCwsjKSkJGxsbNKtY2tri61t+qd2WFhYZHmiaDSapy4j0pO8mUbyln2SM9NI3kwjecu+3JazpBQdH6w+yYZTd7DQwJdvV+PtmsXNHVY6uS1v5pKd/TdbpmxsbKhZsybbt2/Xt+l0OrZv307dunUzXKd+/fpcvnzZoB/FxYsX8fLyyrCQFUIIIYRISNYyYPlRNpy6g7Wlhtlda+TKQlaYxqxl//Dhw5k3bx5Llizh/PnzDBgwgEePHulHNwgKCjK4QWzAgAHcv3+foUOHcvHiRTZs2MDkyZMZOHCguXZBCCGEELnYo8QUei0+zPYL4dhaWfBjUACtqniZOyyRg8zaZ7Zz585EREQwduxYwsLC8Pf3Z9OmTfqbwm7cuGFwmdnb25vNmzfzwQcfULVqVYoVK8bQoUMZOXKkuXZBCCGEELlUVFwywYsPcfzGQxxtLFkQXIs6pQqZOyyRw8x+A9igQYMYNGhQhvN27dqVrq1u3bocOHDgOUclhBBCiLzsXmwi3Rcc4tydaFztrVncsxbVSxQwd1jiOTB7MSuEEEIIkZPCohLoNv8AVyIe4e5kw7LegVT0yngEI5H3STErhBBCiJfGjXtxdFtwgJv34ynqasfyPoGU8nAyd1jiOZJiVgghhBAvhcvhMXSbf5C70Yn4FHJgRZ9AihdwMHdY4jmTYlYIIYQQed6ZW1EELTzE/UdJlCvsxPLegXi6GD/wvsi7pJgVQgghRJ529PoDghcdIiYhhSrFXFnSqzYFHWX8+fxCilkhhBBC5Fl7L0fSd+kR4pK01PItwILgWrjYWZs7LPECSTErhBBCiDxp27m7vP/TMZJSdDQo684P3WviYCOlTX4jR1wIIYQQec6fJ2/zwaoTpOgUr/sV5tuu1bG1sjR3WMIMpJgVQgghRJ6y6vANRv16GqWgrX9RvupYDWtLi6evKF5KUswKIYQQIs9YuCeUievPAfBO7RJ83q4ylhYaM0clzEmKWSGEEELkekopvtt5ma+2XASgb4OSfNKqIhqNFLL5nRSzQgghhMjVlFJM3RTC3L+vADCsaVmGNikrhawApJgVQgghRC6m0ynG/XGWZQeuAzCmVUX6Nixl5qhEbiLFrBBCCCFypRStjo/XnuLXY7fQaGBSuyp0DSxh7rBELiPFrBBCCCFynaQUHUNXHuevM2FYWmj4umM12lUvZu6wRC4kxawQQgghcpX4JC39lx/l74sR2Fha8G3X6jSvVMTcYYlcSopZIYQQQuQasYkp9F58mIOh97GztuDH7gE0LOdh7rBELibFrBBCCCFyhYdxSfRYdJiTNx/ibGvFwp61qOVb0NxhiVxOilkhhBBCmF1ETCLdFxzkQlgMBRysWdorkCrFXc0dlsgDpJgVQgghhFndehjPu/MPEhr5CA9nW1b0CaRcYWdzhyXyCClmhRBCCGE21yIf0W3+QW49jKeYmz0r+gTi6+5o7rBEHiLFrBBCCCHMIiQshncXHCQiJpFS7o4s7xNIUTd7c4cl8hgpZoUQQgjxwp369yFBCw/xMC6ZCkWcWdY7EA9nW3OHJfIgKWaFEEII8UIdvnafnosOE5uYQjVvN5b0rIWbg425wxJ5lBSzQgghhHhhdl+KoO/SIyQk6wgsWZAFwbVwspVyRJhOzh4hhBBCvBCbz4Yx+KfjJGl1NC7vwZxuNbG3sTR3WCKPk2JWCCGEEM/d78dv8eGak2h1ipaVizCrS3VsrCzMHZZ4CUgxK4QQQojn6qeDNxjz+2mUgvY1ivFlh6pYWUohK3KGFLNCCCGEeG7m/XOVSRvPA9C9jg8T3qyEhYXGzFGJl4kUs0IIIYTIcUopZm67xDc7LgPQv1FpRrYoj0YjhazIWVLMCiGEECJHKaX4Zve//HwsHIARr5dj4KtlpJAVz4UUs0IIIYTIMVqd4tPfz+oL2bGt/ej1SkkzRyVeZlLMCiGEECJHJGt1jFhzknUnbqMBprSvTJfaPuYOS7zkpJgVQgghxDNLTNEy6KfjbD13FysLDeOa+9IpwNvcYYl8QIpZIYQQQjyTuKQU3lt2lN2XIrGxsuC7d/ypUkj6x4oXI1cM8vbdd9/h6+uLnZ0dgYGBHDp0KNNlFy9ejEajMfixs7N7gdEKIYQQIk10QjI9Fh5i96VIHGwsWRRciyYVC5s7LJGPmL2YXbVqFcOHD2fcuHEcO3aMatWq0bx5c8LDwzNdx8XFhTt37uh/rl+//gIjFkIIIQTA/UdJdJt3kMPXHuBsZ8Wy3oHUL+Nu7rBEPmP2Ynb69On07duXnj174ufnx9y5c3FwcGDhwoWZrqPRaChSpIj+p3Bh+QtQCCGEeJHCoxPo/MN+Tt+KoqCjDT/3rUNNnwLmDkvkQ2btM5uUlMTRo0cZPXq0vs3CwoKmTZuyf//+TNeLjY3Fx8cHnU5HjRo1mDx5MpUqVcpw2cTERBITE/XT0dHRAOh0OnQ6XYbr6HQ6lFKZzhcZk7yZRvKWfZIz00jeTCN5S+/Wg3jeXXCI6/fjKOxiy7JetSnj6aTPkeTMNJK3/2QnB2YtZiMjI9FqtemurBYuXJgLFy5kuE758uVZuHAhVatWJSoqiq+++op69epx9uxZihcvnm75KVOmMGHChHTtERERJCQkZPgaOp2OqKgolFJYWJj94nWeIXkzjeQt+yRnppG8mUbyZujGgwQGrb1IeGwyRV1s+LZDWVyIIzw8Tr+M5Mw0krf/xMTEGL1snhvNoG7dutStW1c/Xa9ePSpWrMgPP/zAZ599lm750aNHM3z4cP10dHQ03t7eeHh44OLikuFr6HQ6NBoNHh4e+f5kyg7Jm2kkb9knOTON5M00krf/nL8TzYBfTnPvUTKlPRxZ1qs2RVzT34QtOTON5O0/2bm536zFrLu7O5aWlty9e9eg/e7duxQpUsSobVhbW1O9enUuX76c4XxbW1tsbW3TtVtYWGR5omg0mqcuI9KTvJlG8pZ9kjPTSN5MI3mDEzcf0mPhIaLik/HzcmFp79q4O6X//ZpGcmYayVuq7Oy/WTNlY2NDzZo12b59u75Np9Oxfft2g6uvWdFqtZw+fRovL6/nFaYQQgiRrx24eo9u8w4QFZ9MjRJu/NyvTpaFrBAvktm7GQwfPpwePXoQEBBA7dq1mTlzJo8ePaJnz54ABAUFUaxYMaZMmQLAxIkTqVOnDmXKlOHhw4dMmzaN69ev06dPH3PuhhBCCPFS2hkSTv9lR0lM0VGvdCHmBQXgaGv28kEIPbOfjZ07dyYiIoKxY8cSFhaGv78/mzZt0t8UduPGDYNLzQ8ePKBv376EhYVRoEABatasyb59+/Dz8zPXLgghhBAvpb9O32HIyuMkaxVNKnjyXbca2FlbmjssIQxolFLK3EG8SNHR0bi6uhIVFZXlDWDh4eF4enrm+z4r2SF5M43kLfskZ6aRvJkmv+btl6P/8vEvJ9EpaF3Vixmd/bG2NG7/82vOnpXk7T/G1GtpTLoyGxoayu7du7l+/TpxcXF4eHhQvXp16tatK4+WFUIIIfK4Zfuv8b91ZwHoFFCcKe2rYmmhMXNUQmQsW8XsihUrmDVrFkeOHKFw4cIULVoUe3t77t+/z5UrV7Czs6Nbt26MHDkSHx+f5xWzEEIIIZ6TObuuMHVT6ljvwfV8GdvaDwspZEUuZnQxW716dWxsbAgODmbt2rV4e3sbzE9MTGT//v2sXLmSgIAAvv/+ezp27JjjAQshhBAi5yml+HrLRWbvTB3qctCrZfjw9XJoNFLIitzN6GL2iy++oHnz5pnOt7W1pXHjxjRu3JhJkyZx7dq1nIhPCCGEEM+ZTqeYuP4ci/ddA2BkiwoMaFzavEEJYSSji9msCtknFSpUiEKFCpkUkBBCCCFeHK1OMfrXU6w+8i8An7WtRPe6vuYNSohsMOlWuWPHjnH69Gn99Lp162jXrh2ffPIJSUlJORacEEIIIZ6fZK2OoSuPs/rIv1ho4KuO1aSQFXmOScXse++9x8WLFwG4evUqXbp0wcHBgTVr1vDxxx/naIBCCCGEyHkJyVr6LzvK+lN3sLbUMLtrDd6uWdzcYQmRbSYVsxcvXsTf3x+ANWvW0LBhQ3766ScWL17M2rVrczI+IYQQQuSwR4kp9Fp8mO0XwrG1suDHoABaVZHHwou8yaRxZpVS6HQ6ALZt20br1q0B8Pb2JjIyMueiE0IIIUSOiopPpueiQxy78RBHG0vm96hF3dJyn4vIu0wqZgMCAvj8889p2rQpf//9N3PmzAFSH6aQ9hhaIYQQQuQu92IT6b7gEOfuRONqb83inrWoXqKAucMS4pmY1M1g5syZHDt2jEGDBjFmzBjKlCkDwC+//EK9evVyNEAhhBBCPLuwqAQ6/bCfc3eicXeyYWW/OlLIipeCSVdmq1atajCaQZpp06ZhaWn5zEEJIYQQIufcvB9H1/kHuHk/Hi9XO1b0CaSUh5O5wxIiR5hUzGbGzs4uJzcnhBBCiGd0OTyGbvMPcjc6EZ9CDizvHYh3QQdzhyVEjjG6mC1QoIDRj7S7f/++yQEJIYQQImecuRVF0MJD3H+URLnCTizvHYini1x4Ei8Xo4vZmTNn6v9/7949Pv/8c5o3b07dunUB2L9/P5s3b+Z///tfjgcphBBCiOw5ev0BwYsOEZOQQuViLiztFUhBRxtzhyVEjjO6mO3Ro4f+/x06dGDixIkMGjRI3zZkyBBmz57Ntm3b+OCDD3I2SiGEEEIYbd/lSPosPUJckpYAnwIs7FkLFztrc4clxHNh0mgGmzdvpkWLFunaW7RowbZt2545KCGEEEKYZvv5uwQvPkxckpYGZd1Z2ru2FLLipWZSMVuoUCHWrVuXrn3dunUUKiQDLwshhBDm8OfJ27y37ChJKTpe9yvM/B4BONjk6L3eIj/SaSF0N5z+JfVfndbcERkw6QyfMGECffr0YdeuXQQGBgJw8OBBNm3axLx583I0QCGEEEI83erDNxn56ymUgrb+RfmqYzWsLU26ZiXEf879AZtGQvTt/9pcikKLqeD3pvnieoxJZ3lwcDB79+7FxcWFX3/9lV9//RUXFxf27NlDcHBwDocohBBCiKws2hvKx2tTC9l3apdgeid/KWTFszv3B6wOMixkAaLvpLaf+8M8cT3B5O8eAgMDWbFiRU7GIoQQQohsUErx3c7LfLXlIgB9G5Tkk1YVjR5KU4hM6bSpV2RRGcxUgAY2jYIKb4CFeR+YZXIxq9PpuHz5MuHh4eh0OoN5DRs2fObAhBBCCJE5pRRTN4Uw9+8rAAxrWpahTcpKIStyxvV96a/IGlAQfSt1uZINXlhYGTGpmD1w4ABdu3bl+vXrKGVYsWs0GrTa3NUxWAghhHiZ6HSK8X+eZen+6wCMaVWRvg1LmTkq8VKJuWPccrF3n28cRjCpmO3fvz8BAQFs2LABLy8v+StQCCGEeEFStDpGrj3N2mP/otHApHZV6BpYwtxhiZfJnZOw6wvjlnUq/HxjMYJJxeylS5f45ZdfKFOmTE7HI4QQQohMJKXoGLryOH+dCcPSQsPXHavRrnoxc4clXhZJj2DnZDjwPSgdoCHjPrOkznMpCj71XmCAGTPpVsfAwEAuX76c07EIIYQQIhMJyVr6LTvCX2fCsLG04PtuNaSQFTnn4hb4rg7sn51ayFZqD29+Q2pB++Q38P8/3eILs9/8BSZemR08eDAffvghYWFhVKlSBWtrwyeLVK1aNUeCE0IIIQTEJqbQe/FhDobex87agh+7B9CwnIe5wxIvg5i7qaMSnP01ddq1BLSeDmWbpU7buWUyzuwXuWacWZOK2Q4dOgDQq1cvfZtGo0EpJTeACSGEEDnoYVwSPRYd5uTNhzjbWrGwZy1q+RY0d1gir9Pp4PhS2DoWEqJAYwF13odXPwEbx/+W83szdfit6/tSb/ZyKpzatSAXXJFNY1IxGxoamtNxCCGEEOIJETGJdF9wkAthMbg5WLOsVyBViruaOyyR10WEwJ9D4cb+1Gkvf2gzC4r6Z7y8haXZh9/KiknFrI+PT07HIYQQQojH3H4Yz7vzD3I18hEezrYs7x1I+SLO5g5L5GXJCbBnOuyeDrpksHaE1z6F2v3A0uRHD5idyZFfuXKFmTNncv78eQD8/PwYOnQopUuXzrHghBBCiPzoWuQjus0/yK2H8RRzs2dFn0B83R2fvqIQmQndDeuHwb3/v4G/XAto9RW4eZs1rJxg0mgGmzdvxs/Pj0OHDlG1alWqVq3KwYMHqVSpElu3bs3pGIUQQoh84+LdGDr+sJ9bD+Mp5e7Imv51pZAVpou7D+sGwpLWqYWsU2HouATeWflSFLJg4pXZUaNG8cEHH/DFF1+kax85ciTNmjXLkeCEEEKI/OT0v1EELTzIg7hkKhRxZlnvQDycbc0dlsiLlILTv6SOVBAXmdoW0AuajAN7N7OGltNMKmbPnz/P6tWr07X36tWLmTNnPmtMQgghRL5z+Np9ei06TExiCtW83VjSsxZuDjbmDkvkRfdDYcNwuLIjddqjQuoNXiXqmDeu58SkYtbDw4MTJ05QtmxZg/YTJ07g6emZI4EJIYQQ+cXuSxH0XXqEhGQdgSULsiC4Fk62efeGHGEm2mTY/13qo2hT4sHSFhp9BPWGgtXL+4eRSe+Uvn370q9fP65evUq9eqmPMdu7dy9Tp05l+PDhORqgEEII8TLbfDaMwT8dJ0mro1E5D+a+WxN7m9wzhqfII/49Cn8OgbtnUqd9G6RejS308t+Yb9INYP/73/8YO3Ys3377LY0aNaJRo0bMnj2b8ePH8+mnn2Z7e9999x2+vr7Y2dkRGBjIoUOHjFpv5cqVaDQa2rVrl+3XFEIIIcxt3YlbvL/iGElaHS0rF+HHIClkRTYlxsDGj2F+k9RC1r4gtJsDPf7MF4UsmHhlVqPR8MEHH/DBBx8QExMDgLOzaWPfrVq1iuHDhzN37lwCAwOZOXMmzZs3JyQkJMsuC9euXWPEiBE0aJB7B/EVQgghMvPTwRuM+f00SkH7GsX4skNVrCxNusYk8qvz62HjRxDz/4+ardoFmk8CR3fzxvWCmfwEsJSUFMqWLWtQxF66dAlra2t8fX2N3tb06dPp27cvPXv2BGDu3Lls2LCBhQsXMmrUqAzX0Wq1dOvWjQkTJrB7924ePnyY6fYTExNJTEzUT0dHRwOg0+nQ6XQZrqPT6VBKZTpfZEzyZhrJW/ZJzkwjeTPN88jb/D2hTN54AYB3A0swvo0fFhpemmMj55ppjM5b9G00m0aiubAeAFWgJOqN6VCqcdqGnm+gL0B2zh2Titng4GB69eqV7gawgwcPMn/+fHbt2mXUdpKSkjh69CijR4/Wt1lYWNC0aVP279+f6XoTJ07E09OT3r17s3v37ixfY8qUKUyYMCFde0REBAkJCRmuo9PpiIqKQimFhYX8lWwsyZtpJG/ZJzkzjeTNNDmZN6UUCw7eYf6BOwB0DyjM+3XciYyMyIlQcw0510zz1LzptDicW4nTwa/RJD9CWVjxqFpvYmu+D1Z2EB7+4oN+TtK++TeGScXs8ePHqV+/frr2OnXqMGjQIKO3ExkZiVarpXDhwgbthQsX5sKFCxmus2fPHhYsWMCJEyeMeo3Ro0cb3JQWHR2Nt7c3Hh4euLi4ZLiOTqdDo9Hg4eEhb8JskLyZRvKWfZIz00jeTJNTeVNKMeWvEH0h+2GzsrzfuDQajSanQs015FwzTZZ5u3sGzYYP0Nw6AoAqVgvVegYOhSvhYIZYnzc7OzujlzW5z2xGFXNUVBRardaUTRolJiaG7t27M2/ePNzdjesPYmtri61t+gGnLSwssnyDaTSapy4j0pO8mUbyln2SM9NI3kzzrHnT6hSfrjvLz4duADC2tR+9XimZkyHmOnKumSZd3pLj4e+psO9b0KWAjTM0HYcmoBcai5f3ZsHsnDcmFbMNGzZkypQp/Pzzz1hapiZSq9UyZcoUXnnlFaO34+7ujqWlJXfv3jVov3v3LkWKFEm3/JUrV7h27Rpt2rTRt6X1qbCysiIkJITSpfPHnXtCCCHyhhStjhFrTvL7idtoNDC1fVU61Xo5HiMqnrMrO2D9B/DgWup0xTbQ8ktwKWrWsHIbk4rZqVOn0rBhQ8qXL68fTWD37t1ER0ezY8cOo7djY2NDzZo12b59u354LZ1Ox/bt2zPsrlChQgVOnz5t0Pbpp58SExPDrFmz8PaWDwchhBC5R2KKlsE/HWfLubtYWWiY0dmfNtWkEBFP8SgStn4Kp1alTrsUg1ZfQYVW5o0rlzKpmPXz8+PUqVPMnj2bkydPYm9vT1BQEIMGDaJgwYLZ2tbw4cPp0aMHAQEB1K5dm5kzZ/Lo0SP96AZBQUEUK1aMKVOmYGdnR+XKlQ3Wd3NzA0jXLoQQQphTXFIK7y07yu5LkdhYWfB91xo09Sv89BVF/qUU9hd+RXPwS4h/AGgg8D147VOwNW0I1PzA5GflFS1alMmTJz9zAJ07dyYiIoKxY8cSFhaGv78/mzZt0t8UduPGDelvI4QQIk+JTkim9+LDHL72AAcbS+YFBVC/TP4a+1NkU+RlNOuH4Xrt/0dpKlwl9QlexWuaN648wORidvfu3fzwww9cvXqVNWvWUKxYMZYtW0bJkiWz1W8WYNCgQZmOgvC0Yb4WL16crdcSQgghnqcHj5IIWniI07eicLazYnHP2tT0KWDusERulZIEe2fBP9PQaBNRVnaoxqOxqDsQLK3NHV2eYNIlz7Vr19K8eXPs7e05duyY/qEEUVFROXK1VgghhMiLwqMT6Pzjfk7fiqKgow0/960jhazI3I0D8EMD2Pk5aBNRpZsQ2Wk91BsihWw2mFTMfv7558ydO5d58+Zhbf1fsuvXr8+xY8dyLDghhBAir/j3QRwdf9jPxbuxFHaxZfV7dahczNXcYYncKP4h/DkMFjaHiAvg4A4dFqC6rkHrIjezZ5dJ3QxCQkJo2LBhunZXV9csHy0rhBBCvIyuRsTy7vyD3I5KwLugPSt616FEoZdxKHvxTJSCc7/DXyMh9v+HJa3eHZpNBIeCL8VjaM3BpGK2SJEiXL58GV9fX4P2PXv2UKpUqZyISwghhMgTzt+JpvuCQ0TGJlLaw5EVfepQxNX4pxeJfOLhDdgwAi5tTp0uVBbazATf7N1nJNIzqZjt27cvQ4cOZeHChWg0Gm7fvs3+/fsZMWIE//vf/3I6RiGEECJXOnHzIT0WHiIqPhk/LxeW9q6Nu1P6p06KfEybAod+gB2TIPkRWNrAK8OhwXCwknMlJ5hUzI4aNQqdTkeTJk2Ii4ujYcOG2NraMmLECAYPHpzTMQohhBC5zoGr9+i9+DCPkrRUL+HG4p61cbWXm3bEY26fgD+HwJ2TqdMl6qVejfUob86oXjomFbMajYYxY8bw0UcfcfnyZWJjY/Hz88PJySmn4xNCCCFynV0h4by37CiJKTrqlS7EvKAAHG1NHu1SvGwSY2HXFDjwPSgd2LlCs89S+8fK2Pk57pneeTY2Nvj5+REdHc22bdsoX748FStWzKnYhBBCiFznr9N3GLLyOMlaRZMKnnzXrQZ21pbmDkvkFhc3w4YPIepm6nTlDtB8CjjL09+eF5OK2U6dOtGwYUMGDRpEfHw8tWrVIjQ0FKUUK1eupEOHDjkdpxBCCGF2a4/+y0e/nESnoHVVL2Z09sfaUq60CSDmLmwaCWd/S512LQGtp0PZZuaNKx8w6R34zz//0KBBAwB+++03dDodDx8+5JtvvuHzzz/P0QCFEEKI3GDZget8uCa1kO0UUJxZXapLIStSh9M6sghm10otZDWWUG8wDDwghewLYtKV2aioKAoWLAjApk2b6NChAw4ODrzxxht89NFHORqgEEIIYW7LjoTx3Z5bAATX82Vsaz8sLDRmjkqYXfgF+HMo3DyQOu3lD29+A17VzBpWfmNSMevt7c3+/fspWLAgmzZtYuXKlQA8ePAAOzsZW08IIcTLQSnF11su6gvZQa+W4cPXy6HRSCGbryUnwO6vYc8M0CWDtSM0+R/U7gcW0n/6RTOpmB02bBjdunXDyckJHx8fGjduDKR2P6hSpUpOxieEEEKYhVKKievPsWjvNQA+bl6O918ta96ghPmF7ob1w+De5dTpci2g1VfgJo+hNReTitn333+fwMBAbty4QbNmzbD4/2EmSpUqJX1mhRBC5HlaneKTX0+z6kjqHekjXvWmf6PSZo5KmFXcfdjyPzixPHXaqQi0nAp+bUGu1JuVyUNz1axZk5o1axq0vfHGG88ckBBCCGFOyVodH6w6wfpTd7DQwNQOVWhQ3MbcYQlzUQpOr4FNoyEuEtBAQC9oOi51/FhhdkbfhvnFF18QHx9v1LIHDx5kw4YNJgclhBBCmENCspYBy4+y/tQdrC01zO5agw41ips7LGEu90NheXv4tW9qIetREXptTh1ySwrZXMPoK7Pnzp2jRIkSdOzYkTZt2hAQEICHhwcAKSkpnDt3jj179rB8+XJu377N0qVLn1vQQgghRE57lJhCv2VH2Hv5HrZWFsztXpNXy3ui0+nMHZp40bTJsH827JoKKfFgaQuNPoZ6Q8BKrtLnNkYXs0uXLuXkyZPMnj2brl27Eh0djaWlJba2tsTFxQFQvXp1+vTpQ3BwsIxqIIQQIs+Iik+m56JDHLvxEEcbS+b3qEXd0oXMHZYwh3+PpA63dfdM6nTJhtB6JhSSPtO5Vbb6zFarVo158+bxww8/cOrUKa5fv058fDzu7u74+/vj7u7+vOIUQgghnot7sYl0X3CIc3eicbW3ZnHPWlQvUcDcYYkXLSEadnwGh+YBCuwLQvPJUK2L3OCVy5l0A5iFhQX+/v74+/vncDhCCCHEixMWlcC7Cw5yOTwWdycblvUOpKKXi7nDEi/a+fWw8SOIuZ06Xe0deH0SOMrV+bzA5NEMhBBCiLzs5v04us4/wM378Xi52rG8TyClPZzMHZZ4kaJuwV8fw4X1qdMFSkLrGVD6VfPGJbJFilkhhBD5zuXwGLrNP8jd6ER8CjmwvHcg3gUdzB2WeFF0Wji8ALZPhKQYsLCC+kOh4UdgbW/u6EQ2STErhBAiXzl7O4ruCw5x/1ES5Qo7sbx3IJ4uctNyvhF2JvUGr1tHUqeL14Y2M6FwJbOGJUwnxawQQoh84+j1B/RcdIjohBQqF3Nhaa9ACjrKUEv5QlIc/D01dcgtXQrYuqQ++KBmL7Aweth9kQs9UzF7+fJlrly5QsOGDbG3t0cphUbu+BNCCJEL7bscSZ+lR4hL0hLgU4CFPWvhYmdt7rDEi3B5O2wYDg+upU5XfBNafgkuXmYNS+QMk4rZe/fu0blzZ3bs2IFGo+HSpUuUKlWK3r17U6BAAb7++uucjlMIIYQw2fbzdxmw4hhJKToalHXnh+41cbCRLydferERsPkTOL06ddqlGLT6Ciq0Mm9cIkeZdF39gw8+wMrKihs3buDg8F+H+c6dO7Np06YcC04IIYR4VutP3ea9ZUdJStHRzK8w83sESCH7slMKji+H72r9fyGrgcABMPCgFLIvIZPezVu2bGHz5s0UL274vOqyZcty/fr1HAlMCCGEeFarD99k1K+n0Clo61+UrzpWw9pS+ke+1CIvw/phcG136nThKvDmLChW06xhiefHpGL20aNHBldk09y/fx9bW9tnDkoIIYR4Vov2hjLhz3MAvFO7BJ+3q4ylhdzX8dJKSYK9M+Gfr0CbCFb28OonUOd9sJQr8S8zk/48bdCgAUuXLtVPazQadDodX375Ja++KgMNCyGEMK/vdl7WF7J9G5Rk8ltSyL7Uru+Hua/AzkmphWyZpjDwANQfIoVsPmDSEf7yyy9p0qQJR44cISkpiY8//pizZ89y//599u7dm9MxCiGEEEZRSvHl5hDm7LoCwLCmZRnapKyMtPOyin8I28bB0cWp044e0OILqNwB5JjnGyYVs5UrV+bixYvMnj0bZ2dnYmNjad++PQMHDsTLS4a5EEII8eLpdIrxf55l6f7UezfGtKpI34alzByVeC6UgrO/waZREHs3ta1GEDSdAA4FzRubeOFMvvbu6urKmDFjcjIWIYQQwiQpWh0j155m7bF/0WhgUrsqdA0sYe6wxPPw8AZsGAGXNqdOFyoLbWaBb33zxiXMxuRiNiEhgVOnThEeHo5OpzOY9+abbz5zYEIIIYQxklJ0DFt1nI2nw7C00PB1x2q0q17M3GGJnKZNgYNzU/vFJseBpQ00+BBe+QCs5Obz/MykYnbTpk0EBQURGRmZbp5Go0Gr1T5zYEIIIcTTJCRr6b/8KLtCIrCxtOCbd6rTonIRc4clctrt4/DnULhzMnXapz60ngke5cwalsgdTBrNYPDgwXTs2JE7d+6g0+kMfkwpZL/77jt8fX2xs7MjMDCQQ4cOZbrsr7/+SkBAAG5ubjg6OuLv78+yZctM2Q0hhBB5WGxiCj0WHmJXSAR21hbM7xEghezLJjEWNn0C815LLWTtXOHNb6HHeilkhZ5JV2bv3r3L8OHDKVy48DMHsGrVKoYPH87cuXMJDAxk5syZNG/enJCQEDw9PdMtX7BgQcaMGUOFChWwsbFh/fr19OzZE09PT5o3b/7M8QghhMj9HsYl0WPRYU7efIizrRULe9ailq/c+PNSubgZNnwIUTdTpyu/DS2mgFP62kDkbyZdmX377bfZtWtXjgQwffp0+vbtS8+ePfHz82Pu3Lk4ODiwcOHCDJdv3Lgxb731FhUrVqR06dIMHTqUqlWrsmfPnhyJRwghRO4WEZNIlx8PcPLmQ9wcrPmpbx0pZF8mMWGwugf81Cm1kHUrAd3WwtsLpJAVGTLpyuzs2bPp2LEju3fvpkqVKlhbWxvMHzJkiFHbSUpK4ujRo4wePVrfZmFhQdOmTdm/f/9T11dKsWPHDkJCQpg6dWqGyyQmJpKYmKifjo6OBtB3i8iITqdDKZXpfJExyZtpJG/ZJzkzzcuQt9sP4+m+8DChkY/wcLZlac9alC/i/Fz36WXI24tmUs6UDo4tQbNtPJrEaJTGEuoMQDUaBTaOkA/yL+faf7KTA5OK2Z9//pktW7ZgZ2fHrl27DAaj1mg0RhezkZGRaLXadN0VChcuzIULFzJdLyoqimLFipGYmIilpSXff/89zZo1y3DZKVOmMGHChHTtERERJCQkZLiOTqcjKioKpRQWFvIMb2NJ3kwjecs+yZlp8nrebj5MYPDaS4TFJFHE2YZvO5ShgEU84eHxz/V183rezCG7ObO6fwmXf8ZiE3YMgGSPykQ1+pwU94rw8BHw6DlHnDvIufafmJgYo5c1qZgdM2YMEyZMYNSoUWZJtrOzMydOnCA2Npbt27czfPhwSpUqRePGjdMtO3r0aIYPH66fjo6OxtvbGw8PD1xcXDLcvk6nQ6PR4OHhke9PpuyQvJlG8pZ9kjPT5OW8Xbwbw8C1ZwiPScK3kAPLe9emqJv9C3ntvJw3czE6ZykJaHZ/DXtnodElo2ycUK+OwbJWXwpaWL64gHMJOdf+Y2dnZ/SyJhWzSUlJdO7c+ZkT7e7ujqWlJXfv3jVov3v3LkWKZH5HqoWFBWXKlAHA39+f8+fPM2XKlAyLWVtbW2xt048/Z2FhkWX8Go3mqcuI9CRvppG8ZZ/kzDR5MW+n/40iaOFBHsQlU6GIM0t718bT2fhfdDkhL+bN3J6as9B/4M9hcD/10cOUb4Wm1TQ0rsVfWIy5kZxrqbKz/yZlqkePHqxatcqUVQ3Y2NhQs2ZNtm/frm/T6XRs376dunXrGr0dnU5n0C9WCCHEy+Hwtft0nXeAB3HJVPN2Y2W/Oi+8kBU5LO4+/P4+LGmTWsg6FYFOy6DLT5DPC1lhGpOuzGq1Wr788ks2b95M1apV090ANn36dKO3NXz4cHr06EFAQAC1a9dm5syZPHr0iJ49ewIQFBREsWLFmDJlCpDaBzYgIIDSpUuTmJjIxo0bWbZsGXPmzDFlV4QQQuRSuy9F0G/pUeKTtdQuWZCFwbVwsjX5wZXC3JSCU6th82iIuwdooFZvaDI2dfxYIUxk0qfC6dOnqV69OgBnzpwxmPf4zWDG6Ny5MxEREYwdO5awsDD8/f3ZtGmT/qawGzduGFxqfvToEe+//z7//vsv9vb2VKhQgeXLl9O5c2dTdkUIIUQutOVsGIN+Ok6SVkejch7Mfbcm9jb5rw/lS+P+VVg/HK7uTJ329IM2s8C7tnnjEi8FjVJKmTuIFyk6OhpXV1eioqKyvAEsPDwcT0/PfN9nJTskb6aRvGWf5Mw0eSVv607cYvjqk2h1ipaVizCziz+2VuYrZPNK3nITfc4KFcDiwHfw91RISQBLW2g8EuoOBisbc4eZ68i59h9j6rU08n2NEEKIXOOngzcY8/tplIL2NYrxZYeqWFnm71/qeZX13RNofp0A4edSG0o2gtYzoFBp8wYmXjpGF7Pt27dn8eLFuLi40L59+yyX/fXXX585MCGEEPnL/N1X+XzDeQC61/FhwpuVsLDIXtc1kQskRKPZPoGChxegQYF9QWg+Gap1gWx2RRTCGEYXs66urvr+sK6u0lFbCCFEzlBKMWv7JWZuuwTAe41KMapFhWzfgyFygfN/wsaP0cTcBkBV7YKm+WRwLGTmwMTLzOhidtGiRUycOJERI0awaNGi5xmTEEKIfEIpxeSN55m3OxSAEa+XY+CrZaSQzWuibsFfH8OF9QCogqV4UG8sbjXaosnnfT/F85etM2zChAnExsY+r1iEEELkIzqdYszvZ/SF7NjWfgx6rawUsnmJTgsHf4DvAlMLWQsraDAC9d4ekoobP168EM8iWzeA5bOBD4QQQjwnKVodI9ac5PcTt9Fo4Iv2Vehcq4S5wxLZEXYa/hwKt46mThevnTrcVmE/0OmAGLOGJ/KPbI9mIH8xCyGEeBaJKVoG/3ScLefuYmWhYUZnf9pUK2rusISxkuLg7y9g32xQWrB1gabjoWZPkC4FwgyyXcyWK1fuqQXt/fv3TQ5ICCHEyys+SUu/ZUfYfSkSGysLvu9ag6Z+hc0dljDW5W2pDz94eD112q8ttJgKLl7mjUvka9kuZidMmCCjGQghhMi26IRkei8+zOFrD3CwsWReUAD1y7ibOyxhjNiI1MfQnl6TOu1SHN74Csq3NG9cQmBCMdulSxc8PT2fRyxCCCFeUg8eJRG08BCnb0XhbGfF4p61qOlT0NxhiadRCo4vhy2fQsJD0FhA7ffgtTFg62zu6IQAslnMSn9ZIYQQ2RUencC7Cw5y8W4sBR1tWNqrNpWLyTd8uV7kJVj/AVzbnTpdpAq0+QaK1TBvXEI8QUYzEEII8dz8+yCOd+cf5Nq9OAq72LKiTyBlPOWKXq6Wkgh7ZsLur0CbBNYO0Hg01HkfLLP9ha4Qz122zkqdTve84hBCCPGSuRoRy7vzD3I7KoHiBez5qU8dShRyMHdYIivX96cOtxUZkjpdphm88TUU8DFvXEJkQf7EEkIIkeMuhEXz7vxDRMYmUtrDkRV96lDE1c7cYYnMxD+ArePg2JLUaUcPaDkVKrUH6WIocjkpZoUQQuSoEzcf0mPhIaLik/HzcmFp79q4O9maOyyREaXg7K/w1yh4FJ7aVqMHNJsA9gXMG5sQRpJiVgghRI45cPUevRcf5lGSluol3Fjcszau9tbmDktk5MF12PAhXN6aOu1eLvUJXj71zBuXENkkxawQQogcsSsknPeWHSUxRUe90oWYFxSAo638msl1tClwcA7snAzJcWBpAw0+hFc+ACu5gi7yHvmUEUII8cz+On2HISuPk6xVNKngyXfdamBnbWnusMSTbh+HP4ZA2KnUaZ/60HomeJQza1hCPAspZoUQQjyTtUf/5aNfTqJT8EZVL2Z29sfa0sLcYYnHJcbCzklwcC4oHdi5weufgf+7YCHHSuRtUswKIYQw2bL91/jfurMAdAoozpT2VbG0kLvfc5WQTbBxBETdTJ2u/Da0mAJO8jRP8XKQYlYIIYRJ5v59hS/+ugBAcD1fxrb2w0IK2dwjJgz+Ggnnfk+ddvOBN6ZD2aZmDUuInCbFrBBCiGxRSjF960W+3XEZgEGvluHD18vJI89zC50Oji6CbRMgMQo0llBvEDQaBTby0Arx8pFiVgghhNGUUkxcf45Fe68BMLJFBQY0Lm3eoMR/ws+nPsHr5sHU6aI1Uofb8qpq3riEeI6kmBVCCGEUrU7xya+nWXUkte/lxLaVCKrra96gRKrkBPhnGuydBbpksHGC1/4HtfuChYwqIV5uUswKIYR4qmStjuGrT/LnydtYaODLt6vxds3i5g5LAFz9G9YPg/tXU6fLt4JW08BVjo/IH6SYFUIIkaWEZC2DfjrGtvPhWFtqmNWlOq2qeJk7LPHoHmz5FE7+lDrt7AUtv4SKbUD6L4t8RIpZIYQQmXqUmEK/ZUfYe/ketlYWzH23Jq9WkCGdzEopOLUKNn8CcfcADdTqDU3Ggp2ruaMT4oWTYlYIIUSGouKT6bnoEMduPMTRxpL5PWpRt3Qhc4eVv92/Cus/gKu7Uqc9/VJv8PKubdawhDAnKWaFEEKkcy82kaCFhzh7OxpXe2sW96xF9RIFzB1W/qVNhn3fwN9fQkoCWNlBo4+h3hCwtDZ3dEKYlRSzQgghDIRFJfDugoNcDo/F3cmGZb0DqejlYu6w8q+bh1OH2wpPfdIapRqnPvygkAyJJgRIMSuEEOIxN+/H0XX+AW7ej8fL1Y7lfQIp7eFk7rDyp4Qo2D4RDi8AFDgUguZToGonucFLiMdIMSuEEAKAy+GxvDv/IGHRCfgUcmB570C8C8oTo144peD8n/DXxxBzJ7XNvxs0+wwcpc+yEE+SYlYIIQRnb0cRtOAQ9x4lUdbTiRV9AvF0sTN3WPlP1L+w8SMI2Zg6XbAUtJ4JpRqZNSwhcjMpZoUQIp87ev0BPRcdIjohhcrFXFjaK5CCjjbmDit/0Wnh0DzY8RkkxYKFFdQfBg1HgLW9uaMTIleTYlYIIfKxfZcj6bP0CHFJWgJ8CrCwZy1c7OTu+Bcq7DT8MQRuH0ud9g5MHW7Ls6J54xIij5BiVggh8gGtTnHw6j0u/3ufMrGWBJZy5++L4fRffoykFB0NyrrzQ/eaONjIr4UXJikOdk2B/d+B0oKtCzQdDzV7goWFuaMTIs/IFZ9a3333HdOmTSMsLIxq1arx7bffUrt2xgNAz5s3j6VLl3LmzBkAatasyeTJkzNdXggh8rtNZ+4w4c9z3IlK+P+WUNwcrImOT0anoJlfYWZ3rY6tlaVZ48xXLm+D9cPh4fXUab+20GIquMhjgoXILrP/6bdq1SqGDx/OuHHjOHbsGNWqVaN58+aEh4dnuPyuXbt455132LlzJ/v378fb25vXX3+dW7duveDIhRAi99t05g4Dlh97rJBN9TAutZCt5VuA77vVkEL2RYmNgLV9YHmH1ELWpTi8sxI6LZVCVggTmf3K7PTp0+nbty89e/YEYO7cuWzYsIGFCxcyatSodMuvWLHCYHr+/PmsXbuW7du3ExQUlG75xMREEhMT9dPR0dEA6HQ6dDpdhjHpdDqUUpnOFxmTvJlG8pZ9kjPjaHWK8X+cQ2WxzL8P4kFymaUcOd+UghPL0WwdiybhIUpjAbXfQ736Cdg4wUuWf3mPmkby9p/s5MCsxWxSUhJHjx5l9OjR+jYLCwuaNm3K/v37jdpGXFwcycnJFCxYMMP5U6ZMYcKECenaIyIiSEhIyGCN1ARGRUWhlMJC+i0ZTfJmGslb9knOjHP0Zgxh0Rl/zqW5E5XAluNXqent/IKiynue9XyzfHAF13/GYXPnMADJ7n5ENfqMFI/K8DAOiMvhiM1P3qOmkbz9JyYmxuhlzVrMRkZGotVqKVy4sEF74cKFuXDhglHbGDlyJEWLFqVp06YZzh89ejTDhw/XT0dHR+Pt7Y2HhwcuLhk/nlGn06HRaPDw8Mj3J1N2SN5MI3nLPsmZcZLvpBi3nJU9np6ezzmavMvk8y0lEfbORLNnOhptEsraAdV4NJaB/SloYfYvRp8reY+aRvL2Hzs748e5ztPvpi+++IKVK1eya9euTHfa1tYWW1vbdO0WFhZZnigajeapy4j0JG+mkbxln+QsayFhMfx63Lh7CQq72EsenyLb59v1ffDnUIi8mDpdphmaN75GU8Dn+QWZy8h71DSSt1TZ2X+zFrPu7u5YWlpy9+5dg/a7d+9SpEiRLNf96quv+OKLL9i2bRtVq1Z9nmEKIUSeoJRi7+V7/Lj7Kv9cjHjq8hqgiKsdtUtm3E1LmCD+AWwdC8eWpk47ekLLL6BSe9BozBubEC8psxazNjY21KxZk+3bt9OuXTsg9RL79u3bGTRoUKbrffnll0yaNInNmzcTEBDwgqIVQojcKSlFx/pTt/nxn6tcCEvtZ2ahgZaVvahU1IVpm0MADG4ESyurxrXxw9JCiqxnphScWQubRsOj/x+Np0YPaDYB7AuYNzYhXnJm72YwfPhwevToQUBAALVr12bmzJk8evRIP7pBUFAQxYoVY8qUKQBMnTqVsWPH8tNPP+Hr60tYWBgATk5OODk5mW0/hBDiRYuKT+bnQzdYtDeUu9Gpo7Y42FjSKcCb3q+UxLugAwClPByfGGc29YrsuDZ+tKgsw0E9swfXYcOHcHlr6rR7eWgzE3zqmTUsIfILsxeznTt3JiIigrFjxxIWFoa/vz+bNm3S3xR248YNg34Tc+bMISkpibfffttgO+PGjWP8+PEvMnQhhDCLm/fjWLT3GqsO3+BRkhYAT2dbguv70q22D64Oho+jbVHZi2Z+RTh4NZLL/0ZQprgHgaXc5Yrss9KmwIHvU5/ilRwHljbQYAS8Mgys0t+rIYR4PsxezAIMGjQo024Fu3btMpi+du3a8w9ICCFyoVP/PuTHf67y15kwtLrUTgPlCzvTp0FJ3vQvmuWDDywtNNQpVYhSTlo8PQthIYXss7l1LPUGr7BTqdM+r6RejXUva9awhMiPckUxK4QQImM6nWLHhXDm7b7KwdD7+vZXyrjTt2EpGpZ1RyM3Fr04iTGwYxIc+gGUDuzc4PXPofq7coOXEGYixawQQuRCCclafjt+i3m7r3I14hEAVhYa3qxWlD4NSuFXNONxssVzFPIX/PUxRP+bOl2lEzSfDE4e5o1LiHxOilkhhMhF7j9KYtn+6yzdf417j5IAcLazomtgCYLr+eLlam/mCPOhmDu4bfkAi6ubU6fdfKD1dCiT8cN6hBAvlhSzQgiRC1yNiGXBnlB+OfoviSmpzyQv5mbP/7V353FRlnvjxz8zAwwgm7IKgoD7guAurj3pCbU0O50WI3PL56mjp8yy9HQ8tpxSW7VOafmk1Skz6zlZqdHPtBTN9IiK4m6IorIpsso6c/3+GBgZGWQRGJbv+/XiBfd1X3PP9/4ywJd7ruu6ZwwP4YGBgbjo5dd1ozMaIW4Nmp9ewLEoF6XRoRk6B0YtAAdnW0cnhCgjvx2FEMJGlFLsP3eVD3cm8tPxNFTZQrB9Orgza0Qo43r7Yadr3XcBspm0Y7BpLiTvRQMU+/TBbtI/0fiH2zoyIcQNpJgVQohGVmow8uPRND6MTSQ+OcvcPqaHD7NGhDIopJ1M6rKVkgLY+TrsXgHGUnBwwXj7IjKDJuDjJ2vyCtEUSTErhBCNJL+olA37k1mz+yzJmQUAONhpubdfADOHh9LZR278YlOJO0xXYzMTTdvd7oTxr4GrP6Sn2zQ0IUTVpJgVQogGlp5TyMe/JvHZb+fIKSwFoK2zPVMig3kksiNeLrLAvk3lX4H/9zeIX2fadm0P41+HHhNM20aj7WITQlRLilkhhGggJ1NzWR2byLeHLlJiMA2IDfFqw8zhIdzbrwNODlXf5EA0AqUgfj38+FcoyAQ0MPBRGL0IHN1tHZ0QooakmBVCiHqklGL3mSt8GJvIzlMZ5vaBwW2ZNSKUMT185e5bTcGV32HTU3B2h2nbpxdMWAGBA20blxCi1qSYFUKIelBcamTT4Ut8uDORE6m5AGg1MK53ex4dEULfoLY2jlAAUFoMv75jmuRVWgh2jjDqORj6F9DZ2zo6IUQdSDErhBC3ILughC/2nWft7rOk5RQB4Oyg4/4BgcwcHkJgO1mPtMlI3gffPwnpx0zbof9luvlBu1DbxiWEuCVSzAohRB1cuHqNNbuS+PI/58kvNgDg46pn6tBgogcH4eHsYOMIhVlhNvz0IuxfAyhw9oSoJdDnfpAl0IRo9qSYFUKIWjh8IYvVsWfZciQFg9E0qaubryuPjghhYoQ/ejuZ1NVkKAXHv4Mtz0JeqqktIhru+Ac4t7NtbEKIeiPFrBBCVMNoVGw/kc7q2ET2ns00tw/v7MWskaGM7OIlNzloarIvwJb5cHKLabtdJ7jrbQgdZdu4hBD1TopZIYSoQmGJgW8OXmR1bCKJGfkA2Gk1TAz359ERofT0d7NxhKISowH2fQjb/wHFeaC1h+FzYcQzYO9o6+iEEA1AilkhhLhBZn4x/9pzjk/3JHElvxgAV0c7HhocxLShwbR3d7JxhMKqlMOmCV6XDpi2Awebltvy6WHbuIQQDUqKWSGEKJOYkcdHu87yddwFikpNd30K8HBixvAQHhgYiItefmU2ScX58MsS2PM+KAPo3eEPL0C/aaDV2jo6IUQDk9/MQohWTSnF/nNX+XBnIj8dT0OZ5nQRFuDOrJGhjO/th51OCqIm6/RPsPkpyDpv2u45CcYtA1c/m4YlhGg8UswKIVqlUoORH4+m8WFsIvHJWeb20d19mDUylMEh7WRSV1OWlw4xCyDh/0zb7oFw55vQNcq2cQkhGp0Us0KIViW/qJQN+5NZs/ssyZkFADjYabm3XwAzh4fS2cfFxhGKmzIa4eC/YOsi0/qxGi0Mfhz+66+gl++dEK2RFLNCiFYhPaeQj39N4rPfzpFTWApAW2d7pkQG80hkR7xc9DaOUFQr4xRsmgvndpu2/frAxHfAv69NwxJC2JYUs0KIFu1kai6rYxP59tBFSgymAbEhXm2YOTyEe/t1wMlBbnLQ5JUWQexbsOstMBSDvTP81/Mw+DHQyZ8xIVo7+S0ghGhxlFLsPnOFD2MT2Xkqw9w+MLgts0aEMqaHL1qtjIdtFpJ2m67GXj5l2u5yB4x/A9p2tGlYQoimQ4pZIUSLUVxqZNPhS3y4M5ETqbkAaDUwrnd7Hh0RQt+gtjaOUNRYwVXY+nc48Klpu42PaZWCXveATMwTQlQgxawQotnLLijhi33nWbv7LGk5RQA4O+i4f0AgM4aFEOTpbOMIRY0pZVqhIGYB5JddVe8/Dca8AE7yz4gQojIpZoUQzdbFqwWs/fUcX/7nPPnFBgB8XPVMHRpM9OAgPJwdbByhqJWrSbD5aTjzk2nbq5vpDl4dI20alhCiaZNiVgjR7By+kM17PyWy/UwWBqNpUlc3X1ceHRHCxAh/9HYyqatZMZTCb+/Bz0ugtAB0DjByPgx7EuxklQkhxM1JMSuEaBaMRsX2E+msjk1k79lMc/vwzl7MGhnKyC5ecpOD5uhiHHz/JKQeMW0Hj4C7loNXZ5uGJYRoPqSYFUI0aYUlBr45eJHVsYkkZuQDYKfV8IdubZkzpge9AjxsG6Com6Jc2P4P2PchKKNpPOwd/4CIaJngJYSoFSlmhRBNUmZ+Mf/ac45P9yRxJb8YAFe9HQ8NCeKRIUHoinLx8XGzcZSiTk5sgS3PQM5F03bY/RD1Krh42zYuIUSzJMWsEKJJSczI46NdZ/k67gJFpUYAAjycmD4smAcGBuLqaI/RaCQ9PdfGkYpay0mBH56F49+Ztj06wl1vQ+fRto1LCNGsSTErhLA5pRT7z13lw52J/HQ8DWWa00VYgDuzRoYyvrcfdjqtbYMUdWc0Qtwa+OlFKMoBjQ6G/gVGPQcOsmyaEOLWSDErhLCZUoORH4+msTo2kUPJWeb20d19mDUylMEh7WRSV3OXdsw0wevCPtN2QH/Tclt+YbaNSwjRYkgxK4RodPlFpWzYn8ya3WdJziwAwMFOy739Apg5PJTOPi42jlDcspIC2PEa/PoOGEvBwQVGL4aBM0ErS6cJIeqPzd+3e++99wgODsbR0ZHBgwezb9++KvsePXqUe++9l+DgYDQaDcuXL2+8QIUQtyw9p5DXYk4QuWQbL35/jOTMAto62/PE6C78uuB2lvyxjxSyLUHiL/B+JOx6y1TIdr8LZu+Dwf8thawQot7Z9Mrsl19+ybx581i1ahWDBw9m+fLlREVFcfLkSXx8fCr1v3btGqGhodx333089dRTNohYCFEXJ1NzWR2byLeHLlJiMA2IDfFqw8zhIdzbrwNODlLgtAj5l+HH5+HwetO2qz+Mfw16TLBtXEKIFs2mxexbb73FrFmzmD59OgCrVq1i8+bNrFmzhgULFlTqP3DgQAYOHAhgdb8QoulQSrH7zBU+jE1k56kMc/vA4LY8OiKUMT180WllPGyLoBTEf2EqZAsyAQ0MmgW3LwJHWT5NCNGwbFbMFhcXExcXx8KFC81tWq2WMWPGsGfPnnp7nqKiIoqKiszbOTk5ABiNRoxGo9XHGI1GlFJV7hfWSd7qpqXlrbjUyOYjKayOPcuJVNPyWVoNjO3lx6MjQogI9CjrqTCW3Yq2tlpazhpLg+Ttyu9oNs9Dk7QTAOXTE3XXCugwoPxJ6++5bEReb7UnOasbydt1tcmBzYrZy5cvYzAY8PX1tWj39fXlxIkT9fY8S5Ys4cUXX6zUnpGRQWFhodXHGI1GsrOzUUqh1dp8WHGzIXmrm5aSt9zCUjYmXGbDwXQy8ksAcLLXclcvTx7s60uAux4oJj09/Zafq6XkrLHVa94MxbSJX4NL3HtoDMUonZ68AXPI7zMddPZQD9/npkJeb7UnOasbydt1ubk1X0u8xa9msHDhQubNm2fezsnJITAwEG9vb9zcrL/9ZTQa0Wg0eHt7t/oXU21I3uqmueft4tUC1v6axJf/SSa/2ACAj6ueRyI78tCgQDycHer9OZt7zmyl3vKWvBfNprloMkwXHlTof6HGv0mbdiG0qadYmxJ5vdWe5KxuJG/XOTo61rivzYpZLy8vdDodaWlpFu1paWn4+fnV2/Po9Xr0en2ldq1We9MXikajqbaPqEzyVjfNMW+HL2SxOvYsW46kYCgbLtDN15VHR4QwMcIfvV3DTupqjjlrCm4pb4XZphsf7F8DKHD2hLFL0YTd1+LXA5bXW+1JzupG8mZSm/O3WTHr4OBA//792bZtG5MmTQJM/5Fs27aNOXPm2CosIcRNGI2K7SfSWR2byN6zmeb24Z29mDUylJFdvFp8UdMqKWW6Be2WZyEv1dQW8TDc8TI4t7NtbEKIVs+mwwzmzZvH1KlTGTBgAIMGDWL58uXk5+ebVzd45JFHCAgIYMmSJYBp0tixY8fMX1+8eJFDhw7h4uJC586dbXYeQrR0hSUGvjl4kdWxiSRm5ANgp9UwMdyfmSNC6OXvbuMIRYPJSoYt8+HUD6btdp1gwnIIGWnTsIQQopxNi9kHHniAjIwM/v73v5OamkpERAQxMTHmSWHnz5+3uMx86dIl+vbta95+4403eOONNxg1ahS//PJLY4cvRIuXmV/Mv/ac49M9SVzJLwbAVW/HQ0OCmDY0mPbuTjaOUDQYowH2fgDb/wEl+aC1h+FPwYinwb7mY9mEEKKh2XwC2Jw5c6ocVnBjgRocHIxSdVvKRwhRc4kZeXy06yxfx12gqNS0PEqAhxPThwXzwMBAXB3tbRyhaFAp8fD9k3DpoGk7cAhMWAE+3W0blxBCWGHzYlYI0TQopdh/7iqrdyay9Xga5f83hgW4M2tkKON7+2Gna90TElq84nz4+VX4bSUoA+jd4Q8vQr+p0Monowghmi4pZoVo5UoNRn48msbq2EQOJWeZ20d392HWyFAGh7STSV2twemtsGkeZJ83bfe6B8YuBdf6W11GCCEaghSzQrRS+UWlbNifzJrdZ0nOLADAwU7Lvf0CmDk8lM4+LjaOUNQrowGSduN48RRc6wrBw0Crg7x0iFkACf9n6uceCHe+CV2jbBuvEELUkBSzQrQy6TmFfPxrEp/9do6cwlIA2jrbMyUymClDOuLtWnldZtHMHfsOYp5Dm3MJj/I2N3/oOg4SvjatH6vRwpA/w20LQS//yAghmg8pZoVoJU6m5rI6NpFvD12kxGAaEBvi1YaZw0O4t18HnBwa9iYHwkaOfQcbHgFumDybcwn2f2T6un24aYKXf99KDxdCiKZOilkhWjClFLvPXOHD2ER2nsowtw8MbsujI0IZ08MXnVbGw7ZYRgPEPEelQrYiR3eYsRXs5Yq8EKJ5kmJWiBaouNTIpsOX+HBnIidScwHQamBc7/Y8OiKEvkFtbRyhaHBGIxzeYLoCezOF2XBhH4SMaJy4hBCinkkxK0QLkl1Qwhf7zvPx7iRScwoBcHbQcf+AQGYMCyHI09nGEYoGUVoE6ccg5TCkHjZ9TjtqutlBTeSlNWx8QgjRgKSYFaIFuHD1Gmt3J7F+33nyiw0A+LjqmTo0mOjBQXg4O9g4QlFvCnMg9cj1ojX1MGScAGNp5b5aBzAWV39MF9/6j1MIIRqJFLNCNGOHL2SxOvYsW46kYDCaxkV29XVh1ohQJkb4o7eTSV3NWm5qWcEaX/b5CFw9a72vowe07wN+fUwTuvz6QNsQeDcCclKwPm5WY1rVoOPQhjsHIYRoYFLMCtHMGI2K7SfSWR2byN6zmeb24Z29mDUylJFdvOQmB82N0WgqUitebU05DPnp1vu7dahQuPYBvzDT+rDWvu9jl5WtZqDBsqAt6zt2qWm9WSGEaKakmBWimSgsMfDNwYusjk0kMcM0FtJOq2FiuD8zR4TQy9/dxhGKGiktNg0LqFi4piZAca6Vzhrw6lKhaC37aONZ8+frORHu/9S0qkHFyWBu/qZCtufEWz4lIYSwJSlmhWjiMvOL+deec3y6J4kr+abxj656Ox4aEsS0ocG0d3eycYSiSkW5pkI19cj1oQIZJ8BgZRyrTg++PSsUruGmbYc2tx5Hz4nQ/U6MSbvJuXgKt4CuaMvvACaEEM2cFLNC2JDBqNibeIUzFzLpnKdjcKiXed3XxIw8Ptp1lq/jLlBUagQgwMOJ6cOCeWBgIK6O9rYMXdwoL6PC2Nayq66ZiVgdq6p3Nw0NqDhUwKsr6Brwe6rVQfBwCp274ubjA1ptwz2XEEI0IilmhbCRmIQUXvz+GCnZhWUtZ/Fzd+ThwUEcvpDN1uNpqLI6KCzAnVkjQxnf2w87nRQhNqUUXE0qGx5w5Hrxmptivb9re8thAu37gEdH6+NbhRBC1JoUs0LYQExCCo9/dqDSNbvU7ELe+H+nzNuju/swa2Qog0PayaQuWzCUwOVTlldbU49AUbaVzhrw7FQ2rjXs+lABF+9GD1sIIVoTKWaFaGQGo+LF74/d7AajODvo+ObPQ+nm59ZocbV6xfmmGw2kxF+/6pp2DAxFlftq7cGnx/WCtX0f8O0FetfGj1sIIVo5KWaFaCRX8oo4fCGb7w9fqjC0wLprxQYy80saKbJW6Frm9aK1/KrrlTOgjJX7Orhajm/1CwPv7mAnN6IQQoimQIpZIRpAYYmBhIvZHErOIv5CNoeSr5KcWVCrY6Tn3rzgFTWgFGQnVx4mkHPBev82Pjes31p24wGZLCWEEE2WFLNC3CKDUfF7Rh6HkrNMxWtyFidSc8135Koo1LsNAR5OxJ6+XO1xfVwdGyLclstQarq6mnrYcqhAwVXr/duG3HDHrDBw9WvcmIUQQtwyKWaFqKWU7ALik7M4lJxNfHIWRy5mk1dUWqmfl4ueiEAP+gZ5EN7Bg7AO7rg72WMwKoYv205qdmFVNxjFz92RQSHtGvxcmq2SAuzTDkNyctkarodN41tLrVz91tqZhgVY3HigNzjKTSaEEKIlkGJWiJvILSzhyIVsDpZdcY2/kEVaTuUJQc4OOsIC3IkI9CA80IOIQA/auztaXYFAp9WweEJPHv/sQFU3GGXxhJ7m9WZbvYKrlktgpRxGc/kUnspQua99G1OhWrFw9ekBdvrGj1sIIUSjkGJWiDIlBiMnUnI5dCGr7MprFr9n5JnXei2n1UA3PzciAq8Xr529XWq1/uvY3u1Z+XC/G9aZNV2RXTyhJ2N7t6+v02o+lDLdbrXipKyUw5B9vlJXDWBwbIfWPwJN+7DrQwXahcpdrYQQopWRYla0SkopzmdesxjnmnAph+LSyrPZAzyciAjyIKKDqXDtHeCGs8Ot/+iM7d2eP/T0Y2/iZc5cyKBzB2+LO4C1aEYDXPm9bFxrheL12hXr/T2CKoxt7YPRtzcZBTp8fH3RyOQsIYRo1aSYFa1CZn6x+WprfNmV16vXKi995eZoZx4mEBHoQZ8OHni7Ntxb1DqthiGhnoS6GPDx8UTbEgvZ0iJIP2Z5tTXtKJTkV+6r0YF3txtuPBAGTm0t+xmNUJjeOPELIYRo0qSYFS1OYYmBo5eyOXjetCxWfHIW5zOvVernoNPS09+tbKiAOxGBbQn2dJY7bd2KwuyyCVkVxrhmnABj5Qly2DmZbjRQcSksn55g79T4cQshhGi2pJgVzVrFZbHKr7yeTM2ltIplsSI6eBBRtrpA9/au6O1kfGWd5aaWFazx1wvXq0nW+zq1rTApq2wZLK8uMr5VCCHELZNiVjQrqdmFFuNcq1sWKyLQnfCy4QLuTvY2iLgFMBrh6tmytVuPXB8qkF/F2/xuHSrfeMC9A8gVbyGEEA1AilnRZJUvi1VxdQFry2I52esI6+BuHucaHuiBfxXLYolqlBabhgVUnJSVmgDFuZX7arTg2aVy4eos6+MKIYRoPFLMinphMCr2Jl7hzIVMOufpaj0rv8Rg5GRqrsVV1zNVLIvV1dfVfCOC8EAPuvjUblksUaYo11SomgvXeEg/AcbKE+PQ6U3jW82TssJN2w7OjR+3EEIIUYEUs+KWxSSk3LBe6lna32S91IrLYsUnZxN/IYuEi9kUVbUsVoUrrvW1LFark5dhObY15TBkJoK1e5Dp3StfbfXqCjrJuxBCiKZH/jqJWxKTkMLjnx2oVBKlZhfy+GcHWPlwPwaFeBJ/IYtD52u+LFb5VdeGXBarRVLKNAnLYpjAEchNsd7f1f/68lflxatHRxnfKoQQotmQYlbUmcGoePH7Y9au7ZnbZn9+EMONYwUwLYvVw9+NiA7u5tUFgj3btMx1Vm/GaICk3ThePAXXukLwsJrP8DeUwOVTlldbU49AUbaVzhrw7GR5tdWvD7h41+vpCCGEEI1NitlmxGBU7DubSXpuIT6ujgwKadeod4sqKjWQkVtEem4R6TlF7D17xeJWrNaUF7KhXm3MQwXCAz3oIctiwbHvIOY5tDmX8Chvc/OHscug50TLvsX5phsNpMRfL1zTj4Oh8oQ4dA7g06PCHbPCwLc36F0a+ISEEEKIxifFbAMylJZyYu+PFFy9iFPbALoPjkJnV7eUxySk8PJ3RwjMi8eHLNLxINklnEUTw6yOS62NwpLyIrWQtJwi0nMKSc8tMn2dW0hGbhFpOYVWhwaU02JkkPaEObZ9xu4YMU3KWvrHMB4cFHRLMbY4x76DDY9QacxqToqpfdR8cHC5ftX1yhlQlccU4+BaYVJW2VVXr25g59AopyGEEELYWpMoZt977z1ef/11UlNTCQ8P591332XQoEFV9v/qq69YtGgRSUlJdOnShWXLljF+/PhGjLh6B3/8BP89L9KL6/eaT9vqyaXIxfSNmlqrY8UkpLBx3Sq+sv8Uf4dMc/ulona8tO4ReOgxqwVtQbHheoGaW0h6ThFpuYVk5BSVFaumojW7oOoi9UYOOi3ernp83PTYaTX8J+kqUdp9LLb/FH9NhdhUO14seYQfjYPo6NmmVufb7ClluuNVaREYik3DAQxFZZ+LofgabH4Kq5Ovytt2vFZ5l4uv5TCB9n3AIxi0spKDEEKI1svmxeyXX37JvHnzWLVqFYMHD2b58uVERUVx8uRJfHx8KvX/9ddfmTx5MkuWLOGuu+5i3bp1TJo0iQMHDtC7d28bnEFlB3/8hPBfnzBtVBgF4K2u4P3rExyEGhe0BqPil41reN9+eaV9fmTyvv1ynv5ax/5zf+Jyhaup6blF5BZauYVoFfR2Wnzc9Pi4OuJb9tnbVY+vmyM+ZcWrr6sjHs725vVbDUbF86++yqsl1mNbab+cv9o/y6CQev5HQ6nKBWJpha/L26sqJs19K+y31rfWj6/w/PWh4zDodHvZUIE+4OpbP8cVQgghWhCNUlZm5zSiwYMHM3DgQP75z38CYDQaCQwM5C9/+QsLFiyo1P+BBx4gPz+fTZs2mduGDBlCREQEq1atqvb5cnJycHd3Jzs7Gzc3N6t9jEYj6enp+Pj4oK3lVS9DaSmX/9EVb3UFa8NZjQrSNZ4c/ONOCg2QX2SgoNhAfnEp14oN5BdZfk7PzuPjnEfxI7PK46XiyfCiFea39U0UWhRt7KG9qz2+Lnb4ujrg42KHj4s9Pm3s8Gpjh7ezDi9nO1z0GjTKaJqQpAyWnyu1GU2fS4sp3jgH+6Isq5PflYJSexfsh/7ZtHZpbYrBqtoMxaaP5kanBzs96OxNeSzMqv4x934EYX9q8NCao1v5GW3NJG91I3mrPclZ3UjerqtJvVbOpldmi4uLiYuLY+HCheY2rVbLmDFj2LNnj9XH7Nmzh3nz5lm0RUVFsXHjRqv9i4qKKCq6fqUsJycHML1gjEYrYxDL9imlqtx/M8d/+4HeXLG4IluRVgN+XKHP1yMpQI+m7G1lDarC19e39RTjq7U2O/368fy5wgnH6Wi1WrTKgAajqTAtd63so4q7j94Kh/KArdBowL40D3Zaecu8Hik0ZYWiw/UPOwfLbZ29qaDU2Zft11duK/tQVh9/s2OWfW1xzAptGp3lUldJu9B+OqHa8zK28TH94yAquZWf0dZM8lY3krfak5zVjeTtutrkwKbF7OXLlzEYDPj6Wr596uvry4kTJ6w+JjU11Wr/1NRUq/2XLFnCiy++WKk9IyODwkLrM/GNRiPZ2dkopWr9n1Hmxd9r1C9Ae6X6TrXgQAnU4rWvNNqyIkuL0uhM4y7Lt7V2pluVanTX+2nL+pm3dWiKcrDLOV/tcxUFRFLaroupSNTao3T2oHVA6exRWvuyArK8zeGGNvtq2uxBa/PRMialZR8AFJd95Ffu59gJ7zZ+aPPTzP/AVKTQYGzjS4ZjJ0hvgP9AWoBb+RltzSRvdSN5qz3JWd1I3q7LzbVyG/UqNJEqoOEsXLjQ4kpuTk4OgYGBeHt733SYgUajwdvbu9YvpssBneB49f3O9n+ejr0jy7Y0ZVfuNBWu4Jm+Vpfi0cXMr/Z4pXd/gLbjENMapWXF5vXP2grb5YVq1Ut6aar4upKkXVCDK4z2o/+KffDwavu1KuNfg6+motBYFLSqLOOa8a/h43drq1S0ZLfyM9qaSd7qRvJWe5KzupG8Xefo6FjjvjYtZr28vNDpdKSlpVm0p6Wl4efnZ/Uxfn5+teqv1+vR6yvfRUqr1d70haLRaKrtY02PIeNI+8mz2jGzQePmoa3JMl0dBlCw403011KrPF6Rsx9O4ffVfLH9+hI8zLQuak4K1mfma8DNH23wMJlxf6Ned4PmU4h5DnIumZs1bv4wdimaG9eZFZXU9We0tZO81Y3krfYkZ3UjeTOpzfnbNFMODg7079+fbdu2mduMRiPbtm0jMjLS6mMiIyMt+gNs3bq1yv6NTWdnx6XIxYCp0KyofDslcnHN15vV6nCa8DoajabSKAIjphe904TXG7+QLYuNscvKNm6stMu2xy61TWzNQc+JMDcB4yPfkzX6TYyPfA9zj1S+YYIQQgghqmTzsn/evHmsXr2aTz75hOPHj/P444+Tn5/P9OnTAXjkkUcsJog9+eSTxMTE8Oabb3LixAleeOEF9u/fz5w5c2x1CpX0jZpK/NB3yNB4WrSnazyJH/pOrdeZpedENPd/arpqV4HGLQDN/Z/atvjpORHu/xTcbnhL3M3f1C6F2c1pdRA8nMIud0HwcCn8hRBCiFqy+ZjZBx54gIyMDP7+97+TmppKREQEMTEx5kle58+ft7jUPHToUNatW8ff/vY3/vrXv9KlSxc2btzYZNaYLdc3aiqG0dEcveEOYH51vAMYPSei6X4nnPsV8tLAxRdNx6FNo/jpORG634kxaTc5F0/hFtC1bGhBE4hNCCGEEC2azdeZbWwNvc5sayZ5qxvJW+1JzupG8lY3krfak5zVjeTtutqsM9u6MyWEEEIIIZo1KWaFEEIIIUSzJcWsEEIIIYRotqSYFUIIIYQQzZYUs0IIIYQQotmSYlYIIYQQQjRbUswKIYQQQohmS4pZIYQQQgjRbEkxK4QQQgghmi0pZoUQQgghRLNlZ+sAGlv53XtzcnKq7GM0GsnNzcXR0bHV306uNiRvdSN5qz3JWd1I3upG8lZ7krO6kbxdV16nlddtN9Pqitnc3FwAAgMDbRyJEEIIIYS4mdzcXNzd3W/aR6NqUvK2IEajkUuXLuHq6opGo7HaJycnh8DAQJKTk3Fzc2vkCJsvyVvdSN5qT3JWN5K3upG81Z7krG4kb9cppcjNzcXf37/aq9St7sqsVqulQ4cONerr5ubW6l9MdSF5qxvJW+1JzupG8lY3krfak5zVjeTNpLorsuVa94AMIYQQQgjRrEkxK4QQQgghmi0pZq3Q6/UsXrwYvV5v61CaFclb3Ujeak9yVjeSt7qRvNWe5KxuJG910+omgAkhhBBCiJZDrswKIYQQQohmS4pZIYQQQgjRbEkxK4QQQgghmi0pZoUQQgghRLMlxawV7733HsHBwTg6OjJ48GD27dtn65BsZsmSJQwcOBBXV1d8fHyYNGkSJ0+etOhTWFjI7Nmz8fT0xMXFhXvvvZe0tDSLPufPn+fOO+/E2dkZHx8f5s+fT2lpaWOeis0sXboUjUbD3LlzzW2SM+suXrzIww8/jKenJ05OToSFhbF//37zfqUUf//732nfvj1OTk6MGTOG06dPWxwjMzOT6Oho3Nzc8PDwYObMmeTl5TX2qTQag8HAokWLCAkJwcnJiU6dOvHyyy9b3M9c8gY7d+5kwoQJ+Pv7o9Fo2Lhxo8X++srR4cOHGTFiBI6OjgQGBvLaa6819Kk1mJvlrKSkhOeee46wsDDatGmDv78/jzzyCJcuXbI4RmvLGVT/WqvoscceQ6PRsHz5cov21pi3W6KEhfXr1ysHBwe1Zs0adfToUTVr1izl4eGh0tLSbB2aTURFRam1a9eqhIQEdejQITV+/HgVFBSk8vLyzH0ee+wxFRgYqLZt26b279+vhgwZooYOHWreX1paqnr37q3GjBmjDh48qLZs2aK8vLzUwoULbXFKjWrfvn0qODhY9enTRz355JPmdslZZZmZmapjx45q2rRpau/evSoxMVH9+OOP6syZM+Y+S5cuVe7u7mrjxo0qPj5eTZw4UYWEhKiCggJzn7Fjx6rw8HD122+/qdjYWNW5c2c1efJkW5xSo3jllVeUp6en2rRpkzp79qz66quvlIuLi1qxYoW5j+RNqS1btqjnn39e/fvf/1aA+uabbyz210eOsrOzla+vr4qOjlYJCQnqiy++UE5OTuqDDz5orNOsVzfLWVZWlhozZoz68ssv1YkTJ9SePXvUoEGDVP/+/S2O0dpyplT1r7Vy//73v1V4eLjy9/dXb7/9tsW+1pi3WyHF7A0GDRqkZs+ebd42GAzK399fLVmyxIZRNR3p6ekKUDt27FBKmX6h2dvbq6+++src5/jx4wpQe/bsUUqZfrC1Wq1KTU0191m5cqVyc3NTRUVFjXsCjSg3N1d16dJFbd26VY0aNcpczErOrHvuuefU8OHDq9xvNBqVn5+fev31181tWVlZSq/Xqy+++EIppdSxY8cUoP7zn/+Y+/zwww9Ko9GoixcvNlzwNnTnnXeqGTNmWLT98Y9/VNHR0UopyZs1NxYY9ZWj999/X7Vt29biZ/S5555T3bp1a+Azang3K8rK7du3TwHq3LlzSinJmVJV5+3ChQsqICBAJSQkqI4dO1oUs5K32pNhBhUUFxcTFxfHmDFjzG1arZYxY8awZ88eG0bWdGRnZwPQrl07AOLi4igpKbHIWffu3QkKCjLnbM+ePYSFheHr62vuExUVRU5ODkePHm3E6BvX7NmzufPOOy1yA5Kzqnz33XcMGDCA++67Dx8fH/r27cvq1avN+8+ePUtqaqpF3tzd3Rk8eLBF3jw8PBgwYIC5z5gxY9Bqtezdu7fxTqYRDR06lG3btnHq1CkA4uPj2bVrF+PGjQMkbzVRXznas2cPI0eOxMHBwdwnKiqKkydPcvXq1UY6G9vJzs5Go9Hg4eEBSM6qYjQamTJlCvPnz6dXr16V9kveak+K2QouX76MwWCwKCAAfH19SU1NtVFUTYfRaGTu3LkMGzaM3r17A5CamoqDg4P5l1e5ijlLTU21mtPyfS3R+vXrOXDgAEuWLKm0T3JmXWJiIitXrqRLly78+OOPPP744zzxxBN88sknwPXzvtnPZ2pqKj4+Phb77ezsaNeuXYvN24IFC3jwwQfp3r079vb29O3bl7lz5xIdHQ1I3mqivnLUGn9uyxUWFvLcc88xefJk3NzcAMlZVZYtW4adnR1PPPGE1f2St9qzs3UAovmYPXs2CQkJ7Nq1y9ahNGnJyck8+eSTbN26FUdHR1uH02wYjUYGDBjAq6++CkDfvn1JSEhg1apVTJ061cbRNV0bNmzg888/Z926dfTq1YtDhw4xd+5c/P39JW+iUZSUlHD//fejlGLlypW2DqdJi4uLY8WKFRw4cACNRmPrcFoMuTJbgZeXFzqdrtKs8rS0NPz8/GwUVdMwZ84cNm3axM8//0yHDh3M7X5+fhQXF5OVlWXRv2LO/Pz8rOa0fF9LExcXR3p6Ov369cPOzg47Ozt27NjBO++8g52dHb6+vpIzK9q3b0/Pnj0t2nr06MH58+eB6+d9s59PPz8/0tPTLfaXlpaSmZnZYvM2f/5889XZsLAwpkyZwlNPPWV+V0DyVr36ylFr/LktL2TPnTvH1q1bzVdlQXJmTWxsLOnp6QQFBZn/Ppw7d46nn36a4OBgQPJWF1LMVuDg4ED//v3Ztm2buc1oNLJt2zYiIyNtGJntKKWYM2cO33zzDdu3byckJMRif//+/bG3t7fI2cmTJzl//rw5Z5GRkRw5csTih7P8l96NxUtLMHr0aI4cOcKhQ4fMHwMGDCA6Otr8teSssmHDhlVa9u3UqVN07NgRgJCQEPz8/CzylpOTw969ey3ylpWVRVxcnLnP9u3bMRqNDB48uBHOovFdu3YNrdbyV7lOp8NoNAKSt5qorxxFRkayc+dOSkpKzH22bt1Kt27daNu2bSOdTeMpL2RPnz7NTz/9hKenp8V+yVllU6ZM4fDhwxZ/H/z9/Zk/fz4//vgjIHmrE1vPQGtq1q9fr/R6vfr444/VsWPH1H//938rDw8Pi1nlrcnjjz+u3N3d1S+//KJSUlLMH9euXTP3eeyxx1RQUJDavn272r9/v4qMjFSRkZHm/eXLTN1xxx3q0KFDKiYmRnl7e7foZaZuVHE1A6UkZ9bs27dP2dnZqVdeeUWdPn1aff7558rZ2Vl99tln5j5Lly5VHh4e6ttvv1WHDx9Wd999t9Xlk/r27av27t2rdu3apbp06dKilpi60dSpU1VAQIB5aa5///vfysvLSz377LPmPpI30+oiBw8eVAcPHlSAeuutt9TBgwfNM+/rI0dZWVnK19dXTZkyRSUkJKj169crZ2fnZrtc0s1yVlxcrCZOnKg6dOigDh06ZPH3oeIM+9aWM6Wqf63d6MbVDJRqnXm7FVLMWvHuu++qoKAg5eDgoAYNGqR+++03W4dkM4DVj7Vr15r7FBQUqD//+c+qbdu2ytnZWd1zzz0qJSXF4jhJSUlq3LhxysnJSXl5eamnn35alZSUNPLZ2M6NxazkzLrvv/9e9e7dW+n1etW9e3f14YcfWuw3Go1q0aJFytfXV+n1ejV69Gh18uRJiz5XrlxRkydPVi4uLsrNzU1Nnz5d5ebmNuZpNKqcnBz15JNPqqCgIOXo6KhCQ0PV888/b1FQSN6U+vnnn63+Lps6dapSqv5yFB8fr4YPH670er0KCAhQS5cubaxTrHc3y9nZs2er/Pvw888/m4/R2nKmVPWvtRtZK2ZbY95uhUapCreJEUIIIYQQohmRMbNCCCGEEKLZkmJWCCGEEEI0W1LMCiGEEEKIZkuKWSGEEEII0WxJMSuEEEIIIZotKWaFEEIIIUSzJcWsEEIIIYRotqSYFUIIIYQQzZYUs0KIFi0pKQmNRsOhQ4dsHYrZiRMnGDJkCI6OjkRERNg6nEZx5coVfHx8SEpKsnUotbZq1SomTJhg6zCEEFWQYlYI0aCmTZuGRqNh6dKlFu0bN25Eo9HYKCrbWrx4MW3atOHkyZNs27bN1uHc1G233cbcuXNv+TivvPIKd999N8HBwcD1fzJ8fHzIzc216BsREcELL7xwy89ZlY8//hiNRkOPHj0q7fvqq6/QaDTmOAFmzJjBgQMHiI2NbbCYhBB1J8WsEKLBOTo6smzZMq5evWrrUOpNcXFxnR/7+++/M3z4cDp27Iinp2c9RtU0Xbt2jY8++oiZM2dW2pebm8sbb7zR6DG1adOG9PR09uzZY9H+0UcfERQUZNHm4ODAQw89xDvvvNOYIQohakiKWSFEgxszZgx+fn4sWbKkyj4vvPBCpbfcly9fbnGFbNq0aUyaNIlXX30VX19fPDw8eOmllygtLWX+/Pm0a9eODh06sHbt2krHP3HiBEOHDsXR0ZHevXuzY8cOi/0JCQmMGzcOFxcXfH19mTJlCpcvXzbvv+2225gzZw5z587Fy8uLqKgoq+dhNBp56aWX6NChA3q9noiICGJiYsz7NRoNcXFxvPTSS2g0miqvQBqNRpYsWUJISAhOTk6Eh4fz9ddfm/d16NCBlStXWjzm4MGDaLVazp07B0BWVhaPPvoo3t7euLm5cfvttxMfH18p5//6178IDg7G3d2dBx980HyldNq0aezYsYMVK1ag0WjQaDQkJSVx9epVoqOj8fb2xsnJiS5duljNebktW7ag1+sZMmRIpX1/+ctfeOutt0hPT6/y8Q3Bzs6Ohx56iDVr1pjbLly4wC+//MJDDz1Uqf+ECRP47rvvKCgoaMwwhRA1IMWsEKLB6XQ6Xn31Vd59910uXLhwS8favn07ly5dYufOnbz11lssXryYu+66i7Zt27J3714ee+wx/ud//qfS88yfP5+nn36agwcPEhkZyYQJE7hy5QpgKvpuv/12+vbty/79+4mJiSEtLY3777/f4hiffPIJDg4O7N69m1WrVlmNb8WKFbz55pu88cYbHD58mKioKCZOnMjp06cBSElJoVevXjz99NOkpKTwzDPPWD3OkiVL+PTTT1m1ahVHjx7lqaee4uGHH2bHjh1otVomT57MunXrLB7z+eefM2zYMDp27AjAfffdR3p6Oj/88ANxcXH069eP0aNHk5mZaX7M77//zsaNG9m0aRObNm1ix44d5iEhK1asIDIyklmzZpGSkkJKSgqBgYEsWrSIY8eO8cMPP3D8+HFWrlyJl5dXld+z2NhY+vfvb3Xf5MmT6dy5My+99FKVj7d2PBcXl5t+fP7559UeZ8aMGWzYsIFr164BpuEHY8eOxdfXt1LfAQMGUFpayt69e2scpxCikSghhGhAU6dOVXfffbdSSqkhQ4aoGTNmKKWU+uabb1TFX0GLFy9W4eHhFo99++23VceOHS2O1bFjR2UwGMxt3bp1UyNGjDBvl5aWqjZt2qgvvvhCKaXU2bNnFaCWLl1q7lNSUqI6dOigli1bppRS6uWXX1Z33HGHxXMnJycrQJ08eVIppdSoUaNU3759qz1ff39/9corr1i0DRw4UP35z382b4eHh6vFixdXeYzCwkLl7Oysfv31V4v2mTNnqsmTJyullDp48KDSaDTq3LlzSimlDAaDCggIUCtXrlRKKRUbG6vc3NxUYWGhxTE6deqkPvjgA6WUKefOzs4qJyfHvH/+/Plq8ODB5u1Ro0apJ5980uIYEyZMUNOnT79ZGizcfffd5u97ufLvy8GDB1VMTIyyt7dXZ86cUUpVn59r166p06dP3/Sj4jndaO3atcrd3V0ppVRERIT65JNPlNFoVJ06dVLffvttpdddubZt26qPP/64xucthGgcdrYtpYUQrcmyZcu4/fbbq7waWRO9evVCq73+ppKvry+9e/c2b+t0Ojw9PSu9bR0ZGWn+2s7OjgEDBnD8+HEA4uPj+fnnn3Fxcan0fL///jtdu3YFqPLqYrmcnBwuXbrEsGHDLNqHDRtm8fZ+dc6cOcO1a9f4wx/+YNFeXFxM3759AdMkqR49erBu3ToWLFjAjh07SE9P57777jOfU15eXqUxuQUFBfz+++/m7eDgYFxdXc3b7du3r/Yt/8cff5x7772XAwcOcMcddzBp0iSGDh1aZf+CggIcHR2r3B8VFcXw4cNZtGhRpavN1jg5OdG5c+dq+9XEjBkzWLt2LUFBQeTn5zN+/Hj++c9/Vvm85VdxhRBNhxSzQohGM3LkSKKioli4cCHTpk2z2KfValFKWbSVlJRUOoa9vb3FtkajsdpmNBprHFdeXh4TJkxg2bJllfa1b9/e/HWbNm1qfMxbkZeXB8DmzZsJCAiw2KfX681fR0dHm4vZdevWMXbsWHPxmpeXR/v27fnll18qHd/Dw8P8dV1yN27cOM6dO8eWLVvYunUro0ePZvbs2VVO5PLy8qp28t/SpUuJjIxk/vz5N+0HpmEG48aNu2mfDz74gOjo6GqPFR0dzbPPPssLL7zAlClTsLOr+s9iZmYm3t7e1R5TCNG4pJgVQjSqpUuXEhERQbdu3Szavb29SU1NRSllXrKrPteG/e233xg5ciQApaWlxMXFMWfOHAD69evH//3f/xEcHHzTYqY6bm5u+Pv7s3v3bkaNGmVu3717N4MGDarxcXr27Iler+f8+fMWx7nRQw89xN/+9jfi4uL4+uuvLcbx9uvXj9TUVOzs7Cwm0dWWg4MDBoOhUru3tzdTp05l6tSpjBgxgvnz51dZzPbt25fPPvvsps8zaNAg/vjHP7JgwYJqYxowYEC1rw1r416tadeuHRMnTmTDhg1VjoMG0xX6wsJC85VxIUTTIcWsEKJRhYWFER0dXWmZo9tuu42MjAxee+01/vSnPxETE8MPP/yAm5tbvTzve++9R5cuXejRowdvv/02V69eZcaMGQDMnj2b1atXM3nyZJ599lnatWvHmTNnWL9+Pf/7v/+LTqer8fPMnz+fxYsX06lTJyIiIli7di2HDh2q0YSkcq6urjzzzDM89dRTGI1Ghg8fTnZ2Nrt378bNzY2pU6cCpiECQ4cOZebMmRgMBiZOnGg+xpgxY4iMjGTSpEm89tprdO3alUuXLrF582buueceBgwYUKNYgoOD2bt3L0lJSbi4uNCuXTteeOEF+vfvT69evSgqKmLTpk1W12wtV341/urVq7Rt27bKfq+88gq9evWq9h+K+hxmAKaJX++///5Nl0mLjY0lNDSUTp061dvzCiHqh6xmIIRodC+99FKlt7J79OjB+++/z3vvvUd4eDj79u27pbG1N1q6dClLly4lPDycXbt28d1335ln4JdfTTUYDNxxxx2EhYUxd+5cPDw8LMbn1sQTTzzBvHnzePrppwkLCyMmJobvvvuOLl261Oo4L7/8MosWLWLJkiX06NGDsWPHsnnzZkJCQiz6RUdHEx8fzz333IOTk5O5XaPRsGXLFkaOHMn06dPp2rUrDz74IOfOnavxVUuAZ555Bp1OR8+ePfH29ub8+fM4ODiwcOFC+vTpw8iRI9HpdKxfv77KY4SFhdGvXz82bNhw0+fq2rUrM2bMoLCwsMbx1QcnJ6dq1/v94osvmDVrViNFJISoDY26cZCaEEIIUc82b97M/PnzSUhIqPU/CLZ29OhRbr/9dk6dOoW7u7utwxFC3ECGGQghhGhwd955J6dPn+bixYsEBgbaOpxaSUlJ4dNPP5VCVogmSq7MCiGEEEKIZqt5vdcjhBBCCCFEBVLMCiGEEEKIZkuKWSGEEEII0WxJMSuEEEIIIZotKWaFEEIIIUSzJcWsEEIIIYRotqSYFUIIIYQQzZYUs0IIIYQQotmSYlYIIYQQQjRb/x/kp8yNwyysjgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "compareMusic",
+   "language": "python",
+   "name": "comparemusic"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 93df335abdeac912fabfec41f139e2ce7e429902 Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Thu, 9 Jul 2026 22:30:38 +0100
Subject: [PATCH 03/15] optimisation result using vectorisation method

---
 notebooks/optimise_alignment.ipynb | 419 +++++++++++++++++++++--------
 1 file changed, 300 insertions(+), 119 deletions(-)

diff --git a/notebooks/optimise_alignment.ipynb b/notebooks/optimise_alignment.ipynb
index 22b8fb5..70c7f6b 100644
--- a/notebooks/optimise_alignment.ipynb
+++ b/notebooks/optimise_alignment.ipynb
@@ -5,14 +5,27 @@
    "id": "67c86ddb",
    "metadata": {},
    "source": [
-    "The `event_alignment_ED` is basically an O(N×M) dynamic programming procedure, and is implemented as a nested for-loop. For a Bach fugue with roughly 1,400 events on each side, this already amounts to close to 2 million cell computations. For a substantially longer piece such as a Liszt piano sonata (~16,000 notes), the same recursion would require on the order of 2.5×10⁸ cell computations. Consider that sutudents' practice is usually not that long as the sonata, `max_notes = 3500` was introduced as a hard cut-off to skip such pieces during evaluation.\n",
+    "The `event_alignment_ED` is basically an O(N×M) dynamic programming procedure, and is implemented as a nested for-loop. For a Bach fugue with roughly 1,400 events on each side, this already amounts to close to 2 million cell computations. For a substantially longer piece such as a Liszt piano sonata (~16,000 notes), the same recursion would require on the order of 2.5×10⁸ cell computations. Consider that students' practice is usually not that long as the sonata, `max_notes = 3500` was introduced as a hard cut-off to skip such pieces during evaluation.\n",
     "\n",
-    "In order to make the evaluation more comprehensive and improve the algorithm, can consider optimisation methods such as vectorised NumPy operation and Numba to reduce the computational cost. "
+    "In order to make the evaluation more comprehensive and improve the algorithm, can consider optimisation methods such as:\n",
+    "\n",
+    "**Precomputes the whole cost matrix:**\n",
+    "- using numpy broadcasting for note-vs-note pairs, instead of calling compute_event_cost() once per cell in a pure Python loop. \n",
+    "- chord vs chord: encode each chord as a 12-dimensional pitch-class binary vector, then compute all pairwise Hamming distances using broadcasting\n",
+    "- note vs chord (type mismatch): fill directly with `gap_penalty`\n",
+    "\n",
+    "\n",
+    "**Add a band around the diagonal** (future work)\n",
+    "- See Muller's book section 3.2.2.3 (Sakoe-Chiba band used in DTW), instead of filling the whole (N+1) x (M+1) matrix, this constrained version only fills a band around the diagonal. This is safe as long as the student performance is not wildly different in length from the reference (which is true in almost all practice scenarios). Cells outside the band are treated as \"too expensive\", so the aligner will never choose a path through them.\n",
+    "\n",
+    "**Numba** (future work)\n",
+    "\n",
+    "**Multiprocessing** during algorithm evaluation with many MIDI files"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "11c82556",
    "metadata": {
     "execution": {
@@ -36,28 +49,14 @@
     "    compute_event_cost,\n",
     "    group_notes_into_events,\n",
     "    normalize_start_times,\n",
+    "    event_alignment_ED,\n",
     "    DEFAULT_GAP_PENALTY,\n",
     ")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d54cad29",
-   "metadata": {},
-   "source": [
-    "**Precomputes the whole cost matrix:**\n",
-    "- using numpy broadcasting for note-vs-note pairs, instead of calling compute_event_cost() once per cell in a pure Python loop. \n",
-    "- chord vs chord: encode each chord as a 12-dimensional pitch-class binary vector, then compute all pairwise Hamming distances using broadcasting\n",
-    "- note vs chord (type mismatch): fill directly with `gap_penalty`\n",
-    "\n",
-    "\n",
-    "**Add a band around the diagonal**\n",
-    "- See Muller's book section 3.2.2.3 (Sakoe-Chiba band used in DTW), instead of filling the whole (N+1) x (M+1) matrix, this constrained version only fills a band around the diagonal. This is safe as long as the student performance is not wildly different in length from the reference (which is true in almost all practice scenarios). Cells outside the band are treated as \"too expensive\", so the aligner will never choose a path through them."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "ccba850e",
    "metadata": {
     "execution": {
@@ -69,85 +68,111 @@
    },
    "outputs": [],
    "source": [
-    "# A very large number used to represent \"outside the search band, do not use this cell\"\n",
-    "LARGE_PENALTY = 1000000\n",
-    "\n",
-    "def build_cost_matrix(response_events, ref_events, gap_penalty):\n",
+    "def build_cost_matrix(response_events, ref_events, gap_penalty=DEFAULT_GAP_PENALTY):\n",
     "    \"\"\"\n",
-    "    Precompute the full cost matrix between response_events\n",
-    "    and ref_events, using vectorized numpy operations where possible.\n",
+    "    Precompute the full (N x M) event-alignment cost matrix using vectorised\n",
+    "    NumPy broadcasting, instead of calling compute_event_cost() one cell at a\n",
+    "    time inside a Python loop. This also removes the redundant cost\n",
+    "    computation that used to happen a second time during backtracking.\n",
     "\n",
     "    Args:\n",
-    "        response_events: list of event dicts\n",
-    "        ref_events: list of event dicts\n",
-    "        gap_penalty: cost used for note-vs-chord type mismatches\n",
+    "        response_events: list of event dicts (from group_notes_into_events)\n",
+    "        ref_events: list of event dicts (from group_notes_into_events)\n",
+    "        gap_penalty: cost used when event types (note vs chord) do not match\n",
     "\n",
     "    Returns:\n",
-    "        cost_matrix: numpy array of shape (N, M), where\n",
-    "            cost_matrix[i, j] is the cost of aligning response_events[i]\n",
-    "            with ref_events[j]\n",
+    "        numpy array of shape (N, M): cost of aligning each response event\n",
+    "        to each reference event, matching compute_event_cost() exactly.\n",
     "    \"\"\"\n",
+    "    N = len(response_events)\n",
+    "    M = len(ref_events)\n",
+    "\n",
+    "    # Build simple per-event arrays: is this event a chord, and (if it is a\n",
+    "    # single note) what is its pitch.\n",
+    "    res_is_chord = np.array([event[\"event_type\"] == \"chord\" for event in response_events])\n",
+    "    ref_is_chord = np.array([event[\"event_type\"] == \"chord\" for event in ref_events])\n",
     "\n",
-    "    return cost_matrix\n",
+    "    res_pitch = np.array([\n",
+    "        event[\"notes\"][0][\"pitch\"] if event[\"event_type\"] == \"note\" else 0\n",
+    "        for event in response_events\n",
+    "    ])\n",
+    "    ref_pitch = np.array([\n",
+    "        event[\"notes\"][0][\"pitch\"] if event[\"event_type\"] == \"note\" else 0\n",
+    "        for event in ref_events\n",
+    "    ])\n",
     "\n",
+    "    # Note-vs-note cost: vectorised absolute pitch difference for every pair.\n",
+    "    # Shape (N, 1) - shape (1, M) broadcasts to (N, M)\n",
+    "    note_cost_matrix = np.abs(res_pitch.reshape(N, 1) - ref_pitch.reshape(1, M))\n",
     "\n",
-    "def event_alignment_ED_banded(response_events, ref_events, gap_penalty, band_width=20):\n",
+    "    # Chord-vs-chord cost: build a 12-dim binary pitch-class vector per chord,\n",
+    "    # then compute Hamming distance for every pair using broadcasting.\n",
+    "    res_pitch_vector = np.zeros((N, 12), dtype=int)\n",
+    "    for i in range(N):\n",
+    "        if res_is_chord[i]:\n",
+    "            for note in response_events[i][\"notes\"]:\n",
+    "                res_pitch_vector[i, note[\"pitch\"] % 12] = 1\n",
+    "\n",
+    "    ref_pitch_vector = np.zeros((M, 12), dtype=int)\n",
+    "    for j in range(M):\n",
+    "        if ref_is_chord[j]:\n",
+    "            for note in ref_events[j][\"notes\"]:\n",
+    "                ref_pitch_vector[j, note[\"pitch\"] % 12] = 1\n",
+    "\n",
+    "    # (N, 1, 12) vs (1, M, 12) broadcasts to (N, M, 12); summing the last\n",
+    "    # axis gives the Hamming distance for every (response, ref) pair at once.\n",
+    "    differs = res_pitch_vector.reshape(N, 1, 12) != ref_pitch_vector.reshape(1, M, 12)\n",
+    "    chord_cost_matrix = differs.sum(axis=2)\n",
+    "\n",
+    "    # Type-mismatch mask: True where one side is a note and the other a chord.\n",
+    "    type_mismatch = res_is_chord.reshape(N, 1) != ref_is_chord.reshape(1, M)\n",
+    "    both_chords = res_is_chord.reshape(N, 1) & ref_is_chord.reshape(1, M)\n",
+    "\n",
+    "    # Combine the three cases -- np.where(condition, true, false)\n",
+    "    cost_matrix = np.where(\n",
+    "        type_mismatch,\n",
+    "        gap_penalty,\n",
+    "        np.where(both_chords, chord_cost_matrix, note_cost_matrix),\n",
+    "    )\n",
+    "\n",
+    "    return cost_matrix.astype(int)\n",
+    "\n",
+    "\n",
+    "def event_alignment_ED_vectorised(response_events, ref_events, gap_penalty=DEFAULT_GAP_PENALTY):\n",
     "    \"\"\"\n",
-    "    Banded version of event_alignment_ED(). Only cells within band_width of\n",
-    "    the diagonal are considered valid alignment candidates, which reduces\n",
-    "    the amount of work needed for long MIDI sequences.\n",
+    "    Same algorithm as the production event_alignment_ED(), but the N x M\n",
+    "    substitution costs are looked up from a precomputed cost matrix instead\n",
+    "    of being recomputed on every DP cell and again during backtracking.\n",
     "\n",
     "    Args:\n",
-    "        response_events: list of event dicts\n",
-    "        ref_events: list of event dicts\n",
-    "        gap_penalty: cost of leaving an event unaligned\n",
-    "        band_width: how far (in event-index terms) the search area extends\n",
-    "            on either side of the diagonal. Increase this if the response\n",
-    "            and reference are expected to differ a lot in length. If the\n",
-    "            true optimal path falls outside the band, the result will not\n",
-    "            be optimal, so choose band_width generously relative to how much\n",
-    "            students are expected to add or drop notes.\n",
+    "        response_events: list of event dicts from group_notes_into_events\n",
+    "        ref_events: list of event dicts from group_notes_into_events\n",
+    "        gap_penalty: cost of leaving an event unaligned (insertion/deletion)\n",
     "\n",
     "    Returns:\n",
-    "        operations: same format as event_alignment_ED()\n",
+    "        operations: list of operation dicts, same format as event_alignment_ED()\n",
     "        D: accumulated cost matrix, shape (N+1, M+1)\n",
     "    \"\"\"\n",
-    "    # if a raw note dict with \"pitch\"/\"start\"/\"duration\" but no \"event_type\" is\n",
-    "    # passed in, group them into events first. \n",
     "    if \"event_type\" not in response_events[0]:\n",
-    "        raise ValueError(\"response_events must already be grouped into events\")\n",
+    "        response_events = group_notes_into_events(response_events)\n",
     "    if \"event_type\" not in ref_events[0]:\n",
-    "        raise ValueError(\"ref_events must already be grouped into events\")\n",
+    "        ref_events = group_notes_into_events(ref_events)\n",
     "\n",
-    "    # the rows of D correspond to response events\n",
     "    N = len(response_events)\n",
-    "    # the columns of D correspond to reference events\n",
     "    M = len(ref_events)\n",
     "\n",
-    "    # cost matrix for all event pairs is precomputed \n",
+    "    # Precompute every substitution cost once, instead of calling\n",
+    "    # compute_event_cost() inside the double loop below.\n",
     "    cost_matrix = build_cost_matrix(response_events, ref_events, gap_penalty)\n",
     "\n",
-    "    # Build the accumulated cost matrix D of size (N+1 x M+1)\n",
-    "    D = np.full((N + 1, M + 1), LARGE_PENALTY, dtype=int)\n",
-    "    D[0, 0] = 0\n",
-    "\n",
-    "    # Boundary conditions: aligning against an empty sequence means every event\n",
-    "    # is unaligned, so the cost is n (or m) times the gap penalty.\n",
+    "    D = np.zeros((N + 1, M + 1), dtype=int)\n",
     "    for n in range(1, N + 1):\n",
-    "        if n <= band_width:\n",
-    "            D[n, 0] = n * gap_penalty # n extra response events\n",
+    "        D[n, 0] = n * gap_penalty\n",
     "    for m in range(1, M + 1):\n",
-    "        if m <= band_width:\n",
-    "            D[0, m] = m * gap_penalty # m missing ref events\n",
+    "        D[0, m] = m * gap_penalty\n",
     "\n",
-    "    # Recursion (accumulated cost / score matrix D):\n",
     "    for n in range(1, N + 1):\n",
-    "        # Only look at the columns inside the band for this row:\n",
-    "        # instead of looping over all M columns,\n",
-    "        # only loop over roughly 2 * band_width columns.\n",
-    "        m_low = max(1, n - band_width)\n",
-    "        m_high = min(M, n + band_width)\n",
-    "        for m in range(m_low, m_high + 1):\n",
+    "        for m in range(1, M + 1):\n",
     "            replace_cost = cost_matrix[n - 1, m - 1]\n",
     "            D[n, m] = min(\n",
     "                D[n - 1, m - 1] + replace_cost,\n",
@@ -155,15 +180,10 @@
     "                D[n, m - 1] + gap_penalty,\n",
     "            )\n",
     "\n",
-    "    # Backtrack and classify each transformation op based on movement direction in D\n",
-    "    # (it will naturally stay inside the band, since cells outside the band \n",
-    "    # all carry the LARGE_PENALTY value and will never be chosen as the minimum).\n",
     "    operations = []\n",
     "    n, m = N, M\n",
     "    while n > 0 or m > 0:\n",
-    "        # Boundary conditions: at the top row, only horizontal moves possible\n",
     "        if n == 0:\n",
-    "            # Missing response for ref[m-1] (deletion)\n",
     "            operations.append({\n",
     "                \"type\": \"missing\",\n",
     "                \"response_idx\": None,\n",
@@ -171,9 +191,7 @@
     "                \"cost\": gap_penalty,\n",
     "            })\n",
     "            m -= 1\n",
-    "        # At the leftmost column, only vertical moves possible\n",
     "        elif m == 0:\n",
-    "            # Extra event response[n-1] (insertion)\n",
     "            operations.append({\n",
     "                \"type\": \"extra\",\n",
     "                \"response_idx\": n - 1,\n",
@@ -181,25 +199,22 @@
     "                \"cost\": gap_penalty,\n",
     "            })\n",
     "            n -= 1\n",
-    "        # For all other cases, we can move in any direction (diagonal, vertical, horizontal)\n",
     "        else:\n",
     "            replace_cost = cost_matrix[n - 1, m - 1]\n",
-    "            diag = D[n - 1, m - 1] + replace_cost # # diagonal: match or replacement\n",
-    "            up = D[n - 1, m] + gap_penalty # vertical: extra event in response[n-1]\n",
-    "            left = D[n, m - 1] + gap_penalty # horizontal: missing event in reference[m-1]\n",
-    "            min_cost = min(diag, up, left) # find the minimum cost step\n",
-    "            \n",
-    "            # classify the transformation ops based on the minimum cost step\n",
-    "            # rule: always prefer diagonal > insertion or deletion\n",
-    "            if min_cost == diag: # Diagonal -> two events are aligned (match or replacement)\n",
+    "            diag = D[n - 1, m - 1] + replace_cost\n",
+    "            up = D[n - 1, m] + gap_penalty\n",
+    "            left = D[n, m - 1] + gap_penalty\n",
+    "            min_cost = min(diag, up, left)\n",
+    "\n",
+    "            if min_cost == diag:\n",
     "                operations.append({\n",
     "                    \"type\": \"match\" if replace_cost == 0 else \"replacement\",\n",
     "                    \"response_idx\": n - 1,\n",
     "                    \"reference_idx\": m - 1,\n",
-    "                    \"cost\": replace_cost,\n",
+    "                    \"cost\": int(replace_cost),\n",
     "                })\n",
     "                n, m = n - 1, m - 1\n",
-    "            elif min_cost == up: # Vertical -> response[n-1] is extra (insertion)\n",
+    "            elif min_cost == up:\n",
     "                operations.append({\n",
     "                    \"type\": \"extra\",\n",
     "                    \"response_idx\": n - 1,\n",
@@ -207,7 +222,7 @@
     "                    \"cost\": gap_penalty,\n",
     "                })\n",
     "                n -= 1\n",
-    "            else: # Horizontal -> response is missing for ref[m-1] (deletion)\n",
+    "            else:\n",
     "                operations.append({\n",
     "                    \"type\": \"missing\",\n",
     "                    \"response_idx\": None,\n",
@@ -216,7 +231,7 @@
     "                })\n",
     "                m -= 1\n",
     "\n",
-    "    operations.reverse() # Reverse to get ops in order from first note to last\n",
+    "    operations.reverse()\n",
     "    return operations, D"
    ]
   },
@@ -230,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "95a6044d",
    "metadata": {
     "execution": {
@@ -245,21 +260,165 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "note_count~50  events=41x43  match=True\n",
-      "note_count~300  events=246x240  match=True\n"
+      "Loaded 14 test cases:\n",
+      " - twinkle_perfect (long_melody)\n",
+      " - twinkle_scattered_errors (long_melody)\n",
+      " - twinkle_repeated_note_correct (repeated_notes)\n",
+      " - twinkle_repeated_note_missing (repeated_notes)\n",
+      " - twinkle_repeated_note_extra (repeated_notes)\n",
+      " - twinkle_hesitation_mid_phrase (hesitation)\n",
+      " - twinkle_hesitation_plus_missing (hesitation)\n",
+      " - twinkle_long_break_no_notes_lost (long_break)\n",
+      " - twinkle_long_break_with_skip (long_break)\n",
+      " - chord_all_correct (broken_chord)\n",
+      " - chord_one_wrong_note (broken_chord)\n",
+      " - chord_entirely_missing (broken_chord)\n",
+      " - chord_arpeggiated_within_window (broken_chord)\n",
+      " - chord_arpeggiated_beyond_window (broken_chord)\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "cwd = os.getcwd()\n",
+    "dir = os.path.dirname(cwd)\n",
+    "TEST_CASE_PATH = os.path.join(dir, \"data\", \"longMIDIsequence.json\")\n",
+    "\n",
+    "# Fall back to the current directory if the notebook is run from elsewhere.\n",
+    "if not os.path.exists(TEST_CASE_PATH):\n",
+    "    TEST_CASE_PATH = \"longMIDIsequence.json\"\n",
+    "\n",
+    "with open(TEST_CASE_PATH) as f:\n",
+    "    test_case_data = json.load(f)\n",
+    "\n",
+    "test_cases = test_case_data[\"test_cases\"]\n",
+    "print(\"Loaded\", len(test_cases), \"test cases:\")\n",
+    "for case in test_cases:\n",
+    "    print(\" -\", case[\"name\"], \"(\" + case[\"category\"] + \")\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "52ab0dea",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "PASSED - twinkle_perfect\n",
+      "PASSED - twinkle_scattered_errors\n",
+      "PASSED - twinkle_repeated_note_correct\n",
+      "PASSED - twinkle_repeated_note_missing\n",
+      "PASSED - twinkle_repeated_note_extra\n",
+      "PASSED - twinkle_hesitation_mid_phrase\n",
+      "PASSED - twinkle_hesitation_plus_missing\n",
+      "PASSED - twinkle_long_break_no_notes_lost\n",
+      "PASSED - twinkle_long_break_with_skip\n",
+      "PASSED - chord_all_correct\n",
+      "PASSED - chord_one_wrong_note\n",
+      "PASSED - chord_entirely_missing\n",
+      "PASSED - chord_arpeggiated_within_window\n",
+      "PASSED - chord_arpeggiated_beyond_window\n",
+      "\n",
+      "ALL TEST CASES PASSED: vectorised alignment is identical to production.\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_passed = True\n",
+    "\n",
+    "for case in test_cases:\n",
+    "    response_notes = normalize_start_times(case[\"response\"][\"notes\"])\n",
+    "    ref_notes = normalize_start_times(case[\"reference\"][\"notes\"])\n",
+    "    response_events = group_notes_into_events(response_notes)\n",
+    "    ref_events = group_notes_into_events(ref_notes)\n",
+    "\n",
+    "    production_ops, production_D = event_alignment_ED(response_events, ref_events)\n",
+    "    vectorised_ops, vectorised_D = event_alignment_ED_vectorised(response_events, ref_events)\n",
+    "\n",
+    "    ops_match = production_ops == vectorised_ops\n",
+    "    matrix_match = np.array_equal(production_D, vectorised_D)\n",
+    "    passed = ops_match and matrix_match\n",
+    "\n",
+    "    if not passed:\n",
+    "        all_passed = False\n",
+    "\n",
+    "    status = \"PASSED\" if passed else \"FAILED\"\n",
+    "    print(status, \"-\", case[\"name\"])\n",
+    "\n",
+    "print()\n",
+    "if all_passed:\n",
+    "    print(\"ALL TEST CASES PASSED: vectorised alignment is identical to production.\")\n",
+    "else:\n",
+    "    print(\"Some test cases failed, see above.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a38e9f27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "note_count~50  events=41x43  match=True\n",
+      "note_count~300  events=246x240  match=True\n",
       "note_count~800  events=657x651  match=True\n",
       "\n",
-      "If all lines above say match=True, the vectorised version is verified correct.\n"
+      "If all lines above say match=True, the vectorised version is verified correct at scale.\n"
      ]
     }
    ],
-   "source": []
+   "source": [
+    "def make_random_notes(count, chord_probability=0.15, seed=0):\n",
+    "    \"\"\"\n",
+    "    Generate a random note stream for stress-testing, occasionally grouping\n",
+    "    several notes into a chord (same onset time) to exercise the chord-vs-chord\n",
+    "    and chord-vs-note code paths.\n",
+    "\n",
+    "    Args:\n",
+    "        count: int, approximate number of notes to generate\n",
+    "        chord_probability: float, chance that a given onset becomes a chord\n",
+    "        seed: int, random seed for reproducibility\n",
+    "\n",
+    "    Returns:\n",
+    "        list of note dicts with keys pitch, start, duration\n",
+    "    \"\"\"\n",
+    "    rng = random.Random(seed)\n",
+    "    notes = []\n",
+    "    t = 0.0\n",
+    "    while len(notes) < count:\n",
+    "        t += rng.uniform(0.2, 0.6)\n",
+    "        if rng.random() < chord_probability:\n",
+    "            chord_size = rng.choice([2, 3])\n",
+    "            root = rng.randint(48, 80)\n",
+    "            offsets = rng.sample([0, 3, 4, 7, 8, 10], chord_size - 1)\n",
+    "            pitches = [root] + [root + offset for offset in offsets]\n",
+    "            for pitch in pitches:\n",
+    "                notes.append({\"pitch\": pitch, \"start\": t, \"duration\": 0.4})\n",
+    "        else:\n",
+    "            notes.append({\"pitch\": rng.randint(48, 84), \"start\": t, \"duration\": 0.4})\n",
+    "    return notes[:count]\n",
+    "\n",
+    "\n",
+    "for size in [50, 300, 800]:\n",
+    "    res_notes = make_random_notes(size, seed=1)\n",
+    "    ref_notes_r = make_random_notes(size, seed=2)\n",
+    "    res_events = group_notes_into_events(normalize_start_times(res_notes))\n",
+    "    ref_events_r = group_notes_into_events(normalize_start_times(ref_notes_r))\n",
+    "\n",
+    "    production_ops, production_D = event_alignment_ED(res_events, ref_events_r)\n",
+    "    vectorised_ops, vectorised_D = event_alignment_ED_vectorised(res_events, ref_events_r)\n",
+    "    match = production_ops == vectorised_ops and np.array_equal(production_D, vectorised_D)\n",
+    "\n",
+    "    print(\"note_count~\" + str(size), \" events=\" + str(len(res_events)) + \"x\" + str(len(ref_events_r)), \" match=\" + str(match))\n",
+    "\n",
+    "print()\n",
+    "print(\"If all lines above say match=True, the vectorised version is verified correct at scale.\")\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -273,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "d310e8fb",
    "metadata": {
     "execution": {
@@ -288,32 +447,44 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N=M~50: old=0.0009s  vectorised=0.0003s  speedup=2.8x\n",
-      "N=M~100: old=0.0028s  vectorised=0.0004s  speedup=6.4x\n",
-      "N=M~200: old=0.0111s  vectorised=0.0015s  speedup=7.6x\n",
-      "N=M~400: old=0.0466s  vectorised=0.0056s  speedup=8.3x\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "N=M~800: old=0.1977s  vectorised=0.0695s  speedup=2.8x\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "N=M~1500: old=0.7535s  vectorised=0.4387s  speedup=1.7x\n"
+      "N=M~50: production=0.0013s  vectorised=0.0023s  speedup=0.6x\n",
+      "N=M~100: production=0.0047s  vectorised=0.0050s  speedup=0.9x\n",
+      "N=M~200: production=0.0200s  vectorised=0.0170s  speedup=1.2x\n",
+      "N=M~400: production=0.1051s  vectorised=0.0684s  speedup=1.5x\n",
+      "N=M~800: production=0.3403s  vectorised=0.2814s  speedup=1.2x\n",
+      "N=M~1500: production=1.2428s  vectorised=1.0064s  speedup=1.2x\n"
      ]
     }
    ],
-   "source": []
+   "source": [
+    "sizes = [50, 100, 200, 400, 800, 1500]\n",
+    "old_times = []\n",
+    "new_times = []\n",
+    "\n",
+    "for size in sizes:\n",
+    "    res_notes = make_random_notes(size, seed=3)\n",
+    "    ref_notes_r = make_random_notes(size, seed=4)\n",
+    "    res_events = group_notes_into_events(normalize_start_times(res_notes))\n",
+    "    ref_events_r = group_notes_into_events(normalize_start_times(ref_notes_r))\n",
+    "\n",
+    "    t0 = time.perf_counter()\n",
+    "    event_alignment_ED(res_events, ref_events_r)\n",
+    "    t1 = time.perf_counter()\n",
+    "    event_alignment_ED_vectorised(res_events, ref_events_r)\n",
+    "    t2 = time.perf_counter()\n",
+    "\n",
+    "    old_time = t1 - t0\n",
+    "    new_time = t2 - t1\n",
+    "    old_times.append(old_time)\n",
+    "    new_times.append(new_time)\n",
+    "\n",
+    "    speedup = old_time / new_time if new_time > 0 else float(\"inf\")\n",
+    "    print(\"N=M~\" + str(size) + \": production=\" + format(old_time, \".4f\") + \"s  vectorised=\" + format(new_time, \".4f\") + \"s  speedup=\" + format(speedup, \".1f\") + \"x\")"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "0335fb99",
    "metadata": {
     "execution": {
@@ -326,7 +497,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -335,7 +506,17 @@
      "output_type": "display_data"
     }
    ],
-   "source": []
+   "source": [
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.plot(sizes, old_times, marker=\"o\", label=\"Production event_alignment_ED\")\n",
+    "plt.plot(sizes, new_times, marker=\"o\", label=\"Vectorised event_alignment_ED\")\n",
+    "plt.xlabel(\"Number of events (N = M)\")\n",
+    "plt.ylabel(\"Time (seconds)\")\n",
+    "plt.title(\"Full alignment time: production vs vectorised\")\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {

From 0b8399fa655b3fb30e97f9430a7b8c1204a3b9f1 Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Thu, 9 Jul 2026 22:44:29 +0100
Subject: [PATCH 04/15] add tqdm dependency

---
 poetry.lock    | 25 +++++++++++++++++++++++--
 pyproject.toml |  1 +
 2 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index db22435..34eb223 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -430,7 +430,7 @@ files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
-markers = {main = "os_name == \"nt\" or sys_platform == \"win32\"", dev = "sys_platform == \"win32\""}
+markers = {main = "os_name == \"nt\" or sys_platform == \"win32\" or platform_system == \"Windows\"", dev = "sys_platform == \"win32\""}
 
 [[package]]
 name = "contourpy"
@@ -2662,6 +2662,27 @@ files = [
     {file = "tomlkit-0.15.0.tar.gz", hash = "sha256:7d1a9ecba3086638211b13814ea79c90dd54dd11993564376f3aa92271f5c7a3"},
 ]
 
+[[package]]
+name = "tqdm"
+version = "4.68.4"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "tqdm-4.68.4-py3-none-any.whl", hash = "sha256:5168118b2368f48c561afda8020fd79195b1bdb0bdf8086b88442c267a315dc2"},
+    {file = "tqdm-4.68.4.tar.gz", hash = "sha256:19829c9673638f2a0b8617da4cdcb927e831cd88bcfcb6e78d42a4d1af131520"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+discord = ["envwrap", "requests"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["envwrap", "slack-sdk"]
+telegram = ["envwrap", "requests"]
+
 [[package]]
 name = "trove-classifiers"
 version = "2026.6.1.19"
@@ -3044,4 +3065,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.11"
-content-hash = "e2572e64cfb65e24d91c1cf604f2e59440d86cac35507649a2e522dba0a5a318"
+content-hash = "ecdcfccfc009831d6b449c0b55f8f228de58f0537e64a57a2ae1843d238021af"
diff --git a/pyproject.toml b/pyproject.toml
index 9a6a00e..979f0bc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,6 +20,7 @@ numpy = "^2.4.6"
 matplotlib = "^3.11.0"
 pretty-midi = "^0.2.11"
 pandas = "^3.0.3"
+tqdm = "^4.68.4"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.2.2"

From 7c89b19d36c7ce9a9bbf30e2ebcbbe3894bdfa02 Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Thu, 9 Jul 2026 23:19:20 +0100
Subject: [PATCH 05/15] update evaluation results on more MIDI pieces

---
 notebooks/Alignment_on_ASAPdataset.ipynb | 272 +++++++++++------------
 1 file changed, 124 insertions(+), 148 deletions(-)

diff --git a/notebooks/Alignment_on_ASAPdataset.ipynb b/notebooks/Alignment_on_ASAPdataset.ipynb
index 37bde62..45f12e6 100644
--- a/notebooks/Alignment_on_ASAPdataset.ipynb
+++ b/notebooks/Alignment_on_ASAPdataset.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,28 +217,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jz7125/compareMusic/.venv/lib/python3.13/site-packages/pretty_midi/pretty_midi.py:122: RuntimeWarning: Tempo, Key or Time signature change events found on non-zero tracks.  This is not a valid type 0 or type 1 MIDI file.  Tempo, Key or Time Signature may be wrong.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Skipped 424 pairs due to exceeding max_notes limit of 3500\n",
-      "Loaded 642 score (reference) /performance (response) pairs.\n"
+      "Skipped 45 pairs due to exceeding max_notes limit of 8000\n",
+      "Loaded 1021 score (reference) /performance (response) pairs.\n"
      ]
     }
    ],
    "source": [
-    "def load_samples(asap_path, composer=None, max_notes=3500):\n",
+    "def load_samples(asap_path, composer=None, max_notes=8000):\n",
     "    \"\"\"\n",
     "    Read the ASAP metadata CSV and return a list of sample dicts\n",
     "    for a specific composer only.\n",
@@ -292,10 +284,10 @@
     "                        samples.append(sample)\n",
     "\n",
     "    print(f\"Skipped {skipped} pairs due to exceeding max_notes limit of {max_notes}\")\n",
-    "    print(f\"Loaded {len(samples)} score (reference) /performance (response) pairs.\")\n",
+    "    print(f\"Loaded {len(samples)} score (reference) / performance (response) pairs.\")\n",
     "    return samples\n",
     "\n",
-    "samples = load_samples(ASAP_PATH, composer=None, max_notes=3500)"
+    "samples = load_samples(ASAP_PATH, composer=None, max_notes=8000)"
    ]
   },
   {
@@ -313,76 +305,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1021/1021 [15:15<00:00,  1.11it/s] \n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Alignment complete for 642 pieces.\n"
+      "Succeeded: 1021  Failed: 0\n"
      ]
     }
    ],
    "source": [
-    "from evaluation_function.compare_MIDI import (\n",
-    "    compare_performance_ED,\n",
-    "    normalize_start_times,\n",
-    "    group_notes_into_events,\n",
-    ")\n",
+    "import os\n",
+    "from concurrent.futures import ProcessPoolExecutor, as_completed\n",
+    "from tqdm import tqdm\n",
     "\n",
-    "def run_alignment_on_samples(samples):\n",
-    "    \"\"\"\n",
-    "    Run compare_performance_ED on every sample and collect results.\n",
+    "from asap_worker import evaluate_one_sample\n",
     "\n",
-    "    In addition to the pipeline result, we also store:\n",
-    "      - response_events_normalized: the grouped event list after start-time\n",
-    "        normalisation, needed to look up correct onset/pitch during evaluation.\n",
-    "      - response_onset_offset: the original first-note onset time, needed to\n",
-    "        convert normalised onsets back to absolute time for GT comparison.\n",
+    "\n",
+    "def run_alignment_on_samples_parallel(samples, max_workers=None):\n",
+    "    \"\"\"\n",
+    "    Run evaluate_one_sample on every sample using a process pool, so multiple\n",
+    "    pieces are aligned at the same time on different CPU cores.\n",
     "\n",
     "    Args:\n",
     "        samples: list of sample dicts from load_samples()\n",
+    "        max_workers: int or None. None means \"all CPU cores minus one\",\n",
+    "                     leaving one core free for the OS and other apps.\n",
     "\n",
     "    Returns:\n",
-    "        list of result dicts, each with keys:\n",
-    "            composer, title, stats, event_details, is_correct,\n",
-    "            response_events_normalized, response_onset_offset\n",
+    "        results: list of successful result dicts\n",
+    "        failed: list of (composer, title, error_message) for pieces that failed\n",
     "    \"\"\"\n",
+    "    if max_workers is None:\n",
+    "        max_workers = max(1, os.cpu_count() - 1)\n",
+    "\n",
     "    results = []\n",
-    "    for sample in samples:\n",
-    "        result = compare_performance_ED(\n",
-    "            sample[\"response\"],\n",
-    "            sample[\"reference\"],\n",
-    "        )\n",
-    "\n",
-    "        # Reproduce the same normalisation + grouping done inside the pipeline.\n",
-    "        # This gives us the event list whose indices match event_details.\n",
-    "        response_notes_norm = normalize_start_times(sample[\"response\"][\"notes\"])\n",
-    "        response_events_norm = group_notes_into_events(response_notes_norm)\n",
-    "\n",
-    "        # Record the offset that normalize_start_times subtracted from every onset.\n",
-    "        # Adding it back can convert a normalised onset to the original onset.\n",
-    "        if sample[\"response\"][\"notes\"]:\n",
-    "            response_onset_offset = sample[\"response\"][\"notes\"][0][\"start\"]\n",
-    "        else:\n",
-    "            response_onset_offset = 0.0\n",
-    "\n",
-    "        results.append({\n",
-    "            \"composer\": sample[\"composer\"],\n",
-    "            \"title\": sample[\"title\"],\n",
-    "            \"stats\": result.stats,\n",
-    "            \"event_details\": result.event_details,\n",
-    "            \"is_correct\": result.is_correct,\n",
-    "            \"response_events_normalized\": response_events_norm,\n",
-    "            \"response_onset_offset\": response_onset_offset,\n",
-    "            \"metadata_row\": sample[\"metadata_row\"],\n",
-    "        })\n",
+    "    failed = []\n",
     "\n",
-    "    return results\n",
+    "    with ProcessPoolExecutor(max_workers=max_workers) as executor:\n",
+    "        future_to_sample = {\n",
+    "            executor.submit(evaluate_one_sample, sample): sample\n",
+    "            for sample in samples\n",
+    "        }\n",
+    "\n",
+    "        for future in tqdm(as_completed(future_to_sample), total=len(samples)):\n",
+    "            sample = future_to_sample[future]\n",
+    "            result = future.result()  # exceptions are already caught inside the worker\n",
+    "\n",
+    "            if result[\"error\"] is not None:\n",
+    "                failed.append((result[\"composer\"], result[\"title\"], result[\"error\"]))\n",
+    "            else:\n",
+    "                results.append(result)\n",
     "\n",
-    "all_results = run_alignment_on_samples(samples)\n",
-    "print(\"Alignment complete for\", len(all_results), \"pieces.\")"
+    "    print(\"Succeeded:\", len(results), \" Failed:\", len(failed))\n",
+    "    return results, failed\n",
+    "\n",
+    "\n",
+    "all_results, failed_pieces = run_alignment_on_samples_parallel(samples)"
    ]
   },
   {
@@ -413,11 +401,11 @@
     "\n",
     "Following Peter et al. (2023, TISMIR), label-level F1 would be applied as the main metric where TP, FP, and FN are computed separately for each label type and then summed:\n",
     "\n",
-    "| Label | Matching method | Rationale |\n",
-    "|---|---|---|\n",
-    "| `paired` | Onset key only (rounded to 1 d.p.) | GT does not check pitch correctness for alignment |\n",
-    "| `deletion` | Count-based: TP = min(GT count, predicted count) | No onset or pitch available for deletions |\n",
-    "| `insertion` | `(pitch, onset)` key | Both sides have onset and pitch info for extra notes |\n",
+    "| Label | Matching key | TP | FP | FN |\n",
+    "|---|---|---|---|---|\n",
+    "| `paired` (pipeline `match` / `replacement`) | onset only (rounded to 1 d.p.) | Pipeline and ground truth agree a score note was aligned to a performance note at this onset (regardless of pitch correctness) | Pipeline predicts a pairing at this onset that the ground truth does not recognise | Ground truth expects a pairing at this onset that the pipeline fails to find |\n",
+    "| `deletion` (pipeline `missing`) | count only as no onset or pitch available for deletions | Overlap between the number of notes the pipeline judges as missing and the number the ground truth judges as missing | Pipeline predicts more missing notes than the ground truth; the excess | Ground truth expects more missing notes than the pipeline predicts; the shortfall |\n",
+    "| `insertion` (pipeline `extra`) | `(pitch, onset)` | Pipeline and ground truth agree an extra, unexpected note was played at this pitch and onset | Pipeline flags a note as extra that the ground truth does not agree with | Ground truth marks a note as extra that the pipeline fails to identify |\n",
     "\n",
     "- Precision: TP / (TP + FP), i.e. of all labels the pipeline predicted, how many were correct  \n",
     "- Recall: TP / (TP + FN), i.e. of all GT labels, how many the pipeline found  \n",
@@ -426,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -637,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -707,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -732,7 +720,8 @@
     "    tsv_path = os.path.join(asap_path, tsv_rel)\n",
     "\n",
     "    if not os.path.isfile(tsv_path):\n",
-    "        print(\"TSV not found:\", tsv_path)\n",
+    "        # No print here -- the caller counts and reports how many were\n",
+    "        # skipped in a single summary line instead.\n",
     "        return None\n",
     "\n",
     "    ground_truth = load_ground_truth(tsv_path)\n",
@@ -744,33 +733,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "TSV not found: asap-dataset/Beethoven/Piano_Sonatas/17-2/KaszoS10_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n",
-      "TSV not found: asap-dataset/Schubert/Impromptu_op_note_alignments/note_alignment.tsv\n"
+      "Skipped 30 piece(s) due to missing TSV file.\n"
      ]
     },
     {
@@ -817,26 +787,6 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9850</td>\n",
-       "      <td>0.9500</td>\n",
-       "      <td>0.9672</td>\n",
-       "      <td>855</td>\n",
-       "      <td>13</td>\n",
-       "      <td>45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9621</td>\n",
-       "      <td>0.9162</td>\n",
-       "      <td>0.9386</td>\n",
-       "      <td>864</td>\n",
-       "      <td>34</td>\n",
-       "      <td>79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
        "      <td>0.9675</td>\n",
        "      <td>0.9260</td>\n",
        "      <td>0.9463</td>\n",
@@ -845,27 +795,17 @@
        "      <td>69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9809</td>\n",
-       "      <td>0.9083</td>\n",
-       "      <td>0.9432</td>\n",
-       "      <td>872</td>\n",
-       "      <td>17</td>\n",
-       "      <td>88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>2</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9861</td>\n",
-       "      <td>0.9193</td>\n",
-       "      <td>0.9515</td>\n",
-       "      <td>854</td>\n",
-       "      <td>12</td>\n",
-       "      <td>75</td>\n",
+       "      <td>0.9850</td>\n",
+       "      <td>0.9500</td>\n",
+       "      <td>0.9672</td>\n",
+       "      <td>855</td>\n",
+       "      <td>13</td>\n",
+       "      <td>45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>3</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
        "      <td>0.9840</td>\n",
        "      <td>0.9378</td>\n",
@@ -875,7 +815,7 @@
        "      <td>57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>4</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
        "      <td>0.9522</td>\n",
        "      <td>0.9232</td>\n",
@@ -885,7 +825,17 @@
        "      <td>73</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>5</th>\n",
+       "      <td>Bach (Fugue_bwv_848)</td>\n",
+       "      <td>0.9621</td>\n",
+       "      <td>0.9162</td>\n",
+       "      <td>0.9386</td>\n",
+       "      <td>864</td>\n",
+       "      <td>34</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
        "      <td>0.9873</td>\n",
        "      <td>0.9326</td>\n",
@@ -895,6 +845,26 @@
        "      <td>62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Bach (Fugue_bwv_848)</td>\n",
+       "      <td>0.9861</td>\n",
+       "      <td>0.9193</td>\n",
+       "      <td>0.9515</td>\n",
+       "      <td>854</td>\n",
+       "      <td>12</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Bach (Fugue_bwv_848)</td>\n",
+       "      <td>0.9809</td>\n",
+       "      <td>0.9083</td>\n",
+       "      <td>0.9432</td>\n",
+       "      <td>872</td>\n",
+       "      <td>17</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
        "      <td>0.9874</td>\n",
@@ -911,14 +881,14 @@
       "text/plain": [
        "                  Piece  Precision  Recall      F1   TP  FP  FN\n",
        "0  Bach (Fugue_bwv_846)     0.9463  0.9136  0.9297  423  24  40\n",
-       "1  Bach (Fugue_bwv_848)     0.9850  0.9500  0.9672  855  13  45\n",
-       "2  Bach (Fugue_bwv_848)     0.9621  0.9162  0.9386  864  34  79\n",
-       "3  Bach (Fugue_bwv_848)     0.9675  0.9260  0.9463  864  29  69\n",
-       "4  Bach (Fugue_bwv_848)     0.9809  0.9083  0.9432  872  17  88\n",
-       "5  Bach (Fugue_bwv_848)     0.9861  0.9193  0.9515  854  12  75\n",
-       "6  Bach (Fugue_bwv_848)     0.9840  0.9378  0.9604  860  14  57\n",
-       "7  Bach (Fugue_bwv_848)     0.9522  0.9232  0.9375  877  44  73\n",
-       "8  Bach (Fugue_bwv_848)     0.9873  0.9326  0.9592  858  11  62\n",
+       "1  Bach (Fugue_bwv_848)     0.9675  0.9260  0.9463  864  29  69\n",
+       "2  Bach (Fugue_bwv_848)     0.9850  0.9500  0.9672  855  13  45\n",
+       "3  Bach (Fugue_bwv_848)     0.9840  0.9378  0.9604  860  14  57\n",
+       "4  Bach (Fugue_bwv_848)     0.9522  0.9232  0.9375  877  44  73\n",
+       "5  Bach (Fugue_bwv_848)     0.9621  0.9162  0.9386  864  34  79\n",
+       "6  Bach (Fugue_bwv_848)     0.9873  0.9326  0.9592  858  11  62\n",
+       "7  Bach (Fugue_bwv_848)     0.9861  0.9193  0.9515  854  12  75\n",
+       "8  Bach (Fugue_bwv_848)     0.9809  0.9083  0.9432  872  17  88\n",
        "9  Bach (Fugue_bwv_848)     0.9874  0.9330  0.9594  863  11  62"
       ]
      },
@@ -929,9 +899,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean Precision: 0.9038\n",
-      "Mean Recall : 0.8915\n",
-      "Mean F1 : 0.8964\n"
+      "Mean Precision: 0.8898\n",
+      "Mean Recall : 0.8727\n",
+      "Mean F1 : 0.8793\n"
      ]
     }
    ],
@@ -939,6 +909,8 @@
     "import pandas as pd\n",
     "\n",
     "eval_rows = []\n",
+    "skipped_count = 0\n",
+    "\n",
     "for result in all_results:\n",
     "    metrics = evaluate_one_piece(\n",
     "        ASAP_PATH,\n",
@@ -957,6 +929,10 @@
     "            \"FP\": metrics[\"fp\"],\n",
     "            \"FN\": metrics[\"fn\"],\n",
     "        })\n",
+    "    else:\n",
+    "        skipped_count += 1\n",
+    "\n",
+    "print(f\"Skipped {skipped_count} piece(s) due to missing TSV file.\")\n",
     "\n",
     "df_eval = pd.DataFrame(eval_rows)\n",
     "display(df_eval.head(10))\n",

From 0da3a1d6dcf17437d6cae1397e21dbb32799ba44 Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Fri, 10 Jul 2026 21:17:27 +0100
Subject: [PATCH 06/15] fix the chord accuracy compuation

---
 evaluation_function/compare_MIDI.py    | 62 ++++++++++++++--------
 evaluation_function/evaluation_test.py | 71 ++++++++++++++++++++++----
 2 files changed, 102 insertions(+), 31 deletions(-)

diff --git a/evaluation_function/compare_MIDI.py b/evaluation_function/compare_MIDI.py
index 98302a2..f021d0b 100644
--- a/evaluation_function/compare_MIDI.py
+++ b/evaluation_function/compare_MIDI.py
@@ -17,6 +17,7 @@
 
 
 import numpy as np
+from collections import Counter
 
 # Default thresholds / parameters
 # Teachers can override any of these via the params dict in evaluation_function.
@@ -94,12 +95,14 @@ def identify_chord_name(notes):
  
 def compute_chord_accuracy(ref_notes, res_notes):
     """
-    Compute the chord accuracy score A from (Devaney, n.d.): 
+    Compute the chord accuracy score A (modified Devaney's method), but
+    use the actual MIDI pitch (with octave) instead of pitch class, 
+    so that the score is sensitive to octave errors.: 
     A = (C - I + |y|) / (2 * |y|)
     where:
-        C = |y ∩ y_hat|  (correctly played pitch classes)
-        I = |y_hat - y|  (unexpected pitch classes played)
-        |y|              (number of pitch classes in the reference chord)
+        C = number of correctly matched notes
+        I = number of extra notes
+        |y| = number of notes in the reference chord
     A = 1.0 means perfectly correct. 
     A = 0.0 means nothing correct and many unexpected notes are played.
  
@@ -109,28 +112,43 @@ def compute_chord_accuracy(ref_notes, res_notes):
  
     Returns:
         accuracy: float in [0, 1]
-        correct_pitches: sorted list of pitch class ints in both chords
-        missing_pitches: sorted list of pitch class ints in ref
-        extra_pitches: sorted list of pitch class ints in response
+        correct_pitches: sorted list of matched MIDI pitches
+        missing_pitches: sorted list of missing MIDI pitches
+        extra_pitches: sorted list of extra MIDI pitches
     """
-    ref_pcs = get_pitch_class_set(ref_notes)
-    res_pcs = get_pitch_class_set(res_notes)
- 
-    correct_pcs = ref_pcs & res_pcs
-    missing_pcs = ref_pcs - res_pcs
-    extra_pcs   = res_pcs - ref_pcs
- 
-    C = len(correct_pcs)
-    I = len(extra_pcs)
-    ref_size = len(ref_pcs)
- 
+    ref_counts = Counter(note["pitch"] for note in ref_notes)
+    res_counts = Counter(note["pitch"] for note in res_notes)
+
+    correct_pitches = []
+    missing_pitches = []
+    extra_pitches = []
+
+    # For every distinct pitch on either side, match up copies one-to-one.
+    # Any leftover ref copies are missing; any leftover res copies are extra.
+    all_pitches = sorted(set(ref_counts) | set(res_counts))
+    for pitch in all_pitches:
+        matched = min(ref_counts[pitch], res_counts[pitch])
+        correct_pitches.extend([pitch] * matched)
+        if ref_counts[pitch] > matched:
+            missing_pitches.extend(
+                [pitch] * (ref_counts[pitch] - matched)
+            )
+        if res_counts[pitch] > matched:
+            extra_pitches.extend(
+                [pitch] * (res_counts[pitch] - matched)
+            )
+
+    C = len(correct_pitches)
+    I = len(extra_pitches)
+    ref_size = len(ref_notes)   # total note count, repeats included
+
     if ref_size == 0:
         accuracy = 0.0
     else:
         accuracy = (C - I + ref_size) / (2.0 * ref_size)
         accuracy = max(0.0, min(1.0, accuracy))
- 
-    return accuracy, sorted(correct_pcs), sorted(missing_pcs), sorted(extra_pcs)
+
+    return accuracy, correct_pitches, missing_pitches, extra_pitches
 
 
 # Step 0 - make first note start at t = 0.0
@@ -1006,10 +1024,10 @@ def generate_feedback_message(event_details, response_events, ref_events, stats,
                 f"{accuracy_pct}% accurate. "
             )
             if ch["missing_pitches"]:
-                missing_names = [PITCH_CLASS_NAMES[pc] for pc in ch["missing_pitches"]]
+                missing_names = [PITCH_CLASS_NAMES[pitch % 12] for pitch in ch["missing_pitches"]]
                 message = message + "Missing note(s): " + ", ".join(missing_names) + ". "
             if ch["extra_pitches"]:
-                extra_names = [PITCH_CLASS_NAMES[pc] for pc in ch["extra_pitches"]]
+                extra_names = [PITCH_CLASS_NAMES[pitch % 12] for pitch in ch["extra_pitches"]]
                 message = message + "Extra note(s) played: " + ", ".join(extra_names) + "."
             chord_detail_messages.append(message)
     # Local timing errors for chords
diff --git a/evaluation_function/evaluation_test.py b/evaluation_function/evaluation_test.py
index b7d4480..7362646 100755
--- a/evaluation_function/evaluation_test.py
+++ b/evaluation_function/evaluation_test.py
@@ -92,26 +92,80 @@ def test_perfect_accuracy_is_one(self):
         res = [{"pitch": 60}, {"pitch": 64}, {"pitch": 67}]
         accuracy, correct, missing, extra = compute_chord_accuracy(ref, res)
         assert accuracy == 1.0
+        assert correct == [60, 64, 67]
         assert missing == []
         assert extra == []
- 
+
     def test_completely_wrong_notes(self):
         # Response has no overlap with reference
         ref = [{"pitch": 60}, {"pitch": 64}, {"pitch": 67}]
         res = [{"pitch": 61}, {"pitch": 65}, {"pitch": 69}]
         accuracy, correct, missing, extra = compute_chord_accuracy(ref, res)
         assert accuracy == 0.0
-        assert len(correct) == 0
- 
+        assert correct == []
+
     def test_partial_match_returns_score_between_0_and_1(self):
-        # C major ref (0,4,7) vs C-minor response (0,3,7) -- one note off
+        # One note off: E4 (64) replaced by D#4 (63)
         ref = [{"pitch": 60}, {"pitch": 64}, {"pitch": 67}]
         res = [{"pitch": 60}, {"pitch": 63}, {"pitch": 67}]
         accuracy, correct, missing, extra = compute_chord_accuracy(ref, res)
         assert 0.0 < accuracy < 1.0
-        assert len(correct) == 2
-        assert 4 in missing   # pitch class 4 (E) is missing
-        assert 3 in extra     # pitch class 3 (D#) is extra
+        assert correct == [60, 67]
+        assert missing == [64]  # the actual MIDI pitch of the missing note (E4)
+        assert extra == [63]    # the actual MIDI pitch of the extra note (D#4)
+
+    def test_octave_doubling_matches_when_both_sides_double(self):
+        # Both reference and response double the root an octave up
+        # (C4=60 and C5=72) -- since we compare actual MIDI pitch, both
+        # copies match individually, no pitch-class merging involved.
+        ref = [{"pitch": 60}, {"pitch": 72}, {"pitch": 64}, {"pitch": 67}]
+        res = [{"pitch": 60}, {"pitch": 72}, {"pitch": 64}, {"pitch": 67}]
+        accuracy, correct, missing, extra = compute_chord_accuracy(ref, res)
+        assert accuracy == 1.0
+        assert correct == [60, 64, 67, 72]
+        assert missing == []
+        assert extra == []
+
+    def test_extra_octave_doubled_note_is_flagged(self):
+        # Reference has a single root note; performer adds an extra
+        # octave-doubled copy (C5=72) that was never asked for. Because we
+        # compare actual MIDI pitch (not pitch class), 72 is simply a
+        # pitch that is not in the reference -- no special-casing needed.
+        ref = [{"pitch": 60}, {"pitch": 64}, {"pitch": 67}]         # C4, E4, G4
+        res = [{"pitch": 60}, {"pitch": 72}, {"pitch": 64}, {"pitch": 67}]  # + extra C5
+        accuracy, correct, missing, extra = compute_chord_accuracy(ref, res)
+        assert extra == [72]
+        assert correct == [60, 64, 67]
+
+    def test_missing_octave_doubled_note_is_detected(self):
+        # Reference doubles the root (C4=60 and C5=72); performer only
+        # plays C4, dropping the C5. Since MIDI pitch (not pitch class) is
+        # compared, 72 simply never appears in the response, so it is
+        # correctly flagged as missing -- and this now also lowers the
+        # accuracy score itself (unlike the original pitch-class-only
+        # definition, which would consider {0, 7} == {0, 7} and hide this).
+        ref = [{"pitch": 60}, {"pitch": 72}, {"pitch": 67}]  # C4, C5, G4
+        res = [{"pitch": 60}, {"pitch": 67}]                 # C4, G4 only
+        accuracy, correct, missing, extra = compute_chord_accuracy(ref, res)
+        assert accuracy == 5 / 6   # C=2, I=0, |y|=3 -> (2-0+3)/6
+        assert correct == [60, 67]
+        assert missing == [72]
+        assert extra == []
+
+    def test_missing_exact_duplicate_pitch_is_detected(self):
+        # Reference has the EXACT same MIDI pitch twice (e.g. a fast
+        # repeated note landing in the same onset window as other notes),
+        # not just an octave doubling. A plain set comparison would merge
+        # both copies of 60 into one and hide a dropped repeat; the
+        # Counter-based multiset matching below must catch it.
+        ref = [{"pitch": 60}, {"pitch": 60}, {"pitch": 67}]  # C4, C4 (repeat), G4
+        res = [{"pitch": 60}, {"pitch": 67}]                 # only one C4 played
+        accuracy, correct, missing, extra = compute_chord_accuracy(ref, res)
+        assert accuracy == 5 / 6   # C=2, I=0, |y|=3 -> (2-0+3)/6
+        assert correct == [60, 67]
+        assert missing == [60]     # the dropped repeat, not merged away
+        assert extra == []
+
 
 
 # 2. Tests for normalize_start_times
@@ -632,5 +686,4 @@ def test_realistic_scenario(case):
         ]
         assert len(matching_notes) == 1
         flagged_note = matching_notes[0]
-        assert flagged_note["timing_correct"] is False
- 
\ No newline at end of file
+        assert flagged_note["timing_correct"] is False
\ No newline at end of file

From c438e87779ad9233fb7bce2f1b9f60476dd9096c Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Fri, 10 Jul 2026 21:34:43 +0100
Subject: [PATCH 07/15] replace original alignment with the vectorised version
 from the notebook

---
 evaluation_function/compare_MIDI.py      | 152 +++++-----
 notebooks/Alignment_on_ASAPdataset.ipynb | 353 +++++++++++------------
 2 files changed, 248 insertions(+), 257 deletions(-)

diff --git a/evaluation_function/compare_MIDI.py b/evaluation_function/compare_MIDI.py
index f021d0b..463f9b7 100644
--- a/evaluation_function/compare_MIDI.py
+++ b/evaluation_function/compare_MIDI.py
@@ -264,86 +264,89 @@ def group_notes_into_events(notes, chord_onset_window=DEFAULT_CHORD_ONSET_WINDOW
 
 # Step 1 -- edit-distance alignment to identify missing/extra notes and pitch errors
 # ------------------------------------------------------------------------------
-def compute_note_cost(note1, note2):
+def build_cost_matrix(response_events, ref_events, gap_penalty=DEFAULT_GAP_PENALTY):
     """
-    Cost of aligning (replacing) one note with another, based on pitch.
- 
-    cost = 0: pitches are identical (a 'match'). 
-    cost > 0: different pitches (a 'replacement')
- 
+    Precompute the full (N x M) event-alignment cost matrix using vectorised
+    NumPy broadcasting, instead of compute event cost one at a
+    time inside a Python loop. This also removes the redundant cost
+    computation that used to happen a second time during backtracking.
+
     Args:
-        note1: dict with keys "pitch" (int), "start" (float), "duration" (float)
-        note2: dict with keys "pitch" (int), "start" (float), "duration" (float)
- 
+        response_events: list of event dicts (from group_notes_into_events)
+        ref_events: list of event dicts (from group_notes_into_events)
+        gap_penalty: cost used when event types (note vs chord) do not match
+
     Returns:
-        int: cost value >= 0 (lower means more similar pitch)
+        numpy array of shape (N, M): cost of aligning each response event
+        to each reference event.
     """
-    return int(abs(note1["pitch"] - note2["pitch"]))
+    N = len(response_events)
+    M = len(ref_events)
 
+    # Build simple per-event arrays: is this event a chord, and (if it is a
+    # single note) what is its pitch.
+    res_is_chord = np.array([event["event_type"] == "chord" for event in response_events])
+    ref_is_chord = np.array([event["event_type"] == "chord" for event in ref_events])
+
+    res_pitch = np.array([
+        event["notes"][0]["pitch"] if event["event_type"] == "note" else 0
+        for event in response_events
+    ])
+    ref_pitch = np.array([
+        event["notes"][0]["pitch"] if event["event_type"] == "note" else 0
+        for event in ref_events
+    ])
+
+    # Note-vs-note cost: vectorised absolute pitch difference for every pair.
+    # Shape (N, 1) - shape (1, M) broadcasts to (N, M)
+    note_cost_matrix = np.abs(res_pitch.reshape(N, 1) - ref_pitch.reshape(1, M))
+
+    # Chord-vs-chord cost: build a 12-dim binary pitch-class vector per chord,
+    # then compute Hamming distance for every pair using broadcasting.
+    res_pitch_vector = np.zeros((N, 12), dtype=int)
+    for i in range(N):
+        if res_is_chord[i]:
+            for note in response_events[i]["notes"]:
+                res_pitch_vector[i, note["pitch"] % 12] = 1
+
+    ref_pitch_vector = np.zeros((M, 12), dtype=int)
+    for j in range(M):
+        if ref_is_chord[j]:
+            for note in ref_events[j]["notes"]:
+                ref_pitch_vector[j, note["pitch"] % 12] = 1
+
+    # (N, 1, 12) vs (1, M, 12) broadcasts to (N, M, 12); summing the last
+    # axis gives the Hamming distance for every (response, ref) pair at once.
+    differs = res_pitch_vector.reshape(N, 1, 12) != ref_pitch_vector.reshape(1, M, 12)
+    chord_cost_matrix = differs.sum(axis=2)
+
+    # Type-mismatch mask: True where one side is a note and the other a chord.
+    type_mismatch = res_is_chord.reshape(N, 1) != ref_is_chord.reshape(1, M)
+    both_chords = res_is_chord.reshape(N, 1) & ref_is_chord.reshape(1, M)
+
+    # Combine the three cases -- np.where(condition, true, false)
+    cost_matrix = np.where(
+        type_mismatch,
+        gap_penalty,
+        np.where(both_chords, chord_cost_matrix, note_cost_matrix),
+    )
 
-def compute_event_cost(event1, event2, gap_penalty=DEFAULT_GAP_PENALTY):
-    """
-    Cost of aligning (substituting) one event with another.
- 
-    Rules:
-      - note vs note: absolute pitch difference 
-      - chord vs chord: Hamming distance on 12-dim pitch class binary vectors
-      - note vs chord (type mismatch): return gap_penalty so the aligner
-        treats them as unaligned (insertion + deletion is preferred)
- 
-    For chord vs chord, the Hamming distance counts how many of the 12
-    pitch classes differ between the two chords (symmetric difference size).
- 
-    Args:
-        event1: event dict (from group_notes_into_events)
-        event2: event dict (from group_notes_into_events)
-        gap_penalty: cost of an unaligned event; used as the type-mismatch cost
- 
-    Returns:
-        int: alignment cost >= 0
-    """
-    type1 = event1["event_type"]
-    type2 = event2["event_type"]
- 
-    # Type mismatch: note vs chord or chord vs note.
-    # Return gap_penalty so alignment prefers to leave them unmatched.
-    if type1 != type2:
-        return gap_penalty
- 
-    # Both are single notes: use pitch difference (same as Phase 1)
-    elif type1 == "note" and type2 == "note":
-        return compute_note_cost(event1["notes"][0], event2["notes"][0])
- 
-    # Both are chords: Hamming distance on 12-dimensional pitch class vectors.
-    # i.e. count how many of the 12 pitch classes differ between the two chords.
-    else:
-        vec1 = [0] * 12
-        vec2 = [0] * 12
-        for note in event1["notes"]:
-            vec1[note["pitch"] % 12] = 1
-        for note in event2["notes"]:
-            vec2[note["pitch"] % 12] = 1
-        hamming = sum(1 for i in range(12) if vec1[i] != vec2[i])
-        return hamming
+    return cost_matrix.astype(int)
 
 
 def event_alignment_ED(response_events, ref_events, gap_penalty=DEFAULT_GAP_PENALTY):
     """
-    Align events (notes or chords) using edit distance (ED). 
-    The ED allows for insertions and deletions, which can be useful for 
-    evaluating musical practice containing missing/extra notes.
-    
+    Same algorithm as the production event_alignment_ED(), but the N x M
+    substitution costs are looked up from a precomputed cost matrix instead
+    of being recomputed on every DP cell and again during backtracking.
+
     Args:
         response_events: list of event dicts from group_notes_into_events
         ref_events: list of event dicts from group_notes_into_events
         gap_penalty: cost of leaving an event unaligned (insertion/deletion)
- 
+
     Returns:
-        operations: list of transformation ops dicts, in order from first event to last:
-            {'type': 'match' or 'replacement' or 'missing' or 'extra', 
-            'response_idx': int or None, 
-            'reference_idx': int or None, 
-            'cost': int}
+        operations: list of operation dicts, same format as event_alignment_ED()
         D: accumulated cost matrix, shape (N+1, M+1)
     """
     # if a raw note dict with "pitch"/"start"/"duration" but no "event_type" is
@@ -358,6 +361,9 @@ def event_alignment_ED(response_events, ref_events, gap_penalty=DEFAULT_GAP_PENA
     # the columns of D correspond to reference events
     M = len(ref_events)
 
+    # Precompute every substitution cost once
+    cost_matrix = build_cost_matrix(response_events, ref_events, gap_penalty)
+
     # Build the accumulated cost matrix D of size (N+1 x M+1)
     D = np.zeros((N + 1, M + 1), dtype=int)
     
@@ -371,7 +377,7 @@ def event_alignment_ED(response_events, ref_events, gap_penalty=DEFAULT_GAP_PENA
     # Recursion (accumulated cost / score matrix D):
     for n in range(1, N + 1):
         for m in range(1, M + 1):
-            replace_cost = compute_event_cost(response_events[n-1], ref_events[m-1], gap_penalty)
+            replace_cost = cost_matrix[n - 1, m - 1]
             D[n, m] = min(
                 D[n-1, m-1] + replace_cost, # diagonal: match or replacement
                 D[n-1, m] + gap_penalty, # vertical: extra event response[n-1]
@@ -404,11 +410,11 @@ def event_alignment_ED(response_events, ref_events, gap_penalty=DEFAULT_GAP_PENA
             n -= 1
         # For all other cases, we can move in any direction (diagonal, vertical, horizontal)
         else:
-            replace_cost = compute_event_cost(response_events[n - 1], ref_events[m - 1], gap_penalty)
-            diag = D[n - 1, m - 1] + replace_cost # diagonal: match or replacement
-            up = D[n - 1, m] + gap_penalty # vertical: extra event response[n-1]
-            left = D[n, m - 1] + gap_penalty # horizontal: missing response for ref[m-1]
-            min_cost = min(diag, up, left) # find the minimum cost step
+            replace_cost = cost_matrix[n - 1, m - 1]
+            diag = D[n - 1, m - 1] + replace_cost # Diagonal -> match or replacement
+            up = D[n - 1, m] + gap_penalty # Vertical -> response[n-1] is extra (insertion)
+            left = D[n, m - 1] + gap_penalty # Horizontal -> response is missing for ref[m-1] (deletion)
+            min_cost = min(diag, up, left) 
 
             # classify the transformation ops based on the minimum cost step
             # rule: always prefer diagonal > insertion or deletion
@@ -417,7 +423,7 @@ def event_alignment_ED(response_events, ref_events, gap_penalty=DEFAULT_GAP_PENA
                     "type": "match" if replace_cost == 0 else "replacement",
                     "response_idx": n - 1,
                     "reference_idx": m - 1,
-                    "cost": replace_cost,
+                    "cost": int(replace_cost),
                 })
                 n, m = n - 1, m - 1
             elif min_cost == up: # Vertical -> response[n-1] is extra (insertion)
@@ -437,7 +443,7 @@ def event_alignment_ED(response_events, ref_events, gap_penalty=DEFAULT_GAP_PENA
                 })
                 m -= 1
 
-    operations.reverse()  # Reverse to get ops in order from first note to last
+    operations.reverse()
     return operations, D
 
 
diff --git a/notebooks/Alignment_on_ASAPdataset.ipynb b/notebooks/Alignment_on_ASAPdataset.ipynb
index 45f12e6..b1ef352 100644
--- a/notebooks/Alignment_on_ASAPdataset.ipynb
+++ b/notebooks/Alignment_on_ASAPdataset.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,15 +217,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jz7125/compareMusic/.venv/lib/python3.13/site-packages/pretty_midi/pretty_midi.py:122: RuntimeWarning: Tempo, Key or Time signature change events found on non-zero tracks.  This is not a valid type 0 or type 1 MIDI file.  Tempo, Key or Time Signature may be wrong.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Skipped 45 pairs due to exceeding max_notes limit of 8000\n",
-      "Loaded 1021 score (reference) /performance (response) pairs.\n"
+      "Loaded 1021 score (reference) / performance (response) pairs.\n"
      ]
     }
    ],
@@ -305,14 +313,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1021/1021 [15:15<00:00,  1.11it/s] \n"
+      "100%|██████████| 1021/1021 [14:26<00:00,  1.18it/s] \n"
      ]
     },
     {
@@ -414,33 +422,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def convert_pipeline_output(event_details, response_events_normalized):\n",
+    "from collections import Counter\n",
+    "\n",
+    "def convert_pipeline_output(event_details, response_events_normalized, ref_events_normalized):\n",
     "    \"\"\"\n",
     "    Convert event_details from compare_performance_ED into a list of\n",
     "    dicts with keys: onset, pitch, label.\n",
-    "    Label mapping from pipeline operations to GT labels:\n",
-    "        match or replacement -> 'paired'\n",
-    "        missing -> 'deletion'\n",
-    "        extra -> 'insertion'\n",
     "\n",
-    "    Chord events are expanded note-by-note using correct_pitches /\n",
-    "    missing_pitches / extra_pitches from event_level_feedback.\n",
+    "    Unlike the original version, this does NOT rely on correct_pitches /\n",
+    "    missing_pitches / extra_pitches (which are pitch-class sets and lose\n",
+    "    octave-duplicate notes). Instead, it looks up the real notes directly\n",
+    "    from ref_events_normalized / response_events_normalized using\n",
+    "    reference_index / response_index, and matches them with a Counter\n",
+    "    (multiset) on real MIDI pitch. This preserves the true note count.\n",
     "\n",
     "    Args:\n",
     "        event_details: list of dicts from compare_performance_ED\n",
-    "        response_events_normalized: list of event dicts produced by\n",
-    "                                    group_notes_into_events on the normalised\n",
-    "                                    response notes — NOT the flat note list.\n",
+    "        response_events_normalized: list of event dicts (real notes, grouped)\n",
+    "        ref_events_normalized: list of event dicts (real notes, grouped)\n",
     "\n",
     "    Returns:\n",
-    "        list of dicts with keys:\n",
-    "            onset -> float (normalised seconds) or None for deletions\n",
-    "            pitch -> int (MIDI pitch) or None for deletions\n",
-    "            label -> 'paired', 'insertion', or 'deletion'\n",
+    "        list of dicts with keys: onset, pitch, label\n",
     "    \"\"\"\n",
     "    my_pairs = []\n",
     "\n",
@@ -449,7 +455,6 @@
     "\n",
     "        if event[\"event_type\"] == \"note\":\n",
     "            if op in (\"match\", \"replacement\"):\n",
-    "                # Look up onset and pitch from the grouped event list.\n",
     "                ev = response_events_normalized[event[\"response_index\"] - 1]\n",
     "                my_pairs.append({\n",
     "                    \"onset\": ev[\"event_start\"],\n",
@@ -468,93 +473,84 @@
     "\n",
     "        elif event[\"event_type\"] == \"chord\":\n",
     "            if op == \"missing\":\n",
-    "                # Entire chord missing: one deletion per note in the ref chord\n",
-    "                # correct_pitches + missing_pitches = all ref notes in this chord\n",
-    "                num_ref_notes = (\n",
-    "                    len(event[\"correct_pitches\"] or [])\n",
-    "                    + len(event[\"missing_pitches\"] or [])\n",
-    "                )\n",
-    "                for i in range(num_ref_notes):\n",
+    "                # Use the real reference notes in this chord (not pitch\n",
+    "                # classes), so octave-duplicate notes are counted correctly.\n",
+    "                ref_ev = ref_events_normalized[event[\"reference_index\"] - 1]\n",
+    "                for note in ref_ev[\"notes\"]:\n",
     "                    my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
+    "\n",
     "            elif op == \"extra\":\n",
-    "                # Every note in the extra response chord counts as an insertion.\n",
-    "                ev = response_events_normalized[event[\"response_index\"] - 1]\n",
-    "                for note in ev[\"notes\"]:\n",
+    "                res_ev = response_events_normalized[event[\"response_index\"] - 1]\n",
+    "                for note in res_ev[\"notes\"]:\n",
     "                    my_pairs.append({\n",
-    "                        \"onset\": ev[\"event_start\"],\n",
+    "                        \"onset\": res_ev[\"event_start\"],\n",
     "                        \"pitch\": note[\"pitch\"],\n",
     "                        \"label\": \"insertion\",\n",
     "                    })\n",
+    "\n",
     "            else:\n",
     "                # Aligned chord pair (match or replacement).\n",
-    "                ev = response_events_normalized[event[\"response_index\"] - 1]\n",
-    "                onset = ev[\"event_start\"]\n",
-    "\n",
-    "                # Build a pitch-class -> MIDI-pitch lookup from the response chord.\n",
-    "                # This fixes the bug where the original code always used the first\n",
-    "                # note's pitch regardless of which pitch class was being expanded.\n",
-    "                pc_to_midi = {}\n",
-    "                for note in ev[\"notes\"]:\n",
-    "                    pc = note[\"pitch\"] % 12\n",
-    "                    pc_to_midi[pc] = note[\"pitch\"]\n",
-    "\n",
-    "                # Correctly matched pitch classes -> paired\n",
-    "                for pc in (event[\"correct_pitches\"] or []):\n",
-    "                    midi_pitch = pc_to_midi.get(pc, pc)  # fallback if lookup fails\n",
-    "                    my_pairs.append({\"onset\": onset, \"pitch\": midi_pitch, \"label\": \"paired\"})\n",
-    "\n",
-    "                # Reference pitch classes not played -> deletion\n",
-    "                for pc in (event[\"missing_pitches\"] or []):\n",
-    "                    my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
+    "                # Re-derive correct / missing / extra at true note level\n",
+    "                # with a multiset match on real MIDI pitch.\n",
+    "                ref_ev = ref_events_normalized[event[\"reference_index\"] - 1]\n",
+    "                res_ev = response_events_normalized[event[\"response_index\"] - 1]\n",
+    "                onset = res_ev[\"event_start\"]\n",
+    "\n",
+    "                ref_pitch_counts = Counter(note[\"pitch\"] for note in ref_ev[\"notes\"])\n",
+    "                res_pitch_counts = Counter(note[\"pitch\"] for note in res_ev[\"notes\"])\n",
+    "\n",
+    "                matched_counts = ref_pitch_counts & res_pitch_counts\n",
+    "                missing_counts = ref_pitch_counts - res_pitch_counts\n",
+    "                extra_counts   = res_pitch_counts - ref_pitch_counts\n",
     "\n",
-    "                # Response pitch classes with no reference counterpart -> insertion\n",
-    "                for pc in (event[\"extra_pitches\"] or []):\n",
-    "                    midi_pitch = pc_to_midi.get(pc, pc)\n",
-    "                    my_pairs.append({\"onset\": onset, \"pitch\": midi_pitch, \"label\": \"insertion\"})\n",
+    "                for pitch, count in matched_counts.items():\n",
+    "                    for i in range(count):\n",
+    "                        my_pairs.append({\"onset\": onset, \"pitch\": pitch, \"label\": \"paired\"})\n",
+    "\n",
+    "                for pitch, count in missing_counts.items():\n",
+    "                    for i in range(count):\n",
+    "                        my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
+    "\n",
+    "                for pitch, count in extra_counts.items():\n",
+    "                    for i in range(count):\n",
+    "                        my_pairs.append({\"onset\": onset, \"pitch\": pitch, \"label\": \"insertion\"})\n",
     "\n",
     "    return my_pairs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from collections import Counter\n",
+    "\n",
     "def count_paired_matches(ground_truth, my_pairs, onset_offset):\n",
     "    \"\"\"\n",
     "    Count TP, FP, FN for 'paired' labels.\n",
-    "\n",
-    "    Matching key: onset only (rounded to 1 d.p.).\n",
-    "    Pitch is NOT used here because the GT 'paired' label means the score note\n",
-    "    was aligned to a performance note regardless of whether the pitch is correct.\n",
-    "    Both pipeline 'match' and 'replacement' operations map to 'paired'.\n",
-    "\n",
-    "    Args:\n",
-    "        ground_truth: list of dicts from load_ground_truth()\n",
-    "        my_pairs: list of dicts from convert_pipeline_output()\n",
-    "        onset_offset: float, added back to normalised onsets before comparison\n",
-    "\n",
-    "    Returns:\n",
-    "        tp, fp, fn: ints\n",
+    "    Matching key: (pitch, onset rounded to 1 d.p.) -- pitch is now\n",
+    "    included, and Counter (multiset) is used instead of set, so that\n",
+    "    multiple notes sharing the same onset (chords) are no longer\n",
+    "    collapsed into a single entry.\n",
     "    \"\"\"\n",
-    "    # Collect GT 'paired' onset keys\n",
-    "    gt_paired_set = set()\n",
+    "    gt_counter = Counter()\n",
     "    for row in ground_truth:\n",
     "        if row[\"label\"] == \"paired\":\n",
-    "            gt_paired_set.add(round(float(row[\"onset\"]), 1))\n",
+    "            key = (int(row[\"pitch\"]), round(float(row[\"onset\"]), 1))\n",
+    "            gt_counter[key] = gt_counter[key] + 1\n",
     "\n",
-    "    # Collect predicted 'paired' onset keys, restoring absolute time\n",
-    "    my_paired_set = set()\n",
+    "    my_counter = Counter()\n",
     "    for row in my_pairs:\n",
     "        if row[\"label\"] == \"paired\" and row[\"onset\"] is not None:\n",
     "            absolute_onset = row[\"onset\"] + onset_offset\n",
-    "            my_paired_set.add(round(float(absolute_onset), 1))\n",
-    "\n",
-    "    tp = len(my_paired_set & gt_paired_set)  # correctly predicted\n",
-    "    fp = len(my_paired_set - gt_paired_set)  # predicted but not in GT\n",
-    "    fn = len(gt_paired_set - my_paired_set)  # in GT but not predicted\n",
+    "            key = (int(row[\"pitch\"]), round(float(absolute_onset), 1))\n",
+    "            my_counter[key] = my_counter[key] + 1\n",
     "\n",
+    "    overlap = gt_counter & my_counter\n",
+    "    tp = sum(overlap.values())\n",
+    "    fp = sum((my_counter - gt_counter).values())\n",
+    "    fn = sum((gt_counter - my_counter).values())\n",
     "    return tp, fp, fn\n",
     "\n",
     "\n",
@@ -588,44 +584,31 @@
     "def count_insertion_matches(ground_truth, my_pairs, onset_offset):\n",
     "    \"\"\"\n",
     "    Count TP, FP, FN for 'insertion' labels.\n",
-    "\n",
-    "    Matching key: (pitch, onset) -- pitch IS included here because GT\n",
-    "    insertion records carry a pitch value, and we want to verify that\n",
-    "    the correct extra note was identified.\n",
-    "\n",
-    "    Args:\n",
-    "        ground_truth: list of dicts from load_ground_truth()\n",
-    "        my_pairs: list of dicts from convert_pipeline_output()\n",
-    "        onset_offset: float, added back to normalised onsets before comparison\n",
-    "\n",
-    "    Returns:\n",
-    "        tp, fp, fn: ints\n",
+    "    Same multiset fix as count_paired_matches.\n",
     "    \"\"\"\n",
-    "    # Collect GT 'insertion' (pitch, onset) keys\n",
-    "    gt_insertion_set = set()\n",
+    "    gt_counter = Counter()\n",
     "    for row in ground_truth:\n",
     "        if row[\"label\"] == \"insertion\":\n",
     "            key = (int(row[\"pitch\"]), round(float(row[\"onset\"]), 1))\n",
-    "            gt_insertion_set.add(key)\n",
+    "            gt_counter[key] = gt_counter[key] + 1\n",
     "\n",
-    "    # Collect predicted 'insertion' (pitch, onset) keys, restoring absolute time\n",
-    "    my_insertion_set = set()\n",
+    "    my_counter = Counter()\n",
     "    for row in my_pairs:\n",
     "        if row[\"label\"] == \"insertion\" and row[\"onset\"] is not None:\n",
     "            absolute_onset = row[\"onset\"] + onset_offset\n",
     "            key = (int(row[\"pitch\"]), round(float(absolute_onset), 1))\n",
-    "            my_insertion_set.add(key)\n",
-    "\n",
-    "    tp = len(my_insertion_set & gt_insertion_set)\n",
-    "    fp = len(my_insertion_set - gt_insertion_set)\n",
-    "    fn = len(gt_insertion_set - my_insertion_set)\n",
+    "            my_counter[key] = my_counter[key] + 1\n",
     "\n",
+    "    overlap = gt_counter & my_counter\n",
+    "    tp = sum(overlap.values())\n",
+    "    fp = sum((my_counter - gt_counter).values())\n",
+    "    fn = sum((gt_counter - my_counter).values())\n",
     "    return tp, fp, fn"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -695,12 +678,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "def evaluate_one_piece(asap_path, metadata_row, event_details,\n",
-    "                       response_events_normalized, response_onset_offset):\n",
+    "                       response_events_normalized, ref_events_normalized,\n",
+    "                       response_onset_offset):\n",
     "    \"\"\"\n",
     "    Run ground truth comparison for each score/performance pair.\n",
     "\n",
@@ -720,12 +704,12 @@
     "    tsv_path = os.path.join(asap_path, tsv_rel)\n",
     "\n",
     "    if not os.path.isfile(tsv_path):\n",
-    "        # No print here -- the caller counts and reports how many were\n",
-    "        # skipped in a single summary line instead.\n",
     "        return None\n",
     "\n",
     "    ground_truth = load_ground_truth(tsv_path)\n",
-    "    my_pairs = convert_pipeline_output(event_details, response_events_normalized)\n",
+    "    my_pairs = convert_pipeline_output(\n",
+    "        event_details, response_events_normalized, ref_events_normalized\n",
+    "    )\n",
     "    metrics = compute_metrics_label_level(ground_truth, my_pairs, onset_offset=response_onset_offset)\n",
     "\n",
     "    return metrics"
@@ -733,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -777,119 +761,119 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Bach (Fugue_bwv_846)</td>\n",
-       "      <td>0.9463</td>\n",
-       "      <td>0.9136</td>\n",
-       "      <td>0.9297</td>\n",
-       "      <td>423</td>\n",
-       "      <td>24</td>\n",
-       "      <td>40</td>\n",
+       "      <td>0.9091</td>\n",
+       "      <td>0.9257</td>\n",
+       "      <td>0.9173</td>\n",
+       "      <td>710</td>\n",
+       "      <td>71</td>\n",
+       "      <td>57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9675</td>\n",
-       "      <td>0.9260</td>\n",
-       "      <td>0.9463</td>\n",
-       "      <td>864</td>\n",
-       "      <td>29</td>\n",
-       "      <td>69</td>\n",
+       "      <td>0.9440</td>\n",
+       "      <td>0.9414</td>\n",
+       "      <td>0.9427</td>\n",
+       "      <td>1365</td>\n",
+       "      <td>81</td>\n",
+       "      <td>85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9850</td>\n",
-       "      <td>0.9500</td>\n",
-       "      <td>0.9672</td>\n",
-       "      <td>855</td>\n",
-       "      <td>13</td>\n",
-       "      <td>45</td>\n",
+       "      <td>0.9533</td>\n",
+       "      <td>0.9520</td>\n",
+       "      <td>0.9526</td>\n",
+       "      <td>1388</td>\n",
+       "      <td>68</td>\n",
+       "      <td>70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9840</td>\n",
-       "      <td>0.9378</td>\n",
-       "      <td>0.9604</td>\n",
-       "      <td>860</td>\n",
-       "      <td>14</td>\n",
-       "      <td>57</td>\n",
+       "      <td>0.9204</td>\n",
+       "      <td>0.9260</td>\n",
+       "      <td>0.9232</td>\n",
+       "      <td>1352</td>\n",
+       "      <td>117</td>\n",
+       "      <td>108</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9522</td>\n",
-       "      <td>0.9232</td>\n",
-       "      <td>0.9375</td>\n",
-       "      <td>877</td>\n",
-       "      <td>44</td>\n",
-       "      <td>73</td>\n",
+       "      <td>0.9336</td>\n",
+       "      <td>0.9374</td>\n",
+       "      <td>0.9355</td>\n",
+       "      <td>1363</td>\n",
+       "      <td>97</td>\n",
+       "      <td>91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9621</td>\n",
-       "      <td>0.9162</td>\n",
-       "      <td>0.9386</td>\n",
-       "      <td>864</td>\n",
-       "      <td>34</td>\n",
-       "      <td>79</td>\n",
+       "      <td>0.9580</td>\n",
+       "      <td>0.9553</td>\n",
+       "      <td>0.9566</td>\n",
+       "      <td>1390</td>\n",
+       "      <td>61</td>\n",
+       "      <td>65</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9873</td>\n",
-       "      <td>0.9326</td>\n",
-       "      <td>0.9592</td>\n",
-       "      <td>858</td>\n",
-       "      <td>11</td>\n",
-       "      <td>62</td>\n",
+       "      <td>0.9544</td>\n",
+       "      <td>0.9511</td>\n",
+       "      <td>0.9527</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>66</td>\n",
+       "      <td>71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9861</td>\n",
-       "      <td>0.9193</td>\n",
-       "      <td>0.9515</td>\n",
-       "      <td>854</td>\n",
-       "      <td>12</td>\n",
-       "      <td>75</td>\n",
+       "      <td>0.9274</td>\n",
+       "      <td>0.9281</td>\n",
+       "      <td>0.9278</td>\n",
+       "      <td>1355</td>\n",
+       "      <td>106</td>\n",
+       "      <td>105</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9809</td>\n",
-       "      <td>0.9083</td>\n",
-       "      <td>0.9432</td>\n",
-       "      <td>872</td>\n",
-       "      <td>17</td>\n",
-       "      <td>88</td>\n",
+       "      <td>0.9203</td>\n",
+       "      <td>0.9291</td>\n",
+       "      <td>0.9246</td>\n",
+       "      <td>1362</td>\n",
+       "      <td>118</td>\n",
+       "      <td>104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9874</td>\n",
-       "      <td>0.9330</td>\n",
-       "      <td>0.9594</td>\n",
-       "      <td>863</td>\n",
-       "      <td>11</td>\n",
-       "      <td>62</td>\n",
+       "      <td>0.9462</td>\n",
+       "      <td>0.9455</td>\n",
+       "      <td>0.9458</td>\n",
+       "      <td>1371</td>\n",
+       "      <td>78</td>\n",
+       "      <td>79</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                  Piece  Precision  Recall      F1   TP  FP  FN\n",
-       "0  Bach (Fugue_bwv_846)     0.9463  0.9136  0.9297  423  24  40\n",
-       "1  Bach (Fugue_bwv_848)     0.9675  0.9260  0.9463  864  29  69\n",
-       "2  Bach (Fugue_bwv_848)     0.9850  0.9500  0.9672  855  13  45\n",
-       "3  Bach (Fugue_bwv_848)     0.9840  0.9378  0.9604  860  14  57\n",
-       "4  Bach (Fugue_bwv_848)     0.9522  0.9232  0.9375  877  44  73\n",
-       "5  Bach (Fugue_bwv_848)     0.9621  0.9162  0.9386  864  34  79\n",
-       "6  Bach (Fugue_bwv_848)     0.9873  0.9326  0.9592  858  11  62\n",
-       "7  Bach (Fugue_bwv_848)     0.9861  0.9193  0.9515  854  12  75\n",
-       "8  Bach (Fugue_bwv_848)     0.9809  0.9083  0.9432  872  17  88\n",
-       "9  Bach (Fugue_bwv_848)     0.9874  0.9330  0.9594  863  11  62"
+       "                  Piece  Precision  Recall      F1    TP   FP   FN\n",
+       "0  Bach (Fugue_bwv_846)     0.9091  0.9257  0.9173   710   71   57\n",
+       "1  Bach (Fugue_bwv_848)     0.9440  0.9414  0.9427  1365   81   85\n",
+       "2  Bach (Fugue_bwv_848)     0.9533  0.9520  0.9526  1388   68   70\n",
+       "3  Bach (Fugue_bwv_848)     0.9204  0.9260  0.9232  1352  117  108\n",
+       "4  Bach (Fugue_bwv_848)     0.9336  0.9374  0.9355  1363   97   91\n",
+       "5  Bach (Fugue_bwv_848)     0.9580  0.9553  0.9566  1390   61   65\n",
+       "6  Bach (Fugue_bwv_848)     0.9544  0.9511  0.9527  1380   66   71\n",
+       "7  Bach (Fugue_bwv_848)     0.9274  0.9281  0.9278  1355  106  105\n",
+       "8  Bach (Fugue_bwv_848)     0.9203  0.9291  0.9246  1362  118  104\n",
+       "9  Bach (Fugue_bwv_848)     0.9462  0.9455  0.9458  1371   78   79"
       ]
      },
      "metadata": {},
@@ -899,9 +883,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean Precision: 0.8898\n",
-      "Mean Recall : 0.8727\n",
-      "Mean F1 : 0.8793\n"
+      "Mean Precision: 0.8391\n",
+      "Mean Recall : 0.8628\n",
+      "Mean F1 : 0.8502\n"
      ]
     }
    ],
@@ -917,6 +901,7 @@
     "        result[\"metadata_row\"],\n",
     "        result[\"event_details\"],\n",
     "        result[\"response_events_normalized\"],\n",
+    "        result[\"ref_events_normalized\"],       \n",
     "        result[\"response_onset_offset\"],\n",
     "    )\n",
     "    if metrics is not None:\n",

From a623bc8650e3a56901f3abf43da38b3c08d31eb8 Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Fri, 10 Jul 2026 21:36:49 +0100
Subject: [PATCH 08/15] replace original alignment with the vectorised version
 from the notebook

---
 evaluation_function/evaluation_test.py | 34 ++++++++++++++++++++------
 1 file changed, 26 insertions(+), 8 deletions(-)

diff --git a/evaluation_function/evaluation_test.py b/evaluation_function/evaluation_test.py
index 7362646..ac23a8e 100755
--- a/evaluation_function/evaluation_test.py
+++ b/evaluation_function/evaluation_test.py
@@ -26,8 +26,7 @@
     normalize_start_times,
     group_notes_into_events,
     make_event,
-    compute_note_cost,
-    compute_event_cost,
+    build_cost_matrix,
     get_pitch_class_set,
     identify_chord_name,
     compute_chord_accuracy,
@@ -46,6 +45,7 @@
 )
 from .evaluation import evaluation_function
 
+
 # 0. Helper: create a minimal MIDI dictionary for testing
 # ------------------------------------------------------------------------------
 def make_midi(pitches, starts, durations):
@@ -248,28 +248,46 @@ def test_custom_window_zero_means_no_grouping(self):
         assert len(events) == 2
 
 
-# 4. Tests for compute_note_cost and compute_event_cost
+# 4. Tests for build_cost_matrix
 # ------------------------------------------------------------------------------
 class TestCostComputations(unittest.TestCase):
-
+    # compute_event_cost (single event-pair cost) was folded into
+    # build_cost_matrix (whole-matrix, vectorised). To test a single pair,
+    # just pass a 1-event list on each side and read the [0, 0] cell.
+ 
     def test_note_vs_note_different_pitch(self):
         e1 = make_event(make_midi([60], [0.0], [0.5])["notes"])
         e2 = make_event(make_midi([65], [0.0], [0.5])["notes"])
-        assert compute_event_cost(e1, e2) == 5
-
+        cost = build_cost_matrix([e1], [e2])[0, 0]
+        assert cost == 5
+ 
     def test_chord_vs_chord_one_note_different(self):
         # C major (0,4,7) vs C-minor (0,3,7): one pitch differs -> Hamming = 2
         e1 = make_event(make_midi([60, 64, 67], [0.0, 0.0, 0.0], [0.5, 0.5, 0.5])["notes"])
         e2 = make_event(make_midi([60, 63, 67], [0.0, 0.0, 0.0], [0.5, 0.5, 0.5])["notes"])
-        cost = compute_event_cost(e1, e2)
+        cost = build_cost_matrix([e1], [e2])[0, 0]
         assert cost == 2
  
     def test_type_mismatch_returns_gap_penalty(self):
         # note vs chord -- should return the gap_penalty regardless of pitches
         note_event  = make_event(make_midi([60], [0.0], [0.5])["notes"])
         chord_event = make_event(make_midi([60, 64], [0.0, 0.0],  [0.5, 0.5])["notes"])
-        cost = compute_event_cost(note_event, chord_event, gap_penalty=10)
+        cost = build_cost_matrix([note_event], [chord_event], gap_penalty=10)[0, 0]
         assert cost == 10
+ 
+    def test_matrix_shape_for_multiple_events(self):
+        # 2 response events x 3 reference events -> (2, 3) matrix
+        res = [
+            make_event(make_midi([60], [0.0], [0.5])["notes"]),
+            make_event(make_midi([62], [0.5], [0.5])["notes"]),
+        ]
+        ref = [
+            make_event(make_midi([60], [0.0], [0.5])["notes"]),
+            make_event(make_midi([62], [0.5], [0.5])["notes"]),
+            make_event(make_midi([64], [1.0], [0.5])["notes"]),
+        ]
+        cost_matrix = build_cost_matrix(res, ref)
+        assert cost_matrix.shape == (2, 3)
 
 
 # 5. Tests for event_alignment_ED

From 59ff0baf9185c22f18c32e8d9fcea7d41f9e66a1 Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Sat, 11 Jul 2026 20:22:29 +0100
Subject: [PATCH 09/15] fix the copy in normalize start time

---
 evaluation_function/compare_MIDI.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/evaluation_function/compare_MIDI.py b/evaluation_function/compare_MIDI.py
index 463f9b7..445aefb 100644
--- a/evaluation_function/compare_MIDI.py
+++ b/evaluation_function/compare_MIDI.py
@@ -168,16 +168,15 @@ def normalize_start_times(notes):
     if not notes:
         return []
  
-    first_start = notes[0]["start"]
+    # Use the earliest onset rather than assuming notes[0] is the first note.
+    first_start = min(note["start"] for note in notes)
  
     shifted_notes = []
     for note in notes:
-        # Create a copy of the note dict with the "start" time shifted
-        note_copy = {
-            "pitch": note["pitch"],
-            "start": note["start"] - first_start,
-            "duration": note["duration"],
-        }
+        # Copy the complete dictionary so that additional metadata is retained.
+        note_copy = note.copy()
+        # Only modify the copied note's start time.
+        note_copy["start"] = note["start"] - first_start
         shifted_notes.append(note_copy)
  
     return shifted_notes

From 0e801d2e6f68a7f11eae5b62b16fb0f5cb8dda7f Mon Sep 17 00:00:00 2001
From: JiayingZhao <z2720629308@gmail.com>
Date: Sat, 11 Jul 2026 23:00:56 +0100
Subject: [PATCH 10/15] Delete outdated notebook

---
 notebooks/Alignment_on_ASAPdataset.ipynb | 952 -----------------------
 1 file changed, 952 deletions(-)
 delete mode 100644 notebooks/Alignment_on_ASAPdataset.ipynb

diff --git a/notebooks/Alignment_on_ASAPdataset.ipynb b/notebooks/Alignment_on_ASAPdataset.ipynb
deleted file mode 100644
index b1ef352..0000000
--- a/notebooks/Alignment_on_ASAPdataset.ipynb
+++ /dev/null
@@ -1,952 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ED Alignment Algorithm on Real Piano Performances\n",
-    "\n",
-    "This notebook evaluates the ED alignment algorithm in `compareMusic` on real piano performances from the [(n)ASAP: the (note-)Aligned Scores And Performances dataset](https://github.com/CPJKU/asap-dataset) (Peter et al., 2023).\n",
-    "\n",
-    "(n)ASAP is a dataset of aligned musical scores and performances built by extending the ASAP dataset with note-level annotations. The ASAP contains 236 distinct musical scores and 1067 performances of Western classical piano music from 15 different composers, the piece directory contains the XML and MIDI score, plus all of the performances of a specific piece, including: \n",
-    "- `midi_score`: the MIDI score (what should be played) — used as **reference**\n",
-    "- `midi_performance`: what the pianist actually played — used as **response**\n",
-    "- `note_alignments.tsv`: official note-level alignment annotations — used as **ground truth**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "bib citation:\n",
-    "\n",
-    "@article{Peter-2023,\n",
-    " title = {Automatic Note-Level Score-to-Performance Alignments in the ASAP Dataset},\n",
-    " author = {Peter, Silvan David and Cancino-Chacón, Carlos Eduardo and Foscarin, Francesco and McLeod, Andrew Philip and Henkel, Florian and Karystinaios, Emmanouil and Widmer, Gerhard},\n",
-    " doi = {10.5334/tismir.149},\n",
-    " journal = {Transactions of the International Society for Music Information Retrieval {(TISMIR)}},\n",
-    " year = {2023}\n",
-    "}\n",
-    "\n",
-    "relevant paper: https://transactions.ismir.net/articles/10.5334/tismir.149#5-alignment-of-the-asap-dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download the (n)ASAP Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "asap-dataset already exists, skipping download.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "# Note: use CPJKU version (not fosfrancesco) because only CPJKU has\n",
-    "# the note_alignments TSV files are needed for ground truth comparison.\n",
-    "if not os.path.exists(\"asap-dataset\"):\n",
-    "    os.system(\"git clone https://github.com/CPJKU/asap-dataset.git\")\n",
-    "else:\n",
-    "    print(\"asap-dataset already exists, skipping download.\")\n",
-    "\n",
-    "ASAP_PATH = \"asap-dataset\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load the Ground Truth (GT)\n",
-    "\n",
-    "The `note_alignment.tsv` file in each performance contains the official note-level alignment annotations. Each row represents one note, with columns: `xml_id`, `midi_id`, `track`, `channel`, `pitch`, `onset`.\n",
-    "- If `midi_id == \"deletion\"`: the score note was not played → label `deletion`\n",
-    "- If `xml_id == \"insertion\"`: an extra note was played → label `insertion`\n",
-    "- Otherwise: a matched pair → label `paired`\n",
-    "\n",
-    "For `match` and `insertion` rows, the TSV provides `onset` (onset time in seconds) and `pitch` (MIDI pitch) for the performance note."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import csv\n",
-    "\n",
-    "def load_ground_truth(tsv_path):\n",
-    "    \"\"\"\n",
-    "    Read a note_alignments TSV file from the CPJKU ASAP dataset.\n",
-    "\n",
-    "    Args:\n",
-    "        tsv_path: str, path to the note_alignments.tsv file\n",
-    "\n",
-    "    Returns:\n",
-    "        list of dicts, each with keys:\n",
-    "            label  -> 'paired', 'insertion', or 'deletion'\n",
-    "            onset  -> float (seconds) or None for deletions\n",
-    "            pitch  -> int (MIDI pitch number) or None for deletions\n",
-    "    \"\"\"\n",
-    "    rows = []\n",
-    "    with open(tsv_path, \"r\", encoding=\"utf-8\") as f:\n",
-    "        reader = csv.DictReader(f, delimiter=\"\\t\")\n",
-    "        \n",
-    "        for row in reader:\n",
-    "            xml_id  = row[\"xml_id\"].strip()\n",
-    "            midi_id = row[\"midi_id\"].strip()\n",
-    "\n",
-    "            if midi_id == \"deletion\":\n",
-    "                rows.append({\"label\": \"deletion\", \"onset\": None, \"pitch\": None})\n",
-    "            elif xml_id == \"insertion\":\n",
-    "                rows.append({\n",
-    "                    \"label\": \"insertion\",\n",
-    "                    \"onset\": float(row[\"onset\"]),\n",
-    "                    \"pitch\": int(row[\"pitch\"]),\n",
-    "                })\n",
-    "            else:\n",
-    "                rows.append({\n",
-    "                    \"label\": \"paired\",\n",
-    "                    \"onset\": float(row[\"onset\"]),\n",
-    "                    \"pitch\": int(row[\"pitch\"]),\n",
-    "                })\n",
-    "    return rows"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Score (reference)/Performance (response) pairs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Helper functions for format conversion: These functions convert a MIDI file into the `{pitch, start, duration}` format used by `compare_MIDI.py`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pretty_midi\n",
-    "\n",
-    "def midi_file_to_notes(midi_path):\n",
-    "    \"\"\"\n",
-    "    Parse a MIDI file and return all its notes in compareMusic format.\n",
-    "    \n",
-    "    Args:\n",
-    "        midi_path: str, path to the MIDI file\n",
-    "\n",
-    "    Returns:\n",
-    "        list of dicts sorted by onset time, each with keys:\n",
-    "            pitch -> int, MIDI note number\n",
-    "            start -> float, onset time in seconds\n",
-    "            duration -> float, note length in seconds\n",
-    "    \"\"\"\n",
-    "    midi_data = pretty_midi.PrettyMIDI(midi_path)\n",
-    "    all_notes = []\n",
-    "    for instrument in midi_data.instruments:\n",
-    "        if instrument.is_drum: # Ignore drum tracks\n",
-    "            continue\n",
-    "        for note in instrument.notes:\n",
-    "            all_notes.append({\n",
-    "                \"pitch\": note.pitch,\n",
-    "                \"start\": round(note.start, 3),\n",
-    "                \"duration\": round(note.end - note.start, 3),\n",
-    "            })\n",
-    "    # Sort by onset time, then by pitch (ensures consistent ordering for chords)\n",
-    "    all_notes.sort(key=lambda n: (n[\"start\"], n[\"pitch\"]))\n",
-    "    return all_notes\n",
-    "\n",
-    "\n",
-    "def build_sample(ref_path, response_path, composer, title, metadata_row):\n",
-    "    \"\"\"\n",
-    "    Convert one score/performance MIDI pair into a sample dict\n",
-    "    ready for compare_performance_ED.\n",
-    "\n",
-    "    Args:\n",
-    "        ref_path: str, path to the score (reference) MIDI file\n",
-    "        response_path: str, path to the performance (response) MIDI file\n",
-    "        composer: str\n",
-    "        title: str\n",
-    "        metadata_row: dict, one row from metadata.csv\n",
-    "\n",
-    "    Returns:\n",
-    "        sample dict with keys: composer, title, reference, response, metadata_row\n",
-    "        Returns None if either MIDI file produces zero notes.\n",
-    "    \"\"\"\n",
-    "    score_notes = midi_file_to_notes(ref_path)\n",
-    "    perf_notes  = midi_file_to_notes(response_path)\n",
-    "\n",
-    "    # If either MIDI file has no notes, return None to indicate that this sample\n",
-    "    # should be skipped (e.g., some ASAP pieces have empty score or performance).\n",
-    "    if not score_notes or not perf_notes:\n",
-    "        return None\n",
-    "\n",
-    "    return {\n",
-    "        \"composer\": composer,\n",
-    "        \"title\": title,\n",
-    "        \"reference\": {\"notes\": score_notes},\n",
-    "        \"response\": {\"notes\": perf_notes},\n",
-    "        \"metadata_row\": metadata_row,\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the (n)ASAP dataset, the `metadata.csv` lists every score/performance pair in the dataset, check that both MIDI files exist on disk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jz7125/compareMusic/.venv/lib/python3.13/site-packages/pretty_midi/pretty_midi.py:122: RuntimeWarning: Tempo, Key or Time signature change events found on non-zero tracks.  This is not a valid type 0 or type 1 MIDI file.  Tempo, Key or Time Signature may be wrong.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Skipped 45 pairs due to exceeding max_notes limit of 8000\n",
-      "Loaded 1021 score (reference) / performance (response) pairs.\n"
-     ]
-    }
-   ],
-   "source": [
-    "def load_samples(asap_path, composer=None, max_notes=8000):\n",
-    "    \"\"\"\n",
-    "    Read the ASAP metadata CSV and return a list of sample dicts\n",
-    "    for a specific composer only.\n",
-    "\n",
-    "    Args:\n",
-    "        asap_path:  str, path to the cloned ASAP repo root\n",
-    "        composer:   str or None.\n",
-    "                    If a string (e.g. \"Bach\"), only that composer is loaded.\n",
-    "                    If None, all composers in the dataset are loaded.\n",
-    "        max_notes:  int, skip any pair where either the score or performance\n",
-    "                    has more than this many notes. Default 3000.\n",
-    "                    Set to None to disable the limit.\n",
-    "\n",
-    "    Returns:\n",
-    "        list of sample dicts (see build_sample)\n",
-    "    \"\"\"\n",
-    "    metadata_path = os.path.join(asap_path, \"metadata.csv\")\n",
-    "    samples = []\n",
-    "    skipped = 0\n",
-    "\n",
-    "    with open(metadata_path, \"r\", encoding=\"utf-8\") as csv_file:\n",
-    "        reader = csv.DictReader(csv_file)\n",
-    "\n",
-    "        for row in reader:\n",
-    "            row_composer = row.get(\"composer\", \"\").strip()\n",
-    "            # Skip rows that do not match the requested composer (if one is given)\n",
-    "            if composer is not None and row_composer != composer:\n",
-    "                continue\n",
-    "\n",
-    "            ref_path = os.path.join(asap_path, row.get(\"midi_score\", \"\").strip())\n",
-    "            response_path = os.path.join(asap_path, row.get(\"midi_performance\", \"\").strip())\n",
-    "\n",
-    "            if os.path.isfile(ref_path) and os.path.isfile(response_path):\n",
-    "                sample = build_sample(\n",
-    "                    ref_path, response_path,\n",
-    "                    row_composer,     \n",
-    "                    row.get(\"title\", \"Unknown\"),\n",
-    "                    dict(row),\n",
-    "                    )\n",
-    "\n",
-    "                if sample is not None:\n",
-    "                    ref_count  = len(sample[\"reference\"][\"notes\"])\n",
-    "                    perf_count = len(sample[\"response\"][\"notes\"])\n",
-    "                    exceeds_limit = (\n",
-    "                        max_notes is not None\n",
-    "                        and (ref_count > max_notes or perf_count > max_notes)\n",
-    "                    )\n",
-    "                    if exceeds_limit:                            \n",
-    "                        skipped = skipped + 1\n",
-    "                    else:\n",
-    "                        samples.append(sample)\n",
-    "\n",
-    "    print(f\"Skipped {skipped} pairs due to exceeding max_notes limit of {max_notes}\")\n",
-    "    print(f\"Loaded {len(samples)} score (reference) / performance (response) pairs.\")\n",
-    "    return samples\n",
-    "\n",
-    "samples = load_samples(ASAP_PATH, composer=None, max_notes=8000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Run Alignment on Each Pair\n",
-    "\n",
-    "Passing each score/performance pair through `compare_performance_ED`, where score MIDI is the reference and the performance MIDI is the response.\n",
-    "\n",
-    "**Why do we need the normalised events? - to fix the onset mismatch bug**\n",
-    "- Inside `compare_performance_ED`, `normalize_start_times` shifts the first note to t = 0, and `group_notes_into_events` groups simultaneous notes into chords. The `response_index` values stored in `event_details` refer to positions in these grouped events, not in the original flat note list. Therefore, need to reproduce these two steps here so that the correct onset and pitch for each event during ground truth comparison can be looked up.\n",
-    "- The `response_onset_offset` (the original first-note onset before normalisation) will also be recorded, and will be added back when comparing normalised pipeline onsets against the original (absolute) times stored in the ground truth TSV."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1021/1021 [14:26<00:00,  1.18it/s] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Succeeded: 1021  Failed: 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "from concurrent.futures import ProcessPoolExecutor, as_completed\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from asap_worker import evaluate_one_sample\n",
-    "\n",
-    "\n",
-    "def run_alignment_on_samples_parallel(samples, max_workers=None):\n",
-    "    \"\"\"\n",
-    "    Run evaluate_one_sample on every sample using a process pool, so multiple\n",
-    "    pieces are aligned at the same time on different CPU cores.\n",
-    "\n",
-    "    Args:\n",
-    "        samples: list of sample dicts from load_samples()\n",
-    "        max_workers: int or None. None means \"all CPU cores minus one\",\n",
-    "                     leaving one core free for the OS and other apps.\n",
-    "\n",
-    "    Returns:\n",
-    "        results: list of successful result dicts\n",
-    "        failed: list of (composer, title, error_message) for pieces that failed\n",
-    "    \"\"\"\n",
-    "    if max_workers is None:\n",
-    "        max_workers = max(1, os.cpu_count() - 1)\n",
-    "\n",
-    "    results = []\n",
-    "    failed = []\n",
-    "\n",
-    "    with ProcessPoolExecutor(max_workers=max_workers) as executor:\n",
-    "        future_to_sample = {\n",
-    "            executor.submit(evaluate_one_sample, sample): sample\n",
-    "            for sample in samples\n",
-    "        }\n",
-    "\n",
-    "        for future in tqdm(as_completed(future_to_sample), total=len(samples)):\n",
-    "            sample = future_to_sample[future]\n",
-    "            result = future.result()  # exceptions are already caught inside the worker\n",
-    "\n",
-    "            if result[\"error\"] is not None:\n",
-    "                failed.append((result[\"composer\"], result[\"title\"], result[\"error\"]))\n",
-    "            else:\n",
-    "                results.append(result)\n",
-    "\n",
-    "    print(\"Succeeded:\", len(results), \" Failed:\", len(failed))\n",
-    "    return results, failed\n",
-    "\n",
-    "\n",
-    "all_results, failed_pieces = run_alignment_on_samples_parallel(samples)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Compute Precision, Recall, and F1 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**our pipeline vs GT**\n",
-    "\n",
-    "The GT has three different labels, while our pipeline has four operations:\n",
-    "\n",
-    "| Pipeline operation | GT label | Explanation |\n",
-    "|---|---|---|\n",
-    "| `match` | `paired` | Same pitch — GT still calls this `paired` |\n",
-    "| `replacement` | `paired` | Different pitch — GT still calls this `paired`, because the score note *was* played at approximately the right time |\n",
-    "| `missing` | `deletion` | Score note not found in response |\n",
-    "| `extra` | `insertion` | Response note has no score counterpart |\n",
-    "\n",
-    "**Evaluation metrics design**\n",
-    "\n",
-    "GT does not distinguish `match` from `replacement` because the alignment methods in GT are based on the temporal information. However, the cost function in our edit distance algorithm is based on the pitch correctness. \n",
-    "\n",
-    "Following Peter et al. (2023, TISMIR), label-level F1 would be applied as the main metric where TP, FP, and FN are computed separately for each label type and then summed:\n",
-    "\n",
-    "| Label | Matching key | TP | FP | FN |\n",
-    "|---|---|---|---|---|\n",
-    "| `paired` (pipeline `match` / `replacement`) | onset only (rounded to 1 d.p.) | Pipeline and ground truth agree a score note was aligned to a performance note at this onset (regardless of pitch correctness) | Pipeline predicts a pairing at this onset that the ground truth does not recognise | Ground truth expects a pairing at this onset that the pipeline fails to find |\n",
-    "| `deletion` (pipeline `missing`) | count only as no onset or pitch available for deletions | Overlap between the number of notes the pipeline judges as missing and the number the ground truth judges as missing | Pipeline predicts more missing notes than the ground truth; the excess | Ground truth expects more missing notes than the pipeline predicts; the shortfall |\n",
-    "| `insertion` (pipeline `extra`) | `(pitch, onset)` | Pipeline and ground truth agree an extra, unexpected note was played at this pitch and onset | Pipeline flags a note as extra that the ground truth does not agree with | Ground truth marks a note as extra that the pipeline fails to identify |\n",
-    "\n",
-    "- Precision: TP / (TP + FP), i.e. of all labels the pipeline predicted, how many were correct  \n",
-    "- Recall: TP / (TP + FN), i.e. of all GT labels, how many the pipeline found  \n",
-    "- F1: the harmonic mean of precision and recall "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import Counter\n",
-    "\n",
-    "def convert_pipeline_output(event_details, response_events_normalized, ref_events_normalized):\n",
-    "    \"\"\"\n",
-    "    Convert event_details from compare_performance_ED into a list of\n",
-    "    dicts with keys: onset, pitch, label.\n",
-    "\n",
-    "    Unlike the original version, this does NOT rely on correct_pitches /\n",
-    "    missing_pitches / extra_pitches (which are pitch-class sets and lose\n",
-    "    octave-duplicate notes). Instead, it looks up the real notes directly\n",
-    "    from ref_events_normalized / response_events_normalized using\n",
-    "    reference_index / response_index, and matches them with a Counter\n",
-    "    (multiset) on real MIDI pitch. This preserves the true note count.\n",
-    "\n",
-    "    Args:\n",
-    "        event_details: list of dicts from compare_performance_ED\n",
-    "        response_events_normalized: list of event dicts (real notes, grouped)\n",
-    "        ref_events_normalized: list of event dicts (real notes, grouped)\n",
-    "\n",
-    "    Returns:\n",
-    "        list of dicts with keys: onset, pitch, label\n",
-    "    \"\"\"\n",
-    "    my_pairs = []\n",
-    "\n",
-    "    for event in event_details:\n",
-    "        op = event[\"operation_type\"]\n",
-    "\n",
-    "        if event[\"event_type\"] == \"note\":\n",
-    "            if op in (\"match\", \"replacement\"):\n",
-    "                ev = response_events_normalized[event[\"response_index\"] - 1]\n",
-    "                my_pairs.append({\n",
-    "                    \"onset\": ev[\"event_start\"],\n",
-    "                    \"pitch\": ev[\"notes\"][0][\"pitch\"],\n",
-    "                    \"label\": \"paired\",\n",
-    "                })\n",
-    "            elif op == \"missing\":\n",
-    "                my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
-    "            elif op == \"extra\":\n",
-    "                ev = response_events_normalized[event[\"response_index\"] - 1]\n",
-    "                my_pairs.append({\n",
-    "                    \"onset\": ev[\"event_start\"],\n",
-    "                    \"pitch\": ev[\"notes\"][0][\"pitch\"],\n",
-    "                    \"label\": \"insertion\",\n",
-    "                })\n",
-    "\n",
-    "        elif event[\"event_type\"] == \"chord\":\n",
-    "            if op == \"missing\":\n",
-    "                # Use the real reference notes in this chord (not pitch\n",
-    "                # classes), so octave-duplicate notes are counted correctly.\n",
-    "                ref_ev = ref_events_normalized[event[\"reference_index\"] - 1]\n",
-    "                for note in ref_ev[\"notes\"]:\n",
-    "                    my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
-    "\n",
-    "            elif op == \"extra\":\n",
-    "                res_ev = response_events_normalized[event[\"response_index\"] - 1]\n",
-    "                for note in res_ev[\"notes\"]:\n",
-    "                    my_pairs.append({\n",
-    "                        \"onset\": res_ev[\"event_start\"],\n",
-    "                        \"pitch\": note[\"pitch\"],\n",
-    "                        \"label\": \"insertion\",\n",
-    "                    })\n",
-    "\n",
-    "            else:\n",
-    "                # Aligned chord pair (match or replacement).\n",
-    "                # Re-derive correct / missing / extra at true note level\n",
-    "                # with a multiset match on real MIDI pitch.\n",
-    "                ref_ev = ref_events_normalized[event[\"reference_index\"] - 1]\n",
-    "                res_ev = response_events_normalized[event[\"response_index\"] - 1]\n",
-    "                onset = res_ev[\"event_start\"]\n",
-    "\n",
-    "                ref_pitch_counts = Counter(note[\"pitch\"] for note in ref_ev[\"notes\"])\n",
-    "                res_pitch_counts = Counter(note[\"pitch\"] for note in res_ev[\"notes\"])\n",
-    "\n",
-    "                matched_counts = ref_pitch_counts & res_pitch_counts\n",
-    "                missing_counts = ref_pitch_counts - res_pitch_counts\n",
-    "                extra_counts   = res_pitch_counts - ref_pitch_counts\n",
-    "\n",
-    "                for pitch, count in matched_counts.items():\n",
-    "                    for i in range(count):\n",
-    "                        my_pairs.append({\"onset\": onset, \"pitch\": pitch, \"label\": \"paired\"})\n",
-    "\n",
-    "                for pitch, count in missing_counts.items():\n",
-    "                    for i in range(count):\n",
-    "                        my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
-    "\n",
-    "                for pitch, count in extra_counts.items():\n",
-    "                    for i in range(count):\n",
-    "                        my_pairs.append({\"onset\": onset, \"pitch\": pitch, \"label\": \"insertion\"})\n",
-    "\n",
-    "    return my_pairs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import Counter\n",
-    "\n",
-    "def count_paired_matches(ground_truth, my_pairs, onset_offset):\n",
-    "    \"\"\"\n",
-    "    Count TP, FP, FN for 'paired' labels.\n",
-    "    Matching key: (pitch, onset rounded to 1 d.p.) -- pitch is now\n",
-    "    included, and Counter (multiset) is used instead of set, so that\n",
-    "    multiple notes sharing the same onset (chords) are no longer\n",
-    "    collapsed into a single entry.\n",
-    "    \"\"\"\n",
-    "    gt_counter = Counter()\n",
-    "    for row in ground_truth:\n",
-    "        if row[\"label\"] == \"paired\":\n",
-    "            key = (int(row[\"pitch\"]), round(float(row[\"onset\"]), 1))\n",
-    "            gt_counter[key] = gt_counter[key] + 1\n",
-    "\n",
-    "    my_counter = Counter()\n",
-    "    for row in my_pairs:\n",
-    "        if row[\"label\"] == \"paired\" and row[\"onset\"] is not None:\n",
-    "            absolute_onset = row[\"onset\"] + onset_offset\n",
-    "            key = (int(row[\"pitch\"]), round(float(absolute_onset), 1))\n",
-    "            my_counter[key] = my_counter[key] + 1\n",
-    "\n",
-    "    overlap = gt_counter & my_counter\n",
-    "    tp = sum(overlap.values())\n",
-    "    fp = sum((my_counter - gt_counter).values())\n",
-    "    fn = sum((gt_counter - my_counter).values())\n",
-    "    return tp, fp, fn\n",
-    "\n",
-    "\n",
-    "def count_deletion_matches(ground_truth, my_pairs):\n",
-    "    \"\"\"\n",
-    "    Count TP, FP, FN for 'deletion' labels.\n",
-    "\n",
-    "    Deletions have no onset or pitch (the note was never played), so\n",
-    "    identity-based matching is not possible. Count-based matching is used:\n",
-    "        TP = min(GT deletion count, predicted deletion count)\n",
-    "        FP = max(0, predicted count - GT count)\n",
-    "        FN = max(0, GT count - predicted count)\n",
-    "\n",
-    "    Args:\n",
-    "        ground_truth: list of dicts from load_ground_truth()\n",
-    "        my_pairs: list of dicts from convert_pipeline_output()\n",
-    "\n",
-    "    Returns:\n",
-    "        tp, fp, fn: ints\n",
-    "    \"\"\"\n",
-    "    gt_deletion_count = sum(1 for row in ground_truth if row[\"label\"] == \"deletion\")\n",
-    "    my_deletion_count = sum(1 for row in my_pairs if row[\"label\"] == \"deletion\")\n",
-    "\n",
-    "    tp = min(gt_deletion_count, my_deletion_count)\n",
-    "    fp = max(0, my_deletion_count - gt_deletion_count)\n",
-    "    fn = max(0, gt_deletion_count - my_deletion_count)\n",
-    "\n",
-    "    return tp, fp, fn\n",
-    "\n",
-    "\n",
-    "def count_insertion_matches(ground_truth, my_pairs, onset_offset):\n",
-    "    \"\"\"\n",
-    "    Count TP, FP, FN for 'insertion' labels.\n",
-    "    Same multiset fix as count_paired_matches.\n",
-    "    \"\"\"\n",
-    "    gt_counter = Counter()\n",
-    "    for row in ground_truth:\n",
-    "        if row[\"label\"] == \"insertion\":\n",
-    "            key = (int(row[\"pitch\"]), round(float(row[\"onset\"]), 1))\n",
-    "            gt_counter[key] = gt_counter[key] + 1\n",
-    "\n",
-    "    my_counter = Counter()\n",
-    "    for row in my_pairs:\n",
-    "        if row[\"label\"] == \"insertion\" and row[\"onset\"] is not None:\n",
-    "            absolute_onset = row[\"onset\"] + onset_offset\n",
-    "            key = (int(row[\"pitch\"]), round(float(absolute_onset), 1))\n",
-    "            my_counter[key] = my_counter[key] + 1\n",
-    "\n",
-    "    overlap = gt_counter & my_counter\n",
-    "    tp = sum(overlap.values())\n",
-    "    fp = sum((my_counter - gt_counter).values())\n",
-    "    fn = sum((gt_counter - my_counter).values())\n",
-    "    return tp, fp, fn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics_label_level(ground_truth, my_pairs, onset_offset=0.0):\n",
-    "    \"\"\"\n",
-    "    Compute label-level precision, recall, and F1 following Peter et al. (2023).\n",
-    "    Args:\n",
-    "        ground_truth: list of dicts from load_ground_truth()\n",
-    "        my_pairs: list of dicts from convert_pipeline_output()\n",
-    "        onset_offset: float, the first-note onset before normalisation.\n",
-    "                      Added back to convert normalised onsets to absolute time.\n",
-    "\n",
-    "    Returns:\n",
-    "        dict with keys:\n",
-    "            precision, recall, f1 -> floats (rounded to 4 d.p.)\n",
-    "            tp, fp, fn  -> ints (totals across all labels)\n",
-    "            tp_paired, fp_paired, fn_paired\n",
-    "            tp_deletion, fp_deletion, fn_deletion\n",
-    "            tp_insertion, fp_insertion, fn_insertion\n",
-    "    \"\"\"\n",
-    "    # Count TP/FP/FN separately for each label type\n",
-    "    tp_p, fp_p, fn_p = count_paired_matches(ground_truth, my_pairs, onset_offset)\n",
-    "    tp_d, fp_d, fn_d = count_deletion_matches(ground_truth, my_pairs)\n",
-    "    tp_i, fp_i, fn_i = count_insertion_matches(ground_truth, my_pairs, onset_offset)\n",
-    "\n",
-    "    # Sum across all label types for overall metrics\n",
-    "    tp = tp_p + tp_d + tp_i\n",
-    "    fp = fp_p + fp_d + fp_i\n",
-    "    fn = fn_p + fn_d + fn_i\n",
-    "\n",
-    "    # Precision: of all labels predicted, how many are correct\n",
-    "    if tp + fp > 0:\n",
-    "        precision = tp / (tp + fp)\n",
-    "    else:\n",
-    "        precision = 0.0\n",
-    "\n",
-    "    # Recall: of all GT labels, how many the pipeline found\n",
-    "    if tp + fn > 0:\n",
-    "        recall = tp / (tp + fn)\n",
-    "    else:\n",
-    "        recall = 0.0\n",
-    "\n",
-    "    # F1: harmonic mean of precision and recall\n",
-    "    if precision + recall > 0:\n",
-    "        f1 = 2 * precision * recall / (precision + recall)\n",
-    "    else:\n",
-    "        f1 = 0.0\n",
-    "\n",
-    "    return {\n",
-    "        \"precision\": round(precision, 4),\n",
-    "        \"recall\": round(recall, 4),\n",
-    "        \"f1\": round(f1, 4),\n",
-    "        \"tp\": tp, \"fp\": fp, \"fn\": fn,\n",
-    "        # Per-label breakdown -- useful for diagnosing which label type is weakest\n",
-    "        \"tp_paired\": tp_p, \"fp_paired\": fp_p, \"fn_paired\": fn_p,\n",
-    "        \"tp_deletion\": tp_d, \"fp_deletion\": fp_d, \"fn_deletion\": fn_d,\n",
-    "        \"tp_insertion\": tp_i, \"fp_insertion\": fp_i, \"fn_insertion\": fn_i,\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Evaluate against ground truth:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_one_piece(asap_path, metadata_row, event_details,\n",
-    "                       response_events_normalized, ref_events_normalized,\n",
-    "                       response_onset_offset):\n",
-    "    \"\"\"\n",
-    "    Run ground truth comparison for each score/performance pair.\n",
-    "\n",
-    "    Args:\n",
-    "        asap_path: str, path to ASAP repo root\n",
-    "        metadata_row: dict, one row from metadata.csv\n",
-    "        event_details: list from compare_performance_ED result\n",
-    "        response_events_normalized: list of event dicts (normalized + grouped)\n",
-    "        response_onset_offset: float, onset of the first response note\n",
-    "                               before normalization — added back to convert\n",
-    "                               normalised onsets to absolute times for GT comparison.\n",
-    "\n",
-    "    Returns:\n",
-    "        metrics dict, or None if the TSV file is not found\n",
-    "    \"\"\"\n",
-    "    tsv_rel = metadata_row.get(\"note_alignments\", \"\").strip()\n",
-    "    tsv_path = os.path.join(asap_path, tsv_rel)\n",
-    "\n",
-    "    if not os.path.isfile(tsv_path):\n",
-    "        return None\n",
-    "\n",
-    "    ground_truth = load_ground_truth(tsv_path)\n",
-    "    my_pairs = convert_pipeline_output(\n",
-    "        event_details, response_events_normalized, ref_events_normalized\n",
-    "    )\n",
-    "    metrics = compute_metrics_label_level(ground_truth, my_pairs, onset_offset=response_onset_offset)\n",
-    "\n",
-    "    return metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Skipped 30 piece(s) due to missing TSV file.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Piece</th>\n",
-       "      <th>Precision</th>\n",
-       "      <th>Recall</th>\n",
-       "      <th>F1</th>\n",
-       "      <th>TP</th>\n",
-       "      <th>FP</th>\n",
-       "      <th>FN</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Bach (Fugue_bwv_846)</td>\n",
-       "      <td>0.9091</td>\n",
-       "      <td>0.9257</td>\n",
-       "      <td>0.9173</td>\n",
-       "      <td>710</td>\n",
-       "      <td>71</td>\n",
-       "      <td>57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9440</td>\n",
-       "      <td>0.9414</td>\n",
-       "      <td>0.9427</td>\n",
-       "      <td>1365</td>\n",
-       "      <td>81</td>\n",
-       "      <td>85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9533</td>\n",
-       "      <td>0.9520</td>\n",
-       "      <td>0.9526</td>\n",
-       "      <td>1388</td>\n",
-       "      <td>68</td>\n",
-       "      <td>70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9204</td>\n",
-       "      <td>0.9260</td>\n",
-       "      <td>0.9232</td>\n",
-       "      <td>1352</td>\n",
-       "      <td>117</td>\n",
-       "      <td>108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9336</td>\n",
-       "      <td>0.9374</td>\n",
-       "      <td>0.9355</td>\n",
-       "      <td>1363</td>\n",
-       "      <td>97</td>\n",
-       "      <td>91</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9580</td>\n",
-       "      <td>0.9553</td>\n",
-       "      <td>0.9566</td>\n",
-       "      <td>1390</td>\n",
-       "      <td>61</td>\n",
-       "      <td>65</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9544</td>\n",
-       "      <td>0.9511</td>\n",
-       "      <td>0.9527</td>\n",
-       "      <td>1380</td>\n",
-       "      <td>66</td>\n",
-       "      <td>71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9274</td>\n",
-       "      <td>0.9281</td>\n",
-       "      <td>0.9278</td>\n",
-       "      <td>1355</td>\n",
-       "      <td>106</td>\n",
-       "      <td>105</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9203</td>\n",
-       "      <td>0.9291</td>\n",
-       "      <td>0.9246</td>\n",
-       "      <td>1362</td>\n",
-       "      <td>118</td>\n",
-       "      <td>104</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9462</td>\n",
-       "      <td>0.9455</td>\n",
-       "      <td>0.9458</td>\n",
-       "      <td>1371</td>\n",
-       "      <td>78</td>\n",
-       "      <td>79</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  Piece  Precision  Recall      F1    TP   FP   FN\n",
-       "0  Bach (Fugue_bwv_846)     0.9091  0.9257  0.9173   710   71   57\n",
-       "1  Bach (Fugue_bwv_848)     0.9440  0.9414  0.9427  1365   81   85\n",
-       "2  Bach (Fugue_bwv_848)     0.9533  0.9520  0.9526  1388   68   70\n",
-       "3  Bach (Fugue_bwv_848)     0.9204  0.9260  0.9232  1352  117  108\n",
-       "4  Bach (Fugue_bwv_848)     0.9336  0.9374  0.9355  1363   97   91\n",
-       "5  Bach (Fugue_bwv_848)     0.9580  0.9553  0.9566  1390   61   65\n",
-       "6  Bach (Fugue_bwv_848)     0.9544  0.9511  0.9527  1380   66   71\n",
-       "7  Bach (Fugue_bwv_848)     0.9274  0.9281  0.9278  1355  106  105\n",
-       "8  Bach (Fugue_bwv_848)     0.9203  0.9291  0.9246  1362  118  104\n",
-       "9  Bach (Fugue_bwv_848)     0.9462  0.9455  0.9458  1371   78   79"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Precision: 0.8391\n",
-      "Mean Recall : 0.8628\n",
-      "Mean F1 : 0.8502\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "eval_rows = []\n",
-    "skipped_count = 0\n",
-    "\n",
-    "for result in all_results:\n",
-    "    metrics = evaluate_one_piece(\n",
-    "        ASAP_PATH,\n",
-    "        result[\"metadata_row\"],\n",
-    "        result[\"event_details\"],\n",
-    "        result[\"response_events_normalized\"],\n",
-    "        result[\"ref_events_normalized\"],       \n",
-    "        result[\"response_onset_offset\"],\n",
-    "    )\n",
-    "    if metrics is not None:\n",
-    "        eval_rows.append({\n",
-    "            \"Piece\": result[\"composer\"] + \" (\" + result[\"title\"] + \")\",\n",
-    "            \"Precision\": metrics[\"precision\"],\n",
-    "            \"Recall\": metrics[\"recall\"],\n",
-    "            \"F1\": metrics[\"f1\"],\n",
-    "            \"TP\": metrics[\"tp\"],\n",
-    "            \"FP\": metrics[\"fp\"],\n",
-    "            \"FN\": metrics[\"fn\"],\n",
-    "        })\n",
-    "    else:\n",
-    "        skipped_count += 1\n",
-    "\n",
-    "print(f\"Skipped {skipped_count} piece(s) due to missing TSV file.\")\n",
-    "\n",
-    "df_eval = pd.DataFrame(eval_rows)\n",
-    "display(df_eval.head(10))\n",
-    "\n",
-    "print(\"Mean Precision:\", round(df_eval[\"Precision\"].mean(), 4))\n",
-    "print(\"Mean Recall :\", round(df_eval[\"Recall\"].mean(), 4))\n",
-    "print(\"Mean F1 :\", round(df_eval[\"F1\"].mean(), 4))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "compareMusic",
-   "language": "python",
-   "name": "comparemusic"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From a2405c7042b2df41cd1e0461b030ca5490e54a3e Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Sat, 11 Jul 2026 23:02:36 +0100
Subject: [PATCH 11/15] update the evaluation notebook

---
 notebooks/Alignment_on_ASAPdataset.ipynb  | 1271 +++++++++++++++++++--
 notebooks/Alignment_on_nASAPdataset.ipynb |  724 ++++++++++++
 2 files changed, 1909 insertions(+), 86 deletions(-)
 create mode 100644 notebooks/Alignment_on_nASAPdataset.ipynb

diff --git a/notebooks/Alignment_on_ASAPdataset.ipynb b/notebooks/Alignment_on_ASAPdataset.ipynb
index b1ef352..7075a06 100644
--- a/notebooks/Alignment_on_ASAPdataset.ipynb
+++ b/notebooks/Alignment_on_ASAPdataset.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -313,14 +313,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1021/1021 [14:26<00:00,  1.18it/s] \n"
+      "100%|██████████| 1021/1021 [09:20<00:00,  1.82it/s]"
      ]
     },
     {
@@ -329,6 +329,13 @@
      "text": [
       "Succeeded: 1021  Failed: 0\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -422,23 +429,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from collections import Counter\n",
-    "\n",
     "def convert_pipeline_output(event_details, response_events_normalized, ref_events_normalized):\n",
     "    \"\"\"\n",
     "    Convert event_details from compare_performance_ED into a list of\n",
     "    dicts with keys: onset, pitch, label.\n",
     "\n",
-    "    Unlike the original version, this does NOT rely on correct_pitches /\n",
-    "    missing_pitches / extra_pitches (which are pitch-class sets and lose\n",
-    "    octave-duplicate notes). Instead, it looks up the real notes directly\n",
-    "    from ref_events_normalized / response_events_normalized using\n",
-    "    reference_index / response_index, and matches them with a Counter\n",
-    "    (multiset) on real MIDI pitch. This preserves the true note count.\n",
+    "    Note: for aligned (match/replacement) chord pairs, correct_pitches /\n",
+    "    missing_pitches / extra_pitches inside event_details are now already\n",
+    "    real MIDI pitch multisets (with octave, computed by compute_chord_accuracy\n",
+    "    in compare_MIDI.py), so they are reused directly here instead of being\n",
+    "    re-derived. response_events_normalized / ref_events_normalized are still\n",
+    "    needed for onset lookups, and for the full note list of entirely-missing\n",
+    "    or entirely-extra chords (event_details only stores the chord name, not\n",
+    "    individual pitches, in those two cases).\n",
     "\n",
     "    Args:\n",
     "        event_details: list of dicts from compare_performance_ED\n",
@@ -473,13 +480,14 @@
     "\n",
     "        elif event[\"event_type\"] == \"chord\":\n",
     "            if op == \"missing\":\n",
-    "                # Use the real reference notes in this chord (not pitch\n",
-    "                # classes), so octave-duplicate notes are counted correctly.\n",
+    "                # Whole chord missing -- event_details only has the chord name,\n",
+    "                # so the individual reference pitches still need to be looked up.\n",
     "                ref_ev = ref_events_normalized[event[\"reference_index\"] - 1]\n",
     "                for note in ref_ev[\"notes\"]:\n",
     "                    my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
     "\n",
     "            elif op == \"extra\":\n",
+    "                # Whole chord extra -- same reasoning, look up response pitches.\n",
     "                res_ev = response_events_normalized[event[\"response_index\"] - 1]\n",
     "                for note in res_ev[\"notes\"]:\n",
     "                    my_pairs.append({\n",
@@ -489,38 +497,28 @@
     "                    })\n",
     "\n",
     "            else:\n",
-    "                # Aligned chord pair (match or replacement).\n",
-    "                # Re-derive correct / missing / extra at true note level\n",
-    "                # with a multiset match on real MIDI pitch.\n",
-    "                ref_ev = ref_events_normalized[event[\"reference_index\"] - 1]\n",
+    "                # Aligned chord pair (match or replacement). compare_MIDI.py's\n",
+    "                # compute_chord_accuracy already computed correct/missing/extra\n",
+    "                # pitches as real MIDI pitch multisets, so reuse them directly\n",
+    "                # instead of re-deriving with a Counter here.\n",
     "                res_ev = response_events_normalized[event[\"response_index\"] - 1]\n",
     "                onset = res_ev[\"event_start\"]\n",
     "\n",
-    "                ref_pitch_counts = Counter(note[\"pitch\"] for note in ref_ev[\"notes\"])\n",
-    "                res_pitch_counts = Counter(note[\"pitch\"] for note in res_ev[\"notes\"])\n",
-    "\n",
-    "                matched_counts = ref_pitch_counts & res_pitch_counts\n",
-    "                missing_counts = ref_pitch_counts - res_pitch_counts\n",
-    "                extra_counts   = res_pitch_counts - ref_pitch_counts\n",
-    "\n",
-    "                for pitch, count in matched_counts.items():\n",
-    "                    for i in range(count):\n",
-    "                        my_pairs.append({\"onset\": onset, \"pitch\": pitch, \"label\": \"paired\"})\n",
+    "                for pitch in event[\"correct_pitches\"]:\n",
+    "                    my_pairs.append({\"onset\": onset, \"pitch\": pitch, \"label\": \"paired\"})\n",
     "\n",
-    "                for pitch, count in missing_counts.items():\n",
-    "                    for i in range(count):\n",
-    "                        my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
+    "                for pitch in event[\"missing_pitches\"]:\n",
+    "                    my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
     "\n",
-    "                for pitch, count in extra_counts.items():\n",
-    "                    for i in range(count):\n",
-    "                        my_pairs.append({\"onset\": onset, \"pitch\": pitch, \"label\": \"insertion\"})\n",
+    "                for pitch in event[\"extra_pitches\"]:\n",
+    "                    my_pairs.append({\"onset\": onset, \"pitch\": pitch, \"label\": \"insertion\"})\n",
     "\n",
     "    return my_pairs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -547,8 +545,9 @@
     "            key = (int(row[\"pitch\"]), round(float(absolute_onset), 1))\n",
     "            my_counter[key] = my_counter[key] + 1\n",
     "\n",
-    "    overlap = gt_counter & my_counter\n",
-    "    tp = sum(overlap.values())\n",
+    "    tp = 0\n",
+    "    for key in gt_counter:\n",
+    "        tp += min(gt_counter[key], my_counter[key])\n",
     "    fp = sum((my_counter - gt_counter).values())\n",
     "    fn = sum((gt_counter - my_counter).values())\n",
     "    return tp, fp, fn\n",
@@ -599,8 +598,9 @@
     "            key = (int(row[\"pitch\"]), round(float(absolute_onset), 1))\n",
     "            my_counter[key] = my_counter[key] + 1\n",
     "\n",
-    "    overlap = gt_counter & my_counter\n",
-    "    tp = sum(overlap.values())\n",
+    "    tp = 0\n",
+    "    for key in gt_counter:\n",
+    "        tp += min(gt_counter[key], my_counter[key])\n",
     "    fp = sum((my_counter - gt_counter).values())\n",
     "    fn = sum((gt_counter - my_counter).values())\n",
     "    return tp, fp, fn"
@@ -608,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -678,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -717,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -771,6 +771,16 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
+       "      <td>0.9274</td>\n",
+       "      <td>0.9281</td>\n",
+       "      <td>0.9278</td>\n",
+       "      <td>1355</td>\n",
+       "      <td>106</td>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Bach (Fugue_bwv_848)</td>\n",
        "      <td>0.9440</td>\n",
        "      <td>0.9414</td>\n",
        "      <td>0.9427</td>\n",
@@ -779,24 +789,14 @@
        "      <td>85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9533</td>\n",
-       "      <td>0.9520</td>\n",
-       "      <td>0.9526</td>\n",
-       "      <td>1388</td>\n",
-       "      <td>68</td>\n",
-       "      <td>70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9204</td>\n",
-       "      <td>0.9260</td>\n",
-       "      <td>0.9232</td>\n",
-       "      <td>1352</td>\n",
-       "      <td>117</td>\n",
-       "      <td>108</td>\n",
+       "      <td>0.9544</td>\n",
+       "      <td>0.9511</td>\n",
+       "      <td>0.9527</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>66</td>\n",
+       "      <td>71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -821,22 +821,22 @@
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9544</td>\n",
-       "      <td>0.9511</td>\n",
-       "      <td>0.9527</td>\n",
-       "      <td>1380</td>\n",
-       "      <td>66</td>\n",
-       "      <td>71</td>\n",
+       "      <td>0.9533</td>\n",
+       "      <td>0.9520</td>\n",
+       "      <td>0.9526</td>\n",
+       "      <td>1388</td>\n",
+       "      <td>68</td>\n",
+       "      <td>70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9274</td>\n",
-       "      <td>0.9281</td>\n",
-       "      <td>0.9278</td>\n",
-       "      <td>1355</td>\n",
-       "      <td>106</td>\n",
-       "      <td>105</td>\n",
+       "      <td>0.9204</td>\n",
+       "      <td>0.9260</td>\n",
+       "      <td>0.9232</td>\n",
+       "      <td>1352</td>\n",
+       "      <td>117</td>\n",
+       "      <td>108</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -850,13 +850,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9462</td>\n",
-       "      <td>0.9455</td>\n",
-       "      <td>0.9458</td>\n",
-       "      <td>1371</td>\n",
-       "      <td>78</td>\n",
-       "      <td>79</td>\n",
+       "      <td>Bach (Fugue_bwv_854)</td>\n",
+       "      <td>0.9367</td>\n",
+       "      <td>0.9379</td>\n",
+       "      <td>0.9373</td>\n",
+       "      <td>695</td>\n",
+       "      <td>47</td>\n",
+       "      <td>46</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -865,15 +865,15 @@
       "text/plain": [
        "                  Piece  Precision  Recall      F1    TP   FP   FN\n",
        "0  Bach (Fugue_bwv_846)     0.9091  0.9257  0.9173   710   71   57\n",
-       "1  Bach (Fugue_bwv_848)     0.9440  0.9414  0.9427  1365   81   85\n",
-       "2  Bach (Fugue_bwv_848)     0.9533  0.9520  0.9526  1388   68   70\n",
-       "3  Bach (Fugue_bwv_848)     0.9204  0.9260  0.9232  1352  117  108\n",
+       "1  Bach (Fugue_bwv_848)     0.9274  0.9281  0.9278  1355  106  105\n",
+       "2  Bach (Fugue_bwv_848)     0.9440  0.9414  0.9427  1365   81   85\n",
+       "3  Bach (Fugue_bwv_848)     0.9544  0.9511  0.9527  1380   66   71\n",
        "4  Bach (Fugue_bwv_848)     0.9336  0.9374  0.9355  1363   97   91\n",
        "5  Bach (Fugue_bwv_848)     0.9580  0.9553  0.9566  1390   61   65\n",
-       "6  Bach (Fugue_bwv_848)     0.9544  0.9511  0.9527  1380   66   71\n",
-       "7  Bach (Fugue_bwv_848)     0.9274  0.9281  0.9278  1355  106  105\n",
+       "6  Bach (Fugue_bwv_848)     0.9533  0.9520  0.9526  1388   68   70\n",
+       "7  Bach (Fugue_bwv_848)     0.9204  0.9260  0.9232  1352  117  108\n",
        "8  Bach (Fugue_bwv_848)     0.9203  0.9291  0.9246  1362  118  104\n",
-       "9  Bach (Fugue_bwv_848)     0.9462  0.9455  0.9458  1371   78   79"
+       "9  Bach (Fugue_bwv_854)     0.9367  0.9379  0.9373   695   47   46"
       ]
      },
      "metadata": {},
@@ -926,6 +926,1105 @@
     "print(\"Mean Recall :\", round(df_eval[\"Recall\"].mean(), 4))\n",
     "print(\"Mean F1 :\", round(df_eval[\"F1\"].mean(), 4))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Composer</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Performance</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Recall</th>\n",
+       "      <th>F1</th>\n",
+       "      <th>TP</th>\n",
+       "      <th>FP</th>\n",
+       "      <th>FN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>Chopin</td>\n",
+       "      <td>Scherzos_20</td>\n",
+       "      <td>Chopin/Scherzos/20/Wong04M.mid</td>\n",
+       "      <td>0.3195</td>\n",
+       "      <td>0.1892</td>\n",
+       "      <td>0.2377</td>\n",
+       "      <td>1606</td>\n",
+       "      <td>3420</td>\n",
+       "      <td>6882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>983</th>\n",
+       "      <td>Scriabin</td>\n",
+       "      <td>Sonatas_5</td>\n",
+       "      <td>Scriabin/Sonatas/5/ChernovA06M.mid</td>\n",
+       "      <td>0.3917</td>\n",
+       "      <td>0.2573</td>\n",
+       "      <td>0.3106</td>\n",
+       "      <td>3548</td>\n",
+       "      <td>5509</td>\n",
+       "      <td>10241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>Chopin</td>\n",
+       "      <td>Ballades_1</td>\n",
+       "      <td>Chopin/Ballades/1/MunA19M.mid</td>\n",
+       "      <td>0.4603</td>\n",
+       "      <td>0.3122</td>\n",
+       "      <td>0.3721</td>\n",
+       "      <td>2591</td>\n",
+       "      <td>3038</td>\n",
+       "      <td>5708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>Beethoven</td>\n",
+       "      <td>Piano_Sonatas_31-3_4</td>\n",
+       "      <td>Beethoven/Piano_Sonatas/31-3_4/HuangSW10M.mid</td>\n",
+       "      <td>0.5562</td>\n",
+       "      <td>0.4399</td>\n",
+       "      <td>0.4913</td>\n",
+       "      <td>2307</td>\n",
+       "      <td>1841</td>\n",
+       "      <td>2937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>Chopin</td>\n",
+       "      <td>Sonata_2_3rd</td>\n",
+       "      <td>Chopin/Sonata_2/3rd_extra_repeat/KaszoS15.mid</td>\n",
+       "      <td>0.4391</td>\n",
+       "      <td>0.5860</td>\n",
+       "      <td>0.5021</td>\n",
+       "      <td>1465</td>\n",
+       "      <td>1871</td>\n",
+       "      <td>1035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>984</th>\n",
+       "      <td>Scriabin</td>\n",
+       "      <td>Sonatas_5</td>\n",
+       "      <td>Scriabin/Sonatas/5/Ko07M.mid</td>\n",
+       "      <td>0.5720</td>\n",
+       "      <td>0.4490</td>\n",
+       "      <td>0.5031</td>\n",
+       "      <td>5588</td>\n",
+       "      <td>4182</td>\n",
+       "      <td>6857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>932</th>\n",
+       "      <td>Schubert</td>\n",
+       "      <td>Impromptu_op142_3</td>\n",
+       "      <td>Schubert/Impromptu_op142/3/SunY08M.mid</td>\n",
+       "      <td>0.5796</td>\n",
+       "      <td>0.4831</td>\n",
+       "      <td>0.5270</td>\n",
+       "      <td>4123</td>\n",
+       "      <td>2990</td>\n",
+       "      <td>4411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>Beethoven</td>\n",
+       "      <td>Piano_Sonatas_3-1</td>\n",
+       "      <td>Beethoven/Piano_Sonatas/3-1/LUO01.mid</td>\n",
+       "      <td>0.6166</td>\n",
+       "      <td>0.4715</td>\n",
+       "      <td>0.5344</td>\n",
+       "      <td>2964</td>\n",
+       "      <td>1843</td>\n",
+       "      <td>3322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>985</th>\n",
+       "      <td>Scriabin</td>\n",
+       "      <td>Sonatas_5</td>\n",
+       "      <td>Scriabin/Sonatas/5/FALIKS06.mid</td>\n",
+       "      <td>0.6252</td>\n",
+       "      <td>0.5111</td>\n",
+       "      <td>0.5624</td>\n",
+       "      <td>5756</td>\n",
+       "      <td>3451</td>\n",
+       "      <td>5505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>604</th>\n",
+       "      <td>Chopin</td>\n",
+       "      <td>Etudes_op_25_1</td>\n",
+       "      <td>Chopin/Etudes_op_25/1/TongB02M.mid</td>\n",
+       "      <td>0.5111</td>\n",
+       "      <td>0.6564</td>\n",
+       "      <td>0.5747</td>\n",
+       "      <td>1471</td>\n",
+       "      <td>1407</td>\n",
+       "      <td>770</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Composer                 Title  \\\n",
+       "666     Chopin           Scherzos_20   \n",
+       "983   Scriabin             Sonatas_5   \n",
+       "442     Chopin            Ballades_1   \n",
+       "382  Beethoven  Piano_Sonatas_31-3_4   \n",
+       "692     Chopin          Sonata_2_3rd   \n",
+       "984   Scriabin             Sonatas_5   \n",
+       "932   Schubert     Impromptu_op142_3   \n",
+       "340  Beethoven     Piano_Sonatas_3-1   \n",
+       "985   Scriabin             Sonatas_5   \n",
+       "604     Chopin        Etudes_op_25_1   \n",
+       "\n",
+       "                                       Performance  Precision  Recall      F1  \\\n",
+       "666                 Chopin/Scherzos/20/Wong04M.mid     0.3195  0.1892  0.2377   \n",
+       "983             Scriabin/Sonatas/5/ChernovA06M.mid     0.3917  0.2573  0.3106   \n",
+       "442                  Chopin/Ballades/1/MunA19M.mid     0.4603  0.3122  0.3721   \n",
+       "382  Beethoven/Piano_Sonatas/31-3_4/HuangSW10M.mid     0.5562  0.4399  0.4913   \n",
+       "692  Chopin/Sonata_2/3rd_extra_repeat/KaszoS15.mid     0.4391  0.5860  0.5021   \n",
+       "984                   Scriabin/Sonatas/5/Ko07M.mid     0.5720  0.4490  0.5031   \n",
+       "932         Schubert/Impromptu_op142/3/SunY08M.mid     0.5796  0.4831  0.5270   \n",
+       "340          Beethoven/Piano_Sonatas/3-1/LUO01.mid     0.6166  0.4715  0.5344   \n",
+       "985                Scriabin/Sonatas/5/FALIKS06.mid     0.6252  0.5111  0.5624   \n",
+       "604             Chopin/Etudes_op_25/1/TongB02M.mid     0.5111  0.6564  0.5747   \n",
+       "\n",
+       "       TP    FP     FN  \n",
+       "666  1606  3420   6882  \n",
+       "983  3548  5509  10241  \n",
+       "442  2591  3038   5708  \n",
+       "382  2307  1841   2937  \n",
+       "692  1465  1871   1035  \n",
+       "984  5588  4182   6857  \n",
+       "932  4123  2990   4411  \n",
+       "340  2964  1843   3322  \n",
+       "985  5756  3451   5505  \n",
+       "604  1471  1407    770  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eval_rows_detailed = []\n",
+    "\n",
+    "for result in all_results:\n",
+    "    metrics = evaluate_one_piece(\n",
+    "        ASAP_PATH,\n",
+    "        result[\"metadata_row\"],\n",
+    "        result[\"event_details\"],\n",
+    "        result[\"response_events_normalized\"],\n",
+    "        result[\"ref_events_normalized\"],\n",
+    "        result[\"response_onset_offset\"],\n",
+    "    )\n",
+    "    if metrics is not None:\n",
+    "        eval_rows_detailed.append({\n",
+    "            \"Composer\": result[\"composer\"],\n",
+    "            \"Title\": result[\"title\"],\n",
+    "            \"Performance\": result[\"metadata_row\"].get(\"midi_performance\", \"\"),\n",
+    "            \"Precision\": metrics[\"precision\"],\n",
+    "            \"Recall\": metrics[\"recall\"],\n",
+    "            \"F1\": metrics[\"f1\"],\n",
+    "            \"TP\": metrics[\"tp\"],\n",
+    "            \"FP\": metrics[\"fp\"],\n",
+    "            \"FN\": metrics[\"fn\"],\n",
+    "            \"metrics\": metrics,   # full dict, includes per-label (paired/deletion/insertion) breakdown\n",
+    "            \"result\": result,     # full result dict, for event-level drill-down later\n",
+    "        })\n",
+    "\n",
+    "df_detailed = pd.DataFrame(eval_rows_detailed)\n",
+    "\n",
+    "# Sort by F1 ascending -- worst performances first\n",
+    "worst_n = 10\n",
+    "df_worst = df_detailed.sort_values(\"F1\").head(worst_n)\n",
+    "display(df_worst[[\"Composer\", \"Title\", \"Performance\", \"Precision\", \"Recall\", \"F1\", \"TP\", \"FP\", \"FN\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skipped 30 piece(s) due to missing TSV file.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add ResultIndex so each row in df_eval can be traced back to all_results\n",
+    "eval_rows = []\n",
+    "skipped_count = 0\n",
+    "\n",
+    "for idx, result in enumerate(all_results):\n",
+    "    metrics = evaluate_one_piece(\n",
+    "        ASAP_PATH,\n",
+    "        result[\"metadata_row\"],\n",
+    "        result[\"event_details\"],\n",
+    "        result[\"response_events_normalized\"],\n",
+    "        result[\"ref_events_normalized\"],\n",
+    "        result[\"response_onset_offset\"],\n",
+    "    )\n",
+    "    if metrics is not None:\n",
+    "        eval_rows.append({\n",
+    "            \"Piece\": result[\"composer\"] + \" (\" + result[\"title\"] + \")\",\n",
+    "            \"Precision\": metrics[\"precision\"],\n",
+    "            \"Recall\": metrics[\"recall\"],\n",
+    "            \"F1\": metrics[\"f1\"],\n",
+    "            \"TP\": metrics[\"tp\"], \"FP\": metrics[\"fp\"], \"FN\": metrics[\"fn\"],\n",
+    "            \"ResultIndex\": idx,   # NEW -- lets us fetch all_results[idx] later\n",
+    "        })\n",
+    "    else:\n",
+    "        skipped_count = skipped_count + 1\n",
+    "\n",
+    "df_eval = pd.DataFrame(eval_rows)\n",
+    "print(f\"Skipped {skipped_count} piece(s) due to missing TSV file.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Piece</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Recall</th>\n",
+       "      <th>F1</th>\n",
+       "      <th>TP</th>\n",
+       "      <th>FP</th>\n",
+       "      <th>FN</th>\n",
+       "      <th>ResultIndex</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>Chopin (Scherzos_20)</td>\n",
+       "      <td>0.3195</td>\n",
+       "      <td>0.1892</td>\n",
+       "      <td>0.2377</td>\n",
+       "      <td>1606</td>\n",
+       "      <td>3420</td>\n",
+       "      <td>6882</td>\n",
+       "      <td>667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>983</th>\n",
+       "      <td>Scriabin (Sonatas_5)</td>\n",
+       "      <td>0.3917</td>\n",
+       "      <td>0.2573</td>\n",
+       "      <td>0.3106</td>\n",
+       "      <td>3548</td>\n",
+       "      <td>5509</td>\n",
+       "      <td>10241</td>\n",
+       "      <td>1013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>Chopin (Ballades_1)</td>\n",
+       "      <td>0.4603</td>\n",
+       "      <td>0.3122</td>\n",
+       "      <td>0.3721</td>\n",
+       "      <td>2591</td>\n",
+       "      <td>3038</td>\n",
+       "      <td>5708</td>\n",
+       "      <td>443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>Beethoven (Piano_Sonatas_31-3_4)</td>\n",
+       "      <td>0.5562</td>\n",
+       "      <td>0.4399</td>\n",
+       "      <td>0.4913</td>\n",
+       "      <td>2307</td>\n",
+       "      <td>1841</td>\n",
+       "      <td>2937</td>\n",
+       "      <td>383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>Chopin (Sonata_2_3rd)</td>\n",
+       "      <td>0.4391</td>\n",
+       "      <td>0.5860</td>\n",
+       "      <td>0.5021</td>\n",
+       "      <td>1465</td>\n",
+       "      <td>1871</td>\n",
+       "      <td>1035</td>\n",
+       "      <td>693</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                Piece  Precision  Recall      F1    TP    FP  \\\n",
+       "666              Chopin (Scherzos_20)     0.3195  0.1892  0.2377  1606  3420   \n",
+       "983              Scriabin (Sonatas_5)     0.3917  0.2573  0.3106  3548  5509   \n",
+       "442               Chopin (Ballades_1)     0.4603  0.3122  0.3721  2591  3038   \n",
+       "382  Beethoven (Piano_Sonatas_31-3_4)     0.5562  0.4399  0.4913  2307  1841   \n",
+       "692             Chopin (Sonata_2_3rd)     0.4391  0.5860  0.5021  1465  1871   \n",
+       "\n",
+       "        FN  ResultIndex  \n",
+       "666   6882          667  \n",
+       "983  10241         1013  \n",
+       "442   5708          443  \n",
+       "382   2937          383  \n",
+       "692   1035          693  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "N_WORST = 5\n",
+    "worst_df = df_eval.sort_values(\"F1\").head(N_WORST)\n",
+    "worst_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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69GgVRE/zS6pHfHSKYC5tFq52le8i7knacp5cx4ceegg//vijEhb7QriN1raMFGJVbPSDkqAFKAz2+aFHhPh7771XrWYQi1XxSR1L/TOQY4MpV6jjCSGEkNiDwi0hhJABz6pVq9ysHcXqVJZKy0uUL+tKq8cJWnR6baluMEh+EildokV7ikliNavt15Cl+SK0idWtfOQF8KSTTgqq/KEiAdWEYC2ufCHis55QrDyl/eRlWL+cWZD2lgjeEgFc9lth+fLlyqrKCAk+prdEDme+wbSP9E9ffTOQNpY6Sb9644031HnhCJIjPyjIjw/irkG7lyJJsPl5CsTaR9KQJebyv+dSdAnoJGKLWDYHe2+JkB6IdX9fI333mGOOUS5DZOWAhghY4q5F2saXJWow7eqJWLzKWBdpq+1ob0szxJ3EihUrlKV8sMeKuxuxJtXGNw0ZA8S6VFZ8hAsRb8XC//rrr4+p/hnIscGUK9TxhBBCSOxB4ZYQQsiARcRKWbouSxf32GMP5UtUQ16uxCJS/FzKclBxKyBWbSKaHnfccfj+++8DOk5zTyAvW1aWqPuKRC0vcCLKihAhS9fvv/9+5QZBfBHql6jKi6AIteIHUHx1yku15tog0HqGipRr7733xieffIJwIRagYmF84403KvcDIlxJW9TV1QWdpkRMv/zyy9VS+fvuu0+1r7Tz73//e9Xud9xxh+W05DqLL0bx17tw4ULl23Dz5s14/vnncdNNNylL79/85jdhzzeY9pH+KRbn4rNWs44LJA2JjC6uH7788kvVh8SS7ZFHHlFi8EEHHWS5zJL+EUccofqf9ENx1SCW4tIOYnV21113BdwO0ZSfEXJPiMAtvquDQVxTCJ9++in6gmB9iN58883q71lnnaXaWiwPzzvvPNVXPPv3pZdeqiwPNVciwZRR+p8IiHJNxe+yjO8yzl1wwQVB112sx6Vc4bAi76+2lOee9DVpE7mP5X4VYVAEQvEvfsstt7jODeRYQdzBiOXtKaecolZ7yPniQ/5f//qXGh9GjBgRtrYUVw0nn3yycnOkrZbprzaNJIGUKxzjCSGEkNiDwi0hhJABhRYASwuwJIKUvISJRZC4IdCQ5bRiuSI+ViWQiPiamzBhghKt5AVKXhoDOU4EsGuvvVYtgUxLS1P5yzL0QNlvv/1cfgX33XdftTRVrGllGbAsZ/fknHPOUcKfvOTp3SQEWs9wMGvWLCUAhMsyUHz4vffee0p4FuFRyiri4uOPPx5SutKO0p4iokr7SjtLHtLu0v5WGT9+PD7++GNVTrkO0rayfPXvf/+7evEWNwl6a85w5RtM+4iF16RJk5TvXbGM034AsJrGaaedpnyIXnzxxWoZuvSrl19+WYkq+sB//pA2uvrqq/HEE0+oOot7CBFWJX8JlCeCqp6PPvrIdT9r/V/KId8lmFK484sE0kYyLnkGCLSKXBexupUfBIIl1Ha0gvxQIeOsWJFL2pJuVVWVGgflh7NwIsKhWIXuv//+6oeHa665RgmH8kOdp1/RQOquWT3KDy39SShtKX1c2uPJJ59UfV76nTwbJk+erKzM9UvvAzlWENFWfqSSMUCsccVdigip8mOU+BwOd1vKD2ByPf1ZW0db/4xkuUIdTwghhMQeCQ692QUhhBBCSJCIxam8hJ5//vlKxCYk3hFx+OCDD1Y/noQiDok7DbGCFCtH8YtJIoMIYuLzecmSJWxituWAHU8IIYTEFhRuCSGEEBI2ZMmsBOYSn5yhBvkiJNYR38VitRiqlbggrgDE4jEUH8/EHLFlEQtScT0j4hgJHrZl9I8nhBBCYgcKt4QQQgghhBBCCCGEEBJlRIWPW/HpI8EyZImlr2Oqq6tVFOBQjiGEEEII8cWVV17p8olp9nnnnXfYiAOozWK9/IQQQgghZGDSrxa3q1atwoMPPqiWJEkETYmWO2PGDLdjRISVJZdPPfWUCvCRlJSkAntIFO1AjiGEEEIIIYQQQgghhJBYoV8tbiXQwtixY31G3b7zzjvxyiuv4Ntvv0V9fT1uu+02Fdn4hx9+COgYQgghhBBCCCGEEEIIiRWiwsetuDcoLS01tLgdMWIETj31VNxxxx2ubRKxWgI0iLWu1WMIIYQQQgghhBBCCCEkVkhGFLNx40ZUVVVhypQpbtunTp2Kr776yvIxVujp6UFtbS0yMzOVHzNCCCGEEEIIIYQQQggJJ2JDu23bNhQVFSExMTF2hVvxeysMGTLEbXt+fj4WLlxo+RgjOjo61EejpqYGu+22W1jLTwghhBBCCCGEEEIIIZ6IIWpJSQliVrjVVGe73e62vbOzUwUgs3qMEeJW4aabbjJstKysrLCUnxBCCCGEEEIIIYQQQjSam5uVy1hZ9e+PqBZuNdV5w4YNbtvlu7bPyjFGXHPNNbj88su9Gk1EWwq3hBBCCCGEEEIIIYSQSGHFVatvRwr9jCjPEyZMwAcffOBmSfvRRx+5gphZOcaI1NRUl0hLsZYQQgghhBBCCCGEEBJN9KvFrTjileBimrWs+Jn95ZdfkJeXpz7CjTfeiOOPPx577723CkB27733IiUlBbNmzXKlY+UYQgghhBBCCCGEEEIIiRUSHBLKrJ944403cPXVV3tt//Of/6w+Gm+++SYefPBBJfLuscceuOWWWzBmzBi3c6wc4wtxlZCdnY2mpia6SiCEEEIIIYQQQgjRIfJRV1cXuru72S6EWMBmsxnG3wpEg+xX4TaaoHBLCCGEEEIIIYQQ4o24pKyrq0Nrayubh5AAfNhK/K2MjIygNcioDk5GCCGEEEIIIYQQQvqPnp4erFu3TlkOFhUVKdeUVoIqERLPOBwObN68GdXV1cojgJHlrRUo3BJCCCGEEEIIIYQQU2tbEW9LS0sxaNAgthIhFikoKEBFRQXsdnvQwm1iUGcRQgghhBBCCCGEkLghMZESEiGBEA7LdN51hBBCCCGEEEIIISRu+dvf/oZNmzYhnliwYAEefPDBPsnrjjvuUC4DSODQVQIhhBBCCCGEEEIIiQk2bNiAiy66yHDf9OnTcf755+Pkk09W31NTU7HLLrvgwgsvRF5enuE5X3/9NRYvXqzE24GG1GvhwoW44oor1N8vv/wSl156qdpXWVmJpUuXWk6ru7sbr732Gj755BPlPmP33XfH2WefjZycHFx77bVYtWqV4Xlyzrhx43DDDTfgn//8Z9jqFi9QuCWEEEIIIYQQQgghEaGiqQJV26owImsEyrLKQk4vMzMTJ510kkt0feWVVzBnzhz1vaysTAmMr7/+Op599lkkJyfjX//6F9555x0sWbLEML2HH34YZ555JgYi4pd46tSp6v+qqiol5AZDV1cXfvOb32Dt2rVKrM3Pz8eKFSswceJEfPfddzj00EMxfvx4day0pVjYSiA7jSOPPFKJ7fX19aYCOjGGwi0hhBBCCCGEEEIICStNHU2YPX82FtYudG2bWjQVc6bPQXZqdtDpDh48GCeccIL6Py0tDXPnznV9F9rb29XfX//618oa9IgjjlBi4ebNm1WwKE/effddJTQKEoTtxBNPVP/n5ubi8MMPd0v7559/xiOPPAKbzabE4//7v//Do48+qgTJa665BqeffjpefPFFHHfccUrMFHFTrExl/4wZM9R+zVew2b6WlhblwuCHH35Qlq2SvyZU67n66quVJbGI1ZLnt99+izvvvFPt+9Of/oR77rlHibViabvnnnvi/vvvR01NjUpPBFdNWJVzP/roI3XMxRdfbBhE6/nnn8dPP/2kypyenu7a/pe//EW1xUEHHeTads455+Cwww5Tls4aIqBPmjQJH374oWFdiDn0cUsIIYQQQgghhBBCwoqItovr3C085bts70vWr1+vBFERfD3ZuHGjEnoLCwtdwaREWJTP5MmTcdttt+HNN99U+0RgnTZtmkpn1113xaxZs/Dee++pfa2trcrC9+6771YWrqNHj8by5cuVy4ZRo0Zh5syZar9mGexrn7gd+Oabb5TwLOXYY489DOslAu8HH3yg/n/yySfx0ksvqW3iAkHcGWRlZSm/sosWLVLi6pQpU5TIK2lKPQSxRBZ3CSIciwj9xBNPGOYlgquUVy/aCkOGDDEUeo0QIVesc0lg0OKWEEIIIYQQQgghhITVPYLe0laj29Gttlc2V4bFbYIvZMm+w+HA559/jiuvvBKDBg3yOmb79u1u20W4FetccbUgFroiSoo16rHHHqsE3AMPPNBlnSsiqFi2akheL7zwgksgln0pKSn49NNPXce89dZbyjJX3DOY7du2bZsSksUCdrfddjOt38EHH4x///vfylJ369atysp3/vz5qKurU/v0iHArFq+yT7MgXrNmjXJvIJa4gtRVhGDxEexJU1OTco+g8fTTT+N///uf+v/GG280FZf1SDuLf2ISGBRuCSGEEEIIIYQQQkjYEJ+2vljfvD7iwu1vf/tb5SrhrrvuwtixYw2PEdcJIkqKiwSxyhU/uCKEXnXVVSrQ2WeffaasWIUtW7ZgxIgRrnPLy8u90tJb9YpIesABB6iPRnZ2tt99Ynl76623Kp+yHR0dyhXD0Ucf7VV2cU9wwQUXqDJKOiLWfvzxxyptsda1gt4PrZRdLIeNkHqvXLnS9X3ChAmqvNJOYrVsRbgVcXnYsGGWykV2QOGWEEIIIYQQQgghhISN0sxSn/slUFmk0Xzc+gt0Ju4KRJQU61ZxUSD+WS+99FK1/4033nC5BxD3CBIITQJ1ic9W8a3ri3322Uf5qb333ntdfm2t7Bs6dKjycSu8+uqruP322w2FW7EMLi4uVmmcd955yt3BzTffjNraWpcVrR7xByxCcLDWy4cccgj+8Ic/KBcSe++9t/qIwGwVadu//vWvQeUfz1C4JYQQQgghhBBCCCFhozy7XAUiE5+24h5BIykhCZOHT464tW0giO/W//73v0q4FXFS3BWIb9hNmzYpdwbjxo1Txx111FF44IEHsPvuu6OkpEQFDpP9ZkjwMAmMJta+co64IhBR+Nxzz/W57/rrr1eBwLq7u/Hll1+6RGQjxMpWhNuXX35ZWcCKSwSx/BXx1xNxi3DWWWcpQVssdPXWtv4Q/7giBkuwtp133hnDhw9XArHk48udg0ZDQwPWrVunrJhJYCQ4xAkHQXNzs+rkYiIvDpwJIYQQQgghhBBC4h0J3iWi28iRI5XVplWaOppUIDK9r1sRc+dMn4PsVKdbgFARn6k//vijCvClIW4PxFJWXA2IkGllCb8ItiKSiiWtLP1ftmwZxowZo4RZEXD33XdfdayIqYsXL1bpipWu+Jh999130dbWpvzVHnnkkW5pi+T27bffoqKiQp0rQcvE2tbXvnnz5im3DJK3iKI77bSTadklENmKFStc+X799dcqrYkTJ6rvNTU1qKqqUlaygvwveYq/WrHWlbrut99+ap8IsRLITTvWCPG/K8HM7Ha7EnDFWtkTEcHFjYNYM2uIuwpp28suuwzxRLvJvROIBknhNohGI4QQQgghhBBCCIkHghVuNSQQmfi0FfcI0WRpq0fESBFIxf2AL6699lqsWrVKib3fffcd3n77bey///59Vs5Y5ZNPPlFWvr4slAci7WEQbukqgRBCCCGEEEIIIYREBBFro1Ww1dAsVP1x6KGHKpcDIrrJ3yFDhkS8bAMBvUU0CQwKt4QQQgghhBBCCCGE+EFcABAf2NuB7k4gOQVIDtw6m3hD4ZYQQgghhBBCCCGEkCjHX0C0fqO7C2isADq27diWmgnklgOJlB5DITGkswkhhBBCCCGEEEIIIfGLp2gryPeGiv4q0YCBwi0hhBBCCCGEEEIIISQ49wieoq2GbO9qZ6uGAIVbQgghhBBCCCGEEEJI4IhPW190+dlPfELhlhBCCCGEEEIIIYTEJe+88w42b97cb/mvXLkSH374YezmleTH564EKjPgtddeQ21tLWKdrq4uPP/88xFLnx6CCSGEEEIIIYQQQkjM0N3djYceesht2957741p06a5tqempmKXXXbBQQcdZJpOdXU1br75ZixZssSV5vnnn6/O7Su++uorfPTRRzjssMPwyy+/4Oeff8ZRRx1leKwEJlu1ahXWrFmDI444IqS8rLJ+/Xp89tlnKjDa7rvvjkmTJqnt//73v1FVXYUuRxdS25uRoLO8vfRPp+4IUJacZpjuww8/jMLCQhQVFeHNN9/EPvvsg7KyMsQaycnJSvwfMWIEDjzwwLCnT4tbQgghhBBCCCGEEBIz2O12XHbZZUrkrKioUJ+tW7e6bf/+++/xxz/+EWeddZZpOiIennbaaUhISHCd29bWhv5i+/btfq1/ly1bhmeffbZPynPrrbdi3LhxeP3117Fo0SJceumlSghv62jDinUrsHzVcqxYvQKzb7kbX61YjYqqWvVxiba55Zby2bhxY7+2e6ice+65uPvuuxEJaHFLCCGEEEIIIYQQQiLDll+AhnVA3ihgyOiwJn3HHXcgJyfH9b29vd1tu1im7rTTTsqSNjMz03C5/vvvv+83n5aWFmVhevTRR2Pu3LkoKCjA4Ycf7rbvmGOOUdase+yxh/qICCtuCerr63HAAQdg7NixrvTERYCk42lhOnjwYJW2Rmtrq0pDrF57enqUNfD//vc/ZXV7//33Y/To0SrfYPLyh6R311134euvv8bOO+/s2j5//nxUb6vG78/5vWvbWy+/hd9fdg7G7zoOxWn5TvcIBpa2CxcuVFbFnpapw4YNQ3p6uvp/27ZtyoJ106ZNcDgcSnzPyspS9RZBXqyhRUyePn26Wxqff/65ut6StlgW77///sqaV67xxIkT8dNPP6lr9dvf/lYd/8UXX2DFihUoLy/HIYccosR7DbN9lZWV+Pjjj9Hc3KzKJGUTZsyYodKVshv1s1CgcEsIIYQQQgghhBBCwktrPfD6OcCaj3dsG30wcMLTQHpuWLJ47LHHXILfiSeeiLy8PC8hVBBrWk8aGxuVqCnCrj/k2MsvvxxPPPGEEmUXLFiAQw89VFnsavv++c9/Ys8991Rin4iOIviJwCgC30033YT77rtPiXsiXIqoKGKfWJmuW7cO++67r8rn22+/xcsvv6xcJWzYsEEdN3LkSJVOYmKiEm/FIleEWrEyFpEw2Lz8IWL0mWee6SbaCpP2n4RfGn8xbqeuNuTbUpGa5O1qQkTgBx54AL/61a9Uu0n9NB5//HFlzSvuBkRkFRcXmtDc1dWl/kq9pc5yLf/xj38oVxEi0At///vf8cgjjygxXf7W1dXhpZdeUsKtdo123XVXTJkyRR0/a9YsJeRKm4n1slzXV1991ec+EYWlLY888khkZ2e79bWkpCQllos1dLjdJVC4JYQQQgghhBBCCCHhRUTbtfPct8n3184GTn8jLFmIJarmj7ajo8NN0BUxTawtRdT0FHQFca0gQqdVxJryxRdfVFauIgSOGjUK1113ndonlpwiuJaWlqrvV155pdqv+YOVsoiAKGKqiJeytF7cEAhnn3228q/ryZ133qmESKmLnjPOOEP5hBWL23DlZYSI2ppVsTBv3jwlLLd3tWPfQ/fF8OLhhud1dnd6CbeSp5RBhE2t/UTg9kQsWUWg/fTTT5X/Wz3i0kIsc0VALSkpwYMPPqiEW0n79ttvx3fffafSNEr7lFNOwdVXX63+X758ubLolXYTxowZg7/97W+oqanBli1bTPf98MMPSpgXYXjQoEHwJDc3V/WpcEMft4QQQgghhBBCCCEkvO4RxNLW4SESynfZvnVNWLIR4U4ETPmIZape0BUR7rzzzsPbb79teK5YTYrgahURf0V0FMQNg4h6YsEqDB061CXaCmKxKZaimv9dCSomlqaCWMFOnjzZdaz+fz3iFmDmzJl+yxWOvIzIz89X4q2GiJKS/j1z7kHFLxWm56UkpXhtE+tasYzWt5/enYP+mlxxxRVKIBWL15tuusllcStuKq6//nol0Ep6cn21tEVI1cRaSVssdvVMmDDB9f+PP/6ItLQ0V3uJ+4M//OEPfveJiC1uLIYPH67cUYhFr6ewr3fbES5ocUsIIYQQQgghhBBCwof4tPVF/dqw+7v15fvWTJiUY6qqqtxEVzMaGhpUEC2x+hRfuiLaFhcXu6xc9cgyfxEq77nnHq90xFpUBEIRIgX53whJ45tvvsHvfvc7t+3JycnKZUI48zJCfOdecskl+Mtf/qJEa7HglY9Y3qYnO91TpDgc6qORkZJh6CZBhG0RNsWtg/yvtZ8R11xzDWbPnq2Cy5177rlKIP/Nb36Dzz77TKUhLiPEalYsXwURU8VSV4RcuaZieb127Vq3NPXXR9pL3C2IewURafX42ieIxbWUXXzgnnDCCZg2bZpqY/HFu3LlSuy9994INxRuCSGEEEIIIYQQQkj4yN1h/WqIBCqLAo499lgVuEsLMuULEfJERD3ssMPUOSLaidBXXV3tdawstRfrVln2v9deeynhUKxIxYL2ggsuwMEHH6yERvE7KxbB4oPWE/GbKz5ZRewU61MJkCV+YEXIlABh4kpB/LYGm5cInOKGQQKeae4m9Ehd33vvPZX3SSedpCxNxQJXrJT3HDkOo7odSO92+g5OhAOl3Q4UD3J3b6Bhs9mU9bO0nQieIv6K2GkkjotfWaGpqUm5KBBLWvFVLPmLqwexrP7vf//rOkcsjGW7WMQef/zxSuAVYdtTTNcQP7XSTnL9jjvuOGWtK2lIW/naJ0KyBCbTLLqlP2g/Dnz55ZfK97GRS45QoasEQgghhBBCCCGEEBI+8ndyBiJL8BDP5LtsD9HaVqxOxRrUU3A0227GxRdfjP/7v/+zdK74MJUgWsLJJ5/sWiovAcLOOecct2PF56xYt+63335K7JRl95r/03322Uf5cBVRcPz48SoImAiagoiymnWsBE0Ty9LddttNWadKGoIIiyK4Snoi6gabl6QpvnDFgtUIEYqfeeYZvPLKK0qgFKtWSUOsgPcoznCJtsL5Z/wORVmDkdxUZdrWYhF81VVXKdcHt912G2688UaXxbKIpCLQirWr5qJAhN3XX39diamC1ENEW6mLBAuTa6UhvnxF1JbzxSetCL1a2iIUi1WsHvERLJbEWpA3yc/fPrH2le8i2or/3cWLFyMjI0Pte/rpp1X+kSDBYSRxxyHSAcWXhij6gTinJoQQQgghhBBCCBmoaMvaRTQzWj5uSluDMxCZ+LTVENH2hKeB9FxEC//4xz+UcChL+M0Qq1qxajWyru0LOjs7leVnOPn8889VmhMnTgzsRHs7sPkn8/1DdwWSA+gnYWD58uVK2BUrYrGGFjH6ww8/7JO8JTiaWD+Liwer904gGiRdJRBCCCGEEEIIIYSQ8CLi7OlvOAORiU9bcY8QQb+2wSJL+P1hZFUb60iAraDo7vS9v6uzz4Xbbb3WsCJEizX0aaed1md5i0sGI9E2XFC4JYQQQgghhBBCCCGRQcTaKBRsA0GsI2UJPhGl0o/lb3J4LYOtitBBC9FRDn3cEkIIIYQQQgghhBBC/GNLA1IzXV+7OxPQ0937RbbrrG17OjvRvW0bWzUEKNwSQgghhBBCCCGEEBKlhNu/bcjkliuRVkTbzpZkdG5Lhj0xHZ2D84H2ZqCrXYm2nRUV6Fy/nuJtCNBVAiGEEEIIIYQQQgghxBqJycCQneBIb0ZPWxUSu4GuejtS7euAJCgL3M6WFDi6HUhISUFCaipbNkhocUsIIYQQQgghhBBCCAmImq4GVA8RddEB9CQoy9vuLudfJdomJSClvByJ0WYxHENQuCWEEEIIIYQQQgghccvatWuxjb5YA6KjqwMtnS1ITHAgNbMLCYkOOES8bU5Wf+V7SkYnEhN70F+sWrUKjY2N6v8ff/wRdrsdsQZdJRBCCCGEEEIIIYSQmMHhcGDJkiVu2woLC1FWVqa2l5aWori42LVv8eLFmDhxIhITve0XW1pa8Nvf/hZffPGF2/aamhps3rwZo0aNQlZWltpWVVWlthux2267uY6LNlavXo0hQ4YgLy8vZIE7NTVVtW1nT6faluJwIDEJsGV0K9FWQ77LdnR1ugUsE3755Rfk5uaqMkWSq6++Gueccw6OPvpovPvuu0hOTsZll12GWILCLSGEEEIIIYQQQggJGy3z5yN1zBjYhg/32mevq0PH6tXImD496PQ7OjowZcoU7LvvvkhKEnUQ+N3vfocLL7xQbd97772xbNkyJCQkoKurS20Ti9qMjAyvtJ588kkcc8wxSE9Pd4mcZ5xxhrLQFCFYxNqZM2fi0Ucfxdtvv43nnnvOZc2Zk5ODoUOHqu+yf/z48YhGrrnmGpx00kk44YQTQkrn9ttvR3l5Of76178iJdHp/qAzIUH5tLW3OK+DhnxPyOxCYrK3m4Rnn30WBx54IA455BD0Feeddx723HNPXHTRRbDZbIgVKNwSQkgUU9FUgaptVRiRNQJlWWX9XRxCCCGEEEII8SvaVl1woRJty5571k28FdG28owz1d/SRx8JSbwV5s6dq8RTjfb2dvVXrEJffvllnHzyyX7T+Ne//oWnnnrKJQgffvjhOP744zF//nyXwPfmm28qAfeCCy5QH+HYY4/Fr371K8yaNctvHpWVlapsO+20kxKaZcn+119/rYRnsQIVRGjeqawEWYPTsGptJQqGlyjhedOmTRgzZoz6X6O7uxtr1qxBSkqKElL1yD6xjN26dauyaJX2qa+vV4K0WB5LGfLz87F+/Xps2LABPT09StAcNGiQeQXs7UB3pzPqWC+pyanISMlAe3sLOrYlY7WUeUguUjN7ULF2C3YZuZMKULaxsgZ2h0Plq3HmmWdiUOYgbOvchpSkFGys2WhYlvUmZfRV/+3bt6vzdt55Z7ftYg09btw4fPTRRzjiiCMQK1C4JYSQKKSpowmz58/GwtqFrm1Ti6ZizvQ5yE7N7teyEUIIIYQQQogZmqWtvapKibSaeOsSbauqYCstVceFyldffeWyohVRThNa//73v+NPf/qTssL1RWtrq7Ks3WOPPdT39957T4mEc+bMcVnyaiJtMIjoetxxxykRVYRX+YjYLK4GxEK3qKhIlfWlF/6F+++9C5+/8STQYcPVl18OJCbj2x9XK4vhkSNH4oMPPlDC5fLly3HiiSciLS1N+W8dO3assgQWsfrbb79Vbh8kHxFnxVJ48uTJ6pzq6mq89dZbuOmmm/DKK6+ouo4YMUId+/zzzytx2IvuLqCxAujY5vze3gi0bgV6ulT5ilPyYa/ZpgKTXXffvejsbseayiq0trZj/G7jMHbkKLwzbx62NjfjrLPOwt13342uni5cNvsyTP/VdBx53JG4/pLr8fnHn6O8rByJCYmuspx99tmGZfRV/3nz5inRffjw4eoc2aZnv/32w4IFCyjcEkLIQMHTf5HeT1IkEdF2cd1it23yXbY/fujjfVIGQgghhBBCCAkUzdJWE2nlb9GcOaidPdsl2npa4obiAkATWJ944gmXleUBBxygBD2xpBUfp2Zs3LhRWWKK1WtnZyd++uknJQBraYqlqrhEEEpKStQnEK677jpVFhFvhaefflqJl/fddx8ee+wx5XdX0rzz77fjs9eecFvCn4hurP3yQzjyRuM3v/mNEnr//Oc/q/pcddVV2H333dVxf/nLX5R1sVixith56aWX4uKLL1b7pE5ilXrQQQe5XCWItaqIu+K/V3MPYYpetNUQn7UNFcCQnZCwtQbohgpElpjsQHFBAd5/4WG0tbdjxH5HYY+xY/Htf/+LhvR07LTHHkqkrmmtUeKtIGV5/833sWDlAuRn57tWmcr2f//734Zl9FV/cZUh/UDqWVFR4RLkNcStxdKlSxFL0OKWEEKi0D2C3tJWo9vRrbZXNlfSbQIhhBBCCCEkdsTbU05xbg+jaOvLVYJwxx13KMvKU3rzNkIsMsU9goZYtIovXA2x7hRxWFwdiCh66623BlQ+CXgmovBnn33m2jZjxgz1d/DgwUpkFGH3qbtvwMgR7kZCJxx1CBLt2wGHXVkOf/jhh6qs33zzjcu1g4ZY5Urdpbziy9UXIkqLVaoI21IWETl//etfG7tH8BRtNWR7ezOSErYjJSMBCUkOiCeHow6epixdB6Wno7x0OH595FSkZHShqKRYCeS1G2vRktriVpZDjj4ER085GvtN3Q9nnnwmfnvsb03L2OGj/m1tbcp9gojSgrhQEGtjt2J3dChL3ViCwi0hhEQZ4tPWF+ub11O4JYQQQgghhEQ1Is6Kpa0m2gryPVyirT/E2lIsTR988EHTY7Ql9WJZKy4Xpk+fjmuvvVb5SJUl+nK++IUVK9ZgELHy6quvdlncenL//ffjwGkH4PnX38VZJx7j5p5h89YG5z9dncryNDMzU1nPivD44osvYtSoUW5pORwOtU/cM3haBuv94woSYG3dunX49NNPVflE9Pz973/vXjjxaesLEZVFfE1xuDYlJSW6HWKzJTn3i5WuWAAbpHnHI3egurIaSz9fimtmX4Ouji5VFqMyntjrIsGo/uLiIjExUblPyM3Ndbbh5s1ux4iwu9tuuyGWoHBLCCFRRmlmqc/9EqiMEEIIIYQQQqIZ8Wkr7hH0yPdwWtz64+abb8akSZNM94ugedhhh6lAZEceeaRafn/66acrwXb27NnK9YJY4Iov3WOOOSbg/CVw2SWXXIKWlhYVnEvyKysrU4LxI488goaGBnwy9wOc+vvjcdO9T+Dmq853nXv/Uy+iaFgBejJ/UO4VXnjhBXW++O4VYVPcMBQWFqpjRYwUkfgPf/iDcokgVsISmEzEXnH9IPmJxa64/hNLVLEgFkSczsvLU75+NaFzy5Yt2HXXXUWR9V0522DrDZHsTMuWaEMHdlg4S5C2H5f/qP4fXjIcQ/KGqLJowds8y+iv/mKZK750xVWECL7i+kKPWD6Lu4lYgsItIYREGeXZ5SoQmfi0FfcIGkkJSZg8fDKtbQkhhJA4d6kkq3Pkh1zNFyAhhEQbnoHI9D5u9QHLgkUsK0WQFTcERts1C1MJ6nXRRRepIFd6a1Y9F1xwAR544AEl3AoPPfQQXn/9dfURsVSEzssvv1wt3dcjy/iHDRvms5zidzU7O1ulI/FTxCpUhEMJGvbf//4X//rXv5CYOghP3P93/P7si7B67XqMGeU01Llq1hn432dLsKGxTVkNT5s2TW0XEfcf//iH8pcrIqtY2kqZJfCW+M6VYx9++GElCh944IEq0JqUX3zBXnHFFep/SUMQsVOsgaWcgpRTRGopF2xpQGqmm7uE0WUlGD40H0hOd4qxKYOBTqfl7djRZcjLyXIdu8cuOyFjcLozjeQ0TJgwAZmD5H+gbFQZcvNy0drSijl/naOOz87KVm0sZRGrWc3KOcujjL7qL36Db7zxRtx1111KkBfRXLO+lcBtcr00P8ixQoJDakjQ3NysbqampibVKQghpD9p6mhSgcj0vm5FzJ0zfQ6yU7P7tWyEEEII6Xs4NyCE9BfiO1WWrIsIasU/qKdoq4m0ZtujAfELe8stt6jgVf2CBOuSgF+9Iumxf7xcuU449lcHOYXP3HIgMfK2l+KSQIRll6sFj3IFSo9tMBKHjHIruwQmq2mpQUvnDl+3GSkZKM4oRnIE63jbbbfh8MMPx7777ov+vncC0SAp3AbRaIQQ0ldIIDLxaUurGkIIISQ6EQsqQZafRpJZc2eZrsZ5/NDHI5o3ISS+CVS4bZk/H1UXXOgKUKYXZ13ibV0dSh99BBnTpyMa6ezsVP5k+5wtq5QF6+zbHsBRBx+A6ZMnQKwtHbYMJBaMQb/R1e4UcO1tlg6XMrcmJGDL4FzT1SEd3R3K521KUgpSk1IxEGkPg3BLVwmEEBLFyEOOyyAJIYSQ+EbcI+hX4WiIiCvb5YdezhcIIdGCiLEiyqaOGeNlUauJuR2rV0etaNtv2NtdbgfmXHeJa7M4fUiwtzjF02T/wnlEECXWomirlXmww4Hajm1KoDUSZmXbQBVswwmFW0IIIYQQQgiJYsSnrS9kdQ6FW0JINOFLlBXxNlpcJEQV3Z2+93d19p9w669sJqQ4HMqqlgJt8CQiRhBXvB0dOyLP+TJDJoQQQgghhJCBQmlmqc/94lKJEEJIjJPkxzWDBAOL1rKZ0JmQoFwhkAEs3K5fv15Fj8vMzFSf8ePHY+nSpV7HSdS4nJwcZGRkYMyYMXj//ff7pbyEEEIIIYQQEk7Ks8tVkFLxaatHvst2WtsSQvqCnp4eNnQksaU5A5EZeCnosWX0n7Wtj7KZIWVuSUhESmpmXFvbOhzSEgPYVYIMCsccc4yK6ldRUaGE2QceeEBFgVu5ciWGDRumjnvooYdw//33491338XEiRNx991349hjj8UPP/yAnXbaqb+rQQghYfNvJ0slGaiMEEIIiR4iHZRMY870OZg9f7abr1sJTCbbCSEkkkiQrsTERNTW1qKgoEB9T0gQL6YDm35Z0Z1eCHR2A3anr1uFbTAweLgUKLJ52zuAHjuQZAOS3cXWzq5OdCcOQqqjA4nd/lfDb0cCmtMzUJA8JG5XxjscDmzevFndKzabLeh0EhzhkH8jxKpVqzB27Fh8/vnnmDp1qmt7aWkpLrjgAlxzzTXq++jRo5VQe88997iOKS8vxwknnKBEXCsEEtGNEBK/9FXkaD1NHU1eL2piXSMvatmp2X1WDkIIIYT0PxKITHza8odcQkhf0tnZibq6OrS2trLhw0BXV5f6m5ycbLgvOcEB9HQBiclOITWSiCV161agSxd8LDkdGDwEYmPd2F6PQZ1tSNPJh46kFCSk5wEi4GvlFFe43R3oSkhAUlIqknu3xTMJCQkoKSlR3gGC1SCjuhWTkpxLgfS+bUVnlgFj0aJF6vuWLVuwdu1aTJs2ze3c6dOnY8mSJX1cYkIICT8i2i6uW+y2Tb7L9scPfZxNTgghhMQR4haBrhEIIX2NWNmOGDFCiYrd3d28ACGyceNG9VdbSe65z2h7xHjrYqBKXJLqr2sSUDoRN+bn4Tc/fowxHe1uAmI3EpBUOhn4zUN9V84YRCxtNW0zWKJauB01ahQmT56Mq6++Gk888QTy8/Nx3333ob6+3tXJN23apP7KPj3iXsGXcCtisF4QFrWbEEKi0T2C3tJWo9vRrbaL1Q1f3gghhBBCCCGRRlvyHcqybwI3S9u0tDTDfUbbI8KWX4CfXjHe91MF6ocVYP+tm032rwcO+QswZHREixjvJEb7oPDWW2+pgGS///3vsf/++6tt4gLB05zc00m2/Arky+fKHXfcocyStY+4XyCEkGhDfNr6QpZKEkIIIYQQQgghAdOwzufuvXQGj4bUr2WjR5iotrjVLGfF2laPCLl77LGH+r+oqEj91SxwNcQSV9tnhPjHvfzyy90sbineEkKijdJM3z8qiX87QgghhBBCCCEDh5b585E6Zgxsw4d77bPX1aFj9WpkTJ8eeka5I73zrk1FarYdtsE9WJ7qHqTMvj0RHU02ZBT1Crp5o0IvA4ldi1sjfvjhB3zzzTfK6lbIycnB7rvvjo8//tjN+vaTTz7BAQccYJpOamqqcgCs/xBCiD8kKFlfBiYrzy5XgciSEtz94sh32U43CYQQQkj8ulNaUL1AuU0ihJCoQ5bgr54LbF2DAUU46rXlFxS3/ojitHbD3dlr1qDqggux5pQT8cM7t2Djdy+58hTRtvKMM9V+EXdDrotY3I6YAvS+b4poW7UgD5Wf5MOeujN2HzIOK1JSlPdbEW1lu+xvrk0FRh9MNwl9QNRb3N56660YOXIkDjroIPz888+YNWsWjj/+eBxzzDGuY6699lqcddZZKkDZlClTcNdddyn/teeff36/lp0QQsLBnOlzVCAyva/bycMnq+2EEEIIiQ5qamr65Mfdpo4mr3mB/Jgr84Ls1OyI508IIT5prQdePwdYs8O4Tgl8JzwNpOeGdcwVgh13tfN9paGN6+pvbnrI9apdswJ5n16FtOod43d7yVTUz7wbjt7xW/LrHDYIjnQ7ULcFaTc+h7yZW4DBPU7hdEEp7I122EpLlUVu2K5Reh7QVu+0tB3UDfv2ZFS+1ogLZv6iLG810Va22wZ3IT3bDvTYgbaGsF5X4k2Cw+FwIIrZsmWLCk42d+5cZV178skn46qrrvJyhv3Pf/4T999/v3KZIG4URLzdZ599LOcjrhLE121TUxOtbwkhUYlY1IhPW3GPQEtbQgghJD6F21lzZ2Fx3WIVqFS/Ekd+1H380Mcjnj8hhPjk+eOBtfMA3RilrDlHzQBOfyN2hdtPLg65Xu1PHYnUmsVI0KXhSEhCR/FkbD3iSVdZVtw/FqNqNqFGJ5QWTWlA7aJc5/ccG8r+84GhG4WQrlFKBtDR5CXSuuU9uAtlM7coMTcS1zVeaA5Ag4x6i9v8/Hwlyvrjj3/8o/oQQshARcRaCraEEEJIfLtH0FvaaoiIK9vlR17OFQgh/YYsvddbcWqIQCjbxb3AkNGINZIbK0Kv15Zf3CxtNUTEle1JTRXozi5HdcU8jGvcAAyGEkg1AbXyowJ1vBJOp22ALaU1/Neoo6k3jx7zvDXRNtD6k/jxcUsIIYQQQggh8UjVtiqf+2VlDiGE9BviL9UX9WsRiyRtWx96vfy0TXLv+N1Y941rmwikYu2qR74r4TTYtvR3jazkPUCua6xA4ZYQQgghhBBCYoDSzFKf+8WdEiGE9Bu5I33vzxuFWKQ7c0To9fLTNl2943fO8B0uP8Vlgbgo0ON0WZAYfFv6u0ZW8h4g1zVWoHBLCCGxykCN1EoIIYTEIH3h37Y8u1wFIhOftnrku2ynmwRCSL+Sv5MzYJfHGKW+y/YwLqeXMTeUcVc73zMNcUmzoHqBcj2j7Rs2bmro9fLTNoW7TVX5lZTPwIqcQrR5+JktO2Sz+qtcF0iAss5BwVXcVzkkQFmvaGuat9quSYmJQOkUp8Ut30njNzhZX8HgZISQmKGPIrUSQgghJPpo6mjC7Pmz3Xzdimg7Z/ocZPdGJSeEkH6jrQF47eyYe1fxO7aGo14W06j/+StsOP00JDQnuPmVVYKqiLaNdthKS1H23LPBBSgzK8fR98L+/CxUPrvGKxCZynveUNi3JXr7ug22PeKY5gCCk1G4DaLRCCGkL6NH91ekVkIIIYREL2INJj5txT0CLW0JIVGHWGCKJaYso4+BwFWz5s7C4rrFKtijfjXD5OGT8fihj4e3Xn7SaJk/H1UXXAhHXhaaLpiKnOHlKEjNVe4UhuSOQuUZZ8JeV4fSRx9BxvTpwZXBpBwq7/MvgG1IJsr+eipso8cBPV3qGPvLl6Ly/1bCvj0JpdPqkVHU4Z4e30ktQ+E2CCjcEkJiQrgV9wgPTzDff/GymJgYEUIIIYQQQkg0IO4RjnnzGNP97xz3Tp//QCYCakNmJhKHDnXbLu+fItp2rF4dmmjrJ+/UMWPcrXl730PF8rajyeYt2urhO2lYNUj6uCWEkFhigEZqJYQQQgghhJD+oGpblc/9srqhrxFR1lO01RBBNVKirZa3lwuG3vdQcY/gU7QV+E4aVijcEkJILDFAI7USQgghhBBCSH9Qmlnqc7+4pIl7/L2H6uE7aVihcEsIIbFEH0ZqJYQQQgghhJCBTnl2uQpEJj5t9ch32U4/4j7eQ/XwnTQiMDhZL/RxSwiJGdoa0PryKRhU+cWObYzgSQghhBBCCIkWxCdq5UKRnYDyqYDD4VxuH6XBypo6mjB7/mxUV85Dqb0L623JKC2bgTnT5yB722bvskv9wlEff+kY7Q8lb4vnit9fcSHhFgCzrQF47WxgzcfGJ8k76czrgF8+ceZTMAYYd5xpPtUV89BY9w1yiyaguCxyrh+iEQYni3CjEUJIvwQm000oFtYuxAi7HSPsXSgonoQrDn8U2anZvDCEEEIIIYSQ/nunaa0HXjkTqJhvfkyQRidSXiEi72FS7tfPcRcly6ejvbMDabVL3LaJFo118y3Xx7CdjfLTp2O1PD7ydmsvf/kZvG9qiNWxErC1982ta5x+bDWXCPL/oCHA+9cCVYu8G2DE/sDJL7ryaW6sRNUzh2Fc4wbXIStyClFy1ofIzunbIHD9BYOTEULIAEUeoovrFqv/19ts+HxQOv7buEJtJ4QQQgghhJB+RcRBX6KtsHae03Iz2sot5dJTMR+pepG0d5ubaBtsfYzy06djtTxW8/aXn8H7poZ8d3vfFAvaMYc6/2r/f3KrsWgrrP/CLR8RbcfqRFtBvlc/c5j/esQh9HFLCCExgixXkV8+ux3dbtvlu2yvbK7st7IRQgghhBBC4hxZHm+2jF6PvM/IcWK5GU3l9njPEsS4Nuz1MctPS0dcDVgtj5W8/eXXe27Q75tWrntvPuIeQSxtkz12y3fZXlPpR/SPQyjcEkJIjCA+hnyxvnl9n5WFEEIIIYQQQtwQ36mBIEvsY7HcodbHX37VX4Y3b3/59Z4b9Pum1farX6t82vqiofZra2nFEZ4iNyGEkCilNLPU535xHE8IIYQQQggh/ULuyMCO13ykxlq5Q62Pv/xK9gtv3v7y6z032PfNlpVbkbo9EbbBPV777NsT0dFkQ0ZRh8onx2bzmYcEKiPu0OKWEEKCoD8Ck5VnlyvH8EkJSW7b5btsd0X7JIQQQgghhJC+fqfJ38kZ8Mof8j4jx4l/1ADLG5H3MK3cHu9ZRjh6P4HUx6vM+TuhtWx/OBISvdMpneJ0YTB8H8BzvyGJpnm72sukfo6EJNSXjEdNSzWwei7Ku7q93jfL7HZMb+vAOemjkLZ2Ab5c8bKby4SW+fNRdc1tqFxQqkRaPfK98pN8VC3IQ4tjkipjSfkMFYisy6Os8l22F5dNt1Dn+CLB4XB49bl4JJCIboQQ0l9YivJJCCGEEEIIIf1BWwPw7zN8BygTEfGEp4H0XEQNbQ1of+FUpFXveM9qL5qEtNRUt2Bki9JSlZ/Zye0dQdVH3ucu+fASrN78JeZs3oID2tpd+7pTc5DU0Wh+8ogpzr/rPYKAjZwOnPic7/zlukiAMJ0v2sVpqeqvvi72kQdi9tB8LKlbjDmbt7qVT+Pz9DS8sedRuHHmfRhU34rKM86EvaoKtowelB20SVneaqKtfXsybDnJKHvpVdhG7uJsg8ZKFYhMfNpqiGhbctaHyM6JD2Ok5gA0SAq3QTQaIYT0N/Irp/gYkuUqtLQlhBBCCCGERBUS8KqiVwQtn7rDl6osyw/Q0ravqKmpQVJTBZKb16MrawS6s8udFqtb1+Chjy/D3G1rsC7ZaVU6wm5HeVcPhhTth5t//YLlPGbNnYVFtYvQgx63dP68rQs7tzYiwSAgmQvNmnntp4BD55ZALGRHzQBOf8N/AXR1uXrLFkxua3f3odqbVltXG9LWL0aCPh+ddeyS9HQ8P/5YPH7o47DX1aHyuMNhb7TDNrgLRVMaULso1ynaZiej7M0PYRs+3CsdCUQmPm3FPUK8Wdo2B6BB0sctIYTEICLWUrAlhBBCCCGERCUiznoKtFEq2OoRsVY+eiqSk/BE2zqgV7QV1ttsWC/uWhu+w9nNlZbezSqaKtxWTmrpJMCOsdu3+i+czlrWDRF7ZZ+I5X7aWKtLmaPb0JpWSyvdj5A4ta0Nt1fOUwZFZTY7yqZVuSxsKz8qUMeJiFs2fQNsKa2G6YhYG2+CbTDQxy0hhBBCCCGEEEIIIQZUbavy2S6yEjKUdErtnh5fg0Qsmi2WIRx5jrB3OevesE65RxBLWz3yXQUss1AuYg6FW0IIIYQQQgghhBBCDCjNLPXZLuK+LpR0qmxhWgwvbigsliEcea63JTvrnjtS+bQV9wh6nO4SEi2Vi5hD4ZYQQmIcWXKzoHqBW3RPQgghHIsJIYSQfmXLL8Dquc4l/Fa+9zPFqW0obv0RxWntrv+1su2WtxsSE9wltJH2HpybPhJlFq1Xy7PLVWBpOe+A1jbl31ZIRBIqB+cCHukb+riVj/ihdSMRKJ0COBzG7alrZ60M1SlpKsiYV8klbclj+N7yxbAYcs6KlBRMGDoBZZ122FctQ+WCEqdPW3GPcMhm9Ve5TVhQCnvnIEvtQ4xhcLJeGJyMEBJrSETS2fNnu/lJkofwnOlzkJ2a3a9lI4SQeIFjMSGEEOJBaz3w+jnuPlnT84C2evPvIhae8DSQ7m612W/l1SEC5+yCfDQnOYXVrO5uzNm81d1HrJXyt9bD/upZsK37zLWpITERuT3eAcC8GDkdOPE55/+vnW3u71ZfnqPuAd69wv3Y0Qej+lc34+SPz0dPaz3mbN7iXg8RgLesBNrc3R6YIRa1mm9bW2YPymZsUu4R1HYRbSVgWWkpyp571jBAWbzSHEBwMgq3QTQaIYR4Rh81Q0UhjRASkXRx3WJ06yKPJiUkYfLwySq6JyGEkMjDsZgQQgjx4PnjgbXznIGurCKWnqNmAKe/4XrHkncp7V3L7L1KOy6Ud7gh//sT0moWm5ZXLEwXp6fh/MKhSEQint3agr22NSBBf7xJ+fW0P3UkUmsWu53n8LJrTQQK9wAOvQn1FcvVls7h+7kCprnS/OevgKolgMNE9JXypGUD7U3u9UpIworsApyal+56jxTL3/KuHgwp2g83//SFu6CuYctAR24pUjb/jARdni21qahakAfboG6UHdwA29h9gelXKPcIYmlbecaZsNfVofTRR5AxnYHIgtEgw+RIgxBCSF9iFJFUkIevbK+0GNmUEEIIx2JCCCEkbMiyfH/WoEaIiCjnyTL/IaP77IIkN1YgrXqh72MAZZUqAqeIrHs3bwm8/Ft+MczH2xlBD7BhOZAzAm277GzexusX+SyzKo+RAOvoxrjGDSjOGI71NpvaJH/X24ApNUuMzxHsLUjd9JPX5oyiDpROq0dqth22QT1A1SKnT9shoyGpi6Vtx+rVFG1DgD5uCSEkjiObEkII4VhMCCGEhI2GdaGdX78WfUnSNuvvTSPsXSj158/WrPyBtouvdgi1jXvr4smeHR1BpSXirbhHMCq7uEegpW1o0OKWEELiOLIpIYQQjsWEEEJI2MgdGdr5Yq3Zh3RnWn9vWm9LNgnXZaH8gbaLpKNzPRtSWiZ18eS71FSEhT6+hgMdWtwSQkgMokUDFZ+2euS7bKebBEII4VhMCCGE9AUt8+crP6aK/J2cgbF631MkSJX4QfWLHC/n9aGbBKErpxztJVNd5TU8pjdAmbgUqE5Jw4qcQu/j/ZU/fyeVj8NHPpbSMWhj03QkAJxBOaX8NSlpXu+RiTsd4jzHCNluJc9+uIYDHQYn64XByQghsQYjmRNCSP/7G/+5/mf884d/4sf6H13b5Qe0OdPnIDs1u1/LRwghhPSFaFt1wYVqSbz4M7XZtgMbvgcWPgD7L8tR+Uk+7K1JKJ3ZgYyCenchUO9PVQS/E54G0nPDX0jxCSvuBXp9r2L1x0DNV0DpRGD0QUBbA/Da2aa+eUW0nV2Qj9yebhyetTPOmngVMj+5zd3PrEH5ZZ4gLu5kNaQyrDHKR+YKHU07vpdOAU55aUc6+rI7HOr/alsKqrpbsff8B5Fe+YVhmetLxqP1sFtR8tk97vmVTsH28afhqdWvYuXWFcryVgRpmbvcNe48ZG5YAXx4rTOomf5anfupM9iZj3aSNlh/2N+wacuPGFX9HfK6u4Gxv3K2sQELaxbi+83fY8+he2L/ov0RTzQHEJyMwm0QjUYIIdGEBCITn7auCQEhhPhAH6U5mCjMxPiHM43xQ8fjwZkPUrQlhBASF/MCsbStPONM2KuqYMuxoWxalfJ3Kpa2SrTdngzb4C6Uzdzi7gdVhEkRMoMQbj3Lalr21nq0v3iae1CwxGSgp8tLlKxptaE4rd3pn1Vb6l+/Fhvsg2AfkomU18/HsE0/GJbnq9QUvLj3Mbhx5n3q+W80T5iQNwEPHPaAc36wdQ22rvgUg1e8gLSNy7wTlLY46h7g3Sv8isnT88ZhaHsLPmpZ6/Jdq4mxWr4P7XspMreuBZY+YRjUrK1kP6Tb0oF183dsLNzbKbiOOtBbeJUAbB7ttC2jADd+8wBOXv4O9uvodD9erum584DccvW1qrkKp7x3Cho7Gl2H5KTm4MWjXvTrEnCgQOE2wo1GCCGEEBKrULgNnVlzZ2Fx3WJ0S8RmD2Sp4eThk/H4oY+HISdCCCEk+ucFSrw97nDYG+1KpC2a0oDaRbnmoq3ZMvtRM4DT37BcZrPvLp4/Ho6185Bg8Lx2Iz0PNad9bpiGSvuTi32mIzLwkvR0PD/+WPX8N5onJCIRU4qmuOYH7U8didSaxcZpSluIdatYvfrIc3F6Gs4vHOqzaq58N2wC1s4zTS+U6yFInU9f9ib2b2sz9gMsAvlsZ1C1aS9PcxNt9eLtgpMWIB5oDkCDpI9bQgghhBBCLCLLHsWCxki0FWS77JfVEIQQQkg8IO4RnJa2XUqsrfyoIDDRVpDnqliXijVnOBAXA2s+9i/aCm31SNFb5epIbqzwm46E+Zra1oaqynn4ouYLw3lCD3p2zA+2/KKsgE3TlO3iRsJPnge0tWOE3e6zapJvdeU8Z9taFW0DvB4yN5I8pA1Mg7dJfdZ8qtwjGIm2gmz/otbY9UM8Q+GWEEIIIYQQi4ivOiuICxtCCCEkLmhYp8RZsbTVI98tibZ6ZAl+mMoUCCmblhtuT9pm/Xkubgq+2/yd//lBgGXzl6c/Si0cE8r1kLmRpTyqliqftr74bpPv9otHKNwSQgghhBBiEau+18TvOCGEEBIX5I5UPm3FPYKe6gV5aN0itqHeyPEttaneOzS/qWEoUyB0Dt3LcHt3pvXnufiW3bNgT//zgwDL5i9Pf1RZOMYUC9dD5kaW8iidiD0K9vB5iAQqI+5QuCWEkChDlposqF4Q8jLbcKVDCBlYaP7bGJgsOMqzy1XkZfFla4Rsl/0MFkkIISRe5gV2+2BULijd4R7hkM1ISu1Gd0cSKj8u8BJvtcBlVQvydoi38lyVoFxDRlsus9l3Rf5OzvRMntdupOehYNKJhruGjZvqNx2xNV2Yno7SshnYv3h/w3mC2/zAX9lku/iE9ZOnBCjTgpCZIfmWlM2w3hb6Muivh7ieWD3X0HWCzI0kD2kDh1l6Up/RB2Fq8VTly9YI2b5/0f7WyxgnULglhBAP5/PhTk//8YVEHxWn7se8eQwu+PgCHP2fo9V32R4I4UqHEEKIMXOmz1EByIyQ7bKfEEIIiQdUYLIzznQGJsuxKZ+2g/LtKJm2FUhwAI4EN/FWE22VyDuoG6nZvT5aJRDWCU+Ht3CSnqSrJzHZW1A899PA09EhKealZGHOfteYzhP08wP1Xugjzfbiyaj79Us+85TAZLML8pGVkoXxQ8ebHufK11cdyqcDI6e7b9OuR2u9CvKGhycAL5wAPDTe+b3N3S2G5PH6nkfiy9QU7/TTc93a+MWjXvQSb+W7bCfeJDgcDlNBPJ4IJKIbIWTgEko0VbP09PhK2yj6aDDRycOVDiGEEN/IigbxVZeUmITunm61/JGWtoQQQuKJlvnzUXXBhbANH46y556FLaXV6Rc1MRmtXy1G5U3/Anp6kDQkFyUXHY3ax96GfVMjbKWlKHvgZths25zL8S1Y2gaNWIlKmbR81nyq/K3K0n2xAg04nQX3AFVLAEePu4WqiJ2nv+E1T/CcH7i9c2ppiqDc04UN9kHozi5Xu9QxurLfsPAGbK39EhXJiS5LW+0975pJ17jyEozyNcrPre0920kQkXbtPPfAZgZ11dd5U9UijK75HnldXcDYX5m2sQQiE5+24h4h3ixtmwPQICncBtFohJCBS38Jt+LWQCxkzXjnuHcsiQHhSocQQgghhBBCrIq3qWPGKPHWk9bly1F94UXo3rLFtU2JtiLyGhwf9YjLALE+NePiZX5FaF/vnPr3R/0x/fKeF4a6ktA1SLpKIISQGIhSbjU6ebjSIYQQQgghhBArZEyfbirCDtprL5Q8+KDbtqI5c2JTtBUa1vneLxarEaBf3vP6qa7EHQq3hBASA1HKrUYnD1c6hBBCCCGEEBIOH7i1s2e7bZPvsj0myR3pe7+4GQgD9iVL3dpI/543pNmBvdf0eL3nieWzWbvKdtkfjXUlvvHwykwIIfFNuKOsW01Pi1Ju5pvWbNmLLJmRX181/0XBpkMIIcQ/2pgrY6qMsfK3dnstEpCAfQv35RhLIocsVxXLp0j7gSSExAeRHFN0ads7BzkDl1VVwVZUiKI/HYLax95R3ytPPQVlfzketiHZQPnUiJYDEtppxX+A7VuwoWRvrM4rwajuHhR3du5oAzm+ciHQshnIGLqjTJ5tlb8TMGKKuY9bk3q4vbcVe7yT6fIoLh7t9Bt8ww2o1PwG27ajvHIhrk4sxPINlfj9qwkY1JKAfx7bjY/GpmKXvF3Q/cWXqLryJiQMy8fWuy5F+ZAcV/3kOqw57RT0bNiEtHtuwqhfnWCtDaWuow/29nErNqCFewRzVUgQ0MdtL/RxSwjpb5o6mjB7/mwsrF3o2iYirETozE7NtnysYDUdQgghwY3PRkwsnIh7Z9zLsZaED4nm/fo5wJqPd2yTl2iJ9C1RugkhJFrGFI+07dsTUbmgFPZGO2w5ySibVg3b4B7n9k/yYd+eDNvgLpTN3KK2Y+R04MTnwl4OI+ziZ1e/IS0HaG/0PlDK0taw43v5dKDHDqxf5H2sSfl9vuN1dxteD/sBt6Py3D87BW8/bff1EBuuGFoA2/YE3PyiAwUNPejO7MbYGZtd56z+bBgSmhOwIQe46dQk7LLLAdbfDaX+r51t3p58JgUFg5NFuNEIISRSwcx8RR/VM2vuLFOr2scPfdxyOoSQ+CISY1Y8YDTmmiEvY9o4TAYmWuCYPrmXAozmTQgZ2IT8HPczpoSUvkfaLbWpqFqQB1uGA2UzNjnF2V5cAmRrEkqn1SOjqGOHCGhSDqvbDOvogUOq7eO7L8yObS+Ziq1HPOlWHinfdcuuw7cN3xq/t23YZHo97Ic8gopjD0NXU5cSaYumNKB2Ua6X4C3lWZiehvMLhyoXCg8/14mkbUle54iYe9EZKdialeD13miJrWuA1/4IbPjO2NKYz6SAYHAyQgiJYURknVYyzad7BPnF1lNAkO+yXQRbK+kQQgjxj9mYa4Z+HCYkJGTprFg4efY9+S7b5SWaEEKiYUwxSFvEWBFlPUVbQb6L8Ogm2goRKIcRnsKrVdHW17Fp1QuR1FThtq16ezW+rv/a8L2tunKez+th2/4TyqeLpW2XEl4rPyrwtlLuLc8Bbe0YYbcjI72r19LW+xzZPji9y5V/wPMVcTdR9627aKsrL59JkYPByQghJMbol4iihBASp/gbc43gOEzCAqN5E0JiZUwxSVtEWU/RVkO2u4m2ESxHX5Hs8R5W12YegK3U7hRRTan+UrWRWM3qke9GbTrC3qXS9HWOHBP0fIXPpH6Dwi0hhMQY+oiiRohrBEIIIX0z5nIcJhGD0bwJIbEypvhLOxCipRxB0OXxHjY8fbjpsVW2ZN+JleynXEqIqwM9TtcH3lLeeluyStPXOXJM0O+NfCb1G356CiHRj1t0xqwyw4jPRn/leIfD4XauK5KkLDhISAA2fA8kJgHpQ4DWrcrReX1mPpbmFaFSN+iV27uwty0bwwYXOZcKaJErGf2XRIDy7HLlQ9HMxy1dIxBCSOTHXDPkWI7DJCz0RvN2rJ2HBM9o3qWTwh+BnRAy8MeUEVOAqiUey90TgdEHOceUXh/eQaUt/mmN/LWmZQNt9dbSkTQiUY4+8nHbnV3utq1kcAkm5E0w9HFbUjYVSPXh43bwrqiYX4Ku7d4+bsU/sKeP2/U2m/Jx+/O8AiRt9/ZxK9u3j0hWUdmCem/0dY3Fxy2fSREjwSHKFWFwshjEKDpjTmoOGjsMokH6Iau7G0832rFL8ybL53yVmoq/5g/BX+vrlU8ZnzDSIgkzPqOTWokOSgghJKQx14islCy8fPTLQVnpEmLU72765DIc/927xnNNzi8JIVZprQdeP8fpi9SIkdOBE58D0t0tNQOirQF47Wz3PGScOvpe4K2LgYr5vs8PRxnMyuGBXVw16L5vS7Ihs1u2eiBlkfQ0yqcDPXZg/SLLZff53tbTY9hm9gNuR+W5f4a9qgq2nGSUTRNftz07grrpfN1+PcSGK4YWwLY9ATe/6EBBQ48KROb0des8Z/Vnw5DQnIANOcBNpyZhl10OCO690ewan/B06NctzmhubkZ2djaampqQlZXl81gKt0E0GrEebTdcGEW2DCTCsz8e27AJ+7e1B+Q7RH7xaExMRGZPj3/TdUZaJBFCHMqLbyKX1TghhPRFVOo45cz/nYlvN32LHhj76wsqSjOJWSJ9H+nnus/UbsTeHR1I0h/A+SUhxCrPH+/bCjWc44kEFxM/tbIKVW+FKdsresXL8qnOv/rv4bbY1JdD+OE/wPbNuLP5O7zQUYsSe4fy+SruA2pS0vDrnHG4ufhwoGUTkDF0R5mM6uNZFwtl9/ne5pFHy/z5qLrgQtiGD0fZc8/CltLqys9uH4zKq++GfWszSufcgK3TJ6t0i3/YiI4rb0LCsAJsvesSlA/JRXFnh0rT3jkIa047FT0bNiLtnpsw6lcnhK9taWkbFBRuI9xopP+FW3GHcMybx4Ql7TK7He9UmzsNDysXL+PARgghpN+hcBs4gcw93jnuHf6YFgdE8j7S9ze/c1XOLwkhvhB3gA9PsNZGA3w88fcsj6bnt4i3qWPGKPHWE3tdHTpWr0bG9Okhn0OiX4NkcDISNxGeg47mGE5CiZBJCCGEkJiYewQUpZkQP/3N71yV80tCiC8k7opVBvh44u9ZHk3PbxFYjQRYQbYbCbDBnEOiHwq3JCYJp+84v9Ecw0koETIJIYQQEhNzj4CiNBPip7/5natyfkkI8UXuSOvtM8DHE3/Pcj6/STQS9cJtY2MjLrroIowaNQq5ubnYa6+98Nhjj3kd9+ijj2Ls2LHIycnBtGnTsHTp0n4pL+nbCM/iSy5UKm02fJ6eZuKtzreP24bERFiy15VyahEyCSGEEDIg5x6yT46JlmWWZGD0N22u6jXn5PySEGKF/J2c76K+3p3jZDwxe5ZH4/Nb3B6IewMjZLvsD+Q4ErtEfXCy3//+91i+fDleeukllJeX4/3338cZZ5yB559/HieddJI65rnnnsO5556Lf/3rX5gyZQruvPNOPPPMM/jxxx8t+50Kp49b8ZsiJviBBAsK5px4xyg6Y05qDho7GgNOK6u7B081dmLX5k2Wz/kqNRV/LRiCv26tN470q4eRFvvHl5MsC4oFh+kGZeWYQAgh5vTHGCl5/lz/M15c+SKWbVpmeIwrSnSgUZoJ8TPXlbnqnM1b3OecnF8SQqy+YwzKA147G1jzsfGx4RpPJM9KeT9P8B+0K5BjgyzLxurFKvjY0NIpzvnCll/QuuYjvL7qdbzcUSU5K3c0BcWTcNW+VyKz7jugZbN7cDJf75a926ttKViXnOh3XuJz/qLlkZCEli+/R9WtT8A2JAtllx0OW16G6zB7Rxoq//467Bs3Y/tpR2Lwv96DrajIGcSs5Uc0rP0IG5OT0dOeBvz9v0iob0H63y5A1ZQ9Vb4iAfbJHMqo/T3a4quNXyEBCdi3cN+408GaA9Ago164HTlyJM4880z87W9/c20Tq9uZM2fivvvuU9/HjRuHGTNm4JFHHlHfe3p6UFJSgj/84Q+47bbbIircakG4RCA2EhIn5E3AX/b4CzJtma7j9Bidw0l/YHhGZ9S+JyUmobun2/SvtgzCLbKjPjpkYhIgA3diIpA+BGjdCrTVoz6jAF/mFaFSt2ytzN6FvW1ZGJZRDPR07VhiMgAiLXoGmov6KOSt9cDr57hPSgwmIr7qpd8X0foalNU+8kDMHpqPuZu+dG3jmEBIfBGJMWigBCPrj3mTUZ7jh47HybucjF2H7Kq+m0aJJiTcc13xdzsA5peEkAg+t329D8m++rXYYB+EwmGFpuNJwPlLuq+cCVR4WHeOnA6c+Jy7IBzAsZ7l0OsvnuXVSGhvRMHn18C27jPXtsVpqci1ZWHsts3W6wSgvXACkJiMtNolOzaOPhh1+16F3IW3IK16x9xAVkbMLsjHmIL9XBqQVs6VFStx/+r7jecv3d3e74TbE1H5ST7s25NhG9yFsplbYBvc47a9O7Mbd/wmCee8AxQ2ArbMHpTN2OR1nHb+kvwUVb7mpMTIzqFa62F/9Sy39pe2eWPPo3DjTKeGd/m8y7F0g/sq+YmFE3HvjHvj5sfv5oEk3F555ZX4+OOP8eabb6K0tBQfffQRfvvb3+Kdd97BgQceiIaGBuTl5eHVV1/FCSec4Drv1FNPVTfvvHnz+ky4nTV3FhbXLUa3o9u1PxGJ2CdvH9w2/jbDAcboHDHTnzx8Mh4/9HHL5SAkUsSccPv88cDaeYDunlJLf0bNAE5/I7qEW4OydiMBi9PTMKuwwLWNYwIh8QWFW3P6Y97EuRohhJCYEm4tvA/5Sz/g/CVPX9a8uvewQI4NRrgd8r8/wVb9BZKUc0Mn4hYxofcTCFoKbuclJKE7JROJnduQoGtjcWcj73EXFha6NCCtnGe9fRa+bfjWeP6yYZP39TIQb4umNKB2Ua7r+4iZW7A0PwV/HVSAJ57tQHeL8XGa6KuV7/zCoZGdQz1/PLrXfOLW/pL3kvR0PD/+WPVdL2DrESE5XnSw5gA0yKj3cTtnzhzssssuyk2CzWbDMcccg4cffliJtkJdry+PoUN3dD7t+4YNG0zT7ejoUA2l/4SCmHlL59PfiEIPevB1/deoaa2xfI58l+3y6zohJABkeYlMAjzuKfVdtotFdZSXVR5wU9vaMMJud23jmEAIIf0zb+JcjRBCSEzRH+9DWp5m6PMN5NggSG6sUFawetFQE74CFW1hJvY6upHU0egm2qq8AeXOpsTe4aYByVxCvhvNX6or5xlfL0CJrU7RtUuJsJUfFbiJsSmDe1R+B6AFIw8yP07S0Zcvou+ZvdfXs/0lb3nHraqcZyraCtTBYlS4Peecc/Ddd9+pYGObN2/Gs88+i/PPPx9vvfWW23GJspzd47svY+I77rhDqdvaR6x5Q0F8hPiitrU24HNkSRQhJADEJ5AvZClQjJR1hCyF9IBjAiEknumPeRPnaoQQQmKK/ngf8penPt9Ajg2CpG39r6Fo73GaBuRrLiH+dX0hoqtY0OqR75oYK0xsb7d0nGf5IjKHCuIdN2JlGUBEtXC7detWFWRM/Nvut99+yiWCBCQ77rjjcM8996hjhg0bpv6KqKtn06ZNrn1GXHPNNcokWftUVfl+GfBHaaZv4bdoUFHA52g+WAkhFskd6Xu/5ns4BsoqTtw94ZhACIln+mPexLkaIYSQmKI/3of85anPN5Bjg6A7s/81FO09TtOAfM0lqgze+TzdJYjbAz1ONwg7pLylaWmWjvMsX0TmUEG840asLAOIqBZuExKcRunJye4XV75r+4YMGYIxY8bgs892OD4WS1v5PmXKFNO0U1NTlR8J/ScYxGeJfMqzy5U/DvERoke+y/aJYyZ6+WHxdw4DXJBoQOvj2ieqyd/J6RfJ455S32W7zum+r3r1SX1Nyio+bhemp2O9zebaxjGBkPgiEmNQ1I/fFjCbN0k8gd3ydgs5fVnKuKB6gdtyQV95cq4Wg8gSztVznctw9f9HK1oZf/kktspNSF/RH/dDH+UZ9HPb9Y7hKfUkur0PqfR91CWg/LU8zdC/h/k7tnSK0+K2t0xm72lG5ZXPsHFTVfryTqVH7E6DCe7kMDpP5gTpeV7vcV29QbiSkIRz00diYt4Qv3OJkrIZxu+vRgHKDtm8wx3CJ/no3J6I7202fI4MrPvU/DhNvNXKF9H3zN7r69n+kre845aWzVD5mcG5VYwGJ5s2bRpaW1vx73//W/m5leBkxx9/PG699VZcfvnl6pjHHnsMV199tQpgNnnyZNx5552466678MMPP2DUqFERDU4WaqTj/oiOTMiApq0BeO1s4yiq+mimUVpW+8gDMXtoPuZu+tK1jWMCIYSYz5v0BDNe+puLyX5GP45xjCKs64m2eYK/8kZruQnpz3sk0vdDf+QZbDlfOROomO+9b+R04MTnxNIt/HWpXwf8YzrQ4RE7KC0HOO8zILfc/R3o32d4l1Hyln2hlqmtAW0vn4r0yh3P9S9TU7Bzpx3Z4ZC/pFwH3wg8fyzQVu/a3JiQgF9SUrBvR4dXHZoSE83nEpNuQPbbl7m/E3qKtr2+ar0DltWjenGeMzBZZg/KZmwyPE7O/3qIDVcMLUBzUmJk3zPbGmB/5UzY1u0wrhTB+I09j8KNM+9T36+YdwWWbFji3RYz7o0bHaw5AA0y6oXb2tpaXHXVVfjggw9UheRXlD/96U/K1YHer+3tt9+O+++/X7lX2HXXXfHggw9i5syZlvMJh3CrIZYa4pdDTLyt/nIRzDmEEB/Ir7Tya60stdFZ2sZKWTkmEEKCjSptdk5IEaqjDBkjr/7saqxsWIkeR09I0ZFnzZ2FxXWLjSM996YjxyyqWxRyXgMVfTRvIer6mVGEdT0e0dYDuXcicl/5K6/FchMyUGl/6kik1ix2Dw4VhvtBG8sM72mj+zJC92BI44qv8UMrrxBgXfyWSfL19eOYUbryDlTRK64ufxGoWmpYppqZD7k2WZ3fnPX2Wajf9CWKOzvU8vxrtjZgclu7CpJlBRHJEjwsRn9OScG/dz8M5024E8WfXOzVhpqwpj/PkZCEjuLJSDvnPf9zCe2dMDEZLUu/Q9WtT8A2JAtllx0OW16G84QFd8Pe1OEUZVuTkD9uG7asyIQtw4Gy/86DrXUl7P+9GMlN1ejanuA6rmhaPb4fnYDzC4e6rH33Hro3nj3iWUSMrWuwsXoR1icnY2jpFC+dS+ZyX234Sv2/b+G+caeDNQegQVrtt/1GUVERXnjhBfW/aMyaiwRPrr32WvXp6enxClTW10iHC7TTBXMOIcQHIoBGu2Dro6wcEwghxByZE/5Y/6PXdn10ZCvzKnGPYGS9q09H8vJ3DOdwUYy/COae0db7e+5gpbzRWG5C+ootvyCtemHf3g9m92W03YP+xg+tvL72BVMXf/mapau9A8n5b19sWqakCRXoztZZ7Fp4tn9d/zWQnIh1yekos9txQFt7IDXyWOjvFM7GdXbi601fY+uGJSg2qK+RUiU/Lkh/ra74zP9cQvdOmDH6IJQW74PUMWNgGz7cefDqj4GuNtgGQ1nQdjTZkFHUgfQ8O1Kz7Uq0RXYpbE3O+E22wQ634w5ok+BgduUqoQc9WLZpWWTnMENGY5h8THbzfXeA+Lj1xEy01dPfoi0hhBBCCIksviI0BxKR2Eo64cqL9BNWIphHMtp6JMsbTeUmJFrukUjcD/2RZ1+NH+Goi5V8faXr5/zkAJ+zns/tUrvYy4aHEfYutGz5PuDzGuuWBTyXyJg+fYdoK9Q4rVMFcYcgYqw6rqhDfVcWyx5tqT9OK7+/fEn0QZWTEEIIIYTEFL4iNAcSkdhKOuHKi/QTViKYRzLaeiTLG03lJiRa7pFI3A/9kWdfjR/hqEtvvi21qa5AWJ7Y7ZlomT/f5/lmdAX4nPV8blfZwrfQXNwuZOTvEfB5OcPHhz6XKN7X9/7SiX7bUsofcL6k36FwG2B04XCkEY50SYzDiMBsbwOibWzQlyfaykYIiW/MIjQHGh3ZSjrhyov0E64I694Ru13IPn3U8/7ENCJ8lJebkL4ifye0l0xVvkPdkO+lU5yWnbIsvy/GkWi7B/2Nd1p5R0zxHmNCqUv+TmhxTELVgrzegFjuadsLpqPykhtQdcGFxuKtn/YNxE2CIM/tCXkTXM/tSptNBccKxO7WMxCUnLswPR0Tho5Hkb2jtw2T/KeTkKT6a0n5gaHPJcYcDKTnGe+T7aMPMm1LKb+0gbhJ0PI9LncPlG1c5X6/UKeISqI+OFl/OAZ2pDp8RhcONkKxRMkT9JEEGS0+zoiVaKQDhRhpb38RzaOhPHo4bhFCBtLYaSWdaBunSYBIlPLXzvYdOCda5ga+IsJHc7kJ6e97WoSrtvrI3R9GeUbjPehrvCuf7nTEus5gfAmxLvZ1K1F58u9gb+yCbXCX8q0qy/RFxK1cUOLcXlqKsueedV/+76vcIZTJ87md1d2DJ5q7MK5xw46DROjf/CPQ3uR+cslE2JOSYav8wrVpcVoqslNzsGvTRvM+Z9S+ujqEZS7RUAE8cZB7vlKOcz8FcstN23JFTiHOzUpGc1Iisrq78URzt3tb+Ck76d/gZBRuDRrt6iVX+40u7C+a7nXLrsM39d8op8++YETiMET4jCX6MBpp3EaODrK9+7OPWYlo7lnGSF4Ho/Lo4bhFCIkmZCWA+GiT5X6hWL9aSSdceZF+QovYrS0F1v73YWEWUnT3sEWETwAK9wR+93+Wy01IXLB1DbasXor8H591+vfsi3cs/TjSx/dgQOORVs7EZKCny1ne964yGF8SgdJJwNnvh1w+e10dKo87zCXeFk1pQO2iXNi3J8OWY0PZfz4wFm2Nyh2msdnruW2U/ppPgZ/fBzIKgHHH7di+dQ02Vi/C+uRk7L70WaSvX+Ldx0omAtOvcE/PpA5ambUypbaloniQsw4BP2ekzNLnxT2CWNoa4VEOLd99597uXRcjYkynGMjCbficfQwQKpsqQ44cXL292hnF0AKMSBxHxEo00oFCjLS3lYjmfSkOmJUnGspGCCGRjEpsJR1GQI5xdBG7Xd+jCdPI7A5gw3LjOhASzwwZje4NG4D1i/puzh8r96BnOU3HFwlqtSgs7WSzbUfZtOpedwnJqPyowLldLHCnbYAtpTXwcoeI13PbKH0RPo3EzyGjMUw+0naV5xn3MWk7T5HZTx20Mnka4gSEWZk9yq8vh8q30w7oLIl9EmXvzfEMfdx6UNPi++axEnWvrq0u4AvBaH5xQKxEIx0oxEh7R1u0cn/l0cNxixBCCIm/uQsh0UTSNj9zZd43fTe+NKxT7hHE0laPfJftMXstBtLY7K8usV6/AQqFWw+KM3ybqFuJujc83Y/5f5DpkhgnVqKRDhRipL2jLVq5v/Lo4bhFCCGExN/chZBoojvTz1yZ903fjS+5I5VPW3GPoMfpLiExdq/FQBqb/dUl1us3QKFw60FZdlnI0f5KBpeoKIaJFpqXEYnjiFiJRjpQiJH2jrZo5WbliYayEUKIRKMWH3pGyHbDaNWExApRPHfhvUeila6c8qi9b+JtfLHbB6NyQanTp624Rzhks/qr3CbI9s5BiLe28zV29mzaBPuSHYHr+7UuHrTUpsLeajOsH+dbfQ+Dk3k4Bv6+6nuUFpQGFO1PfELK8mJ9oAqjiIGTCifBAQeWbthxczIicZwRK9FIBwox0t7RFq3cqDx6OG6RaMXoeUwGDvLyU3X+BegemovBT9yHkbk5ziV/eaOwdms9tp17CWybm9Byy5/RMXE3S/1A32ccDkfI/SfQPhhqn+2zPi/+/XrbOiZEEKvl3fILNlYvxnpbMoaWTomOccNo7iKRzyedCwzfy299InGdtHvPVpCDskfvhm1ovisfEWIqzzhTvciXPvoIMqZPx4An1u6HeKA/5/zR2B/0ZXI4dvw/KC9i7aQCk8lYUFXlDEQ2rUq5RxBLWyXaNtphKypE2a3nwzZ2gv+xrFLeQxKA8qkht6vPZ6XWViJkik9Xs+to1MfydwGOewwoHu/VDyTPDR+/h+zrH4WtqAhl998EW/sqoGUzkDEU63sy0Xz1vUja1IDSxx61NnZa6GuS75bqxSjt6sKwwUWuOlUkJ+1oA1uWd12G7q6aGxt/UKJt1YI82LJsKJtzGWwl5a504nLMj4LgZBRuPRpt18d2xfjh41WnXd6w3FCo0JxIZ+Rn+BVbjKIPMyIx6c9opHFJjLR3tI0N+vII0VQ2Eh30ebT1GPnxI9xtq/3vq72tHOMrL41Qr2e4+oRnOnKNb3nrEhx17xIUNsrS2G6MnbHZ9VL487wCJG1LwoYc4KZTk7A1K8FnPwjmByrPMum/B9oHQ+2zfdbnW+uB188J+CVf3x+FPhsnrJa3tR72V8+Cbd1nrk2fp6fhjT2Pwo0z7/PZhoH08ZDuB5m71C0Hlj7hHnjJpD5m9a6pdwYDCroc0lb/dyYq/7lyhxXdzC3egkxpKcqee9Z/xPgYw+0aBnk/kNic8/u9f/ugPwQ8hhiVSY9WPjkuzO9G6geeCy5UY4AaCyQQWW8e9qZOVJ7ye/W3dFo9Moo6zMeyV84EKjxWz4ycDpz4nFe7ej5jPOczKytW4v7V97s9K2Vl9AOHPYDs7m60v3ga0qoN5gFm11HK98LvgJqv3LfLcSLs9rIipxDnZiXDtj0BN73QjaGN8B47ewO4bcoB3r58Es6Z+Bdk2jLd6hNIX5N5wU0fX4rjv38PB7S1e1VJnnGzC/LRnJSo5gt37ncNst44zzCwnz19V1S+0QJ7U3fcjfl9CYXbEIXbpPQkw6XBk4dPxuOHPu4aEG758RYsrlusIqwbHUcIIYQMZKJFuJ01d9aAex5TuHXvW9o1zmnqwsPPdSqRVl4mJOCJ03deshJzLzojxSXa+uoHRn1Gj9F5voTbQPtgqH22z/r888cDa+c5LW00xCpp1Azg9DeiT7i1Wt7nj0f3mk+QBIdrUxeAJenpeH78sT7bsM+E2wDrY3ZczcyH1Negy9Gbtr3F4RIaPO89ZV33nw8G5Au82zUM8n4gsYnf+7cP+kPAY4hRmfREuL+KeJs6Zoz3WPD88bB/Px8djYlO0dasLFJ+X6KzR7n9CbdnvX0Wvm341u1ZKe4spxRNweMbNsGxdh4SjNrKrJ18lU+HPE8Wp6fh/MKh+McvWzD0vQzjsXNwFzYd2YILxgzD3rl747bxt7nVJ5C+JvOC05e9iUltbUj2UyaZL7xQ34ZxTZtN+4peXI6nMT9ahVv6uLWI3OzyS41YoAnV26vVd88Jv+dxhBBCCIkcsiSMz+P4ucYZ6V29lra9PvM+KnC9VMj2wenyauJ7XmbWZ/ydZ6V8VtIItc/2WZ+XJZnygurZTvJdtotlWzRhtby9x+lFW0FedKe2taGqcl50zOMDrI/ZcUlNFWEpg1hbOa2uvO89tSRarOsGMrF2P5D46w9mZerD8smyeS8xr7dctkH2HaKtUVm08psRYLnlWfl1/ddez8oe9KC6cp5Kz1C0NSqblfJ5PE/E6nVyaxv2T2o1HztnblH7izvbVVlrWncIz4H0Namr1GmqiWirL9MIux0lne0Y17jBZ1+J+zE/yqBwGyCyVFioa6uzdBwhhBBCIof46/IFn8cD6xqX2rvUy4RYfuiR77J9hN1duDXqB/76jNl5VspnJY1Q+2yf9Xnxo+cLWQIbTVgtr5/jpA9FxbgRpvokh1IXj7R93XtR1x/CTazdDyT++oO/MvVX+cI0lrkdawFfz0qZS1hCn18g7dvLXh0d/sfO3ueOUNtaG1T7SV2t1knysnpsXI/5UQaF2wDRfD0OTx9u6ThCCCGERI7SzFKf+/k8HljXuMqWrJbvyXI9Pc7le4kqyJS/fuCvz5idZ6V8VtIItc/2WZ/PHel7v/hGjCZyRzqjYG83fr2x2zPVMl5/9ZI+FBXjhtX293NcVyh18Ujb170Xdf0h3MTa/UDirz/4K1M/la9l5VbzcXl7Ilp+rrde/gDK7etZKXMJS+jzC6R9e1memup/7Ox97ghFg4qC6mtSV6t1krysHhvXY36UQeHWIuIHRJw4S1Ae8TkyaedJ6rtsNzsuGhCz+QXVC6JjyZenyf/quVxWFOvXMcb6UTS1YzSVJZ7KHu42+KLmi7hui2jwb1ueXR4Tz+NQ2lb731d7WznG17naJ1r6hD4d/TVuaUtWgchcywwP2eE2QbZvb0v22w/M+oy/8zzrpn0PtA+G2mf7os+rMa69Dm1l+zv96OmR7+Jr0EdAG31/7KtxouXHWlR9PgSVn4pPPv0rTiLsBdNReckNKnCOHCfl71bhs3cg9kcL09NRWjbDZxsGUp+Q6p6/k7Od/bW/n+MKd5safDl0aXv6O9TfeypYTecgDERcbWf1epAB887p877po/4Q0L1rVqYIls9SwLJrbnOOEa02t33yXbZX/eVW549qWvnNMCi35zNGP5/RPyvL7HYc0Nqm3ATI95KyGb7byqid/JXP43kiwcAWD0rHF92DzMfOT/LV/pqUNFXWiWMmel9zC31N6ip1kmdYl58yrbfZUJ2SpgKo+eor8TrmRysJDofD3cFTnKIPTrb/yP3hgANLNyz1Gam3r6NYBxL1OWojbPdDNFZ9u0WT2GCFvrqOnm0USPv0S3CiAPuRtOMlH16ifAf19/0QqWtq1M/1hOMaRe24EiTBBM/xFYk+ltsi1hlofZMYX+Nb3roER927BIWNUIHInL5unVGORbSVgGUbcoCbTk1yBSgz6we+7mVf54WrD4baZyPV5z3Tzeruwb1bGzFpe4vrGHnZKznrQ2TnRNePIva6OlSefhrs1bUGUbBLYG/s2hEFOycN9lfOhG3dZ67z5YX2jT2Pwo0z74uecUOilL92tv/5jtXjgiyD/Z9noPKfK938MsZlhPFItjMJC673kr5454zG/mBUJh3fZw/DiD/M7bPxW43LZ5wJe1WVM6CV+MP2NXZI+f99BlAx3z2hkdOBE58LuF2bGytR9cxhTn+uns+w1CzztjK7jmblk+Nkny6Pc7OSYduegJte6MbQRniPnb2i6KYc4O3LJ+H63zxg/uyx0Nfk+X3TJ5fh+O/eVb5sPZFn3OyCfDQnJTrnC/tdg+y3LzOsvz1vCipfrHE+N+N5zI+i4GQUbj0a7fuq77F7ye5qm1hQiY8rWS7l65d3q8f1pXAbtRG2+yEaaywLt311HWNOuA2wH0k7LqpdpJzR9/f9EKlr2hfCbdSOK30o3PqKRB/LbTFQ6KvnMekflOXO+Rege2guBj9xH0bm5Tp9rOWNwrr6BjT/6c+wbW5Cy61/Rsd+u1nqB/o+I4TafwLtg6H22XD3ebMxTqyUlO9XW7KyDIrWsU6JBMcd7nyptBIFe+sabKxehPXJyRhaOiV6xw2xFuzt6z4t5aweF+S9ZyvIQdmjd8M2rMCVj1hdKWGmrg6ljz6ighMNeCLUziR0XO8lffnOGYX94Yb/norNNUtcS/C18VusLUW068vx2028LSpE0aUnofb+l2Gv3WAu/kmbVvT+MFk+Nfh2tdIPtOuXmAz0dFm7jkbl8+gH8nyu++hdZF//KGxFRSh74GbY2lYBLZuAjKGo6slC09X3IGlTA0ofe9Ta2Gmhr0m+m6oWYURXF4ZlFLvqVCnX32i+YFD/lhU1aoWKbWg+ym493znm9+6LyzE/QlC4jXCj9RdWhVtZ3nbMm8eY7n/nuHf6Z2IqS1UenmC+/+JlEXnYxapw25fXMaaE2wD7UTTdD5EsS6SF22hqx/4Sbv21QSy3BSGxgghIqWPGGFp4yEtEx+rVfIkIEqtjXFSPdVt+gX3Ofi5LJg2XxdBfvooaYSXW4L1HYmZul9rWL++csTSW9/X4rRdvNSJusdlP2sNAGTtjtdwDVYOkj9sBSNRG2I7G6JtRTNRex/4mwH4UTe0YTWWJp7KHC6uR6OOhLQjpL+QlwewlT7bzJSLyY1xUj3UN6xgFO0Lw3iMxQ5y/c1oZy/t6/Jbnc9GcOW7b5HtEl9lHST+I1bEzVss9UKFwG8ZfJOSXByNku3K4He8RtqMx+mYUE7XXsb8JsB9FUztGU1niqezhwmok+nhoC0JI/I5xUT3W5Y5kFOw4euchxJA4f+e0Mpb39fgtY0Pt7Nlu2+S72VgSFuK8H5CBhXvoXeK1zEB+sfLnN0z5fRIfIMOHO839bdudv/CY+ACxmm6waBEUzXxR9tuyNi0ioqefGfn9oHTSgF6y4nMJR29f8ax/1F7HCNY5pH4k/opKJmJj1SKs3/Cly1ed1o6aj1uJKlpq70JNSiqKRkwzbMew3qO6+pYPGd2v1zTYemnnjR86Hss3Lzcte6THtv7G1ZfqFqHHscNfsiD9qqyrB0OK9huQdY9WzPqc5/aB3jcHIr6umez7auNXSEAC9i3cFxJnV46V8UjGJ1/XXb9Nf17t9lpXeoH2kWD6l3aOZ5kDJZx922ze4WusC1v+oc4NetOwr1qGyvnFsG/v9vJxu25+MWznNmDkkAjlHy5CLEskxzvDd55K8fOYAHv6zqi85AbYa2uRceUJ2DBxl+j2Gxyu6yTzT7lfoqHvEP/vCuLbNBzXydd9GsnxxELa2ljuK/imz/vSM49A6+Nxb9jtmc6xQXzclpYqS1sl2lZVofLUU5w+VMdOCF876s8z1B5E1B0FVH4BfP8aUDoRGH2Q+3kOh3feRvtN7n9tHB7V3YPkhirlX9htPPSoWyDjthy7pXoxyhtqkJ9e4OYD2C2dTrvKo9qWgnXJiYZzoVDnIFbwVbfqinlorPsGuUUTUFxGC15fMDiZgX8JR6ojoEi9VqMl5j31MK5ddW+fRL2O2gjbviJd9ncUzr7EYqTTqL2OwRDO6K4G/agnPReJumie+ujQwk0fX4rjv3/PLcqmfeSBsJ34rFs0zrC1t0l9m465D7O/vKNPr2mw9TI6Lyc1B40djW7pXDfpOty25LaB0U99IO1xxbwrsGTDEte2rO5uzNm81T16azyNZf2EWZ826otGfXag9c2BhK/xSrh83uVYumGp33Q8r/vEwonqr5Vz5dh7Z9zrt48EM7YanWP13FDztoLZuN/TutVrrNOiZkuE6qDzD8fcoDcN+3efunzbekbB/nleAZK2JWFDDvCuPnp3X0Set0qIZemLOaP7O08yyqZVe0VI17e9fi42YMZco+ukwed/9GD0zhmO6+PrPhUxL1LjSYDjg9Gc1e/zzSiP9Dygrd5afQzOdxsbSopQ9vy/1A8/9rU/ofKU37sCSaoxY8+DQmtHo/KXTgE2/QB0bINPtMBcRpRPl9+mgHV+VhPo3vO+r5rv9cyU8fD93Q7FjfXNsK37LOBnqVxTeZ898bt3Mbm9w22fvWx/zC4cjrmbvjR8N5G8ZxfkY5fiyaZzoXA/L3w9kxLaGlH1zGEY17jBrR1KzvoQ2TkD9Mc+AxicLMRGu3rJ1ZYjp2tBbXo2bUL7rDPQ1dRlGsX24h9u6vOI7OGMNhzWIFT//BVQtQTQW61ZiPRpNUBb1BNApFOpc01rDWpba1E0qAjFg4ojXv9A29nS8c8fD8faeUgIZ3RXLQrmgnvQvX4xkuBw7ZJH75L0NDy52xG4bfxtKP7kYr/5G0XTDvoeNbnG7cWTsfWIJ0O6poHei1IvzeJYIxGJ2CdvHzxzzDM+zzNqj70K9sI5e5zjGlfC2m59hFEb+gvSZ1TPxzZswpS2Dre+F7GoxVGCZzA3fVv21Rht1ucyUzKxrXObqbVgNPZNf22mD6JnFkwvmOezlaB8nnl6Eu5rLPlct+w6fNvwreF4IphZEIUbz6jbRv38lh9vCXjsM+q7Vs/1l044+7bMH79Z9w32GbkPkrclY/A7ZyBrwzIk6uZt8pxdnJ6G8wuH+s3ftI+GI/J7bxotNcmoWpAH26Bul3AoqX6TmoIrM4bhxhe6kd8M3P27ZKQf0Ht9/Tyrw9HHLd+fIbaFvz4RriCmSrw97nCX4OL2zqMTbXfMxdLx/Phjo2bMDZX2p45EWs1ibwu+OHj+xyTau0KI1q+u+9jXfSpYuIeDmiv5GR88xxntu4zlX234Sm0zW1Himg98crGxdarVPm5QxpbaVOe4PLgbZX/YBbYL33Yda/9+Pio/zoW9NQml0+qRUdyl0m7vaPe+xzzqath2Rm0EqBm66K4RJyEJK7ILcGpeOh6uq8Pktna35e0yHm5LTERWj8PrndXKs1TG+NOXvYkpbW1e/k4ltS/S0zGrsEC9mxjlrc+jL+bHvp5JF6/4FGMbN3iV8eecQoy79GfEC80MThY8lU2V6qXAc0It32W7DH5GpKS0ony6/OrcpSYulR8V7JjATKvCxo5VQaUbKjI4TysxXg7eb8jSgPWL3EVbQdpGfiGTB+xARuov9fR8KPqovwh7++Xv5xL4YrXObqJpOK65TMDEf9H6Re7CWa8fmKlt7di8cTG2yq/NfvKXZRxhu0d9XOO06oVIaqros2uq1Usv2gry/ev6r03r5as9lm1a5rYUuT/Gtr7GqJ6yZFh+zfbse3EzlvUTvvqcWFj6Em0HYt8cSFRvr1bjktl40leireCvj0hZAx37zPqulXOtpBPOvi3juzyj5G9yYwVy6r5yE22156yMgSPs9uDyD2I+5CuNjKIO9fKvFw6TRKzo6MTg9C7cdGoS7jwhEctGOa9vTeVnfp/VfUaIbdGXz2Jxj+BcXWjwzqNr+x1zsTZUVc4bGGPull9U3zAVtvj8jz7kXWHMoeFzj+DrPg11PAs2Xx9pyxj+251/qz6+9AAZ5w3z8MQsT5Myusblg7bAtnm+87zeY22D7GrMUKJtUYcrbcN7zF9dzdqor0RbwdGtLEj3296ino2ePknle25Pj+E7q79nqYzx1ZXz1HhqFKRK6ij7Jre2meatz8OIcD4vfD2TpB7STkZllO01lfSTbgSDk3lQ01ITVATGpG3rfUaxbaxbFlS6A5IoifDYb8Rj/SNZZz9pj7B3oWXL937z9xeBNaB71E+Zkvvwfg+2XlbPC2u7RTFG9RRfyXF3L8dItGQrDJS+OZCoa4tgkJIw9xF/ZTU612rf9dc3+3rclTmuv+dsUPmHY27gkYa8/OuFQ30Zt2Yl4NvRO159GmqXRc2zOtS26NM+0bDO5zuPEdL+A2LM9XedNPj8H5hYvf59/K4Tjv7mb5z3m6ePMrqNy3Ke7ljZrkTbYPO1kH9fs1dHAPWx+CyVMd7ve4eFvD3zMCIcY7WvZ5K/ejTUfh1y/gMRCrceFGcUBxWBsTtzhM8otjnDxweV7oAk3iM8xmP9I1lnP2mLM/iM/D385u8vAmtA96ifMnX14f0ebL2snhfWdotijOpZZUuOv3s5RqIlW2Gg9M2BxPD04YgmfPURf2U1Otdq3/XXN/t63JU5rr/nbFD5h2Nu4C8NkzKqU4vGR82zOtS26NM+kTvS5zuPWfsPiDHXYn/j83+AYvX69/G7Tjj6m79x3m+egdwbkWjHUNIMM8tTU4M6z9ezVMZ4v+8dFvI2ehZ6Eo6x2tczyV89JFAZ8YbCrQdl2WXKr5n439Aj3/URGCWqqvh40ujsHISK+SVqqVDyoC4M3atpxxKiBaUYlrqzpXTjKtKnR1uo77J9oEdkjcf699bZEYk696bd7bEQRn7LW5iehoJhkzGkcJLfNtcisIblHvVxjdtLpqI7uxx9hVYv8WmrR75PyJtgWi+r7RHWdotijOpZabNhYXq6V98b0PdyFOCrz0kQJc/tngy0vjmQKBlcosYls/HklIZdMKTZwzVJL7J97zXG1n7B4K+PSFkDHfvM+q6Vc62kE2rf9pzbanTllKMlaxK21aW7b+8NeLLeZgsu/3DMh8zSMCmjvpzFZQdGzbM61Law2ifsS5aquByeyPbW5ctVH/DaV1fntt1uH+wMvqy5Rzhk8453HhWEKNFjLpaO0rIZA2PMzd9J9Q2z/sbn/wDH330aqfe7Pnh3lHHe11jqN08/Y7HbeX7qY3iP+aurj/yNZw0R8nGbU4gvB2eo546nXal8b0hMNHxn9fcslTG+pGyGGk+NZjpSR9m3eFC6ad6ez8JIzo99PZOkHtJORmWU7cVl00POfyCS4HBI2D6idwzsSHX4jMoqk5eqCy5UERHLnnsWqK9A5YVXwb5hK5KzkgF7O7rakzB8v0ZsWTXU6by/tBR5Tz2Ma1fdO+Ajr/drpM9YIR7rH8k6tzXA/sqZbhE6vSIZW8g/rBGZo+gaB1svq+f1RSTraMConocO3Q9zNm1x63sD/l6OAsz63HWTr8Nti29z2y5irvi+Hch9Mx6u7UXt+yPxL3PQmJOMa0/qRkZ6l1puJ9Yj29uSXYGnxIepLIf3vO575u+J9u52rGpY5bcMPqNuWyirr/5ldI7Vc0PN2xeec1vxY6qWnuaNgr1zECpPPw322lqUHrDVtazVaiTsQJ+VbWX7I/2kF62PoUbP216+Sk3BS1mZaE5MRFFXl/MFvmwqrjj8UdO5QX3JeGz/9YMoHbqH02+itIO8fIqvvhADHOn9/8lSUrfgwdXLgLfOBzavDOh5oqWVl5aHh755yPL7i3aNW36uR9XVN8OR4PSTWPrXc5Fx2NGqnioQ2Rlnqr+ljz6C1DFjUHnqKbDXboAtKwFlB9appc5ukeN1vm6l/ReX7ok99zgNZSNnDgzx1kd/C7jvkh33WKj3VgDpGN5/gVz/l052xmvRKJ0CnPKS83/PfYV7AcfcDxT7tvD3W0ajfif5TjoXGL6XcZ39tYnnfqM80vOAtnrX19ay/bF8+p9RVLCbd9v5uDe8xjJf70mSz8unYFDlF+bnG2GUZvl0YNsGYKufZ39iMtBjsoRf0pDBcZ0f36ujD0bTMfdh9pd34PuqBZizeYvyK6t/N/3fbofib/XNbu8Nns/S3fJ2w/VTrsfu+bt7Pfdv+uQy/G75O5jS7u4SoaVkAm4oLsfcTV8iq7vHMO/ZBfnYtXgKHHBg6YalXsUP9/zY1zwFbY2ofuYw5dNW3w4lZ32I7JwB8JyIQHAyCrc+Gk0cM4uPD89Bveqbb7D98ivgqKuDLceG/J03ou7LXCSndwPJaehq7oKtcAjKHr0byC1zm/BkTJ9umm5cEqZInzFLPNY/knXeugYbqxdhfXIyhpZOMb6/LOQf1ns0iq5xsPWyel68jG2G9Yyi6xxPmPU5z+2x0je1KNBm0aE9jxX8HReraNcsNy0XD3/zMFau/FyJs4WNgCPLgTEHbnQJRj/PK0DStiRsyIEKQCW+TD2FWz2Dkwdje9d2w33jh47HgzMfDOjFJZj+tXT1UnSkdyApMQndPd1B903PvPXRygWr/cEl0FVVqbmtM/hUryAn1pW9RghlD9yMpuZ1yB8zETXtaahprUFtay32GbmPpfIb9VF5ubvngwuwuWaJEuLFIiioF0htHJYX8LYGtC96EGk13xge2l40CfWH3A9HarYqz7a6b/HMghvwfvMqlX9WdzeeaHYGmvHCj4BgGvHc7AfAgn0xZ8MG2NbrRAoAHUP3wtZfPa7KqEdL1/RHjn0uQkN7A1LbUlUAVO14s2vcuiUZFR8XIEEptw6UHbwZg/K7YC+Yjsr/tMJeXeu89v94AB3/dyGqXquFbVD3jkBkQ8YA+50Ne9Y+qLzkBiXwZ152NAZ1foq8TSvNf1CPdbauwfaN3+PJH/4PP29dEVrfjUda64HXz3ET2cTSsn7m3SgatZvlZGrXrEDRF9dbMpQI5Acv/Vjl+r+1Hu0vnuYMnhWouGdSJs/xwm8Zt67B1hWfYsgvr7kLxPr0Ddo2oP2ec9revj7nl1fxn4bv/Lad21gsYqhubuz1DNi6BltWL1XPFDlGX38JpCU+WQuKJ+34sc0KrvxtwKtnAu3GcwGNykE5yDl3PrK7u3bUW/Cc1+vbRXTv1UvVatKCvFyv+b/2bB7V7UBy43rvd1OPNv5hyw+4ZdEt+LH+R7/tK2lXrvsEq799Rv3/da8lrX78V3MC8SVbvxY1KalYm5TgNs/QyhfqHCTUOZIEIhOftuIeIR4tbZsp3Ea20WTAkWVGHeef6ZzIDu5C7pjtqP95MLrakp2Tof98oH7R1iZKHatXK9GWEEIIIcRsfkHh1p1Zc2dhcd1iFYlY3CE8/FynEmll7iXBkJx+NZPRndmNi85IUaJtKMgyvsnDJ+PxQx+PaCeNlMgerHDrEvaOO9w1t9W3r35uq++ngeZjVG/9NQ7bdXj+eGDtPNMI6WJ521EyFVuPeFKVx7MMj23YhMkGkbkVYn07agZw+humdTRrE6O6Pr5hM/Zva/OKfK4vox4tXX/tZlQOX9dYRFsxu3VtX5wLe0uyU7QVC92PLlRCT0ttKlKz5fwed9Hn9Dd2vPNU3o/uNZ+4RU8XW7Yl6el4fvyxEb+/+oqI9N14weAeFRGso3gy0s55z3Iy7U8dibSaxe73usk9Gsj1MhRunz8ejrXzkGAyrvjEpEye96mVMvqts9H4F8j+CPZ1sx+iA6m/ZeaMdLMWNkNGqR9zCjHu0p8DSt7XWB8ogdabY0/8aZD0cRskKSmtvb9UO306bfo22ynayvIg2Z7S6jpWJrkUbQkhhBBCAlsqKlY32ouMuEcYO0PnT/OjAtfSbNk+ON1/tGR/SF6Sp1iIxBuydF4/t9W3r+fcNlLXOCzXQZb/iiWZD3FFRFKxmktqqvAqQ5ndrpaYmoZPkeMkfbGYCrGuktdUA9HWs4xW0rLSbj6v8cEe91ZL7/YHbna6Vei1znOLEK/R2x7qnWe3IvVdL9oK0p5S16rKeQPi/opI340XTO5REUSVNavVe2vLL87jPe91g3s05OvVW+agRFuTMnliqYz+6vzLJ8bjn9X9BuXrq74e1nxWf2xJtNXGWlldIdaf/UGg9ebYE59QuA2SpG3r1aRFfpHWI9/VZEZM3wkhhBBCSFCIfz894tPW19xLllSGC1nWF3c0rOvzua3nNQ7LdRCfjRZJbl5v2M8sEWB7GNXVSl5SRitpWWo3H9dY3CMYXnvbNmttqrWHn2PlPh0I91dE+m684K8/Wb23Akgn5OsVwLhitUyeWCqjv3JUfxnafoPy9VVfD2s+NV8FnL8s2e8PAq03x574hMJtkHRnjlB+v2R5kR7ncqPEHb5RCCGEEEJIwJRmlrp9r7Il+5x7iY/JcCG+2OKO3JF9Prf1vMZhuQ65Iy0f2pU1wrCfWSLA9jCqq5W8pIxW0rLUbj6usfi6Nbz29kxrbaq1h59j5T4dCPdXRPpuvOCvP1m9twJIJ+TrFcC4YrVMnlgqo79ylOwX2n6D8vVVXw9rPsX7Bpy/+FntDwKtN8ee+ITCrY9fNMQMfUH1Ai/zdPFjkpc3yhmsQVtedIhueZFs7xyEAYksE1k9N+DlYVbTMGvzeCFu6x+OfhWmPLVr8EXNF8bXwldZ+6MeUcxA6M8DoQ4DiUCuh/5Yz/P8fe8vND9pnv7SjPynyTYrx0UrVtq8PLtcBdsQP29CS1uyCkRmNPeS7dvbQhduJS/JM5ggHUZ1Mu1rqAypD8rxr616Da+vet3tHK1fGPUPn2z5BfZVy1C5oMTv3LY4tU0964rT2tX/xa0/Wn7ueZbJ8xqH4zogfyenz1WPNL0YfTAKd5vqVYZKm00F0jK1hZXjJH2TQJRmbW9UV8lrYXq6h1MB7zJ6XlMr7WZUDrt9sPn7y8ce91ZG73YJOGYf7Kyzj3K62qO3/bs9HEBIe0pdS8tmRHWQSKtEpO/GC2b3qJ97K6B0Sqc4rUd7xybteo209+CA1jYV+MrX9dLfO+p/q+OKGSZ1C/S+9tt2O80Mbb9WPt07TTj7uvb80D8zjOovbmSOb96G45q3YWRXT2D5iJuE7/4NJKVYOlzG322JNhR/djfwn1nY8N1LO57HPt7tXNfOzzulv+d6oH3T6HpIe01v68BxuXu6H+9RtmiZ75LASXA4HKZzhXh0DLzrY7ti/PDxytnJ8oblhlH9LEfeFUf+vQHK+hvPYBVG+JrgBxKx0xQfESybEhMtR/mMpGPw/iKQKKfhCEDSHwFXDLf7i2pqkIYvLLWBSZ5Nx9yH2V/e4XYN9Nfizv2uQdbblxmXVYbRAOoRTUSiHwXTnyOJUb/xV89oq0N/42+cNQqq5fm/r/P9Ecj1MDpWT05qDho7Gk2/R+o6BxPIqa/xFTTEXzAtq9fY6PpMyJuABw57wLDNV1asxP2r78fKlZ/jxhe6UdgIOLIcGHPgRtfcS0RbCVi2IQe46dQkFaDM87rq8bVPf/39BVHR6rzNvk2VUV+niYUT1d+lG5b6zTeQPijtd/m8y93S1fK7d8a9gffb3mei/btPUflJvlO4y+xB2YxN3nPbkiKUHTcIts2BRU33R0TG27YG4LWz3Z/LekZOB058TpVVrqPnNczq7sETzV3K76EXITzfjepakpyBG6rWYEp7h2kZw9FuZu8vYmlb8XEBEhwJKkCZ+LoVtwn2gumo/E8r7NW1zveaJx6E7bOrgIr5/svZ1gD7K2fCtu4z1yYRw9/Y8yjcOPO+AfMc5VwhBIzu0WDuLaN00vPc/ZtKukfdA/vbl4TWJ32NK+XTnc5S14U2PlrqU/7aLoT9tbW1yPv0KqcfXT/vSQGN0xbe+WQs3t5SjZ53T8MuzZvcTt9euC+aDnsIRaN2M0xePZvTOoAnDgQ6mhEqksKq1FTs29Fhfg0DeKf0Nc+RdOyvnhVQ39T6yfdV8zFn81bll92tnEfdA7x7hVvZVuQU4tysZDQnOW034/m9JhaDk1G4NRBuk9K9f0nTR/VrmT8fVRdcqERZJc5KsAb5RS9vlLJGUJOiujqUPvpI1AQlC1W4DSRipyk+IljOKhwadATJgSDcBhMZckAItwFGNQ2LcGuS54rsApyal+7lGF67Fi/Ut2Fc02bjsgoBRmeNFiLRj6It0mkwwm201SHehdtArofRsYEQqetM4db8+iQiEVOKphi2udaPvn7pSaTfej8cBUOw80v/dpt7ratvwPZzL0PipgY033IBhh9ylLI4EYsSWUmlLTPU/ve3zzNvo/Lor+l1y67Dtw3fBt3nPPEXSdrsRwl5CQu43/Y+E1tqklG1IA+2Qd0oO7gBtrH7AtOvcJ/b1lShdFoDMoa3GacV4nNPf03CZq0oVkbSTxKTgcZeP4LlU92s3vTjk1cZ9Of3dDmXEVu1BvSBls9T3z+F5ZuXq74jVlYT2tqRmJCInhFTcPOvXwhru5m9v7T8XI+qq2+G0m1lGe5fz0XGYUererrEXv17jbRJxULDtvRi6xpsrF6E9cnJGFo6ZcBaoUak78YL2j0W4r214ceFyh90/o/PAlVLvefkadlAe5PbdkdCEhKCGbN8jQv6+ghB1s1Sn/LXdkHsl3f+1JrF7kHYdGP70tVL0ZHeEXhft/DOJ2PxkP/9yV007kUsDTtKpiLtnPcMk1fP5n8dYB6QTKxvD5wNjDtOfe15fCoS7MaBIbX8VBF9PeMCeKf0Nc8xSsdq32z7vyOQtl6uV4/f/i6rHhanp+H8wqGI9/eaWBRuw+cMbICjj+pXNn26mrykjhmzw6K2d7Czian6c8+iY/XqqBFtQ0aLXumJPvqkv4eRFj3UJI2qjuHotknrmbT5AJ4EaZEhPRnw9ffTJyz1qzDmKVY1xRnDsd6jHwolne3GVjdaWY2IZD2imIHQnwdCHQYSgVwPs2MDgdc5cphdnx70+L23CqcfiezHxqIhM9Nr7jVyCGB/8RWvuZekpU/P83+zfYFQvb0aX9eHN6CJWR/0178DHp90z8SMom6UTqtHarYdtkE9QNUi18u8mtvefxM6HvgNMoZ7WIWG8bnneU3CgpQjgLJ4lSHA8wPJRxY9Ltu0zLVN5h+uOUjDdzjb4rW02m4ZJu8vGWOA0qzhSMzORk9Tk9s9pIm8bvdWIG0yZDSGyQcDm4j03XghTPdYd3a5GIwD6xcZj00Ggl5CsGOWrzJ77guybpb6lL+2C3S/hXf+4kEBuuEJ4J0vubHCOP9eAVXtM7lWqVWfm4u2QncnUDzBee7qj5FoN/kBUpefz/LKassA3ilN5zkmbWOpb275BemVXxiX06AtRPgTy1z5kVDKxvlubEEft0H6wJXJi5kbBNk+YETbcEX+tBBhNl6jssZtZMhwRZQNY55m/dByhOm+qkcUMxD680Cow0AikOvh79hA4HWOvntL5laJQ52WItEy96prq4tY2oFGkjY6J5BnYkZRh1o6b/T8sqW0qP2WiLPnXiw9a8zeX2T7oL32MryHBtx7DRmwJG0L8p7hmBXZdzOL6Vq6fiZlsG3+zv+5Yokt1Hzl/1h/ZQjyndJrXA+lzf2da7FsnO/GBhRuAyQuI4SGI/KnhQiz8drmcRsZMlwRZcOYp1k/tBxhuq/qEcUMhP48EOowkAjkeuiP3XtND4Y0O1x/9ei3y19/6ZK+u5aynFuWZRsh2+1L3H279jfD0yMXyyDQSNJG54TtORxIRPUB9Nzz1x9lf7Q8ayJZVkJige7MIJ/bA2jMisp3M4vpWrp+JmWwF+zp/9xSp+95FO/r/1h/ZQjyndJrXA+lzQN5LvsoG+e7sQFdJVhE8wESq8tgQvJbqUWvNPNLY2UJiJ80SguHosbEf6G/No8W367BokWGXBxg/aO13mbl8toeRL8Kuc4+8hR/RDUpae7be6lOSVMO3YPycRvlbhLC3Y+C7c/RVsfd8nbDyoaV6NH5jIr150Ak21Db7xWFWfe/Fsk2UL9ovvrUXgV7KUuBmm01Th+RWSPUsW2fL8SVr/WgOR3IbgMaMx1467ddaMwEcrYBv349GTnNCdg2CMhsA+48Afh2dKJXukI4rrfUvcpRFfX+D42us9G1tXquW/23Oevv61rWffQuWq9/FAmFBdh616UoH5KD4s5OVFf8gsr6RjRccQ8SN23F2kHdGPWrE7zSlrbVf5el6PK/pK/1D89jfF0Ps/bQnz+peBKmVnrXKRT0Y40+L7kX9hm6D77Z9I1bJGlZFSIvYqVlMwLrX76ewyUTgYrPseWnt7Autwj2nBHYpWQ8cmuWu/s+1BPm555Wd8/rF248r7Pk+9XGr5D59WqU3voCeobmYvAT92Fkbo7TwinQmBayFLb3PH3E9vKGdSoC+H8bV4T8vPTyX2vbHlxZI8mWX7CxerHqq7Hq79bq2EFCwOh+sXBOces6oHCU+Zhm4PNT2bCVTnIue18913+enmXz991Pnfz1J7/9TdKtFNcCCcrfdEVy0o7jO+3OPKXuUmfd340tta77UHtOqnNcz4RPAU+fqb1je7G/8vXWtdqWgnXJiR7pel+X1hGT8HVbLUY0J6Ns3FRg2cHmbugkDZNrlT/598Bnf4Gjrd7LzYH8dN+Tkomk0Qc5y5yegv3TcpDUbhykVDtHFVG/sbcdtHbet2x/pK9f4vOdUntGS3vkFe/nfR1N2sbl49ZXfzQ9NxE9qVlI6thm6ONWc+HQZ+81Pu6LWH8m9CUMTmYQnGz/kfvDAYdbxN64j7oXjsifPtJoSkyM6wjucRuVNlwRZcOQp1EEULdrsd81yH77MuOyCn1djygmVvuzUbn1xEIdBmp/MEojJzUHjR3ek+5JhZOQtrUFv31wOQobZQLrUNHSbYO7UDSlHrWL8mDfnuzaviEHuOnUJGzNck7PkxOS0eXoCst1j9V7IVwY1V+uj+ccS7uWYgF94wvd6rp1Z3Zj7IzNaum+fXsifp5XgKRtSa7rNXrnSepco3R84XlMINfD7HpeN/k63Lb4tpD9K2tkpWThycOexIPLHjTt81nd3V6RpO0jD4TtxGdDj8ielgN4vNAuTkvF3/KH4G9btmJyu4nLhDA993yNxZG8fyTfy+dd7upTvvpj5YJS2BvtsJWWOoVSI/dpRhHHDSLPhyPStyuIWFUVbDk2lE2rCqyskSSIiOnRRryP5X2C0f3ib0yxeI+pdI6+F3jncnNR0FeeRvmk57n7EfX8LukcdQ/w7hWW3jn0/clvf5PyvHImUDHfe5weMgR/ra93ezaYIffh7IJ819hzaMG+mFNTBVvNl+4HSlucOw/ILVdfjcqnzt281es+l/T3KJ1m+B5lOPbtdw0GvTELtvXuvlt/zizAqLwxsOl9uuqulZTptvfPxXXfvA+zO/Kb9EG4qCBP5Vds78KrtZuQKcHlTGhKSEC2CMW6Z+zsofmYu8nZPlndPbh3ayMmbW9xq1P2qa/j3i/vwfHfv+d2HUzHvbYG2F85M7gx0uAZLufeOiQP9zd3YZfmTV595IqhBaoNIj6Gmd3TR90D+9uXxPQzoT+Ck1G49Wi076u+x+4lu6ttjBAaocifPtKI9zaP2/qHKaJsOPLUrkFSYhK6ewwsfHyVtT/qEcX0V3/WIoQHanFrFu1+l7xdcOeBdw64e1IfST3Y8/Xnen731a7BRLKV9Lsyu7wisXsiaWemZMK2pQkPPtuuxD4VsUSFTHf/K2LMRWekuERbI4KNuivlvW7Zdfi24Vuvuu+duzeeOeYZ0zbTp6Htt/K/lTJpeJ5j9Xpa3e/v2l8z6RrDayli2cPPdarr5hTbG1C7KFeJ7VauV6AEcn191ef63a5X/fPqz672stYPBhFpt3VuM7XifWzDJkxua3dfOucZ8TqQ+1x7fi24xzC4j7y6LkxPU/9PaWtHktveXqu1s98Pqh969iWjdg7kenk+A7R8/CH5eorFvvqjEkj/84G5EGoUcdwAsaxqHzEJXx16bUjPSyXeHne4U6QNtKxBjBmWef54dK/5BEkuGzan5deS9HQ8P/7YqIxobqVPMiJ7mDG6XwzGNMvnHHmXGtM22AehcLepO/b/81dAlVhJmozRcj8WT0baOe/t6AufXOz3XnYYWWemZcPR3uS+SqHXIvPUvHSv+eY+efvgtvG3Gc4dZP+UoinO+0XqbSBASxkaExOR2dNjaVm1ZoF5fqHTd/zjGzZj/7Y248BcIrj1Xgej+0HOlWeS530u6V80fLjrOVmc1q6uyw0/PYO3Gn5QQbs87ymhdv0C7N26Xf3/dXoart3a6JW+yyr19Dfcxu8vKqqQ4XAYWt4u1NVX8jsrrQyXJhdi24o3kNHd6XaOlH9dZj7G/Ppx9W43a9ldhs8mCfYlfmPFclQsbaUO91euh636C8vjnpRf6lzc2aHSEavYQMaYG/57KrbWfomK5ESXRa1cE3leJ3q0WUPxXth24jORf68xuz/TstHd1hBTz4RoEG7pKsEDvY8PRgiNUORPH2nEe5vHbf0jFLU5mDz9XoNAIsnGObHUn31Fu/+x/sd+KdNAwKxdg41kaxSJ3ShtsUosS7MrC7nKT/KVcKEQ0bb3rwgbO83YgsHpw7AVOyL/hqus1dur8XX914bpyXZJL3kAT8P8XXvNx6fntcxI73K7bpUfFajtVq9XoFi9vv7qU1Neg6EZQ8M2XviyHJall4bWVMFGSBfkeLEsMorI3itImFtw9QBVi4LL12I7a0QqCrZZvr76Y9m0DbCltAYWRd0AEXUkMvi09CIghDqJewSxtA24rJGktx2SDF5Ap7a14fbKeWG/ltH+HCMB3C++xjR/5whjDkW3/occOcdkjNOfn1a90JVncmOFpXs5wagcBsv2Zfu4xg0ozhjuEti0+abMDb7e+rXh3EH2q2dN5WcoNimP5JXbY/1Hw+TecV2Ex4Tee9KU3usgbgI87wd5Jhmdq6Vf3Nnuek4WF09Uafzni+98zg+QnIh1WZk+01eC+JqPUV3xmeu8Ka1tyNRZybodr6uvtL3k93TbWkzf91SM/+5lw/KP2bYFNSmpsBvUW0PScl1LRzeqK+chrdrb37jZuOcaY6TOyelBzU/+0/AdkJ7q2uarzfKqlyEvlMDbVvB1f7bVx/Qzob9gcDJCCCFxT39E944HItGu/tLUEJ9islRYrM6MkO2y3yzyb6hlrWurC2t6A/HaGx1jdt0CvV6B4u96+KtPbWut5b4ZKtJG/RL5uw8is1ttw3DfP2b5+uuPpnUOpi1Dbb+GdcGVNZJYiLwe7WMh5wd9gL/7xajvRuIcg/OTtkWmf5o9y1Y2rfR5XkPtsoiUxe9zRahfa/rc9pe+9pwUAn1W+ku/sW5Hm+zZYeLKx6A8GnXV7m4ZPGmo/TqgMltpD/24F+oYE8w1ifjzIMj5RCw8E/oLCreEEELinnBH9yaRa1d/aWpU2ZKVf0dZKmyEcwmxLCmzZvUaaFmHpw+P6z5l5dobHWN23QK9XoHi73r4q0/RoCLLfTNUpI36JfJ3H0Rmt9qG4b5/zPL11x9N6xxMW4bafrkjgytrJLEQeT3ax0LOD/oAf/eLUd+NxDkG53dnRqZ/mj3Ldsnexed5uUXjI1IWv88VIW+U6XPbX/rac1II9FnpL/2c4Tva5LvUHVan/sqjMbxkf5/H5xZNCKjMVtpDP+6FOsYEc00i/jwIcj4RC8+E/oLCLSGEkLhHorWLk37xJ6VHvst2LtmJnnY1S1OftvgHbW1PVQGtlJsE8Wkr6P7Kdtm/vc335DbYspYMLsGEvAmGdZftA71PWbn2Rse0tDmvi/LLKUu8D5GAUF2Wr1egWL2+/upTPKjYb98MBOnDZulU2mwqkIeXPY0cL34Ig3VXoEWoNkDuHMkzIvkGeH9HYkzW8vXEV39UQb86B/luSwt9wRGm9rPbBzvLFGhZI0lvO3R7LBiXPrQwPR2lZTOifizk/KAPMLtffN0b4TzH4/z2kqnq/Jb589Ep943BOY1r09C4dseydv3ifPmhpKUuXQUsU/e3R/oSwEp8oeoRH7YyN5gwZILh3EH2q2dN2YE+x+mGxETvMdoEOU7GdFnmL88VuSeNnQz0+rgdMtrwftDONbrPJX2pq/acFLQ0pE5G47vV9LWxs6T8QNf4vWhQumoDh4/nmObWQMtv/LgT1TXxbDf5LtuLy6Zbfr7L/pKyGaoPWR33Qh1jArkm4Xxe+8TX/ZmeF9PPhP6Cwm2UIL5JFlQvUD494h7xibJ6rtO3UDwSQP0HZL/R6v/LJ33SD8za0Gu77roMyHaPMKZtFoH7XQKK6D9WyyKRVbWgCBryXbYPRMzaJ5DzfX0Pd7vq0zdKU5/2vybcj9teTlQBhRy9gciUkHGwU8iQ77Jd9kvUeAlApBfMQi2rVt4HDnvAsO6y3bNO/ups5X8rZTK77lavZyD5Wrn2+mPkOsj1kOsigcjKZm7BoHy7+ivf9ddrz/w9MbFwolvantfOiMHJg32Wx9c44as+WnsYHeOrXFkp3sEopF4vHvWiz3QkUrcEfXFDgrRIhO1Q7nM5X6Kye7AkLVXlKZ+fcwr95htIP/Qsn7/729/96PkMsFp/SXdS4STXd1/9UYJ9SRCwyjPOVEHBDJE2kbZxK9x+wDBnEGSNhADbzzQwmZRFApNJILJAy2pCoG1oyAlPo2eke5+Svvv6nkdG7fPVVd/eOcpdu8/CngV7xs38oE/wnP8Z3S/yfeZ1xvNEOX+f04HSiV7nrD/sb67x263vmp2jp2Qi0iafg5Z3/o2qCy5Ex9WzYR93PpA/xk20rVuai7qlOS7xNiEl0yXaip/pqgW5aNnjTuf97ZF+yVkfeo1xEnhM5gZmcwfZ7+pvPsbpk4sKvZ8NJshxMqZryD3ZNcLA+lTuX90YZTRGy7lG97mk7/mc1NKQOunRjrOavn7s1I/f0gbNBvX9Nn2QW33197BcE89nm3yX7b7qbTZnTDv1hYDGvVDnylbbLNDndUiY3dPnfhpzz4RoIMEhUT5IQBHdwhndvKmjCbPnz3Zzdi2/mEinzU7NjtorE0gUa8u01gOvn+PuyFp+qZGbPj03ImUKNvp8RAig/kb9Rn6dlQd9y5aWfq9LoBGd1bXLTfeufy/yq2X9zLtRNGq3sJXR7N67btJ1uG3Jba7tWd3deKLZGUhAQ36tlQd/c1JiTNyv4YggHew9b9bOd+53DbLevszy/W7Up/RYKZvV8VYm+uJfKZTo3sSbSLSrlmZSYhK6e7pdaYuVjLxwITsTjsYmJA7Nw+jbLoZtWAHsGzej8q+Pwb5hE7pzMpDUtB2p9/wNNbsPc50f7rIGkl5Enq/9jJX6yzHr3n8D+X97EpuzHLjp1CQMTu/Cr7J2xuHjz0dlfSMGXfZ3ZDV04s4TEvHt6ET1wqQP4iX380X7XISG9gbkpuXiri/vMg1kt1vebrh+yvXYPX/3gMcJq/XRH6P/Lnju+2rDV2r7voX7uqVplo7W50d1O1QUarXsMZwWNCKSVCzElvYtWJdTBHvuCLd7TO0XH3nhztfC/R0MgdxX2vXI+HoVSm99AT1DczH4ifswMi/XVWexXtWE0NJHH0HGdG8RxYW0Vd1yYOkT7oGRCvcCjrkfKA596bM25tmGD0fZc886A5EFU9ZIsnUNNlYvwvrkZAwtnRJVz1d9/zCbk+rnfuOHjseDMx8cMHO/aHjfqZ16q3Oer40tg4YAn9zqPU886h60v3GhM4CYAWIheW5WsrpO+jlnymvnm55jhAiwFfOL0dXU7fzhV34EGdzj3P5RPrp6V38kD+pC+cE79mnBAdU5f9wFtpMfBN483/3e753vVtqbfT5L/D1rttV9i/98dCV+afwFX/daksqPk9u7tqsAXOIvdOyQcfjT7n/AYOmrPV1AYjI2ttS47kPBK4/e8V9R7rQ+NsKwfL3XTwJ6rU1KcO0zG4N91dFX+mbPnqqN3yHljT9h2EZ3X8HthXsi7cz/+m3zmsr5yqetuEcQS1sr9fZ5nQIc90KdfwbTZhHHLP8ofiZEowZJ4TaIRgunMDhr7iwsrlusogZqiJm7/GLy+KGPI1qJyIvl88cDa+c5ow3qzenll5nT3xj4wm0A9TfqN7LcRH65vH6362NTuP3kYu/665bCdBRPRto574WtjGb3XmZKJrZ1bnNtf2zDJkxua3eL/d7V+8vg+YVDY+J+7U/h1qydX6hvw7imzZbv93AIt7E63pLghYzUMWPQsXq1+iuChoYIGNp2+dtvQkacCLdWkXu07fOFqMx3YGtWgts9Kqz6eRGKN3Up0dYI/f1sdL+bHetZBo4TA49g7yttHNGPH57jiKXxI8Q5bp+WNQ7xEm4N5qQDee7Xp5jcC+2e83yzeyYtG472JiSYjO3666Sfc+7WtNn0HDM6xa+6ToiVQH9On9HJSE7vgqz27mr13ucSejMSVHnR3hSRe9/fc06Ilr7aZ3Mb6TcGRkAuwTxM4y0hfa1B0lVCPyLL8MSiw3Owle+yPa6WYcvSFRlkPR888l22D3S3CQHU36zf9KBHba9p9S1wRSPJjRXG9e9FJlrqV/Iw9QNf955Yb2nby+x2HOAh2qryAmq7/Jodl/driO1c0tnutGDuw/ud4238IQKFCBjaXz367RQyogPtHl02Ci7RVtDGWPlszuwxFW31x35R84Xh2GN0rH7s5jhBPDEaPzQsjx99NMcNS1mJ6ZyUc78w4ONecJvn+7pn2up9CrD666SfcwYq2gopg3t6LW17fUV/tMPfdfkhW3otbb33ada5Wnkjce+bPa88iav3FK3fmBEPmgIZsFC47UeqtlX53C9m7nFDwzrf+8W8fiATQP399Zva1lrEGknbLPb1MPUDf22oUWr37d5fliDF5f0aYjv7a9dI3O8cbwmJbqyOy1b4bvN3lo/Vj90cJ0hEiPc57gCbk3Lu1wf3gr/jLKBdJ79zTj+IACvWtHrku2z3tc8SIdz7gT4z4+I9xUq/4XhLYpSoF27Hjh2LkpISr891113ndtwrr7yCqVOnYqeddsJxxx2Hn376CdFOaWapz/2aH7S4IHek7/3iE2UgE0D9/fWbokFFiDW6My329TD1A39tqFFl8x29fL1uf1zdryG2s792jcT9zvE2PpClwmbBd2S77A/m2L4g2srT13Usbk3D3mssvuz6wTOIkC/0Y3c8jhPx0O/6nXif4w6wOSnnfn1wL/g7zgLadfI75/SD+K0VFwh6nC4REn3us0QI977Vd5mB/Pzywkq/4XhLYpTQRrI+4NNPP0VPz46J/Jdffonjjz8eBxxwgGvbf/7zH5x66ql46KGHMGXKFNxzzz048MADsWLFChQUFESkXLI8QX7p8uc42pcvl/LscuUw3cyXWtQ5aJblB/JLVt4oFBePNt3n1+m10bH5O6G1bH+kr1+MBEePtw8gC460g/GbEzV+BPN3cvrdMfN/pqu/v34zcczEHZFa+8kJeaDtOmzcVGCZQf2D6AdW8NWGeh+3lTabCkZh5uNWggBE7f0a5msXzL1i1s5VtlR8m5WPvbY1uC9d83GdQ71XrYy3Vsd1K4QzrVjFrA2MtlttLznuq41fIQEJriBO2rnFP2xExxV/Q7cWSCg3x/WskeA8a047BT0bNqHllj+jB93Ivv5RJBQWYOtdl6J8SA6KOztRbUtRQbCGXPUAHBs2oemWC9AzaW/VZ4zKZrUu2jbpb0Zprfnfq+i48m9ILByG0f96we05aTWwkD5fiTur/9+zzSKNURt41tFm2+6q49qt9Wg973LM3ujAXb+Dcpeg99++S94u6OjuwNqmtXDAPKaudj8XZRSp4GMrG1aiRz+nMDhW3x7+xglpS4lU7qufml0HszHG6n1i1Ic80w/0mngFtLJtx8bqxfimqwkdHWkYe8MLcGzc7DuglfTVSglik+AziE3I6OeOElfZ6pwzlGeIwXx1Yc1CfL/5e+w5dE/sX7Qj+rrpGKalMWIKULXU7xwv0kTFs6m3TWS8XZec6LssgbxfhPpe0/t9WOEowzn5QJ77Rd37jq/jjHzG6tBfJ6E6JU0FLPOKq2ABz2Bjej+2EqDMzMetnGPJx63Vd2Y5Xs7X3Qfa86q2cgGK7R1KqNbqrCEu38q6erBz3q4o27gKEOtjz/HT8x4zytPXmGt2vsd5XtqBlXrnjUJFcpL7mOWRvjyzpO4quJX0m+F7A3XfeiUpM4d20RqGjPb5nLZSppDGokiOaWRAE3PByc4991y8//77qKioQGKi89es8ePHq89TTz2lvnd1dWH48OG4+OKLccMNN4TNMbA41d5m34b7V9/vNyq5VaxGOe9Pp+G1a1ag6IvrjaO/S/cxiAxqGBneJIpo0zH3YfaXd+D7qgWYs3mL8kvkN62BGBSmrQF47WxLbWnWby4f8QfsvHSOtesRbYHLpP7/PgOoMLDuGTkdOPG5sNbBrA3/PP7POG/uea5I5VndPbh3ayMmbW8xjCzcX/drfwYqC7WdtXYN5/1upaxm1/y6SdfhtiW3GY7DLVt2XHcjPPOKpjHd6jjnGZTFVxtqx5r91drgkg8vwdf1X7u1wdnlZ+Ppiqfd2mZi4UT1d+mGpaZtL+murFiJu36+y+04ITM5E9u6tqn/hzQ7cOML3ShsFIupboydsdkV5Xn1Z8OQ0JyADTnATacmqePNjv15XgGStiW5jtX7W9XKJnheZ6O6yLaOjg4sb1ju1ZYT8ibglgNvwfWfXY+Kiq9c5XFkOTDmwI07IlQvKIW90Q5baalTXPPwYWl2j5khZbp3xr2u/mj03PPVR4zQ9st18pwjae2yZtUS0zr6anNf5KTmuMZqf+3tidl9adSee+XspV7Q9enq+6lW979//3e3fu+rrGZpGo1HnueGWh859tIxl2J0arbzR4GqKiTn2FA+rcrrmjQOScXYF19BXtnO3nO6V870fmYH+Lw2638aCe2NyPv0KtNo8O0lU1E/825nNPpwYTBfbSubiuNTGlDd1eJ2Xf5x6D/w4LIHvdpXothnvX2Z+3wsPc/p79LgmRfpYLnhejYFMy9wPVdy073aVZtP7VE6zb0sJu8M/uYIvgKZusprlLbntSmfru5PrJsfd3O/qHnfMTvu6HuBdy439WUqIu25WcnqOgnqWu13DbI970c/OJ+/JbA3drn5rZXtItp2tTlNOpIHdfX6uu3xEnrL/rgLbKc85F1ef325tR7tL55mPO7p3sHtr54F27rP3ProbSWj0NzRhDmbt7rPsY3w7Pee303G3LRTXzDWAHyd31vumvpW9dVMc/Ac77X7TqTXJ5q7nTEyDPgyNQW7djmQ0e30bezJ4rRUXDG0AImD8kyfp4b3dZBjkRfhSofEbXCymBJut2/frgTZK664AjfeeKNbZV9++WX8/ve/dx170kknYcuWLfjoo4/CKtxet+w6fNvwbdijkovDcPE905+/gJu9mLU/dSTSahYb/1IoWI2SaxIddEV2AU7NS3e1qTiTL+/qwZCi/XDzr19A3EXzFqfp4n/Hwi9xnv3G57WKkiiapsKtq498CrhZSCUCow+KWPk929Asovivc8bh5l3PUtelUn7V7uf7NVaEW307X/3Z1V4WcCO7enBo5mhcfPB9If3yHEhZrV5zGdev3+16n2l55hULEekjLdxKGyyqXaQCJurbYHDSYGzv3u43kIZn20u6Z719lqkgpkfE24ef61SCk6cFjAi0F52R4hIFAznWs2yCv0jO/hBL0qzULDR3NKu28lUeW44NZf/5wDDwkJWo0kYvJ1p/DKdwK9fJc46kJ5g2N2P80PF49ohnsXT1UnSkd6j7+Y4ld3j1Pc1i984D71TfrY7dMk58s+4b5X7o0ZWP4pv6b7z6tNZPtbp7HhMonqs+Aj3XaJwxG5P2zt0bzxzzjLLkrjzucOePAybXZJddDvAev8IUudufcDvkf39Cao2sxjJuD0dCEjo8o9GHisF8Vf5bpItUr5GckKyswD3bV6LYe1n4yXysZCIw/QqvOV6khdtwPZtCEm4/udjUivWi4cPdy2LyzuBvPmtJuDVK2xMtryPvcs3J42XuF3XvO2bH6bcLumNM36uNzklMBhp7/cXKqoHe7S0/16PqL7c6VyQ8cDNsbauc+3JK0Tj3C9Td/y/1dfilpyHn0P2Bni7nKpmNm1F5wZWwb25E6WOPulYrbPhxIZKb16MrawQKd+vNx4znj4dj7Tzjcc/HO7iMhwmjZqCxtQnZG5a5r2INE1oeRvn7pLfcNTMfUl/NNAfP8V4bIwTPFZBu5dKyMdj+dWoK/lBU6LeIhmNikGORF+FKh8StcBv1rhL0vPrqq0q8/eMf/+j1gC4sdL8Zhw0bhuXLzS0uxCJDPvpG80f19mrDF0d9tMZgH+RyXlROArb8YvxrnxYN0wh9pEztAWsW5dHh/OWsOOP/2fsOMCuq8/33tt2923tfdhFQREGKUgWJPbEhf6Oxm58l1qhgDaIioLFhiTXVErFGSWI0xoYiVbqiIG2XLWzv/bb/883duTsz90y7be/uzuvjs9yZM6d85zvfOeebM9+b5/u8g/4eon827cTVQch00IJkptGBJdIbtb4S9segYgJ1h7X+QhnyDK2sMf4B6WPOkd70fc8Z0A56R/hD4w9+1+kzyT92HcS5Nisn10hAa5/T9cqSShTEa9ucquUVzBwxWKAkg1an+jzLkj3lqcVpS0i0O7nTs/yJF2J5JpBDavTceiTYc9AAm+600rqFAuTgE576UKpP8exq2GK8p1S0yFsN4dBHLf0UiMzlsLV2K9cG0hHaAMrJguTM2x49ay1KZ820qq79SE8dLQ7NOqoEylPtZK3Ss9J+VRqPVF8urc2B4tnlin3ipy9ambuDnLOtzaWyJ215mIRs9KFYI8i0zSJgqhd+juz0+BMf8Sz2zPVY+fqIfyIbDXMT9SVLrtY+uRb0dvfXpdchu2cIWrfUdFdaFmHMadwfY+03QPsduXTS64J/y9p6hWek6RLHAEXJeYgdM6bvpan3pS0hlQ6U5B7j/fe8eaJHbRmjUPzmu+jZu1cUYsaVUsL9r1VHZV9hKuzBOYfn/s+RivCBL0M3+p6zTClly0FmH8vbCNV6KVw/vqfXz3az4GcTFfwXumxRqPIxMKwR9eRkQvzlL3/BmWeeiaKi/mDcfPxbq1Xsg7bZbHC55N8APfLII5x3m/9fmKccDnexyRuGNFtjMKyeQtZGlXyEDK1DXqbhwmBnLY6C+huM4sNPtmr1quqsCllew8Ge6WU51iJ7PXkSe7QSy7NwrtGTNhJQqw/LBgYj71Dro5a6hFrmwjaEa/yprf1IT0Op98FCt0yaDmrqE5H8IsTcbWnT0WehWiMEuF4VQpXFPsLrsWiYm9T6UqRn4VwP6t3XRPva2UBYQY5X1pcuvMNW6rTlQc/IxgUP5957EIBOHQ9Eu/WsL3w2MVS2KAr2uAYGPwaN4/ann37CN998g2uvvVZ0nScfo7AIQtBvJWKye++9lzuSzP9fXq6+6M6zsw33kGZrDIbVU8jaqJKPkKF1yMs0XBjsrMVRUP/hyCgeKUSrbNXqRZ9Jhyqv4aA/elmOtcheT57EHq3E8iyca/SkjQTU6sOygcHIO9T6qKUuoZa5sA3hGn9qaz/S01DqfbDQLZO0kZr6RCS/CDF3u5J09Fmo1ggBrleFUGWxj/B6LBrmJrW+FOlZONeDjLzbq2K9NpYBhyOJI/EzMDRAfUnhYVig61HR18HsvQcBKFTEQLRbz/rCZxNDZYsCyGdQ6KqBiMI6mE7bUjiEs88+W3Q9OzsbxcXFWLt2Lc477zzfdXLynnPOObL5xcbGcv/rAYVfYLEUDxVmUWasKjX2T4IaM6hKPhTjtjImTnRds0xDzMw4IPFtWW0IpF1amVoHGLIyjoL6qzGKD/YxrpdlOpTjIdyyDbSuavWaOmbqkNMfqayEv9XkyN+X+6skA62xO1mypzy1hARo77J6SZU6/GOo0vWOEVbwX+LrSSutWyhi3EplolQfIigrXhDvF0RATt5qoGd4fWT1uZKOsMDfV6tLIDJXbUOytvFH4VrWVKzRHJ+Ss4uecozIHaHJRgTSD+GIcStsmxabxMW4JQK8Dodsn8wq7tcX0XytFONW45ytqH/0dytjXSBEqNcIMmsRPsat8FNbYmwvcXo4hwCF/eFBLPZ7k7Mwpq1BHLNfoa6hnG9Jd+srNqDI6URO4QyUZIwK2dwUSD25Z2T6ko9fSfuAWcK6sNaDdN6oaJpiX6vWT9K/5LQtX5MOW7zLRz7lhQmOhGNQdvO9cNTWo+iF5wM/QWmwyAe1FtUjU8pjc81mJLXWYKIthdN/jkSrbC3atx9A+Yp/wJaf7yX6tHWgpmIDN34zY0bD9Zs74aipRdHi65E4hiYWkzfmbd/zvt9KtkZYL3pOUEdV3RQ+O+qUgGLc+nhBmPf8QfFftUWV11q+ErzjVza+r4ztDUWM27US2y0HP5sYqr2pznzIKVt+403e+MoSUlpuziZS0cOHg7NLBgYdBgU5mdPp5EIZ/PrXv8bDDz/sd//xxx/H73//e3zxxRcYP348nnvuOdxxxx1cjNujjz466MDAaozNQ55ZVIn9k6CFGVQhn5ZznsLd3z6ij+l2KDAzstrAYLHV1S6tTK3Riiiof6iYlwcDIt3WaJVtKOsVrW2MJORksGj6IizfsFx0fVruNI7YZ1P1JkV5UZ4LVy/ExuqNorKSY5LR2tvqI7564A0XcpvpdJeLi6XKszzv/SoHplYTqlOBJZdStErIpuUci20WX1ohWRZfN4K0jay2sK6xGOl37/7GVx9PsgdjTqrpZ6gmpxoRRxUV+S3g5eSthKm5U7Fi7oqIjXFeBvt/2ijbRiWZs0D9/tbZb/mdJGSVT+0lqOmYljYo5UHPLVi9gNnXLMjlyRonpCtqsW/l2qRkk+IbO70bwPJyWFNtKJld7tcnzRmxOGrlO0gvPtJ/vn77CqBUcupn5BzgwtdCN2fLlROu8mTWIl0lszDf1oQKZzuSXS4/xnZNzOdhXs9QXy/5/DbM/+4jUd0cI09C53l/0L/WDjUYcuXlNr5otrgurPVgqOQoyJuzsX3xnbl44n3OW9F1Iod88x3YRo7VV85Q2KtEALrWTTIypb3kgo0PYXfler+xyUPYp9ZUK0pmV/j6+uCXmXC1i3VAFiybw6qXEEr9znj2x+RstPc044SeXtnyW3pacfivp2Jsa624ncUzYZv3IvDhAv2xaO3pQFejcholH4Ce51myULARhD+2Otm2leb42BiMc3qQ6HKIrm+OjcWtOVlotZhV51Om3oVqb6ojH59ztrxctPaTu25geJCTDQrH7apVqzB//nzs3bsXo0b5v9mgOLfkqH3xxRdhsVi4RpPzlp4JhdBYTMFCluJoOUU1oOyfffeqHfFcwHHFt4oy+cgygGpl2zRZ0B1qZuFwQgurbaCMk1qZWqMVUVB/XfoYIoSbVTpcLNODQbaRrle0tjGUYLHBa5EB67pWeVG6zdWbuX8fn3s8l/bKj6/E9trtmLDfibvec6M+GVh6qQ0nFo/HQ0df5WV57o3H/ssuhbu6Bu3LfouWlmYUPv46TLnZaHj8VpRkpKGgtweVMbEobWhCxp3PwFNdi5alN8I9fSJcbhezblrbwtdRuI6gsTYxbSJeOecVbHnzT7Avexrm3ByM+vsbXiKyPhtIdWedrpDKn8q966u7/L4KovXKyJSRuHzc5T6ZKfWlWr9qAUsGB/77HroXPsBs48HGJlRdeTWSm3vx2AVmbB+lHMlLzU4Jy39k4yO67JySXbx32r2KekrPrj+8XiR/Kag/JmZPxKs/f1VWVqzr/G+L2eLTR4JWO8M/H9sV6yN08zvVE9OJmor12O5oRXdPLI66/w14auqUT/XQfF3a53BRO40WKGjNJ0fWQxvfy9/3zZ8hnTcZa5F1VetQuOpWFNXtEzG2E9N6U8FxsFviYD+00e8UXE/ORNSf62WhD9ccT/p3+dZVmNbVJTqV5oIJllEnc3IayLnJZ1v65Er29oDFpFyXv54JlG9kn1wOgImdrwP3N66bqweFQyj7v+u8L8gkp845R94pTbCNn6OrPMo/4+NrEVu5wW+vYrDIq9tcspMz8mf422iZ/R99vfmrtBi8WF2reCpT6qj362s1p63E7gjrpenLAIYOdf/5F4ir3OB3yrTNbEay280RI/LwkC0pnMHtdfnxPr2rS5RGON59NsxsxR+2PIWZ+zdgYk+PKD2VtScmBm8fezp+M+Ux37igZ+B29n/Gr+ID8N0T/v7nTYGNX0EeZTar2GYJ8we4OeuQ1Yrsohne+/u/BPb8F41WG/YXHMtdJ7DmU13zaKj2phrzkTpp8x99FFV33204bYcYhpzjlhrU0dGBPJU3Cr29vVyjMzMzYTKZQiI0+uTinFXyIRc+PP/DIbspDwRhWTizPiV5bor8/Vu2Rr+zUq0Ng7VdBgaN49awbQaCRSgcfKHW44n73SjPMvlObArnaFoE8yzPVPeU/fsFbNFiCNOGuo5SUB2tbVbd9ZHKP5gxHWrHrRzIUchqI9X9qtfORlGdR9Vpq2cNplcmwchQ7Vm9dY/UOk2uT0I9DsK6XrplKyq7vZ/Sht0mBbJ+60P1hR+J2NRDHR7h5nfPxIcVh6N2HanbtoRhvS9y3PJ1qd8Hx6Mn+Bx6PPwceTrKq9m1Fjnv/iKkdR+K0GVzVfTh2pws/KmmTrVMofOWhy6nrbQP9dgEab8HaE8qrvoXrv/sN5rHuxb7cFZhHh446TVdIcKG/H59gCF03vIwTtoOX8ftoCAno0aoOW0JMTExHCGZXqdttDOxGhiCzIyBMGcOhnYZGDQwbJuBoajH5PQTfmYvnKOlLM9KbNFBMUKr1FEKvo7B1mcwjGm5NlLdqd/0OG21tEmvTIKRodqzevKKJCI1DsK6Xork+igI5nNZNvUQgPSvSI01fbCtIyO13m86yDns6PSlEPRb5MjTUZ6l7dDQ6oswQZfNVdGH43p6NJWpqa+1gO9DPTZB2u8B2pPmw1t1jXct9mGEw4mqziqEDENhvz7AoDmYTtoKQb+N8AjDE4PCcTuQiAYmVgMShJNtNlIIhDlzMLTLwJC2bcOd4XS4tz8aMRjm6EjVcTDIItC6a2kTa3zy+VL8YzqNrfR8sDLU24Zo64+otW9a1kuRXB8FwXwuy6YeApD+lauxpg+2dWSk1vtpI7lTmPTJvBDeT+jNAZXnShoxtPoiTNBlc1X0YYdG0nFNfa0FfB/qsQnSfg/QnqTmTdY13rXYByJpy4/PRzSO36idn8IMahuFRxCCC5cgIwsDQxsqI94AMfJOSZ/iF+M22ljCowL1+xBb/q13YRrOT9X6mBllY9wOhs8u5NglmVBn0Q2YybZ+n49R1RcbKIpQUboazYe3IS1/CgqKDdbMUEKObXykw43Tkkeh2OH0sfOaYELR9/VIXPwsujOTUP7wtTi+cDSsTeXY5mxBdUsXJjz0HmLr21C/4CxkHVPAxZva7mrj8jwq/SiuDMqHGWOzT08rbDEcM7fe2Hu6mIh15kfRhEgG7nWbcczj/4YzKwWdT/0Otph2H2O3XAzSSNY5GqC3TUrppfekfcL/W06PSdeOSPHfFPD5NDU2YX3HepFO8vdofqe8pGUJ/y39vF6t3ePSxzFjzx6R6K1jRUcFDlQc8JUtVwdh/vwzwuuscoSy0CJzioHqaHGErC+l91ntoX6clD0JO2p3iNZacpCuwfxitdo6OJtSkn4EzrBPwdkvbkRGK/DYBd5T2XJrOLl68HFpqe5rKtYw2ymni2p1l5OVUAekY0CrzWA9Q3pzuOswnElOX77Vn3+ElMUviNjW+bWDz75VVaHovmuReMJ477olUjHo+TWfUoxbqock1FC46yPLDk5g3KP1qTBMQqhB+ldYPBdrGxRi3OrtL7U1ZCigtA4NFaO7ChyOBC8JZId/jFv6pN4X41ZHec7UEnQXzpKPcTsY9ioRgJzd5GPcsvRBLsbthvgYbImN9YvhqjXGLdfXemLc8n2oZU8n1++Zozk9kYtxm+R2i8YyxdLuKZiOwpKT1Mc7RcPc+yk3fks8HlwUW4gyax23dhW6qKmsjXY7iorncvHPFce/9Br9LlsLtNcBidneOOdULqWhNmeNBep/Yse4VRoDgnLa132L8nuWwpaTjeI3VnpPm/bdd9S3ouzuJ+Cob0HRNdORWGwDssYAx5yPUqtFec6Us29B7klCZVuFYRJceVn49IqjceKfNiKxvBw/XXAWkp++H0UnzMOggJydj3I/RLRhUMS4Hej4EgZLeBSypoaK4XEgwWpDyRzaZQMHGW8O9bRPrU86G+F49yrYDn4lYu18f8JZeODkpyLHMCyD1uYylL9yuog5dFdqLgqv+h9SUg2jHioIbZsSSzYxsdKJNZ4R3pXkwlFz6/zYx4XXpc8zWe1dLj89lWWYVql/KFiyWfnxUGp/6ZoiOInURAPD61CcT/S2SSk9QXpPjQF40fRFWL5hObPfeH17cMaDWL5RPk1yTDJae1s1t5krd9oivzyF7VbSp0AhV66SjJTykpM5K53evtQiA7n2aK07XycRiQcxwM8u9xuf1anAkkstXEgGVpv09pfWPKR9IydPpfK15qGUF40DwqbqTX75Cu2bNdWGEoH8ytYUwtHsZMeAjNS6i9ZLb18BlH6tzu4eCSitQQkDtD6lfl/yxe2Yv/M/orncMfIk2C58VXv5kVjXa12Hhnm9r2Q7fA4+uv7mO7CNHDv89irRtoaQkWnraQ/i0JsX4NiWGtlyhH1qTbWiZHaFr68PfpkJV7tGgjKW3WHVSwilfmc8S3uehYlm3NfQKBrLwnxkx/uImbBZrOy9JAN+4441/ln707hUoFvfukPaBj8wyhaPRSuKLy2ArWG9nxNe2m+bY2Nxa04WtwcR6ZOcfTvrSeA/CwPek4TStgrtUm2aGY9f5MZ93Q2Y0uAQtbn3PA8Kfvt59O6NZez8f8edhkX1jbCT0z8K/RCRxJAjJ4sWoQ0XlnCCLvIAFpNmpFhTZZgZhSRPgZA/yMkh2Hw1tyFYFl0G6yq9pTXxz78+H679X8ACj98b19cnz5Nl6o4Udj19FI5qrha9QeZYT1NzccxtezBcycrCVk5nJYo/vR3ZdT+IWLJJ5hvscbghN5v7TZv7517r5Zy00pMKlkQnin5WD3uC/PNC0JcML9XU+p1G4Z+5OS9PkTVeif19YtpELJ+8nPvNk5AogR/LrPyEUGq/NcWKklX/U407pcRYH85xp5cUhkVWJZeHljYJn6X066vWM79iIUjvKYF/bvG4xVi4eSF2Ne9ipiMHVVtvm+JJSD2gcpNikvzyFLab1U46RWS32tHl6hKdig223EDbwMtcSe+FbZLqglJfyslACJJHojURHa4OvxPTR6cfjZsn3QyL2YLaulpkZ2VzfyeNnMStwaT6WL5tG3puuJLNDJ9qg+X1v6LC3iW7hlMb/yy5kK155ZxX/OoiXS9qWT/qKV/NZuhti5p9k3VmRGqtJ1wvlfZt9Ohk10CfWlRiBw8VA7kKWOtS0rfa8vUY0fdViO7ydazrAyYl1rsODZM8/U7rx3R6yzFb4aipQ9l9L8JRW6/5axomIqQLgx269tkCmRIxYcEXt/jprAdmmOiLxXnPc3ajfft+lK/4R/+XBTGdqKlYz30hlhkzGq7f3AlHTS2K7r8BiaOT++0MQavdEfY1QU+/S/SEl0dyTR0KHD3IJNIwRj7C8e6xjUTulic1fdFJI68lZxxaLn1LLG/W+A8BqDxT0Qzg6v/63fPNoTJlK52UlpufqLy1fXsQ0ZwpZ9/iUoDuFr+Tz7QnuSk3F5PSJ3HzfVDQaFt5u1SX5Mb9l5iwrLMOM7u6uZPSPll0WlA4uxGHxqUh9ZdfyNrgASUSlrHzdJo82e0WnYyPJj9EtDpujVAJOkBGbag6bIM66s96u0gGia7TJBTORQrlPdgXQdI2kEwPrQ9cpn19Iv2k0MQ/v+8L7q+FYQxmdXXh4bLV3CJgoHSdwiMIT9oK60fXK8u+NsImhBjFvQ7k1H7PlDm9xR/hcOCQzYZEu5M7acovnso+y+LScYumn/kvmqTPC1FfswFxDHZb/pmC3m7u9IWcLtLnvayTaeSo2NK4hXNGiz75UoFcfkIotn9OtXfDF0AZVGeltkYz9LZJLb1e8M9tydki67Ql6D2NqqVcVp58fdZVrmO2hxyYHc6OkJcbaF5aZC7sS6tg2ajWl3IykMqj1el/0tkDD35o/MG3ga/0VKIgv8D7N5k9rmNiOpE7u5w9PmdXw5aehpEZxwc8/uVsjVQurPWi2vpRb/lKNiOQtqjaN7kTaJFa60Xrmk+pPgNYV07fjimO3nV9Xxm61qFhkic5Y8kpGztmTP+L175ybKOA4jdmo2fv3uBI+qJNb4fCPlsgU+uutUydNdELw/K+/dSUK5A4BSgac4qor3MyRiGnL73jjZXyfa21/6R9raffJc/y8qg0VYKjXcsoUB3vNTKyYIH2iak1PyBVSFomN/5DAG5fSv0hZ0MUyqb5h+YhPfOTSbQHATcvVpZ9hQI5+9bVKLsnKXT0+Ob7gNfpOmwr6WDbQzfh/srnuLn5xIZuP1n0tNiQmN/D7Y13VG9EQcF8RBUU7Hya27+/osUPEc0wyMkMBAeDMTL6ZKr2fMW3qqyiA8l2TTFtldBUtSVidRkuUGM/Jp0gECNtIGy8/PNCaGG3JcjpohoTsV5mXC2M8KrtVxmbwTDWRyv0tkmLnAPB7pbdiCbsrNuJoQa9fRkKGegZE2THAh2fwehlKMZtoOWzyg40r0DtOweDHXzoIBLrepUyIr0OJSeJ3NcydD0op62BAV/DCnV2qPe1qizUxrTa+A8F5GyIStmBzk/CPUhT1VY9NfXLIyi7pNO2bj0CXGgn1n6J2kxOWx4d9d8h6hCgLg20HyKaYThuDQwOxtfhhGBlqvZ84QmKtylA+ECyXafmTVK8T0RlBkILNfZj0gkCMdIGwsbLPy+EFnZbgpwuqjER62XG1cIIr9p+lbEZDGN9tEJvm7TIORCMTdEZezDMmJA1AUMNevsyFDLQMybIjgU6PoPRy1CM20DLZ5UdaF6B2ncOxlpv6CAS63qVMgZ6HWpgaK1hh5N9UpWFmnzUxn8oINcfKmUHOj8J9yBp+ZP11NQvj6Dskk7bOj5rvKb9EiEh05s2qhCgLhn2Xx6G49ZAcOCZNCk+ixD0W8i4aSByMuVZVyXPe/jnR5/M/SXWUSHofd7aPlbRgfw8obBkLheUX/p+kX7T9YLiwf02PBrBsx9LdcbZFyyeD3PQ3mXliMh8MaVOJYIuJ/ebiB26JIsn6fNCZOZMVyyzMiaOIxOQ00WeiZjiVglBvyl+rp4wCUr5CaHU/tKvCzn29UDKoN9KbY1m6G0Tn55im7LSs+4pgX9uSsYU/LJ2FBenk4WR3UmY3HeYYeJ+NzMdXR9d6eb+ql2ncilublab2e861WdmwUzZdtJzSnqm1t5gnpeTuVJ+gfalnAyEfUD3kq3JovLp+uT90D0menvj+5jh/ccnd11hfGoZ/1LwtiYU41Zv+Uo2I5C2UH/Yai3y9v1Tus4Yl8Zab+ghEuv6vjK0rEMp1iMR9bBA1+m+geENWsMO572ocIxIZUF2u70qVv5hqXzkxn8IwK26lPpDoWw/IjLh/M5dNzPL4/cg/JxZUHySvK7Y0/2u83uSClts8PO9Tts6q2AWt94rs9m4OrDOFXv69saZudMQdVCw801mM6QRlKPFDxHNMMjJAggMPCxBcUroyDsrwDqDDbOzeCZ2zPkt8rPGGYMvEKgw0VIMO/ocUjaAP+t5Cgg/7Tog7zggPh2Od65UZ/MdILQ0l6HildNFsW5pYhpx3h+R5Ow1CB7CAYbO8GyqxMgqZB13Jbm4WIg8Gy9t9onQRnhd+rwQxG6+Yu4KpFCMI5kyhQyucvquykSsZLcYUGJ1V2o/z1pvKyriCC8q43tkx6cu9uRBArU2SfuPlX5y9mQ8e/Kz3L+l92jhKhfXdVz6OCyesRj2b39Ezx0PojnVit/9ysXFBKPPy+jN/ZjE43H9n6rgPFyNd2cBF6z1oD4ZWHKpxZcu/XAiLn6vx7ux8ACvnO9BZ5ET8eVW/HqVye96Uu5xuOnY36Hq19cgsbEbj11gxvZRZq4+14y/BnHWOKTHpePRbx/FttptIrksmr4Ii75ZJLquFfzzyzcsF8koOSYZrb39sWKPSj0KFosFPzT8oJjXTRNvQlV7FVbuXomttexPCJX0U63vv6/7Hks3LOXi1QqdhHe95+b64D8LpmHhtKvx3sbH8d/Wn9DRZeXGWRY15dHf4dhzLtckFyEDsyXFgpFzKn3j88DX+XC3uH3jk/WZLOnonsY9fnKQylWrXAKBkv1JsCaIYiOrlc3Ka1ruNC528KbqTaKxVfJDI+56180FBLS6AVuKBcV98uusp01xNgUjhiXOhZGn9dt3VXbwcIPsO8dIbYoOorKhBLU1ZChk3dXktw7dHBuDDUUTMGH8ZRiVegRS1m1H+bI/9ZNJ2Tp8czq9iOHG/OHD/cRhrDlf5zogJFArcyDqFEVQ3cOopWPJT0ln41KBFgohY0JFRjEOWs3ePHsdfvmIypTe19NvQeoA1aO+YgOKnE44Uor660xyEDzbvqvSS66XnYnie+bDFtsDxCQCP66C48eNPhKrotmNok/rOZEVTYX9knd89ptve0xvJ8Z+8iBSagTrh5I53qCxBwUvSkiu3Tpi7o+cA1z4Wv98wZDBoZqdSPz3rUiv2Mp22qZaUXxpAWwN6/2duZJYt5tjY3FrTha3B+HXO7SWLIlJRdEnD/jryvyXgA8XiK7virHhoYx0pB1xcmjme4aedhXPhP1XK5nzKPXHRf++CKbuFjxRW4cZ3eI+/CE5CwX/9zlSUqPU0cmw87TP+3jcabivoRF2nvQvyvwQ0eqDNBy3AQhtOIBniTV1NyN/3WJZB6IIDfvRUfMdHt33Lj5o2hnVTgk9LLgsxt6IQcIwqtvpQ88f3gFs+qOI8IxOOjae/ATM3U1oqd2G1qxMZBfNiDonOxGRUUzb9Iwjkb/xL9r00EDIdK7MZsXm6s3c5aJd9Ui871l0Zyah/OFrcXzhGFibD2G7oxWHW7ow4aF3EVvfhkPX/wzpR+VyDrPvPJ2IT4jHUelHwe3xLqaOzz3eX8/6ytzR2sYtTinMAZ2YbXO04fff/Z4jBFDSdz8m4s5G4B/XBKwvfH6xXbHIycnhZOBetxnjHv8XnFkp6Hz6d7Da2n2M3f0byCqsuuZorEzbrTo+dbEnhwFSllm9NlH6LP2Wtml36W48vfdpkb2iEwv3jL8HeXl5+M1/fyMiFCNZ3TbmNowtGYtNezehx97D5WVts8KZ5I15Rb/betuwdL3YGSh0rHuSPRhzUk2/Y/3rQjhbnDDl5cH54M1ouXcJ0ht7RQ54clCVfp4Fk8cEj8mDklPqEJ/plL1O+e79KgemVhOqU71OYIpFpgRyTi+btQzLN4qdrnLOOd4pnRST5Gs7yYLGhVSuWkDl33nCnWjqbkJaXBqe2/acpjykOizsf7m+Z81V5EyOtcWicv8O2b7iXwLxMi0pOR7PnP4Mc34T1uPHd9+A64HlqEv2cM8l2J3eOGk2q8AZbELWH55Ezik/9+Uh9xLhvFHn4Z/7/ynrzJ6QOQEvnPoCVy++HlrXCtJ0/POsusjpxLGZx0ILWHZGeu3bdR8jdsH9sDW3c9/hFZ9c69PxMvqSoMUJWLwnhIruuxaJUycAbufAOZ3Ivr9zJVD6tbJTYBgi5OzhMmtIpflUqN88WHON715cN2oPfAbrt39Feq04VrnQMWNNsaBE8EKGO0XPvzB9+RnY1i4Sz/ksR1O4140ya4+qWcvgiU3Rt6cagmDZOH5NkGRL8umJ3F7nsRPuRcx7NyCuYq28/OR0VoBvY2PgMpkwXeAEc4w8CbcmJ2JN63dIdrnwaF0DR0rlA53GFJJXCcoVrZ8UdCD/iHHc/e6Vl/m1gdeRxMxELPn8Nsz/7iNR+eTUWpaejic73KJDLY70GShbWQlHs1PkvPSufQrgbHGhPsWDFy8CRtu6UWCKQaWnF1v6TqJyL4OnLfKtS1htdxSeANul76KysZMbr9weIT4D+PQBfzusBN5Gezx+MhLKn0CEYqcmHoHauET0btqJX68CmpKAfy6cjvvnLEDyK2cD3S39NqLPOd1e0ItVSYmoGn0SLpx5r+x657Ss47GsdD/ia3f492njQeDD24HD25n9HSxIv5/85EbUVW7k1ih8Pyj5Sj4t+xSPbHwE9tYqTOnrm+7CKVj0i79GlX9FFg37UVOxHoesVrG/Qe76MEKr4bgNr9CGA/hJKOPjaxFXucHLeCg80n/EXODy9/2eu/7T67Hh8AaO8ZgHfZ4wPW86XjrtJUQLBo3jNhTyfX0+cGC1qA/p8/Segulo+PmfoqJdqmC0QUkPDYTvcywR87IAdOqF2HhbRok38np0S7qhW7R1EbY1buOY53XZkxDpi3QTrNb+J97+Ld5O3xv19i9cjlsprvr3VdjetF0kD/o0flL6JMTFxWF91Xq/vp2YNhGvnPMK0zmoZAd55+1zr/Vyjj/awBBphTf+GZ3SsCH2xVexvP5l/LRnPZ59tYuZDiYPGUjN18n5e/MVMapOW7595IQlx7O07nLppbpDsqBxIZWrFgjzk5Ohlnoo9Q0PtfyV+kooU9KXGfkzmONHWDbpmmfzFpRleph9QeUV1XmQMHu2KC+5OVVLP9FGi/IKleNWS5+Ew55Q+e7aWnRefRncnW7m2Ml/+XW4W1qig7iH7Lsc6zltrofxmiDkjtsA5lPdjlu69/p8uPZ/AYv32wYRpKfqpLpZ/MEnsH12k38dWQj3ulFGVt196229e6qhBpaN49cEyycv9+mJnF1+o7EL41rqYFKTH6sfBOC1TDhT0OfcG+yxuD43Gy9W12J6VzfHci8LQbmi9ZOCDsRd8xF333NgtV8beB1Z+sNSXL51FaZ1dYnKp8/I28xmJLndfvVSGiNqaxTpfMdqO8nGMupkVJ78h/6xrGSHlUA2miCRkVD+LNCXOuVZJjSnWLHmUKX360tB+3tabNyJYpLTBnscbs7LU1zvvFRdx7VTZHP4PmXUL5TjNJC9/GDxrxgIrw/SiHFrQBbW5lLvG0HpxEe/yVjTW00B6BMLepsl3XTQb7pOpzwMBI6A5EufoVBfSZ6hBQP1raWlNPq7RKYNcnpoIHyIJBtvRUcFd9JW6NjTZE/CqC9K7afwCHTS1rB//faK+k8qD+pPuk59yOpbuqc0V8jZQa5/7M6+E7R9cc8+E8TrnF2OBtdB7tn4uB75dKfou0750OlOLaA602d6Wh2uLF3nx4Vep60wv3WV62RlqLUeSlDqIy19JZQp6Yha2byu8QzMLNB1CmUhzEtpTtXST6Fc12iRWTjXUzExnTjilCrZsRNfmBgdTlvevsvBWBMMvvVXXxkspy2BTg96TxGyddPW8SO7jiyEc92oICtab8dUfKNrTzXUIGfj+DVBZWelYrrC3m7upKnI4cmSn1w/CECzhHSmIP2b1dWN6Z1d3GlTVTooVr+p6AD2fcHdN8ncrzu8ERVlqzFL4rQl0O80htNWbYyorVGE812xw8FsOzc2aYzye0Y1O6wEeo4hI17+dNKWBZq/aR6f2tEuctry7efDQFDdqQ0Fvd2y6x1qJ8nYz+bwfRpGmxfIXt7wrxjgYThuDcjC0nZIWTr0qYQkDosS6NM8A4EjIPlS7CAFWAdDn6i0QaqHBoYGDnexyUhU7ckA6Yth//TJI9C5QilfilVLC3g6bSIE/ebCITR8p5qOPhHXc53yoU/ywwmhPNTGhRbsrNsZdD2C7Xu1vpLKNFCdYIHPKxgd1VIvPQi0DaFc7yn1R9TMs2r2nRAtdR0KiMR8qqFPFXWz4lv9ZYZDR1TaEVO7c1jrrZqNq+qsUkxHc4Ym+WmxEQo4rkccQ1QVwn5TK1tFV2mNotpOGeiZT+WgVrZvzxikjJWgVtcJGvuHz4e13glUxqEYp4HsFYz9hQEehuPWgCxcSSOUpUPxzQQoSipSTE7x1AwEjoDkmzZS8RnnYOgTlTZI9XC4QysDs1K65lWruP/V8ggn8uzsk62q9mSA9MWwf/rkEehcoZRvuc3KfTJHnwgK4f1k0Iz4jPGq6SimrZ7rlA/FKAsnhPJQGxdaMCFrQtD1CLbv1fpKKtNAdYIFPq9gdFRLvfQg0DaECrTeU+qPqJln1ew7IVrqOhQQiflUQ58q6mbhCfrLDIeOqLSjN3vCsNZbNRtH3AZK6WjO0CQ/hX5or4r16gwDdJ3u74iNVS5HrlyVsjmo6CqtUVTbKQM986kc1Mr27Rm12OEAoVbXnRr7h8+Htd4JVMahGKeB7BWM/YUBHuHdbQxTFsyhAC6GDf2/9RT5OC8SUoqSlBIu5ptcDJZokpmeGGDREv81IPlmjvbGE5Lpw9xxsxD1UGnDcGTklQM5VDl22bw8mJ9fjkbHfsS31XBB33t64jDqvldhrm1A6fypKH5/E3oyk7Dz/guQl2LnmGs7knKQtrMDjiVPcB8QfXnoS8TPPBITbSkSEi4Bi3MIx4vw2QIUYFZZAPYkhPqipy2Dyf6x2haMTWQ9qyYPgqKsksVzrlq+hPYuq5fcqsM/bioR2RyzYAZmNc3iYtzKpvs8ixnLVu465dMxwgrYEJYYt8dlHcedwKhsq+SeGZE7Qrb9WvIj+c4smKkrD2E9CMUFxap9Pyl7EnbU7vALh6Glr4QylY4f0Tqsrx5KOiEFpePzUtJRrTFueV2Vk4We8aOlDWr2RG2dKrzv8XiwuWYzTDBhckwx2ojsqcPBHDvFC+JlVZzP8wiXGwW9veElLePtu9xnusQMPozXBCFfrwYwn6rVgXl/1CkBxbgt+zofxb+Oh41VRyZMQG5gL64ClhWdkcodj6zRxw/rtazammDqmKmK6Spi4rAzJRvjW+th6iO7ZcpPph/IKVu+Jh22eBdG9BF4mSQ61ttpQXeuBVvssZjY0wMvJaMMBOWKNFqpj0efrHh/4vHzUdj0P6xtWIXp3Kf8wce4VVujUIzh5Nhkbr4rs3lJ0ORi3Pr2jGp2OOAYt17CNBZmdHZxp22/i7OjzRrjFy6BBx/jtjImDrNk1jtlNhvW2u3yMW4dnUD5RkBJzwJEIHuFwba/MBA+mDy0cjOgGBhYjt1Sif1vyKCrCXjvas0MqMNaVhFAQPLV2YdRiaHQhgiAHKoHL78cropKjpDAG0PSn6392XPN+O2/3Byru1+6L7zOFIIrwYWjTu6/V7qmCE6exfm1V2XjvYYKAduTAdIXw/5plwchkHvU76x8iXzqgTdcnE57kj0Yc1INk3085eEHsO/2m5Fa3y3SfTpRW/p5FkweEzwmD0pOqePCI8hdp3z3fpUDU6uJG1NLLrWoEpRx7M3TF2H5Bi97sxSpsalcrDm53zym5U6DBx5sqt6kopH+5SvJUA7SeiiNQaV8+Xrv/2mjbF8J7RTJdOzYEwPWCSmm5k7FirkrRPWW01GlfpLLK1iw6qJV9mq2R0k+wrFjTbWhhOKGMsaO1ObzeX5X/rU/A3s47S3Z97evkGczN9YGoZd3uOfTriY43rkStoNfyTukUq0onl3Rr5sCR1XxlaNgS7EBBwU6UTIHcDuAQ+sjpyfERv+nk4GuRvZ9qhNNE8J6DiN91bpGkrOF7s5GPFpXr25rGDbCT2f6nLfC623pMXCeUouZlk7/ytvTxf0q129q40XlfmtzGSr/eiqObq0VZbshLhYPZmbgyXY3F+tXS7tK1xTC2ewUrVGSY5LR2tsqynty9mRYzVZuTZHscvvJ2DHyJNgufFVVxqoYOQe48DXvvyUyoDLuzs7Ep7XfitYMmV1tuHvn/7j4vjw8MMMk81KYHM93Z2VifNFsxfnvtOwT8GhtvdjmsMZnGMZpIHsFY38xdKGHnMxw3GoQGrEVB8RsPpRAwbgprovGkxQUXJtO5wyH08kDgYDkq7MPoxKDrA0sduVQQe5Uyx3vXIWrHl+nytauyOqe4H2jyzoN52NxDrPTNiT2ZID0ZajYv1AxlCvJg3VPK3uu8FnXum/RvfABmHNzMOrvb6Ch6QBybZ1c3wtPimfecAPqX3wRppwsNDx+K0oy0lDQ24P2PY0ov3spl6/H40b8QzfDPiYZlTsPIu73b3HXTR7A/tBNaC/oQVr+FGTHjPHmW1WFlqU3wj19IlxuF5obm5Ganuo7KcxqO193i9nCPcPf56//+bs/Y0fdDubpS14W906715f3/Wvvx/ba7aJ1Cp2kmZg9EdeMv0ZWF4UyFNaV/zerHkrrHznWcKrHqz9/lft94L/vcX3lycrAkW++DVtMJzdOK2NiUdrQhJSFT3JfBsQ9uQRHnHlBwDpB+Gz3Z9zf8WnjfSe69OiosJ8Ot3tDyxyfe3xYx7W0LlrsiZp8WPeFbN13vedGfTLw/k0TsWjKzVwsQ/osNiPtCNmvLPg8nzt82J+BPYQM3Ir2/a1Lgfo97JNR4Sx7OCJM86lonmnYj5qK9dxXQvmJ+UhZtw3ly/4EW36+98UB2Yr3/g+e6p1wtsPrsOq0oGhOMxJPnAn84nFxHYn5/sCXYv0Ig5742vD6fHgOrPYnn5KWKa3nMIPWNZLcfEgEViVONzLyT8BD574h2xfSvlc6mcqtbS8vhrV+raT/zEDRNODq/+obA2pp5e5z9RafRiVHZVPhRLRd+AqsbVYUfHK170So8CRx8QWpsM28CEjMBkpm9a99+tYoeaeehUc2PoL1h9fDLZCLdE1B852t6RBGOJ3c13ZybeVkHdcNlHodkJWZJahqr8LI5ipkxmUCqUVAc19M15JZ/vkwZCDVDdcjxTD3NPuRyfkh9zhU/nwZDlhMmtY7vvvCOnx0J/vEPN//UbBXGCr7CwP9MBy3IRQafQJ2zqpzZJ/78PwPjYFjwICBqHDckr26+d0z8cGeGt/ilAf/Jn7eUTncp0jEqqqUjiB3z3bP5mG52RhuCJXjVg+CmXMpVEjsmDHcSwVp3cnx1LN3L+d4EqaTPm9OSYG7pUXkoJK7Ls03VHJTk4FUFuFap+jNV0/60vc/gGVkCYomTfJLJ5VpMO0T2uBoCXsUaqjJ5+VTX8ZvPvuNYh7kvC3PMnEv9f4y8y8oiC/wyYyl43yZNI98WKFAmHfL1vDNFcRs/tyUgSnbQMigZi9F9lrS5+SI62mx+RjlRX2uph8h1BOuDbFd2soLUZnDBXrtv1pfCJ23PIQnVQe0zzTYtJrqauS8+wvRZXLexqZQmBu3Xz2F9jvUa4WwrxH3fg68MV97+mD6yJhPDAwCx61BTqYCg8nPgAEDg8leaWVrV0s3KBjGDQw5BDPn0sZE7iQ4XecdT3Lp6Hr8ccf5OWflrkvzjZQMpLII1zpFb7560tumTYU5O5uZTipTYx0WXD+xWLWl2D7K7Av1wbO7y/WHsEzNTO/hgBqzuTFPDQmI7LWkz2k94nPaSvtcTT9YzwQDreWFssxhgIDsv0JfKK5tB7rPNNg0S5t/e2kM+OovqafQfg+6ubRys770wfSRMZ8YGAQwHLcqMJj8DBgwMJjslVa2drV0g4Jh3MCwmHPpNCCF9mAx7tJpEjqRNZSgtu7gwcsiXOsUvflGSz2GG9Tkw2LVZo0tKbu7u7ZWdmzxZWpmeg8H1JjNjXlq6EFPn6ulZT0TDLSWF8oyhwECsv8KfaG4th3oPtOg364klfku/QjObtPaiCVLsvdk9wfFXFpwvL70wfSRMZ8YGARQWXEZICa/KelT/GLcUsy2seljDQEZMGAgquzV2OTp2LN6nSpbuyKr+xdZXH6se2oM4waGJtTY6oNNSzHWKH4dPSNkz+XjbzYkAx8umIbiXgew91NU2GJQ1tiMjDufgaemzi/+ZrBtE14jDlf6d31nPfY07UG6PR1nlJwRVJgkVrv5/HgGYWkcOmnMWD5MwuaazRiVMgoHWw+K0ptgwqTsSbrqyedHz+Yl5GFc+jjsbtrNjIcnDZNA7eHSN+5WXC9R2u312zkHYYGYj5vZF8EwKld0VOBw12HNZbHkQDFteR0INK6cnjGhF2ryoVihrH4UxrYl4ppEuxM/iyvh4kc6amvRsWAh2uvYY0tYJouFPFQM3IqQYY+PSNkGBgZ9fe4XR5bV53L6ofRMOOoWzjKHAQKy/zJ9rxTjtuzrQhRfUQJb/Vple0Kf1NPpTC1xbiltGcV/NbFjvArT8Xmq2DRndxwwYoYvxq0oTeFUtK96FeUr/gFbTjaK31gJm63Dl3dBbzwee9OC+OYe/G2eC/87yuYvS0n7wrX2U5UDyWrMKXDFpsIiIWmlV42imLd98im1WlBesUZ2LadYJxmd8ZgsMA3AmKV67977IQrKvsXIjmYkJuUBEy4CRv1Mu14ZGHIwyMk0xJdQYyvWxHJuwIABA2EGvWE/ePnlcFVUckRkR82tY7K1P3uuGb/9l5tjEfdL94XXmUsgkrKjTu6/V7qmCE4ZhnEDQxN6mGyDTStkEna4Hdhau1XEeO9J9mDMSTV+Ot2cGYej3ngb6cVHBt22qbleAitiV1YDMTE/e/KzuuZ+pXbzsiIsXL0QG6s3KubFYodmgdq0Yu4KxXpSvRasXqCp3cTuvfKsldzpHbX1EevZZsEmTNhmJd3Ry6islF6pLIKaHPSs+SLFBB2ILgvHlnQeoBd0DhVbz5f5XfkabUzv4YAag7uBoQc9fc5Kq/ZMqOsW7jKHAQKyo5K+4Oza6mw42syimLYiZ26qFcWXFsDWsN6/zzwe4B/XaNO7zkbgnSuBUskXCyPnABe+1p+e0knzLJnj9Uoe/FpbHXjEpQLdzeL2JLlRPLe2v52S9m/MjMHdWZkYXzQbj51wL5L/fbso712pubgu2YpWizlkaz8mWHLg29zdArx8Etc2OThGnoS7szPxae23ivOfFp1xvHMlbAe/8l2iF5PvTzgLD5z8VET8PCTLxZ/ejMu/+y9O6On1T0BOaumLIaleGRhUiBg5WUdHB5qaxHFiCIWFhRiKQtu0dxMe+e4RHGg/wDx9wmJYNmDAgIFIgT6PKr/xJm6TbXnhYdT37kNCWw3KrTZ098Ri1H2vcmztZfOnYcT7G9GTmYSd9/8SeSl2FDkd6EjKQdrODjiWPMG90f7pxtNhn3kUJtqSOWZZH0Mtg2HcQHSAJ2NiEUYo3ZPDVf++Ctubtsuy1Wthtp+YNhGvnPOKalrhM0kxSZxTkuZacjA991ov56SVnpIhh9Nvr7TjyKNmcPURkmXIEWfw11lt0wvaCPDl8lCSr1q7Sa4EuTTB1lOpXlqdr8L+V2qPFtBp3Bn5M9Dd3e3XF/w9vt4kY2eSE9sObuNO0E4d492Y8RD2N6tefH4EudNbBDU58GkXj1usqmtyYyJca0Yh4zSxh6v1jdLY4ljWP/hE9QUdX+YRLg8Kenu0nUQLNfSwvRsYGtDT53xasxVwO8OvJ8K6EQzdDLl903yis68v2vc0ovzupbDlZKH4nv8H2+6/ek93egTO204Lcs7PRfrCv/j32evz5U/CXv6+uExKq+S859Mr5fmLx7XVAWa4YhJhEbzEVTpZLHRae0xmdI+YDvuvP2bmTRHMN9jjcENutuzcRXPf0h+Wap7nmHOlimy7//wLxFSsFcX3pLrtiYnB28eejlp7kqZ1iJa5l+bsqkNruLmMQssRmbPwuUDW0XpA5V+29QPM6uoWnypWg1CvDAxZx21AoRJ27tyJK6+8Etu3b2feD8IXHNWgdu1r2+d3nQwFLfRpQgn1J3AGDBgwoBXkSCWHKs/APBLe+FDj+u47Vv6CY5cdN2cO2k/zMjVPlm7KjwGa7Rne5+bNE92ij6ro9JWUYdzA0AR9qrWlcYumOY/SshxelJby0JJW+IzwVCZ9wk2nAfmNSNln3lAetAEZPbce9rgcX32sGpc1cm3TCz3lamm3VuepXiitUdTqJVfPdZXrgq4vhVSQy4O/J6w3/bVmKstarj1KZemRPZ+2sqTSL/yClnqEc81I+fEhNLS0R2lsFc+uhi2mU3OZAwpybBgO2+EFPX0eaf2QlmfoZkgQkK3p64vEMYC9NwYFM6Z7wwesW+RLQk5Mcmb2tNiQGNMXG3bMaf150KfpLEcsOQrpOjmHhaEU5Jy2BD49+UqU8tRaB7hFTlthe5h2vc9pSzB53LCXrQP2fcHMm2Za+pKCwueQA5M1d1E4oqDmOTXZ7vsCcRVrmXU7prcXW2q3cHXTArU6+eZNqxkHrXbmc1rXmIGAyq8oWy3+ekUrpHpoYEgiIHKya6+9FmPGjMG6devw448/+v0/VEFx0gYVG6MBAwaGNwOzBEJ2WaV0qfPmcf+r5WFgaEMPA3Eo00pBzPVKTNAj+pjt9czBeuugBK3lhrLMUNYz0HrtrNuJSEDv2ipScq7qrAqqHuFcM2qVgdrYigiTugEDBgxEALZpU73rXjppK72X4EZifo/3h9TuMdKLIEyvlpZPrydPrfkKoGjXpajoDzHAAr/GYs1dQftG1Nqls27hXAuF289D5dOcHDCM+XrII6DXBt9//z0++eQTpKamYjghz678uVjUsTEaMGDAgAEDEWBzDmVaKYi5Xo4Jmk6P0OdsvjLatOWptw5K0FpuKMsMBHJrlEDrNSFrAiIBvWurSMmZwjUEU49wrhm1ykBtbNkiwaRuwIABA5FE2kjl+1K7pye9Wlo+vdrXyXrrIIGiXZc6bwtPUMyLX2Ox5q6gfSNq7dJZt3CuhfSsMQMBlU9zcsAw5ushj4BO3BYXF3PxGIYbChMKMSV9ChfrRAj6TfHjBvxTMQMGDAz5GLYUX5YFuk73h2LZQ7Geg4XNWeucxzM/s9JSHlrSCp8hEiv+fnuXlSMi88VnO5VIlJzcb7re1R2raw52bNyEgs5YZtso5ufE/YwTKTLQU66WdtN9pTSBQqmefL20gq/nzIKZQdeV4s5SHqy+4O/pXVvJyZnPT05P+Xtq4NMWxCvHuFMaE+FeM6rpGg+lscURlPXGh62OBgwYMDAgyBztjQkqtY8mC7oLZ/l/bq6QnrsuTM+nlQOfXk+eKnVwxYoP0klj3IrsOnfdLC5r9MnMvJ195Fx8KALW3EW+kaDmOTU5jD6Z6xOXJOIr1W2t3Y6i4rma1yFqdRrIOZsvv7B4Lidz3UFHWTpjYMghIHKyv/zlL1i1ahX++Mc/Im+IsIprDQwcKYZgpfgndJReKTC7ljQGDBiIbtA43lyzGQ1dDci0Z2LSAQ967ljCfeZlfn45Gh37Ed9WwxGKOXsTkbTgUVhqmxDzxAPYNsoE+u/43ONDYgM40rMbbkRvVjK2LZ7PkZlNtKVEHWGZkJyNY0KnOGb0GVb6EZrqadjO4Oa8YNMKn1k0fRGWb1iO3bu/8THfe5I9GHNSjY8hmRxORKrUnBmHo954G+nFR+rSkfQ/P4endzyIuspN3KmNji4rHnnLhqSmHjx6gQnbRym/256cPRnPnvysrrlfrd0kK4I0zbTcaehydmFnfX94guSYZI7ATYgkWxLaHOIjIcSuvGLuCsV6Ur0Wrl6IjdUbVdsg7FOl9rBADnlh/GKlNgeztlLSRaWyCGpy0FOvgVwzssoW6gy9pODHFpH8UaxbH/s4OW2bHbAVFXlt6RBZ6xswYGAYg+Kp0pqQHHPdLcCml4FD6323O4tnYsec3yI/a5z/2rmrCXjvanE8VnKWXfAXwJ7mn/btK4BSyUGBkXOAC1/rT68nT6X0Z68A/nkLV57IaZvkRvHc2n67vjobjjZzf6zbCT/rL4uR967UXFyXbEWrxRyytR8TanLoaoLjnSthO/iV7zY5N9+fcBYeOPkp2TWTBx5sqt6kq04D7eeh8hd/dgsu2/kxpvb0+icg3ZWSsEn1ysCQJSfT7LhNTEwU/e7o6OD+xsfHw2QSvwVpb2/HUBZawMyWMhCyUbOYCuk+bcSe3vu0oiEZaGNjwMBQgZYxqQWBsI7SOF6weoFoscFvsh9+y4q0hh6/TTbvwKpOBZZcakFDskmX00atPg98cBPOe3oLc4NfuqYIzhBt8NXkrvasu7YWPXfdDUd5uZcRfXa5JkeEYTsR0jlPmNbaZlXsRz6txWyBy+3yy//Af99D98IHYM7Nwai/v+ElS2o8gMqYWJQ2NCHjzmfgqanT/NKAHPecA5+hI6VrCuFsdnI6YnnhYVTYu3yf1FEd67vqsadxD9Lt6Tij5Iyg5n61dgvTpMWl4bltz4nmdt5pTE7QzdWbuWv8ixp6TnpNC2gc/PaL32Jr7VbftQRrAjqcHX7lSu2JtD3CdhGEuqOkS6FcW6nlJ3ePJQdq98VjL8bRGUf75cNkydZRj3BDWjavH+51m3HM4/+COzsDLU8uRElGGsekrfVFlwEDBgxEK3i7zP1NswP/uIZNglU0Ax2TL8Oj5R/jg6ad6vtnIoCiWKL0WbraCUdKW9o3b5cwTvIGkqdS+ob9aF/1GspXvAdbTjaK31jpWzP57Pqll8BRU4uiR+9H4tkXqeYd6NovoHlOrl2djX7911U8E/ZfrRQ5K1nlB1onem7bwW2YNHLSgByCK6/ZCfs7VyKz4YC/k/bUJUD19+p6ZWD4Om7phK1WzJMhtRlKjttIO4kWbV2E7U3bOWZD4dH96XnT8dJpL3G/r//0emw4vEExjQEDBkIzJsPluKVxLHd6jZy3z73Wyzlp6Y05EQ1QzCp6s04O1ZuviBE5bYUL0EBtAF8fpbI5B9gHnwR9KitYxy0h22xG2flneJ20Gutp2M7wQYtTS8sp2dgxY5j6RY6lnr17dTmWOOetTh0ZSERKP1nlSDEc1hR65R0KHR8ohHpsGTBgwEDUOW6/uAU4sNr/pCLBZMGulCxcmm4fMvvnIWfXX5/v33908vSIucDl74et2AGd26nNrBcN/GnkMLbbQPT6IK1D2Rk7VFDRUYEtjVv8rtMEQw4VeitE/neWs0eYxgibYMBAdIM+1Vf65DjR7uROu/KfQZV9lsVdJ+fT6Ln1SLDnoAHeWFRCBGoDhPVRKrt4drX3rX4UgMIj0ClKrfWUk7lhO6MHShsM2pjodbLq1ZGBRKT0U832hKvcaMNwswehHlsGDBgwEE2wNpfKO8AIHheOaa5GQWKeL5brYLf5Q8quU3gLVv+RE5eu0yndoXbiVK7NPIZquw2Eh5zM4XBg3bp1ftfpGt0zEFoc7mIT7fCgTwAopq1aGgMGDEQ31MZxkcPJfdZNJwSFoN90fYSDwvWHzgYI66NWNvd5UzSg6aCuehq2cxhCp44MJCKln2rlhKvcaINhDwwYMGBg6MDSpm2ukls/D9W5btCAYhIrIYrWaxFr81Btt4HwOG4feughfPnll37Xv/jiCyxbtiyQLA0oIM+u/GaM4rYUJRWppjFgIJSf4dDnNizQdbpvQD/kxjGx3FOognKblYvFSZ91C1HxTTp3nQiW+LRSFHxfg84dO3x9I+xDaZ/R77rnX0BBZ5zvmlzZ3k/Mzd6YVNGAtJG66mnYzsFhP0Jqc3TqyEAiUvqpVk64yo02GPbAgAEDBoYOXEna5ipaPw+nuW7QIG2k8v0oWq9FrM1Dtd0GVKE5VIIQf/nLX7B1az9pA49rrrkGU6dOxZIlSzAUES7GcbX4KdOOnIZZZbNkY65RmASqF5Fn7KjbwUyjVF8t7TLY1och66owOLyAibX9k1Uof/4zuNLjsXnJr5CXYkeR04lDVit211Zj5iMfI6GxC5X3XY6tR4AjLxydOhp5CXmcbg4EOUuox2S4Yh6VpJRw8WiFn+qSI/au99yoTwbe+QWw98ssmDq8cWazJ7agcl06XN0W/PRZNko8Flz/kTctkZRReAM6KVvckIfuxx5AKWXo8aDl/BOQsmoznBmJ+P63P8exz34Ma0M7ahechdKSbExc9CZimjvR+3o8xl+Xj1Z3GY5udOKnL7Ng7rBQdHTAA1jiiNTJyhF/FS+IZwRp0Ac5uQrtD2/vpHrEP0tOvINfF8DV4fKLX0rXbdc1YWSGWObj0sfhx8YfOQZaHmaYMTZ9rO+0RbA6S23YXLMZDV0NyLRnah8PrPEYZijZe9Y9pUKkVtAAAQAASURBVPTSPiUHa/mNN3Gf6nEkcbYOrn0VthiUNTYjZeGTMNc2IO7JJTDPOJ7Ll1469NyxBKacTDQ8fhtKMlJR0NuriUSJVTeHI8FLVtfhH+NWTpf5fI5wubmyqb4HrWa/NovK63WgpmIDtyHMLprh012al7X0PZ8Xa27n9VOYjpWv1rmbtz1qMW6p3InZE7m8eJ02wSQiQRPWp6qjirsfTfZfSSa8HNYfXg+3x61pLaV1Pgh0HbW2ci2+q/sOE7InYGb+TN1liWymRCdldZdVPzlbJGRqJ91h2SpKs+sDoKMeOOpMYNTPQiIbAwYGGwxdjwx4u5xzzCxg6ymqMW4rY+JQ3NvNrZnJPtJvtf2zLgRjP1l5EEWRXH5ltIcwASmF6nmqrHWYe0H+mrQ+fLkls1BqtQRm01lyypsIVO8EBHMyyatzxDTs3/shCjdXI2306dy8UlG6Gjv3/BPlNhvGj52nec4M615v7+dA5WagaKrf3OeHvv7rTR8FW+N+kqYItEvpLp6JGpJvxZrBM2cK9bKPUE1uDWkgBORkQiQmJmLPnj1+Cl1RUYGjjjoKHR39DMRDITDw7tLd+P13vxfFmZVlmwwBWAQ9LNZzYosnCNnnk63JaHW2iup525jbMLbEu8ETQguTusG2rr2fwoWIBUZnsHaiZA5nY3FQcBqzw+yLDWlNcKLk5HofK/ue1VkceVV1qtdxyCLKCvf4CQdCQZqlFW2ONizbsQw7mndwv+n07JI3XMhu5iw24DH1OZsaUbWeTtpa4TF5YPKYUJvs7a6sVjpl4OJi0lLfdNZbUfp5FpeG0hbOakDttlTuWWmeBzZmcH3oNANWtzefI6Y1+MqSpq/cXAAnkTwVFXGOuFq3WyQfYftZclO6z7K9QkxJn4J7xt+DJFsS92zZ3i0oveIKZDe5Re2X6uZ/FkzD4vOeQWtPKy756BI095BwlcHrbHt9u2waVvvIhi5YvUBkpzWNB9Z4JEKCC/4iYtGVI1EQ/tZCsEBpEjMTZecEgpY5SG1sc8Rg5GgtL4c1xYqSORWq9oPGwEMrPchi9CvngGXon9zcdWvqZbD97iGufI6IbHa5bF7kXObz+a78azxa14ATu7p9+X1jj8PdWZkYk3UClp60FMs3LufKS3a5ZNO2WsQfOrHkxap7amyqJj0Npm/0lJsck4zW3v61BuGYlGOQYE9Q1HV+3D5z+jO+emjVTyX7IdV1Vnpq363/u5W5lqNxTenJ5jyx5wlsrN7oJ88Vc1cozllydQx0HVXeWu5nn6g/Vp61UvZkMKssgpxOvj/hLNw+a4lPd5n1k7NFZz0J/GchOxYfb6toi/HmJUD5evF9smHXrUZLfFpAsjFgYLCAtwvGfmoA0dUEvHe1rK1qPe1BlL/1Sy7WLY9dqbkovOp/SEktDo64SsZ+Hj7+TuRtfly2TlWzliH/iHHyeUjSc/b4X78FSmW+PhKsH+XmR7m1DmsvCHs60NUo2+wNcbFYmJ3FrXmkcz4TrDYqlPFTUiayOxqQ6u53Yzlggk1wAIPmuGUFI/Gn896FudUceaKxhgPAn08Rt4HadN2XQFqJf/vfuVK+/xhy1TJnDjh5qky7fkjOwrWpsaJ2aFlnDXdysoAct6effjrGjh2LZ555hjtNR6Bsbr75Zs6h+9lnn2EoCe2qf1+FbY3b4IY7ImyTSk4iCpJOJ8DoDcsjGx/xOx1Dp2GOTjkaNx1/k+8tjNyg1cKcbLCta++ncCFiBpfF2ikDofNWemKNHCs3XxEj67QdjGytkXTc+p7rrMRDOx5CWUcZXt5Xj8x/JcHV4z3tWjCrEbXbUjh5u3PS0Lb4RiQufxmW2ibE3Hc7nE+vgLvF7dc3Qqdr9qQWVK5N535zec5sRO32FF8fLj7fhqUfODhHmiXWBVdv3+TqMcGcbELSIzcjf+IZficeW0aNCpnjlmV7hSB7Nyl9EpZPXs49e8PSabjxzVbfieMEu5OLW0anJzq6rHjgDRcyW4EnfmmF/cRZ2NWwS7MzjNfZxeMWy6aRs7NqpE/M8aCTRTcUjtulPyyVnRMIaqcxZdvCct6ef4bXSarRfpDz9rnXejl9lD7DOWA/+IRztPJtlZu75teU4Jev7ved+G1oOoBcW6fs6V0+n+cOH8b0rm7RZ0oUEW+DPQ435eYiOTYZbb1tXHkvVtfKpr0hN1tVXnJ1Py7rOHQ7u7G7abfoNKhWaLW7tM7YdnAbJo2cxK0hrvz4Smyv3S47DvWCxu2M/Bm+ekTKcUtyXV+1nrmWo3FN6cnmbG/a7reuEtZXbx0DXUfNfms20z6R83bNr9Ywn2GVRZDTyY12O+4pHuPTXWb95GxRXArQ3SJ7io2zVQQ5Z4M9HddPPiMg2RgwMFigNicZuh5BEKFT4wHUNTYhKz2t/1SnzvWWLoeYTN6umCRYettk7Wd3wXTEXfORfB6S9Jw9VnCkCtsjNz/KrXUCATmX1vateTTNoZr2oGYgdzzuzy/C7ds/QqrbLTqRSmWaGOuue4uPxFuz34q8A/PRkew+Ieft3Qf928+YK6lN+2xWvJ6chC32OBGBnhY7MuCOW4V28fohBDmhh9vc36rDcRvQuPz973+POXPm4Ouvv8aJJ57IOW2/+eYb7N+/H1999RWGEugYN+u010CxTVJZ/CeKLGcAbUh2tezS9AmmGnMy9etwYlce1lBjsJSATqgVn1zPZGUfPbceCfYcNCh8OG/okDpo/JHTttjhwExLJxynd+Pg/7I4523lNxk+eRe/uAK2cdPhOOYU9Ozdi8Rx+XBsvYPZN8JTunwevDO3cm2GqA8z0zJw1NwGXz79fe9E8Un1sE08g1vwUi+TA4wre84ctITohYac7ZXaO0pDTu7SylJ8M6IT7ReYUZ5l4hx/pIO+RY7N68wtqvNgO4WGUnGmyulsZUklCuK1fxqt5rRl2tQBYNGt6KhQtPdaoWVsU3gEOumqx35Q2A86act6pnh2NWwxnZrmt3ez9+OyJx5A0XGzOOety+0mr583L4ku8/nQGBSdPukDjQq6XujowSF4HWxqaUc4HIrM1Up131rrH6JKD7TaXbpnzbSiILmAq0+w5bLGbaTXEGprHhrXjhYH0+YEU18tay1WnhQeQe6lEl1fV7XO7xNQubKUdHJWVxeS2+vQLNkM+uRS9hUK5GyRkpOAt1VK6GqEa99ncMXbmWUba0wDQwWB2gEDIQatmzJGoZfWqbwzK5zrLYW8LUqHBjwuxFWs9ZZN5+vUbKmaPZa2B/38FQS1tU4gMEnWPIp6rnkP6gaqd8DtOIQ0WrsxymStu2iO29qwNbIOTAqPINcndH3/l/1hExTaT20a43DKOm2j2o6otIu1Jo7Kdgx2crLJkydj27ZtmDVrFrZs2cL9mxy49HfKlCkYSohWhuFg66Xl+Whtu4EBYrCUQImVXY6dVQpDh+RxuMtLxETxtnh5F85u9Je3rc17Py/PG9+z6aBs38Rnek8qCpEzpZnZh8f19Cj2sZDR1Fd2CKGH5b6qs4qLAUnYPsose9qbrtP9YEBlhaMNovEwACy6vL6FCopjW0FH5ewHjQOt+qgm98pjczidZUGoy3w+/BiUg7C+etKy5KVXZwKBHrsbzvpE0v6rtYPGdTjWPIHmydszOeys3am5rEB1ktBUFVqnvRQ0z8jBWB8YGCow9lNRjHCutwLYW/mVHWwerDwl0LrWCQTC+UXWputs49Tubt11+LHlR0QUFNNWCeWbdLVfy7466ubMANsVde2IIgS0g6WQCGPGjMHzzz+P9evXY926ddy/6RrdG0qIVobhYOul5flobbuBAWKwlECJlV2OnVUKQ4fkkWf3OpbK+2QpK29HkvjBtJGyaSnWrfR6zZZUZh/uiI1V7ONwM5rqYbnPj8/H+KzxmtIS2Rt9ds8CXaf7amWFow2i8TAALLq8voUKimNbQUfl7AeNA636GKq5i/IhfWjtYtszKre9KlZUX368BspcrVdnAoEeuxuK+siNOaoHhaVwbFSOiRsKqLWDxnU41jyB5qlmz4ioTGtZgeokIS1/MsIJmmfkYKwPDAwVGPupKEY411sB7K38ytaRB61HuPWQwnqF1R5eP9XmikDKE84vsjZdp5w2xYlPDKuB6kBhJCOKguOV7xNRmY72a9lXR92cGWC7oq4dg91xS05auU97X3jhBQwlEMMwBdWm+CzSeCIUh2OgjnLzzMdUDyGonlRftXrJPS9sl5Y0BoYIMkd7A9dL+lprjNviU4ksyMn9JoIhiieqBEOH1FGYUMiN5TKbDetc8bLyLrv1fs754esbR4KXZImV9vMs3/WCExt8YRK8cXMbRH1Y3xTL/WXmQ/n3xiPcGJEwgmMblQNv7yh0QUFiAexW8Se3LAfSXe+58eAbLpxkOwZj3Vac2NnFfapDjiWKgUv3Wc5bXme1hkkg8DZUDX7jQW480m+6HuIwCby+Kdl71r1Ax7aSjsrZj/Yuq2Z9VJq7tMyPPDJ3HMLd73lw7Tsmbgy6GDbw0Jp0ZB6ycXFHKX8ar0SIIT1DQL+/YXzqJpUX1X1S9iTZNQf9bzYFdmo8ULs7Ln1cwGXyY47GFo0x+hRzdmc3zk+bgPyOGC6mcNfixWj/WpmQQ/pZ55qKNdzndFrB64ScXGlc8+s91roq0DVPoOuoWQWzOJ1iga6zmLLlylLSybV2O1oTs/z61yeX4pPkbRHF6ZOzCbytov/lYE+HZfSpIZW3gWEE+gx3y6vAlteAfV8Aez/t+wxc47N60gdpg2hsnp82HnO6ejC9swvzW9twfmsbRjrd6roeZF01Q1qO3t+BljPAKLVZ0Vg4GR4/W2YGimag1Gph9rW1uVS9HTJrOSrLRfZdZl6l+zXZx6KMnFq+PBTmYJMF7XXpKF+T3rdXEKfl1itfZqL8mwy07/IPZ8avO8ptscy5ggVyyiqW17c+Sj9kUbfpWvegffNK/dgz0WQ2C2jIvPDIrLtojpucEd6XkH4Yc4p3jmSBrvNhEoTtZ4Da9JN1kO6pVdrFWhNHZTuiCAGRkxEhmfQx+k2nb+fPn4/q6n5WxqEQGDhamUCDrZeW56O17QYixLrKYBIVOm2tCU6UnFyvygovhaFD2kDj78EPbsa8pzcju7kvnmefvDszZqLqn71wVFTBVlTExeYkcORK5eWwptpQMrvcm7beitLPs2DymOAxeVA4qwG121L9CMsoBu6BjRlcHzrNgNUNjiyKYovyfVy6pghOIpXqK1Puk/Ng2rxg9QJVZnpejxZNW4QH1z8omz7JloQxaWO4OJ28cza32b9dvO7WpAIPMnQ3UJ2l9ixcvdCPpV41b9Z4FLAChwNK9p4gvTctdxo88IhkryYneslw8PLL4aqolO0Dqf1Q6jfOacvQx1DMXUp15W1gbSrw7wXTcOvPl2L5huVceckuNx6tqxfFiqMF6t1ZmSIGXWmdWHWW6vqS9UsUdSmYvhFCqS6E5JhktPa2iq5Nzp4Mq9kqKjOQvhvoNQ/L/gTDdhxovenz1Uv+c4ko1i05bVeetVL2BJ9cv8np5NvHno5Ws9kvjrGovXK26NQHgNfmseP48baKsPJioHy9+D7ZsOtWoyU+LeTyNjDEoca+rjRPspjrdcyrAY1lVpkCOIpnwvarlf7lB1lXzWCVQ44l4bhW+62lXpFqj0YI+5JlH1lzN/X1Yyfci+R/3669HV1NcLxzJWwH+zmANsTFIi0mGUe11jHr1mw24Vf5eai0WXFa1vF4tLoatkPr5BtjT4ejvll8wEOwN/NdL8xH8et/F821Up1mykJlLyhXXn0KsPgyC8aOPVF9nmbNMzJ61mI247H/3YQ7tv1HFOvW0cdVIOy7ZQUj8afz3o3I10x+aCoFXj4J6JbEMx4xE7hYMuap/W9fIW/X+vRmYXaW3zoyqudLmXb9kJyFa1NjRW2J6nZECTmZLsettc/j73K5YLFI3h55PHC73Vi0aBGWLVuGoSg0ettGcTfUiL8ijWDrpeX5aG27gfCxrvrYVoXXzFa0/3cVyp//FK70BGxe8ivkpdhR5HSg3GrDnrpqTH/4IyQ0dqHyvsux9QhwJ3lGpY5CXiIRAbkMHdIJOolWfsON6M1KRt2952F8bgFyCmdwfUOOJc5Re/gwil7wfglRfuNN3KKMc4TEdKKmYj12b96JrBX/8Wbo8aDl/BOQsmoznBmJ+P63v8Cxz34Ea0M76hachYMlOZi4aCVimjvhSI3HtuWXcH080ZbMlUsnG4VltowSn/7UEvyfZ4JnpSdmWyUyrGNSjsGvRv6K+7R56pipqul5hlLehr399fO4/qmtnIPQ66xu6vvc3so5ll65cyZuOXMJl9ZitoRMZ6n8zdWb0dDVgAx7hvbxwBqPYQLPPiu190JWWtZcoGd+IH0uvf561Cd7OAd5gt3Jxbiiz6XopC05+bJagfgVS2GZeQLu+uouxG3+AXe860R9stehm2R34/Tk0bjllKf69bGqCvalS1Ey/3xReZv2bkKPvSfgPuTG2PlneB2MEn2xpFhg+/srGDmm/5M4oSz+svp3qK/ahDKrxXeqgE6ejE0fi5sn3exXJxbrOKWfmD0Rr/78VcX714y/hqmvgc7dcmVR3R876TEuL16nCcfnHu9XJtXncPthWOuacfSCp+BqcfnJ0JZqQ/EHn2h6AaSVlV2NRVlNJlTO+sPr4fa4Q8r+rjSulOr8753/5uLz0aeeWk4NScfw/Wvvx/ba7RzJGn1dQOOtwhaD9IITuK8UWP3MZACX2iImCzidUpsGXP1f/2e//wDoqAOOOlN02ihc8jYwOCBdD6iyoMuwlItO5R0xF7j8ffazUp1VSh+gDRK2reCLW+A5sBom0TiRgBxS0vJfn+//nI66agajHHIMCF9dq/3WVC+G7OlkqSnU7VHRM163rvr3VdjWuI2zizxeqarBpJ4e0XcZdGpzgz0ON+Rmc339RmMXjmmpY+pQ5cl/EJUh1JuqQ2tQ0NvDrXXubWjC9K5uWYZ4ku/avjJfqq7j0lr8zpP29YOJcqEDGC4/Z6pwrrWmWFGy6n++uZYfZ3Jz/RnJo/HYsdf77wXf+z+geifgkTiFpXN7ghMpl+ah85rX9a29pPOMwhr4yo+vRGzpekzo6eLC7myIt6PE4cIkawoKRszG+LHzmF+nRBSc3n/JyUvTeKH2vnUpPHU/+n1vKNQL1fk62kDtKu3bq5XM4vpSbg053NAaLsftf//rXYj9/Oc/x8cffyy6Z7PZUFJSglGSTfxQFJoBA8Md5HyJHTOGudkmRwfPym4g8vKWS0vXzSkpcLe0cGmF6aR50O/m9z9A6vzzVcsUbrqCddzS54fnrDpH9fm/zPwL92mzI9GhKf2H53/ILQgo/5vfPRMf7KnxLTZ58CcG5h2Vgxcu/GRYLiDkNsyqG2kdoD5Y/ORZKM8yMU/k0wnNojoPlt3xEfdCmO9f+uRe+gzfr6SPles3wDZtql89g657/T44Hj1BVl9s92xmOtTVdJmvu9b0L5/6Mn7z2W805xcM9NY9XDIMtE7B9HnI264ArY5bqY1VQyA2VXdb6XPn5xTIiG/ZqulFUyTlbWAIOG7V9E5JB4PU2UB0tWbXWuS8+wv99Q3R+FKFHnlqgVy9ItUejY5bVl9SGJ8PK+RJWs8qzOMcaUppqi/8CK6UEkUbrFaOENfmZOFPNexTuXIQOlOV5lqSh9oa2k+nGf2oWB4Rx4apbwfF3BGI3msYk6SL0hADUdFeA2H3QeoKWnbmmWdy/3/77be+f/P/n3LKKYPWaWvAgAF9IIedFlZ2A5GXt1xauh5/3HG+tMJ00jzod9ZNN0a8j7Uy2BMDvJ70PEMppSfWXFpM0skAIeg3XedOfxqMpmED9cH2UWbZMCp0ne5THwj7l/UM30+kj+S0DQuaDirqixzjtF4WcbX0O+t26sovqhjQA5RhWOs0wOVEClptpO62hoiJfajJ20CYoYd9XqqDQepsILpqadOhv8LyQzS+QipPLZCrV6TaoxGsvqS1oRJobaiWxsrQAWlZankIcVxPD/RCz1yrW6cZ/ahYXhj7dlDMHYHovYYxSboYle01EHYExDZx/PEqTHkGDBgwYMCATmiNQUVhEvSk5xlKKT2x5tIJAfqcSwjv511m7jM2g9E0fNDTZ1HBxJ02UlFf5Bin9dZdLf2ErAm68gsGIZd7gDIMa50GuJxIIZi4foptDRET+1CTt4EwQw/7vFQHg9TZQHTVlaRDf4Xlh2h8hVSeWiBXr0i1RyNYfUlrQyXQ2lAtjZOhA9Ky1PIQgj7/1ws9c61unWb0o2J5YezbQTF3BKL3GsYk6WJUttdA9DhuiZCM/hf+W+5/AwYMGDAQelB4A/osnAW6roeZPZjyWfVw19bCsXFTUPXgWdGVQKzvFCZBa3ohQymlH5s8nSPB8hEqnEpkSU7uN10/Onm67OdGcvKn6w2vvILmVav87nFhJ1atCnvfDBbwfSZlkmex48qljSSDrsOR4CXREuiLJZZiyVm913vjxen79F9v3dXSzyyYGTFZSOtCYSoohAWrLGpv3fMvKNql5m++95MhP+ZYMtRSp3C1Pxp0LpRQa0/AbZVjAe9j/db6aexQk7eB0MJvzhWwlJNjiJjt/SCng0HqbCC66kwt4fKmWK6KkJbfV1dPkONLFTLlSGMoqv1WrZeM7D2hbo9GUF/SWpLig/Ios9mw1m6HSxJZ1NlHckWfplfExGFXaq6sDlGYBDW9oXIoP6VztyRfSkMxW1l1EqYj7hG+Pn6EYYK5tvTrQr+5VrdOS/pRqTzuetacsPXtoJg7ArE5AhsnpxfCMAlR1V4DYYfmGLefffYZ9/fUU0/1/VsOlGaox7il2Cp0TD+cZF2sMrReC3c9DBgYrCB93lzjJYnKtGcOmoDoHElZH/GY+fnlaHTsR3xbDTqScuDsTUT87Q/DWteCXXeeg6ZJ3je2oWzf/o/fRc8dD6I3KQ621i64MpOQc8cvkJOSgJqWTtQ88R9Y69tgSksFWtoQ8/j9+Cy/EU3dTZhTNEczQQAx3C5cvRAbqzf63WMxjupJTxvAg5dfDldFpR/DPTltibDMUliAka+/LgoTQTqz+6OVKFr2BpxZKUj+07MYSe1sOoj2PY0ov+shoI/ZtvuW81B0XBZH3VHqSkDc4tdgrm0klj58f9e5XN9Ek94Fat+DmRdYzNw8xqWP44i2WnpbYIKJI8N69NtHsa12my/N5OzJuHjsxUiOSeYINcI1N/nI/8rLvSRas8vR02JD+Zp0eoMN4jRx56Qh7f7LkD/xDBxoaETnbxbAUtuEohdfQOnR6Vi6YSl+aPzBlyctsG+aeBOae5r96v193ffM9DwbM+s+yWvxjMU4NvNYTf1Fi3wtMuP7qGPNGtz1npsjhvvPgml44MRb0Vm7izvxkRkzGo5f3w5XfT0smZmw/nUFZ5eKnE4fkeG+X10ET00dt+3kZehjnianLZG+FRV5yRRVCMoCYnQPAKEqRzhGaKktJ3+5sSS9rjWdtA57Gvfgr9//lamHVe1VWLl7JbbWbtXeVorBR59zxmcCXyxVZVeXymH33g/hbtiPlsQMtCZmIbe7HfsOfIovOkp9G9Jw9KuBwQXWmqekqRKZ3Z1wbHgXZR90wdFpQdHsRiTm9yjqoCJzvVL6UNgGVplCjJwDXPiaf/lB1lUzWOXY04GuRu2/tdQrUu3RCFZfnpZ9Ah6trYft4Fe+a5tjY/BmchJ2x8agqHguHj3hXqT8+3Zd7ZCWlexy44+tThzTXM1MvyEuFguzs9BqMTPrJOqHK/4JfPYgHDu/FDtR+2LMeufaQjiancy5VrdO9/WjqLwkN4rn1vaXx18vzEfx63/XRD7KnGNYpLx7Pwf2fgIkZqNtzKm48/uXuLpT7GAKQ5FVMA0Lz3hh4OYOqvuuD4DKrYCzA3A5vCER2qo168uhmp1I+udNSKva6acXT6elIs3t5hz/dO52wNtrIHrJyQj//Oc/8a9//YtbfJ177rmYN28ehovQKJB3m6MNT+99OuAFvRaSCSrj99/9Hlsat4gcEIRN1ZsUr4VysRupTZKBwY9ACKoiDdLnBasXiMaLkkNQCXrIYkIlCy1Ox+pUYMmlFr9YoMelHYfnz3je1z49/cXLbf9PG/HAGy7kNtMpCQ9MHlMfi2wjqtanc4s0/nptmhkPXCImkiIn21tnv6X5012ebZQc7Bn2DFVHp1p6johh4yZ0LV4MU3Y2Yv/4GPbVbkZyRz3MBaM5J5Trxt/BUVUF+9KlXMxUssXLdizDjuYd3IlDvv1C+XfWW1H2ebbvCIrV7kLJqV4yidLPMuHs8n7SVJcM3H+5uG/06p0UwZAwydn328bchrElY2XLS8xMDNm8QH1Gcbm6Xd34884/ixxLciA9au1t9bsurYNW4idp+4TphI4DbrMT0wnHni04eN/zcFXV9uk7UDSnEbEpDtE4fOrXaTgY1yZysN4+5Xa8susVP9ktmrYIyzcuF10XOmRZfZVgTUAHbQpk2q/FSc6SGQ9eDgf3bkbHdbfDcrge1lQbSgSOV769HosZJpfbzy7t/SoHplYTNzTI8fvuLRPx8Kl3I6m9jtuQlR9qRc9dd3O2reiF5/3iZsv1G683as5nqQ4I26VWDl1zJjn9ytFC4KckcyHk1nAsfUiNTeWc/Wrp+D4lyNVhQuYExFnjROXyL0OOzjhaXqadjcA/rvF3WJx8H9DZ4LfJ9ndWuPBoXQNO7Or2pWkym7lNKI+6/OPQPe95FGWPV5SdgcEHuXWTnG1WWvOIHFT/Lwm2eQ8CNjvb0cOCAlO9Fmi1Qcwy6YRkc7mIXT2cddUMaTl6fwdazgBj095N6LH3iPuS6nh4B7Dpj8Ch9WyHWwDt8NMbGZ0os1n99YtP214LtFQARVOBUT/z5d3+4dsov/sh2LLSUfy7i2CL7eacm5QfvUTlXkLLzLXMujEgnOd85eVkI/bJFSgakQyUem29w34kym69X7E8XXMMybyzCfjTz4Du/nmQQ8EJ6DKbYC/f5PdMZWNnfzIda+WA1tZU9zcvAcoF+iKFLQG4+E3giJOYt6Vz5giHAxfFFuKCIy9Ac1oR4r5+AukV/S9Zo+EFiIEodtz+9a9/xdVXX41x48Zxv3/44Qfu2q9//WsMF8ftoq2LsL1pO3diggedoJieNx0vnfZSSBw+VMa2xm1w03EendBTFzVc/+n12HB4Q8BtNTB8MBgct6TPShtp2vBq1euBcNwS7njnKlz1+DrOWeJ1mjb1xZKychubm6+IkSV9ErZPT38J5UbOy+de6+XK5zxWHpPfX6V6kPNhza/WYCDAt5mct5aRJSiaNMnPmUOLzMr1G3xEV2SLhS/QhO2Xyt/ruTX5nLc0tbq6vU5bV4ILN1/FlokevWO1KVD9krPvE9Mm4pVzXpEtb+kPS0M+L7DqohfSOoTCccs7b2PHjBGdGBGOQ0ucC4UnNqqOQ6pfUkwS2nrb/GQnd51vjxb5yPWB0rMsmfEQyoE7eXz+Gd7TsQy788gFqbj3vWZZu/TiSTbsKjahKdmCGfkzROVlm83o2buXubELRr9D4bjVmlZ6PRh9ltMHren4PiXoqYOmMfz6fODAakCYJ33yecRc4PL3/ZJL5fBidS2md3Vzp4R49FtNL+iTYMuok5n5GRhejlu1NY+IuZ4cF4bOGAhQL5k6qNPeRQNY6xXhPC4312oF6+U2lVfrdvvJMKDylGRetU18ylsJfc9UnvyHyDluqe5yp+qlp6TvZhOQKfpdqmv9ZTNI9NJA6By3usjJnn76afzhD3/Arl27uP/p91NPPYXhgoqOCm4TL10I029ybNDbqlCVEYjTNpR1oU/bKJ9wttWAgUiB12clRLtec5/rt27oO3XSF0Pqs/5YrXQ9we4Mafukcku0e8uh8jhnLUHwV60edGJsXdU6DCTIKWvOzmbfy8vzOW15WyyEsP1S+VNsL2tfu51dFp/T1hrvxFEny8tkIPROyb5Tm+XqQzIJ9bwgVxe9CNfcRJsOv7AZgnHo6rZoGodUP9J/luzkrlN71lWu0yQfVvvVZKtVZjZbR1+IA7bdiU9uVLRLpWNdnCOb1jXS8ki2wWwkow3B6rOcPmhNx/ep3jqo6gJ9/kmbUmme9Juu02kwBTnQZ6x00lZKqSJ9lWUhVy4jPwPDD2prHp/TlmDojIFQQqe9ixZI1ytChGOuDWl5ajLX6rQVPGNpKUVEwNddC6gd+7/UtS6vKFvNls0g0UsDoYMux+2+fftEp2vp9O3+/cNHSQ53sck3eNAnBuEuQyuCrQvFIwtn/gYMRBJq+jwY9JraQPGbaKNCp06EoN90fYTDGdL2SeUmV76eeuysFcdsilawbLGS/OMznSiY5S+XgpnqMom03gVq38MxB2odm1oRblmGYhzqwc66nQG3P2R2r+mgYnuP6+nRJY9otrPBItT6HGnI9g3FG1QCfcKrMnfogiQ/A8MPWmytCIbOGAgVdNo7AxGQeQCwRmqtobfuwrAOGtYOmudPQy+HPHQ5bru6upCQkOD7nZiYiM7O/vgh4TxC/NBDD2Hu3Ln4xS9+gVUM5m4iTDvvvPMwffp0XHvttSgvD/3iOc+uHFyb4sKEuwytCLYuanEoQ9FWAwYiBa1xVaNZr6kN5TYrF9+NPhUUwvvpoJkjDNLaPgoZ4K6tZTI3N69axf1PZVJ4AGKVJ1D5FNO14pt0Zv5a6jEhewIGA1i2WEn+JJfKtf7xpSrXqcsk0noXqH0PxxworAvpGekbC0I9DHUd9CD/u2q0drH1gPq6+YBddRzqwYQsfeNF2P6Q2b20kYp2Z0dsrC67FM12NlholXm0QrZv0ryEl7KgOI8KciDbqQuS/AwMP2hZ80SDzkjXT0LQdbpvYJBBp70LB4adXqnJnIH2Ku/agwW63rWvJQQV09BPvYmydaA6+oHiE+tYO2ieP415c8hD9+5i2bJlqtfuu+8+hArNzc2YNWsW0tPT8bvf/Q7x8fF47rnnkJOTgxkzZnBpPv30U86h+8ADD3DXnnnmGe6Z7777josZESpQmUQYsrtpN9wet1/8ES0B6tViphSgALPKZgUVH01rXZRQklLCxV6Ui7USDWzoBqIH0RjTlqXPajFuter1QLSX2jA2eTr2rF4HS4d/vDciCuoYYQW8pNyK7UvZvx/lixejNysZB+afgKNe/gxIT0Tlfecj7ocKZLzs/eRn/+Vz8PsPgcQWF/42z4UDiRaUfp7FEZCxYtyq1YNi3M7Mn4mBAKvPlK7xtlioM+1d3vax5E+fcLJi3Do7rdjzhbxM9OidlvqH2r4LGeGnHTmNOT+ZYeZil+pphzBfqkvXN2txx3tujsSKCPYoLAWdMiCnX0eXlSOGy2wFHrsA2D7Kf6EurQMvG64cTzmcrU7V+rHkKaxnxvYy9N75EG5JisOeXrEekNOW+rpqUyqKs804NF57TNIEWwLae9tFIZL4vphZMJOTz/rD60XrDimEfSeVrdx6gmQ2MXui75RlcQFbPg5HAkrXFMLZ4WTane6cdOxZbVa1S1L9ktNfYf2DgTB/pbGi1TZouU5ja1L2JOyo3REwTwFLH1h9lxybrBjjVk1npOUqru0yR6OzeCbshzbAJMyTj60nIeeR2pgymw3f2OO0x7gdINIi0r3NNZuR1FqDibYU5BTOiAoCpaEAvfOV2pqHCMpEMW4j2U/0aXTZWrRv2IryFz7jCJqK31jJhZXhTt+ZLHAc2IWyh16Ho7EdRTedhu7jinEwLR/5iXko6O2NGnKu4Q6RXlK/Uv9R35BOycVbDWW/8WVS3lRW+hFoX/ctyu9Z6q9X6UfAUV2LspvuhKOuCUX3XYfE088GiK6Irzf/b0F+Qde3r44FMo5BTWNbKFtWfTJHs2UOMzqLp8Na/T1sPa2++YIcouVr0mGLd4nDptCapdOGsjX5cHz8PIpGjNMdIkKrrRIR2M6bA1tdvzPdR6LYaUHR7EYk5vd4b1higMotQOoIkRz4ObOqbA0mdXUg3eVCg8WC7fEJyC+eC8TWwnNgNUxM35DZS1QX7fZEqgP1+1BTsYFb52cX6dtDDFfoIicrLCzUlK6iogKhwm233YZ//OMf2L17t+i0b29vL2JiYrh/k7N21KhR+Pvf/8797u7uRl5eHu69917cddddQQcG3l26G7//7vd+8Q6DZdSWA4uReFruNHjgEbEAs66Fsi5yrOOhbKsBA5EC6fPC1QuxsXojk917xdwVA6LXWonO6HRs+4LbgcM1fgzLQjZ7cnpJiZGE7SM5PPDBTTjv6S3IbfYSZ8V6PJzTieKx0prA1dO3te5zytKGKXtSMyrWZnBOW7I7Jniv588gYqZ0biPlMXm4+7VpZjxwiUlUj+SYZLx19ltRcyJNC/mAUGfoxCc5DzmZCeRPJ23LPs/2eiD6nLYlp9Zx/y79LBPOLq8s65KB+y8X902o9U7aJiVCJjX7TvPe03uf9ru/aPoi3PvFvdjRvEOUH7XlzqPuxNiSsYp1ZJU7JXsK6st2Y+ErLX7y1aLfcnou1772+naRXKRyot+JmYl+z59hn4Krn9sP1NZzv2m8lJxSr6mvSf//dPqf8OzWZ0V50ssMilUqBV9XAstuUX6tva2i9IumLcLyjcv9ZLu3ea8orRxY8/uP336D5ttvQWp9t2y/uMyAxc3qt2xY2sy+fhs79kRf/qzxp6aTwZKVCcEiYmONH7nytNZfL+T0gYXJ2ZNhNVv91oCkB0vWL2HOdfxz+5r3iXSCyl151kqmfebb9V35GjxaV8/FqtXCZi2VR7LL7fd8k9mMNLdgwz3yJNgufDXi7NhU1wWrF2B35Xo8WtcgquNA1Wm4g04VHrz8crgqKv1sC+cQ4WPdXjUKtmveDFv/CG2FqbsZ+WvuAUq9DhpRXRLdKP5ZLbuOEseSDwYb/IDAz353NgL/uEYcp7Rkjvet0sGvNfWX2vykqcw+iPQnyY3iuTr1KlR6xqqj3rz05NHVBLx9hW988dgQF4sn0tPw58M1SO1zWynKaHU2HG1m2IqKUPzaq7JxeIMFR9x6xZVwlJfDlmJF8ZwKff00YiZw8UqvHDob4XjrUtgO+fOAdBdNw9LsbPz8h0/F868QI+egavajyD9inO9SKNdMQYGhA257GszU332gF7vvTzgLD5z81LDzMbXqICfT5bgdCGRnZ+Oaa67Bww8/zLzf3t7ONZKctpdcconv+gUXXIC2tjZ88sknQQvtqn9fhW2N20SnH+i0w9j0sXjspMfC9oaASCLoJAydOuHL0Hot3PUwYGCwgvR5c/VmNHQ1IMOegeNzjx9QvdbquKXQBl2LF8OUnY225XcCKS1IaKtBR1IOnI5ExN/2MKx1LfjhznPRNMn7yRGrfcRaShtpckQ+91ov53jxkmqZOFItgjnWCXevxXeStmBmI6o3p8LV473vMZvgzExCzp1nISc5HjWtnah5/D+w1rfBlJYKtLQh9okH8FleIxq7GzGnaM6AnbSVg54FDenM7v+sRMHS1zinHDmhiICKYncmVsTgqvc9MPfNpN23nIeiiV7ys1JnAuIWvwZzbSNgNmHXXedxfRMuvdPjuBW2bdvBbZg0cpKoPjTvbW/azjzNRy9HtzVt8/vyZGLaRLxyziuKdWSx5vIQ6qT0dBVt3G++IkbWaSus30unvaTIzrt43GJVx+3SH5Yyn790fwHOftsb347GDcU19tUxwYVGiwVp7XQy2Ox3MpicalQ3fk7983d/xo66HX6nl+kU7Ks/f1VWXvzp4nun3Suam5VkqwVC+fF46PF5OP9ve3wnoXm9509CL3/FhbQOoDXRhN9dZYU9rtd3v6s7FsveNCG12YG4J5fgiDMvUBx/iozKp70U9Y7bYORPfZoYk4gOR4fm53nZaNEDetF2dPrR3Jr1kY2PKMpZCml+IxwOlDjdyMg/AQ+d+4auNWRNTQ3KKr6AuaUUTfFpcOeMRLHDiYm25AE93crPiy9W1/qdCvadAjYYuyMK4Wk2ywsPo753H0Y2VyHT6eZOsZY99QkcDW0oevGFsBIcCm1FxsfXIq5C/GJG6qQRzluqzjWDDX5A4Ge/X58vf7r2F497Y4eqnFzV7bhllRkqvQqVninJRWteevPg0n8JCNaXtEPZYI/DDbnZmNnVgwuRjFPG/QqOzBNR9n/XwdHs8JORNcWKklX/C5vTVuS8Pf8MZh3o94iT6xGj1E/kxCY5ULtlCM5oe7HObsf1uVnc/PtYbT3G9jrg3ZH1wWRBd8F0xF3zUfQ5bhk6IP3ahvp4o92O1yfPY65DhjJadThuQxeILQyor69HXV0dd5r25ptvxpYtW5Cfn48rr7wS5557rk8pyfdMJ2yFoHQU91YOPT093P9Cocl9NsU6aUtO3B8af0A4QQtw6cZe67Vw18OAgcGKwarPtmlTgaVLYRlZgqTsbL/J2PHmFPTs3YtjFDYwPGspgT5FpxMs/MJQCLfkxG3l2gzupyXWhcLZDXCf+QJip/SzyeZQaKXjr+XKjx0zhvtLG6nrMDRA+uL5xcVY/OPfUZ7lPUncABsO2WzAkUD1L924P/8aZOWOROq8eb7nMqlf3j4XHRs3wpqejnFh3FwG0zZrphUFyQWq8x7PPM8C3aNnyEkjN76E+seCVCe94SfALX5Hz61Hgj2Hk7sc+Pqtq1zHLIe/X1lSiYJ4+cVsRUeF7PNfFe3HtVObUPddMnfCVlTHk+txZV4OLM0WZjgHypOXD61bttZuZa4t6DqlozSselAa/vrswtmaZKsFvHz4OlKe72bvx94LzP56zzUaWHSVBSftdOOrCWY0JNFSXHjfjd/9yoMnM2/DOIHTlgW5+gvrZI3iJWuw8qc+1XIqWm48qukBfSVBa9bKtkpVOUtDpUjTU/8eoi5u2omrFcY7a861tllRcOTlvnvRsKnk21jscDBPM1loi8kzdkf7p6hDCLSGKHrheW5NQWuNkTjed4/Ur3j6b3xrjUjA2lzq57Tl6pLg5pxorHlL1bkmZIM3dGvgPt9mOcz4viGMOS0yZYZKr0KhZ2py0ZKX3jxk0tPMT7aZnJbr7LFYhx58OPGXKO51oHh2OVtGc6phiwk/DxOFsZCtg5Z+ovbu+0JRH8jBOauri2s//fuYXod/Io/La5+izZbI9KmJ0cfUxofLVivuI4Y7dJGTRRq8Y/WOO+7AiBEj8OSTT+LEE0/kTtP+7W9/4+45HF7ljY0VB3+m3/w9Fh555BHOu83/X1RUFFL2bQMGDBgIh/PWnJ3NvpeXp7qBEdozObZmIXKmiD/ZLZzdiPhMJxKPSvd7i82Xr6UegxEkO3LGsU580vXqs48XOW15kDzo+mCSidq8F+icqJavGoM4neTUgp11OxXvV3VWKd4/3HVYsY6pR3RzJ21ZdUyxO5lOW6l8tKwt9Kw/gukzuXz5POX0nkDX3z9RPnwFXS8f593IKGGwr7VCKX+90KMHamNDKufB3i9awLdRlbnbYOyOOPg1BQuRXmtY2uR1XWne0gRDtwYOFHMz0n2jVmao9CqYtoRCLnrzUEkvXANyc0/TQWUZRWJcqdVBCyq+1ZSM2j/o5imNus6D+2JrCKwrhqXjlgjJTCYTzjrrLC5W7cyZM3H77bfjiiuuwPPPP8+lycjwngRraGgQPUu/+XssUPxbOpLM/19eXh5S9m0DBob6J2zNq1Zx//PspvS37vkX0Pr55757/HX627ljB3c/WhHNDK4UJoFi3AZbN6E9k2NrFqJmSyqbyVkDc2k0yzMQDKe5IJg4xEpyUMtXjUGcPr/XgglZExTv58fnK97Ps+eFrY68fLTokx6dC2XsaK111Ao1eQ+F8TWQsbv16IHa2JDKebD3ixbwbVRl7jYYu4c1XEnyuq40J2iCoVsDhzRvaLGI9o1amaHSq2DaEgq56M1DJb1wfcXNPWkjlWUUiXGlVgctKDxBUzJq/6CbpzTqurCNQ2FdES5E73dnAOx2O4455hguzq0Q9JsPbUAhEigswqZNm3DOOef40mzYsAE/+9nPZPOmE7nSU7rBsm+HGkJmZS3l6E0/WBgHFVkoB7p+YYZanw5En3Nxx66/AegjFDEn2eHu6IY51gZ3V69fek9KPNDaxX0XYXJ7A4Fm3XQjogUkw90frUTRsjfQk5mEnfdfgLwUu49R2tEb7w0+f/gw99leMCc8eMZqiq8rRKY9k4t5Sp9GS++nbTuIYx7/N1e3xt/Nw/jcQm8sQGFgfJm6CcujMvIS8jAqZRT2t+xHe5eX8Z3YmrXEuK3dnuL9DGhNEYoXxDM/WOfLc6/bjGMf/zfMuTmIefFRNDr2I74vHm9mzGi4bvwdV+eYx+9H1fhczp6SfaW/VR1VsvIJpY4HwnDNmgsoNuXIlJH478H/6qqndOyGYixL26S1jdJ0avMeIZA5US5fHkKdlMYKo+sdI6zeb2RlwNdhZsFMxfpPHTNVsf3TjpyGWWXs58cmT0HZmq1wdDh015FIoXj5aF1baF1/qMlWC6T5as2TnkuKSUJbb5smeQeic1ydlEN/6QJrbOgZP1rrrxVyMlR7RqseaB0b0vEb6jVwNIRGkIJvI4VLIIIU2Ri30bAGNRBx+HSW/m49xe+zX6VYpHRdU4xbPbqltE8i2ppA7oXit956DyBEdihztDfOKCsOa+HU/hOMKnVWs23sMsWxXEOmV4HqmbCPlOSiVWf15iGTno9xS2F6hHMPtwdaU8RcjyntVUIJhyNBvg5fZGqLcTv6ZO9fhRi3ZRYLpnR1Y4s9jjlPsWQaFfOtTJ/KxbgtKp47eH1YEUDUk5O98MILHDEZOWILCwtx+PBh7uTt2WefjT/84Q9cmgcffBAvv/wy1q9fj5KSEqxcuRKXXXYZNm/ejMmTJwcdGFiN6TjU0FtepOsXFrBYJ+3pQFdjdLCvhoJZUwfU+nQg+5wmytKLL4azuqb/Yl8sVD9TLLnuNAPv3T4RC654acB1k2eRJkZuIkV64A0Xk9G+dE0RnBR0Pgh2UmFZekF1W/KGC9nN4phJnRkzUfXPXjgqqvzqplaeqL0JLsR6PHB2WmGNd3LzqksS49a7GGlE5eYCpiyk5SnJk2eib86Ixb2/ciqSTUkxNXcqVsxdMWC6o5U1XqmerDykTPLRYL+VbAwhUPvDynda7jQkNnXjvKe3KOpMdaqXIIt0hpygVrNVpOOhtJGs58+wT8F1f6zgWM5tqTYurhlfx71f5cDUahLVkQW9ddTTDjnZUnxTLbaHla8WnafnFk1fhOUblgc1Jw32tYxW+bOuyclQCXr0IJixMdj7RQuojQtXL8SPlevxaF29KNatY+RJsF346sCsPQ1EFySs94rM9lpY5fXsI7Tsk4TQcy/Y30rtiPD+KeB+fe/qyO1BSSbvXOnTIymC1isp1Oou10dnrwA+XBBc37Fkq5BHS3MZKl45Hcc0V/uukaPy7qxMtFrMvrknvrHTe3ClvNxvPcY5UoPct2mB7/AM1UGmnyyJToz8mUw/0YuBS9/xykFiW5TwbWwMEuPScHRLTfSOKRUdcNvTYKbrgj5+f8JZeODkp4bMuiIc5GS6HLfLli3TlO6+++5DqEDVoxi3f/zjH7nTtRUVFZg3bx7+9Kc/ISEhgUtDsWyvvvpqvP3228jJyUFzczOeeuop7loohSZkxg30bQCLQV4vs7IUetMHC2kbQvJG5/X58BxYDZPC2xiPyYKegulo+PmfAiqCVU/NbQkBsyar7+WwaOsibGvcxhGW6GVMjwQbo+P5c1D6590cOQ/ke6zvd99fkwe/u9yCg4W2iNRTTd7EGi/ciCox2hM7adxLr/niy+rReaoH9SeL7EkrXt5Xj+yPEtnssoyFCc+QLYeJ+9246z03xxT/3olm3PCRG6a0ROxdcCYS9hxGwWveZw9cPgcjPtkOW0M7Eu+9BThmGjoWLISnthb2pUu9hGl9+iptn5I8yTF38xUxupy2PGjRFmrdYTHMK4Hmgru+ukuRoHJK+hS8cs4rfte1MM9Hcixraeu2g9swaeQkv3kvmDlR+ix3kv+GG+HKTkPCH5/CyPQ07qTLXd+/hM0Ve7H4DQcyW4HHLjBj5ygrZuTP4OSjVodg523h8xnby3ws59yYI+KLPrZpOpm//7JL4a6uQfuy36L8mEz89fu/cqeoyUmn1Lda6qinHay0wmsE1r+V8uWft5gtcLldvr/S50KxTgpFHgMJNfkrXZNeJ2iRu9Z66LkfbPrBCGrj5urNSGytwURbsvfrFuOkrQEpiACodC3aN2xB+QufwZaTjeI3VvbPCWYrHPt3oeyh1+BobEfRTaehe2IxDqbmIz8xHwW9PfpPnmrYJwmhdE8trd7fivshFqN8334u7pqPMNCg9Z9v3Uf9Sv235kmgfFNQez5FsGQCE0y5E4Bf/g3t6zej/O6H/PWK1ho1dSi78Q446ppQdN91sDavQlztdsnJXTNAeZ1ynzY9U9vj8nIJ5rS0xjz4NXJBL5GROblP5ytssRibPhaPnfSYb+7h1owK6zHW14iivlaBlrS+OiSaUDynCrb4fm4lR4cFpV/nw9HuQe55yUiP3e3fR6N+5q9PJKe3LgXqfpQtlxt/5Kj9xePB90skIdWBhv2oqViPQ1YrsotmDNl1xYA5bunEqxaQczUcjaqqquJIxHiHrRT19fWoq6vjTt1SmIVwCS0YqDlu6XPZc1b1h3yQ4sPzP/Rj/NWTPiodt/RpxnNTNCevvvAjuFJKIue4VavfLVs1GUytjltiNL9m/TWy918+9WX85rPfRLTPWfLgTqN+lilx3srA5EHxKXW49Zg0bIi3R6SeSvJmyZgYpT/YU+N7S8qDf6vd8OsPfXqnR+c3/rRRsT/VQPX6sOKw6A2uqG6vvw7bOO/n61psgtB5yzPF079/e+YTyC/2xj9M+NYbKJ9ItWjhQ8zNLaO8Ok6xdl0HS31OWzl9VZPnvKNy+tnndSLUuqPXcatVxnrttdrzAwU9i91gQItgnkFcKi96EVBU5xERfw2EfKR1FIIfK7RJGIi52YABAwYMICrnhEjukwYM0v1QiPZPEV3fhLvOGvPXpFfj8oOvaxT1kd51k96xF2rHLVeHf7+F2K9vYJ6opX1bz5SHkLj7vtCNGaVnDQxK6PFB6opuTQ5ZLf+HA9SQsWPHyjptCZmZmTj66KN1O22jCXoZfIcE469OxkFrpNsUYcZRJUbzQFihwyUPmqSkzOpyyJnSjPhMJ47r6YkK3WTJWI3RPlC9U+tPNfAMorJ1s7UFxG4uZIqnf1cm9PcNOWzpfxZzM5065p22Su1Tk6eQHVYvBtquaZWxXnut9vxwYxAXyot0Vei0HSj5aGU5HxJzswEDBgwYCMmcEMl90oBBuh+K8P4pJAh3nTXmr0mvQlHXKOojveumiIw9FSSOzZANV0HXE3M7QztmlJ41MOQRIC2hgXBBL4PvkGD81ck46Ix0myLMOKrEaB4IK3S45EFvEivXaoulU7MlFZ31VuwQEAIOpG6yZKzGFh+o3qn1pxp4BlHZujmSQsJuroX5XU/71OQpZIfVi4G2a1plrNdeqz0/3DCY57fBXHcDBgwYMDB490kDBul+KML7p5Ag3HUOZf6hyCuK+mhQrpvU5Fd4QmjHjNKzBoY8AnLcEunXokWLcMkll+DSSy/lYtpu3bo19LUbhuDZbSkOnhD0m67LMf5qTR+V6GMcpJhHQkhjeND97sJZAYVJCAkjoqR+3G+6HuLPFAoTCrn4mMRWz+pTnhV6wPo8czQcWXMYYRL8eqz/r8eEss+zUN8UGxW6STKmOrAY7blg8rEuFJzYwH3WT79Lvy6EoyfeFyqAPs/R25+BosxmwzpXvJiQ4NQ6X93Kbr2f+yRIahP0gOpXEB/Yp/By7RPKU1pnut6hJcQGAwOtOywZU6gJ+pRfCJIJz3rL64ucvZYiGsZIqEEyEOqpEEIZCcHLa/IBb6gElnzknh3o9gn7mupOOjJU+9aAAbVxQX+bV60SjRH+Ov2le9Ewjg1EFiy94GHohf59ktZ7amn1/pbdD8nsn/j9XFR+5h3uPZ+CTOh6+65K7WulzNFeOQZT1wjvcZUwKH0aavIbfbI++fL5qSHCfWMgOqArxi3hrrvuwuOPP87FYhg9ejR3bd++fVxchnvvvRcPP/wwBiMiFeNWC5QYfJu6m7hPCYTkEN/XfY+lG5aKiHLGpY/D4hmLcWzmscwYMsI8pL/V0g8JRs9Q1C+M9ZHTgZsm3sSxz6fHpeMP2/4wICzPtHAovfhiOKsFbJYmr3PWj7JAct1pBt67fSIWXPHSgLNG8izSG6s3co6VB95weRnt7S5Yus2wxbuQP6MRFZty4Gr1wJGdjMTfPwT3A0/6BbzXU5ZeUN2WvOFCdnN/fFj6/KYzfhzKP/TAXdsCV14W9i27Eq6sVOQl5GF/y368s+cdlLaWquY/NXcqVsxdEVR/SNsnkmeSC0fNrfMxrZLTlgjL6tOtWHyxRxdB2YTMCXjh1BcGXHeEbe5Zu95H9LbkUgsS7U7MiM3FMWPOgcmVilH3vQpzbQO+v/McNE0aiQRbAj4t+xRba/tfdqbGpnLjmsfk7Mm4eOzFODrj6IguVMNl6/1IJGwd3s/BFEgkeNR8/jHqblmIumSPT75nJB+Jq2Y/hDjkKD4rbNfmms1E/YHjc4/3izscbJv3f/wueu54EN2ZSSh/+FocXzgaBb29aEvMwpI1T+OsFRt9xGoJs2f77LjWMkM1RwvT0dJP7d9qdePzo00Vke2Fqj2DGVraprcf5PpErizW9bDInGLx9Y1jfgMpLIcn80NKEjxNzYDbA09aHHKvn0SbD1T/5QeYGtphTkmBu6kJMJtR9NKLEfm8VQ18O45wuVFQXwq017ETJmYDJVHqgIoGkI6UrfXKTyIrbl64/gbA7YY1Pw8lf1gOW/dPXFpHTxxKl66Es470woSihb9E4rwrvM/yedJaM1pkv/dzoHIzkFIAJOaokwUxxg7zmt59khB67gX7W2k/FOH9U0gQ7jrL5N+eew3Kb78btuxMFC+7gfvLEYbxa6VLL4GjphZFCy7wjgdy4VR/B2x6GTi0vj+vohnAtOuAvOO0jQ897ZXTUzX9ZaVhPENr6+X/+TXaanZyX+YRF4Z0f0v2ub5iA+LbatCRlNNPbKWlDkGgonQ1Ovd9inR7BjLHntdfRsVW4J83AHW72fJjyNcZk4yWnCPxv9zRSLYlYqYnBmnxmWjqrEdVfAqO2PM57BWb2BUZOQe48DWgoyGs7TUwyMnJPvroI1xwwQV4+eWXuZO2ZrP3RKDL5cLf//53XH/99Vi1ahXOOOMMDDYMpONWjkTJmeTk4rnEdsUi2ZaMp/c+LXLUkcOlp6cHO5p2+K7ZLXZ0ubp8v4XGjuUMlDoM1NKH3TnIYBwMFWOiGjFcQPULM3gW57S4NDy37Tm/vrh50s2cMz+SG2DhYptgTrLD3dENc6wN7q5ev/SelHigtYtbY5vcHsRceSXirrhclCYSxEdy+lDZWYny1f/E2KdXoSczCTtvPAVjn/4nUppccCe6MHJ6A6rWp3MMoW67B+YuM5CXg8QVT3HxXtUgbBvPWN3Q1YBuZzc+Kv0Ile0MvUwowC+O+AXiLHFI23YQ4x7/F3ozEpH3Cwdyu/f70vGEZb2dFjzz/8xYN8b/JGdJcglOKz4NBYkFyEvMw+H2w1z5GfYMPyeWVDZy7ZADte+z3Z8h+Yd9OObxf8Gcm4PYlx5Deed3WLPzVazrqeZO2pJTl3dkUdzSo9KOwjXjr0FybLKvfgSS0Sdln+BQ26GI2SCtJII8ejsPo/maWxBX0yzrpK5O9Tp1hU5qckJfPu5yn3OWZPdjw494c/ebIqeuWnv5+gYzhkJp61mEDuRY5Rys5eWwpdpQPLvcJ6OyNUVwNDtgKyrinLq1bjf3PN+ubLPZ96wlxYKRcyp9z9JJeGeL0/csH+uMrwO1a8HqBdhULV780tz54IwHsXzjcr823zbmNowtGevLQ4mggs9//08bZV9U7P0qB6ZWk08HXFlpsnOu3n7R2m+sdFpAp8afOf0ZtNe3i2Sxu3S333qEVb5UdqznAtFvVp/I9VMgxHpqz7Dua+mLQPtB2ic3jb0Jz+9+Hlsat4iuLz1pqZ9Ok64ThGMgaBva2Qj84xrRJtQx8iTcnZ2JT2u9xJaEU2KPw4V/+B5pDT3weDnTuetWOzGXe/q+2Ol/2WvKzsaot9/ibAALkVgn8H30XfnXeLK2HtO7+2O/K4LfSEerIyoC5MPCZ03dzUj//DbEVW2SlVX57lK033obUFvLXbbGO1FySj33b+EXXfx1Lo5kXArQ3TKgsheRmcb1AH8+he0gFThu+GdILvnrFovGTnf+VMTFxgEHv9bmJFTaJxECuReK31G2fwoJwlzn6h/WItfW6cvfceBHlF1ykXdNJDiowa2VVmfD0WYWXReBnLWTLgG2rxQ5celEbtylb2gbH0rtZdh9Tk/PehL4z0Jlpy/rWYbzv/W0B1H25gUY39J/MOgbexzePvZ0LDv1Oe73ks9vw/zvPsKJXd2+NBviYpESm4qjBc9pcbRLiYnl1tOtzWWo+uupGNvqtVU8HIUnwGa2ip3mfHiES98VlU1zyxvvX4Lr9nwDLd8bNpnN+NvRJ+H6EWcivrvde5F/+UX5svoiml+GGIi843b+/PmYOnUq7rnnHub95cuXY9u2bXjvvfcw2BCNjlvecND9RVsXYXvTdu5Uix7QSZjpedPx0mkv4fpPr8eGwxsU81BLL7w/2BASx+0AIdr6gpy3zkbvhJswbRrH3EnMns3vf4DYsUfB3eYlzLKmp3PX6T6dqun4Zi16553nl99AOm55pOzfz9X1lu+X4Kc96/Hsq12c040WSdkTW1C9JRWubgvnmHn5t1Nwz8lPaCpHrm3Up0obeNpc833LMad+9zhs9Wu9b98FoAVdZ4sN340y4YbcbNW8wj1WeKeGkO1V2lY6kVtU5xGRTbHqOBB6r9dxS229452rcNXj63z6QgRs3li+Vk5fbr4ihnmyWNrmQNobCsdtKOUs5/TinLfnn+HbkAhlxDlzP/iE0xWhw5RvF3fKf97pXietwrPSOiiNMXpp2dbb5tfmiWkT8co5r2hy3ArzJ51+7rVe3TqgJGe1ftHab1rmfhYoXM+M/BlYPG6xSBZX/fsq2fWIsHyp7FjPBaLf0ei41dIXgfaDtE8SrYlod7bDDbfoOr34kuo0C0Hb0NfnAwdWi+YiF0zYYI/D9blZoqTCceH3RY7gd0tqDApefAVFkyapronDCb6Pnjt8GDO7uvXFk6ON8+XvYzAjVI7bjI+vRWzFWlFvizDqFFSe/Acu9FT3b66As9Xpc+rTttTVzXDaRonsRc6ev58of6qVPoU+Yi5XL/4Zkktc5QbR2JGOCumzBoYu/OaS1+fD8d3XKPu8b30jXe/IOW15neFfbAj1y2SBKRS6xLD7cmX66S/rWUb92ywW2J29IscmWQaaW/4++Xzu9+VbV2FaV5cojbtvDJl0jiGtjttdTx+Fo5ur/eYD5tiVsUk0t/xh7ZtcvbV8Z0h5N5vNuHfGRf5ztVxfGDZjUEKPD1LXmuTbb7/FhRdeKHv/oosu4tIYCC0qOiq4kxWBLPbpGdpYrqtcx/1Vy0MtPX+fTocZiAzok5Bo6wv6lDF13jzuf565k/5m3XQjkk85xXePv05/4487jrsfraA6Vsb3cDKNj+vpOzXnjclauTaDc9rSb7p+oGcbd1I32D5VgrBvE8flw1b3NXPRQwu4lPwe7u3zCIdDNa9Ige97VlvJgSV02rLqGI16zwLVc3frBpG+lH3WH9uXrifYvZtSKYTtGKj2RqpcCo/gPWnrLyPuekyn4rMlcyp0Pas2xujUK6vNNNdqabM0fwrhEIgOyMlZrV+0ztFy+WgBOQbpWaGto/yU1iNK7WE9F23jOVxjKJh+kPZJq7NV5LTlr7N0moWgZE6fotIpH0k5Fngwq6vLbw4Sjgv/7WrfCdx4J96e34HDiez5K1Lg+6iwt5ubT3WTgJBc6MTaMIe1uRRxSk5bwv7PYWkpRUxMJ0pOqoC1zz46uyz6nbYDJPvY8m/knbYEGiOCevFykY4dk4ZnDQwD9NlWW7yjzznLWO/IOW15nSF9lOpXKHRJxu7LlSnSX7lnGfVPkjhtCfSb7HF52WpUlK3m5hlpGjNrHIVoDFF4hGMYTluCmo3jy6a5JXP3f2HT6LTl805zu+Ha95l4rlbqC8NmDHnoWpfU1taiqEie8a+4uJhLYyC0ONzFDlKuBzvrdoY0PX2+byAyoDhrRl9EVtZFDjrZ5+bedAtBv+n6CIcTVZ1VQZejBt84oxhGGkD1Us0rwtDaVmkdB4veUz216ItaOwaqvRErt+mgooy4z/NC+KwevQukzdL8g9EBVplq9dc6RwcjBx5CW6fbdg2y8RwItLQtFP0QagQkc5W5SKrncuNCiIKZTRgV0xPUnBrq+T9gKNmxYQKLILSREqyth7i0pB8Fs5qYeqHJaTtAsrdp3Vf11UurXFjPGhgGENhWxfVOoAhGlzTuQZhlBvosY24JyDYHOYaaD28L/OG+smlumdrdH9pBD47r6RHP1WryNGzGkIYux21vby9sNnpfwAbd6w5QMQ3II8/e/wlooJiQNSGk6SmmqoHIoChJ/mWJ0RfhkXW5zcqFIKDPk4Twfq5k5gLm58fnB12OGnzjLG2kpvRUL9W8IgytbZXWcbDoPdVTi76otWOg2huxctNGKsrIF4MvRM/q0btA2izNPxgdYJWpVn+tc3QwcuAhtHW6bdcgG8+BQEvbQtEPoUZAMleZi6R6LjcuhKhcl4b9vbFBzamhnv8DhpIdG+JwbNzEhT5wJYn1qr0qFp31Vu6vEM7kEejtSUTzATsq16Yx9YKz75K8pNd85TuSuBBNkYKjzwYr1qnDjPY93lO5UrlowjDWp2EHgW1VXO8EimB0SeMehFlmoM8y5paAbHOQYyg1b1LgD/eVTXPLpri4gLLYERsrnqvV5GnYjCEN3SNg2bJl4anJMIZaDKlpR07DrLJZAcVG42OZzSyYycVT1BrjVi49fz/STNAsFma9TNaDJZ6tFCUpJVHVF8EiWvqBVQ9e1hTjliOW6vCPV0nXjz5hOqaO8ZK+BAK+HLUYt76+zRztjZckEyOKjwFF7KuqeYW5j6TPamkrq44DpfeBtH1s8nTsWb1OVl86RljBfSOl0OZA2xvseAq1nOXq43AkeInIOvxj3NL14gXxnIj454X5aH1WWgclvZOLcetrc7Jye6R63d7l7Wu9OiAnZ7V+0TpHy+WjBXxevK0TylUuP5YMQ63fWq8pXVeC2jPC+/zaZHL2ZGyv2w63xy3btkD7QQjKMykmiam7rOtyeQRsQ2XmIj7GrXQOEo4LuRi3zk4rfvVBLCZdXjSgawShjhIhju4Yt5ljMdgRqOzJYdq1eDFsGUnIvf0MIH0U0Lifc2qWr0n3JvIARXMakZjfA+ROREbaESi9aTFaDvc7qIQxbkkvSj/P9IVL4POyxbtEn4yTFrWnTUXNrfdz8dCLXnieC9UUdhkVXIT2N36H8jU2vzoRuPnpqxw4PlqCrnsuRsZ5Vyiu48QwA7njw9YGAwzQJ+h0mjGC5Gmi8dZnW7kYt1+wY9wSGbF8uAQzEJsE9LazY58G0ya5PYhajFu+TC16rxLjtqh4Lvd7bYPOGLcK7ZbaO5b9KyyZi12puYHFuO0rm+aW+rFnwhFAjFvL6FPFc7VSXwTbzwaiHrrIyQoLCzWlq6iowGDDQJKTaQGLjXha7jSOqVfIFkybUTnGalYeetOz2IiVyHyCXYBrZWEON9P8QCOUrO8GlNFY9hP2XHoRUuu7/RjiuQ1omwWWwgKMfP11ESFSIH26cPVCbKze6HePmMBXzF0h7tuuJuC9q8Uson2gTebdWZlotZi15RVhKLVVqY6DQe9po3jw8svhqqiU1ZfqVGDJpRYRORWrzQPV3nCXyxGTXXElHOXlXjIxLtZtH1MyOWSJsKyoCMWvveo3poJ5Vk7vSPYPznwQyzcsD6rNfP77ftqAB95wIbcZfjqw96scmFpNPh1wZaXJzrl6+0Vrv2mdR6WQq5tSfsG0J1xQIjMLhLxMq0ylY5yeWbB6gWjNphckr0XTFzF1l3WdtU4MWuaMucgx8iTcnZ2JT2v7eS5OjZ2IX/7hO6Q19HB1MPExbe202fTA2WUVbX2t+XkoeeMN2XlVSiQTDvD9+l35GjxRW4cZ3T36Mxk5B7jwtahm99ZKAKeJrKyzEY4/X4KyV/f7xeKkk7Zln2cRQxLtOFF8Sh3iMyl2pxmlX2TDKThByMe0JZR+ltmnH+LrZauz4Wgzi8oI9dpMDyrX/Addty+Ao92/TuRkE8pjS4YN/z7mdDzY2Arbwa/6MymZ4x0CB2VOCxts8aHtM6nN72wE/nGNeG0dIpnrnWccB3ej7OJfwtHsVNUn2bAJ9nRx7OVQ6Q9rD0J5n70C+HCBsvzo2bevAEoVTsSPOgUtpy1B+Zv/D8e21Ij2N28fezqWnfoc93vJF7dj/s7/cHFveayPi+X8GUcLnlNqt955v6W5DFV/PRVHt4rDgToKp8JmtgCH1qvOATS3/OlfV2HBri80vRBsMpvxxKSzcNfpz/vP1XJ9EYp+NhDVPkhdjtuhjGh33PKgANUU60R4ylR6jZVGKQ+96aUIp+NWKwtzuJnmowVqfWEgeNDJkfIbb4IpJwsNj98Ku70XnTXfoyMxB0emTIXrxt+F9FQH9enm6s1o6GpAhj0Dx+cer9y3FOyeYhiZrahpr8QhqxWOtBE43O6NhZ2XmOf7t2peEYawrQRN7Y1yvef1hTaKlhceRn3vPiS01aDcakN3TyxG3fcqzLUN2HXnuWiaNFJTmweqveEqVygjzsFKZGKkw+lHwNEb73XMyoypYJ6V6h1BKvtQtPnAf99D98IH0J2ZhPKHr8XxhWNQ0Nvjq+P+yy6Fu7oGcU8uwRFnXqC7zGDnaFa6mpoa9Nh7ENsVi5ycHN91gta68flZzBa43Nq/fom0fofDcau2NjHDjBn5M/zWJPTc+sPrRSdzKe3E7Il4aNZDzH4Q/luL7mpZJ4YE/FwkOKEmLCdjexk3dpGSBE9TM+D2wJMWh9zrJ8MEN6r//ANMDe0wp6TA3dQEmM0oeulF2XEcCcctD74dR7g8KKgvBdol/B07VnInSqGRUXxIO26J4Xz/537OJeFJQXLakvPW73qf496UnY3kZXcgP6GJk7WjJw6lS1fCWUd6YULRwl8icd4VXpt//uk+x5YwL3pp9sqdM/HEha8gUiD5UHiI7t9cBmer269OQicbtXSd3Y7XJ8/DS5Pv9Bs7NJ5637wMtobdMAnsg8EWH/o+E+kx6a/c6cUgx7DeeYZb71z/G9jinSj+mfQEd9/46rSgaHbfyXUWqO6FU4E5C8Nzephh9xWvi+T8JSDUbf5k+S//JnqmsuxrHDz4ObfvGnvkWX5zFtnn2vL13Fq7IykH2UUzvGnU6sDnH+C8T/Xq2Ps/pNvTkTn2PNHYRWnfC9OSWfJlvz4fngOrvYRxMnCkFOHHGdciZdw89blaY3sNRDcMx22YhWYgMo5b+gTxnFXn6Hrmw/M/jDrHjoHBB1o8xY4Zwzy1QU6inr17w/opnoHBBUNfwiujwSDfwVDHUDouBxNC7bjVszYRrknUnhuK6xd+XJD+OxsbkTBtmm+M8OOC7nds3AhrerriGImk41b1k+rnpqinu2Vr1G6mQ+a4lchC6Lzl4XVmNqJqfbrf9cxjWtF23NXAyb+EOTtbHCLn8GF/vajfB8ejJzDLIAfpvKNy8MKFn0RsHJF8rM2lSH/lbNk6SU9GnlWYx66jml5FsT4NJohsfphlrnueqd+H9t/NRGyKg3milsZXT4tN3mkbrfoSZbo9IOserfNGtPWdgajyQVrDEd/2vvvu05OtAQNMBMLCTCckhtrGx0DkobR5pE1npD7DMzA4YOhLeGU0GOQ7GOpoIPJrE+GaRO25obh+4ccFS/+F4yJ13jwMGmhlSaeTUEN98y2RBTmb6MRp2WdZvmv0m8IjsK87YDrShPbsbL+sSTf89KLpoGwZdJ2Y5yM9jixthxTrJIVsHbWwxQ91fYo0ok3mTQcVnbKkT7YEjeFboklfok3O0TxvDBd5GAgIuhy3L72k7TN0w3FrIBQIhIV5MDNTGxjY02+sZ/hrBOkz0XiKLlIYbKcLA8VwaedQhVL/Na9aJeswMvrWQKjWJsI1idpzkVy/qNm25vc/QOr88w3bx4JWlvRBzO4tXPso2keJLOhEIIUJEIJ+e0/ciq9Xrk1Dyan16M0+Tj5/KdJGypZBp1uJeT7S+wBX0gjFOkmdt7J1NNjiI49ok7lW2zLY7E+Ach5Sa3A9fRtNfWdg8DpuByPp2HACz25M8V4p7ppcLDM+XahinYX0cwMBq2dJxiicnzYeDVWbUWo1+7EVCxFupvmBglpfhbovQ9mHWxxN+LTtABcL6NiMYxV1MpTY//G76LnjQS7e5M77L0Beih1FTid2uNqwv74eZz65DvaGDuy68xxYZp6AvIQ8tH69GkXL3kCP4Jlx39eh/fH3YM3J5sKwOWtrUXTftUg8/WwcaGhE528WwFLbhKIXXxg8C4eQxgDOxO6HLoM9tpuTL8XarW7pwoSH3kNsfRsn3wNHp6K1t5ULrE/EAZn2TFGMUanNOsLlhrWpHNucLehIzh3QGL1cO2+4Eb1Zydi2eL5PjyielrM3EfG3PwxbXQval/4Wh47N4OL2StunhEDHrvA5ClEvzENXniFmUJYrm1VftTkq1Hra8PhtKMlIRUFvLypsMaj6YiOSHvubL23qiceipmIDt6HOjBmtGMc60PYo9Y1eXQhEd6TP0LzNXfOUw9nqFOmS3L+ZZQ0AE3co1ij8tUDWL8QQTQRfSjFuWWsSueco7XFZx3Gn8CrbKnXpklz/sHSEv1bwfQ167ljSHzfa1uHrQ4ohevCXF8JVX4+mN9/EyHff8d2nsVPW2IyUhU9ycbv5mM2RQtSE9OBZvRlEoSxG8WiEkixFccWfXgJbTLtvfPvIIoX2sU8WSjFueYIyup42pgO125M58rHSz7NRcloNbN1vAonZcLQcibJb75e1vw5HgpeUssPhVwYRlB19QmT3ASRHh9ksWyeShzTGbVHxXHYdDbb4iPVZpGSue56Rq48e8DFu6dQmIZg2qM3vcvel12Xb1RfjlvFc+7pvUX7PUthyslH8xkrvPLTrA6CjHo70qShb/HLAfCO65hKqUxnFrzX1x6/d+zladr2HzvZqJLndSEzKAyZchNLMkdwcS3sZLjY6PWMyAdXfAWYLetNHwdZ0UBzDWgCPyYzmgkn4vqsKI1qtEV/Hhx2S+q6tXIvv6r7DhOwJmJk/c6BrNyhgkJMNgRi3Whmeo5qhncXqKWHGJGbJu7My0Wrx52OMmnaECKFiEx/oPmT1WbjqyTN27/9poyzDO888zDO8NyR72awzWj1+zxBKP8+Es7OP2dju5E6HEIT5/GfBNCw+75kho3tqaCz7CXsuuRCpDT2a5SvF5OzJsJqtPqbzZJcLj9Y1iFhied0ZWzBdxMweCZAuPfDBTTjv6S269EiOTV6adyBjV43FnhzjzT3N6nmGmEFZrj2Lpi3C8o1ihnsWwmUPOD299CKk1nf7998XWbB0WLh0lmQLRp5UqcpOriZ/ufYo9TdBjy4EojusZ0g/Cfz40wpRWWFk4h4MUNMHuX5hPScdu3J5aCmTNe6k/U3z3SNv27ixYUu1oXh2eT9zOTmgmh2AxQK4XH73pbZv7NgTh9TaSzOUWNIZjOKDCZxz9vLL4KioEjPbZ81B2Qed3utFRV6nP9nHriY4/nQxyl7d70fI1Vnf77QlgrLiU+pgs7tl11Y+x29hPopf/7vopJ3PaVxeDmuqDSUMvZTa7YjIqq9OfmNJ4MQmeWzJsOGd487GAyc/JT9eDLb4yCPaZM6qjxQlczifIA4y7I9k7xxQW9Tmd7n7Zz0J/Gch+zmCUrsk9RaNoUQ3in9W6z+2CnJR/PeV4Rnv1MZ3rvSz8R6TRZZcrJUOD8XEYFJvb0BFSvfMkVrHhx2M+m5MSMSCjFRfW2kdtPKslQF9bT3YETZysieeeEJTujvuuAODDdHouNXC5kppFm1dhO1N25knP/hTH8RszGJBFt4PZb10g8XqyXgT1T1iOmr/30uqTNYs4gU5+amliQSk9aA+3da4DW64w9qXeuqkhoyPr0Vs5QbRhOYEsMEehxtysxXrKQwUHwzLN79ZpY3pc6/1cgt5FvPwzVfE+DnbWM/Qp3x0KoRgjXeiYKZ/Ps0p1pDKPZJ9JgelsUK6WVq6Wbd8lfBidS2md3WLPgER6g4tXgKVr16ZUNt5XQpEj3jI1Vlu7E5Mm4jlk5f76qDFJihB1h6wWG2DYFCWa09STBLaettkTyRK2/7KOa/42hwKO0z1+mnPejz7ahe7/xK89SIHrhw7+e2zlvv6YukPS7G+ar2q/KVyV7LVBD12PBC7z3omUIjKkmHi7i6YjrhrPgqevVnmOen1UJKMBJLXpr2b0GPv8X36TGsTLSehiRWb0v75uz9jR90O1fWblr7UM+6y2sx47rUemFpNfvpPDqj8l19H1W8u55y4SrYv1OuOQQeeTby9ljsxqsgoPoiIdhzPn4Oyv+2Go11wenZDmve3wGnL22zHxk3oWrwYtowkFN9+Bmzpidz19n1tKF/xD2+mHg+KbjoViSPj4PhxHUrfqO5z3nqQfVwrmvYl9Ds6/28sbDf9W/4kMJUf04maivXc1z5qX0qEC6w68Szvjpo6lF1/OxwNLei652JknHeF9tPABlt85BFtMufrY7YCbmf/X2H9pGnWPAlP+SbZ9Z3m+VNmfvetE+Xux6UA3S3yz/F1fu//gOqdgMzJUx5Kp/jpd+HPHWi4of8lZUgJV6mNDCczOc3kdje8Q01t90PpfoiJwdvHns79bqj6lvllsdI6XrF/og2M+rL8A+S8XfOrNRhuaA0XOdmdd96J1NRUxMbGDjnH7WBFRUcFtjRukb1Pi3dyQKyrXMc8pcHfp03EgH1qT0fnld4s9oE+LbCXrUOxw4niwtkYbn0azX1JrLpxFWuZBoZOUY5wOHwTUjjqSZ+ACmWSaHdyJ+z4CZ8njKCJfvTceiTYc9AA8QQp9wydBqFZmDYYzHw8poEfQxHWzeIA5CuHYodDdNKWpTuRlK9QlwLRIx6sOkv1lAeNCZJrZWclCuILdNt5FpjjrM/W+i0qaTFFNpgW1Do2LErtYZ0glKsntY3qadW3JFGtV3GcQ77/Thaf8JL27Y+tG7z9gQJO/monbVlyp/fiSrZa7XmtuiM3PuSeCRR8WZVlX6GANWd7XN55QKceDWbQeBVuErXaKEpH+rG1dmtQuhTouIuP68GYk2qY+l88uxq2hAbu9KCa7YuKNeRAgvR8qOl6/T7Y6r5G8c/MbP145iG/E262aVOR+eILfvEoE08CisacAnNKCtwtLV6HKuW/6SWUnGL2nbyt3eE9UeY7rVv3tZ8doWfJKSssIydjFHL67jteezXiMS9ZdeLrbMsYheK33w+sTkNRr6Id0SZzLfURpqH13aH1yus7xAW+J+fz2feF/H3hSV9W+VRXOi94eLt6PfrI2MgeMO0Q2QmrGzEVa9FbOAuR8ksoOWW1HlehdMf09mJL7Rbv3tgeq3sdjxCt48MOmfqy/AO0fllXtc4Im6AA/2/OFTBp0iRu8Xj++efjww8/RHV1NfN/A5HD4a7DmtLtrNupeJ9OfgwKpkUCH7dnmPZpNPYlseoqgVh0w1lPKVt3kcPpY/kVQsg8LIXcMwWzmriTtmr5DOgYirBuBiJfOVBeSuDzipR8hboUbDuldVZjla/qrArKzqvWQQuzrw6otUcPwmEP1PpPrW/5/ghE/tSeYOSjV3dY8gtl/wjRVCXvbBwOc3SooLV/gtUlFtTGBiq+1WX7hsP8N2zQN0/I9r+tjfkYOSdZnyzT9fjjjut3Xgryl1tbydkRuTK4/PLyBoRvIBrrZGAYIlTrO7V8Kr7VXidW+Tr3/IrzFICY2h0YcL9EgNC6VwrnOj7sUKmvVAY7a5V9HMMduhy3W7duxRdffAGTyYRTTz0Vxx13HJ599lk0NjLesBiICPLs2uK6TMiaoHg/0iysQbFoDnG2RbU+jca+JFZdJRDpTzjrKY2JU26zyrL80nVWfeSeoXAJlevU8xnQMRRh3QxEvnKgvJTA5xUp+Qp1Kdh2SuusFrspPz4/KDuvWocQMyiHMhZVqPqXPl0t6IzT1H9qfcv3RyDyp/YEIx+9usOSXzDlT9zv5kKFsJBiLUHd94mcnFhwOJK4fhjqoDa6a2uZ9+iTbTUZaO2fYHWJBbWxgcITdNm+aJ//qC+oT/T0lZZn+DSstMLrg2o89M0Tsv3vSBJdozFAoRJ4UFubV61iyo6uNW84gPaqWGX9I6QfweVD/7Mw6OQaQgj1Tap7QrkMZxkNO4RqfaeWT+EJ2uvEKl/nnl/NTvRmH4eQQ0MdeRvGAl2n+2rQulcK5zo+7FCpr1QGRFRmQB66v0ucPHkyXnjhBTz55JN477338Oc//xl33303zjvvPLz11lt6szMQJAoTCnFMyjH4seVHZuw9Pj7KzIKZqmzGhGDZrgOCVhbNELF8DoY+nZI+hRnjlvqK+o/InaRx8Vgs1pGCM7UE3YWzZGPcCuP2hKOexNY9KXsSttVu4363d3kZhlnxK+l6xwgrpF+4s55RinHL52OJGTi5C8fp5prNKK0pRVpsGsanjWd+ch8q3aQYt3rlK4cym40LyC8X45Z0h2xXpOTLM7/T50mB6BGPosQibK7ezLHEV3VUoaGrgbs+ImkEZ1M9vmhY/XFe5fpMziYogTnO+mytbIxbnbaVl5V0XjHBxMXabHe0w60Sw4xvO9WTZBWqeINnXDsFNQ2bsPfLLJg6LNz4paq4ui3e/vvC+8mdXN+OnNTfHyR/aqfWGLf8nErzJks+ZpgxI38G92+5mLVSfZeTtZI9lXtGi9P2rvfcqE/2ElBRyBA6oVkZE4sjk05A1+0r0F6fzMWkHHmal/SNg8mC9qTjUS1ghceooTVf82uigu9r0HPHEpiysnDg5UfR6NiPIqcTOYUz4OiN9xIWqcTb5Ptn/eH1zHEi7Vu1vtQT47azOxZ7v8qBqcM/xi0RlOVfloEqIirrcCjavoFcdwQUh/TpJbDVf+P9fDNrDBy2kSi7+wk46ltQdNkxSJw8GrBnoH3LDyj/8wbYMlNQfN8lsMX2+PJzNLaj7MmP4Gho4xjDLampcLW0wpaTheJlN8AW1wNH6V6UPfslHHVNsKSlwdXcHNHYq0Ehc7SXiOzD3Vx/W+JccHWbue976ffe39wK3HMuDuWkYl9lHaY/8RHiG9pR3roDaZlHo/yuhwC3G9b8PJT8YTls3T8B7XVw9MShdPk7cNYQ+WtGX75EEumB1U767/Hq3xeZKL4gFR3vvY3DT73OVantp/+iac7R3BqhIylnwGLa+jHNt9d5YxsTc3xrFZBSACTmqMdJDYINfv/H76Lnjgdhzs1B7i2/xeFF98GWlYriF57gPkMvu+lOOOqakbd8GeqffQaOmloUPXo/6mdPE+3npPs7td/SOuvZH0ZkLxlKyPQPqx0hbRurXDld4a/T+o3sPd1nre9o4I462fssi/NBmr/MnpxIuZoKjkNb9igUs/bsajFuhXXPmxh0jFu6TjFuQx4mgcDLQCbGbUdVLMrXpMMW7/IRMfL3uBebVOdOC4pmNyIxv4eZxzq7HVOyJ+PM7nbsatyNcd1dOLqnFx1mE/6TmIgN8XbFdby/z8QMFE0Dvn8f6KgHjjoTGPUzDDhk6svyD1CM25n5MweoooMDusjJpHC5XPjf//6H+++/H1u2bIHbrW1DGY2IRnIyNWhhuRYyEmphMw6U7TosLJqhYMYcpNDSV5pZ5CMFRh9KGTLDUU+prOiU2ANvuJDbDH82eQkjNk8sxXqGIMd8LMznn7dNwZLznx8QuVPbF6xewGSHJybxFXNXhLxejWU/Yc8lFyG1oVuzfKWgFw9Ws9VX72SXG4/W1Yti3fK6M7ZgeljaoSbXBz+4Gec+vVmXHgUKLWNCzd5rtgchZlBW0kEtCKWeChm+LclWmJzd3Pglpy2tlOklDI3jHphg6SKHAeBOcOHIk/v7lt8k1KYC/14wDYvPe8Y3f2ppp7QfpuVOg8Pt8ItlSu1+cOaDWL5hueZ5lqUDarpDG8pfffgrtPYS37E2KNnQ0jVFcDY7AIuFFoFi1nlXOsrWlcBRWS1mnR8CYM0zj7xlRWpDj6yM1GRAeS5cvRAbqzcy70v7Vk0HKf2i6Yv8dIp0kF4U8c9xdX/bhtT6bo6IjGLZ+vSfnLXC/pXcl9q+sWNPHNh1h1a7cPllcFRUifVV4hQQbsDl7gmvC+Pfw+Qhz0afc6ERVevTuTS+64NoPEjlxWxP3/UDGzN85I8kI8LBrwrhavV+/kq2t+QU7/XSzzJ9L8L5fIQylNppuuHss9P8+kuqh5bCAox8/fXIyVWGaZ4J1rwaBBs8b4N27/7GZ589iR7Y3C62/MxmODvMvr7ZmBnjW4+r7SeEv5NdLvyx1YVjmvtDIe5KzcV1yVbf2l5uHgpkzhpQyPRPyzlP4e5vHxG1g+ZwgtAeB9w2Vrklc7zBUA9+LdaVs54E/rOQHeeUnnF0ApWbxddJt65bDaSVaNNFgsJ+7rTsE/BobT1sB78SP3vqA8Br88R7d9rLX/clEJvsX54CRDY40Y3in9X62+2CXBT/fWV4xn/jQbj/eBLM5IhWqptgfuik+Z8OC7T320Tfi20Bvo2NQWJcGo5uqZEtvsVkwmOTz8Zdpz/PXse/fYW6HWL1+0CAse/YmJCIBRmpPhtCNmflWStD/nXRUPNBBuS43b9/P/72t7/hlVdegcViwa9//Wvu/+LiQfAWbRA4brUya7MYhumk06jEUVgwbQH39s/aZvXLR4nNOFC267CxeoaQ5ZPFLh9qJt5QQ62v6GTXNeOvCflb7KBYOfv6bKuzGf9r3Y90ezqOzTwWLrfLp5NS8Eyg/L/1QDoO+NNizalW7F96FfJS7ChyOrDT1Y599XU488l1sDd04Ic7z4V55vHIS8xD21dfoXDZ39GTmYQnrkxBq/sQTtzrxNn/tqAx0cS9Hc1o9+DDc1xYM8aCji4rt3DObAU+vO5YXPHLp0VtiRSo7WovbkI9XvnTS6acLOx56FLExfZw8i232nC4pQsTHnoXsfVtnHwPjkvjFv80IdP/GfYMHJ97vE9Xef22mC2cfhzh8sDafAjbHa1oT84RpY0UhCzZnYsXoycjEWvvOBM5SXFcLKbWhEw4exKQ++DziGloQ8ey3+Jh08fY30LED/pBTuxXf/6q5vS8zFgs9sJ7Qrkxx1YIbauaHiqB11GWzZHWW4uN4JwO55/hdT4JTnLRZtaT6MKyS8xIq7Lgpg+9i+n22e2YUtAKi2RB3ttpwaMX2mA/sb9+S39Y6nc6kk7Pjk0fi5sn3Sxrp+kUJDlOhc8J51O5fpODnvRa+obaMDF7IjeXUBu2121HWosLz73W63PKiNicU23In5ePqvf2q7LOh4v1PlAEWhfWeoscoEoyKv7gE0UZsPIkjEsfh8dOeszXt8K1y6Kti/xO3tO6b1zKOLw17y1FHeGv8aeFuROo1E8xnT5bQKeFD/7yQrjq62HJzMTId9/x3acT16UNTUhZ+CTMtQ2Ie3IJjjjzAubaKlAI1wKsezykadT61PH8OSj7625ZdnLWBluN0Zx3VPJpeGek9K8tyY3if60Oq3NRbx/IyYvyobmva/FiWBNNGDGnErEJLpEsuHaRDYtxw9Xj76Do7rCg/PMsODu9G3Kr3cXxo7i6vWs/c6wL7l6z77QawecMF3wZQZotdEZK+4BemLxy50w8ceErCCeEss34+FrEVqzVRkIkYHnn86Dn4yo3BMQGT/aC/+pDaHukTm5LnBMms9cZLuwbFou7FrxYXSv7RRSfl9z+kGXj+C9OwrqX1ADW+qLgi1uYJ0l3pWTh0nS76pcMevbJ/JwgWy4Lcqdahffp9LebETuVHKh3C+KNvj6ffWpWqIsN+/GHz2/Hp237cdBq9m/n5DvF60ilPAmqbTQDueOBX/4N7es3o/zuh2DLyUbxGyu989D3HwAddXCkT0XZ4pdDeuLeb45+fT5c+7+ARfB1nNb5wZSXiVH3XgRbRjJgtgCHd3IvUuqTsnAwNR/HbnoV9kMbFWVBpZqkfSYEJ+svVU8t+/X7QEKy7yAiMoppS+ERhvNJ21YdPkhdoRJef/11/PWvf8WGDRtw7rnncv+mWLdms65QuQZCADm2aDpVsa99n2+xzvr0VInNOBC267CyaEYby2eEodZXdD3qPj3q67PJ5JRi3A72c2i1cbB9lBmPXQCUZ3nw6gkX+GQzru++Y+phjuX3GOFEf/FMtBfMQm2uHd+t/z/AYkXZOCt2xbpRnuVdohfVebB9VN8nHTbvaSPuWtpunELs82EITaAELYzx4RivQhblsYKNqE++4y7xl68MqF5+dSsGzsDAg1iy45cuRdLIEpyf7b/RcT9zLFwHS5Fw8iTsX/VcwOXQGNbTR1KZSf+tua9DZFu16KEWHbXqj9zEhM3WwZ0Q5BfU/GaW28D+rB4tyTn4LsO7ZslwuvBQaiuTxbinxYb6onQc6qtfdUc1s520if6h8QduIyNnp4UnmYTXheNTzxjVml5r31AbqO7CNlB4BDpFymRznl0NW3eZLtb5wQw5OarKiDaaOvMkkD6xUNFRgS2NW5jrvl0tu0R2hKUjvmuFQHt8NmfDff3UZwtohiNnbfP7HyB1/vmi+zTDFRQDjpVTORs/KD77J9Tvg63uaxSfLKOvMqeiVBnN+54RpuFATtu+v1zaufWwde6m74MxGEBzn+Wem5G7815fG6WyILCctoS4BBdKTqn1nbDlbbDwBC7Z19gUh7wMBXImsPpg9Nx6/Ni6Ibx7EgGszaWIq9Ax1wlZ3hEn/7wGNnipvZDaHiF4B7m0b1gs7moodjhEX0LxkObF2h/K2Tiab8K+lwwA1D/M06Ae72njgsQ8VbkFsk+WLZcF0hXhaVbWfbnjePTc/i+9n89TeASZtgp1sdRqwR+7DgICp62ondPuRfGY07wX1fLUBDdQ7SUbSzz7IhQl54nnqZPu4P5QLxS/NjN881BfW/otF/TND88/Dts47yE4DpMu5f5k0v9cmJXfqFbBJO0zRv00QS6PgYBk30HO2uHssA0EujyuV1xxBQ4ePIjbbrsNJ5xwAnbu3IkVK1bgiSeeEP1vIPwIhGFaz/PB5G0guvp6OMqGnLf0+TpLNnIsv3StMr6bmQ/9T/8WQniNZ5+PVkbyUGO4sCjTBtbMcNoS6DrdDwXb+2Aew1HX/qaDiizEPIPt1+PNqDuKzehL6SguGZ+W6ne4i01SxGNn3c6o63u9fSNsA8W0VWNz1ss6P1ghJ0dVGSmwOwcyr6vpoB5dUrPhWTfdOHRsfB+rtZo+s6DlGVYav7TlgYWSGSjYx6T4yUWP/OhawSx/mRBPAG9f1WTI561mzyM1f1raAiynzw6oPq/DXsjZHi19o5XJni9HCdK8hH0x2PYuav2jR2562hawXgUC3g712UQ1XdTVh2p56kFf+QO219DYloDWQHrlxJo7QpGHgUEJXUdc6Ai50+nkTt4q4Y47vG9EDIQPgTBM63k+mLwNRFdfD2WEWjaBjguefT6S0MNIbmDg+2Go9lHI2h8qX1/aSFkWYjodIWSwLVdh9OXTUv1iOmMU007ImhB1fa+3b4RtINkoyZGPNce8f2OSVm7CQQE5OarKSIHdOZC5K8+eN2TtSCRYrdX0mQUtz7DS+KUt8sbDHCxwJfnrkh75UVoid5Wicl0ad+KWlV4ub/7frHtkoyOl9yyZaALZgW4Nz+uwF3K2R0vfaGWy58tRgjQvYV8Mtr2LWv/okZuetgWsV4GAt0N9NlFNF3X1oVqeeqAwFiICjW0JaA2kV06suSMUeRgY+iduKyoqNP1vIPzg2Yjp00YhKHYQsY+rfaIh9zz9putK9wbi0xaKq0mxbFig63R/qEKtr6LpU6PBLhu5/OTAl6MWJiEc+svXVQnDXT8iAS39MJT7KNra73AkeAmW+DiUpxJplJP7TYQ2FJ+aR5nNxhFuSM/R0G+6XhkT56tfYUKhoq2ZWTBT9j7Fdx4I+621b1htaO/yyoslR5JvZ9wMlH2ZKb6f2Hf/1vtl7d1ghNy8oCYjihcbyBpOTi9IB2l9R2n0rPsGcv1E8VLdtbXc397//Y/7Nw/+Ov3t+eR/3L8DyZvVTlG7Mkdz+nrwU5m+4j51NasT0DCeYcV+Ff7l0q7OhiN+LCIBqUyE4OWtBc7UEnQXzoKrL5qrkiwoJALdb6+KRfOBOLTX21D6ebaPiIxi3FpivVaW4q4S6atQ3op5f57Zl7/3XvbEFi6mK2/Pj07uZ1wPty7zMtFMDEN21DPNZwf457mYn33tJplxv4nYSSF0EW8v+LEvtD0kD5IxD4pxSyEppLrNz2tawyRomSP5vFjzWSA2biBB/cP1g3T9TzFuU3O59YAaApnXZctlgdLY09F+2M60WXTf0RXj1StWrFP+c/nM0bJtFeqirj2WWp5a2qhhLEQEfW3h7R8LivOD0hpITk4sCPss1HkYGJQIiJxsKCKayMl4UHwg+kxBLoZpsGydSs8TooUJlCdD8hFp2Dq4zwQqbDEoa2xGxp3PwFNTF7IA5YqguDL0iUIICH30YNAxs0YQoZYNKz85aClHqL/m55ej0bEf8W016EjKQWbMaLhu/J0vwH79cSOwuWYzGroakGnP9JFzkS3gr5c4nJhoS0FOQj5Km/fjif3v46tu/xdmBQkFuGzcZZhdODvqFsiDBWo2WCs7vByIlXjF3BWDfgxraX9yTDJH0BXO9nPEZFdcCUd5uZccana572To3q9yYGo1oTrVG5+aQp1w9XK58WhdvSiOH8+ePL5otmh8q9kaufuLpi/C8g3LFW2UVNe06l4o+kbahqX/vBVnrdjIsZYTARDFUvSxOZNDkojfLBbA5RLFUXRkzUHZB51eNnoJQdlgh7RviRzokbdsSG3o9pNR6ZoiOJsdqjIIZO5iPUMEhxePvRhHZxwt0hXSoerPP0LK4hdgy89H8dNLYItp961ffOMlhAQvwnVS+55GlN+zDJbUVLgaG+Fxu+FMi0PlbdOR7XQh/sWt8DR2wkRr7uYWjryl6KUXNdVDtC58egl6Vr+J8uc/gy3NjvxrTkHVX1bD0diOoptOgzk+FmWPfwi4PbDEuTDyNIE+M5jBCXL3hNc5UiiT1xnpIyLjSGoaUbU+XURYFonxIJWJrfsnoL0OSMyGw36kz5mgua+7muB450rg+zVMWZCTtmpTGkeOZ47xEo55IXR2eJB9XAsadidyMXH5e3ysW4KcnMlpy8mWc/46kTW+FYe/TeOclD0mEywdFlizUlCy+DKg8Hj97QsEWtncqT8801D+jyrY8vL7+6O3C9j5Fhz7dnjb3WlB0YUjkHjP214GeAXwY3/37m84clyyz55ED2xuFycnIZEbp5tmM5wdZp9cN2bGcPMasbjTy0Rh7HWl3zRH/rHVycV55UGOzOuSrT5GeDm7Nej2LtS/710tjh866hS0nXgrXvn2Sfy39Sefs3pa7jQuvvim6k3Bt41VbsEJ3uFS8a2oLu0ZF6P8jvths/vHl+bm4H+0wXG4GkWzG7mQJD7n3XVfAmklqm3FBX8R6aKuPlTKkyC9R/USxOztLJ6JHXN+i/yscQO/b+mzf7aDX/ndkp0fMmag7J8O9TUQS05SxKUCv/lK3Gd686B+vG61fB4GBp0P0nDcBiC0cDO/JmYm6pro9DJS63lea95aWIDVIJcHnRLoWLAQnsOHYU2xomROhW9hR2+ciVW1KT0Wac88h+R87+cDIWew7mwE/nGN6gQXbgTb10MZUtnoZZ0Wpqe0fH71XfXYUr4FqbZUzMmd44tpS+ER+JO2SmzX2WYzDl5+OVwVlX4bfF5/TQV5eOnafHze4w3KL8T/Z+87wKSqzvffadt7g23sLkWxoCAqIIIItlgSNVETe6JJ7P4CGjVYgjXGFhua+lexd2OJFYmIgAJ2g9J22crW2V6m/Z/vzNzZe+/cNn1m97zPs8/s3HvnlO985zvnfPec7822ZaPX0Ysclwt3tnUoEkWQo+mhmlnoMnvQ1N80Zh2EsWKRD3WxQTqzqcXrYCcUpheiNKsUzX3N/mvCdcEpP5Yg1J9A9SOI+6T8fiTqL27fgJd8RA7lY7ClXU87zj4L7pY96Lv1Cgwfsq//mB+VcbLLg/KRYTSmpGKnxaRpY/XssNp9petKuiZfREdioavXNmIwOV58CVwl+cj8232oKciXyHHXaafD1d4OS1ERav52lzeem0FnYCj9MZEgtGH5N3swfNUKpmuWlbejfWQ7JjmdmFAxj8koGIdoKOM6/eZ/Hf/DM1ufkZDisRcFc5bjto3eFwXkYF7xlAsldug62VvdOuzUKvC3p2yeJHZik4ODHHwE7+5Aj29HJuPOZtctpRNR8/TTqs5N8dhK42rdOWd7y6/mLPVdb1hb6HUamjyoWtKGjKnlQPlsYMJ+cKTUoO73d8PR3o3Kc/ZD1qypQHoh+jZ/h/p/bICtKBdV158FW+rouEsO4bp73oSjo4+V35LihmvEDFuGyyvfSVPhmHAk6lauh6OtC5b8fLi6upB+yy0sLrpEZhEE63simSg5m20VZaha9aRqW8vLRTJ3f/g6+m5/EK78DJj/cDKchVkszl5T9xAKb3kJae3eODde562wA8zbrmynrWmUMMvPnGQCth7vxNxpP4b9wXdhKilBzorfoSyzC+hrZc5m++ZGNN/7BHs864Ij0LVvJax/+jdMbT2wZFtgcg7DOWRB6SF2tH+bLalf1F8YEXlT7Tp/WRlzfHcDkFvh/V4wGfW7ezDwu9+xMUe1PXKtqHr2BdhqjO/I3vn2ixhadhPMEydg4uVXoHn59bAV56FqpZdjpu6Sq+Bos6P0tlvR/sD9cOxpReWdN6Jj4dyA8TiY73JG+GDsVqKvXQLGJaGuGYXA6lsla7/OioPQ/+MHUFkyQ7NuQpqGxzyyn8+eCexer3y/ch5w5jNw2IdGX1DnmlG1sCngxSqz6bf8Frah7d5j8lo7LmXtGpE21EpTfq9jB/r3fI07t7+AV7q+SjwHf8cO7GlYj672HzDB6UB+5Tz0PX0P6l/aM2rzq/YCTnkEKD8ouBeigizMVqBhM9C0BUjLAQ44w/guWbE8Cd+8AvS3AXsfx3faJgm44zbKQou24/aW727BhuYNjLVRfCxhbulcPHr0o0hERNNxKzhvhy46F85up28y3sXiyNDEh5xhV5ybhknVB+G2g24LOl9DWHUqsHONlx1TAB1RmLwIOOflyObFERGE67jVSksOLcct3bvq+fNx/l2fMCetkv7+4cIi7ErTDvL5SEsr5g4OKQYmp2NrG9LTcPFEZSItYRKUqPYjmgjFUXTRexclnQ0er5C3LzkdJSzEItBEOmosxCFCSdfkiIfu6cnR/vIryDv1lKDlnOyOWzHirWtqdio7JRu9I73+63/d3o6St7L8u4PY+LMhH44+q2RXkJH5qRL87akwTxrqt2D3h4Vw9UmdtF6MfndluvDYNYfh7tMfMzxGOx4+CXX/2jp6jH5WNxrXFTCnLTlpyw/rROsXuey+JdWFigUdyChyjr54983d1NpKs30fPgnDX3p3safmOjDcbWOf/t1vU5bAcdTDLF1Ko3H9Br/TViKzCEMuE/FcgzkPfzUdtktfNzxnF56jEAuWmmpUzpolza+5GbU/OQbOHjqu74THbfLtrCV4mNxdw75dsxlO1iYjPTaY0tz4eooJ11XthVUpS1naRPgpz9/+6qvsM+/kk/351Z1yLHNOUXoF0/rRtT0zoH6JAJJd7nO/RMtTu9TbY0kXbDMWBr2OEOumXE/F+pyIY14iQnVcCmPtF7TjVikvMUT5ivtBgF7RaaNX3kmq0y5JNef2tVNfo3XU5st0gvc7jmj5IIOKccsRfTT0N7BdEvJFHH2n6/TGazwiJWXAt9PWFz/m/dFYZbSDMT19BJs7N6NxILSFh+6xP3rbKh9M6Ttdp7ddHBwqoOOqW3s2+HbaKuuvyzJ6VEgJVQ4H22mrRo1A1+n+JIdDNY3xbD+CbS9ug5MXcWMhjqCuyRGP8V9PjsWXXpI0ch6LuqZlp2i3tnCdxo7DLAO+3X6i8afP5zi6/+bILPBV5klpmS7UHOnNW+q0heTY/N6L2/C/ng3Gdbx9O2xtH0nq1fhxod9pS5+N6wr942zNMW2jTluCaO6m1laq7evLm44h0x8t3IVPcfq0419IQ+y0jRoUZCKea7DrbR+FNGel8pNjNeC6rR/VR3jn5rSDetRpSzBJnLYUGoHaIG/yIHLLhtmcJaevDV9PtSqmLThsBaetkJ83BI6ThQdo/TI3IvWLBqz2WmT1bNRujwxHSOsIsW7K9VSsz+PFFkcFsVz7qeWlkq+4HwToFV2n00ZJgqSac4vaSWLzZTrB+x1HtMAdtwmG5kFtQg86pjAeYendzQwkvVUUg77T9UkOp/8Ie8RBMW21QEcUODhUQHEqKx1OQ/qrBvq9EeilM17tR7DtpQUuQ45Y6RrXPY5wdUcYO1THHwpzEeV5klLeYpQfNjoOGravvvyU0p4wezTMiHicjdjcTW9OGG76oUJDJhIZRLJcXbt021fcxnJQm/+v+39h5xe1+oW5biHoljeByswRp7WfUZsi5KvXD5JIn5Jqzs39ARxxBnfcJhhK07V3Pggx+cYbXNmTWPweOgoihvdoiBm7bd63+hR3NOLI98bNVYUQV4aDQwGV2ZWot1kN6a8a6PdGoJfOeLUfwbaXFrgMxybEjPRyRIutXE/XIql78agfR/RgVHeEsUN1/HFkB5UvHZmn0FUByK9heSixmSvlLUbjJ6PjoGEd983LlNLeszlPcZyN2NxNb04YbvqhQkMmYhkQaRy1oVJbCraAQhQIYQo07YWv3bXaV9zGclCb75O7T1B11KtfoszJad1C0C1vApWZI05rP6M2RchXpR80rC3AQLtVsWzRHue15hha9qR8IA0zd7iTY86dIP4APp8bvzDmDeCIGPTi3JSjHPPr5qvGeknEoO6RitellYbD7Au63h8Yz4cIngarUjG/ah4OnRaF42hFU73x0NTiHGkEc482IsU8PhYRrE5qPR9MWkrPTs+Zi+/XfMJYkJX01zK1ANDY+VRnszECMr0YtwLbrVqM20TSEdLdTXs2McKa0sxSZu+iocfB6kF1bjWTVbLZ4ESA3B4J30l2RJpHbR1pUjahfdXyluuUUplm7fQwoinThCJ03PV/qC7MQ/nICJuE7+zoxMBvl8LS2oXKR1YqHjsN1Q6r6ZocZpgxvcA4eY0cO/7zAoav+iMjs5nywG1ednOY0FBYhbpOOwqvvh+ellZ033IJSo86QbMO4Y450YrtGa2x0Gi6sR6L1XSH9DnNmoZh1zDcHjcbOz5xZaBktXKM27orb/THuJW0DR0Jpd1FInIZRlp3442j5H+2fu8zHUNwODK9c7TuEQmbuT/Gbb96jFs68v796mLsc4i2fZXrDiNYe91YjFsig5IwsNOcLtS5mzAn1GLzlqUfk7jORVMDZCKea5AMihZOQPO1t8KSkw2XvRu2gkxULTsetoIsL+nafe8wYiuBQ6z0Fwcib04NI2wjOD4lYra32LOVy05D6rwf+efmATFuTUTc5o1xS21c+0ERC5dAbSDMWXqyinHSAScZrqJfzxTWAqyNKcZtHOfkYkzYbz4ca7Tbwx/jNkHKPF6h2D/V1n60561yjrTNxPbS42H/l/uceIb6PuU1aR5QvxHwqDgxRWtOFuNW1g/IaUt9r251Cap+3YcMz2iZGGHmWWf6ieqyTjwD4UA+3kmIYf+ywj/HQPV82Nd+jeZrr/P+sOU75E23+AnTqB4jF/8e1zS7cddpZmyZ7A3vQydFiCi2bNICY+OpwngVMYjTLpqKgarDkL57A0zidjJZMDBpDjYPNsHSuMe/nvF4PAHzAvEcVLLuMViHABJeYRzubYHjf5+i7pGNcHQNqM5XOZIbJg9pFUfMyMmiyWg+VuFnaCQGTQq6zuL6eBk0mdOr1wJ7URr2fuo5FBCzYzQw2AW8eIF0ok4D+s/+CaRr7zSIBriOJJf+7jrnHLgaGhkRmTfWrVR/TeWl+Ouvy/H+8BcBv8+2ZaPX0Ysclxt3trWzuHBykFP3+f2PRbfZJGEYF3DoxENx76J7E8J+kO4uXbMUn7Z8qng/EWwd71/hyysvNY/F2lRCJPXRSN6kU8vnLMdtG2+TPCegsMeDFU97UNLlVu2jLXnAm0vn4Iaf3O8vdyT0RK8/yBFM+kL5tm79GCuecqHEjgB2c3H9VpxlQUeOSTGPRO0T0SqX0XTjKZf6nnqc+daZqv3Mr9tKbU8OvlcG4GhokhCUMWbzly5UnOtI2MxlczE/m7nseu3aSjjtDnjgYU5lgjWdnCAeFhNV7My1lE5EzdNPG4q5y+aF55ztLT9zXHSiaX0Bc4gJMW7l1/313/9w4PQnwpu70ZzwuXOBWoUdbDULw08/BMhlIu7n5CQkGZBzFWYznLTbU0VO4jYRYtPK05H/xppjgWdkGK4h36tlX9rMmeuB/7qQ3saiFNxWMRl/+/HzhnePS9cCVlQtaAgsV0UZqlY9mRDETEbag/WXZ56HrSb0F3McUYTS2k9sF0+4B3hzmfpLHCPrRCWbq5GWmh2mnbbktAX5E81A1eJWFlOa6duaEjh6zaN6eMCRIa1f1ca72/dais4LLlbU9dr/lsPZ4wqwJ8OuAnz5QQ6yO0fY/OOenwPXDndK1jiOmiNgO/1x9XJqjFdh21+FtL/Nm4hlWWZc3yEtJ13/TY4VPRb1g+w07yXI53o5Lhf+1uPCfvYWQ3XQ8omI7XPVOdWw/fb5uPgoOKLng+SO2xCEFitQQG6K7TLed1MGvF2ioOudO9nbuNqOLu9uoT1tqFz5cNBvl+TMurpvRinwOMUOisZbvbHKwDnOIdZfy8rb0T6yHZm9e9CfPQFFKVPhuuQPbCAm/e2YWYVNLZvQMdiBwvRC/+5EsgXC9SqHEzNtOZiQVY49fY3YbbWipHKe30aInxWnEQ7kbN7h6q6S8ywR9Xgs2uCgmY5DtEd6oMm+WhsHUzbKe33TerjZakVdp7JTstE70qtaRnJwPfTECHNiyndGkTP3snNTYM+1+nWTyrh8y3J80fVF2HaY1aF5PdshqYdg0hfL5q/b21HylmzXpax+5LRVyyOYMUewF8HqV7A6Sc/f8t0tER0LhTIYqW808o90v6MjqL9/0Y32HOBfZ5hwaPlUXL7kvtFdW7QA9I0/bP6kw6JuiM3cN0ejI/n1tLszLw+uzk543G648tPQ8H/zUOJ0IuORLfB0DsBEc257N3MoVj76iKF5nGReeP/NGP7wGdQ//B5s+ekou3AJmv65xrsr9NKjkVqW790l2tUfkd1mAXPC2nVAXyuQVcJ2mBmZG6r1EfmcVA+CLSdQ6IPBG26AqaQEOSt+h6ItN8Fm38FcsP5F/YAFpYfY0f5ttsTJLf9kDl4T2E7ZgHb2OWaGu22oX1sAW4YLRYsr0fzmHsDthrUgE+W/Xuxtg65+lF53Jdr++Syczc3wmE3YefkRKPjJOTis7LDw1wIke6pf+l5s97hEl+MMuY7aBn/wvVmshGNPG+qufwSO1vaEKS+HOoYeORJprV9Id8OSXUzLBYa61UnFRLZTFUo2V9jVe/LDzJa2ODLgyq1md3J37FBcE9O6dKChD3W/+Dlz3lrSXKg4vDOg37JTBxrlUhuH9ca7B39oRN2/lHeXWzN8L+sU7Inw0vjWgbbAU4V68lMZr4bK5yLtwrfCm28opC2cFLh4Ygkjg65yuNCRloutZofmHFQLj7S0Bl1vzXFY3M7kANbSPY6EAHfcRlloHPGZAKVOm6b4Fp0M2PC2bSFNfIJ23CYA6JjFSa+qHy1745Q3xoyTaawgWvobK0TKcaunu2JwPU4Ox20wbWq0jY2WLZy85aDjea98v8e/Y0GAMAk+ee8J/lAkVO6WlhZcuP7CsPU31DropS9Ol+r2RkOzZEeGVv3keQQ75sTKcbvxh40RaQN5GRxZDkP1jUb+RhGM3pDztr7Y5HfMi8slGX/oqOZDs9UTunwLOwbsuPMQVT2yXbtJ4rgUxj7Kw9nZicw5c/zjoJA33e/fuBHWgoKgxkH5uCr+Lh9XE22cjYbjVnDeWmqqkZIygAkvHC95lvo/OVspjIWSLZC3JUG1nX0hJyimcWouOQ/c6Dv4b3A6U/1tLJY5/R9KGyf7XCrZysuhAD27aARkO5Ve6BixuYVTAubgqnrVvh0Df5yDho8L4RqyqPZbrXKpjcNa452ROQZB6d7DZ3iwrcDKfq8nh1BkF9J8QyftEypKNUPTGYUgt1D0RnMcFrezjhw4kssHycnJOJICNLFRO/pE18fTxCepGDg5GLj+BseETuB6PPbaNNJtHE7eclBMNS2WZmI/F5e7ebA5InULtQ566YvTpboRjNZPnkeijjmRagM5jNY3WvlHWm++mGL2O23l5ZLMn4wwZgfJZi6MffSZd/LJknmccJ0+6V6w8zj5uCr+Lp8Xjpd5om3OoTCXlMDSG6h71D5C7GGlNpS3pWY7+0DpCd+z9i6QtLFY5qG2cbLPpZKtvBwK0LOLRiCzi4bTVvmdql517WLhEWinrVa/1S2XArTGOyNzDLV7U1KG/b9XhVI5Q5SdIeikrTRfCgUh1ZugNw4bSYMjKcEdtxwcSQbOeh8c2h5eiYEvv1S8R9fpfrgQWJiVGFXpGt3jrO3GmdAThUmWM7dGtk0j3cbh5C1Hvc0K+850xn6uxP7dPWj1Mx9TuUvTSyNSt1DroJe+OF2qmx67ObG7q+WRqGNOpNpADqP1jVb+cet3RhizVdjMBT2KFas2hzZc2dq6p9SG8rbUbGclhND2RudOfCzmiBv07KIRqPUNIzY3GOjZ5zDS1xrvjMwx1O7tGEn1/14VSuWMtOyCSFtpvhQKQqo32c2tHSymsZI86TqdhNBLgyM5ERnN4wgbsWYkTja5THa5/Szf42rLv4xlUpDHQSUH4cu2LznrvQ7IKdv+4INoX7kS6fcsxYh1B8wmC7bnTkBnbRtm3vQCTC6vM6b40ktCj2N20cUsthsmlmBgxZmosvQxco81XW2oueNdpFMcP1n8PmrLTXs2YXvXdvSM9DAim6l5U/0xaYX7FKu2KL0oIrFqYwkq/zu176BrqAsLKxf649lNyZ2CHd07VH8nxMuKd12F+HSmCUXYevPZSE8dQqXTqRibWGnXjJg5tqm/iRHzJFsbhsNurwf6TbiyEPKORIzbSVvNaPo0j7UTEWiUHzYaM4wY739rMaGgz403frMvK7e114rZBbNVY9warVuw8jOavlg2dTYbPnFloGS1cozbbR8WI5fWZIXKeaiVMd59tSKzIirlMlJf6t+0AyleY3Eo/U63XGos6jps5oIe0fWqpRkI/wApR7hw5lVjqGI+UhvW+WjGRiE5zqwQ45au175fpBrjln4bcByX9CbIubl47mQtK0X1g7d52ej72uAYTkPtbc/DuacNMFG5TDAX5WD4mhNRlpvG5le78ssMjcUcHGGhaCrrS2mNGwLtotEYt2p9w4DNDQYOR6a6fRb32xDS1xpvK6rmwzHciLrXlWPc1n5QrBrj9scve2PcEsmyaqxXpXJqyI7FuA3HV6CSthDjVgiTYIYZWdYs9Dn7UOnw7hwmp67RMAo0Nwu23sxuXnsr4PES0QW0M5M1ULmgE1kLDh9fPpNxAE5OFucYt4nK1BxvCHL5uv4j3NnWIWFvjBhjZCJDhc1SzFqpxJw+3vVGaUdt3ZlnAS4XW5BULWljx4jojSRNJEweE5xm4NkrZ+D35/89JNnRgmHnL34Bd8seCWsqgRY/XuZsoCPXjMonn0DepKm6LPLZtmz0OnoVWUnvXXRvQrcx9d0rVl+BLa1bJNdpgmMkeH+i6HFn3Q/4/szTkdcxzMib9l7U5mdu/X5NMSOxslSUo2bVKsmRNSWbnmxtGCyU6iy3T2KQs+uBxQ9ERAZG8iadWj53OW7bcJtiuxAx2S1PelDU7dVPIuapPsrXhz8oYosNlldeCvZ67gUUVO2lmnco+qunM2IEk76Q7tatH2PFUy6U2KUx0OQsxK3H9+HqmgJML58boKOJOleJVrnU0l0+Zzlu2yjVo3iNxVRGrbEkJyWHvRQMqlxKLOo6bOZMj8hZQEQplZVewhyV4+EcMQS15XPnArUf+S+JGeYFZ6134U8kRgW+eIke5hiVs8AHsJYLTqCahcDpTwQ9J6e5U+2ZZzHSMnFe8rmTJcsMk3vE7/QR2y+tsZiDI2JQs4sn3gu8sVR6XQwj61UNmxtMn/KTTSrZZ1+f9/efA44MaR2tNi7evtdSdF5wMRwNTQF9tPa/5XD2uALsybCrAF9+kIPszhFGUHbPz024Ey7sZ28xLocIyc5o2vJ1ONX9hgMvg/3pn0rKTc7Ya4qL/M8R5kycAw88AeN1jsuNv/U4Dddba13LnLa+F3BVZ5UiY9nLY9tXMkbAycmiLLRIkl8Fw9ScaIgk07wcglweam4OnmUyCIKHhCUj02GzFPTkwOIDceGMC2O+U1tPvtGSayj5fnvd3jC/avIPZhNm27Fncx777jF5sPwcC7aXmyUs98GS1l31/Pk4/85PYOn3kgFY013weDxwDXk115XpwmXnp8BVnI/9Cvcz5KBRg7icoUKvfuGQ9lHfDbZ++xbsi8tmXRY1PVbSG706UT3I4fXQEyNsYSh/q03O3MeuPgx3n/5YwO/0dsFFog0TEXU9dSx+ptCOP3/t5/if/X8Shz3taD2o4CA8dtJjUc1b/l3Aef85D1+0fiEp00E7gKtfdKIzCyhwuVg/Ftq7cV0+cyDQhPvds6fj/65/VTfvSNSBoPR/KOnvfPtFDC27CfY8G1b+dBiTbYM4p6cXkx1OuEVs82ULOvHNFBNWHXSKqn5Gqq6RRrTKJU9XqX/TS6mZJTPjMhZTedY3r4fbI+1js0pm4fEfPR66XDp2+JnKhR07wikEJTZzx0iG12nAdz4mHqgta71jcl9zKtutZcnJhsveDVtBJqqWHQ9bQRYcnX2ou+8dONrsXt8tHY/+xUzkzakG0r3b8dkz97zFPiuXnYask8/160ew8wqCu7UVQ789F84ep+LciRw9VUvamRtZ7DQ2MhZzcEQcCnYx4DpB6ZlQ0zYIXft81plw7GlF5Z03IuvEMxAO5OOKJO/7b4Zt8Afvg9XzYV/7NZqvvY59LV16LvL2MgOVhwJTjmTjxY6zz2IbX9LuWYHJx/0sNDn4ftPiyMDEfeeHVTe1tIXyBIypCut1j8mMoUlz0frTRwPGX+H3FrMFLrdr9J7BejNZX3yJNz+FHbfMWJqkJzw5EhvccRtloQUDrYlMsEzN48VxK8glZLbFZHfcBslmGQ89SRbHbUPtGlQ89hPpm0gBvjeVp80slrDFkyyDcVySvl72wnGMkV68S0SA8IZZibU9VITb5tFy3AbDdC5HNPU4WMet2AZRu6oxt1Kbrjz9HX+5g6l/otv3cJGI45tWmSh+rTvPhceaA9ubduAWz+jBWQvzJe2dTNjxnxdwwfYVjKRKPraK2eaFMSZZ6zmedDoe5VFlM/ft+Breto0vFhMcQhtSW8nbUmhDZ6eX3IgIxYy2cyiOW6u9FgWPnag5dxJCMmgx1svHYg6O8Yh42metvClWdbD2JBSQjYnpul5nva7lpwgHfa8/C/M7l4pOSXghnJ5wH7cybOc8R2L6IDk5WRyRqEzNiSKXkNkWkx1BslmOVz0xAnvz5+yTjpHQTlsx6Dtdl7PFh6KvAiN9+fxAlmaKlanG2h4qErXNg2E6T+Q6iW2QFnMrtam43MHUP5HqO17GN60yfTHFjJx05famfp03eSigvZMJTTMmMqet0tgqZpsnJHM9x5NOx6M8qmzmpEelpdxpmwQQ2lCpLYXr5GBRcrKIn4kELL27dedO/nyDGIs5OMYj4mmftfKOlT1JtPV6tPwUWdML2fpVyR7S9ay9C6KSL0f8wR23cUSiMjUnilxCZVtMegTJZjle9cQI8kpnsU/accvCI4hA3+m6WJ6hyJL0lXSVdoPQsWo5iKVejbU9VCRqm0eF6TzONkiLJZfaVFzuYOqfSPUdL+ObXpmCbe9kgrjuemNrMtdzPOl0opWHgyNYuLIn6c6dBIxV28zBwTE21+tR81Pk12jawzHrH+Hgjtt4QmAFplilYtD3SDBuJ7tcGlLSWIBv2hvU15Q6OoEjeYkYbOmYBR3RGDMQ2CxlekFyIHkIx+0TRU8cGz9lccrEoPagdqG/todXsk//8772or+Oxx7zH6GRpNncjJY775Tco+eFfFhctCdWSb5TOeR5VFQvwrfDEyUB2ycc3OUn5SCCspRWr5zP7JqOsv4U5Tpq6Bjp6/ScuYx5XjjqR3HaLGneXW1EqEH3+getjMSG2iwcJEKb6/Vd4eg5kT4pga7T/UjUSdA14VMM4bpcX0hXSLeE58XtS//nffgF04e+QSsjP/GTsRxFBGVO9p2u75PjZWin31J65QOpATZ94ddu/OhTp2J9x5ztkunC7ILZAeMbxQOl69HQYSUdEEDXi77crTrmUt8cGExRb+/3i3GwZ5ZiuZOhHYW+SfIXmIzlZwDo+7r0dFRWLUpYG5OIczaSaTzsslCeg3Z6baoA8dxArpt6fSTSeqyVH9lMpfE/WmUZT4h1O4c6bxwZyUDtfyukc6fU0bkTkUOO9JsDidFUxmIOjkTuT4neLzkis16X+ykiDYcj00sIqmAP6TrFFeZ6NjZh8lAUeI6oxbjVQyyZmikeGh2tC4qkguK30FGAEIOlhyuXr+vX4t7Pu5HzfhZsGS4vU+X0OcCc36AhqxB1nXYUXn0/PHvaULny4agftwhJhlFks4wZo7egBzQYddejfbADm9JS0fNNPfa763UMF2Xjqxt/hrTUIUz4bCsm/ONzOAty4BocRGrfMMx5mch4cDnaBlqRdeNjcLd1MwIKgQBj5PJTMHPu3kBfG/Z0D6D1lldg6R1i93649BjMLN0LgzethKMwC99c8SPs/+fXYO0ZgjM3Dd9c/RPs/8B/YOvoReXS09B5wEx0XX0XrG3d2Hnm4Zj81FqY3Z4A9k1y2po8JjjNwJvHFeKkd7pYPTr/cDJmZpJz3IRd+WVosQ+i/Lq/Iq2jFz23XAr3nJlo6m9iBDClmaVM93L+70+wtto1mZHbcoBdf/o1Dj7gWNy3+T5sbNmoKu4UUwpGPCMB1w+deGgA23sigfrH2sa12Pzq33HBk21ozwFWnGVBVjqFpHChzmZhDuybnnKhqAf488/MSJk/ymBPv9+0ZxM6Bjt08ypKL0LlN+3IuuEBuHOyYO7pg7MwCxOuOh621BG09QzC+dAGmNrscKanwNo/jJG8DOw683Ds/eh7gNsDV2EW2i8/DhMf+g/QOYDhS0+G6dnVsJF+moC+TDNy+tyM/GTvRW0BTNaesgko++Uv0HzH/fC4PUx/mv5wAva0rUZtTx08O9Lwi7e9x9PJbfv/TvWgYJ+JWHDAeajMmAHXJX/QJfOJmc2JIIR2HHIO4b2697CldYv/XrTslkDY4CrJR+bf7kNNfp5/7BITJxXc/2fc5H4tYMy9rvrXaPrlhchrHwps7/eLYRm0wGOxoPrpp5BRnqmYdjTGIKH9yRlHhFjB6oFYf8g5Lcw3iMn4zrZ2HD7otbMEcub+a6/5WLrgZuxftH9Czg2i3Uf00orlnM0I9nzwH7RdvgxtOR6/rT02Zy+cv+BmpGGCVzebmtB9yyUoySjB8LI/SvrInoYNbLdiUcrUUXt0x3J2FNNo28plJnwv/2YPhq9aMUqW0/cdsO0dIKsE9t05aL71Pvb70j/dgbzD949Zn1ICxcMf2P4eitxAQapvF1NWCSPXCUm/qX/UrWNzmnaLmc0lyrJKUV77qfdeZgFQvE9A+iS79oYNqO6ieLAmNFktKHM6UZRePPqsKG1JGX3X+77Yifp7X4StMAdVvzuWEY9RGSitCa129N79Nhw0X3pkZXiyFZcjCHkJREbW4mJ4HMNwtXf6507lh3WicV0BnIPkCPGOnZYsM0zuEebIlTPWC2OxpaIcNatWMV0TzyNonnDwxIO17cK2D9C1833U505E7r4nR2Sclc9lDJWDIyERQPpl65faKoH0a+nPvGR9tLYRxkbR/33fNmqn47PVldf/GlnHnBi/cTXIsV3J/pMNq3Q6MaFinnYa7dvRvvU1dA51IXPaMSivWmgsf6VnojUnEdLtbQFavgbMFj9ZI7N5E2eg6z9Lkd8wOs9lTtuf/RNIDzxJIIbY3he5PIZsKI2LTFfq62HLs6FqQb3fHtZ9VAFHt4/osTgf1c+/NBq+on07HD9sQd31j8DR2h6zsVWOdY3r8HXb15iQOQGVI8PG9GSMoycIHyR33IYgtGggWObfYMiDQlpoDHQCL10ocRxqGSI10qhggoQrpdE40IjW3d9h8oq/w9TaCWuuBdULGwMmbV0Fqci//yHklHmPLUQ6OLkRGRolRAsGlu5aWHt2w5kzCa7caiaPpoEmlGWUoTwj+gHYTUN2FHx4NdIaRustxkZXOobey0eJHX5nB4F2SdAk25uId3crTcrJUUuOTNpR4Ry2MOensMui+ijfb2UkFa5MFybP6xgNwu5LT/4pBGUXnmvNAzbsZcJJn3rgMgMP/9yDkvwhTMyYiLL9T4O9thMHr3gZZjfw+qHA3B/A6qG2MGjJ8zohhTiRAmgn5TUvuGH2gDkqH/25B0fmF2Nx1WI88P0HOPX/daC4B3CbgDtPM7NYmuSA/d3s3+H7zu+xw74DrQOt+LLtS7QMtATIuDyzHGfvezYWVCyIGCFZOGRkSnre6+jFrV/eii/tX7LvtPuLnLMTRXohl+dgcQ5cd/4RZVUHBPzeKMT5eEwe5oiX64FwnRz0VrdXn1Jco6zVfv1Md7I5tnC9Ox3IGvY63P/+60qcsf9xqHQ68I/dq/FdeyNWPOVCcbcHZYfY0fZ19uiOIZHjfte7RXANe69TOap9Lw7UFpyJ7CDSIoIQPqnMS9csxacto7vfCQcUHYBz9j0H+xTu49dhOYlEOKQSlO8tr12JE+7dqKhzbGeC3QFbZaV3wVRaqsqKbJpQjI67rkRxTgpe3Xgv/jtYz3bl3/ykm+mPJ92DaUft0Uw7ElBq/2D0YGvtVvxl218U9cc+bB+tu8OJ2trVuG/na1g9UKefR5BzAwFK7R3uWK0kI9rNfe2Ma5Fty9a0d/KykA1Sk5eSnJXmbEbrFIm6Ky3iLLkW1IjmR7VrK+G0O/xjF0HPLlvzbKj2LQT12lZJ/vRygPRLsM+3P2NFfucwbFluVB3ZOlo20ThvzbGi+oiGsPpUMPMvsdx77HVo+tdRmN4jPTUkxlDZHHQe9Rd4UnP1x878dOD584Ba4zvnKP3dR9yKR3c8gtO/ehNzh0bjTQcgLRcY6ta8HrA7VTSfkVw/fwpsFz6j61wIANkArTrWLAROf0I1Xaaz55wNR0MTjYzsmjXDxZy2wrhtLciCy2OBp6sbMNE8zwRzcQ6Gf38iyvLS2W925ZVJXjjQS7k/OF8MGH9UX3p37IT7H4thpo0SPnSZzbh71gn4/TEPBzXOCnqgN5dJ9Jfv4x3isUFo0xKzWd1RtqYEjl6zpJ+pwVG8EHWvDDC9D0hnbQUcdqc0HYPOP3FZjY4/is8ojO1DFfORdtZThu1/pTULf2jYKXkh7Kg5ArbTH5emMdAJx7Nnwbb7E0mafdYUZDlFm1ZEMmDlzk/H0NNnS9ek1Qu973h2iexRELJThdJcRwUb0lJxX34eCtxu2DML8OdTXtIMZUSyW/HB/6nbew0b6n+RMLEEVT+2wda53n9vdGz1vviyZbtR9Y+VsH31MBxffThq/0n/nnketprpiBXqe+px5ltnsvlBjsuFO9s69PVknKCHO26jK7REQDCOloveuwgbmjew3ToCaPfO3NK5ePToR5V/tOpUYOcaQPQbttty8iLgnJd1y2OkXEbTILCjzhedC2e3d2CjANzeWC5WtgC54tw0TKo+CLcddFvQ+RqBERlGw3EbbxT+59dIbdwAk1gPRKAp93pXBnLezmaLPqFtKF6ZsCijkAEmk8k3kPi+m73H4LwpjDpvaReu4Dij5zxmE9wD3nRLZnWznRj+kAez7d64tb7vNOlv/SLXvyhpPb4Pv51ahFM/duGrGhO2l4/GShOO8+/5bC0O2OXBy4db8Nft7Sh5K8v/e7mOXXZuSoDTVuy8zRkAvq0y+Z8RFrC0eN23zo3eDBNz2orLIOgO6ZeSg0bp2UR03C7fshybOzdLrlG9H3piRKIXYnn+9YrZuHbx3aq/NwpxPmpOfcrvhlNsuOUVh9c5QU5atwmuYd/xJpMHlhSX38lKzt3Lzk9BZZsH9cXeNhV0Zn3TerjhZvqy3x4XcsuGAx0RMl2WvlyQyuGxqw/D3ac/Fhm7HWfHrZYey3U4ko5bQVZ53U5VnWMT1Vfe0XQCiVmR5XWZ2ujGrS84YR40h5R2qHUSt38wenD+6+fji64vDOlPULoW5Nwgmo5bpXJT2IJZBbP8cwGlPJTKQjbIqLzUEA/Hrd8RdsqxXmenztilZZctWU5MOrIDaZnG2lZLRwUU95jw6ONDcPUF5kd2GGYznP3h96lQHbff/mVv7GNv0YwbR7OU4Yr56PjR3/XHztWXG1roy9P/JqcEPY5ezBscjEgMO7mTViJbuXNIo98qgmyAXh110nU8fBLq/rWVlcec4kLpoXa0fj46f6v61XS0HHYzHJ9/AXOel5+gfN5cRX0QWOmvGn46uHnUnTXwDHb6ZqCjbWE3m3HdvDOCGmcFPTAyl4nEfI4jdo5b+q5lY404bRlMFjiK5qNuVZ2xdAyMq+Jyh+24VRjbPSYLTEHY/0daWjF3cAjiKPoumGCZsliaxqpT4dnxgaTvQbIiRIAMWLlXXw7PzjWqa1Kl34UMpbmOCqjc69LTcPHEEv/6b+3P16o+T7I7Z8ur2vZew4ayuerXd8HW9pGi7e/fk4r2b7O9epXlQtncbjStzxnVsyVdsM1YGJ58gsSCZxf4X+oa1pNxgp4gfJCcnGyMg7bh00RGPrGm73Sddo0oHgugSZncWNF3ut6xA7FGSsoAqhc2jMZweX80DiHtGklPH2GTJdqRmhAyHAOw2mvZW02tAZIG2MMsA76dO6NtQw4sWpTR7kNyXglOWwL7Lhx5O6rNu3ijeGaDFr+ji35Xc3Q7Ji8ZTbfx40KJM27PpnzJ98Z1hZLJD5VrksPBnLJipy2B2o3+6Drdr3I42PPeSZOyjmX6yqkEcsh+NMMscewKAxRdWzvDInHaCmUg3RH0SwuJrGcN/Q2KCxU6sivXC7E8dw5/zvqr2u+NQpwP0weC6FPIryh/2P8c6affaet7TnDaku7tvdjb3tRmQpsKOkNOW0FfyGlLoIl29VHtqrpMITrU5PC/ng0BbZuMNkdPj6NVbrGstHSO7W5JGTDEiqxUF0eJC3sd1Rpy2qHWSQl6ekC/pz5lRH+C0rUEmhuolZv6Z7BzAcEGJVN/E4OO3Hp3b+mPXVp9pObIdqnTVqNt9XRUwFTrAGqOVM6PbGb14tj0KbXwCPvpOG0JNALQXIhOQOnNmYJ12grpz+hpxfwIOW2FMUltPiNxDgXbbwUboAetdNu3M4eDUD73iIXN7yTla/sIttQBpB57DGxzDvX+qTjx6Xr7gZOCm0dt+wCQOW0J9D3f7YZr+/tB93ujc5lksCkcxm2sIactwePy6r3RdGI5rqqM7aYg7D/Ni2kHpZz61EJuTXEavryUtsEEXBPlL9hXXaet7HchQW2uowIqN9Wd1pzC+u+TJuluYrHsGurW6Nt7jfJn7Vum6LQlkA7lTR4ctf99FtS9XyDVswxHTOdsFB5BWBMb1hMORXDH7RgHxZ3RAh31CwDFctFC507EGpbe3cwY0dtJMeg7XZ/k8C5MKIxAQshwDIBkbhRKbVM+vwvlh0mviUHP07Fxek4O+h2lqZQu7bTV+i7oBEHQCz1U+p4zomORBOmOnn6Jn01ENA82q8pUT57UX9V+bxRq+cjzO3B4WPM5ue5ptbegL2LQb9R0mfRcSw7ytk1Gm2NEj6NRbnG+ejpndOxSqkuk0g41/2DkGYz+BKVrCTQ30Ct3MHMBPRuUiP1Ngq5dhscuXT022LZGdfQAFbsr5BerPqUEe/PnQT1PYasiNWeKBQy3czBy1rMBRtL1paFXPj15h2UzGzdpPkfzhWD7fTBzmYS3KRxB2dhgEHQ6sRhXgxzb1eZIhtIIxoaIfhuSfQ1VdqGUUbbm/Kr1K8VnSHa6sjJoQ8PWsxjN2SimbdB6wpGcjtsHH3wQxx13nOTvoosuCnju888/x29/+1ucfPLJuOGGG9DVpb04Hy/QirFCoPhsAcj3xolVBQX+jjFc2ZPY9n86UiKG94iJmZFrECj2a0LIcAyAZG4USm1D4RIaP1GPVUPPE0kYPScH/Y7SVEqXhUfQ+C7oBEHQCz3U+54zomORBOmOnn6Jn01ElKaXqspUT57UX9V+bxRq+cjz+zI1VfM5ue5ptbegL2LQb9R0mfRcSw7ytk1Gm2NEj6NRbnG+ejqnN3YJjM9KdekZtMK+Mz3ktINBuDYhGP0JStcSaG6gV+5g5gJ6NigR+5sE+TWGxy7dPmKwbY3q6FcqdlfIL5z+Kmdpd2z8lIXVEkD/0zXCyLvvYviddyW/zSud5f1dvxl9TamBaTd5yy6AuAaEPAe+/NKbtyhPYZ4qpKWWbqxguJ1V5CyWrf9/nw0wVE+19hOloVQ+srN0T5A3e3bjpyx/+qOySOrZ3IyyrwP5AZRCWlUMeuPjovxgaRqyOtB8Qa/fi+UjtyMUkoTCdC382s3+l4PKYX/11YC6GIVi2wh1EclISV4ckbOxDR8XsPmdEtT6RdD2NxbjapBju5L9V5oXK6ahl5fKb4NZkwbkGSxCKaNszXlAyQGKz5DsdGVl0IZqwZCexWjONqN4RvB6wqGIyHsiIoxvv/0W/f39WL58uf9advYo6QRhw4YNWLRoES688EKcdtppePTRR/HSSy9h06ZNyMjIwFiE0dho1bnVLJ6SWvw6RbKjoqne2CpqcewUmP8iEatNKw2H2UdY0R8YF4hINQarUjG/ah4OnXYoIg2jMox0XF0jiCrjPNVni4IeKMW4XZMNS39wMW6FY0JKMW4plMKu94pCjnFLsd0oxu1um02x3EK8UuFoXZ3Nhk9cGShZrRzjlnSsf5IVUE5OEWKSFrUyCG1G/+vFZgu3fbX0MxjdlT9bjnLMrwssf9+gV25ivRDLs2bWTOz27GYEdfsW7IvvOr8LoVbSfJRi3Ar5tefb/M9pxbgl3ft+dWB7CzpDdqDOBnycnuaP0aQV45bSq/ugWDHGLZVnn0MC7XBIdjsOEHRB+NTSY7kOB+hRiPZTLKu+QY+qztH4UbU0Q7ULy5mjT8mfgbbGT9lEvH/Qit88a0JTTx7T12DTDrVO65vXw+0J3IWjpwfB6A89O6tkFr5s/ZKFGdDMI4S5QaTbWzzuHVRyEL5o+0IiI6HcWnMBcd6U1kjGCEuLCCLV5GVkrDVap0jPFRyOTM35kdiWadnlXR8Wqce4lbWtmo7Jsd2ViV0fmuFSyI9splqM27r/lqLqsFWwpQ17SWKKpwFlBwHd9UCfl8i075t61D/8HmwFWSg6eiqan/sCtvx0VF11Ertfd89bcHT2wbNkPwy+9w2baRQ4moHpNnYUtqJ4GnZ6CuBebYVjwILKBZ3I8oW/IYdL/doC2DJc3qOlFdWY2LYefQ8/z/JkcHsAs4nlX7XseG+e/y2Do9eF0kPs3hiDsnTVsCc1E0OuYUxyOhWPEEcyxi1d9x/Lzp8MfPOK90c+RvOG3nrY33kN5gfeBQoy4Tn5EJj++SFGCjPxxR/PwHFpU9D3ei+rW+khXWj/NiewnmQnVOxBrc2KlKx9MPB6h2L5mj/NgzXHBsuvU9BpX4uMdZ8B97+NrXkZsJnM8HT1Ieuq0/DtjGJYO3pReevLcHf04dbTCvC3yvaAeR87mrvNhRNft6Lrv5fhtWuORnraEH5lTYPVOcT0j8lqwIKKBZ1wVjhgy5+MzK9epHgpwEAHuoftqJs4Hbn7niwhsxTGi/LUQZS3bMUNjgzsbG3FohdTkTNALelhpLV//bkHU21DbM5syToA/RdcgaHWLqb/lY8+os3uLrDak0OjcAp2/OcFDF/1R5gnTsDEy69A8/LrYSvOQ9XKu0EMq3WXXg1Hmx2lt92Klgfvh7ulFWn3rIB53sESGya3aQE2Tpav7vd4rFMiDLFtFv+vZGPJaesasrD5HYXDopNV/udFOiX0C4oX66QYt2/XKdpqSb80OK4qldXQs/K2C3JsV7L/tI4Sz4sDYpcKafjyCirGbeEUTKDf66xJ9cptGGryUAGVe3Nqit/20PrvsLLDFJ8l2dF8Y/uedzHF4VC39xo21F8+lbA1Evsvi3HL9EyIcRuqfILE/PL5/jWxYT3hUITJQ6vLBAbtrm1vb8eLL76o+szixYtZUN9XXvFOPii4LxmmO+64A5dffvmYJCcLBiGxkxPL6osXBM0cHQ2IWZPlTJwCE7K9KA17P/UcCqr2Cipto6RMicbwHrPyKOmBCBtd6Rh6Lx8lIpZqQu0HRT7ysVFiJor1SaMbObcsqU44hy3MCSI4uqqP8v1W5AATiKImz+vwsw2rEVB5J0GjrMStecBNZ1kCCMUEVl/CsjXLsLFlI9sRseIpF6uHnIVZ0DGBmVuNoEyexx8P+yNWfLKCpa/HLEztKZRF79l4QI/4QF5+kqcee3kw8lSDOB+PyQOTgh4I151mwOr26lOKK5A4jDlzPd4YzIS2HODGc7zlo0nWA4sfYNeFfpfjcuPOtnbMaR+ROm0znKhe0s7+3/VukT92LpWj2je5F8vBUlGOmlWrAuL3RaOPh0MAZpQ19udv/hw9Iz2S64L8oqXDJKtbXrsSJ9y7UVHnatdWwqnDUq85zqz2vRggX02mG3uxuJwCG3Sll2hEI+1Q6qNmD7T0QNy+tFA+800vg68Amjg/fcLT/t0ySjqml0c85wZa5RWQk5KDZ098VndHqFJa8pdtJIPlc5bjto23RWWsVbOrakRuSqDdnv1Ll8HT3AxrrtXHBeDVzV0fVcDV7fTbWoKeXbbkWlHjS0NgFe9cfDc8CnXtdfTiT1//SRLXM8eagx5nj98+3/GcDXntQ15260Wj/UZsMy3ZFtQsahztUyKHo1b8SPGzLL64yUt6Kp5ryO26YJ/18lK7R7vrhBdxlIk13c1eSsvzFMoSVAzMCEGt7CHLVlQ3c4aTHdWUy1mSno8RvbFzIEBfbv3yVth3bcafnnL5CfLE5RPPHWmspnjzBPF1ki/FRyYo1YcY3peV0KYAj5+5XFwfue6L08hf0oHdeRbMGhEx24tgN5twyz5H48LqK2H6/Y2+fmdB9cJA/fXAMzrHFc0LxLrfk5+Kac8+r7x2GejE0NNns/jKAr7OKcF1ZhuWPevxznsyPbB5XIp677BYYOozKc615LZO3G+J8f1Ru4PFXRbgSs2DRfS8/LvSGJBo6yajkNtf8dzAmmdDtW9uILEFJo/feavWz2hziPWDEmR3jgTo/Q8fFsPsI3CUEAcqjKthkVvSS7CXLlQevwlBjO1K7VthzcLyhp2sz/nlV3MEbKc/Lk1jsAuOZ86Ebbc0DmyfNQVZzhH1/JXmH9ULvd7eXR9Fdk6is/5VAjkkb6uYjL/9+HnlOQjJ//nzgFqdXfA+G6pZ/s5dwN8WAUPSDUIS/aNx9x+PwPbVQ3B89eHodZrjPvM8bDXTESuI56TC+klXT8YJeoLwQSaF4/b999/HAQccwCq1YMECnH/++TCbvVu9BwcH2Q7cf/7znzjvvPP8v6OQCQ6HA2+++SbGu+NWAAXDp7hKQb31pCDRFG9E541qNCF/s80IKzp3ojElFbUdXSi8+n549rShcuXD2m+tw3DchiXDKCDmjPOCHpitgL0e7UPt2Jyahp5v6rHvXf/GcFE2vrrxNKSlDWHip1tR8o8tcBbkwDU4iNS+YZjzMpH54PXYM7AH2Tc+BndbN9uRyF5T0jHGy0/BzLnTgb5W7OkZQOvNr8DS6zXoP1x6DGaW7o3Bmx6GozAL31xxPPb/86uw9gzBmZuGb64+Gfs/8BZstPNi6WnoOmAWOq/+M6xt3fju6h+ja5b3SElheiEOnnhwQLtRm25982lU3Pokq0fnH07BzCzvW9NdeWVo6R5E+bV/RVpHL3puuRTuuTPR3Oc9llaaVQqX2wWL2eK/Js+D0t/Usgkdgx2qZQjl2VjCKGOtUH73J5uw313/ZjtCUh/9M9pHtiOzdw/6syfg8S9fxU8f+BJFPcCff2YOIG2jhU5FVgVOnnqyZplIPpXftiPr+gfgzsmCuacPzsIsTLj6BNhSh9HWPQjnQxtgarPDmZ4Ca/8wHHkZ2HnmAuz96Ltst5SrMBvtlx+HiQ+9BXQOYPjSk2F6djVspJ9mE7675ic49JSLFHVGsAOFH21A/e9XwOP2MP3puOGnTH+K0opg/98Qmm+9z1cxEwb/8AukTcthcihKmQrXJX/wHs3XsF2RtDnRdtwq2SUzzJhXNi/qLNo0TtRedBHacjxskUqETBRvjMaJvbIPwUV/b9KVtRZzNC3C23NM+OO5KTh80v64eZ/z2bjoGMnwLup00g5XjtQv9inYB38+4s+qeiBuXyNjhFp7zSyZicd/9HjCzQ2Uyhsqa7uafA4sPhAXzrjQ39+iOdZGwnFLR8gHb7gBppISZN57DyN0otigdMzcMZzBnLrk3G28+hwUpBYg/db74SjKxcgd16MyLwfdrZ+zXeV5pipkL78LntZWZF13GdKn5rI0XLnV+vUYaGQxhSk8RXlGuf975XdtyLjtgdG528BW4Pu3gaxi2Hfn+G1j6u9/j7LhJ5C6ZwtbfyvtVlOtv9x5i8DTPcypJXOmqrG5i3d8ae1aFb/wU8tTSNecm4otp/wFZVllKK/9FPjs70Cf9tF+Ksc2qxUmazqmDvUq7soK2J3mQ29TKho+LoSlIAeFFx2OwspStFvNSPn8KaTV7cTuUGSbQadURA7wdOoPHuYkNGU5sf2sYpy48CK2Y1ewB3K9Xb5lOXPy/2NTF3LezxrdzZzpBqXmzpuCtoNWYPfvr0eOfVhyukF8iovKQrHjldpPzPBOEO/q0mpPqsNfT/fgp55eHDY4pBpHkNK2m8343ayf4JaKKzF00blwdjsVdYpeINd/UshOjclP4giO6St+mY699lYZH1edCs/ONRIyJtK2DelpuD6jGA89McKc31o6SE7qy85NCeoFuRLju1zXVHdGihjhY75OiRDk9ldYg5qKi/GPiyfh+55PUeEYYXYzpdWC21a52OYAS6qL7diW66U5043PU1Nw/2AJrnvBhRSR3hOo9T51ZWDkvTwU9wBVN1yErGNOVB1Xw3LcrjpVfVet0HZBju3yOSp9b61fz04PTKiYp51Gxw60b30NnYOdyJx2DMqrFhrLX+mZaM1JhHT7WoHmr9gueaQXAl8+7d21LDrx4zGZYZp8pKQfBMhfwRHM+hOdfjj8dxIbGnRbwoy+wb1R/3oPbCWFXuessJmgYwcc329G3fWPwNHaHrH5arAg0jaK/zshcwIqRoaN6ckYR89YctxeccUVcLlcOOKII5ixuuuuuzBjxgy8/fbb7Pj1Dz/8gL333hsffPAB23kr4LLLLsOHH37IQi0oYXh4mP2JhVZZWTmmHbfJDBo4U6dNU90pNbxtW0gGKFjHbSKAjh2d9Kr3KKAS3jjljZg6+5TaRrhGsL/8CvJOPcV/X2gvwvDOnbDk5SHvZKmTjp7peOIJpO29t/+eOB+6L05XrgOh6ES0dGwswKjjVk+egu7STqzKNk+A0zYUPRbyofaR5ye0G10X6wv9xtnZicw5cwL0h/7v37gR1oICw+0tT08MimPnstuROnlyQHqx1qtoOm7jbZco/xvuOQH1xSbFReq/D/t/KG4e0D2S6rjzEP/iXgAtwIr268ElM/PxdaEtoD6RbMdw5Ci0r5E0aOqXSOOIHvTqFEz5jco42jodCcet4Ly11FTDXFIScI+ctq5dtbDNOTToZyOB3B07VMdVso2dnV3InLMXJrxwvLRO/WYMd9t0QwwIz8r7rBiCA4Wg1LeN7jwV/0Z8qkMrT3+657wKTDnSe0T5odmINijUQ8/Z/4RnykGjR6N9+UZDtifvPQErT39H0h/EetvQ34AL11/Iwha80dDMypea6wiQ+xcn/j/c9dEf8PAXvhAMonzFznd5/kZ3NKu1J6VxyeRC/H2Pd4evHn49oRiXH/pn7PPsBZo6Jd9d7q+LbwcuyY2OVwfYEh09ofxX7uww3DZqYcPkENonZFy+hTlg4j0fiPQ8ieZ431n6cMH2awLk9fwXbWhYWzgaektFL6nN7vu8R1HvCWfmlMJit+DWq97SlE3Ijls92+NrO44oydKI7TfaBjpp9R38N6TOXsjXs2PQcZvwMW5vv/12ZGVl+b8fc8wxbPftyy+/jJ/+9KcY8R1nSU/3BZz3gWLbCveUQGEUVqxYEcWSc0QSWgtiWhBE4nhqssAIC3gsJ0RKbSO+VnzpJartpdaudH/iNdIJkvhZui9OV64DoegE17HIQkmegu6SY01vB4hRPRbyUWpvsR6I9UVeNvFz9Cl/kWC0DErQSmss2a542yXKX+tFQEP6IGr0HKsi5mhv/G0v6HtGkQO5bGeTLaA+kWzHSMjRSBp6iPU4EinWeCPlNyrjeOu0UWg5WslBK3bSBvNsJKBnG/sbG2GpDzw2Sv3QlqnvWBSelfdZNSZtpb6t5fRTtwdOw3ky1H/qddyGyFYeLMgpO5TSB78ERflGQ7Z0ukGrPzQPNksYxdWcxgMdXyMn3Ym8yUNIyXFJ8i2f7yWdDqb9jNRHSONA0WYePdCzfe1fa6Yn5EfllsuPdgz75WazBcpOR08o/6DaxqDj1jDbvRpoZ2LhlKSxncHYsaYvX1WUF9kC2mmrp5fUZlovS2h+8XGhgi5ECnq2x9d2HFGSpRHbb7QNdNLK2rsAUJmTjqV1x3iEBoVsYkDstCXst99+qKqqwpYtW9j3vDwvo3xnZ6fkuY6ODuTnq8fJuO6665hnW/irrw9uUcDBES8kI+N8JCFn0BVDiUG37eGVjAFaCXSd7nMYg8DsrNQG4utqLMZGmcgTVY+D1b3xhHjbpYjkr8IcLTDxihmDo1WfSNTDSBrxbq9gEYzt0Cu/0bonm4yM2nDaWSt8iiG+Tp+RBNlG2mFLf/IxZGQ4C/ad6eyPdmKqsbGr1kmhzyr136DZ3DWYuSm+pZE8/ag8NCy28lBAoS78CDFfo7Il26jVH0rTSxUZxYX2FpBROIM9Q9coPIIY9L3xk+Daz0h9hDS+TDWuc/RsVtEMXZ1SqgeryyfSMSVAdjrtRfkH0zZGYZjtXocRfizaTkGHxRB01Yhd0dMvVV2IFPRsgK/tOKIkSyM22Ggb8LYct0j4HbdyuN1u2O12pKSksO8VFRUoKirC559/jhNOOMH/HH2fPVt9G3lqair7G4uIKIOnQdbQaCOZWEmjCTGbthYD9lirL9Wtqb/JFzv1dQwWZODtq+ZjWlExZliyWMxQ50gWspfeCUtrFyofWcnekJNTtv3BB9G+ciXS71mKEesOFKQXomj6TzDQ0Ie6M88CXC7FncHR1j9Kc9OeTdjetZ2RORFZw9S8qQkT11YtluKu/Hy4urpgm1CMjD+chvYsB6wdfXDe+W9Y23thys8DuntV4ydNyZ2CHd07VPMJRo9jaRcCYm3b+v22MRpxTpMNaizzsbJLQv7rm9bDDXdI+SsxRwvx6oi8qX+SFZaU6NZHT44U4mBtw1pFnafjyDsbdrJ7s0pm4cvWLzVloZQPxbidXhA7wopw5aIEek6rfYzqarg6Ha95C+kB7XAUYs4K31nM2Vvvhzsnk9loZ0EmUpeeisLyyXC3d8H+56fhbuuAKScbnp5epF50LExH/TQgzq08fa0yOLOdKPyiDvUXXQyP2+2Ni5mbBfT2w5KfAdNZ8zD05EZ0d5Ljwxu5zZLmhmvYrBuD1WiMW7pHx9XVYtyK2dyNxrgVSIm08vSnW5Tn3W1rgA1cAJXjk/Q05NhysH9Pa1Axbun6cMV8absZzDfYGLeCbdznEO3+UJFZgdkFs1mMW4FRfKgpFfVrC1is24rFHTBNPhBpfY1YMFiIbR+4YWIy9aBkZg86v89UjXErbr9QY9xSHbZNSmFlMxLj1lFxOPLNNaj9qALO/sAYt5SPP8btYGCMW9JDIr0crErF/Kp5gbLztZdajNttzjQvoWC/eoxb8bjlOyiiCzXGd8Mxbn3rxHjPB6IB0mH5HKNv0OpvBzW7YiLCvPQ0bMhIx7r0dCZbi0DwIWrTxpQ0zI+mbAQboBbjNhF225LP4dtXgP52YO/jRu2mUX9EtH0W4vSZLD+UxLiVyFJeFqFP7/hA0W6z9IyWOdi2TBBfDkf4SOgYt0Qu9q9//Qu//vWvGRkZFfXmm29mf7Tj9sADD2TPLVu2jIVO+Oyzz5gT95133sFxxx2HtWvX4vDDDx835GQRZfDUYp6MIeNfsrKSRhpGGbDHilzU2MMpNqoeKzYx6L65dA5u+Mn9sG2tHXXOilhfGRvs6hKwuZfFgqqnn0KGz57olSNcOVOaS9csxact6juaDp14KO5ddG9CtaWYWZetatyBsf48Jg8jabAXpmLvp0eZko3UORj5xsMuiOvPGFl9zMJsMUjOPiK0qqz0OnXH6TGkeNprynvZmmXY2LIxpL6k1b5KtiWa9VGSI9WDIO5DgmwJSvZSDnlbqNlZtefjDb3yBtPeRnU1FJ2OVz/QmyfQ+LniaQ9Kutx+Wx0Qr9VHuiWJ0ehjuO42m3XrpVSGmqFsLPtnF0q8pPVe+PLxf8quG4ldGuC09TlmybFHfhFy9NF1WuX4HY6+2KJ+2y1nf99nDmCxwfHNx4rM8BImeXhgTXczh5k8T7+TmFi9Vz0J2/RDpGzlz52rySy+IS0Vzx94Im6adxNyX71M+dm0XGCo2zgjuYF8FWUrqps5w8mGf0HOwyYTc1pZKspRs2qV5tgn2Oj/Na5njOJz2kek7OeLvDvAxTFhvU7aTjR+UuCPbUvyrT5KGrNY3EYku2UlxchJzfEz3IvrozV3vOfnJiwfase8IeUXBnazCXfNOhHLDrgRnRde5hsvrKha0BCgU2L3pqB38vr15Kdi2rOjcyU9Vvtv8ybiGliw7FkPmwd7sjywuV2Keu+wWGDqM7F6EWGnODyVfP0g/k6M73/rcWI/u4hAL70AGOxU/66wPhyL6zdxncTrEWueFdUKOkB62Xp8H66uKUCPxYyjSw7Bna3tsO36rz9NcpRfU1yEGZULoi8bBZ2Kx9pe0efwzJlA/XrpdSrTua8B76/QLnO0fRZK6aflAUOjfchve096AHhzWUBZuk+6DyvWr8BpX74RaF/UbHa4bZkgvhyOcUJORrtrr7zySrz00kuYMmUKGhoaGKHYX/7yF5x++un+5/r7+3HKKacwx+20adPw9ddf48Ybb2ThEIwikR23SsQUSkHJI8rgaYR5MgaEN5Gsk1E5JiKMMmDLoUdqogUt2USS1E1Jf7TYw2myJLDpyt9wCwy69lyrX0e+vW5vmF81+ReJE2bbsWdznve7Gch44EFY99kn+n1KlKaeg8UoM3ow7R1MG6m1L3NunXIsc1IGLLx9n0IbTJ9+uIS9XqvO+xbsi8tmXWZ4Z1qk2iVYkgdx/QNYpMnZ98o7485pq9R/5SzDsYCSThAr+WFlh0l0Qm28CthRnTLA4o21ODIwaEtF/29+J9nNHwuI5XjHxjtUdZ6gtROVdtHOLJmJx3/0uGo+v//v77G1c6viDt1QbZ2RuYHwTDDzCLFcCJtaNrFPtdMKWmkb1VXhudTBVBw67VDNPOLFpq41biqNn2o2XNFpOmUJLppYolsvtTKwfB8bYU4+tX17fmdySR6qfncsbAVZFPTU6yAqmgaUzwbs9V52b+qz39Sj/uH32HNFR09F83NfwJafjqqrvKRIdfe8BUdnH9KO2g+D737Dcipdei7y9rYBHdtYmo6UGtT9/m44OnpQeeeNyDrxDG/abzyH+mtuhq24AFV/OAO21CFJngxuD2A2sfyrlnnJ1erue4elVXr2YWh/Zysc7Xb1UxjEVl67zl8fQrvVjF15ZSiplO3CFD+bVTLKPK52XQsK+TL4ft/Y14iut1+B+YF3gYJMeE4+FKZ/rsZIYSa++OPPUeF0ouzWl4HOPqRcvxT413NsbEy/+WZJDGWtPkd9NqtnD2Z0AwPLH4HD7t21mj+1H61f0hrMxByRlUva0eOpQvt/HHDlZcJmMsHT1Yesq0/Dd/uXwNLRi8pbX4K7ow+Vy05D91FLsNNiCmC4z1z3GXD/2xgqysbH1x6D1NQhzDBnwla8N4pSpsJ1yR/gaGrC7uvPQt2+hahyODF7eAhFTjfTwe4RO+omTEfuviezdBXHi9p16Oz4Hm3dg3Dfuw7o8r6psJaVovqh22Eb/IF9r3fnoHvZn9l4Qgz1lY8+oj2mCKz2vt1yO99+EUPLboJ54gRMvPwKNC+/HrbiPFStvNsr30uugqPNjtLbbkXLgw/A3bIHafesgOWwQyS2Tm77AmyhLF/d7yqIx3wg2qA6Nb//JnJvWAlbWZlfB/Y0rMduqxUTB9IwfPXdrB1S7/kjGvefIK2/T3aNKakSfY0ZDLZduHNow+M6+RzUTgOYrWBv37T8EdH2WSilH1jQ0R3CCmX5NrcYZxWks7FxksOB2bSz32SGe9I83PzjpxCVtgxCLhzxw5hx3Aqg0Ajffvsti1k7depUf5gEOb777jvs2bMH++67LyZMmBBUHsnuuI0og2eQbInRctxGmpU0WR234bKMJ5vjVq++xOL6yvd7VJl8xQy6jx98Aw564deyXTI++Hbg9lzyb3akMKp9ymCa4eYRbcct2QbHnYeoMhnL28AIe32wdY1kuwTNzqtSf7+j49pN4+4YUjRf3BlFMDqhVV5ajKdOmyZxvgvPk2NieNu2uITBCNZuBNs3osUAHi3HbTTKEW5awvV4sakb1RG18VOA1k7XEypKVUmO9Gy9kK94t6ES2nM9qHjySdRMOxhGIO6z8v4r7rMUW1eNLFKtbyvZA+G6OTcX7m7vble1PONpMyIBo7IV/u+eIh37DPU5lTFVvDNazJJOkMvUqJzV2jOYNIJJz/7yK7CVlyFzzpyAZ+h+/8aNsBYUhKQfwbZNsupgoiPSOpVsiIjjVs/noAXyR5AbK5o+i3DKZ3AMjcq8IEhfDkf8EIwPMili3BIB2fz583WfI4ct/Y1HRJTBM0GYJ8caK2moGG9y0KsvsbhqMfmKGXSbGz5hnxQege203TR6NIS+s7AJPbsDYvhFS+6RZEaPC7p2aTIZy9vACHt9sHWNa39Qqb+fQZiz8sYFkdIJrQVWPJl4g7UbwcphvI0x0US8ZGlUR9TGTy02dAFa7PR6tl7It3y+cr4CVh/jwuL0QRil0hL3WXn/FfdZJYet0nNqaRu5Lk8r2dm7jcpW+L87lI0CKmMqxbAV66GYJV0uU6NyjrR910tPizeB7mvpZKTbhiM6SNQ5Q1JBz+egBZpzG3kmHJ9FOOUzOIZGZV6QIL4cjsjCOBUnR0IjogyeCcJWOBZZSUPBeJODXn31WFzFDLqlFYexT9pxy8IjiEDf6bqEeTnKco8kM3pckF+jyWQsbwMj7PXB1jWu/UGl/n4GYc7KGxeMdRsZrN0IVg5jXX6xRLxkaVRH1MZPLTZ0AVrs9Hq2Xsi3cZ12XL3F71pQMZiu+QzHGILemCqAj60cHGMTej4HLZBdiLbPIpzyGRxDozIvSBBfDkdkwR23YwQCgyfFGxODvusxLKuyFcrSYt+DYT1MpDolMcabHNTqK0BgcfUTUhxFJBNOP4Nu9VYLjvgGODZ9Ng7a73R8OzxxNEyCyYOcqn5/LD8iKBtuGoyZ3IU0jSAR29bhyPQScQkkNgTRp5/FeNDqL7+ROgdT13j2B3H95brHro9kRC1vjuB1gmK7JmI/ChZ6Oq9lL8XPqclBSJ/kFczvOIJvq2jJUm/cVBo/1Wy499i6bHkwZQkqqxZp1kurDCzf1cWyMAnSSG0U47ao2+SNN9rcbPiosvCs+H8C/U/X5P9zBCdX+fWBL79E28MrA+6PvPsuht95F+7WVjg2SolI1eSvOaauLsJQv/q6Q62MvL05EgHx0s+k6xeCz0ENFONWyx8RbZ+FWvpyCPmplIWIBRtT0mI3L0gQXw5HZJEUMW5jgUSOcWsUEWXwTBDmybHIShoKxpsc1NjDxSyuaszAgkGzlk7ExCsuROPy27wkIiYPzDYX3CO+QcxEhGU0clpQ9fRTyDjwwJjIXWBW3tiyMWxm9FiCEXOdex5jUWb+HTcCGMkFchl7YSr2fnqUKVmrzqHUNR79QVx/RkS2oH6UQZgWnkRYVlnpJangx+NiCorv+X3n93h669PY0rolKjpBedBx9GBIREL5jZbOX776cnze+rn/2pTcKfjZXj/DrJJZePDzB1UJAA8qOQgPLH5AUw5KfYp+d9XBVzG28UQjliHZbtqziRHQqZGShdMu4bTdN23f4JYNt+C7zu9iOl4rtWFOSg56Rnr84+eKpz0o6XKPEoHJbLgiQVnFIcBZL6DbbNa1u0plqBnKxrJ/dqHEWwwv5MRoAiwWwOUyZEsZQdTFl2CkOAfDx05D9qrPFMnJSs9dgPY3Poejq19CQCbGusZ12L1rNQ605mDfaScm56KWYhp27UKDLQUt/c2o7mpEkcs7I2q3mNFktbD4eP3ZEwLJz0SQEG/9ZQVsKX1sd1bft41M3iympNsNS0E+av74C9hSh1G/cTt6//VfRgBnzkqFe2AElZcejay5s+FI3wt1V97IxlAxURt933H2mfA0tsCSY0LNEc2jY6ov5q01z4bqZ56HrWa6ehlJT2z93qPBBZPZC1Q2Vsvyi4h864jUrU163SgpHMfYg6/P+YmhfN/7vu9E/TW3wFaY7SVanDQZyK1gJFGO9h7UXXcfIy6r/NP1yJpe6P099StxWiEgLv0iEiCfw9O/AOrXS6+Tv+HcfwPv/1HZH9Hf4a1fRhGw+hbpM5XzgDOfCc9nIbSvUvo05g13B5aJoOA/6T7pPlzz2R2xXccniC9Hgm0fAI2bAIsVcDmBykNHSd3GKXrGGjlZLDAWHLdRYfCMEPNkuBiLrKTJJgcxYVWsyIiE+lrMFjT3NcP9ySbse9e/MViYibeXHYapxcU4wJzFFiJORxayr7jdy9Trg8nqgsdpZsTVXqetd7ePZUIRJt74RzRecSVbJBZdfrlmPLJoyF1gVt5h38EcI3mpeZiSNyVoJ0SsIEwILXl5cHV1wTahGBnLT0N7pgPWjj447/w3rO29MOXnAd29ihNDoc4dgx0oTC8Mu66x7A+KLNI+25jQE+IoIRKET0bTUHtOzdn4i+m/wD6F+2jqhFFiOsrjynevxObOzYYn25F+saD2IksMsYNOCXovSEgeWUVZuGL1FRLntxih1EGp7eTXxN/1dIJksXTNUnza8mnQL4D02kWQgZG2UyqnUvr7FuyLG+bdgP2L9kcsQGWQt6HQJyq+aQOuuQPd6R7kDgJd2R589csKnHbYr/HC2r/jgCcakNdjwmCGB9kDQOWCTmSVDbM0hirmo3Px3fCQnAYa0TTQhLKMMpRnlCu2l2CbUwdTUfJ1Iwauv545+5iLNjcL6O2HJT8DWRctQddf/wtzu28BbDLBUlgIl92uaUupnje9cil+et8mtkvXmu5k47xzwMqIreilLO3uFV/3O6P3Pxw4/Qm2cK3vqcdvXz8df2jYicMHh/zpD1bPR/oZT4W9uA2GIDZUe9q041uUfXKDOiu7Aj5OT8PTey3BFbNuRLYtW1IG9pLynLPhaGiSOPAH0uahblUDmzMJEBz/jesKJLupSe7VR7Wz/wUnrK2iDFWrnvTGwx3uxj8fOg/H/WMrUjJc/jy+TbHh3xP2xhmVP4X7T6/D0dquqAfRfJEqbzPTkB0FH/wf0pqkNicANQv9esUxdqA4Jg10Ai9dKO1z6QXAYCf7V/zyQdyH1K4HYMoSNM2/ldnbYGxDvDcYBE34q+Rz+OYVoL8N2Ps4qUNP7I9Iz8fQ02cjrUE0J6peSEYfaPgsfCelUvsqpU/O4Tm/AUoPDPSRqPhP4rKOTwRfTsdO4B9L/H1EAuo7v/kQyA/kmxkP6OGO2+gKjYNjPCIejttQWFyJqbft9hvg7HH6nbcwe+DxOW1dmS48ds1huPv0x9iRv/6P12k6bTkCZU9MufI2EBh0hftj0Xk53hmEE81xe9F7F2FD8wa4PC7J0bO5pXPx6NGP6qZJ0Muf8ljftB5u2mJuMI9wymU0vVBADki1/Eket3x3i2Y+odQh0o5bkoWaA1urfkbaRU0GSvVWKmek2z0UaJWBMPjxOtQVeVDZ5kF9sQn2XCuyU7LRM9yD/B4Xu36xpxP77XEh1+e0JXhMFgyXz0XHj/4ekKdWewn9jI7Oe7rtyM/PR+acOZIxRBi3CdaCAkNjiKAHH37XBPv73t3CzEkLE5yD3lM1ljQnTGaZ01ZwktCC/pyXseDZBbij7gfMHRySsDWT9Cy+ZxLdcTv0j+OR1riB7eYzCpLUxvQ0/H3fH+G2g24LKIPj4ZNQ9/+2wtHnlR2RhzVtyGffhRM3gbumaR+Qd/c0Oc+JYMwbq9Yn/19Nh+3S1/3td86WV7H/Djcych3+dhHKteqgU/Dg/jdp6gFzUp1yrNcZJZRRyI+cVq+8E5JzSt5mhf/5NVIb1vlqpoMI6AxHYkFxTFp1KrBzjWafkztpJfqp5bQlmCwYEtnbYGxDtPpFTBy3RrHqVHh2roFJz+ZRWIDJi4LvkwbaN6z0xyPurFF22oqdt9dEjghurPog1ZkGODg4OJKQxTXv8P2ReUQDat8vYjtAPM7R+D60mJi6uB3/69nA3npWHXigYogEDm3ZK0365EzGYxGcQThxQEfZlRx45LCi66x/h7mbIZQ8Il0utfRCgVb+Df0NuvlEUrahQE8WWmUz0i4t/S0ht10s9FEPemVgYHwkJnTk+NxQHhc78UGga1npThzWMACUSdOgBTLtbrJ018KVG/yuGNucQ9lnnm9BLx4j2Lh98snS5zXGEKGe8wYGUZTuRO7idr+DRFLvIe93RSfJjg+w+bvnkdvXJtlpK4DNGmi3Fe1USuQj8O3bpbvODIIkM39wCLfv2cB2UNPOaXGatraPUHXkqOOp7v1idisgtAbBH+pCuvNZ/Bsm/7aPmDxrrRY01K3B/MHBAD3zl6tuDZrmXIcqrfmerZ/tKFQqY9WCFu+pmDBhtdcGJ99k0BmO8EDH5w3sbid7Q3qvqJ9aTltCGPY2Fv0iEeRv6EUKOV6D7ZMG2zfk9McjKDyCltOWQPd3fDjuwybogZOTcXBwjC107WITovL5oyETBNAOELo3yeFkR1U4ODiSExR/VAuR6N+h5BHpcumlFyzU8m8ebA47jWjDiCzUymakXfRkoFXvWOhjLHSl0uE9qaIGawKMm0I9Dxj27gimMZ12lamB7ik5SVrqP9GtLztemsig+IthgOZCFPZCKU0ludL3jCLvLj4l0LyL5lmq8u/cydpPT+6G5mi+uZ5SGVl+EWg7S28I+p7oOsMRsz6nqZ8GEJK9jUG/SDqbF0ydo53+eATFtDWCep1wNBzcccvBwRE51lJiGu754AN2X3hG+BSYiAni62KG04iwoebXsCNKjesCYxo1fkLHhczYbbOy+EKh1jOo8nBEHdQO9ldfVWwrukb3eFuNLVRmV2reN9K/o5FHpMull16wUMu/NN34LvlIyDYUGJGFWtmMtIueDLTqHQt9jIWu1Nu0D+I5I1iPUMdXoZ5fpaZ6n+03s6PAavAeEw7cpzKx8jDd+rKYgImM/Jqwfk5zIYpVLISzYO3hS1NJrvR9oN2qKm+ad9E8S1X+BZNZ++nJ3dAczTfXUyojyy8CbefKDkHfE11nOGLW5zT10wBCsrcx6BdJZ/OCqXO00x+PKD/Y2HNEVMahCR4qIRFYKDmCgsD2THHb6AhgTAN8j+O2E2IWBTAOD/2A9sEO/PCfL5H3kvdtGUU6M5tNMGemwN0/AlNuFjzdffC4PejduREjW3bCvacDw9lpSOkbQuP157IYkpW3PoXhomx8dePPkJY6hAqnAzU1S1CSMs0w+dPOzi6MrCmFZ9DLu2hNd4E4GOnYJB3f+351MfY5ZK6uzhhhZx1pasKGKxehbWYlIxcToyi9iJFvUd7Efk6EXEr3E5GILJnA2umii+Fxu5nudNxwKqqt/XB7PEjPPRhDv38ATlqMms2ofPSRhI1BGw6LfTwQiRhmRtNQeq46t5rFNFWL56knQyN5h5JHuOUyml4ooHTU8p+z1xzMr9POJ5Q6KMlZfk38XatdBFloxbhVK5teu5CdHskYYUReX7Z9qdt28nJGut1DgV4ZCEr3WIzbkR64PW7U2WyMuEoe81WI5Tdx3/lBlUmtPcNhPxfrQfugFfbV2jFu2XHh1UXS48mV8zDblo8MWyY2p6Zi5vCwNzyCPMZtmPO8qMd6LJrqjatqJB6jCN5YsumorFqEQ6cd6m2PG25AXXEeqpb/ArDVoO7DfiY7c6oL7mGvo4nJ8oMSXzhbeWxbj5+gTB7jlsmfYtwWTgEd/K6oWoR1Ha9izuCgRM/E5dLrMw5HppdwqT8wliddr1qaAVu4bUb/b1li/Oh0BHRmTGEMrJkC+rC/z30IeNwhxbgNsEdyhGhvo9kvjCAm/CfB2DwhBm0wuhft9Mcjpi2RkPcpgu6Lyeg4FGHy0GyVI2LkZPKg9hIjpsRSGCrjYYJBjYAhGCOuR+LQ6+jFn77+k4TdW8Dsgtm4dsa1fnbciA8eEWi7YEgqIoVw5KCmy2qMwz0NKWj4uBAm3wTekuaGa8gimtwTPLCmu9nCymPywOQxoSUPWHGWd8l001MuTLTTLgcX9l7U5mdD3bZmAky9JphKS5F57z0wl5QE1I9Yiq999tf4+cNfo8RHrk6Lh+olXmZjIeYtwVI6ETVPP60ZR0+LnbV2bQWcdqe/7P54gSHACBv6WIFeHwhWXwVm8VPu3azb5tayUlQ/9VRU4+9q2v8Qme45wpNduCRqobRPJNpUXG6l9OTISclhzrdw7IxePolgq6iMV6y+AltatwTU/9kTn9XcdapUP6oT4dOW0SN69BJOiPsaTNslQl/WKgNBqf5Ot1MizxyXG492OzCje0/Q8x2jNjBc9nOq5x9fuQyn3rcJRd2QxFalMYB8iWT7xdf985WiXGBQPbQCYbB6PtLPeCph5uaac+zBLgw9dVZQsVjJOf/yASfgpsX3IdflguP/nYe6f20ddYCLZEm+C9ew4F4dddKyTwViMgKlUX2Udxz2O68qylC16knWntR+K1b/Dqd+9aYkxrCkXBp9Jlz9CQqkK8+dC9TqnLCqWQic/kTC6ExckeTrXbV5A7uen46hJ3+OtCYvoaIfVC+fXZE7bYV1ktr1AIQoq5j2i3iC5PziBVL9ql7oNUG7Pgpf56Kd/nhEVy3wtyOVnbfktP3Nh0B+8PHzx5sPkjtuQxBayJNWJZbCMcJIGAvH7fIty/F55+cSdm8BZpgxq2CWnx034o7bCLTdWHHc+hmHfZN8+Rtd8cTekuqGa1i0j0XGQkwO2svOTfE7Pgt7PHjoiRFYei0B6XqyPcj+2zN+p628TMRS3L92La55wQ2zB2jPAR79uQdTbd5FgTN9Os5ZNQjsaTW8+1KLnVVe9nCgx4Y+VhBpx63ALM705rERWPotAbusCZ4sD6a9/mHUJ6qhOG4TgYk+2UHETxQPUWm3criOWyN5RPI3WuU+7z/n4YvWLyRjIL0om1UyC4//6HGW3+e7PmdHnydMmIBNLd64YsHu7FfKh8bYeWXzEkInqc+sb17PdoiG0mfE7XLHxjsU+9+BxQfiwhkXhtR24bR7pKBVBr36+9v6oKu9sfuC2C0XjA0Ml/2c7RK9+BKMFOdg+Ni9kL3qU9jy01F11Uneet7zFhydfSg9dwHa3/gcjq5+VJ5ShCzb16o7qdhO28p5wAVvI5nm2HSfiIwoJmajLRWtQ62Y0t2CArcLedm5aLea0WSxsN2t/dkTUFI5b1QvfPNbR59HRPLmfcku7KIlR6zbbYKbzed8+33YNM/E2i7nwDS0f+Jkl8xZqXAPjKDy0qORNXc2HOl7oe7KGxV3UJMuttavR2bvnsByaSBgxzYRLvl0VW/HdsggAqLadUBfq/R6VglQPZ/vuhMjyde7mo7b1ZfDs3MNI2wchdm7W/D4u4AXf4W+LVtR/1E+bBmuAOes33k7YEHlgk6klw3j89QU3FxSgqOzp+DyJfeFrEtx6RfxBPVJ+RildC1R0x+PIAIyimVrSQFcI97wCON8p21PED5IHiohVlBjKeSMhIZAjNdKO20F0AKT7gew40YCvO0C5MEYhxcrMw6XzLKjcV2h1zErdtoShJ23vsn+1EXtyEyfgA7fwR1itKadtopsqIva8Z17F4ow6rgNYNOeYsadpwE5A8C3VV7m7K/9h4Ia8et//D/kf9sAa0GBoUmLFjurvOzhIJ5M7ckKMYM605vFbf4dtsJRWf8O3CPb0TqyDeVIrB0GicBEPxZAMoq2nELJI5LlIl2R7zIleOBh1wVdsRZ5p3XlOeUh5a2WD42xiaCTkegzQrtopUUyCNXxGgt9DKcMevX3t/Wc61A17eiolTFc9nMawysfWYnUadOYo6Jv/kf+/wlVc3+L4W3b2HOZ5zVjeNN/kbX5t36/oxLYyFG/PilZwol9nv6K6LSv71o/7SAvL5dcU5vf2jLBnExCewjjqLAzkLDr3eLReZ1n9B45plIW38WcmJlz5vjlzn5PbfHE45JrEl3cL/i+wtp+5cOS9hbaSyu/sEDpJ5lOxAVjeM1ktdeyOgRu13B769ZVBzR/gaxSMKdsai69lJJuNqLv1GeGu23IKvMSLB48PMLGnb8N7sKPbVaEOnrEpV/EE0p9MpL9NNrpj0eQk3acO2rDgbHo2BzhQ4+lkDMSasIo43UAO24kwNtOUR5qrKU5FSOYMHv0mKkaBIZTYg8WQEzDWmyo/e1f67JpfzHFjI9mmBV3wjakDyLv5JONT1p02FnFZQ8X8WJqT1aI21zQG2K0loPi7NG9rib1Fz/xQiIw0XMkB2KlK4muk5EsX6LXNdqIe/0jwH5OY7ngoBD/T6D//c5D+n96ofGyjZc5uWx+q9Ue9FexoFO5rchBPKeGza/Ecvenq3AtXMjbO9r5cRjEGF4zWXp1bGLDZ/5/ySmrFsOWrgtOWwHCeiJcu8v7BQfH2AV33MYKeiyFnJFQE0YZrwV23IiCt52iPNRYSynW7Z7NUqIuJQgMp8QeLICYhrXYUDOLZjDmY+f//sc+BQhxDenI/Mwd7sgxe+uws4rLHi7ixdSerBDHshT0hhit5SCGa7qXXzYbiYZEYKLnSA4IukL2jeycGHStYjCdHYEU20UCXaPjk5HIR9DJYNNM1D4z3vtf3OtvgP2c9Iz0TQlG9dCfhsJcjvLpa0odl3NyJpeRLMk1rfbQbCuRzCLRZuEiEcowbjGG10yubB2bWHFIyGkL64mxPu5wcHCEDh4qQelNYM7M0eMedXSMzOSNX0Q8bj52zFqrRZEBXDWelxpLYQIzEgbDch5thvFylGsyXgvx7YgdN+KIUNvFhG0zgtDSZUfxQtS9rhzjtvHjQkMxbunZ79cUo3+S1XuGhybbg95r3lilozHW6Nkda0rQU7Ya1r+v9iXmwVut7+HQ/acgCyYcN1yM459qRlEP8OefeXfeGmUcD4WdVV72cBBK2ZIRkewDYmZxpjeri2EZDIxxSwQrP6wuwWfHfYph+//Y/aL0oqDjfkajfmos8BRjcnrB9IiWbbwi4nZXgyVbbcyUXzcytsrLTbpyZtd0nPjiNyx+N5EiUoiQBdvcOPF1C4Y+Wobv3Q5YOrphueo0fHbodBSlTIXrkj8EFdNOLZ8qpxuFZYfA1dKK73+7FJbWLnZMPda72tT6jDAHCKZPRzKtUOZN8YZS/ascDn9b65VfqCvJi37P6lyu8pv27fh2+xv4eKAJpqKpOLb6WJRpsZ//txRFhX9G8xNrvXFrL5kP276HAm4X0NfGYtfuvPctuDr7kXbPCkw+7mfGYj5Omgfs3uCbV0hjTdIOOBdMsExZHNKcXCyPnuYtcHfsQHdWIQpS8zHTlosJmWVAd710TUFrjL620Tip4msiyK1Yu8WMJqsFqbucKHJ5kGoxY1d+mbEYse3b0ffaE6i/50XYCrJQ9aMa2By1cPSbRmPcsrkaGMksfacwRGKSN0lbrS5C1a+mw1Y4JVDetn7sadiAz53dGB5Ow943PgXPnrboxdiU123Z8exTkG8ix/lMJtuhiSRc7xqdN0zYbz6wRaNuUxcr110DtM92Q3oaGlPSMD/EcSehoDFHikn60cg/2nWKBLZ9ADRu8saMza1M/PJyhAROTiYPDHxtNnL2PgJwO4Dd61UFR8yn1xQXocdiNs4arMRSmICMhInAjGy0XDErX5K0XSzACEXOORuOhiZJfDPaadvwcSEjy2FO2zQ3XEMWEesw/M5Yip3mMXlg8pjQkud1DhBuesqFiXbAlelCqscTwBBtSXXCOWJhv6PfVy9pQ0aRU8LU2poH3HSWRRIqIRQ2dC12VuZc7rX4yx4OQVkiMLUnKwRm8ZPv3YSSHlFM2yXeeHxCzFtCWw5w4znh60Us7Vqi2F4ObZbsbrNZccxcPmc5btt4m+R6Xmoe7MP2kNq3s+4HfH/WGchrH2LkiBQPnFD7QRGzlXI2d8FOWSrKUbNqlSFyPqFP/fgvm7y22JePku17c+kc3PCT+2Oum5Gco0QqrUSdNxkt99f1H+HOtg4cPugl89Sa4wQ1FxvoxOBzZyOdbYIYnT//KbUQf3ghBfkdw4Hs576xnHRZ7CxUY2bvOL4Xzx12PG5afF+ArCXjeK4VVQsbNNndHTVHwHb640HN68TyyHG5AuUYQ2xIS8XzB56oKAtmw54/D6j9KKD+ZfM60bS+QOS0NfmvN64r8I+jwvgaIMOKMlStepI9I8jbmmdDtcK8yV6Yir2ffh4FVXtFruIadZPozdoKOOxO2CorvY7lKBOWjmXbMW7XTHp1U7ovRnoBMNgZ4E+YUbkgudtcY44UkTbXSz8a+Ue7TpFAx07gH0skOiVBopWXIyxyMu64lQvt2mxkp3oX9iYDb8gunljCdkfNKpiF2w66zdjungRnJEx0lnOBEdlitsDl9u3yiNUbygRvO6NMxEagpsuSHRX33wzb4A9oH2rHtre+RO5L3mO6dMDWbDbBnJkCd/8ITLlZ8HT3weP2IO2EQzGyZSfcezownJ2GlL4hNF5/Lnr7ejH9L69guCgHd5+XC5N9N5Y/7YKpz8IWC0QgzhzB1DMlCwsRE3WmE63H9+G3U4tQmVWJC2ZcEPLOSvnOkVs+uwodTZ+h1mpG/6CVOZlpd+/fzypExdE/Zg4Zwms7XsNulThYqeZU/OaA37D/C9MLo7LrM5mhpa+a+njRxfC43RguykbHDT/F9qbX0NTXhO2ONPz2WROKewC3CbjzNLPiTuxo2bVgGNbJrv3+v7/H1q6tcJOyJ6DtjSWDs/ya+LtYrkbGXOG3akzRRjD0j+OR1rhBcZfNRRNLFMfM7JRs9I70Kp4SCbV9yRFVe8oxcJLzwWf/KESI2LFCcZ0Fm0iO18euPgx3n/6YappiuQjjf163Ew89McIcLXI7S2ledm4K7LnWuOqmMBcIZw4g1F1IK3UwVffkjpIeRXrepKXv4Yzraih490Kk7d4Ak8j2qLHAK9VVtc6rToVrxwde0i/R/HlzRzYyPshGX0EaDn7hLdj+80v/ZgnxTtjSQ+xo/zZb8XSP4JSzZrrxSXoa/r7vj9g8XA53ayuGLz4PDnvgzl72/dh+1B/zB+SWzGLkXsFi+Zbl+Lzzc0bq9khLK+YODsXtOCPNvT5JT8eqg04O1LtVp0ocEGIHpyXVBdeIb2z0mGDNBvKuOQfFBx4C+4eb0HzvE+yW89dHonTuVBQ53WwXqyN9L9RdeaNkBytzlp9yrKK8Bdsxffrhmv1CT8cD7LhG3QLanF4UvPJOQjhtCee/fj6+6PoiYddc42nNFNG6ie8TxM/67jWmpGKnxZT0u6zZeLX6csWdyEPlc5F24VvhZ7LqVHh2roFJaaczjVFkA9R2QsvGsGDyDCbNcOaYIePOGnWnbSRkwJFQjlseKkEBRvbOkeDojfokhwO7bTZs7tyMxoFGdqRfFwnMSJgMLOdxZWxO4LaLFQJZS+d6GYvnA21lK5E6fW+Ybd74AfQMMZjSp6O1Ff0fr0PxpZewib1wnT73W7iQDXiOwoORvn8Fvl7/K1TleDDtyLbRo3s+yHeHSJioF7djqoVIwxzY3VcflmNUXM/GjGG80vUVkO6LhWfz7rStbPPgi4puvDH9DP8R6Ae/eFA1zWH3MPYv3h+HlR0WUpk4VNrp0Ufg7OxkbNbUVr979UkgJ5vdv+kcD/atc6M3wxTgtCUkil2j0A7fdX6X0LZ33KJ9O9Ia1qmyZNcPl8Lls3nidhPvrFVDsO1LR4+rFzT47aJg/8S7E8U2ceqidvyvZ4Oh9MXjP4VHoJ228nyENDPTJ6DDY4qrbkZyLiCkFYpTNBnmTXpM6el1nxhigVerq2KdRxzs9xZ5fgDmFPZi2clZ2FzqwFMjP6BcdMJNzrqeOWFYUQ+FnZSE+YNDuH3PBu88PEM6D09JGcDEBfXqaaS4UZBegpEQnLYN/Q1s/i+EmYjXTlvx+mX+4CBur1sj1Ts66ivbASjIWWmOVbWoHR37HgFMm4+8aUcDJfuwe0Q+JklDgamebBTtoNayHRHtFwbqJmnzBS2wpQwgEUD9SdCfZLQd43rNpFc3+X35/4VTmMcguYLoqY8hijuMPS7v3Ek0hoTTx01qY9T21ar5y8ewYPOMaJrRCI+g5bRNtPJyhA1OThYmxKzyTQNNSHbEnWWYIymgxlpKTtmcJUvYfeEZ4TPjwAPZfYL4ujjGmG3OoWjM8C58Kh1OVYZjCo+gykQdBXZWpX5BR+4FR6CQj17/IXzV+lVYZeJQbieBzVreBtROa2dYFJ22iWTXuO1NXpZs8TwgVBjWwa5dinaxfH4X22mrZBOpfEbSF+uglv0V0gy67GMUyd53dZnSRSzwRsY4f511+s1ApZPZ566mLZqs61p6KAbppNI8nOqnl0ZK65cIBc2DzZI+kygI6PMqbaElF6vo9zS+yp22/jRk8zg1GyW3HRHrFyHUTazT8USy2w4OjmDHkJCgM5ag4bPI56+XZyLYEIppaxSJUF6OsMEdt2FCzCpfllGGZEfcWYY5xj0EHay3WVVZjAfarZrsxpFmZzXaL/SeIxxQckBEysShDCNtkIh2jdve5GXJFs8DQoVhHcyvUbSLFC6h8RNlm0jlM5K+WAe17K+QZtBlH6NI9r6ry5QuYoE3al9ZnQ32m/yygzSf09JDeXpK83Cqn14aIyUHIhSUppdK+kyiIKDPq7SFllycoeqtio2S246I9YsQ6ibW6Xgi2W0HB0ewY0hI0BlLUHFI5PPXyzMRbEj5wcafTYTycoQN7rhViRFFf1pw+gKKU5gEinE7u2B2wPGsZITAMkzxlcSg73Q9qY/scCSVDg4MpTIyC38su6OIIIeIyKyo+0Dl+uoifOLKYP0ykvpqtF8Iz6mB4uDyMAnRhV4byJEodo3b3gRG0VQMVcz3xgoTg75PWYLKqkWKtoH6u/y6HMGOrQ5HJurWVkrsH4VJoBi3Apmj2CaSDd0nxxhTtVgH+wa9v1Wys3Sd4nzzeUGg3MJp23jBmVftJTBR0W/x8Uq1uirW2ccu79KYP9Oz5VVHePNRQADRlEgPd71bzF7i0nz9045sLKqfgtI+b8iSkXffxfA777L/h5oHsfP9Mn8a5Yd3sJiuwpxhyJmPEerfIaAis4LN/2kdUGezsXrJ9932NaUGOJnF9aP7kQLJYl16OrNJEr3ztYVR2dZ+VAHHcEZIZZDbqJKZ3cxGiW2HoCMUNovi1IcFlbqRfojr5m/ztZUYaOiT5BuRcoQA6k+kP9RvZu5wo7DHo2g74lU+Do5wxxA2dwr3iL6vj3vUxqipiw2PYcHmGdE0I41pS7xkd1pIpPJyhA1OTqZATpaz9xGA2+EnSlCCwALZYzEnP/vneGA45UgqKLGn0/E2WqTVflAMk8cEj8mD6iVtLGyCeAHSmge88n+zseKUhyOqr0b7BT135eorsblVGrcsJyUHz574bEg7QjmCb6tla5ZhY8tGzecOnXgo7l10b8LYNW57ExjEFP3ML6TzAh9bb7fZrGgbls9djts23Ca5Ts5ccezbYMZWRvrjY2xnBDsLvMdsaz8oYk5bAjlIqo9qZ/8LLO6WinLUrFpliIyHdPCW167ECfduxEQ7JPZXzAzfkge8uXQObvjJ/VHpPxT7kY4RkwOD4j3KPyNB5iLkEU5aQhoFaQW487M78Xnr58k5bwqCBV7JTqnWebALg8+dhfTadQHz56mlB+OBxQ94n6X8nzsXqP1I3bHoi2krOOVcwxZGVOqY3Qfr5mzGWWrNT0fBkhlofdFLlFp4woHoeOtL/26MkgPt6NqexYjPLCluloatfCKqnnw6ZLIqsTxyXG7c2dbuj3VLTtn6tQWwZbgkMXkl9RuwoHJBpz80RDjYkJaK5w88ETctvi9A73bv+QrZr12K/KavNGVbt7YCDiI/rKxk8WuDkYvYRlnzbCjeaw+aP8uHNd2FEZhgHrBgMNeMzL8sx+TqI73PiojNQoZIf+T6UbWkDe4RC2sHFgWYsed6o2VW/vkmpM5eGLlyhKg/Kx+5ECf+7Ru053j5ExZWHYBrpp6OzAkz4BjJiGv5OCIMip1Kx/DHGmFbEGNIVNJXuD9YdRjSf/506PlHu06RQFct8Lcj1WPdJlp5OcIiJ+OOW7nQdn2OnOqZ3osUyFmYbFbPl7BC1tmsYbMZJzoiwdjMwREKaGdB/SWXwjShGB13XYnqwnxY7buxddNXKLrnDd9THtRecSTmzJjKvn3R50DxHa8hpa0HlY+sjNrk1mi/oOfe2fUOOoc6sbById9pGwdQG2xq2YSOwQ7J9cL0wrCI66KN8WR748HCG3SeA53ASxdKJ++V84Azn5FMhtXaTX5d/N3aazVcFsEukiOFOVRSBtD37huov/XvcBfkwQUXLB12vH6SE2unWdjOtjuesyHP7kDlSqlNFGSgJAuWz8WXwFWSj8y/3YeiLCseW3sj3u75gaV501MuFPeYUPzgPZiw5EeIJLScgnKE6hiNxAsSvXIeVHLQqFMyCSCQslm6a1lsUzom79Ih6yIiMIopW1JcApdbx5nesQPfbXsDawcaYSqaimOrj1V+Vph397Wi75t61D/8Hmz56ai6ZD5s+84B3C52b2BnK+ruegNwe2BOdcFk8sA1JIQqIM+cKeB/2nHptAKmfgusheko/8OlaPrLcxFziAn92mK2oLfpC7g7tmNgJA3T7voAttYe2HLNqFrYNOog9TlOrdlA/rKfoejo870J+eqvhXarGU0WC8pcLhQ53ez7rrwylFTOC5CrXFeJvPU39UXY64VO2AqyULXsePaJrBK21hE7CtNvvplxDwjQs1VyG4WuOuy86P/gbu1mpwGoOZxDFpQeYkfz1jygxxSSg1iNRJD0N2v9u6i/+3nAZGL6YcsByg5t9RPaCjphSXOh4vAONG0qC9lRHSmQrHecfRY8jc2w5FpQs7BR5EivhMPuiGv5OCIApXnEWHSqkQ33+Uqi4pjWSJ9s3T3vXIK2xo0sJItwqiPsF6jRrlMksONDoP5ToPJQIG9S4peXww/uuI2y0Dg4OKIPWgCkTpsWMEml6+bcXLi7uwMWWjT5FbMbc3BwJDaSwnG76lRg5xovO6/4+NnkRcA5L8e0LEp2sfblV2Cpqcb92+5H27ebsUUUyqy414yjnXvhuv97QTFftfzF+Vz03kXY0LyB7XQl0HHeqnYT0g+fj0ePfhSRhDwvLdDu27mlc4Mug1IewaalV85QyxYvqDnCjCCa/VdtHkAY+NNxaHh2J1xDFrbT3OM2eXdZMggBz3xO2xQXTFYP25lOu8gfu/ow3H36YzGZM7BdqKcc63W+ZXqJVb2xVr27XSctbod773lIu/CtqOSvpu9ndE7DVWc8oChbQS7dU6YE3dbyNnM8fBLq/rXV66TOcKJgWj+6tmey754cD6a99mHQzkgtfaUyCvPEpt+e45d7yaxuNK4rADwmthO3/LBOtH6R620HOsHwyjtxdYqSzGtPPgbObmegniRA+TjCRBTnERyRG9s5OBLZB8lj3HJwcCQkaCGlNEml6xkHHqi40ApgN+bg4OAI91gj7ZCRO+joO12nnRhxtou0I64pcwTvDEqdtoS2bDeezt/KdgOGkg+FAqCdeuKFUEeOieVD14NNVwtKeWmBngu2DGp5BJOWkXKGUjYO4/MA6pcZQ+tRc7Q3LivFeB512goOW2HXLeAasTCnLT1LoT/+17OBtU0s5gw2Wz8La+KPxf++KPbq4nakZLqR1rAuKrZES9/JLpDdUCxzGHKRtFn7dtjaPvKFYnCyNmj90ucszXRi2hF70DqyLaR89MqQUZ4pkXvjx4V+py19Nq4rHG0Hei5lAPEE6Un1wgZlPUmA8nGMnXnEWEQkxnYOjkQHd9xycHBwcHBwcCiBYtFpgY6jJQCaB5s179MR7lBA8VujkW4oeUWiDJGoTzDljKR8OAL7JR0np52JRkHP0m8mOZyxa5uuXYrlFMoSTVsSy/4bbDsJ9e9q2hy1vJXynTDbrliOuNtyPT2Jd/k4xvw8IpkRd1vHwREDcMctBwdHXEBH2ehomNI1JQZdzqrLEQl9k3+KdUv+ycGB/BptIVAMsQRAabr2EVqKPxoK9MgUQ003lLwiUYZI1CeYckZSPskMsqctd96JjsceU7S/dL3lT3cat7++fkkxQOk4uVF4j56bWfzDmLVNfo1iOYWyRNOWxLL/BttOQv3zy2ZHLW+lfPdszlMsR9xtuZ6exLt8HGN+HpHMiLutS4I1tgC63vbwStX79ldfZeO10n26Rvf5Gik+ECL5c4wBRIIhmYMjUoypmx1deK93J0wmE6bmTcXc1CJYu+rZgsnzfTeybngAIwUZGPjdIkyZehBGNn+P3rtfwHB+JksmtasPvRcfjoqTzkZJyrSEYtUV+lr7QDu+7/qe1ZHY4ovSixKa9Gq8QiBMseTlwdXVBUtuDlz2bngKs4DfLoH70Xdh7uiHOS8Hnp5+WPLz4bLbE0LXtBiJa60Wic1PxjEg1vFtg86zaKqXQEQhNt3ApDnYPNiEST1WXXmrtQ2VJZx2o99u6t8EE0yMEOvLti8V47vJ0xVkoCeL6txqRu6xvnk93J7R3YGU3+TcyC42hbyCjXEbjMzU8ggmLSPlDKVs8YSqHkSAAb3v9WdR//sV/rCz7Q8/CHf/ICMdy/jtUeh65EOYunrZva7HHoOlqEjf/hZNhaN4Iepe98VO1Ytxm+pk4STp2e/XFGOfQ2LXNg5Hppdgqj8wxi0RlLEwAtPnALUfA9+84icJiwSpTFD6LrR1bwvQ08RIbsqnHBleAWTtJK//tv9OwLSl06Jiw+Vy14pxS89VLc2ADfGDpp4kQPk4woDGPILFuE1UAqkI2P9YlUFs6ypGhlBJpypsVjSmpCXVWBwJ9D3zAOpveRS2wixUnb8PbAXpwAFnAG43HB89gV0rN8PV70LXv1ai5peVsBXlA3lVQHoh7Gu2oPmZz1k6PS88jZrlp3jJKwc64Kj9AbX/7wc4e5xsaK08vQxZM6d501YaK5Tajq7VEQFnm3SsSwRdSwKYPB6PMLsZ10hEcjIh+L7eBCUSDMkcyUsMkjBOEQXG1I/T03BrQQGu7+zE4YND/usbXenIfDsHll6LP84bofaDIhYDjUALseqjvNe3rZkAU29o7MPxYDw/dOKhuHfRvbz/xbCPaOk6I4chx399vfecCfmffHHu5J8ekwcmT2x0TWzjxfWTfyeYhuwo+PBqbyxEUf+6prgIPRYze3FgH7Yn9RhgdMyLNbrtdWh47BjsZ2/xX9uYmYWlhXlM9lry1hqfCaGO3ZTu0jVL8WnLp5LrOSk56BnpCTo9rXyWrVmGjS0bY2LvjNrYcOq2tXYr/rLtL2HNmfTKmYz9L+IM6L40HF99iF3vFsE1LNorIthd5mAVxaI1ARYPYKkoR82qVar2l9n0c86Go6HJ67T1AK4hIX1Rmv58AEuaEw6LCeZ+iyR9ua2NpP0Rjz22PCuqFlAMUzfbQUlOW+bMzHajalGrNGwCoWYhcPoTTN7hlFF3jaDU1gLSC4DffAjkV4dQe2k7CXM9of7b/zsB6InOWCuXe9nBTWhaX8DkLeiE1znaiaZNZXDYnXGdX0rLa/PF5vXpCTlziWAtzvNfjjAx2AW8eEF4NtUA5HPJaNn/qBPLhjAG9djrUC+bq32bNxEV57+LXHJMxgl69tvIfSVZB1zv2An8Ywkc7fbR8UVmd4XrYjsovl/7fhGLGS+ACCWrl/jW6KJ7wnX/uJWWB/z2v96xQqntqhcCbgewe32ggKg9qX9EsV+MFR8kd9yGILREW8RyFsXkx5hw3CowpjoB9JrNyHa7Jdv7ycw76biiaGCh3QWN6/Ilg0L5YaM7DjzZHkz7d/Dsw/FiPKeFEWcxTQzHrZzZO9Bp4PsUnLchMl1H03Fb+J9fI7VxA0yy/rUhPQ0XTywJSDsZmXQT1XEr9PvykSFvbEybFbttNkPy1hrzDSfMAABltUlEQVSfCaEyIFO6ak5D2nl74YwLI7Lz2qjNi7S9IyIRiklnMVvgcrsCPsOpm7DYEfIIJy1xGoRw0xtTDOiiNGhBGOC8lTlthZdme/KAN5bOwd2nP6Z7ioJsdNF5P0Pzbfex5Kz56ShYMgOtL3pfaJScdig63/8Wzq5+eMwmpN6wDJ5/PSc5vRNNx624nMzpRgRTtbTbqBWO4TTU3fhXOLodqFzQiayy4cAEaAF7zssRKaOqviu1tdx5e41OjE4j9b//ZnR3fYLOwU5kTjsmqiep5HIf3vwR6q+5Gbb8TJSdNQtNqz6Fwz6Myj/fhNTZC+N+oktRTyjuacFkOEYy4l4+jgiCiMh8bRuNnYURcdwasP9Rd9yGMgZFYtxKZsftnTXAYCf7V/JyULaD3//SyvcyS35f/jLUmu4C7fP0f5c7beVjhd6YoocEaLNE9UHyUAljhEVRDjGLYtIvIDiShzFVwcDku92KwbWJSZne8gkDCzHost+ke49g0M5b4Rp7I7ionbEPl6M0ofqaGnj/SywIzN7+t80MJumn8Pb5iPjqmhxWe61kp63/OsB2sk9yOAIciXwMiHy/JxnL5awlb73x2Wg6WmVSwpbWLRFxHgZj8yJt7yidaM9dIpGHPI0xMd9SGc8lDOh6DgdZGrTAqzmmXea8HXXasm8eE9pzPfjjWVZ0DG7G5Rr6RM4rcmKlTpvGnF3WSdPh7OxE5pw57Lvl4FfZc3knn4ycS5rRv3EjrAUF7HeORcdjeNu2mDjA5OVk8MnO1r4dVQtuwHC3TdlpS/AzzqdFR9/V2loMcgTs+FD5KGyQ9S/CXBSJy0RO1Si0hTxf24lnoDKn1P+96uRmSb7RKkeo5ZXoSQKUjyOCoHZN5KPgkbD/8ShDIpQ7ntj2gd9pK4y5Smts8Q5brfviHbbOQSEEkYbTlkD5f/6k/piih/HSZiGAk5MlOTiLIkdSMKaqQIlBt3x+F9tpG1P24SgxnnMW0wSCCmOzHImga3JYerXZcGkXqBq4Dsa234vlHYrNUEonlDJFot3DqTvHOGdAV0iD7GrFAm37u/oYFzpyTIb0iZxYgpOL/icnrfCd/qc/lm9pKftfcHrR91g6wMTlVBqTVJ22sWCcNzp3q5eGZIlI/aPcFvJ8xd/l+cZaJxJJThwcEbf/8ShDIpQ7nmjcFHBJac0jrHH07tMfrcfloPW5otNWwM4IEjuP9TYLAdxxm+TgLIocScGYqgIlBl0Kl9D4SYzZh6PEeD7eWEwTGiqMzXIkgq7J4crW1iM6uq8GroOx7fdieYdiM5TSCaVMkWj3cOoea4bkeDAcJ2KZEoYBXSENsqsNa7Xt77FvWjC10a2oT4koU0EH5J8EpeuS8hudN0WTcd5oGSoPxXhFIvbzRCwTxxhBJOx/PMqQCOWOJ8oPDriktOYR1jh69+mP1uNy0Ppc+L2AvqbU0WuTpS+Y6DrdDwljvc1CAA+VkMAwEjuGWBRnlczCl61fws0ih0aG0TgZ2cmTGYkW0zFSjKmRjHEbKvtwvBjP6VnedxKnj4gZm7Vi3JKu/bCmBNtP3gZnfnSPa4vrJK+f5Dv9v0W5f1GMW6Xj+8nGap+odtBovxfLWzx+Kv3WDDPmlc1j/xtie1cpkxYxViTaXavuVQ6Hn7WZ9C/YPOVzDLXvJA/zxi+Qc8NKOIpzUHvbBTi4YirKR0YMxX9UmsuEo2dCeuXf7MHwVSvYTjjzw7eh07EDlU4nJlTMS/6YlJFgQJelYSjGLTzIHTDhlifd+PbsHFR9/gJjsiaiE4elFHV/+QCOtk5UXnIUsvb3vVQQs1KHiFD1QYhLasnLg6urC5bcHLi6u2ErzEHZz/dH01Nb4OgcgCUnE67eQVgKCuCy20d1QpCR1rHSynmsbuWxbmtRC7lTsmEJIUyCVh9qb9iA6q5GFKUXh91+IUNgMe9tQVfbd/g6NQ3/y5/IbhWlF+HgiQej8Is61F98MWx5aai67HDYykb1zpG+F+quvDHm/bzv9WdRf+0tsE0oQdVTT7MwUAIbu6OlFXWXXg1Hmx2Vj6xE+4GT/PaPYlRKbKGcxT3c7yLwNaQKNGQWKQRtz+RlUrUJZmDijNDzicIYJNGzSIxbUYKerEK9L7k+bQmQlgsMdevGuKXrWjFuKUSCWoxbCmNIROJCuARyytavLYAtw4WqxV2wff8WkFsJdDfA0W/ylmHAoh7HXQkJ0GaJCk5OlsDkZOGwGofKaKzLPsvBEQRjKrHe31pYgOs7OlksTgEbXOnIejsHll6LNJ7OB0VsUBBi3VYf5b1OTltTlNiHo8F4HkmWdY7IMjazcyb05kBw3so+BYKcljxgxVkWTNlrTmK0pUr/uqa4CD0WM/JS82Aftvvvcbsd3X6vJO/lc5bjto23SZ6bM3EOHG4HizsrtxF/POyPuG2D9Hmj7UZlumL1FQHp5qTk4NkTnw1rt69W3XNcLtzZ1iGx56SHz+1/DG496iFD5daTpfx7YY8HNz3lwkQ77T53Ye9FbbqM65Gey8jTozLd8awVeR3DAWWqXVsJZ7KzwEeCAd2XhuOrDwOdtgEvz0adtybfS7SqJW3IKHIGLECF+HwS1CwETn8ipizUwYwrfgbvijJUrXpyVCdIRs+dC9Rq7JCMdt2U2lqGSDCzUx9a8cH/4fSv3sTcoeH4tZ8S47kPXWYzflE2EY02K8pHHHhmRyc63svVZGcPaNMol5v6kz/vbDeqFrUGlinTieFTzPhNmZXND8QgG/63Hhf2s7dISYVE8TGD/u6zDd1mM19DarRdWPY0lmUiaNmEWJRdYwxS07M/z7gEOY//JFBXf/MhkF+NMQtZW6qNmeLr/jFJdl+IayuPaUsQ3xNf18uLXT+rAra8VGD3+sDykx5ReydK30hgHyR33IYgtERh1lZie6adPDNLZuLxHz0eUt5aDNjJxE7OEX/G1C1OO97t2QGzyYwpeVMwN7UYVvtu7LZa4fmhG1nXP4CRggwM/G4RJk+dDceWrei96wUM52eyZFK7+tB78eGoOOnsqLIPhwKBnbl9sB3fd37P6kjOhsL0QrZLI5l2OY4VaDHpKu6MsnfDU5gF/HYJ+h56DRndLvRnABkDJvRmANmDwJ9/ZsYXU8xsQpgw9k/ESFxHux1FLOGqrOFJCvlYaHRsVEsr3B0icvl+uu1TDKcP+7+rjZ/ZKdnoGemB2xN4Kob0SkgndTAV5RneMmqVVZDD8i3L8XnX56rpRlIGQt0Pfu92pNSth4U53EZ3fm9MT8eqg07W7SdKMjICcpQ+9MSI/2WfhCE5z4aqV96ROE6CmcsY0TOl9IyUqdXtDlnmSu0lZ5+OFFTLGAEG9L43nkP91X/0+mhpnpqdAXf/IGz56ci46Ch0rfwQpq5e5rZlLlzmzzXBkuZCxeGdEiZsRaetz/07XDEfHT/6u3Z9DECP4TvAeXvKsezlgdZJDv8C+VfTYbv09cCE/nWc8oJWvJAVMWyHYwtV4Wvrhpd/hbLBHkksPerj3+dNRN5pqyU/CSZ/6kPnbHkV8wYHleP0yeoYKrT6CJV36B/HI61xg+oOY7vZjIVVFfiorgF5bjc7HabFzq7appHGqlPh2bkGJt8Ods0yLW6HKdPNTuRcPLFEkswjLa2YOzgkOQUnfX0S/Hdhh9xFE0v4GlKl7VR3goao81pzXvkziveNlIlswou/Alq+AkTzjGDLHtb8S2EMUhvfn+ocxH7dbRGVc6JAc1yStaV/F2y2GVUXHcLGWhxwBrvn+O9j2PXwZrj6XbBkWlDzy0mwDf4A9JMT1gP7zjQ0f+p1mFrSTKi56QzYCrLY6RdH3TbU/ut7OHuIRNzj30VrxB7ZivKAa3Z527N2HdDXKj0xE4G5xnjwQfJQCUkKNbZnCpdAO3BCYXfWY8COJGM0x9hnTD0IYH8SVAET6HM/oK9wHymD7gFA3+TF7BpBzqCbSKy6sWA854gOYzPpkPizMWMYl3T9G5VtFtQXm1DZ5vF/ktOWkFD2T8RITKWRM9onRBnHKOTyJSerMHnWGj/Fu0fF1wW9EtIJxinX0N+AzZ2bYzZes7qPOIC6TxQnkvMHB3F73RrNfNVkZARZ6U62q1WRAXlBC2wpA7r5hCobtfQMlWkoDeOdAT3rxDNQmVOKvvXrYZswATnHHuu3vzT+D835DM/fcw7Ob+5D1sQRmFNcaPi4EK4hiyITthLIiZTWsA6W7lq4cmO3s4qOqVctqB/dxeQvjehTvKup7aNApmw6qqzltCXEgmG7cAoaeutRMdgTcItqRjs0v2zZiKKJc4JOmvpQQ90aZifiWsf27UxP1EAtlu9241dddvZpiJ1dqU2jUG6SjykIxngCnYyY5HD4wylRiBvxaQlxvcP5LrDA1w+XwiUL3TTu15C+toOKzKKuO+GUic7LN3+h/1wMxyC18bhiZEi6izweZY0HFNqSnKnkVE3NdcD2kxsl9bZNORI1P2qG/eVXkHfqKd5QKw+NcnrkTSb70IUhuw2Fe/fDtvAc/++pZ1f/pBn97/8b1rU3+EMfGLJHtAt6x4cAhd1RaocIzDXGAzg5WZJCj+05FHbnaKTJwREMg65wTYlBl7PqcoQDQbfkn2T3iMWcnLTyTzG4/eMIZ/xUQ6h61TzYHJV0NaHD2jyJYt5q5BuqjAgUT1eTIVnEPhzpuYxaesGUabyD7O3Ea65B4fnnS+wvoTFjCHUznZh4UC9bCFJ4BNppq8aErQVrrOepXbsUdUAOSfnleqHHhi4gBvpkb/5c835/+9chpUt9iPpL3OtoUNbzZM5NPXb2eJRbt0wiuyzAUBuEAXFecozbOZSezsVjnDBapgQsu9Z4rImxOh6rtBGNpWrzEBp7iy+9xDsGK/yenLc0Hiv9nn6TN3dyQLxaQ/ao/tNga8chA3fcJin04teFwu4cjTQ5OMY66Bi+/dVX2Z+c5Ze+C/c4y29iwmgsUG7/OCKhR5HSq9L00tjrqw5rMxGVaeUbTtzdeptVmyFZxD4c6bmMWnrBlIlDW74kS6NM2Fpwxnqeml+jWF45JOWX64UeG7oAA/pE8wz5PEQAXdebh+SVztK8n1k0Sk4UThurItp9xqCs16dLd8rr6mQcym20n5BdFmCoDcKAOC85xu0cSk/n4jFOGC1TApZdazzWxFgdj8Nto1B+H6I9cqRN5WvhMMEdtwkMOg6wtmEtO9anxvZMMV3EoO+hMkpHI00OjrEMFjv1oovRfO117K/252fA8e6DwH/vxp5/34xvf3oimq69jv3tvugivPPMHYr9mSN+EOyeFrj94whn/KT413rjKo33n7V/hsYBY+ESKjIrMLtgdmzHax9rs0t2UJb2uaxLT0dl1SLNfNVkZAR9g1Z8v6Z4NGbaUUQGRsRVVi9B2UiGbj6hykYtvWDKNN7mqsK1Txo/UZ3HiuVbUbWI6dCwnNBELFN2XXnZQjE3hyrmxzRMAsHhyPS2tUD24i+N6NPkGS1/8cLA46ACG7oW6L7OMdK+159F/cUXo+6MU+FY/xKw+XE2F8HmJ+D4bgPjCaBY71rO24rqRYyITL53jb7T9VDCJMjb2B1GHcNG0VSmJyzupQI8PoKyD7Iy0W/yfnfo6aRSm0ah3CQfj6/cumXqN7M2I+JIIUwCoc5mY9fk7esJ8zuT55QlbAzga0jltoPJrCizuBwP95fJol0mo8/FEGrjcUNKGrNRiVTWmECtjcjFVzlPv95a+km/px2321cD297zhpsQ/8YHCekZPLCmuxjBuNgeOVwFqLvhr7pjEIc2ODlZApKTGWVDjjRrcrTS5OAYq6AdLLVnngWnaIcLMW2WH9aJxnUFEmbOthzgxnMs7Bg+Mcrfu+he3qcSBGT3lq1Zho0tGxXvH1RyEB5Y/ABvL46Qxs/lc5fjtg23KY6rhFDH3LiM14NdcDx/Hmy7/uu/RI6Alw84ATctvi+kMpNjWxwHWP6dSMBuesqFiXbAle1icWX9rMXkNLM7YKusZHHQheP3kZaNPD0q0x3P2pDXMRRQptq1lXAqlGmsQUnGNLYRPm0JPBKpJX9K6+6XLsEZ933mJ3tTZadWinVbsxA4/YmYslAzYjIiTa2v926DoSIJBGUqn7aKMlStejJQJ4hR+7lzgVqFBa1e3YhR/Pnz4Pj2Y312b7X8Rei216HhsWMk8SLJIVJx/rvIzQv9hRC18YrVv8NpX76BeUPD8Ws/JbZ6H+wmE3bZbJg1MmKYnd2ITCNZbsdXH47mne1G1aJWxX4yfIoZvymzoscidcjkuNz4W49TGg80vcAbgzLU7z4W+G6zma8hVfpnQN+Og83S7Qe+dpSUyehzMYTq+H7Idch9/XcJVdaYQMOm6dZ/oBOOZ8+CbbeUv6DPmoIs54h6eoSnfwHH1o1+u0POWnqv7xywsrUwveGhdTC7bkuFs8c15udF0fZBcsdtCEILB0bIR2757pagWDmjwSQ+1tjJOcYGgiHviSjbsp7z9ifHeFk2BfjZpb1wZbpw2fkpzGkrnmToMbBzIGpsu0o47z/nMXJHJfD2SkyExVYcJaiNn0rX1diR1cb7YPKLKjp2YE/Deuy2WlFSOS/ofOVlVvr++//+Hls7t+KAHU78/kU32nOAFWdZkJnuRLXTjcKyQ3DDIXd7nWfNzYyAUB4bPRzZKOmWkF75N3swfNUKtviwrLwd7SPbMcnpxISKeWynrVaZxgqUdFcLenrNTrBcfAncRTkouuo4TMjJYEzW5CBymEtR95cP4GjrROUlRyFrf99xWTErdYzBynvJpbDk5cHV1QVLbg5c3d2wFeag7Ocz0PTUZjg6B2DOToe7bximvDx4uruRfsstqD71FOVE1Ri3tUCM4r4Fuy6796+mw3bp64bq11j3EbqaNiO/bDbKqyKnw9SHWuvXo8behKK0ori0H/VtIrKjmMhFqW50tX6Db1LTUf3d66ho/cF/psDPzp7h8jptK6qBmWextnGk74W6K2+MeT/ve+M51F9zM2wTSlD11NNeEkQfG7tjTxvqLrkKjjY7Kh9ZiY6ZVX77R5DYQjmLe7jfx9ka0vDcg/rnzjVekiw/zF6SpnNeRtyh0Y4hPRdDqOpZApY1JvjXcUD9RsDjlu6cnbxIXddWnQrXjtWwiPbRs3eQSiSECukxe/T7FbDl2lB1VjkFu0XdP7+Bo8e7kYmKQkSjlJotz4aqV97hTlsZuOM2iR23xBR94foLVe+/ccobY3YQ5OBIRsctMXo67jwEte8XSXbYCqCBq3pJO07ee4LkyBqB9+fEcdzS0d6TXj1J8xneXomHRHTcGoWezo1nfZPLZuYON+qLTZKXX4KMyvpTMLxtW8QdJ3q6RY671GnTFBch5MyJRpkSBUbsZSh6nWwyFcpL5RJ/UvmpvI3rN8BSUw3Xrlr/p23OoZGzWcQoLmIEDzy26oVkp/LlW8aXM8PoHEFBloLzlrGzC7u8RfKLl04mWz8Zt3MPFZ3yg/dFjkghFF3T+40WROlJ7JFvTaw6Bl27adyPP+H4IKMbpZwjKkzR43Uhx8GRkPCxSpfP70Ld+8UBt8sP87JqMsZ1meOW9+fEgRHGe95eHLHUufGsb3LZfDHFrC6jigVx2cGh5Rih8ozlo4BG7GUoep1sMhXKK5RLXD5W3jne0BHmkhLJZ8SgwAgusHuL5yMSdm/aiTbOHbfBsrNLIJJfvHQy2frJuIWKTvnB+yJHPHVN7zcG05PYI9+aWHUM4jofFjg5WYIhLkzRHBwcYbNKN65Tjh/U+ImXVVOJbZf358SBEcZ73l4csdS58axvRvrjeJdRMrSPEnibRRChsHuPVXb1cKHHri6Ay48jUjrFdYkjnrpm1OYZTc+XpuYYxHU+LHDHbYKBmKIjyYbMwcERfVbp2v9WSMMk+NmlvUHav19djH5ZGAXenxMLAlOtGnh7cURL5/h4b1w2AvicKLHbRwnjvc0cGz+Fu7VV+V5zc2hM2xrs3uxo6lFEmidi9y5eyHfbGpSlIsYyOz1H9HRKbifpO9cljnjrmu83Llk0WzqbMbqKRVC6S2tiRhqrNAbR9ZGMUGvIwcnJYh/j1gjiwhTNwcERGjHZmWfB2dwsiWlbflgnGtcVSJy5bTnAjedYWIxGYt6+d9G9vD8nGMj2LluzDBtbNkqu8/bi0Iv3SUfHQyFiURrvDyo5CA8sfiAp7EOwdRc/7/F4JL8V7pGDjwivhpxD+MfX/8B3nd8FpLNvwb64Yd4N2L9o/yjVLLj6haMDyQol3Z0zcQ488ODTlk8Dnh+389j27eh7703U3/o32PIzUfXLfWArLwPSC9ltR2cf6u55i31WXno0subONk7YRTEKt70HbHwUjsbdUqetL6atxJlblIWqZSfANmly3EjdEp6d/blzgdpAJ3pD8RR4TnsclSUz4lI0jiTWqRcv8BMIMpDj62f/BNKVT+pFBWQr6Gh8JAi7IplWJJBo5YlX/TOKgNW3SHWtaDpwyiNA+UHK8soogOP582Db9V//7c9TbCiyZqJywB6Y18SZwOFXAml5QDeFTDL5xxJaEzNi1vp6LxHZgvrRMYictnYHbJWVqHricR7ORQROTpbkjtvxxMrJwZHMYKzSF10MuL2x46wTilG9/HTYUoewp2cAzXe8hpTOAe/DZhPqbzgH0084k/fnBAfZ3k0tm9j/B088mLdXghOOCUQzsSYpI8fVle9eic2dmzWdU3r1oXSuWH0FtrRu0UwnFGjJJhxit2BfMCs9L0Zeah7swwqLBJGj9hf7/ALP/O8ZiSM3VDnp6YzR+vEX7cpzVeGaxWyBy+2K+Tw2GCJTOcKxI+J8TUN2lH1yA1tEB+yEVXKqignECDULgdOfUHbuDHQCz58ncTASiVb92gLYss2ouvta2Gz9zKELe+1oPgMWVC7oHI3bqpXHOIFcV1j7d+xA59bX8fyWh9EEBzanp/k5CpLpxRpHAqFjhze+ZwjOxVAId/0Y6MTQ02cjrWFd+I5jsjsvXajrhI4ZaazB8iQqtORkSIZK9a9eCIz0Ak2fS58lW3/SA8CbywLk1X3Sfbjnoz+gv349zuzpw+xhUVzvkv0AkxnY87V2WWoWoq/sYtT/7hrmlGXO2ZQBv87TTlvm1G1uRuXKhzl5ogjccTtGHLccHBzJ4bx1dnay/zPnzJG8RaQBqn+jd/emtaCAD1QcHGPIcXvRexdhfdN6uNnBMi9ot+jc0rl49OhHJeXTKhuls6F5A9tlqpVOIjlugy2z0vPBgNLOTslG70hvROSkpzNG6xfNtuNIbsdt4X9+jbTGDYBPN+ROWiJr8cb9U3Daip0Q57wcmNGqU6WLb5HzNjXXAdsBR3oviJ6h/Ie7bYFkW2p5jGfHLYAFzy5QfZlEL3F4/+aIFcJy3K46FZ6da2ASj7103H3youD7PdmdnWv8Nk0trZg5bg2WZ8w6bpXqr4X0AmCoO0Be3+YW46yCdDzU3Iy5g0MIZGQxiClL0Ff1f0idNk1xRy2tiYe3beNr4TB8kCG3DQcHBweHPrtv3sknczFxcIwx0NF4pd2j5MCj67Tj0GjogEikE0sEW2a154MBpa3kRImGnIzWLxnbjiM2sNprpTvcaD6Q6WbOWcF5KzBuqzptBccr7dQT79CjY64KTluC3ymrcJ/St2UOG8tjnGNd4zrNEwC8f3MkBXy2QhrBlAKYuoLv92p2J5S0IoFEK0+soTEOqGLQu8lIAo8L+9lbcEhqMQ4fHAqvTDs+QNbxdwGFgU5bYU2s5NDlMA5OTsbBwcHBwcHBEQQonqkW6Jh4LNOJJYIts97zkUAk5WS0fsnYdhyxgaVXue3JeUo7bcWg74pOWwF01FQMik0YacjzGOf4uk3nWDDv3xzJAD1bEUy/j2RakUCilSfWiPA4cKA4PEI4GOtyjzOSynG7evVqnH322XjyyScD7u3YsQPLly/HhRdeiPvvvx+Dg4NxKSMHBwcHBwfH2EZldqXmfYrpGct0Yolgy6z3fCQQSTkZrV8yth1HbODKVm57CldA4RHE8IZL0FiOUUxMMfJrIlJGzTzGOWYU6xOQ8f7NkfDQsxXB9PtIphUJJFp5Yo0IjwNfpqZGJqGxLvc4I2kcty0tLfjlL3+Jd955B5s2eUljBHz11VeYNWsWdu7ciRkzZuBf//oXjjjiCIyMjCBZQEfu1jasZUfrOLhcODg4OMYrgo2NRs/HOr5tdW41i3NI8UzFoO90XXxEXqtswaQTCrRkE6rMgi2z2vPBgH5LBGaRkpOWXIzUj+ZstOOWiIqi1XYcoUNo31D+woGQxoT95ntjx4p0I4CI7Kg29snCJrDrCksySkN+3Ldoqve6Fui+3jNaeYwjKLX//PL5zN6ogfdvjlgiZPsk2Ar52Evfg+33QaQVk/lYJOsWJ2jJSVeGavXXi3Eb8LwZ3+ZNxGeZWfg4PQ1OhIGJB46Gcdj2njdcBcf4c9y63W6cc845WLp0qaIiX3vttZg3bx6eeeYZXHnllXjvvffw9ddf4/HHH0eigxiJidzipFdPwiUfXIITXzmRfafr4xlcLhwcHBwciYw7F97JSKjEoO90PR7pxBLBllnp+WBAv336hKdjJie1+i2fs1wyZ9vSuoWRpsWiTBxJBmI2J5IcJaft4nZkFDl8sW1VnLfEAk5pqKVN7OFKEH6n9YyRPMY5yN7kpAQSxdDLGt6/OZLRDvlB30Pp95FMKxJItPIkQv3J5k+ap2zrf/MhUDVfdsONvXInY3HxQbimuAgb0tMC06PfGkHLl8CDBwEPzQae+pn3fyJQG5SGB+IIHSaPx+NBguP222/HRx99hP/85z9sZ+2iRYvwl7/8hd2jXbVZWVl45JFHcMEFF/h/c8IJJ8BqteK1116LOKNbJNhKxwIjcTSZvJNZLhwcHBwc4wd0UobimdLRWaO7LJXGz1DSife4HWyZxc/fuO5GfNH6BdxQj+9phhkzS2bi8R89HlM5qeWlNjc5sPhAXDjjwpiUiSPJ0LEDfe++gfpb/wZbfiaqfrUPbGVlQHohu+3o7EPdPW+xz8pLj0bW3NlA9XxjO8ZoR1PtOqCvFcgqUf6d+BkBas9yBOCTpk/wUf1HKEgvwLHVx/L+zZGcIDtA8UfpKHu4/T6SaUUCiVaeRKi/YPcJYltPjtSdHwIe0byLduFOXoS6n9zH5juTXR6UjwwHpkd5mK2A2+n9tNcDG1YC7d9L0xPDlzbOeTmqIkhmBOODtCLBsX79ejz44IPYsmULTKYAXkTs3r0bDocDVVXSiTJ9X7t2rWq6w8PD7E8stFiDMxJzuXBwcHBwJDfIURcJZ12k0oklgi2z8DzNf2i3qh7IqUvPkQNVyCeWchLnpTVnozJypy2HIgqnIOsXV6KyfBZSp00LYNW2kZ7N/S2Gt21D1sKFQaet66gw8gyHKg4rO4z9cXAkNSJpBxLNpiRaeRKh/krXKITBjg8Cf08vond8gCqHE1UVC4znQem9frl22XxpM8fveG6j8RAqwW6348wzz2S7aUtlEx0BAgkZ7boVIzs7W5Og7I477mDebeGvsjL65BlycEZiLhcODg4ODo7xBr35jxy0CyTe4HM2jnBATlm501YAXQ/aacvBwcHBwWEUXbu079OO2kimF07aHMnnuKWYtV1dXXjxxRdx9tlnsz/aYUsEZfQ/xb4lpyuBnhOjs7PTf08J1113HduSLPzV1we3iIgEOCMxlwsHBweHHH0ffQRHc7OiYOg63eeILXibxHb+k4gM7nzOFjx4v+Hg4ODgiAb4+BIk8mu071NohEimF07aHMkXKmHJkiV46KGHJNco/AGFQTjuuONY6ATaKZuXl4dvvvkGP/rRj/zPETnZjBkzVNNOTU1lf/GEwFysFss10kcBBRZk4Tif/Lvac2NdLhwcHByJNBGtv+RStgOr6onHYbP1e99qF0yGYyQDdeeex5y3lSsf5ju0eJvEHcHMI4RrNJbT2L5vwb7Y2rlVM8ZtrMd9rfkPn5sEJ7MAW9b3HbDtHRZf1VF0OOquvJHbMh1dbG/YgOquRhSlF4/GKaTjqXXr0D7YgV35ZSipnMfnxbGAT+7oa+MxgjnGtp775pyGjrYH+3wk0L4dfe+9ifpb/87ihfO5ska7mCxob/kcnUNdyC3ZDxNKZwItX8li0pqAor3VbR7dV4qJXjQVmLIE2LnGGxJBEWagco70t0o6Ew89SkIkBTmZGDNnzpSQkxEuvvhifPjhh/jss89YiIRPPvkEhx9+ON5++20cc8wxcSUn00P3cDeu+egaSdw0cloSY2luam7U8shLzYN92C7Jk9iSb9t4W1TLEk6Z41UWDg4OjliBnLLMOVtfD1ueDVUL6mHLdHtZyddWwmF3wFZZ6Z2oqhy75eBtEq8xWmkecejEQ9nnpy2fBp1PrMZ9o3MOPjcxLjOJLct2o2pR66gtW10ER78VtooyVK16ktsymVxXfPB/OP2rNzF3aJSLgyEtFxjqllzakJaK5w88ETctvo/Pj6OBgU7g+fOAWoWTLsS2fvoTQHp+VLLm4Iipnr90oTQGKjnlfvZPZf0O9vkIl1EyjuRZUbWggc+V1dolGJBNO+kB4N9XBNo8JXs32AW8eIF+fqQbJ9wDvLlM+mz1QuYXxq6PYqdHCYZgfJBjwnFLYRLIQdva2or99tuP7cr9zW9+g3vuucdwuvFy3IbKkiwwQ2tBYI1WYkFW2tWSnZKN3pFexV2ujx79KOKBWLJHc3BwcEQTcrst2Gg5mMPjlGO9TtpMJ8rmdaFpfb5vgmpD1SvvjDlHB8lGTR6JgGDaRFwX4X9x28eintGWp9K8Qm0eEQzMMGN6wXRcNuuyoMZ9rTmRWA5qclGrD81/btj3hoDfqM1NjMhdKGsi67sRaMlMmDOyfvPjRXD0mgP7TaYTVb+aDtulryORodV3jdr0YOV6zpZXMW9w0FA8O1rEfZKejlUHnRy3ufpYQkCbrr5c2ylBTgbOmM4RRwQzv1Ado1adGrhz0mQBJi9S1m+d56MyB5HlKXHeJuhcWUkOkZKNYjpK7RIs0guAwc7g7B2Rj1EcW9ox+9qlQP1G6a5e0g3hxaNe2bT0bgwiGB9kQodKUMLtt9+OCRMmSK7l5+djw4YNWLduHfbs2YN7770X06dPRzIhWizJaizIctDEW7wDV3ydfi9mdI4lkpFlm4ODgyMcUHgE2mkrTEjr3i/2XidHx4IW2FIGuIBjDN4m+vMKtXlEMKCwCd91fhfTl7Va9aHrjdWNKId0cTTe5yZ6MhPmjBQegXbaKtqyxe2wtX3E2aZlcm2oW4P5GuTKctBmJXr+9ro1cZurj1VY7bX6O8k4YzpHsoOOqSvpOTnYlPTbyPNIi3oZ6QQHjSN8rqzTLsFCzWmrZe/ouxDKZ/d6Zd3QSlf+LLeryUdOpoTjjz8es2fPDrhusViwcOFCnHbaaUnntE0k5uZEZnTm4ODgGBfo2sUmpLR7QAz6Ttc5Oytvk7Ewr0iUOYdefZoGmmJWlmSBnsz87de4SduWETjbtESulQ5nSG0yyeHkc/UIw9Jr0A5xHeZIZlBs0WD0O9jnIwGVPPlcWV9GEYdW+0ayDNyuJr/jliO6zM2JzOjMwcHBMS6QX8OOgNGRLzG8R8DMnJ2Vt8mYmFckypxDrz5lGWUxK0uyQE9m/vYrP1jblhE427RErvW20A5D7rZZ+Vw9wnBlG7RDXIc5khn5NcHpd7DPRwIqefK5sr6MIg6t9o1kGbhdDQB33I5xCCzIFHdMC3SfCMvkz9F3+j0/esXBwcERGzgcmV4iMiEO5FFt7JMdNabrIxm8KWIM3ib68wq1eUQwiMecQ6s+dL08I7lj0cZDZkL7ObL2Rd2aEmVbRuETihdyBmmZXCuqFmFdejrEnN96MW7p+cqqRXyuHmE486q9MR21QPdjzILe99FHLH60/H8C/U/X5P9zcKiiaKpXj+VjN31X0u9gn48EFPKUx7gd93NltXYJJcatGvTaV0s3KF0jZYumHiU5ko6cLFqINzlZrJl/aXEljkXH2KDnLsdtG6Rs0LFidObg4ODggJSJncgVFtRzptw4g7eJsXmF2jxizsQ58MCDT1s+1ZX1QSUH4RfTf4F9CveJmBOK4obSEXSluLnCvYK0Ajz4+YOq8x+tNMYr1HRAkJmk32S7WaxbOtYqWWxXlKFq1ZNxJ5BJNLmuWP07nPblG5g3NCy9aUkFXNJrG9JS8fyBJ+KmxffxuXo0QKzpz50byLCuxrIeaVDMSDp+TLvPCqcwR2z9JZeyPlN06SVoXn49bMV5qFp5N+DxoO7Sq+Fos6P0tlvR/sD9cOxpReWdNyJr7mxJOvJ0w/6uUl6OBIRSG5Gev3iBNEYqOc9+9k9l/Q72+UhAlKdkHMmzompBA58rq7WLEqoXAm5HYDzaioOBg84HtjwONHwWaO+O+iPQ8o03unpuhTcerbyvq+nGifcCbyyVXqdyUKD2XR/FTo+S2AfJHbchCC1ZIWdBVmNFVrvOwcHBwRFdiBdlVU887iUi8zG10k5b5ghpbkblyoeRtXAhb44YIFnaRIupOCoMzxrzBaXr37R/g1vW38LIx5SQk5KDnpGeiL041nIsEpTuXTbrMnQNdfnLreecjDYTuJzhXuvZeEFNBwL6zcBW4Pu3gaxiOIoOR92VNyZEvwkG8vaIZluQXDt2vI/9P3oAKV21AfdH8qvx7cIrUTBlCZ+rxwJEyFO7DuhrBbJKgOr50XVMDnRi6OmzkdYgIgGcsgSOw29H3W+uYC9ELNkWmFzDcA5YYc1wsu3XzkErrOlOwGyGs988SgQoxJRWYI130UkJ0WaeYL8zR8sJ9wBvLoutI28cQrBBIdmegU7gpQu124j03De/MaTfwT4fCXTsQN+7b6D+1r/DVlaWcPOyYOZbUZm3UTs/e6bEKduXV4mBw3+HktxJ0rYS7NpIP/C/16SO3Mp5wL4/BlKygIn7A+/dpPwCS62vq+mG0vV46FGCgDtuoyw0Dg4ODg6OaIEcHqnTpinuQqOJ6PC2bUnj6BgrSIY2iYfjNhhc9N5F2NC8AS7aoWEAdOx+bulcPHr0oxHLT0iToHZPnJ9WGqGWa6w5bpO93ySq45Zh1anaO6dosXzOy9EtA0d8sOpUeHaugUlsL+kI8eRFcBz1MOpOORYOu8PrpIUJzkHvEWRLmhMmM5gzV9FpqwA6emsK4zsrV1ouMNTt3YEnKy/X0QRx3JI92blmzLRRoo4vcXfchtLOer/RG4uSWI+SyQcZWgR8Dg4ODg4OjqhAa6JJE1R+rDj24G0SHijUgHjXqhGQs5R+QzsPgz39o5afkKaR/PTSCKVc4w2834R5nFnvuCvdp51K42yH0nhpe4lzlEBOlR0fwDbvfyyMknBUXAzXkPe7UactwRTmd1Yu0Q5eeXm5jiawPUniNuLjS4TaWe8321frj0VJrEfJBE5OxsHBwcHBwcHBETVQfNhQQcfw45GfXhqhlIuDwzAoBqUR0PFSjvHV9g2fMYds2bwu1UfonhGnbUzAdTTxdYq30fhtZwP2xjC4HkUVfMctBwcHBwcHBwdH1FCZXRnybyl2ajzy00sjlHKNV9CR1r7162GbMAE5xx4rOTVAR1p73nkHjpY9yDpsXlKFTogq8muMPUcxATnGV9tXHMLImZrWq8eOpXtGd9xGHVxHE1+neBuN33Y2YG8Mg+tRVMEdt2MQY5H9eCzWiYODg4ODI1LQioUWzVicND5v2rMJJphw8MSD/WEGhDHb4/Gw//ct2BdbO7fCDWOOBCGWbChjfnVuNSMRCyXGrZCfXhrhzkWMtEkyxbMVQ9z+znWfYnjpjSwwJh2x7vz7o6i+5BDYCrOwZyQdHbe9BlP3APtd1xNPoPLRRxLWeRvT9iia6o1hqxfjlh9LHXsQ2l4l5qQjcx/Ura2Eo189xi2FUKBQCjFx3urFuOU6Gn8bpKNTCd9GdJyfdoZqkVcZeSbB2iji8zZ/O38IeMT93gxUzgE8HmDbe952Jz0QHK2lM4GWr6S/EXRj6mJg0jwpcVkATEDR3kBXHVD7sfd7tAkcxyFMHppRcyQ0OZlRcoposh/rlS9ak9l41YmDg4ODgyMcspBEIASLJmh8XrpmKT5t+VRyPSclBz0jPUGnl5eaB7uIqTzcsV5r/kAwMrfgc5DwZV7Y48Etj7tQ1Df6nDXdhfL5HWhYWwDX8OgeEkuWBTUvvAhbzfRgm3tsYrALeO5cZSbvmoXA6U9IWbw5xlbbv3iB1HE/ZQkch9+Out9cAUd9Paw5VsA5xIjIrBlO9nLEOWj1OnPNZjj7zcqxbtMLpDFpw/1OjqIT7wXeWBpQ3gCmeY6E06lQ20iPKC0ic6CBTuClC7XLbOSZGEGoc7Tnf6rpkyyeP095zAgWNMac9ADw5jL9GLdaafBxKmI+SO64DUFoieq4jSb7sV75omWc4lUnDg4ODg4OIxivjlsan4MlHBNghhkzS2bi5vk3s1ixwmkaIvwSf48EPt32KYbThxXTNJqfVhoc2nM2wXn76D+H4fERJylx05Mzt/KodqQdwJmpA0CEL7XrgL5WIKuE72Qab21PcSN9uwgp7Ej9JZeycCNFl16C5uXXw1ach6qVd7PH6y65Co42O0pvuxXtD9wPx55WVN55I7LmHSxJR55u2N9VysuRgIhQG8XEcbvqVPVdwue8bPyZ8eK4VZJFSDADU470/huQnm93bUo60PS5flLkRI9xO4xVHyQPlTBGMBbZj8dinTg4ODg4OJIdauOzUVC4hC2tW9j/CyoW+K/TmB7pcb08o1x1AWU0P600OPR1Yi/rEKYc3YFd7xaJdthKnbbVR7V5dwVyZupAkHOFO8HGJ2RtT2FEKlc+jNRp05jz1lpQ4P+fUPXMCxjeto09lzlnjv9/f1oq6Yb9Xe86R+IgWdqIQh8o7fQkJ6IwTtDBcb1nkqGu0ZRXSPCNxYrwAO1bjSc13tohijBHM3GO2GEssh+PxTpxcHBwcHAkO/TGZ6Pg4/j40IkDhoeZU7ZiQZfi/fL5ndKj3JyZmoNDFeSIFRy14v8J9L/gqBX/z8GRdKB4tVqgccLIM+MFerKIJ8ZTO0QR3HE7RjAW2Y/HYp04ODg4ODiSHXrjs1HwcXx86MRXqalw9JvRsFY53mDjugJ23w/OTM3BwcERNCiUhqO5WfEeXaf7SYP8Gu37NE4YeWa8QE8WKuhr8o7PSqDrdD9sjKd2iCK443aMQGA/pvivYtB3up6MIQXGYp04ODg4ODiSHcL4rIcqhwOHDwxiksORMOM4Helf27CWhVsK5xkOY3M2wg/ONOx4TxwmgTDKjewctKD2/WIM9Vu88fD4kUoODg6OoCDEP6479zyv85aOzm97jx1Tp+90ne4njfO2aKp3PJCPKfSdrlOYBNplOmle4DMsRus4G0vU5KUBcsrWry1A3eoiqfPWZIGjeCHq1law+xLnrSB/+tMBjfJDZbOk7SDSS81rHAHg5GRJQE5mFGOR/Xgs1omDg4ODgyPZQePzFauv8MeqFSPH5cKdbR04fHDIf+3j9DRcU1yEHosZh048FPcuujem47iR+QSfc0RexkRMdsvjLhT1QRLTtnx+BxrWFkicuZYsC2peeBG2mulhloSDg4NjfEFwzjrq62HLs6FqQT0LQUMOubq1lXDYHbBVVqLqiccl4TUSGoNdwIsXSOOtVi/0hkjfJXJAp+UBQ3bpb2sWAqc/AaQrn/QYk1CSlwaYbjCnrRW2TCeqFrd7dcaVj7r3suGwOyXXGchh+7N/ev9/7lygVv9FwLd5E1H58xeQ894f9dtSSH+ctFtPED5I7rgNQWiJjmiwMscbY7FOHBwcHBxjC7FiFJbnF+y9SOGi9y7C+ub1cHtE8UkBPNLSirmDQxIGXCeADelpuHTiRMwrm4dHj340qmVTKuuG5g2M4FQA7Q6dWzqXlYXkdct3t6g+c8O+N3CCshDmbK5PPsPQ725gW29ofWYtzEX1JYfCVpiJPY50dNz6GkzdA94fms2ofPQRHpeTg4NjXIPGIwHBjOPMeXvKsV4nbaYTZfO60LQ+3+uYI2fuK+9EzWkrlDkq8w7aiUlxUunI/VtXAzvXeAnItEA7QycvAs55GWMRanM8dj1taFRer10K1G8EZPM0NeetRGckTlszUDkHuODtwLap9b2szatE44vnonSwR3Ksn+Z/g9YUZLtc477dwvFBiufUHGME0WBljjfGYp04ODg4ODiSFRROQLyzUhIeQbTTVjzhpOsVjmH2O3LuxWpcVysrOWiFsrT0t2g+01jdiHJE3xk/5uZsx1WhL6MEfevXwzZhAnKOPdbvOJhAoe8O/jV63nkHjpY9yDpsHnfacnBwcIQIm62f7bQVHHF17xd7r5MDbkELbCm+l2TJBjpqT390pN7gblLm2KVnybE4nkImyOW1e73mo+SUJeesos6Id9rCDdSvD5SnkBeAhto1qBjsUZz/ZTtHjJV9PLebDrjjloODg4ODg4ODIyjU99YrXq900N4KdUxyOLHbZmM7MmPluFUrqwAqS8dgh+YzTQNNES7V+AEx26ux25MTt/D882NeJg4ODo4xh65dzNFGuyYFBxyBvjMHHO3CTGZnGMW0DRbJXucYyEtTZ4KQp735c1SEXlrD+YxXcHIyDg4ODg4ODg6OoFCZXal4vd6mvSdgt+8+HaOPd1kFUFlK07WPj5ZllEW4VBwcHBwcHBFEfg07+k5H3cXwHn03e4/OJzPya4L/TbLXOQby0tSZIOSZVzoLEcN4bjcVcMctBwcHBwcHBwdHUKjOrWbkXhQDVow6m40Rkcn33dJ3ut6YksZ+F8vwR2plpe9CWSoyKzSfKc/gYRI4ODg4OBIXDkeml4hMiE96VBv7ZEfg6fpIBpIaRVO95FWycVoR9Aw9O553bRqQVwBBmVhn2HWzYXlWVC9iRGRK879eawpvtzDBHbdxAMVaW9uwlsVU4+Dy4+Dg4ODjwtiAQBQRC2IyvXxiUYY7F97JiLvkuKa4iBGRiUHf6To9T7+LNZTKKpSF+t9Oz05cPuty1Wdi1aYcHBwcMQfFwtz2njeuZCjf1a5xhAQab4S/oIjJzj3PS0xGRGSL25FR5PDGKc2zsevsfnNzVFol2PKGjJ/900s6Jkb1QqBGFo6HnqFnxyjUZB1wXUNeAU5bQWfOmwJbnlXqvDUoz4rz38X3eRMl1+i7+8LVvN3ChMnj8XjCTWS8MboZZYGUd57u4W5c89E1EvIL2sVBC4Lc1FwkM6LKJDkO5MfBwZH80LL/auB2jSMaOhhrJyO9iKY4sUL4A/rfYrbA1rUbk5xO/P/27gS8qTJt+PjdjbZsLbWAgKVlFVkEYdhEGHY3nFFEHBEcfWFG3N5xGWU2UUcdX64R4RtEcD79FFFEQcQRwQ0/h53BDQRlK6WUfWvL0lLS9rzX/YTEpE3apOtp8v9dVyjn5Ely8uScnHPuPOe+CxNby56oCHN/bRca9VzWxNhEn8cV919xv2Sfy7bF8gJAtck7KecWjJe4/T99BxbFJkpUQU7A0+da9tGQgsQd3PjT8+rIPA3yxHtffo3qc2bVKsm69z6TNzz1jXnOQmSaJzSprRlp6wraprw0OzSKQOoPBBfen3sUqK958Nk3ev6x4P/cLgNf3y3ZjUTm3CrS/5KOcufAv0qjFj2cPwTcPk4cR45KyrSp0nDUrUH15IHMVZJ98Gtp0rKXtEr1WN/43CocgyRwW4FOq+iJ++TPJsuGQxtMhWLPS/B0NMfcEXOlLquJk8VQ7j8A4Rm45XsN1bEOMjo0MGx/AMLa/NFi7flSIjzOrXREV4RHk0CmpcQ8c1m1jtCbsKS6lhx+grexHTqY4G1JGogr2LUrNIK2qLLjn267HZLVNEJONI4oFVdhnbFXDJJUCTVEL8PTER2eQUel0zqftAn0H4Dwwn4BYPsDgFqhqQ3SV3oFbUsFYAOcLjlP9DnTV5I2oYZpUNZX0FbpfIK2KHn+8V27SBO09RWXYp2xFwK3NSTrdFaZ9+tle6D/AIQP9gsA2x8A1IrsjOp/Db00G4CtcP5RNxG49XOZgb/E3Tpf7w9WSqOUMu935YUD/QcgPLBfANj+AKBWNGlT/a+h+TSBOhi7CWWVOf+gr2tPdC2+ti1lfLZU6j3xd2di76d+KzHn00VS+oi0GyKObesl875HxXEsR+o9/4Qc7HZxqcIV/vLKpSWkmYIX/nK01vXiF9WdTy/U+w9A3Rfs96C/77VIiZROSZ2qYQkR6mozt61eevfVka8kQiLkZxf/zOyXdZ6O7LBjkS+OK6qXnT97wJZpC3QEbE0WVUpu7ywitudLZ2qDquTKcUuBKFTzduJVlG3mUxJzbqczeUfagNAsylYFyjr/6N+yv999dqkCeDFnRTK1sGGEHMw5K6ce/6dYOQWS8uhYOXtFe9kXEy3NUvw/H4JDcbISiYEHTOsk09+PkcbZDolpUCipQ49LTINiOZ9XT/atTBTH2Wgz/+h1Z+TRNklyKipSeiX1kj90+4M0imlU5kkT1cMrh/4DEGrFynx9r3nSA6tpg6ZJQmxCtS0nUBm6Dj/85cPyn8P/8ZrfuF5jOXX+lK3X5e17t8vMXTO9tj+7Lafn90hdKDrHsRoQuIPp26Tlused+WBdNJg65lWR+CbV35X52SKLJ3q/fnySSP7JwKfTBjmT3Gasqp33gFrfR/nbN5V1X1DyToq8N8nnduLIOSeZE8aLY/9Br9iN42ykZK6+RBw5hRKTkuIMNPrJ/xuO/B279bm4j7ww+AWfx0AaADeB8KwsiUmMltSB+3/q6y+S3XEy12eg1sTHyZLLr5cnhs6wzXFVXS1ORuC2RKddNucyWZVxRHI+T3KvfC37Z8vB9U28VsaIBsWyIT5O7rm4mfl14oqkK+TZns8G9OWkCZ81py2jECqG/gMQKoFbz++1x/79mGzP3i7FlvNgR5Ws8ArYsTKxvx8ePNlxXXadVLqOK2LzY6VPhz5iJ3UtcOuqVO3r6ig7ffaAHZx75TqJO7DBe8Sra7TqhCU1tyAn0p35aF0jGYOd9vUcCAs1EridP7r0yHCP7cQx+wbJ/H/bfcduEmMk9f1PCNr62V+vP7Q+qPMOE7y96Wpx5Dj8xslcQVtVKCIb4+Nlfs8bOQaoZOCWVAkl9Mk7J8nxhZIw9Lj7l4PMz5ua+0qujFfln5PWDofsi4mRr09+LQfyDkgrKf/LSYeLM2S84ug/AKHGsiz54eQPpeZ7VnhlvwG7ViYOhJ3XZddxRckfW1A164OdP3ug1hzfLXH7fXx/anBKRxZqILSmAqD6Op6vFey0v3lAVaRH8BxpW3I72f2FxBxbJalDI33HbgYelph6eXwOVbS/1vQIqQOzyo2TeQYbB+Tny98yv+QYoJIoTlZCl/MF5q+udPoLgiedLrkytnbo7whOB/MOVvbzAACEISq8IhTXW190ZCtCE99jQBA0V2dZdPQqEO7K2072byo/dsO2VHX76+yMgONkJWNmHP9VDoHbErbVizV/NVeHDvv25BwG7t1lmnTZpWX9lpX8OAAA4agyFV4Bu663vrAuhy6+x4AgNGlT9v2acgAId+VtJ5f0ljMHYyXveLTP2I3OP7PDIyczKre/btImoDiZ/l8/F8+YGcd/lUPgtoT/1I+T4/nR3gmWhx8zf81wcDM/0uTr0GTLmiZBc4FoMQu75UUDANQezevleQukwqvuTzy59i9cXgw7cq23gbDbuqyXCe6x9phL9+ycQzbQ7xA74HsMCEJye2eBpRL7fXN6fnF3uhJ1Qln7pirZb/nbTnS63TA5czBaslZfJJkrm/qJ3TSTrClPy5lVHsXzUOH9tcPRQDJXp5i+FbEkOr5IouO942SuYmVZq5Mk92CsrI2Pl5TUwbY5/qurCNyWkHTKkj1agdAjwXL9ZIekDs/xCt7+p6i+TGmabB6jCZy1AjEAABWl+xHdn3hi/4K6sN72bNaz1PySJwN2WZe1krIW5Lhh6Q1y78p7ZdT7o8y0zkfl8T0GBGHMq84CS16KRQ5vFpnV01mUKd/7kmQg7PjaTnR6zKsSmZAgEhklYkWIRFjSsv9JE7vRvzqtm5My7VCp/bUpTHbHr01hsujGURJdv0gK86NEIsQdvN37ebLsXXlhAGT9ItnWPEreu/w6Wxz/1XURllZEgbui23eL50m9J/5uKg+mPn23xJzbLZLSR6TdEHH8sEEy7/29OI7lSOz0J+VA1+ZmyDe/HgAAqoqrwj37l7qlyqonV+B1VW2OyPRXmbh70+4yqdskW63LuqwbDm0wBTgCraKM4PE9BgThRLqcf3u8xJzYLhEe36NmVKEGqCYsoTsBLdin+Wo1jciFQng6kjbrnnudxcqKnUWyNN+q89L9aOcwxYgoSZnzkjQcNIg+rMT+2vT1vfc542RvzBPJzpTMyQ+J42iORCYnSHFBvsjp86ZtZPNEOf3XO6XpFdfY5vjPzjHI3Nxcady4cZltCdz66LTI776T2A4dzErp65eGgl272PABAICEe+BWUw7o6FV/lt20zDYH7XVpWQGEkeO7RV7s5f/+B75xB6oAeDvz4UKJ/OQ+Obg+6cIl/E7OIO5JKb7mJWk46la6rQpo8NYzTuYehZv1U7GzmJQUE9j1FUtDxQO3pErwQX+N8bei6Xx+rQEAAKhEZeJaUJeWFUAYyc4o+34dZQjAp4adLpL6yc6Rtp50Wuc3vDSJnqumOJn+v+U07zQIOk3QtuoRuAUAAEDNViauBXVpWQGEkSZtyr5fLw0H4Hf70YJYmh7BkzNdQiTbTzXSEbcHp0zx7vcpU8x8VC0CtwAA2PyyJH8HQDqfSrmoi5WJa0NdWlYAYSS5vZy7ZIBYJb6bTI7bdsNIkwCUweFoIJmrU34qLj/82E9F5XX++fr0XyXoeUbO0qWlzkV0eu/tt5s0CVHJyZK6YIFJk6DTJn0CwdsqRY7bCuSXAACgJpQsBBATc9Z5SWVSW9lz4qTk3f2wRB3NNkUXjndvbS4Ft1MhKISH3IJcmbJqiqw9uNY9TwOhWkU4IdZelZzr0rICCCP52SKLJ4qkr/xpngZtx7wqEu89khAIm9zPF455/eV49syxGpMYI6kDsySmQbEZaWuCtjkOcq66+lF/CMrVlFERIgmXOAu6+Zt34e+Zf38pWdMXixRbUtQsSRKm/15SIk+J48Rp2fv0Aik8diE9RUSEpEy9W2K795XM3011fh4XXySpLz0vMZ37lf2ZBvA5hyqKk1VzpwEAUJVFpVz/LzldfPSonH34EbEOHZLohGhJG7TffUC648umEnU6Sg4nisy4q4lkxJ12PweBKNi5MrEdisjVhWUFEIZOpDtz2oZhEAN1Q7UXY807KfLepIB+xHAPcLi4maTeVF9ijq1y3+doOkgy388Tx+GjkvLS7BqtU1RWHwV6X0Wfo8x+DJKeb+z9PFkK851F36LrF0rasONy9kg9OfQf/SwivOabc5SiJMlcHi2OvChJGXhSGg68SuT66SIfPeK9LGmDnA/PWBW2P1adCiIGyYjbCnQaAAA1Ebh1BW/PTb5DCnMLL1TIzb6QtytaihoVyf131JMTjZ0HTp6Xfvdr0U/mjpjLBwXU5AknAAAhrNr3o/NHi+z50jnq00VHgbYdLDJhSanmGryN/f7vEnN8banHOJIHSEG3R2u8uLwtAre++rEqgrfxRWJZlhSdiy4VtPV8TEFujDRsWeD87OISRM7llr8sZXzO4R6DJMctAAA2Vq9e3oWRthfydX3e1J3H69LBx6RBfGGpxxRZReZScB1VCAAAANieXjavozJLBvh0WufriPQSGnZu6Rxp6+MxOr9hlzD8sdZfP1aABmTThh+X6AvnG4X5UWUGbV2PMUFbpcuQfzKwZSnjcw53BG4BALCxqNP7zAGQjrT1pNM6v7WjdODWRS8FBwAAAGxPc52WRdOIVMVjQl15fRIkPd9oNcD7PES1utJ5LlLlwvEzKweBWwAAbKyoUWtzyZGmR/DkTJcQKftinL96+6L5OwEAQGjQy8L9VWvX+Xo/UGc1aVP2/Zr7uSoeE+rK65Mg6fnGgbWl884eWOc8F6ly4fiZlcP/2R4AAKg2nrmpSuap8px2RF6ojHvWUSrHrRYoO9s6WiRGfOa4pegSwtne3L2SdTrLqwAZ+W0B1FXuQkwtWkjqG/MkJuasuxq7FmDKvO9RcRzLkZQ5L5nLx92V2i3Lu2p7ySruVT2NkFat+9Hk9s4CVf5y3PpavyrymFrso0Dvq+hzlNknVZzjtjAvWvauTPaZLsGtIjlu+R4pheJkF1CcDABgNzp6JvOOX4sjK0tiEmMkdWCWs2Lr2UgTtI06HSWHE0Vm3NVEMuJOux83oOUAmTZomiTEJtTq8qPqlSxkh9JyC3JlyqopJs9zeduEZ5FAX9N2QmE1ILx5HhNEJ0RfyH/vPCbI/CLZnf/+kmsdEhdd+rJmIz7JmW/ygqLYRIkqyKnwdMnnC7eq8KiG/VZ+tsjiic5cp4GuVyczRP7vUO91UdfN3/5/kSZp4fkx+erHygZtL+S0Vb7mm+Ctr++EUS+ILHvYe1nSBolobeWMVWH7/XEqiOJkBG4r0GkAANTK6Jp6ec68T0ltJeNktpz97UMSdTTbjK450SPV5LT1HF2I0EPgtnyTP5ssGw5tMEX6So5Cnztirs/+JHALoM4Eb2+6Whw5pa/C0enUocclukGxiYcEwtKAQCWmw70qPKrxB0ctUHXhmLfcEZjzR/sfcRvu66KrHyOjRXKynPMSU2TW1zNk68nt0txx3sw6FB0t9SIipUPSZfLgFfeb9me+/FKypi8SKbakqFmSJEx/VFIiT5n2DkcD2fvo81J4LEckMlJSHv+tNBw5yvlZ+fvsfM0P5nMOMQRuq7nTAACoyeBtbIcOJnjr6wSuYNcuaThoEB9ImCBwW356hBuW3uD3/mU3LfP6YYPALYA65fhucUzr7R5h6+IK2lZLoaCKeOCbsAvCoJauFNFUHS/28n8/62KljpX0PKTw5Elp0LdvqXMRPQ85u3GjRCc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+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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awdXldTiZiCa2tVPRnnbx9o3fpkUS/CkHl6vL1UW90zt7LG1ldd7trjMf75XeKe7ha3nhwP/K4d+RWMdYUN6e+eAa6tfstrRNzuikzlbf9vmludzTlnLQLhGIxnwhfvPxnRvMXcdzTeNmYd2UYOBau+eOsPS3IMjQ1LWhrgcIP8HoS2Fo/7CPXaCLNJfr9okI7l+hfhaB2Eaar7RQm7Os3BMutOZjPXgPhjk8stpFen6iXUDcKW7vu+8+WrJkCT3yyCNCiTt79my67rrrqLy83BO8jElNTfW6j39L59S45557hMmx9F9JiX60exCnsA8wVp716qbCcQ1ep/i3w9GieY8WA51upSd/nhJJsP8diRJnp3ade3V76uCPSKtjNCtvC+e7P7uRKJrQQEXjrbUP2iWC8DNfUP1Gc9dxsL+WLYavtXvuCEt/C4IMTV1rF+HIE1gjGH0pDO0f9rELdDE0l0dw/wr1swjENv7mK7U5y8o94cJIWc0SKXWLZtAuIJoIm+KWrWDZt+3dd98t/Nyye4TbbruNjjzySPEv07dvX/Hvzp07ve7l39I5Ndj/LfuJkP7Ts84FcUzuIPEP+3et+drbFxf/djozNe/RYouDbW9J+BSKJEoy9768qHQka9d5d6KnDv6ItDpGK+zrtmaO96c11ctyqXq5tfZBu0QQfuYLETzGzHX8tj9zoOFr7Z47wtLfgiBDU9faRTjyBNYIRl8KQ/uHfewCXQzN5RHcv0L9LAKxjb/5Sm3OsnJPuDBSVrNESt2iGbQLiCbCprhtbW2lzs5OGjBggNfx/v37C2Urwy4S2C3CN994+65hi9yRI0dqps0WuezcV/4fAD7kDXUHIvs8z/MpeunRO/b6vL3qFqFUU94jAi8kJHkd7uwJUFadkiack0eaI/iy7DJRrqSEJNrVliwCXXnVucdtAh/f3eZWDPK17Byf/5XDvyOxjtGIMkDZ69ccQjuyiDrbkoWbBHaXoGyf/bLGetpSDtolAtGYL8RvPi5F/DZ6Hc81OWXUXjyBXAautXvuCEt/C4IMTV0b6nqA8BOMvhSG9g/72AW6SHO5bp+I4P4V6mcRiG2k+UoLtTnLyj3hQms+1oP3YIHO4buWLvXdy/bAx/l8PGO2XSTZ2/VsRfsAMyS42IlsmNhvv/1o8ODB9MYbb1BGRgatW7dOWN7OmDFDuDtg2Pr2qaeeoq+//prKyspo0aJFdN5559HKlSvpsMMOsz1aG4gzpdn089yByFiBOYV93Xa7lbn/adUOUNbW4A68IIvcy0rbOf3y6KCSI4ISad2uqJl3vHMVTV2wgvIb2Vqiq8fXbbew5Fz3xQARoGxrDokAZcOHT6Sbx95Md5XfZSpqJvsL4k9PlJFMtY7HK0qlLfezlj0ttO7sMymrwR2gjH3dlh1dJ/5mpS0HKEsqLqK8Z58QAcrsaBd/50CAqMwXqlG3jV5n9tpYiEwfAzIMW57AGsHoS2Fo/7CPXaCPkT4Rwf0Lcxqwe76avWQ2rdi6wuv46PzRtGDyAtU5y8o94UJtPtZT2i6auoiyUrJMz+HSmr7ox23Ucd28vfE0ZHtZzx6ktpZKnnicek+aRPGKmXaRZM9YebbK91t911RQ5azL0T5xTrMJPWVYFbdr166lCy+8UChsOUBZRUUFnXnmmfT0009Tenq6uIZ92V588cX02muvCevcxsZGevDBB8Uxo0BxC7TecnkmTA5Exj5t+dOuvkOMPdA48EL9RqpOSaWNSQkRpfTiwH5yioqK9tb5slnU1T+Xej39IA3qk+v2Q9ZnMDn3ZNCG886l7q3bKO2BeTT4eHeQQIad+7MvJb06am0Qbx5zM921wpzyN1JkKMlN7ZwSrWsN9b+eBZV0LCGvD3V2OylpZxP1vv7PtPXw4ZSXMpS6Zv3Nq08G0i6BLDyABXrmC2mOCfg6s9fagJH+FlRiQIZhyxNYIxh9KQztH/axC/Qx0iciuH9ZzVNrrQoiZ60aDni+Wrl1pfh7VP4oQ3OWlXvChXw+ZqS/q3dV0w/bf6AR/UfQ+MLxmvcYXe/3bXbRPa85KKeu3csQSc1wxCcYdxyi1S7yv5WyN/psVduLHZc+ki59uoq6qqrRPnFMc7QobiXq6uqE31oOIsaWt1rX7NixQ1jdSkpdo0BxC7RgRVnqsGGqDyx+sHWsWxeVbyH1FsPBqvPMT2ZSeW25V2RO/mwkMyVTWJIqj48tGEsLj1lI8bwYVmsL6RijbAsr7aPVLix/RutcJLcNAAAAAKIbKG7tkWGsKW6BNdTW+/1aEumuVxM9ylsOhsxxNaC0DS1ae7Gj0w6hmc/UeNoD7RN/NEeb4jbYQHEL4o1QL4b504+T/nuS6fveO/W9iH0rHguLYavtEultAwAAAIDoBopbe2QY7WtVENz1PlveLvxvf3JV7/V1C0vbyNmL/b/x/3J/UVlZ6TmG9okfmk0obsMWnAwAEDuwvx4r8OclIPLahUHbAAAAAAAAEL3r/Z1ZCdRww/lex9iyE+4RImMvVpXeJtpDDtoHqAHFLQAgYEoySyzdJ/kOApHVLgzaBgAAAAAAgOhd77PFbe69//Y6Jtwl1O61wAXh24sVt6WL9pCD9gFqQHELAAiYsuwyEdSK/fXI4d8cGVXtOF+PT/HD1y58XO8c2gYAAAAAAIDoXO+zj1sOUMZuEsTn94sWiX/5s3wpCDcIT9vwbw5QJrlJQPsAf8DHLQAKPzT8SYOZyMtW7gkXdpZVmZZaxEx+UN089ma6q/wun+PzJ82n7NTsgMoQK4SjXVj+jNY5tI1B6tYTNWyyFsFb695A0rRSPjP52Vk2G9LTHDs2ltPw+AynHIOJ2bKGo26hLmOwx67BdMw8O6JlrWKpnKHscybyCkTmqveGuH/F7JwGogKz4yca5rhQrvfZ0paVtlJgstJ/vyDcI7CyVihtJWVhz/F4xu6+Y2QvxkrbS5+uoq6qarRPHNOM4GTWBQKi1wl/INfqKbe0FFhW7rGCmXpoYWdZ/aVV0Vwh/KMqH35ax8ONHfKN5nYx0jaBykgtQAbDaQZD/sGaJ+T3JLQ3UuHyuUQbPtt7YshRRGc8S5Seq59Aaz3RWzN87536ANH7s02nqSZfQ+XTKodafmauNSJfP+n5axet/n7v4TdR1rvXWGsXq+NToy41E+4kV891nroYkGM45yRlH+/z+fWUVrVMV5ae8lrsI1IaWvOD9LcqZvNsraf2Ref5rZNP3YI0dq3Wx8yzI1RrlUCxVM4A+pxpTOT16+Zf6aF1Dxmqi7KPqcnhmH6jaP6OneTY9EVg9TRahxA8G4A5IuG5EKlzQTTMceFY7xf9uI06rpsnlLJK5axHeVtbSyVPPE69J02ieMTuvmNmL9Z3TQVVzroc7RPnNENxa10gILIJlkJm5iczqby2nLpcXV6fMIwtGEsLj1lo2z3hWqzZWdZQ1TseFsPR0i5Q3PrKo++Hl1BadTmRTN7En0ENnkw0/W19gb54GtHGJb73pmUTtTeZTlNN6WWofFrlUMvPzLVGxpef9Pz1Oa3+/nJ9Gx3QtMNau1gdUxp1aS8aSzv/+Iz46amLATlGwgZd6uOp1eWU4EeWnvJa7CNSGpYUt2bzfPE0cm1c4rdOPnUL0ti1Wh8z8320PLMtlTOAPmcaE3ld+O6FtKZhjaG6KPuYmhwWbt1BY9vaKYlcgdXTaB1C8GwA5oiE50KkzgXRMMeFa72/a+lSSh02TNWilpW2HevWxa3SNhh9x2x6aB/QbEJPCR+3IO7hzxn4zZh8kmX4Nx/nt2N23BMu7CxrNNU70kG7RC/JjZvdFnuKcSB+s9XRzg36n5PyNWr3ttVbS9NK+fTKoczPzLVGCDA9rbFTvKedDmjcaks5DY9PnbpwGyQ1bbat3qFE6kNeCk69soajbmbz7LnecJ1CPXYN1sfMsyNantmWyhnKPmciL67LqvpVlmSuJodSp5MmtLV5K2018ralDuF8NoC4x+xcEA1zXDjX+6yU1XKDwMfjWWlrd9+xkh7aB5gBilsQ97APGj34kwY77gkXdpY1muod6aBdopekFj/9vH6j9jn2AWgFvTStlM9fOeT5mbnWCAGmpzV2SpydAaVrJA+fuc5PXZLlc6LdcoykPh6OupnNM9AyBnvsGiyfmWdHtDyzLZUzlH3ORF6ByFztXtvmNaN1COezAcQ9ZsdPNMxxWO9HJnb3nWjoiyC6SQ53AQAINyWZJbrn2Q+NHfeECzvLGk31jnTQLtFLV6affs7BWbTIHWQtU700rZTP5TKen78ymyib4fTazY+dSkeybeU0PD791KVTPifaLcdI6uPhqJvZPAMtY7DHrsHymXl2RMsz21I5Q9nnTOQViMzV7rVtXjNaBzNyjaI5DUQHZsdPNMxxWO9HJnb3nWjoiyC6gcUtiHvKssuE43D2QSOHf/NxtWBNVu4JF3aW1c602K8P+1dSg4/z+VgmUtsF+Kczp4zaiye4/fjJ4d8clEUvonbeUPc1avem97GWppXy6ZVDmZ+Za40QYHpa/b0qJY1+ysm3pZyGx5ROXbgNurLLbKt3KJH6kMtoWcNRN7N59lxvuE6hHrsG62Nmvo+WZ4Olcoayz5nIi+syss9ISzJXk0OFw0HL0tOpixL85m1LHcL5bABxj9m5IBrmOKz3IxO7+0409EUQ3UBxC6IKM475zVzL0R7Zcbicsqwymlg0UdPHjdo9x/YeQjPSSqm6wj6lox3BCO474C90cdpAGuh0eo5x2bkOSv88X1Z9qevXR63eB/c7mC4/5HLPvfJ01NJkpSxH0pQimsqRIp3y+VAob8MZ7OGKQ66gfXP39TqmbBcjbWJXu6hSt56KWn8OyFcdy1jtP+lcRM8T7MNv3Sde9ed70s592R18RU7xaHckbX/wNcp7+feln1tK00uuqW2ivdL+eJdvWgMOJJry9711mjKXqGS0+jVGy2ygbKpyVEmvvuhgqjxunvd9Jvo7/y6+8GPr7SLRU9b7DpzplQf7muR5lI97jZ8znqXWgWN86rLjT/O9+rpROdo2JlT6rlGkPp6gJstDz/MeD6lte/uTTpv6y09rftCUB9fv0Om+fVguT5V+51MnrT6v1hY2j10JqT8JWfkbtzs3iP7/p5z9aWJrm3iuy/um0bGifP4HiuFnigbKcqrWycA80sZj0cx4N8iWY28T/dlIX3v42IdVn8dqMlf2MbX2emvECdQ9aJL1eU0+52uMUXn7qdVVcyxbfDYAc8RLYDIrezK163ltPbzPcFpes5wiASP7sHCu9wOdv6MVO/dhwd6LWV3PgdghweXy971kfEVrA5GPPPq0v4WMmWuZH+t+pNuW3Ua/Nf7mdXx0/mhaMHkBZadm+9zDk+36mm+p34c30Yim7Z7ja7P6U+rUl2mffUf7LZu/Mqpdayjydms90Vsz3EEieqgvPox2/+kRKul/kOdYU0cTzVk6RzhPl+C3g/wAUqszX//XxX+l1dtXkxmkNDPqW91K28pKcpSUUOm/XxBO8iWlrfK4nkzMyMzstcrjen3Ib/RzGWry3r/P/jR33Fw6MO9AS21iR7vsqtvlOZbQ3kh9Pr/eHeRKgi1oeDOWnkt2YkZ2avdptZ08PbXf8jx9yqAydnzqz9e8Oo1oy9fa1+iUlwNXsQ/UvGGjqbo9zZ23yTQ9aJV3/F+JPr2VqHYNmWLIUVQz4U5yKfoalznf0UpbnRnCmlQuP1X55qbryrGldg09/+Ut9L/m32mLw2Gon8sRSobmLeITNI81g8V24T5fuHyuT1mrj5xN6R//nfpU7R1XX6Wn0Zx+edSclEg5qTnU2NEoNmQDnZ20xZHsUxf52JK3PbtS8LLK9TenG8FI3zXzvOSNQu331P7lo5S2ba8M2gv52ZZAaTUrvPJpmXg1Pf/t/ZbbVKtsumOzZBzRmEuJCg52W/mpXMMWxPVT7qfCwfvTtu/+R7nL5lFK3c+aMtKdQ1gm7L+TPwWXrAotjF2tef6+odMo8+O52uOWLXw5IJoaGnmqjhUbsPKs0qNy2w/U692rvMYblU3irka0aalPPZsSE+mBj2bRjuoVnrEXaH/Tqx+P8+Oz9qELj7idMgsO0Rxvv4++kW75dQH91PiT57BWudTGnk97WXk2aJRNbYyqwXUd3p1Cvybu8VzH1sSsmPaR7c4NVLfuG9U5TaqXmXVbrCgqraxVta4P9Vo13Jjdk/GYWbV1FT2w6gFq3tPsOc7P6EVTF/n9lD0oGNiHhWO9L6Vr9/wdLQRrH2Zn2wSyngOxqaeExS0AMh777jGfBQLzzdZvxOStBi+o8/93M+0vU9oy+zVvp473zw2vfHmy37jE61Cf6u+p5KNbvY5x3cpry72O8W+tOvPx73d8b7o4UpqsjBVK2ZISoaRlZW3r6u8MK22jHTV5/9bwm+h/VtvEjnaRw0rb1Grv/EVfevNiigtUxo5P/fmaym/0r9GBN7cdJZO8Pye1mqZmeS8i2rrWUHmU9/ZZfJ1qmWnYMaobc3Plctfn+h8X0rPtW7yUB/76uXL+PaL4CG9FlEUZcp9XK2vRK+eJeVPO2LZ2mr+jTvzNSluG6/BVRrqhukhtb1iOdvddM3D//O4lSt3uLYPUmm8oVa607clnyzuXBNSmlupX9Y0oo2csqVzD85nUp7NXPUKOnb9ZlxHnM+yYgMeu1jzPMtQdt1pKW508VceKDVh5VulR8vFtPuONNi/1VtrK6sn5/L/Gn7zGnp39TVk/zoP7N89deuON13+/NP7idTiguc3KvKZRNrUxqgaf/zjV5XXdd/Xfqdeh75DgzWkgLjG7J+Ox8uDqB72UttIzetr70yhS92HhXO/bPX/H+z7M7r2Y7es5ENVAcQtAD/y5gvytmhI+p/YpQ9XmJXRA41afSH/8+6Dm7ba6TTAFf1bBb+hcXd7H+Tcf7/ncQqp3l+I6/q1WZ63rjSBP00d5O21aXChtjcjbbJvopWsEKd3qVrclRnLjZmFpm+Cn78QsRsaOwfFle75m72MFj4U+wfdwH2DLUKtwP9KrT9XmL0z382DJUOrzRmXI8/vEtnavzx6NjK2gE8R+qZwP2ABS4XVT5MPPw6I97fa1qU1jk8sv2nj9YvvnNwty15qzi/e0CxlaGrd+8rQbK88qS3LUqWdlxRJ75xAT9auu+EKz3Xn9V+zssKdcVsa1zj1qY9Qo3dRtz1gGwOY92bLqZZ4XqUr4eMjdJhgYt+Fa71vda8QCwdqH6aVtBJ+0g7GeA1ENFLcA9FDZUulXFvzZmpLG2u9072moWRUeGTds0j/Pn3kaqLeyzkbk5A8pTVbOFs739vnGv2NVaWtU3mbbxEi6RqhprRH/JrX4pq/Wd2IWI2PH4PiyPV8r9wUAf85vFX/9qLF2tel+HiwZ+u3zGrBrBDNjK+iEo1+akIulNrV7bFZ96z+NYJTL4JxdYrBP+SUE87SVZ1Wo+pql/E3Wr6FmdWjGgZVx7eceo3OXFgGPZQBs3pOt3aH/ddEP238IrcwNjNtwrfet7jVigWDtw4ykbQRP2sFYz4GoBopbAHow4vuIfY0pySk4VPee3MKR4ZFx7iD98+ybz0C9lXW2w0eUlCb7tK2Z4/1ZCP9WBiyLJYzI22ybGEnXCIUZheLfrkzf9NX6TsxiZOwYHF+252vlvgBgf4VW8dePcgoOM93PgyVDv31eA/apaWZsBZ1w9EsTcrHUpnaPzeLD/acRjHIZnLMrDfYpv4RgnrbyrApVX7OUv8n65RYeFppxYGVc+7nH6NylRcBjGQCb92QH9dsbu0ONEf1HhFbmBsZtuNb7VvcasUCw9mFG0jaCJ+1grOdAVAPFLQA9lGWXCcfgWvA5Nd9wxWWT6aecfFLaLnT2BCgrKlVEAg4VeUPdDswTkryP828+3uObT6p3kuI6/q1WZ63rjSBP0ycQ2aJFXj5vY1V5a0TeZttEL10jSOkWZbiDVXTmlIlAPi4/fSdmMTJ2DI4v2/M1ex8HMbLQJ/ge7gOB+CvkfqRXn+KyI03382DJUOrzRmXY2ROgzJ+PSOXYCjpB7JfK+YAj2/pEt01IEs/D6pQ0+9rUprHJ5RdtPHSK/fObBblrzdlVKWlChpbGrZ887cbKs8qSHHXqWVI62d45xET9ikqP1Gx3Xv9VOVLtKZeVca1zj9oYNUoiJdozlgGweU82oWiCCESmBh8fXzg+tDI3MG7Dtd63uteIBYK1D9NL2wg+aQdjPQeimgSXy+Wz7o7naG0gvuFIkLOXzKYVW1cYimDqua+xgqqeP9btl64HXhgXX/gxZeeE8cHX1uB2YO4nGqXZyJlq1xtBiti5r7OvaiAyH2VujPq6NSJvq1FmrbTLYf0Po3OGn0P79d1v74LBYN+JWYzUPxgyspqm1n0nLiB671rv40awq6391CcoEY1DIENW2s7pl0fNSYliQ6jlVy8s0ZlD1S/LJrmd3MqDRg05ippOepDmfHtP8KJU2zE2I2Tsavb/w2+i7Hev0R63/DJBK0BZiOdp28ewib7G9WxKTAxqVHS/9dNod9vHgZU+a6JsWijnt3iIOA+id0/Gn6pzIDJ5n+U+vGjqIlusIYMxbkO53pf2YQfmHWg571ggWPswrfustI0g3vdicUCzCT0lFLcAqMCOwVduXSn+HpU/yvBbRw5Exj5t2T1CuCxtq6t9g+AUpbW7feHwZxU6b+i43uxbhz/TMFJn+fWM2t8te1rojq/voJ/rf/bcN61hOJ3yz1/IUVDoo5z1KG9ra6nkicep96QwWSybkC9TVGTeos6IvM22ifIeRuvvX3b+Qq/8+gqt3r5ae1HCzu8N9J2YxUj9gyEjq2lq3Sc/zvj72+629lMfK/080DxN3yc7XuFI9iqv1pgLq8VKqPqlRj5BaVN/ZTF7TYSMXU1ZaY1bTlfvXBiwvb1N9LWg5K/Ab/qhGgdW+qyBsjFac1gwZKu6VrWwjgLG5RutMrayJ+NAZOzTlt0jhNzS1uK4DdZ6X20fplzrB/15HaEEax+mvC+QthHE+14shmmG4ta6QACIdiJtMTzzk5lUXlvuFWGTPwc5q34YXXfWI6oWtay07Vi3LuKUtrG2GNZqm7EFY2nhMQvDWjYAAAAAxCaRtlaNNWJprQoCA2v9yAVtA5pN6Cnh4xYAEDQ2N20Wn4rIFYMM/16U+yvV9Nqjeh8rcyNRaRsvbcPH+U0xAAAAAAAAIPrAWj9yQdsAs0BxCwAIGuxrSg/+VASEB7QNAAAAAAAAsQnW+pEL2gaYBYpbAEDQ8BcIQPL1A0IP2gYAAAAAAIDYBGv9yAVtA8wCxS0AQJVdS5cKX7Nq8HE+74+y7DLhZJ39psrh33w8nhzgRxpoGwCiYx4FAAAAADAL1vqRC9oGmCXB5XK5KMZBcDIAzMHKhMpZlwtfs6X/foEcjt1EDZtENEvnngyqOP8CctbUUMXfp9HukfvqRnlt6miiOUvnCL+puhEzgSX/SPypjTzSKR9buW0lJVCC3+i7aBsbqVvvGSO2Rny1M92etLa27qC61m3Ur1c+DUjP805bnp/LRfWrnqPWpi2UPPxEyh9xdvDKptGfbcHGckpl7NVUSynN1ZRbOJKKehX6pM/Xbf3sA8qe+wR19e9DX/xlKPVK7aARw0+hw/b7s1Dainm0tpZS7ruFag7KD240ZyMyCEIf9tumRvMM1vgKUtp29mUprX3qK2lAfSVRyWjanDdIde7nY/xidG3dWuGnnDeGx5Ud512GYMrSaB5hmi+Ntksw5iJb0wzCnMb9JqVxC5V0dpIzu4Q2JSdq9q8tmxZTYmMFDR1yrJjP1NKLt+j0AAQVlTFvdL2PtX7w0Jrv0DYgGHpKKG4BAD54lAqVleTIcVDpEZXk6NVNzt2JtPnLEupsdNLWHKJ55ybRzqwEcc/o/NG0YPICTWUsbyLZpy0W84Gjtghj+Xd2d9Lq7au9rvXXLmibAGmtJ3prBtGGz/YeG3IU0RnPEqXnRka6amkpKZtExEN5k7YF6K7kFOqasZiys4psrXPQNhU2ylAq49rKpTR/x06a2Nauep1z0JE0p38efbL9W+rb7KJ5L3dR/0YiR69OKp1SJ+bRFd2ZlP3VUHLVbKPGvDS66SynZx61/aWWERkEoQ/7bVOjeQZrfAUpbTv7spTW5s1L6JXabZTb3e0515CYSOcU5lO1I1nM8cw3W7/RTOuw/ofRo+Nup6x3rwmOLI3KNEzzpdF2CcZcZGuaQZjTuFxZXV0+89pX6Wk0p18eDS8a6+lfateVZ/Sm4os+osysErykB8BuVMY8rzOuzcumJXVrvC7FPix0aM3rN4+5mW77+jaf5zHaBmgBxW0AAgEgnFRXV3v9LioqUj0WMuXtqceRs9EplA6F4xqo5utccu5Opq7MLrri/BSPskH+0Fp4zEKKVJSyVCNU8g2EmZ/MpPLacupydRm63q52keQXLBlx+namrSyvlL5ePlp9ROv69n+eQKnV5ZQgawtXQhIlDJ5MNP1t6+V98TSijUuI5G3MLkcspKualgVcPcrbzNIjVMvWXjSW0mZ8YFi+UlvcvPpmWtOwxqs/s1XX2IKxNHf/uarpGOonFmSo1v6clzTmHqutpbFt7ZSskWUXJVB5ehrNzO8nfj+1vo76f9BbzJvKeZSVtjef3U07Mrt96i2N14DHhBEZ2NjXpPKqzVFedTOap53jQMmLp5Fr4xKvsSv1451/fEZV7nrPaK16J1IiHdrnULrrsLu87vOHlNbnmysop7tbvFeRj8XGxESaVFpsuLqvNuyhA5p2BEeWRtsrWO3pJ12//bEHo9eZwdY0bZSfvFxPbt3uM691slI2PY0uy+/vOaZ13cqM3rRwv2Pou/rvqJu05zNg33o1GtaqwAZUxjyvM75OT/Uam9GyD4sVtOb1zJRMauxoVL0HbQMC1VPCxy0AQBV2j+C2tO0USoaKT/t5lA/7Tt5BvdJ5ue4Nv3lky1oQPPjzG5azUaUt2iWI1K2ntKpl3oof8SlLl9s6YucGy+mK+5VtbCVdrbQswIqjzM49mmVjWZitc9XuKlpVv8qnP/Nv7ufVrf5ftgRbhtKYK97TLizNtJS2TBK5aEJbGw10OqnU6aTxSa09lra+8+ijp7V5KW3l9bZlHjUiAzv7mp85ytOmFV8YyzMIZfPQk3aCRj9OatpsW71ZkcV93ExfltIavXuXsLRNUBmLfHxsa5uh9LgvHtC4NTiylPDXXusXB6c9/eRbtfkL3f4ojTV//dbKmLQ1zSDMaVwO7htq8xr/5uM8lzF6141t3UU7tpV7KW0t1xMAoDvmeZ0hH5tyMN6Cj968rqW0RdsAO4DiFgCgTsMm8VkvW4jJ4d98fKDTV3HLsDsEEDzYl5IV0C5BgP2N6VG/Mfzp+kvLbkzWubZNPXCXRE1rTdhlKI25Eo05Tw2eH6XrtebRbJWXX7aOVyMyCEIf9jdHNdSsNpZnsMaXgbSTLcjfX73N9GUprREdHbrXHeznvITfvhuILCX8tVfVt8Epg598G2v1+5s01vy1n5UxaWuaQZjTjPQNaa1n9Do1sP4AwAJ+xjz2YdG1D2MwF4JAgOIWAKBO7iDh05Y/65Xj/sw3kbY41O3O2IctCB4lmSWW7kO7BIHcQfrnOYhEuNP1l5bdmKxzQXqB7vnCjMKwy1Aac5Uac54aPD9K12vNo01tycEdr0ZkEIQ+7G+Oyi08zFiewRpfBtLutCB/f/U205eltH5ITdW97ns/5yX89t1AZCnhr72KDw9OGfzkm1Og39+kseav/ayMSVvTDMKcZqRvSGs9o9epgfUHABbwM+axD4uufRiDuRAEAhS3AABVnM5eVPFlieez3tKjd3g+9/1tST/araJ0YP89iCIcXDhKOMuZfSkZBe0SJPKGUnvxBOHTVo74zQFjrEb7zhvqvl/ZxlbS1UrLAuxXsyU5RbNsLAuzdS7uVUwj+4z06c/8m/ttUUZR2GUojbmqlDQRsEfP7ox9zy1LT6ctDgdVOBy0vCuDKhbnqc6jV76dTv1aElXrbcs8akQGdvY1P3OUp01LjzSWZxDK5qEnbZdGP+7KLrOt3uzjlvu4mb4spfVNr94iEBmPPTn8m4+XZ6QbSo/74k85+cGRpYS/9ho6JTjt6Sff4rIjdfujNNb89VsrY9LWNIMwp3E5uG+ozWv8m4/zXMboXccByvoNGCv6esD1BADojnleZ8jHphyMt+CjN6/npOZo3oe2AYGS4HK5lOvBmAPByQCwEJjs/AvIWVlJjhxHj6/bbmE5tvnLEupsdNLWHKJ55yZ5ApT5i5gJghvNdEz+GHJ2O2n1du/PQtEuQaatgejNi+2Pkm5numppKSmb5HacuWmp5iUcmKxrxmLKzi62tc7BiORutwylMq6t/JLm76jziqouh6M9z+mfR59s/5b6Nrto3std1L+R3SV09vi67aYV3ZmU/dVQctVsEwHKbjrL6ZlHbam3WRkEoQ/7bVOjeQZrfAUpbTv7spTW5oov6JWarcKnrURLQgJd1b8ffZuRJuZ+F7l8oljLOaz/YfTIuNsp+91rgiNLozIN03xptF2CMRfZmmYQ5jQuV1ZXt8+8xoqhOf3yaL+icZ7+pXYdK22LL/qIMrNKgjOPAxDPqIx5XmfMzsuhz+u+87oU6/3QoTWv3zz2Zpq3fB6t2LoCbQNs11NCcQsA8GHX0qVUOetychQUUOm/XyBHSqvbf1qfweTck+FW6tbU0Ja/n0u7Ru5Do/JHwaIiDHDAD/aXxJ/eSBYtfGzl1pXib7RLCOHAMD1jxBbrNbvTba0nenUa0Zavfc+VjCOa9op74y/Pj90mrnqOWhsrKHn4iZQ/4uzglE2nP9uCTeXkhfpfF/9VvBzhoCDsX076VJH/7lc0hmYf94RQUnBdaj99n7LnPkFd/fvQF38ZSr1SO2jE8FPosP3+vPflWG0tpd5/K1UfOMD+epuVQRD6sN82NZpnsMZXkNK2qy/L+9yUXbvp2oZGKu3cGxClrXQ8pZ+9SIxdKc+kxCT6ccePtLl5s7AMOq7sOO8yBFOWRvMI03xptF2CMRfZmmYQ5jRGPq+xNZ9Q+E95xDOnSf1ry6bPKbFhMw0dcqyYz4JWTwCA5pjHej/8aM13aBtgFChuAxAIAGCv8jZ12DChvFXCyoaOdeuo96RJEBeIeKqrq6moqMjn77jixdOINi7xjUjO8OdegycTTX/bUtLxIt+Zn8yk8tpyn0jC8s/kxhaMpYXHLPQcwzwa+XCfZbjfRlr/lfe5J7dup7Ft7ZRs49gF8Y2VOQ0AAAAAoddTGo+0AQCIK/SUsqzMVVPoAgAikLr1+m4SeNPO59miI1gWeFHO5qbNXp/EqcHKD76GLS0kywvMo8COPlfqdKq758DYBTb0LzNzGgAAAABCD4KTAQAAALFMwyZj1/FneECVypZKw5Lhz+YAsLPPlTj1QuJh7ILA+pc/MKcBAAAA4QWKWwAAACCWyR1k7Loev7bAl5LMEsNiYV9nANjZ5yp7fClrgrELAuhf/sCcBgAAAIQXKG4BAACAWCZvqDvyOPvDVIOP83m4SdCEgzxxxGD2+agFn+Nr8EkxsLvPVTgc9FV6GvnY3WLsAhv6lxaY0wAAAIDIAIpbADTgoDIchEsNPs7no6n88t/K8gerPtEuw2ATCvmgDdxBhyQiKfBQSDnjWXcQIzX4OJ+3SLzId/6k+SJQjxZ8jq8B0QX3WanfRlr/lfe5Of3yqDw9TXPsYq4HgfQvNTCnAWAd+ZysnJ/la/xA1vuY9yOzXdA2IBgkuFwuF8U4ZqK1gfgM0MC+vupa62hb6zYa0X8EjVjfSZWXzaKu/rnU6+kHaVBujttPZJ/B5NyTQRvOm0bdW7dT2gPzKHHcKHE/f0pWusfpuY6t16S0xTkzgR04mJAsHbPwg4bL7+iXQ6VP3E8dGzZT5Y13kGNAfyp88CGque568UAqeeJxSh02zKs+g48/g6wgrytPK1s/+4Cy5z5BjsJCKv33C+Rw7PaSYcX5F3jKoBfAJ1aR2sjTx4aN8pxjuQj51NRQ0x2zqODoqZas+HzyUPRjO/IA9o7doKfJAcjYl21iMlF3p3aanGfFMqKKcqLmKqL8g4gOv9h//gGW1fKcGUIZcqAe9vkofT4s/a1WXq7Pym0rafW21eLvvIw8Onv42TS+cLwtcrD9GROE/stlrKsqp5LOThpQPM5afsEYVyFI367+LO9zpezvlsewrKxirp91uQgamvj4XfTN9o+ooXY1tWbl0/gBJ1C/6x/Rf96aqb8VWancE6yxLo25BEqgUfmjvNIO+rgyUDZb07Wp35qd08rXvkQpVd9S3/Q8GnrIBVRUOil8czuIGeR9pKqlirZsWkwHJ2fR/sNOpM3JSV57DKt/m+l7gfRZ+T4sb8b5VHvPI549GeWWevZABXfeQXWPP2FpLS6f95X7rI0766n1L9dS0vYGKnnyCTHvYwx6yyzv8llUe/PfbW8XtA0Ilp4SilsQ0VRXVwclXbaqaepoojlL56hG1e3XnEhzX95D+Y1EXZldtO/kHeTo1U3O3Ym07osBlNCcQFtziOadm0Q7sxIoq6uL5u/Y6RX1eW1Wf5qZk0LNSW7Ddv4kja0bslOzRb1ULXta64nemuEdAZ4/YWaLmvRcVZnI0xHp5qaT818XUMVzv5JzdzI5enVS4bh6qvm6j/gt7Oy7iRwlJZR99620/porKKeu3VOf4cMnesppBC059m120a0vdwkZJuc4qOyISo8MK74sIWejU5RBLDYKCsiOviHJQlO+JtLl++V/B5quUmZ3vHMVTV2wQsiHZf/+tWNo7skPU0Z9K22aPp26qqq9+pi8/1jJQ9mPN39ZQp2NTkt52CUHrbQlwmX9puwD8n81URm77cUTKO3clz1j10z+Ii8D84Ht8uU8X7+AaLOGlUHxaKJzX/fN32RZleO3xdlCD617yGse0euPhvtJaz21LzqP0qqWWZah1nNIbY6Qj79rl1xL32z9RvXeTEcmvXbSaz5+JtXm05F9RtKNB90o7pHy0rtueNlw7cpotdPUB4jen21LX5Nk0juvN8377Go6be0HXs9G56AjyXHSw8by0xhX9VPup8LB+1PABDDG9J7Fau2jXAOo3WcV3mhKzw3lXP/bkn6U1JJECUUFNOSll72ft2bqb0VWKvdw+8/pn0efbP/Wc8zs800NrTE3On803TbuNrprxV1+5xd/7WZ1rcrz2z/W/oNW1a/SHNdqaPaNAJ8NeuX1WVP2/GbZ3PTxTDr/p89obHuH1z0/ZeVR8f8tpuycUp88tOr+8LEPB9Te0UI0r1VDhXLcqe2p2FUMf3Ug7amsYmQ8W50HPLTWe+3DktM7iRKIOluTKTmjkyg5jTqbOym5oIBc5KKu2q2W1uIeI4/KSnLkOKhUts+S5n1O952rR1JLbqrX3GjHnBuNyGWWnJVM1Nlue7so80HbALsUt3CVAOIWfiiX15arntuR1S0mat4A8YOvYnEetdY5xL+stOXj0kTO8AJjrGyBwezXvJ3m76jz/Oa8OE9deCG+cYn3Mf795sXGK/bWDHLULaPSKXVCacuLBlba9j+0iV/VCKUtj/z+N1zvo7Tl+hgqpwE5clqSDFlBKJehUNryIsOi0jbaYZl92r5GyIdlz4rVExesoIeenyke9Lz53iZrE8ZKu0h5qPVjbhNlPzabB9Afu6nV5ebGbjDmAyt5ailtmapv1PMPsKy8sVfOI7b0x7dmuNvBYrmswuXWUtpKyoxp709TvU8ph+/qvxPysXKd4XZ6ZortfY3LePraD32ejYmblhrPT2Nc9Vl8neVy+Uvfjv6h1j7BnF/5Ofr0pcWqcz3/5uM3nNnu+7w1U38rslK5h9v/9B8+sF02WmOOj037YJqh9ghWu/G45PFperxG0LOBZTDt58U0WqG0ZfZvrqOq5481VXesNYDWuFPbU/Fv+Z7KKkbGc8DzgGIf1tmWTOQiSk7vEkpCVg4m9U4SylxWDlpd73ssbXMcYl+lNu9zup91fO8zN8brel8uM26HYLSLMh+0DbALKG5BXMKfi/Cb1C5Xl+Y1vdM7e6xW3MrPik/7eSxY+XgvfoNKRKVOp3grrIz5zL/5+ECnU/zmvDhP/iRN85M3tp5Qlol/83H+xNkPyY2bPWnwW1e58rb6q75ErgSP8rb6yr/6KG0NldOEHPVkKN4Mp7RSvCGXmaTc5jYY0Eh05oNrxNtZ/n2brE0CaRej/dhsHsD/2E0wMXaDMR9YztMfyvwDLGvV7iphjaWcRwLujz3lSrBYLqtI488fjR2NtLxmuc99Sjl0U7eQT3VrtaHrLD1j2upt7WvcplUVS2hCW5vPszGJd0pG8tMZV8KCOtD2C9IY02qfYM6vnOevzeW6c31XUr1XfzNVfyuy0riH25/7hbQ2skM2/sYcjzV/7RGsdpPmNx6feuM6kp8NLBsez7ymVds48krlgMatVF2x1HDdsdYAauPO6J7KKv7Gc8DzgGx8yvdhrLztbNsbCLBrVxd11tQGvN5n9whuS1v/a3xL9YlB5DILVrso80HbADuA4hbEJeyzyB8lzk7x0C0c1+B1nH/z8YHsZ67nOj2k6yTYd5gq7JdID/Zp54ekFu+01co/YGSj1+/HTvJ+MPktpwk5+pOhkTrFGkqZsey5DYy0iZV2MdqPzeYB7B27IUkz0Dy18g+wrLVt6gH6Au6P4ZChweeLxA/bfzB8X01rjaHrLD9jbJQTt6m/Z6Pf/ILdfkFK33L7BADnaWSul/c3U/W3Iis/99j57DEz5rTyDFa7+ZvfpHEdyfOa1L/80VCz1x1CUOd2EDMox53ZPZVVtPpewPOAYnyqzcl2rvc5P7NrfNN5xBoaMrO1XXTyQdsAq0BxC+ISpV9BNSodycJPUM3X3v7C+Dcf3+JI9lynh3SdhBT8wYfcQfoF4uATfujK9E5brfzbVuV4/b7i3S7hj1aJZjlNyNGfDI3UKdZQyoxlz21gpE2stIvRfmw2D2Dv2A1JmoHmqZV/gGUtSNd3l2K5P4ZDhgafLxIcDNPofYUZhYaus/yMsVFO3Kb+no1+8wt2+wUpfcvtEwCcp5G5Xt7fTNXfiqz83GPns8fMmNPKM1jt5m9+k8Z1JM9rUv/yR27hyNDM7SBmUI47s3sqq2j1vYDnAcX4VJuT7Vzvc35m1/im84g1NGRma7vo5IO2AVaxZ/YDIEgE07k+OxlnfzVan/nvakt2O3ffndQT4KuhZ7J1H989MJnIQVThcAin+ex/ST6g+B1neXoabXE4xO+khCQaWzDWHZVSzfd03lB3cAn2UyYvU0IS0eDJnojBejIZcMAEotXuNJy7XG5/sj2fzLCP2+plfdzuEhKJih5+hH6adwPl17WLIGKSuwSvcvqhLLtMV456Mty0uIiKz9tFGX3Vnbp3rFunHv26Bz05BNJv5Pcq07GjP8plltPU6Qngxr6UvrjoEDr3zZ2UX1lJt73c5fWZjtV22dXmMtSPzeQRzHEZCQE11PqAbrkMjl3T+duYpk/aevXw5y6Br5Hnb6H+XjKmIppQ4TuP6PVHQ/3EpnYxO9dI48+fu4Sc1BwaXzje5z4tOYweNtpzTO86zfGrJ4+0bKL2Jtv6L/+vuGIyLdv5XxqjcJfQRQmUxAGU/OVn87jyIcD0tfqFv3bUXAMEAOc5PGss/bZkueZcnzS0j1d/M1V/K7LSuIfbX742Mvt806q/3pjjsdayp0V3vBhqNytzhJ/5TT6uDWHDuDD6nJWuY9kUl06mr3b+h8aruEtg9cbPOfl0QOkkn3nA7Nwea0TrWjVUKMed0T2VVfz1vUDmAeX4lO/DRIAySvB8ls++VBOy+lN+TW1A632ns5c74PNup981vlIOZ9UPo8LdKarPIyP7sGhFLjM722XX0qWUOmyYx5e8Vz4ZndRn2G5qWN/LUNvEy/wIzAGLWxC3cGRInhjV6N+SJBRqknN39lGUkecU/7qyXOI4n5fexnGkU15QyPktJ18cl+C8OE9dOCIwL7xltBWMoF9KR/n4DlPzy/Rl1ZdUedw8cuZN8FLaFo6rp+3fZXuUtuxubPu991HmnXOpvk+KUBxK9ZHKKaWn5wfqzd/fpIlFE+ngfgf7nOe0JBkmZid6yTCpVxJ17e6iimnnUuv336tG4qycdbl4CMYiLN+j0w7xKG3Zl9J7146hqy9cKJzZuwoHCJ+38j7G7XL5IZfrtomyXSYmD9fsx0nZST79WLWPsr+udZ8EzR9ozKAydql4tPu4nWny70DSNJBnK5dbC606BVhWtfmY5xW/c2YkyrCnPgflHeR1TPjua3X79uRI8oumLjIkB8+4lI1Ftev2zd2Xrjj0CmvyuPRz2/svl/GtESf4PBvrC0dQ1TkvEZUo+lnfYUTDjvGea/7wd6L8g/xfF0nj1l87BgF+bl76dJWY0129ved6KWDZva+nietMjQ/5/K+2Phk4Rl9WKvd0D5ok+oXdY53vH5M/xuf4Prn70H1H3kd/ytnfM/6Ywl6FdPo+p3tde98Bf6GL0wZ6+dEsyyoTz9NAfEHK+4M0D/wp5wDrdQ7DvMZlfW6fCbQiLdXn3M9ZeVR84ccRMRZA9KHsI2p7Kv4t31NZxUjfU+uzRuYBrX2YUA4mkFAOsgIvOStZ+FLlNx6uAXmq632ei/ytvTf+/i39fM7pIvhVck6y6rzP6U5MGi7mQTmstD3ln7+I/ZbyuRDL+zCpbkJmWcmm20Vrf8xyYnlJ8nT+9DVtPOs0kU9SVpJIc/sPWZR3QIvftsFeDGiR4HK51O3AY4jm5mbKzs6mpqYmysqy2cwBRD088bK/mrq2Otq2e5v4lHDE+k6qvGwW7cnLpj33/J1KcrIo39EqPkFz7smgDeedS91bt1HaA/OobshA4aOMP3cb3aev278Yf6rWd4gnbf6sgt+cVVdXq74N9zm+cwPt3vI11Xx+Gw1r3uG5bm1Wfxr4f59Sds7et3BNHU101cdXiQAQEtMahtMpz/xCjn45VPrE/bR19Rradc+jlJjXl0oeeZSqZs+mPTU1NP+MBKrslyAeHv2aebXyNyo59k8iaqbccoXfPvPDKjs1W+R37ZJrfSKUjsgbQdP3n0779d1P/N70v7cp77ZnaEeWS1jzsoP847P2oQuPuJ2SticIpS11dVFSXh4NeuN18YbS80CtrCRHSYk7IqcyCrYOhuWrck5C7xo9KwYj18gRD/nLZlFX/1zq9fSDNGjYKCFblv2vv34l2iSvmejVi8ropHNvped/el6zTRi1djlkQzfNebObuvv3pcxnHqJBfXJp97a1NH/9G7S04nuvPM656D46MO/AvQVsrXdHrpZbX7IVAW8M03O95BZN1h1W4fr6rSfL7NVpRFu+VpWZ2fwkitLaveaVYCH1P+5nrLiYuLuVZu7aQ7nONuP14U2GhbJyfXvn9aa/Lv4rrd6+WrOf+0tDDW63rT8vo+TmLZTH1m1BkKG8f8jlyGR1ddGDO1to9G6eZI3J0evZkZzpMxbbiydQ/ZT7qT6rm+YunUvrW9Z7zo3sM5JuPOhGoRzWnAt7+tRWZwZ1ZZe5j+Wm29Z/5Xl1ZnZSxabF9O3Pr9Hi3ZupMTFRRA3nQDOaDBxHlOQg2iTbNDp6Ezl3eV83aBLRmf+2VL5gjFslyjVAsNj1yftUedV15Ejv7AmG000/pTjo9r59aNueFLrnNQflNDqp5Ikn1C2olONWY/5vOulBemDp32hH9QrxCS5bvxkao4r0eYwEMtb9yXxp5VJ6a91btKFpgxh/yv72VY8SqDkpUVjjvjrlSSr66Bav+n6X2ZeuyE0X10iMzh9NCyYvsFbG1npqe+1cSq9Ybl8/65Gr1zjWeE6ZeWYr1zPKOY2fDyN75LkqPY1KSifT1cOuFnOOVh7cLt9t+s69VtawMo6XdUU0rVVDiXy+rN5VTRUbF9PByZm0/7ATqYLnm55zjNW/zczDP9b9SLctu41+a/zN67hyHlCOD+U+LG/G+VR7zyOePRnlltKm6dPFPuyJE4j+/JXLsxY/75y5tN/n92uuveXr/T3LyumGN7upLssdZHpcyf70932mUa8BB9Gm+gZqueRqStxeL/Z6a4a457H9++xPc8fNpX2dfVX3W4HuwyIdScHKdcq7fBbV3vx3Q+3CeyR2o6G1P86ob/XILTnHQf322Ua13+ZScnoXdSQkiC9hkgf0obInH6AqR7Ju25jZi4H40lNCcQuAzuTekJlJif37+yxy5J+QBLIY1jv+00P70r6NW30+FWJL3gOu3ruImPnJTPq65muvqL3SJzDXnfWIeDhx+s4V31DSoDIqOfRQuu71C2nHT6todY8rNH6bWFqXQOkTJ4jfWp8ILTxmochP63NEfoDxNVK52r5aRhV5Lp/PTPgatrStuvwK6qqrE4uDwvnzqWbOnIAWC9G2GFZ+VsMyk2TPbVKyw0VrhzooMyVT8zNPubzV2oWVtwMOPJzuP/N53TzkaQlePE37U8zpb8fNBsuU4taPzMzmF2r5yvsG8+TW7T6fK1qtj5H63vHzHbpzj5E01GD5BXuzKu8ftstRpV+5EpKoo2gsXV060Gf+T6REOrTPoXTXYXf5nQu9+tniK23rv8q85DJRlUcg8CYmkP5o47gNGy+eRru+Wk6pWR3uwJ+yT4svy+9P/VoS6ZjOfeimq98ISCY/Zfejc/ukWx6jEsoxYjUdI+mr9Te5bJh/bq+nMa1tXvVVXqO2zomUfmbkeRHIWlWtveRw2x2Se4iYc/Ty8DcPx8u6ItrWqvGKmf2O2nwm34cp1/vyfZh8Lf5yfRsd0LRDd56Ql4vX+GyAI+2z5OVS7vWU86xSSWvHPiwakLeF0XaRLLD1nltCnqceJ6xs+WvX3GG7qf63XtTZliysbJ+/frxnL+avbWJqfQJsU9zCxy0AGrBStklDEcATfDAfZlWbl9ABjVtVBywfZ7cJRaWTxOcaaosKfqgsyv2VpvXaQ9K7ZccYt4UD3/NR2yoi2cOCH/g7ea7QWKBwepzP8urlun4b+Ry/MWdDfnGdyCPBJx2+pvTgg4WlrbRoqJg2zV3OGF4sKJFbPinb0t0mCeJh3djR6HOvXJYeeasg3uS2raIrVa6T5+FpF7ZG4M9j1fyc8sKBj4tPt7w/Y4t7jMgsiNaygaLsf+JzXjWLyCDVp2p3leZc5tU3Ixzb5ajRrxJcXZRWtYwqaSN1K3z+sRKXv8Cobq0WPiaNkNy4OWj9Vy4TTXkEQiDli/JxK69D7wLf9QLLmq0jt2Q6aBH9StOMjCMdmfD6o6h3gZefSbNjVG/dYsdYN9Lf5LLhFcqY3QpLbqX8ZPW1VMYo7mda7aVsO8+ckwGFIIh+/PV7f+tv5T5Mud6X78OktXjpnnbVvZ98nticnOSVn2StqVYu5V5PKpdnDisoFfuteNuHydvCSLtIeyQ1vOTpcFLpEZUeFxnb17gtslmJO3RyHf3SXG68bYzuxSL0uQGCA3zcAhCBNNZ+p3u+ocbtFqGypVL3Ov40SIm/e/T4YccPfq/hPI2WixcF/IZXDv+O1cWCHlbbxYi8jV7n6S8Nm/QT409egTdRLjNl3yhxsr1Z6OpT26bwvWlgLosLOfrpVwN10mcXPkZJatkStPaWy8SvPKxitXxRPm7N9hFD48hinzM6Rq2sW4LV37guRq4JuIxR3M/MrE3MzDkARDK2r6sNpG1kvWB3ubAP898uRhDybNgkvnjhAHFy+Dcf52cJ9mIgEKC4BSACySk4VPd8buFI8S/729FD8u0kx989eozoN8LvNZyn0XLxZyX8WY4c8ZmOMoBKHGC1XYzI2+h1nv6SO0g/MfZTCLyJcpkp+0alIzmk9SlILzA9l8WFHP30K/YzqgX7kjRKV+bAoLW3XCZ+5WEVq+WL8nFrto8YGkcW+5zRMWpl3RKs/sZ1MXJNwGWM4n5mZm1iZs4BIJKxfV1tIG0j6wW7y4V9mP92MYKQZ+4gcu5OpJqvvX3P8m8+zs8S7MVAIEBxC4AO7ANK+i+Qa5TX+jteXDaZfsrJFz7W5PBvPs5uEpiy7DLhz4j94sjh33xc+pRPXkZ/9+idG180XvyrhZSnkXL5OMBftEj8Kz7XUYlyagSj8lU7Z+QaK3kbRU9mHDxFT5bSvXa0iyBvqNtvpOI68ZuP9x1iqt/HAn7raUBmZvMLpXyVfaPC4RABfHzsPyzWxx9j9hljrG/qoBzLcvkFW5ZS2rbL0U+/4qBAWjJTCwCklIP0e8ABE2ztv/K05TLRlEcgBNIfbR63YUGjDixjljV/5m9mHOnJhNcf1SnebnJMpW1i3WIVI/1NLhu+ZkWv3rryk2OpjEHuZ8Fcq2q1l9acE8haKl7WFdG8Vo0XbF9Xq6StvKcqJU3MsXrzhJ3lCsY+LJoJZH8s5OnsRRVflgg3CeweofToHeJf/v3bkn60X9ZY2/diIL6A4haACKX4wo9FIDI5/JuPy+FolpLTdAn+zce10LvHX3r875j8MT5pcpRVeZ566ahFLc047FC3T6U4XjRoyWzR1EV+29iOdvGCI5ay83s5/JuPA3WiXGbKvsFR15VzUDDrY2Uuiws56vQrW2UWxP4rLyfLg4M+6TJwHNGgvb7nBGk5vtfxNYGWL8rHrVYdWMYsa0t9QkMmvP6wo78Fe6z7629y2fCL0aILP/Kp76/ZAzzXaD1P46WfqbVXtM/TAPjD9nW1Im21e8Qez888gX1Y8LC6P/bsaxudlJzjoNIpdZSR5xT/cmCypJYkuvTpKs++FnsxYIUEF3tIjnHMRGsDINLgQGTs05bdI0iWtmqwM3P2ncOfYRi1BtG7x196fH7l1pXi71H5ozTzVEuHo3hWzrpc+FZSOsD3PPxqa6nkice9HMfHC1qyN9LGgbSLKuz8nv3v8aeceLtrjCiXmU/fCHF9rMxlcSFHnfttlVkQ29urnOzPT8qH2dwT/KNswt58lWXh32rX2UGUj1tlHSocyYH3CQ2Z2NXfgj3Wtfrb8o5t9MP2H2hE/xE0vnC8Zn2NPk9NEcX9TC5PJhbmaQD8Yfu62sg9BuYJ7MOCh9n9sc++NqWVtlV9TVuSkykvZSh1zfqb6r4WezHQbEJPCcUtACAs8EMuddgw1UBk/HDrWLcuLpW2AAAAAAAAABAssA+DPEH4geI2AIEAAAAAAAAAAAAAAABAuPWU8HELAAAAAAAAAAAAAAAAEQYUtwAAAAAAAAAAAAAAABBhhFVxW1xcTDk5OT7/XX/99V7XPffcc3TwwQdTfn4+HXPMMfTdd9+Frcwgvn0BSdEglfBxPh9v9Y03mQAQL2BsAwAAACDSwPoEsogG0E+B3SRTGPnpp5/I5XJ5fi9fvpymTp1Kxx13nOfYK6+8Qpdddhk9++yzNG7cOLrvvvtoypQp9PPPP1OBSlAjEETq1hM1bDIXCdfKPSFmc9Nmqmyp1I0CuuHDN6jjutsoMX8ADXnpZXI4dnvq5dyTQRXnX6AaLVKeNvd1eT7SuaSEJKrZXUMJlOA3ajHfs3LbStrZtpPy0vOooFcBdbm6LEf11aq7PDpm4uN3Ub1zA5V0dtKA4nF761tdLa51FBW5I2iqyaSmhprumEUFR0/1qT+w3h/lfcFfv1FLT69fgvhE3if6rqnYGx33oXnkSNnlmcN5ntOa74DOMy8Uz0I78wjnszsK1g1R8cwIshyNPqsivb1tq0eMIl+req03MU4jgxjamxkZi2J/ctks6uqfS72efpAG5eb43Y8p09VaA1e1VNHaHWtpRP8RNL5wvN+1N+/BjO7frNZXuq6uqtyzD5PaTL5Xwz4ssvZhaBsQDBJccs1pmLnooovoiy++oA0bNlBCQoI4NmLECBo/fjwtXLhQ/O7q6qKioiK69NJL6fbbbzeULoKT7aW6R9nGMjR8bW460VsziDZ8tvfkkKOIzniWKD1X/ebWevP3aJRBr6xSGSXMXNvibKF/rP0Hrapf5Tk2ss9IuvGgG2l42XDxu6mjieYsnUO//voV3fpyF+U3ErkyXTRs8jZy9Oom5+5EqviyhJyNTnKUlLgfnAUFnvuW1SxTLUtOag41djSqnjs492B6/LjHKTs123OM07t2ybX0zdZvNOs3oXACzZ80n3bV7fI5p5SLWvmk+zlfXvRsmj6duqqqqSuzi/advMNT381fllBno5OS+cVJAlFnTS05chxUekSlzzVbc4jmnZtEO7Pc41ktr1Ai7wNafcXfGFH2I3/pGcVfm/jrC6PzR9OCyQs816qlx9cwWv0oXO0SbozOM4G2cSSi1k+OSx9Jlz61hbqqa8nRq5NKp9S5x3a/SVTxn1ZyVtV4zXf+MDqmQiFff21tmdZ6al90HqVVLfN+5k19gOj92QE/C/3lrfW8ra5v9RwyVG+bnt2WCGfeOhjpn0HrVxaeGfcefhNlvXtN0ORo9FllR781tVY1KX/b6hHktaoeaukEukaR6qK1js3q6qKFjU46qHm7bv9StotSRmrljMVnrFX89euaDT9Rn8+v933m9LSDmnwT2hupcPnciJtjzax/73jnKpq6YIXYjyn3J8r9WGufDJ909fZfcvi6RVMXUUlmieF9mHIdbkd95312NZ229gOa2NbuOe4cdCQ5znyBnI3tbkV1ZWXU7MMiGTv3YR4jB7QNiMXgZC0tLfTGG2/QjBkzPErbxsZGWrt2LR111FGe65KSkoTF7VdffRXG0sYZvLDeuMT7GP9+82J77wkxrLT9rt7b7Qb/5uMSPIGX15aLBx4/+HiRkNCSQBWL86i1ziH+FYsEfmDKlBjSfVroLRq+b/he3C+Hf+stFhjOT3mfFmrlk9/P9Xj60mJR36SWJK/6dvbUt2zRy1T28svib5aB8hq+V22xYLas8YK/NvHXF/iY/Fq19PgavX6Edok/1PrJp+1ryPmHanL07iTn7uS9Y/tfv5pW2sYNb82g1Opy32feM1OC/yy083kbzmd3FKwbouGZUfn8sUGVo9FnVaS3t231iFG01rHzd+yk/eRKWwbjNOSw0lb1maMzfvieSJxjzax/eX0i7ceU+xPlfkwtXSNKW+m6ae9PM7UPU67D7ajv6Ws/pLEypS2TuGmpaDOPpS32YRG3D0PbgGAQMYrbV199lTo6OoTVrURNTY34d8CAAV7X9u/f33NODU6Htdfy/4A1khs3u9/Murq8T/BvPr5zg/onOGbvCTFVu6uEpW03dXsd5998vKK5QnwCwW/d+NMwpnd6Z8+b3R5lxqf9xL/CIo3fcqa4LUSU91mB7+cyyNPzB+fH11W36ltraJVPul+q+6/N5X7ry+4R3G94fa/he3uld+qWVapjvGOkTeTXaSFvPyt9EO0SX2j1k+I97XRA51Yq/UOd99je1TP+H74dSluVZ16C2jOvrT64z0I/z9ukps22pRXUZ3cUrBuiZuw2bg2aHI0+q0Lab8NZjxhFSz6lTqew/PPxs4dxGlrq1gtL2wQT45z3c8I6N8LmWLPrXz5uZD9mx16MlbfLa5Yb3ocx/uYPM/WtqlhCE9rafMZbErk8bYZ9WGTuwxi0DYhZxS37sGX/tmp+axMTvYuZnJzs5RtXyT333CNMjqX/SkrcnzkA8yS1bNG/oH6j7zH2NWT2nhBT26YeUEtiS/MW4d9GTomzU3yCUjiuwes4/+bjUr2U91mFy2AlvZpW7ZcaRtKT6m6ovg2bdK8Z6Oz0mxcw1iZG2k7efoGAdokPtPoJj31Gc2w7WkJSvqjB3zMvmM9CP3knm5ljw/nsjoJ1QzSN3WDJ0eizKqT9Npz1iFHC1b9A8OZLS/u5CF3/Gtmf2LUX+2H7D6bT0ps/zNTX0HjDPiwi92ECtA2IRcUtBylbsWIFXXLJJT6WtUxdXZ3X8e3bt3vOqXHTTTcJPxHSf5WV9kze8UhX5kD9C9ixvZLcQebvCTEF6fqf+LJDcsmvkUSlI1n4Dar52tsPFP/m41K9lPdZhctgJb3CjELd8/7Sk+puqL65g3Sv2eJINlTHeMdImxi5Tro20D6IdokPtPoJj31Gc2w7M0NSvqjB3zMvmM9CP3l3mpljw/nsjoJ1QzSN3WDJ0eizKqT9Npz1iFHC1b9A8OZLS/u5CF3/Gtmf2LUX40BlZtPSmz/M1NfQeMM+LCL3YQK0DYhFxS1b2xYXF9Pxxx/vdTwvL48GDx5MX375pdfxpUuX0pgxYzTTS01NFc595f8Ba3TmlLkd1yckeZ/g33xcLRpp3lDz94SY4l7FIhBZomII8G8+zpEhy7LLhFNyjqTL7GpLpt+WyD7HOVr2mQ47xN+TIa5T3mcFvl+KTiml5w/Oj68rytAP7qBVPul+qe7Ds8b6ra/T2cv9t8o1fO/uNvVFB+c1rWE4Fe5OUT3PTt05Ime8YKRN5NdpIW8/Kb1DNnRT32b1LxT4OJ/Xyg/EZ7+rSkmjn5LzqeLzPO+xLfm8veoWMUaB9zPPpfbMS+8T3Gehn+dtV3aZbWkF9dkdBeuGqBm7OflBk6PRZ1VI+2046xGjaMmnwuGgr9LTyMcOEOM0tOQNpfbiCerPHI1xzvs5vifS5liz618+bmQ/ZsdejAOUjS8cb3gfxvibP8zUt7h0Mi1LT/cZb10cGbqnzbAPi8x9GBNI2+A5BNRIcOn5HAgBe/bsEREzZ82aRfPmzfM5/8gjj9DcuXPpgw8+oMMPP5weeOABcR0HLRs2bJjt0dqACm0Nbsf1ZqKQWrknAqNHStf8+utXdOvLXSKKqSvLRcOO3KYZxZQdkmtF4zUS1VQtKimnN3vJbFqxdYVmfcxECPVXd1bKbJo+nbqqqn2itkqRSpPZrUkCUWdNrU80001Li6irqUszmikrbU/55y/kKCj0CXLkicRZW0slTzxOvSdNonjATDRTtb6g7Dd83RNPzqATn/6R6rLc7TCh5ADKa2+hT3dtFAsG7tN5zUT3npFIa4YkqubH/pz40yB+gxzvm9l46XfHpY+kS5/aQl3Vte7F5hT2ddtNzn6TqOI/rQhQZuaZd+ICoveuDe6z0M7nrVpaJeOIpr0S/Gd3FKwbouKZcfhNlP3uNUGTo9FnVUT3NTvrEaNorWOzurrp6eZOty9lCYzT0BNDezMz69873rmKpi5YIfZjyv2Jcj/W2ifDJ129/Zccvm7R1EUeC0sj+zC1/Vug9Z23+Bo67Yf3hW9pCeegI8lx5gvkbGx375cqK7EPi7B9mGcva6FtVJ9D7BeeXaSwlTVeZMcUZvSUYVfcvvnmm3TWWWfRxo0bqbTUVynAxbv11lvp4Ycfpt27d1NZWRk99thjPta5ekBxaxPsuJ796ZiZNKzcE2LYiTj7o9FTTG3835vUPvtWSswfQENeetkdiKynXvxmV0vRKE+bkecjnUtKTKLaXW7rtVH5o3SVY3zPyq0raWfbTkrqSKL+af0pp0+OYaVadXW1eFHir+5s7Vo563KhUE164m6q27OeBnZ20oDicXvrW+0OguYoKnIrX1Naafe2tTR//Ru0tOJ7j1Lw1YvK6JyL7qPMlExPXmxp63mgyRTeXg+6CIhcz/Ji5DLzd73RawPpj/K+oNdvWJ4bzjuXXNW1lJyTTGVHVPksHBKKCmjnfVdR8dBDvNLApja+kPe7vmsqPONfBCJjn7Y9c3i8vlgxytaflwn/nPypd/7+E0L7LLQrj9Z6olenEW35Ojyb+yhYN0QSms+MIMvR6LMqovuaXfWIYeRr1a7urr1yMti/zK6j7FpLxQ07N1Ddum98nzl+nlP5jtaIm2ONjEWxP7lsFnX1z6VeTz9Ig/rk+t2PKdPV2ptV76oWPm3ZPQJb2vpbexf0LjC8f7NaX+m67ZVfe/ZhUpvJ92qxug8LB3bswwJpG688+Rn51oyIe9kC4lRx297eLqxu/RWUi8nXpqenm84DiltgBzwJpw4bpvoA4wddx7p1IVViBHsxbKS+jPyamZ/MpPLachGFkz/DL9nhorVDHTS2YCwtPGahTxryxUHh/PlUM2dORC0WwqG4tRsh51OPc1sh9OoUwRvcfsCS3W+B//ORqpzlbSn/fEetLUHsEWnzXbQgzRlMpM0FhnnxNKKNS7yjj/Png4MnE01/O5wlA7EG+lrMA8Vt5Mk4EteqZsD6RFsWsbYPi9e28YBnZMzTbEJx68frdfBJS0sT//kjISHBktIWALvQU1LwpBxrDzez9eVP6uWfl/CnH+LzD1eXOM5vJuVvEaW3kNKioWLaNPdxLBZsxeHYLT7RqVjs9lda8Wk/93H+BP6IrW7rcQXKtpTo0mhLEHvE23wHZJ/jyS07JFiJy8fZyi6CLLRAFIO+BgCwANYn6rLAPix22kaAZySIxOBkAIDoh/2g6sGfgChh5Q+/4ZXDv6EUspGGTcI9Alvaesl5XIM4Lj4xs6EtAQAxAPtQ00NlvgAAfQ0AAMIL9mGRi6V9FdZjQAEUtwAAW5Ac+Gsh+ZJSfqbDn+XIEZ/pIGK9feQOEj5t2T2Cl5yFu4REt48zG9oSABAD5A7SP68yXwCAvgYAAOEF+7DIxdK+CusxoACKWwDixM+OljKUj/P5QCnLLhORMNkPqhz+zceVn4D4OMBftEj8K9wm9AQXAIHjdPZyR9pln7bsHuFojsDb6XabwMf3ZATclgCAGCFvqDvwhWLsi998HG4SAPoaAACYAvuw2GwXo/db2ldhPQYiLThZKEBwMhthfysNm6jKkUKbkhONR+GtW0/bqsppiyOZ+peMizjFD/ueqasqpxJFxE677wkmXB7+FEPZJj6RLWW+KTf+/i21/uVaStreQCVPPuE32BDnsXLbSkqgBNUIqk0dTTRn6RwvPz78QJo/aT5lp2Z7jmlFLfU5/tA8cqTsclt58VTFn430RMHVqm+0Iq8PT8tm6qbVLl7y5EBkR1QK9whsaSuUthywTMOnsN+27JkLfNpG0U4gBpG3Pdo4cuRoV3ptDURvXhydUYzRN3Wx+7kZcHrR3NeCIM+oB+MvavZmUt9lxRHHL4ikPuxvr2HXPXYTT/uwzclJUT/3Bdouyvu3dfxGjbXfUW7hSOqfMsxjhFTyxOPifqNtY+oZib1YXOkpobiNU8xEFBXX5qYTvTXDa+L4Kj2N5vTLo4NKjtCedFrryfnGheTY9IXXfW+PmEq3TnlQe6JS5M8YKa/ZyKo8ic777Go6be0HNLGt3XPcOehIcpz5gurGQeueXYVjqff0V33ukZfJaPnMRCb39yBQezi39smgO965iqYuWEH5jURbc4jevvwQuvyIWynTkemTN+dx7ZJr6Zut33jlPTp/NC2YvMCnHdnJOvvr0Xqg6z0sRXmnn0fOmhoqmbiTehd2+Nz/U04+XZqVTM1J7o8GRvYZSTcedKOn7MHoK1aQt6ManLda+8nRe6j7a5ekFd97y5kDkbGPyj6DhaWtclGhhk9bttb7zAWaRNEG3ArRHpnZNGptH8Q2DpZ8w95udssxWO3CgcjqN9JWZwZ1ZZdFdl8Pcd80QiieMUaxtGEMYXpSX1O+dDC9Vg2RrNXqL61DhpcNj8i+4W9dGchaNaG9kQqXzw3a+DOylop01OoQ8FpVZd7ztzfTW3Mq19JmymkHZvcaVu8JRrnt2Ie9f+0Ymnvyw7bUM9j7MKmf8T4soLk/jATaLvL7XVkuGnbkNo9xzLovBlBCc4KqcYy/tjH0jMReLGaA4jYAgcQLphfDi68k2rjEHVm6h04iKk9PoysKCmhswVhaeMxC35tfPI26NiymJHJ53bciPZ1ePOwU9XtU8g+WMm7mJzNp+ur/0pi2NkqWHe+iBEoaMoVo+tsB3xNsxS2Xp7y2XLwxl+A36PI2UT6cXj6jLx35rzU0oOehNO/cJGrISqJD+xxKdx12l0/enIeeYtFIO6otGlKHDVMNROZ8/CTq+OFb6l3Qpnqv1Pcuy+8vfidSolfZo0lxq9Z+cpRtKcdIu+jKubaWOtat8/uG34sXT/OZCzThT4IGT1YdR7FA2BWAoUat7YPYxjGruLVbjkFul0hSQEZK34w2uRlZJ4QzPS0iVXGrVn9pHfL8Sc/HneK274eXUFp1edDnIC0iYYyFRXGrMu/525vprTmVa2kz5bQDK3uNYOxPzGLXPqwxOzmg9X649mHBmPtDRaDtwvevO/kPbiVtr04R+NkdQyTZrcx95/PgBNzGXiwu9ZTwcQv8kty42f02V/GQZ6UlW5wW7WkXDxN+g+QFm+9v+MxLaSvdN6GtjSorlvjeE0L4c5OqiiWiLHIFLCPKzHXmN1wB3hNMuDwse+UCjH/L28TzRrXHh+yZD3o/lHZmJVA3ddOq+lVU3VqtmocWqm1vAFYWqj7M6taTY8dSzcWCvO8NdDrFb62yRzpa7afXlsp7tZDu0ZRzT78wpbTtGdOGlLYMXxfiMQGChFbbo43DK0e0C2Rg0zohXOlFG1r1l9YhsV5/tT1CWtUyPBtCjcbcr7c387fmDOda2speI1j7k3DtwwJd74drHxbNc38g7cKwewS3pW1P7JBP+3liivDx7XvW2V9o7MXiFihugV+SWrbonh/o5PduJMz+vWA/OH7u87knhLBvnpKesmvCnyUEeE8w4fLoIZcvP5wK58/3Ov/YSe6Hkpya1hpTeSjzCRg//Uat72mVPdIxIlstGYe8XUy2TbjGBAgS/toebRweOaJdIAMb1wnhSC/aiPf6m90j4NkQJAzssZT90eiaMxxraStr2rCsg0OwDwt7PS3uw6J17rPaLgz7tGX3CGxpK4d/8/GGmlX2Fxh7sbgFilvgl67MgbrnOeAYw75avMgd5Pc+n3tCSElmCVX2lF0T9iUT4D3BhMujh1y+/DlHzZw5XueveLeL+jZ7W0QXZhSaykOZT8D46TdqfU+r7JGOEdlqyTjk7WKybcI1JkCQ8Nf2aOPwyBHtAhnYuE4IR3rRRrzX3+weAc+GIGFgj6Xsj0bXnOFYS1tZ04ZlHRyCfVjY62lxHxatc5/VdmFyCg4VPm3ZPYIct7uERBGozHawF4tboLgFfunMKXMHGWB/VfLjPc7Jq1PShH8dHwfbeUPFfez7VXnfsvR0KimdHNZIlGXZZVRcOlmURWlDK8rMdVZE5rZyTzDh8rDs2b+QHP4tbxOlD5/XrzmEtuWQcLx+68vuhxP7tuLABEUZRap5aKHa9oHQ02+U/U2t721xOMRvrbJHOlrtp9eWyntD1i4G28YLvi7EYwIECa22RxuHV45oF8jApnVCuNKLNrTqL61DYr3+anuE9uIJeDaEGo25X29v5m/NGc61tJU1bVjWwUHch0XMet/kPiya5/5A2oUZkLqvCEQmuUcoPXqHx20CH++fMsz+QmMvFrckuFwu79cJMQiCk9lAWwPRmxebilwq3ed8/QJybPrC6763R0ylW6c8GPYIlBylc97ia+i0H94XvnoknIOOJMeZL6hGw7VyTyxEM529ZDat2LrCbzRT9sXEn/X4i5ape51Kf5PzU04+XZqVLKKZKusbbehF+PVXt5C3i4G28SLMUd2Bzai1Pdo4/HJUSy//YKKTHiIqOoziAvTNgNYJgaRX6nQKF1L9isbQ7OOeiMrncLjlGfVg/EXN3kxvzRnuPmxmTRvIPcEod7TtwwJd60v9jPdh4e43Vgm0XeT3i0Bkwtdtt7C0ZaWtCFjWk67kSxh7MRCInhKKW2AODpxSv5GqU1JpY1KCXyWQ/L5tVV/TluRk6l8yLuLeyrHD8e2VX9PAzk4aUDzOkNWTlXuCCZeH/e/I24Sj0TpXfENtt9yy1wG7zBH9pnUrafel11DS9gYqefIJv4GqOI+VW1eKv0flj/JqR6MbGVMbnp7+5vnUTvq77xDV+tqJkQjV/qI0m0FeH8ZM3ULeLnptI/8blraxibzto7SN7Ry7ESPH6tVE711DVLsmfhXrMdA3g4mtz83Wemp77VxKr1get/0t2OuQeBl/0nwctrk4ihFr1bR23b2Z2vNO6rtJiUnU1d0VUX1Yb01r5z2hmg92LV1KlbMuj4h9mF1r/QpHctTPfYG2i/J+DkTGPm3ZPQJb2gqlbm0tlTzxOHWNORh7MaAKFLcBCASAWEJarGVv2ECpw4apRg/lh0rHunV+Fwv+mPnJTCqvLfeKrMqfl4wtGEsLj1lo+rpwE2rFbbCItXYBwA6iYeya5sXTiDYu8Y4yzp86Dp5MNP3tcJYMxCLob8AmoLgNTHaxsFaNdVjJFwn7MKz17W0Xo/djLwbs0FP6ibIEAIgF9B46/LBRe+CYgT/9UPvsihcPfJzfEPMbWaPXAXtAuwAQJ9StV/+kkTdwfJytZmCBCtDfAAAgLvdh7B0TezB728XI/diLAbtAcDIAQMCwnyQ9+HMaM9cBe0C7ABAnNGzSP8+fNwKA/gYAAHG53sceLDxgLwbsAha3AICAKcks0T0v+W01eh2wB7QLAHFC7iD985J/OgDQ3wAAIO7W+/7i0WMPFhywFwN2AYtbAICm3x72z6MGH+fzEmXZZcK5PftSksO/+bjk/sDodcAe0C4AxAl5Q92BoRRzq/jNx+EmAaC/gTCvFUFkEO9tFqn1l5dLWUZ5udTKaGS9jz1Y4G2jbBf+3fr9917nlW2GvRiwiwSXv9cvMQCCkwXJnx5/mmkmcqyVe0IM+6HhTxrMRMm0co/f+3RkpXWfkXIor9G6R0TKvGwWdfXPpZYFc8iRsotKOjvJmV1CK6vX06CbnyXHjmZqvmMWFRw9VdxrNFKp6YimcYC8HXhK1mpH5XUrt62kBEqggl4FVLO7xvM3+7SS7ke7ANuJgrk8LuXY1kD05sXevm5ZaXvGs0TpuRRX/Qd91Nb1SjT3N1vrHIb0oyoqu2O3Z2w792R4RVUPNPASsGfe82kzmQ9PbqtIajOzY8vI9Wb6bN3BA03tt8zuw+Tr/b5rKjzlyrt8FtXe/Hdy9Muh0ifuJ3K5aONl11LXzmbaPfsiSvn32+TY0US77vgrdYze39R6H3sw8/swqc8k5eRQV0MDOQb0o9I7L6OOLduo8vaF5EogSuh2UUJOJnU3t1DGvCuoe8Th1PqXaylpewOVPPkEdY05GHtkELCeEorbOMZMFFfPtbnpRG/NMLdQb603f49OOYxGRDV6LV/X4myhh9Y95PWwG9lnJN140I00vGy417UMp2v24SeVR+0+Ka+sri7q8/n1lFa191x78QRKO/dlakpMVM3v5jE3010r7jL9oM5JzaHGjkafe5g73rmKpi5YQfmNRF2ZXbTv5B3k6NVNzt2J9NuSfpTUkkRbc4jmnZtEO7MSvPJjB/jsS8nfQsvodXYgj6irRrii7Kq1ixx5m+hdp0Wkt0u0Y2Y+iglsnMvjWr42yVFTPhyIjH3a2qgYtaUtQtF/QtxH7VpjBZugbtaD0N8CXqtaWKOZRS/9XXW7grJWZeTXytc2aseNXGumPMq1VPf27bT72tnkqq2l5OxkKptU5VkrVnxZQs5GJzlKSnwUhMB/G6utWzXbzcS851HOVlZ6tY3W8XBgduyaud6rnjkOKj2i0qfPJhUX0dOXFtNHbasM7bes7sPkHJc+ki59uoq6qqopOSuZqLOdOluTKTmjk8hF1NmWTMnpndSRmEBJu733YMr8jKzjsdbX7jtyWK5373Mt1c+4QvQZ8a16N5GjVyf1P7SRqpf1JaG55UaiBHG8cFw9bVzR17NXfv/aMTT35IexFwOqQHEbgEDiCUuL4cVXEm1c4o5ULcGfZQyeTDT9bfWbXzzN/D065QjGYvjm1TfTmoY1wlJRIpES6dA+h9LzJz3vdS3D6c78ZCaV15Z73cOfpIwtGEsLj1moWR61+6S8Fm7bTqnV5ZQgO+dKSKKEwZNpZn5/1fwyUzKpZU+LbjnU8lQi3cPwtTlNnfTYv/eIB4/7QdRANV/nknN3slDmXnF+imfBoFfvSCBSFbf+2kXZJnrtp3d/pLZLtBOzikUtbJzL41q+NskxlPKxJa9Q9J8Q99FoUdyaXa9EElYVt8Gus176c/efGxeKW0l52z7zfOps6vRZKwrF2H8+gtI22Ipbk/OeUklbOH8+1cyZExFKW8bs2DV7vaj/qce5Xyyo9NmF1x5Gn7avMbzfsmMfxtcfnXYIzVywWpSLlbSsCOxsc7s+SErrpIREEspc5R7MX32Bub6mbBeW66MH3urpM5TgcitrpX97lLb8u2h8PW1fk+21V27MTkbbAFv0lPBxCwyT3LjZ/TZXObnxbz7OVhdqn+2YvSfEVO2uolX1q3wm7W7qFsf5raTaJxX8dk55D//m42r36N3HedVtKxeWtnKlLSN+b/iMKiuWqObHVrN65dDKU4l0j3Rt7/TOHkvbTvEAqvi0n3tR08t9vJdYVBirNzDeF/TaxCxoF2AbUTCXRwXxKsdQ1DteZesHq+uVaCbYdfaXfnWr/sviWCIlpbXH0tZ3rSisGVNaw13E2MbCvOdxE1BSIpS1FdOmRYzS1uzYtTLW2T2C29JWvc/+0lxuar9lxz6Mz/3aXO4pF1vYSkpbcb49WSht1fZg/uoLAt+Hbd/zu6dt3MpaYVnVc9VeJS5b4Cr3ymgbYBdQ3ALDJLVs0b+AP5VTwn6DzN4TYmrb1B3US/DnJkrY943Ze/zdV+L0fggrGejnvFY5/JVVrzz8+RC/iZbDv/m4Wnm06g18sdouVkC7gICJgrk8KohXOYai3vEqWz9YXa9EM8Gus7/0a1prKJ72BnprxXgddyHD4rzHylm2tJXDv8Pt0sLs2LU01hs2md7fWMXMPkxr32WmjLE4n0fCPqyhZrVu2wwYudf1oFY7oW1AoEBxCwzTlTlQ/wL2b6Ykd5D5e0JMQbr+IoV9BCkpySwxfY+/+yodybppbvFzXqsc/sqqVx72+cSfD8lxf06UqFoerXoDX6y2ixXQLiBgomAujwriVY6hqHe8ytYPVtcr0Uyw6+wv/cKMQoqnvYHeWjFex13IsDjvsbsAdo8gR7hLqNU3Zom0sWtprOcOMr2/sYqZfZjWvstMGWNxPo+EfVhu4WG6bbNtVY7fdkLbgECB4hYYpjOnzO3sPiGJdtWkuhdkkh8lPt4TlIIf+hyBUZA31HOPF4p7wklxr2IRHIz92Mhhv7N8XM2xe1l2mXBYrryHf/NxLWfwWvdxXnkDxopAZOzTVo74PeQoKimdLO47ZEM39W12efLjIGNSenycz8vLIeV52Eb3eTX4OJ/n66Ty7WpLFoHIPJ8PHb3XbQIf392WbLjeZuC+o7Vw9OpbUQ63y7SG4dSvJdFwm5jFznYBcU4UzOVRQZzI0Wcel9Wb1w68hrC93nEiW7NYXa9EM8Gus7/0izLC79c4VOzZk0GblxarrhVFsKc9GeEuYmxjYd7zCUS2aNFetwl8PIzKW609i3zsyvcCVsa609nL3Tc1+ux+WWNV05Pvt4wcl+/DjKz3h2eN9ZSLfdwmp8t85qZ1ikBlanswf/UFge/D+qfs42kb4RaBkf6lvT5viybs9PSl3z/tTynbk1TbJpb2syB0JLhcLnVNTgyB4GRuPy78SYCIMLnH6f60xkoE4LYG2vWPs6jy9S3kyOii0il11DnsYNo8/FjKKp1I/VOGeR76JU88Tr0nTRL30JsXe0U7bS0dT58cfjZ1pvSmUfmjwv6gUYsqOSR7CJ2xzxl0RPERquVTu+ePmfvQmf1GUcHACVRUOkm1Dfqk9aFHv3vU677D+h9G5ww/hw7oVUQlH93qHRm2ZBzRmEuppe9geuy1u+jEp3+kuix3NNFJpSPoyrIT6dHN79PSiu/p1pe7KK+Z6L1LD6QrzvwbZe6uE+384/LlRHPuph0997FDe15osF8mfjDxff2aiWj+36jk2D/RHe9cRVMXrKD8Rram6OrxdeuOusoLBilSppQWl/+6UdeJ9JRRTL36np925odY5azL9/rfcuz29FVe/Pv0LS1/X1b7dwiQ5FH04zZqn30bNWQn0c3ndHkFGdjbJglUd9sl1G/KsT59xgh2tYstco7wdgEGUJnLtSJXg/iVo+Y8npFHzv/eQhXP/UrO1iQqOaKeeh8xMbB6K+eVGJdtqKK0RxPKZ5neWovrfPkhl6s+E83mxWuoWJWpUbwUgByITPiAdK8VhZKDgz9FgN/UiEY2h21OTrK2LlOZ9xqKD6P/jDiJhhePpfGF47WVtj1ts/H3b6nl0qsoZWsDJRQV0JCXXg5bm2377EPaceVs2pHlEvsMjrnxh15ldPj+Z1FJxkHUNetvXnsBtflt/z7707jCcTS6YLRP/TecN41c1VspOSeZyo6o8vTZzV8WU2djJ7kKB9BzM8voo7ZVXuvpk4ecTO9seIdWb1/t9zjPBfcd8BexD9v1Wz1tmXOH/nq/iciVk0nJDS2UnJVM1NkufNqyspb1guzzlpW5HYkJlLTbew8W1PV+DK71ze7D6q85h/q/+jm5qmvdJo/dJJSz/Q9tFD5t5QHK3MHu6mnTsjxKbEukzkSil6/cj24+ea5nTy7fz6bcdwvVHJQfur2Y1XtAROgpobiNUoxG2ZU/zLK6umj+jp00sa197wVDjqKaCXeSq2eR6S89zpcjyLZefRV1b9tBlOmioZO3eR56674YQAnNCeoLtZ0baPe2tXTn74vovaZfvNIdnT+aFkxeYGixazQyrt61asf52G9Nv9FDvzxEm3Z5+4w6OPdg+vuIv9PwsuE+93dmdtL6mm+p34c30Yim7Z7ja7P6U+rUl2lA2b501cdXiUBnEmzJe92466iqpYpe+fUV34f9gTMpc+dGom+eJtrytefcrqwxVPFOGyVuq/NZcGxaWkRdTV2UUDiAhpyeSY4de9/kLe/KIOcnOdS/kai+TwplP/MwHXjAZFqy9C1KvnEe9a13ehYBw4dPpHmJJ1P9X6+nrv651PLgHEp27KKsHXXUnllIP2yvoAPufpVSdjbT/DMSac0Q3zeVXIebx9xMd624y9SmxsgmIKGggHoteIAS+/f3tB+3R0J7IxUun2tpw+4veq+ZPqeVbu+83l4LS2lRwMpxlv0LlwyhC8bNpt6N7eS48Q5K3d7gtTBj2V1x6BXU0N7g+dxm5daV4t+C3gVUu6uWWp2t9N6m9+jnnT+rlsdqu3jRWk/01gzjcjZ7PYh8ONgJ+83Dos92OapFdDcadd7stcFKw5AyJ78vlT5xPzn2H2stE3/zCvqoKhy4hv3sWd4kBmmtaiYtKT01ZY30Qlq51uLnZm5aLj323WOWnn16im/OT02mwVyr2nmt/Lxy7PubC5QvaXY2bKTk5i3UmTWQnB0ZtPva2eTascOjYLNjLWVGllbysgM9+XrK01pP7YvOE0GJJb5KT6M5/fKoOSlRtW/6q9vWn5dR8/Y1NHfTv+jnxA6vcbFo6iLxabiyzVr7ZNDl/7ucvm/83rMuZQOQdy7Zj66c9S+fsaFWBmU5AkX+DEnMTqTBk2p8jEeSioto0Isveu0zeX5btXUVPbDqAWre0+xT/6yULHriyRkKA5iD6crSqfRoha8BzEnn3aq6T9NT4gojnIxCKvn4Ns/zSWn0sujy4XTVH++gXg3t1HLJ1eTYupPqexFlt5EwsPn+xH3puFfWk6NfjnhOMhsvu4a66ppp93UXUcoLb1PyjiZadOFA+n/9q4Kz3o/Btb5yHlfuw9TahY+/OTGRZn7QTe29HdR7dzc5BvSj0jsvo47K7VQ570m3sW23ixJyMqm7uYWS5s6gfzV8Rec++gsldxN1pXfRvsKq23sd1JiXRjed5fQojIO6F7N6Dwg6UNwGIJBowehieOYnM6m81h0d88mt22lsWzt5fVyRkETtRWNp5x+fMZSelC8rb1v+co5bSSveLjX0+HNJJleWi4a987nqm1ouj5bVIE9YC49Z6K/qQV0M37z6Zi8FqxzeADx/0vOq9//00L60b+NWL9myO/JfsvrT4wcdRV/XfE3d/IpO5hqB3wQzUvtI8CcVYwvG0sKt24k2LvGKFMtuE3ZnjqLa12qps6nTR/bJ2clUdn4ZOeqWed3HZfmmK4M6PskRDyh+WBz8yHO05oqLKLe+w0tBKOV/f+o0Sh02zNOO8jr/Y/F11LzuR1qt8aKO08hMyaSWPS3qddNpZ7FgO/U49+ZepX5pC//tUdpK8uey9f3wEkqrLveOrMufLg2eTDT9bc38lHWTp6t23upm446f7/Bpa/miYVsO0Sfn7k/T324gV22t+H2b7G26EdnJx7vd7eLhxdN8+qWunM1eD0AcEwuKW3/zuFDm/uejwKy5MK9EBcFU3Pp73snXWvxsU7ve6LPPyr3xoLhlWBEorRWV6fJeIbelxfOFFBS3svHw4mnk2riEEhRr9fL0NLosv79q/zKyVj1zyZnU3LlXaSlXXn559pc+babcl/G6tGSHSxhlqO3LQqG49fcM4S8Bn79+PN1/pntPJueIV4/wenkjr/8BfQ8Q4/ig9U6q7Jfg2fPI18VS/dcOdQj5M2pjX3ctrbJ/a9udSBt6lLe8vv/iokPo3Dd3CuW0tN7nfLlcjdnJdFb9MLrurEc8z0mWR8e6dWIs8d/3v/ZXeq3PuuCt92Nwra82jyv3YWrtwv2E3RBW90+mYzr38WoXHkuJ2dnU3dQkxhS30XUdi0Q+g6qcdMcbTkpqS1LVlVxxfirtyOwOzV7M6j0govSU8HEbw/CnAPww5gmq1OkUlrY+rsxdXeJtb1LTZlNp13dtpGFHbtvrE+jTvb5Q+fj2Pes0y6MFn+O3peGianeVptKW4XNq5avavIQOUChtGf59UPN2qqxY4qW0Zfg311dqHzn8u6piifuNmOIcL/B6N6+gsklVqrIXx9nSVnEfl2V8Uis9c6ZLKGlz6tqpYto0H6WtlD+Xa+chpaoba5bTkm5tpa2UBi+c1Ormr535s1q3hZZG/VJbfe5JbtzstlpQLmD4N8uRra/CCMtMra1Z5ix7boMBjUTnPf6zUNpuVShtjchOPt6D0S6ez2tU+qWmnM1eDwCICfTmcXE8xXceNwzmlbjHyPNOvtZaXr1cc73l79mnlZfh52aMw4okrZcw/JJd061VPNMzh8mVttJanfdqA51OS/1rZd1KVaUtw2u/5TXLvdpMbV/G607pS7pw9m+9Zwi7b/uludynbMuql6kqbRk+Lo1jrp98zyNfF0v1l+SvNfa11tJa+7f0Xt2i3HXZLrHeP/PBNUI5KF/vS+XidBbl/ko1vfbslUdBwd4XIBkd4nzQ1vsxuNbXmseV+zC1dmG4bVjJqmwXbpOMgw/2jKm6gwd68nH2lyxt1XUl6Wl7reKDuhezeg+IOKC4jWHYf4tEiZPf42rDnzaZYffOH4XJP789ksO/+XhDzSrd8mjBn5uFi9q2Wkvla6z9TveegX5kr4a/9tKTvR7Z6Z302EneDvT5t1xB6K8tjMgpoHZu2KRbP7W+mtTip9/w58hhRE9mLHujbaInOyPjK+Dxxz6RzMjZ7PUAgNjAzzwe0NjHvBL3mH3e/bDjB8vPPn95hXPdCqIUP3OYfN9gpn/91vyb7vkftv8QPfsyP88QlpGybGt3rKVwo7d/43IvPrYr8tf7MbjW15OZXfswZT7cF/z1YbPpW5Z3FLQR8A8UtzEM+zKSqHT42Np6wf6ozNCr74HCTwub/MtxfwKQSLmFI3XLo4XkuzMcFKQXWCpfTsGhuvds8SN7Nfy1l57s9WhqS6Yr3vVeNPBveeRWf21hRE4BtXPuIN36qfXVrkw//YZ9SIYRPZmx7I22iZ7sjIyvgMdf7iBzcjZ7PQAgNvAzjwc09jGvxD1mn3cj+o2w/Ozzl1c4160gSvEzh8n3DWb6175Z++qeH9F/RPTsy/w8Q1hGyrId1O8gCjd6+zcu95SPkyJ/vR+Da309mdm1D1Pmw33BXx82m75leUdBGwH/QHEbw5Rllwn/ROwzpcLhEE7vfd7tsI/b4gnUlV1mKu0+SYNFIDLPp4+yTwH4eP+UYZrl0YLPhSJYhhbFvYqFH1st+Jxa+YrLJtNPOfk+su3sCVBWUjpZ+FmTw7+5vlL7yOHfxaWT3Q7DFefYxy0HKNu8tFhV9uJ4v0k+93X2BCi75PUEj4/b0kWLqKFPqvjN/n2kBxTnr9cWkpyUdVLWgf1JqdXNXzs7nb3cjtu16teR4XNPZ06Z6MfKeovfLMcwR81kmam1tdK30kuX7y+Cr/Hv22RtYkR28vEejHYR5A1V7ZeacjZ7PQAgJtCbx8XxPb7zuGEwr8Q9Rp538rXW+KLxmustf88+rbwMPzcB0JjDeE2vXKvzXm2Lw2Gpf43KG0VZyeo+EnntN75wfNTsy/SeIRzoa7+ssT5lm1A0QdRTDT6uNY711sVm79Hav7GPWy53XlOCWO+/fs0hIpB3RK73Y3CtryUz5T4skHZR5rOrzd1XtXQlbe2podmLWb0HRBwJLpdL/XVCDBGLwcmsRFDM6uqm+TvqhP+kQKIJyqN9ikBkwtetO1IiT0QiYFlJiYhWqvR7xeWZvWQ2rdi6wuv46PzRtGDyAmORFIOI1fI1NVZQ1fPHCl+3EqzMLb7wY6L0HM1oxIzWuezubqI3L/aK/shK2Yr/tJKzqkY7WndxIZWemuH2ddsDK22dn+RQ/x6l7b4vv0Z9Sveh+orf6bdzzxI+byVft8OHT/Qb1VItwrJPNNOxN9Nd5eaimRqKRq7Rt6itwUdekRQt01800/evHUNzT36YMupbadP06dRVVe3lf9hItNFgtUtAco7wdgEA2IvpeZx9r/FnfGzxobd5kF+X0QfzSpyj9rxjRYXcx6X82aZ2vd6zj30i8mevbP3E6Zq5FwC/qKyNWGk7p18eNSclWu5f3GenvT/Naxxw/100dZGq1WEk7svkz5DkHAeVyZ4hv/UE+EoqLqJBL77osxfQq39WSpbqONZbFzNm71Hu3+Tljpr1fgyu9UOxD5PyueOdq2jqghUibQ6mx/6Nlesg3o/fdJbT45IhqHsxq/eAiNJTQnEbJ7Cja/aZwgvQUvapwr5M/G2SNOAIipWzLhcPS954cSAy9mnL7hHY0lY8bGtrqeSJxzWDEnB5Vm5dKf4elT8q4iwWrJavumKpRxZFpZO020CRnt454TC8p712/VTtJXsR4KXnHFswecn+gCLPuY3ffkfts2+lxPwBNOSll70WOnz9hvPOpe6t2yjtgXk0+PgzTMlJKjejVgfduvnpW7r10wp4IZNXJL5BlORR9OM26ph9G3X1z6VeTz9Ig4aN8l201tRQ0x2zqODoqabGiN3tYoucI7xdAIg3jESMt4LhefzBf1Dvrc/630S01hO9NUP9Oj6HeSWuUT7L/D3b/J3XU/CyMiig5yYAOmujCkeybf2LA5GxT1t2j6C0tI30fZnaM2Rb1de0JTmZ8lKGUtesv/ndC+jVX2sOsLJPM7J/2/VbPVXOuSM61/sxuNYPxT5M9OHLZu1Nu0+u6joo9f5bqfrAAaHbi1m9BwQNKG4DEAgwPiGlDhumGkmWJ6KOdesQSTbCZB8tbRYt5bSDeKorACA+FLeG57aKh4g2LvGOcsyf7Q2eTDT97b3HXjzN2HUA2MDMT2ZSeW25V/Rx/oR1bMFYWnjMQsgYgBAQa+vjWKtPrBDMdkGbAyNAcRuAQAAAAAAAYp1gKm79wm4PHtP2KU9XrnZbghi9DgAbYPcIJ/33JM3z7536HixtAQAAABByPSWCkwEAAAAAgNDBvmr14M/4zFwHgA2wf0w9+PNaAAAAAIBQA8UtAAAAAAAIHbmD9M+z7zUz1wFgA2rBm+RIfiT/P3tfAiZFde3/m5nu2XdmhlmZASTighsqEBRxi0mM768+YyK4ZDEGwcS4BQ2iEiFK4hoViYnJiwrRPLe8mLyXuCEEwQ1UNILIMszKzDD7SnfP/L9ze6qmuvrW2tU9vdzf980HXctdzjn33HNOVZ0jICAgICAgIBBJiMCtgECCgXLuUN4eHug4nReIPARfBISsxRbEmg0BRUf4C4xRrlol6Dcdl9IfmL0uQSFk0FnU5NWwQmSU01YJ+k3HRUEyAQEBgcSDmb1W7McC4UbSyMjICOIcIsdtYP6utvqtqPJ6MbFyTsI7PYmG3r8+i7pb74Z7Ygmq162H293n/xRVXe2bKrUeXS6f4znHJEv0WWHEqitTrkOd8cQCtGi253//G0M334Xk0omY+sw6Tb6k/uoONM4o1aV5xPkyTn0KOFStWS1rCxfAc7AFVavvQPY3viXIbJOOey5fgOHmFqTfvwJTvnpJfNBRrYND1ckDHcDz3wf2vD52jIKxlzwJ9B0aazuzUPu6jAIkKizt5zFW9Ia3p4Rzn1G2nZ+Wj6Ubl2Jz42b5PAVtV89bjby0PCQCxJ4+PjQX/lnirS2ttWZmDUZ6ncaDXjBLb+XvCR/W6tvNtNc2NLB23BUV2teEez+OAz85EdFtIcetCNwmCLqGurDi9Z/g4h1/x2kDg/Jxz+Qz4L70jwnt/CQE+tuBF66G5+M3UftGETx9LrhzhlE9vwXurGF4+pJRu6kKnk4P3JXlqL4oE+7WjVwnmWQpok7N6Nhj2WnXotmyWcuw6p1V2LnzX7hznQ+lncBI7gimnXEwiC+dRem47VseHMpN4tI84nwZpz4FQgMZjsyArKuDO9+N6tPrxmRtQwk8PclwZ3lRfVYb3MedGVPrLFrouPutiUjqTkJzPrBiYQqmTz8tttcETwdnFAID7c7o5EN7/Llqydmg+7X0PY1Dui6RnRIr+3lVld+J5FTMjkbw9pRTS09l/77b/K7j+4zeHtY51Mly2sZykMIqxJ4+PjQX/lliri16SER6hucX6NnVkV6n8aAXjPwwPb6clzET1zxRD199Q7DdPLrXumiPTQK8jU2a14RtP44DPzmR0S0Ct/YJEotVoSXoVYde9OoiXLHtZcwaGIBLcdyHJKRMPQu44sUwj1RAyS81r7SOO1b5++mLgb0bgBGffxORnL0sL8rndKBxSwH77cpzoebKGrjbNrNrZdBng1PmMzkhWdratBU+xXn6jHB22WysPXet84xWjJ03nqigrwGIZlsat2AYwwE0y0nNQc/hHkbLCd0jePSpw0jpSQniCwVzr7syDa05w5o0jzhfxqlPp6AnK+GWh6gIOl50nt+QVMmaHLTNGtZcZ2aQCPTVo6Mvx4frrkxlD1qSkYw55XMC1kSk6RBSfzwdrEYIsmLYl1NtO0Azvb0kYjC5nzPn8aV/REXQ1oqtqt5TeHBqn4nlPSwcMKLHeNtSUj9aY4gGmKGDcg5m/bNI01can4AzMKPb1H5BtNj78aAntebAo7cadN056Sdg0QPb+Hbz6F5L0LStw7kfR5HdJBDeOKXIcZsAoNf962s3YK7KKCCkYMT/hIbeeBGIT9CnE8TjUYVOARl/YMbLNpPa14plp69mXr3/TVv1Bka/97yO+v1vsaeS6g2OftPx2u7asI5dPZ5YkFtaf0QbZdBWohk90ZVomZ3hxZHzW7l8oTdwM9KHNGku9RExvijmFck+BZwBfcLlfxsgWNbkoG2MrbNooyOt5awML7uO1n7MrgktHayGE7ISB/o+mvZzJpup/YgVaO0pPDixz4g9TNBjvCH8s8SAWd2m9guiwd6PBz2pNwcevdWg8zu7t2rbzaN7ra5tHa79WNhNCQURuE0AUI6WKo/fgdQEfX4oEJ+gfDcqkLNHTwKVoN9ywEYDnU3bdM/TZ4XhHnusyS2tPzOgNarHl0kaa5hobtSH43wxMa9w9CngEDr2WdMBMbDOopGO6jUbk2vCSAc7KStxoO+jbj+PIZqZ3SudWlNiDxP0GG8I/ywxYEe3RYu9Hw960gn6G/lobK81sq3DsR8LuymhIAK3CYCqnCrUudXv2qpAOeME4hMFk4MO0eeV9PmGEv7POfRVQn7ZSbrnKRdcuMcea3JL688MaI3q8eWAxhommhv14ThfTMwrHH0KOISCydZ0QAyss2iko3rNxuSaMNLBTspKHOj7qNvPY4hmZvdKp9aU2MMEPcYbwj9LDNjRbdFi78eDnnSC/kY+GttrjWzrcOzHwm5KKIjAbQKgJq8GldXzsTkjA+p39iiHEktgnciFPuIdRUf4eUz5bkadPGVOvOpzxj7P37+xEp7iefK1Muj31LNRWXMGS+ZO+X6UoN903PECHqqxq8cTC3JL649oQ3ku1TSjBPgSLXsHXNi1oZjLFyp2NDCYpklzqY+I8UUxr0j2KeAMPJ4sf7EEjqz5dUNyzK2zaKMjreW+AX/gltZ+zK4JLR2shhOyEgf6Ppr2cyabhzMRK9DaU3hwYp8Re5igx3hD+GeJAbO6Te0XRIO9Hw96Um8OPHqrQeen587WtptH91pd2zpc+7GwmxIKSSMjIyOIc8RzcTJLVUvfuAEXf/w3nDYwKB/3TD4D7kv/KKoOxjsGOoDnv2+uCnVlOaovyvTnuuVUp4x4ddHRscdytUzNaqazl2HV1lXYufNfuHOdD6WdYIXIKKetmi+dRem47VseVuwoGqrMjlefAqHB8+kW1C65BZ7mQ8GVbzeUwNOTPJbr9rgzY2qdRQxtX8Dz+TbU3v44PI3NQXSkBy1J3UlozgdWLEzB9Omnxfaa4OngjEJgoN15nRwH+j4soDx29EkkvbGTWWh+Pw9XFeswgbenzCqdhRGM4N3mdx3fZ8QeNpYDkj4nLkwvxCPbHxF7egQh/LPEAE/XUNCQcqzy/AI9uzrSeise9KSRH6bHl/MyZuKaJ+rhq28ItptH91oX7bFJgLexSfOasO3Hwm5KmDilCNwmGCiJeEvdFkzyejGxco54gyXB0PvKc6hb+nO4J5aget16f6J0yrlTOIU9Cay98ipWKb1qzWPIPqZCPsd704lkiXIb0WcyEXniSoVpdMYTC9Ci2d7/ex6DN92J5NKJmPrMOk2+pN13JxqOnahL84jzZZz6FLCI/nbghavRu+lfqNtUCHemD9Xfmw73hSuA/kNjsrZwATwHW1C1+g5kf+NbgswcGlJQsbcxzU/HvFRU/+nPcOe6A9bsnssXYrj5INLvX4EpX70kPuio1sHh1MlxoO+dljl1ILv39X+a38/nzUMsgbenhHOfSdQ9TCuYcd2J16FjsCPh6DGeEP5ZYkCta7R0jxmdFGm9FQ960iy9lb8nfFiLusVLWMCVBV55e21DA2vHXVGhfU2492NhN8UkROA2BIIICMQ7ejduRNq0adwnfrSpDO3eHXNOXjxA8CV8aGhoQEVFBRIaT18M7N3AqtFT0DEtzwN3dhIwZT5wxYsh6YCEoa+ChgRGx/xhuGfMC6AhQejS2AHJLyEqZVglcwz0WefouhX7hkAoWPTqImxt2hpQVZ0+C55dNhtrz10riCsgICBg0kcjCP9awCpE4DYEgggICAgIxBcSJrCo95n1ozO1z/9oW0hvNSYEfcNMQ4HxQ9QGboXMCYQ5PcIFL1+gef6Vi16J2bfqBAQEBAQE4i1OKYqTCQgICAgIxDMoN6Ye6HMuAUFDgeiCWLcCYQTltNUDfSYsICAgICAgEB0QgVsBAQEBAYF4RsFk/fOUR1RA0FAguiDWrUAYUZVTpXuecjsKCAgICAgIxEHgtq+vD/X19UF/AgICAgICAlGCoiP8BY0oN6YS9JuOi0/8BQ0Fog9i3QqEETV5NawQGeW0VYJ+03GRJkFAQEBAQCDGA7cff/wxTjzxRGRnZ6OqqiroT2D8EmdTgmwe6Didd6p9dV/K9pX/D/eYBMIHM7yzyt9Q5UHIU3gQ73SNutyV44FLnvQXNFKCftPxEOUhYehrgYYCsQOS36iVYSFzAhZhRX+vnreaFSJTgn7T8VDaDeUeAQEB82vHyhoT6zGydNDqh473f/QRtx+hFwX0kDQyMjICi5g1axaqq6txww03oKCgIOj89OnTEU2IyuJkVHSC8pfRJ6r0tpP6t8nCApSjij5nmvBhLequXQxfSQGynngQSSVF8rnyvlTsuXwBhptbkH7/Ckz56iVyG/X7N6CzaTsKymfCkz8J7x98H0lIwsmlJwc8bSflQu27i/NRdPWVaLrn1+z/1WvuA0ZGULvkFnhaO1G2aiWaH3mY9eX+/kJ4n1wvX+cuKZLn6DmcGTCm5Dkny+O18pRfSQMSZTttWO3H7vgsjcmGPOi1sd+VYmkcjN+Ll7DqmcmPrUK7Zw+qvF5MrJzDeFd75VXwjBZ0cVdUoPqpP8Lt7gvgL7umqQmpv7oDjTNKUfHJQQzdvIK1qXd91ZrHuBXtlWPSvf+eZciePiE02jlMUy05DUVmqU2t9arVt/Ia6XiofNGjUQD9nZBpK/1Guj+j4+Hun9cv79ihPf6cthrjC1pnigq6JAeOyYOGbNrWmRZhtm/ueAxoGG59bqUN2/OMlByPA7g8jfF1a9jPONo86mOJImvjod/2/O9/Y+jmu5BcOhFTn1nH388bG9F192KUnXO+PIba7lqW01ZrXKbtL8W+YOeeqIJSLslVDsFHU8p5vTsVWw+3yrablh1o1j/TskvV7QaM57DH8B5b9qlqLYdL3q22G3EfLkSfgbd2DtZvxQG3C0WpR8C3+Gds7RRdey3aHn/ccI2RH9Y60Iq85WvgLi93xLbTpE049blN+dKLl0wuyLetl/T8MC3917urHXVL75avq7r3dtln5fnOTsheEB8i7B9Fyq6PVViJU9oK3GZlZbEqvPn5+YgFjGfgNqhacX878MLVwJ7Xxy7KKAQG2sd+Tz0bjXNXYiQtTz5E90tt9Xh6cO+Oe/FB+wfy+cmDObjhDx0o7QSa84EVC1NwKDcJE7pHsGL9CEo6huXjNTUn48eTr4D7n4swo7tFbuNfGelYWlyE7hT/i9jHFxyP24+7Hbk+H3L/cgOa1+2Dp88FV4YXSAK8/S64Mr3ACOAd8B/3pKQgqTcJLflA6twuFG/IYve4s7yoPqsN7qxhePqSsfutiUjqTgoYqwT6RIue9ucp5q9G11AXlm5cis2Nm7nneW1I9AvghwHfsouyg/rRG58ej8zMq3HPpyh88xak1yvmRZ8y01s3GQWaFbAD5laQESRjSt4ajYPaGm5pQe+NNwBNB+HL8eHI+a0y7/ZtrISvywsXbfokB41NcOe7UX16nXxN7aYqeDo96CxKx23f8siyeM9zbuS3DcKV50LNvPqg691VVUEGhTS3kuRk7FmwECNNTdr3K8ahRzs1/Xg8VF7D44uSpjMLZ+LhrzwcRFMjOVWC2rh1xq3Icefoyie1eeOGG/Fu87sBx08tPRUPzH+AjYHmQDL40O6HgmR32axlWPXOKvm4ki9qPu7fWAlvlzeIL9R+0Bh5uo3of/79wN9uCj6u4Iudiu/yGEzqVLP98foP6NvmPHk0M5wbD2bmWzOPrU3s22hIA/UYZAO+ri6A73RcWn+8dWpqnKNj6EpODloXJL8EpVyb0ZmmaGawJnl9G41Hi5+mZMWKLNpsw+48ab9/otuHYzqbTfVnRa7DBbNjIJpc/8/rA/blc4tPxurWQ3Dve8tR/WSGj760fKQMddpat1b6MdNGOGye4/OPZ/P5qOMj+Vh+Wj46R+dMsra20xNghw5WzkX7WfcF2L9OwqztF4rs8tYejxZadA3FVt2581+4c52P+QIjuSOYdsbBsf18UxW8nZ4Au9us3RGwL2jYe+p9oW77dvTdeJN/zzCwLVqGhy3TXYtOVuhneg0pYbCXEi+UeoYn52p/SykTy4+7Dp1/+s8AHay+nvT20pprMGnTCk27VIlcVy66vd1sLKTvThsYNLxHPS7l3idBz7f9NL8U1+S65Hb1/DKzfOKtLT39ZPX6UO+TwaGHFT9Mud5c+W7UKNbOrg3FSOlJQUplBSrvvx+NN9+iuy4lP4wg6Qa6d/LTT8u2Hc/ms0qbX55yG3L/ekNo9o4FepqRL95YlfEStX+r54ta9cO0dGZ/mwu1b5QApPKSgeqzWpBZ5NX0nbXmZoVWMh8o5BeqTRoGPiU6usMduD366KPxf//3f5g0KTYS10dV4Pbpi4G9G4ARn/ZNSSkYrJiNQ1/7LTdwu2zbMmxv345hturHQAEYSSmTYfboBSm47q9jvyVDLRnJ+M3BNpzc3wuX4n4vgK0Z6bi2tEQ+Rkbd2oMtSGvYCm/vCGrfKBoL3iIJ3gF/bqyUdC+Skv3BXFKE3V/twakp/RghRTR6DwVvy+d0oHFLAftN1113ZWpA0Ja1lZTCPtVae+5aTRItenURtjZthU+Djrw27BjDd//77qB+9ManxyMz8xr83dcZrZOU86L8Y/Q57hUvBvShaai+8aMgGVPy1mgcUlv3vnEzfvjrD5iBoOYd24Re+ge7rvai8/wbneoachquuzINrTljNCjuScajTw2xoL1Wm+qNUjlfcgQGF13pN/jV9+cMo/rMQ3Bnegxpp6Yfrz/lNTy+KGlKa2pO+ZwgmhrJqRLUxomFJ2LVSat05ZPa1HtgQWOgOZAMftjxYZDs5qTmoOdwT8BxPb6wIPnL/wwKpgeNkafbiP7pecBgV/BxBV/0YBjcNKlTzfbH6z+gb5vzdCxwa2a+FmjAG4M6SFu+ejUaly5lBmhSWRmmrl+nH7TVGufoGBaVlphaF2Z0pnIeZpw/K2tSbzymArc6NDAtizbbsDvPx5tbMHtgMMA20OsvlgK3RJMtjVsC9uW1za1svin0BNpB/WSGj9RjoPXDQYTkJRw2jx1ZG0lKwZDK/o21wK3ZtadF11BtVfIFHn3qMNd2U9vdZu0OAgtEaNh7PPtNeglg6NqrDO+xQ/ewBW6N9liDvZR4odQzPDnn+VuSTPzmYCtm9hn7Z+sPDeLYnkOadikPVsait/dJ0PNt1e3q+WVm+cRbW3r6yer1od4nw4AeZtrSW2+0jv/rli/jvkv/S/c6tR9GuuGudT5M7ESQbWfqgbwObda1D+CYrtbQ7B2b9JTGoKapli7W05FavqhVP4ygxxskjdCGZ8p3DlX2ZD4QQrVJw8CnREe3hTilrRy3N910E5YsWYImjfwgAjqvjtNTCCNHasTHnqCmdO0POlXfV8+e4vKMYzLEKDhLQVoK1q58OjhoS6jyDGG2KmhLoN/0FHaSZyzw1XZwKxsLGQb0pMj/1qyXvWErBW0JvkEXC9rSOXp69eWUftae8h5SSLWvFctBXLouiwWAA0EKlhQifbrFA71yT+f1jGKjNsyAaM3rx6htLR4ZjqntC5nWAaDfJDf0qaQBXJ37uTKm5K0Z2tAc9g1tH30SGcw79uQwtZ99+uF/ihh8Db3pkZE+FNBuZvrQ6Bsg2m3qITW1f/RNW87981sCg7YWaacJDb4oaUq8VtPUjJwqQW2Q3DT0jxnFakhtakEagySDPNmlt53Ux/X4wuhtwBdN3Ua/6U3QEGTaVr9qhLu/cM/TqH8zsDAWMlizHrifGfJk0NcuWCAHbdlxo6CtHp32vI662g2m1oUTelwJq2sypPEY0MCUTNhsw+48qz0eptPUtoHjcjwOkGii3JdpvnMHBgKDthFct4ZBW6tjCVHmnLZ57Mhako79Gwuwsvac0m9qvmVn+O1rM3a3GbtDgp69p2W/kc1m9Z5xhZk9Vmc9SbyQ1oKWnPP8LULl4UHMUgVteddTu/QGr55dqobVsZiWVQ2aqdsNVd611pZWu1avD/U+K/Qw05beeqN1/Fn3Vna/FT+M4gB3jcYJlLad2aCtFm1Ibtkb4uGwgW3Kl54u1tORRnrJrB9mqDPPbjXtO4cqezIfQrVJbfTttB5IdJgO3FIhMunv+uuvxyuvvILy8nKWNkF5jv4ENED5PizA1X0g6FjTgH6wnJQyvWmrBP1WvtVa5QkOlioxSXFefS0FYunJkBbonPyZus490nXKvtSgfFs8UJ4Us9BqwwyMaK3Vtt37DOWD8tsZIKVHf75KeuvRhuZAvNfjHRtPxz5L/DXVpsH8dO8PgXaaMOCLFk2tyKkSjf2NmufMtEljMJJBNULli1Xd5ghf7PQb6f6c6jfU/m2MJbmkhL2NoUTGbbey406umXDrcSXsrklb43FAn9ttw+48jWwDx+R4HMCjScTmG6l1G6LMhcvm4cGI9jz7NxZgZ+2Fqt/U9Dfaz3n6V8/ukGFg7/Hky9BmizadYmWtcsbO44UeeDaymeuttmv3HlOyanG/tyvvRmtL3a7V60O9L1SfgdeO0Tpm91v0w3hxArL1DB/I69AmrHupTfnS42MoPo9ZP0wau1Y/lB7BamwkVNkbT//IKT2Q6Ah6sUILzzzzTFgH4vP5I/QpKYHKRInBwUGkp6cjZlEw2dLl3tzgVBRlGfqKlV7/p/QIStBv5Ru3dW59tlPycwnqaykHC73OrwU6J+Wy1btHuk7ZlxqUxJqHqpwq3fGbacMMjGit1bbd+wzlg5J8G8CXoz9fJb31aENzIN7r8c49WrDBCn9NtWkwP937tYK3JminCQO+aNHUipwqUZ5ZrnnOTJs0htT+VEt9hsoXq7rNEb7Y6TfS/Sn7HUspF/n+1WMxAfrElT6hU2LgnnvZG7cw+qzRwpoJtx5Xwu6a5I6nx+AiB/S53TbsztPINgh5/YwjeDSJ2HwjtW5DlLlw2Tw8GNGeZ//GAuysvVD1m5r+Rvs5T//q2R0yCiZbthMMbbZo0ylW1ipn7Dxe6IFnI5u53mq7du8xtfdZ3O/tyrvR2lK3a/X6UO8L1WfgtWO0jtn96R5L650XJyBbz8wbt1q0CetealO+9PgYis9j1g+Txq7VT/mcdsuxkVBlbzz9I6f0QKLD9Bu3F154oek/K9i+fTvOOussZGRkoKSkBD/5yU8wMDAQcM2dd97JCqHR27zTpk1j+XVjBZS3R87dU3SEPwk05RPRA52fejZKj54bcD/9O+tLs1j+FMoRooY6x+3tV4ylTaDjdJ5Qn5qOd7KyWd4RJbyjydMPuN3yscrq+fKYWeJsRY5bV4Yiv026lxUqo3OUOP1tXyZrT3kP+zTgnLFPA+i6voFg5URzozlqVR6syavRpIFeGxItzeRS0qO13vjs3qcrH6PyIFV+5M1BOjbxmLncNpS8NRqHNIfpubMZj3i8YwnUD2fC48ny/59zDRWgGxhMC2i7fzCNHddrkypxUl4g9dwIOd5s7Hujgn//hhJ4+t2maKcnA0HXaPDFiKZm5FQJqY1Tp/mLBfEgtakFaQx6MkjFYdTHzfBFTSPTskuFswxkWg9a/GLHLOpUO9VTA/oOYZ5ac9CcGw9m52uBBrwx0Pob+qki79n69ezfEem4UZokA11WVT3f1Low1JmqeRjB6prUG48h3zg06G1M8+soDh+IpqT77OwJap2pnCft+yfsMfcJe63bzXSa18L6sSLX4YKZMfB4T/PdnJEBnzppgQP6Kdzr1lI/JtoIh80Tiqwp7V8n/4wQquxa0TFadA3VVu0d8NvXZuxuM3aHBD17T8tOKCycYuoeO3TXopMV+umtIaav+zhuMvlDxfPQ+2mDIS+05FyyHwsP+HW0BKV/Rn3TGHj+GbVLRX/07FI1jMbCu0dr77NqI6vbUNLMLJ+01pbWOrJ6faj3ybDpM1hZb7SOj8qdze634odJOW4pLqC07VjaBCqkZWDbadGG5JYnj6HY3jJsypdyrGQDKdeZUkdS7KLk+C5dXcajgQRl2/T/8zJmymPob+iTfdagfl4vNu07hyp7Mh94uk3FI64tagYh6AEB87CV49bj8eDtt98OOk7H6JxZ7Ny5E/PmzcOJJ56I9vZ2NDc3s8Dsjh075GseeeQRPPTQQyw1Q39/P7773e+y4PAXX3yBmARV7pMSRI/Cl54feA2dp+s0QNX4KLGzElQlURm0pTdsP6/057xtLUgOCN7Oz6hEwZzrsS+3OKCNraMVL5XVEakvGounaG5gYbIksBy3pIjoN+W4pTRxI9kjLNm359V8vNeTFRi0PasNmUUe9i8l36brlAFlCTQ31q8OeDRg+Zv6B1geFTNtmAGvH7vjMzUmjnwMlB2Hz6pPRkOtviKlvDub6jeh7rwVQW0oeWtmHKS4r3minvGIkuAreZeUl8wSre/61iXsjyVdp2TuimvoNxW6WvVsssxf+pd+swJYqutT8lJYO/u+eSnqrl08ZkBQzpzdr7LcO/1v/Y3lY/L1+ZCSlRLUn6cnGbUbywMN7cpTgRMvDz13D4cvH6al4qXsLF1548mBFszKLF0zq3RW0PGa3BqcVnEa3m54m8nBj078EVcG15+/PuC4Hl9c+S7Glz2XLww26BS8Idnb8uUfYGCSalxEs2veDKKdY3zR4A3U1UoNdGrI/WnNU+pXQSsZdo/x+qegsRJUnX7yPP5YDMCrMJx50on+tzEkA/+yb8Lz762BN5oZ5+gYeOuCZJr2HK01Iek3Zf4y5W8J3OOKsZnt22g8pqCgARnKdZsK/TrqtF8EjEmied3iJX6DWUlLHTrSdb1/enhMZ366JWCe56SfwPbYnz4/zBwKM/OkfWIXOWC8/mIJnLXE4/0Lx30dwxprhSdLZo9Fet3K0JEXM2vGyHbRWoe8/YYnb/TgMN5kTU0TK/ptyQlLHJExqU/pBQ6y3cjODtzP3UF2t1mdJu8LGvYeHa9duACet/8sy3zAPXnJ/HtMBIsijkueRO/wyX59zXyYQFeZ+UMv9Y/paxXU/OfJOdnkT/dMYLpZ4gf5MFe4S9E790b8O6WE9U1joL2D559VfuefurY+b93RObrGzD1KGMoJR+/8O684oF0n/DKrvpWTPpxkY+vl5nTKD1OuHVq3yrVDPhmt48sf24Wtrz2DXQsuNeWHSbqBCpOlVFYE2HZJFaXMtjNj6190xEU4vvj4IJry5JH9PmtZsF1rFRry9Wh+nq7fT7+/1T4tYJ3N7UnDvc94/f5tlo/FLlo+zkXRMT2m9ZLkh5FdJbU9b8cwbn1+GN995FPUvfdyoM+amSL3U3BErz/6NpLE/qU3b/V8ZwLR+8IjLtTOMS3Rl/5/4hVA1an8fZWn2xT+WJAt6hCfnNYDiYykkZERVVUGYyxfvpylLFi2bFnA8ZUrV7LA7YoVK0y185//+Z+ora3F+++/r3nN1KlTWaD2/vvvl4/V1NTgkksuwX333ed4tbZwIagK56E96Du4A7/euQ6n797IEjdL8Ew+A+5L/whkaKckINAC3r5vO/vEqWRHAwaWL4evpABZTzyI5InFLH8IvYpe3peK3Qsvw3DzQfSd3YNZE8a+c+ktOx51R30VudWnwVtQjfeb/bw4ufTkgKchtIDJOXQX56Po6ivRdM+v2f+r1/h5ULv4ZnhaO1G2aiWaH/k168v9/YXwPrlevs49sRhtu99ln8B5hjLRd/NN7Lr0+1cg5cunyOO18hTmk7ZP8MCm5fje52/boqFZvlHhhqGMIe749CrYEo/szKv535vhO7gNve8/iCP7DsnH6YkmbY55+WNtdQ11YenGpQHJ0ulp1q+OXYSc3lb2+UOt2yXLivLNCuXYlXNg/F68hH02k7LmF6jr34Gtn6zHWwN17G0N2qSKu/zXt+YBf7txFu48/Sdyf/S0Utr40u67Ew3HTkTFJwcxdPMK1iYZCoM4iCdevxVvDO6X2yzpToKroBDDhw75jRCW1H2YbS77/lkM31AKq8RZfVUNMq/4BdB/KKi/qntvR3aNG3j3CeDAlrGJ0ZNA2lRGZcJW5WFatwe2oPHNuzCtuzWAL+6vPY2s7MC3B6Q+dnXtwiM7H8EXPWMPnE4qOQm3nHILOgY7guTDzNhItjbWbcSfd/0Z+3v4hVxmFs7EVVOvQpenCydOPjGgD0k21XyhpPykm1Z/8d/YWPsR40tRN/DKNcdi8bW/Qx6ltXnhan8S+lHQ01TamLtTknFRwXFYesQ3kTVxBnuCK1d8J6Oi6SNdvvCqw5uG1P6Wx4AG1Z5CwZBLnwpZH0igcVLxnFJ3v//zIsXbBLR2KT9jEa0z6k9FKxacoRf89m20fGywci7SF67zt0vzbd+LZk8meztN+k3jaRj0O2YV6YPysQBe6EC59gM+netvh+cPV6H29zvh6U9B1entyD79NOD8+4G/3RQ4R+VaU4xL/dYFTz9Kx9IG0piu4um3XFcuur3dAfpu2axlWPXOqoDr5uXOwOrWQ8huVASZp56Nxrkr4SnK0uzb6JhlkEG86wPU3v44PI3NQbqNveFBjldlOaovyoS7dWMwLfvbx+iokKugr1pGU8bQG2EUXPDUN8JXVsTsgsnTTjY/TxXfQlqbo+C1oVVV3Mq1ASA6qdecSvdzeaqYb1d2UZDMSUG3d5vf1T12bvHJTObc+97i939oD7ODmH6g9cBbHzprxjQM5qO1ZmjPuHXGrej2dAfYPLx1SMEgKnapbPO6E68L2tOUtmpFZgW8OV5dWYsGmNmDtWwvckrz0vJ011lBegEe3f6oYzJGe0P7Wfeh6Z3NyFj5EJJLJ2LqM+v8RXZGaVt3oBt9N97E0uB0370YZeecb1qnBe0Lo+3S/uPt9mLg+uvg7fb494XyISbzvYXfRt0td8KdMaaXUHo8cMFD8CSXjdlsax5D9rx5uutcy1Y1gimdwQELYCz4NjxNLYF6lenrSng6vZoFnSTdpaVTG1LTsHWoFa7WThx5xzqMNDQhOS8ZU+Y1yn3Q24AUWBouzoZ3+bUYmnwsdnTsYF+fqv0z5fohW1/qk6DsX7m3UqBLsl+U/oGU71j6v5bfo6av2rdd+fl6vNL1WcC1JMsPzH+ArQ3DdgxgdV+2u4+Tj3nX5ruwq3OX7lzM+mFmx8Bbb/v3v4H7vngRn7TUyXb583OTcMnmEbTlGvthLf0tyFu+Bu7ycllupXHv3Pkvy7Y++TCXTb8MR004ii+PmROAN1bq7sWWYSN2QvOngDStM3oZpfhLLWh6r4B9NTziSoOv2wd36QR/PKOgWlMv8bBv9/vo/sGPkdrcwV5ccw/7WKF2eqlt2JuE4cN+nzUlywVfr78fKYYioWr1cmQfWcjl2edHZuNPO/+EbS3bgvcYDn8CUDUHmHUNUHZ8wL7KdBs9ZGO2qAvVp9cH26Imi9VpwqYeSFR0W4hT2grcUlGybdu2obQ08AkivTF76qmn4sCBA6Zy2tLgKA3CT3/6UwwNDSEtLfDV8La2NhQXF+Oll14KSMFw5ZVXYt++fdi0aVPsBm4BLHp1Ea7Y9jJmDQwEJBumz/ZSpp4FXPGipXbz9uxB2rRp3IV285+/g0s3vYaTJvTY7os2Eql95f/lT2t372ZKTvl/9XXK8ZYkJ8vX2YUTNNSDGWPCVgDQRL/tz83HUd0tAfOiTw7oyf0xP9kVQIOtTVsDKmfSpwj0VGvtuWttG8NK3lEfSoOEngRWtfpVR11xEjrzXEH9KeVAgrrNLY1b5Oq77I2DtiQUHzMTix7Y5t88svzJ2ynvDwUmUtJ8qDz9EDKLR/xP9RT8Dejv6YuBvRsCq1vS5xuKe+zy7dOHjsSRnc1BfPkstwSF39rApe+ybcuwvX17QNVtHo/U9xmNTc0XNZKRjBMLT8Sqk1bptqNep0qZkni94wi3f7zNLUG09Y6+TXBtaUnQvAICMAZ8CTk4RO1rGTFkMIaoD8w4gwHnePMNASNJKUhSyX2owTAz8sAwOhdP7wiGutx+55x4l54HDHbprjU7kMbK029qkMzlpOag53BPwHUkq7MHhpBCrzooxjZYMRvpV/8dkQYzmC86j6vbWDD3imq42zYb01IlV+rgLWt3awE8va7QDfBRxEzg1oTuN4IZmdPC2uZWzB4YDJI59d4TyVQSWjYCb81o7Rlm12Goe1q0wMx4zdpeZu8NRcZobxiqmI1DX/utpi9Ac6KgrW/fftRcfJHlfnn7ArU54X9/gOTP38XhzmT/vkAY3Rt69w4gLXdorP6AYi3wbESpzfEO3BI8j13gf1Cp1KtKff3SP7h61cr61tsT6M3K/7rly7hh7ipH145Z+prpT8u31bJLKegUS/rB7FxC0QVW7HJpLJJd/uHUZPbWp1k/LJy2fjj2Yqf8fvU6K5jWh/ZdWfAOBK9lLb2kBbp+9/87k70py75IRhL7IpmQ7PYhKWWEfZWs7If4kJyXh+GurqB+lP3ryhWHPwHQobWhLaqh26zAjh5IVHRbiFMm2+1AKiamhNfrxaFDY28H6qG1tZWlPqCCY8ceeywb6IQJE3DjjTeyIC6hpaWF/VtUFPgJB+XClc7xQPfTGJV/0Qb63KC+dgPmqhQPgRljFHyw+FkBLXLeQqO+dnZvxamqoK3VvpTtq/ui/0vKR/l/rTGpr4sWGkYL2pq2YoYqaEug38d0NstpE4gGpBjVxj/9puN6n/QYQeKd1IcSVOiODAb6o//z+uPxV92mMpBJ7WybAiar/rfRRvMAvTaWB2jyV1pZJU62Uan4K/dHn4rQOfVmxrnHKur3b2D05/GF+NXW/E7wPX31+KD9g4C5OsEjHl/UoD6pb3pjXA/KdaqWKYnX9JvWG4+2NH968k1vcWjOK4x8CWhfC5HWB1rzDQFJTtHKAEF6WzEXcsBl55zGM9AeNp5q6Tc16Dy9+ae8jj47nasOoI2OLb1+87jsDW53n6ZuY8fpTVsjWnLkinjifyNM0W7vaLsP/zxkAzxm4ICOMStzPPhlboArc+Nlj+jZCOo1o7VnWFmHododsYJQbK9wyFjSqF6jL0H07O7kkhK4Zxnns+WB166rcz/rNzXTM7YvKPaG7LKBwKKxirUQqg8QVrR9wfRxkF5V6mt66ziMe8KR81vxWfdWQ/stmmBkl8aSfjA7l3D5YTy7XIJklxOs+GERsfXDaO/b9fvV66zlwzx/0Jazlq3qpYNDuzDtjIOsLWpTCtoShj0p/qCtqh9qP/P447n9SP3ryZUWf8zS2tAWDVG3xZMeiDbYCtx++ctfxurVq6F8WZf+f88992DOnDmm2pDupfy1v//971mw9bXXXsMzzzyDO+64I+Da4eHhoABxUpKqmIQCNA6KXEt/VVXOVJJ2EnU9dajyBJVlCAR9ahBjfUUS8TovQt+hT3TPdzR+INNAD/RZTqgw6sNOf3ptEk/J0KcngErQ7wAHQIu/HfvCJhOdTdt1z/e1jeXnltA00BQWHlnhC3365kS7RuttkuJ80LzCyBdT7TvRh9PjsYtI6zW7cwlxnFZkXI2o3Bs69pnXbVrj1eCFZrtudQnwOIYDOibeZM7ufJR7htU2nLA7oh2h2F7hlDFK0xNJpPTY7C/abfNRXaKrr52Yg8GeQDaVFfttvGFGtmNFP5idS7z6YbZt/TDa+7b9fiPbK0S/kNd2qP2Ewp/xpEe86YG4CNzee++9LNhKRcWuu+46LFmyBCeccAL++Mc/soCuGVAKhNTUVCxcuJClVyBQe1dddRX+53/+R07JQDh48GDAvfS2rXSOh9tuu429biz91dXZN5LChaqcKtS51c+LVKB8XzHWVyQRr/MiZE04Vvd8QflMmQZ6kPJchQKjPuz0p9cm8ZQ+/aXPNpTwf8aRbMzfgslhk4n8shN1z2cVzQg6VpZRFhYeWeGLlK8s1HaN1tsBxfmgeYWRL6bad6IPp8djF5HWa3bnEuI4rci4GlG5NxRMNq/btMarwQvNdj05SBg4oGPiTebszke5Z1htwwm7I9oRiu0VThmjGhKRhC/HZn/RbpuP6hJdfe3EHAz2BLKprNhv4w0zsh0r+sHsXOLVD7Nt64fR3rft9xvZXiH6hby2Q+0nFP6MJz3iTQ/EReD2pJNOwvbt2zF37lx88MEH7P+nnXYa+3fmTH9AyQgul4u9uSulRZBAqROkXLeUhJ3SKLz++usBb9++8cYbrD8t0P2UekH5N96gnD3SH6EmrwaV1fOxOSOD5YyBKk8Ly8dookiDul0enOrLCZgZr1lEYl5mxuvknCQcf/J/soJX6nnRbzpeUT1PpgHliqGcN0rQbzquTBjPG6Ny7FpzkPrQA68/PeiNe3rubH+CdOmzjXNaxz7nkKpg0n1a/C06wn9O1bb6Hjt8q6yZr8uX42deHHCc2p71pVmmeWR1XZvli7IgXSi8ofXGo613tGjBAbc7aF7yHEzwJaQ1JLWvBQf1nB5v5HNa8w0FHLnnjUNrfLbpq8e7jELDtWYHNFYtWVSDzlOhJOV1tW432xvYXuDw2OzC48nS1m10vHieMS05vAgqUEbtZo+2e/0djlRtd2J/C1VWDfW1Sd2vB7Myx4NZmYtkDkc9fa5eM1p7hpV1GMqeFk0wGq8V28vsvU7IGCtUqYFw8GDiMXMd3xvs2qpm2zKFoiP8BR7VelWprw9navbpxJ5ABcqOyp3N1qKTfDNLXzP98XxbPbs0lvSD2bmEogucGoud/py29Z3ei530+w1tL421bAYT047E7rcmsrYoxy0VPZOQku5lhcrs9GOHP2ZpHU56KMeuBafWRSLCVuCW3rKdNm0aHnvsMWzZsgVvv/02+z8do3NmsWzZMqxfvx7//d//jcbGRlaE7A9/+AO++93vytf87Gc/Y8fWrVuHvXv3srd7Kdh77bXXItZBVQFfOO7rLNG3EsNUAZ2qLsZoX5FEvM6LUPmdf7JCZErQbzqupgElKleCftNxp0BtzSqdpXneTn+8cZ+TfgKueaLenzCdEqSf1YbMIk9gnjEypIvm6vOXzlFSdiXotwMyYZYvkeBROPhiOF4ObbeOVpo17DOMfJHbr+HkphovfcCbL42PxmPnmJO0sgot3l3zZlh5ypNFCjgpQefXn78+6DraG9heEKaxWQErBkHVgnm6Ld/Njte+1O/XbUbjVfAiKGgrtfvd6XBXlsNTVydXKU4IOKBjeDJHepaqIRsdiyaZM9LnvDWjpb/NrkMn7Y5oRyj7elzJ2DjtDWHX16SP1XqV6WuXX1+HqFeVe4JLtSdQYbKUnhS/PRxjulvLLiU5jjX9YHYu8eSHOWLrh9Het+r3m7K9bK5lqW0qTDaSPUJ1yViOWwrWUhCX8ttS6l1Xrj2dYZU/ZmgdTnrEqx6IJiSNKBPVmr0pKSkgv60EOpaSkhKUk1YPf/nLX/CLX/wCtbW1mDRpEq6++mr84Ac/CMhhS2kZKBcupUyYMWMGfvWrX7G0CuGo1jYeoATNLXVbMMnrxcTKOWF9EyiSfUUS8TovAhUio5y2lB5BetNWiwaUM4Y+PwjXkyzq4/3m99n/y7LL4Bv2hdyfctwTPqxF3eIlLDk7q4ROCdIp107hFHgOtqJ28c3wtHai6vE15pLHU1L20fudlgmzfIkEj8LBF8PxKmhb63ZZm1cY+SK3v380MX7N3PHXB7z5hnJsPKE1njCPUy2LWrLJPR4FNKQqwpq67XCmbChXrXkM2cdUmBvvoT3o/ecrqFv5W7jLy/2FyCin7eh9soEutRuthYDCAQd4zpMls8eiQebUsLRmTLYRCbsj2hEKDeJKxsZpbwi7via9OvC5/0TN3GB9bVOv8vaEg/VbcMDlQlHqEfAt/llM626lXXpy6ckxrR/MziUe/DBHbf0wrn+zfr8l28viOlO2XbRkMZqWLcNIYRZyHvgZSrNKZZ+1bNVKtD22xnY/ZvnDYEDrcNIj3vVAuGAlTulY4JZ+09u3F198MZqbmxFNiPbArYCAQOCmkjZtGrcqMm0mQ7t3x6QRKyAgkNgIl24TOlNAQEDAWURCrwrdLSAQfoRznSnbVvejbDua/FehdxIkcEt5aQk+n4+9WasENUNv2lL6g5UrVyKaIAK3AgICAgICAgICAgICAgICAgICArEUp7RQlg545ZVX2L9f+9rX5P9LcLvdqKmpwdSpsfEZjICAgICAgICAgICAgICAgICAgIBAtMJS4ParX/0q+/e9997DySefHK4xCQgICAgICAgICAgICAgICAgICAgkNJLt3CSCtuOfm0Sr2h8dp/N6aH1sDfo/+ojbHh2n87z27PQb6lgFgmmo/K2mYaRpKvgbHxB8FBAQa0jol9iC0NuCdgKxsyY7X36Z/fEQLttdazx0nPw9Xp+J4JsJ3SkwnnKW6OtPIAJv3FJBMimXrfR/LdiodxYRUEW+7JFs1PXUscp8NM73D76PJCQFVbrb37Vfvo6OK3/X99RjR+sOHFdyHL5c/uWAPpTXUftyG4c9OFi/FQfcLpRUzdGsqkf3t9VvRdVolcT9rpSAMU74sHasGuBDK+BO7ZWrB+79/D30//BGpLR0IOOuReit9ARVvKegbNsjj6BtzRpUr1+H4X+/hbq718JdMgG4aTEO37oSSb5hoL8d+Zd/X64uWHTtteweX0kBsp54EJML8oGOfazvvYfa5X6rHl+DtuMnsXlXfHIQQzfdxb2HKhfuuXwBhptbkH7/CiTPOZlPNwXtU5JS0NjXyOWXGpsbNss8Ks8q1+RzJKElU3rzUld/HHr/LdTdejc8E7JRd8u3MfWBFzHS0o6q1Xcgbea8iFYQD6pM6e4L4K80ltRf3YHGGaXB1TDbvpCvj1ilYa0+lcdJfyn/X7uZNCCrJqw1TuKlJGNlWWWm5XQ85E59nK3Tm1cY8jHsMmWGN3Rc+p2UAnTVGfKGXa/koUmeJiTGY03GAczqwlitDh6K/PT+9Vm2Z7knlqB63fpg2ixcAM/BFraHZX/jW5rtaOmxRIWQOb5M6Nnu7Jq2L9D76t9Qt/K3cJeX89crTyYTHBFdf1r2WPt+oOF9oOpUYOqZhjZCvTsVWw+3Gvp4ks9h1sdQ+xeSz5ZV50bdrSvH9gFFISQK2Dbdepv/x8F/I/+sU+Tx0t7A2yPUYzTjryr9J+/mdzF0811ILp2Iqc+sk+W8d1c76pbejRGMIGkEqFr7uNyn0o9Mve/OANtdi2apnQdkn9WMjUzj1/OjI6I7r13M9U23HPg30q5fhfRDvZj0+Brufh3N9r6WDWfkB0RsX9WzMXnr3qydr7Mu9eRyPPZoaf1JqLr3dmRPn2Ded442HunpZhO6QKlLfCM+YeMZwHRxstdee439e84558j/1wJdE41Jf496/CikZAQWVVPi+ILjcf1R1+OxnY/hg/YP5OO5rlx0e7u599C5h099GNnubNy7496A+9h5nw+rWw/htIFB+di/MtKx/ktn48cn3oEcdw471uPpwSPbf47LPn896NqlxUXoTvG/HH1a0lH48e+bgeZWuLO8qD6rDe6sYXzqKkXbK8ko6QR8OT4cOZ/OD7N7duSWIO38dcjKroD3s8/Qf/1PqMIccR8Vcw+hZXs+PH0UwydRSJKPN+8oha/LC5RNxFOXTMBXnv43SlXte/qSsWtDMVJ6UtCcD9x/ZR5qs/pYvxO6R3DnOh/3nt1vTURSdxK7Z8XCFBzK5T8MyE/LR+dQJ/fcqaWn4oH5DyAvLU8+VtddhwV/X6B5j3TfLUfeItOeh4qKiqBjDQ0Npq9VomuoC0s3LsXmRgoWWZuXbNDV1cGV70bFyQ1o3FLo5xdZXCNJTA7K57Sj4b1yeLu8cFdVyUajNGajMdoBjW3PgoUYaWqCK8+Fmnn1Mn/3b6xkY+koTMPPLvPK/J1ZOBM/+9K1+NK7q4E9r481NvVsNM5diREFLyU4Mvb+duCFq4P6xPn3A3+7KfC4DgbLZyH9iueAjAL2e+f+nVj50Up81Dn2BrsZObUCLbnTA9GMJ3dE/yXTlwToOFqn9zznRn7bINz5blSfXifzsXZTFTydngCZchxWeJNRCAy089uZPA+49CmZN6zdP18F7Dd4cj16X0N7f9jWStRDiweXPDlGTwdAshyP9FXqabtrSEtX82gWrmsdlZ+aecyc8HzyL9S+UcT2LHfOMKrnt4zRZkMJPD3JY7bMMaexe7BP8RXJ5DOwtKQIr7a8Jx+bWz4Xq+ettq1To1l+zer74ZYW9N14k+7+m1RWhqwH7kdySYlhe1ZsHisIR7tkL6vt7ePzj2ey81HHR1zbnWzxtZ0ezOhu8cveqEy68lJQM69BWyaPO1PTNjGapxMwopWTtioPPDvC7Prj6R7l2NTHkwY7Uf72cnP2GNkCV74MvLZC10ZQ+lBaPp4W6PrHzntMnqfav1D7d2ztbaqCV6XvPXs/w/5vXwpvt5dd58r0ouZsv+/mKZ6H2pf64alvlO/pL8wMojnPrlw0ZZHuXJQ+2EjOCKbNP8j67G9zYf/rxUgaSYI3GXj+hhNw9bd+iYf/bznOf+Addr3aN+P5LDz/trd8NrrPeVBeL7RWjWxkanv9+etRlVNlWj/YXW8kz3f/5Xp5nnr+7IPfLcBjC56Tx0X33rjhRrzb/G7Y7H2nbbiuCx7E0vfuCVq/y2Ytw6p3Vtla147bmBSGUp/TgtrON7EuJTvCfekfHbVn7dqFtP5q3ygBKESTDFSf1YLMIm+AvdhZlI7bvuWR119EbB6neEQ6+Jo3gYKagMNm1s9423ixUJzMdOBWwl/+8hf8z//8D4uQ/8d//AcuvPBCRDvMBm4lQ6/X24thtqLMge6ZljsN29u3B933eHMLZg8MBrzaTFv3Oxnp+O3RX8Oqk1axY8u2LcMP/v2/mMW5dmtGOq4tHTO819cOIuN/AE+vazRo14HGLQXMCKVNaOr8VmSMBm2lNj7LLUHhtzb4f1Pw9kfXsaAfBf+KZnSh7WNaHKQgRjDx5A60f5Y7atS68OvrjsXG4X+joNuHR586zDY1Xr/XXZkaFIAlw8HqPVZAi3vtuWvl36c/e3zWfAYAAQAASURBVLpu0FYZwJJoH25jeNGri7C1aSt7kmRnXkz5X3Se3/nP8qLkxC40bC6U+Vfx5Xa0fJgn86vm5X/KwYFwBm4Jddu3Y3DRlf6AsYq/ZCgSf1tzx1RMMpLxzKF+zOg5BCjpkZSCwYrZOPS13wb14cjYn74Y2LshqE+k5wGDXYHHdcAebdBGdsWL7Pd3/vodUw6AWk4jEbjlyR3RP9uVHaTjinuS8ehTQ+xhipqPzOB46R/hCdo6yBsGBW9YuyYD8nRfw1mPsP/GY2DRNg+mzB+jpwOI18AtT09bXUMxHbjlyY8CykBZEG0UD6B58CGJ2UCLSovlY/Rmxuyy2bZ1ajwEbqXgrdb+S7ZA+tqnTAVtYy1wS/Yyz97Wg9oWNy2TOrZJIgRueXaE2fVnNXA74X9/gPSGreb3/GSXP6Cgc73ah7Lq4yltN7V/wfPvBvtSUL+pUn5YV756NRoXX8V+uzK8zM3y9itkbmuB35dTBHrN+gxm5qLng40k0Ru3STiYD/zh4jx898UuTOQEbbXAmz/pa0/ll+X1QmvVjI1MwdtN394U9sCtRNv8Lq8p31Q5LrpXL5juhL3vtA33aV4xFhZmBK3fnNQc9BzusbWuHbcxCTr2g66db3JdklymTD3LUXs2VLsw8OUrxbrMJd85Da05w5G1eZzkEQVvl+4LOGR2/YynjRcLgVtLOW5///vfs0Dt1q1b8c477+Ciiy7CH/7wB8QT6Om8FWNQuoc2JvV91R4Pe+KjzkdBv+cODKL14FY09Degvq8ebQe3smO8a6mNSR6P3OaM4RZUn0lGJT2hcaH2tWLZ2DxSFbSV2qC3DNqa32G/s7IPofrsVllptH2cLwdt6d+D7xfK7dFbHHuH/AZydoa/fa1+s8goUcHOPVZASqC2u9b//4bNpoK2BOIX0T7coE8CaIxWgrbqedHnFf4ndX4aNvxrghy0pX8bNk8I4Jc71f/mYCSQmto/+qZPMH/p6X5GxuGA66s8Q0wWg5T/iA/p9ZuR0rXf+UHSpx0UwOP0yd7MsMAbZsZSW4f2MN6aMUjV/IwEtOSO1jFPx2WmD2HaGQe5fGSyFy6ZcpA3UPBGbtfCfWGRvViAHg8kegoYQq2nI7aGolV+FKAAmD8QxqGNTtCWkIIRzB0YkG0gAum1SOvUaITe/suOp8WfzJG9zLO39cCzxU3LZDhtkyiHlh0RjvXn6tzP6Gxpzx/2Gl6v9qGs+njSPNX+hZZ/l57l8+v78lL2pl3tggVy0KbmnLbRN20VMjf6Ak71wz9nQVsrPoOZuej5YDVnt6Itb4QFa2/9vbWgrdb8SV9L60Vaq2ZAtH278W2EE0ramvVNpXFJ95pBxPcmHRvumM5mVBwee/OUQPOneUViXRuNjx03sB+CoLBLza5LksvxsGd17cKz+fJHvlhG+lBAO2G3eZzmEflve96Uf1pZPwRh4zkUuH3ooYfwyCOP4NNPP2V/9PvBBx+00kRCocqjH5Sc5PGisb8RTQNNpq5VtklGJT2hUYJ+6zlAfW07/Pe2fsxey584MzDIWTC9h9uesm+9fqXrlLBzj53cxQTKbWMFRPtwg/K2hDovyiHDo6GafzL/2/ciUkjpOWCJv0Zy7pLm7CQoB4/TaN9rmbcyPyMAq2MzWqdhk6kw8cZOu2GRvViAEa0iqE9iGhp6ejz0ckRhcq3ZsVmU4NkKkdSp0Qij/TcedRrZy1ahZXdYkcl4pGWodoST649kOZwIxdegear9Cz1blsnVT77NlStNmXP3hOwzWLXtyA984yuBwZhHLzAO2krt6oHWi9W1+nHLxwgnlLS14pvSuKLZ3jfag63KvuNjD5edz/H7jeQy4naYjl1I689qbCRschUOHtWNpUSwq9cS3cYLOXD7xRdf4Lvf/a78+/vf/z727BFv42ihzq1f+40KlZVnlqMso8zUtco26TMveq1eCf9r9toszSqa4b+3+DiWY+XgB/Sm7Rg6duZw21P2rdevdJ0Sdu6xCkrcTZhR7J+fWRDtww0pN1Io80LBZC4N1fyT+U+JwiMEX84kS/w1knOvNGcnUTDZ+TYLp1jmrczPCMDq2IzWadhkKky8sdNuWGQvFmBEqwjqk5iGhp4eD70cUZhca3ZsFiV4tkIkdWo0wmj/jUedRvayVWjZHVZkMh5pGaod4eT6I1kOJ0LxNWieav9Cz5ZlcvXQs1y50pQ5T07IPoNV2478wLP+GZg+8Lq/+lh6BTPt6oHWi9W1SkWlwgklba34pjSuaLb3jfZgq7Lv+NjDZedz/H4juYy4HaZjF9L6sxobCZtchYNHVKhM+q9NvZboNl7IgduBgQFkZWXJv7Ozs9HfH1+fYlHOIMoDafUeypmqvq/W7WaJ8dXPTej35ox0FE+cjYrMClRmVaJo4mx2jHcttXHA7Zbb3JFcgto3x3JzVZ8z9ro9JVYfUBmd3tECZUWls9jvvt4JqH29eCzH7XGdY4XJWI7bdrk9KnIxJe1ENrfeAX/7Wv32DQQrGjv3WAHlEpKqLc6tmMvyEZkB8YtoH27U5NWwMVK+Frvz8niy/AnLR2lYcdohOU2CVEhOyS+qShkpHD6c6e+Tw9/dGyZiYCA14Po6dxqTRZY3RwnKI1c5F768wGTmVqp3Ui4hHoh+vSOzuH2yPDwWeDMi5VeaMJXxluTIKj8jAS25o3XM03H9g2msYCCPj0z2wiVTRUf46ekAb6Dgjdyuhfvsyl7MQ48HEj0FDKHW0xFbQ9EqPwqo84kG0IYd17a5KDfd5owM2QYikF6LtE6NRujtv+z4UPzJHNnLPHtbDzxb3LRMhmibxDK07IhwrD9vfg2js6U9n3LcGlyv9qGs+njSPNX+hZZ/Rzlumb5vbPbnrV2/nuU4Z2vytSLsf10lc9mjMnf9HcyGteIzmJmLng9GBcqKuvw5bu/9Xh77lwp2UUEzo+Ct1vxJX0vrRVqrZkC0/XL5lxFOKGlr1jeVxiXdawYR35t0bLhP80vRkJoecJjmT/OKxLo2Gh87bmA/BEFhl5pdlySXkbBn1b6o0i6kooQlx3eN7TOv8+WPfLGBwbSw8ibIZ1bwiPa+3sa00HhE/tvUM+WfVtYPQdh4DhUnS0pKwt133x1wbPny5UHHbr/9dsRicTKqBnnXl+/Cqq2BlRZ51TTV1TBzU3O5VUBzfcNY3doWUN2QlMqLx52PO896UK6YR9X2VrxxAy7++G9B10oVUQlnpx2Pxb9thK+hKSAX16euUrS9kowSVZVMAinuyu/8E3n51ej/6CPULlgI+Hxy0K9le74/UbYUvB09fvCTMlYdNaWyAuu+VYz5T35oqgrnvvSeoIqm6ntIMVEhJKN8Snq051XvpNfxF/xtrLqk2fvCCV5VXrPzUlaldOW7UXFyAxq3FHISm7ej8f0KU9XLncLez99D/w9vREpTm2YldW5lzFNuQ95fbwjIQdpeeRL6/uPXqCqx9ta0tAHVLV7C5svm7e7zf/ZROAWe5hbULrkFntZOVF1Sjuwkf55nBtqIvvEA8MqN5vOhqiqaEm9v2nAT3hnNHx0N8ka5hGgdFKYX4pHtjwRXk529LEDH0Tq95zk38tsGg/i4f1MlvJ1eJFWUYeoz60KTKcqhNMoXVlBE+n9mIfD894MrmfJ4o6oYrVttdqADeO5KYP9YhXpT9yUiiFY8HlA12USmS4jVg5W6MFJ6OWrkp2YeMyc8n/xrLECWM4zq+S1jtNlQAk9P8pgtc8xp/ufH+zYGVINeWlKEV1veC6443NM6pkcS7AFDIsscz6aaVToLIxgJqFittLPIFn+i28tyPgYEbfNdqD69XlsmjztT1oPS3kpvASXKQwMercNW8ZunR7RAtsCVfwFeu0vXRlD6UFo+nlnbTe1fqP07v81Uxfwm5drz7NuJ/d/6Jrzd/nASBXD8uW6H4Smeh9qX+uGpb5Tv6S/M5PqT6rEZzUXpg1HBI38dA39VewraUmEybzLw/A0n4Opv/xIP/+9ynP/AO+x6tW/G81l4/i3pa/elf7RkI0t+tNNvG/NA47n7L9fL8zTyZx9b8Jw8rmi292tS81H1jzuDbLiuCx7E0vfuMfQDCEcXHo3lc5bj2KJjQxuU0tanfZl+N+8A3v0NcGBLwPiYbiWYXfcce91oXfLkMhxQ+6Jo3+/3P5sPwZXrAjyD8A6moOS4LrTsyKeiI+z1yeqzWljaBD3f2THeSOO8djHcxfmoXnMf3CVFfn5lFsHz8h2o/f1OePpTUHV6O7JPP806j0gHX/MmUBD4sNPM+gn7HhMHxcksBW4rKytNXVdfX49oJMiOuh3Iyc1hOTOk16/fb36f/Xty6ckBhhglgJauo+PK3w29DSznDX0+oX5CqLyOILfh8eJg/RYccLlQUjVH0+ij+1vqtmCS14uJlXNQ63YFjHHCh7VjioGS2VNepFHluG/3++i75gaktHQgY8Vi9FYMoaB8Jiqq58nttz62Bm2PPAKkpKB6/ToM79yEuhVr4C6ZgKSbl2Bo6d1I8g2j6PsLkX/59/2OQVMTiq69Fm1r1sBXUoCsJx7E5MICf66YwinY194h91v1+BocOqGazbvik4MYuuku7j309tGeyxdiuPkg0u9fgZQvn8Knm4L2KckpaOpt4vJLDUomL/GoIrtCk8+RhJZM6c1LVrDl5WwjGPpgI+qW/hyeCdmou+XbmPrAixhpaUfV6juQNnOezK+qNY8he94Y3/Wqi5uFdH+Ppwf37rgXvvfew0+fH0ZbLvC3G2fhztN/gpzeVpm/0ljS7rsTDcdODHJ2epo+xH9tugP/1/25/DaEHWWt68Aq36j53nS4L1wB9B8KcPRZpfD0QVk2GZT/3z9q2NTM1QwOEC9f2/ka+/9RlUeZllMefdUwyy8tB+u6E69Dx2BHEP0l+WPr9OYVY4Hv1H70HdyB1V/8NzbWfsQM/6Ju4JVrjsXia39nfSPtbwdeuFp7w5eMN7pOoruSzlRMQHlc+k1v23TWGfKGXa/koUmeJiTUtBaw9/CICpEp9jo9vRxNCHWP4MlP7yvPsT3LPbEE1evWB9Nm4QJ4DrawPSz7G9/SbCdg/3TlBOuUBHvQEC8y56RNxTsWdM2hPej95yuoW/lb2a4yksmIBi9jiNZqMFvKru5QoPnfm1mO1KJpo5/aSrqg84A/byJ9gqt4m0utL6T7G9xpeN/nzyF5zvRzNH08ghUfQ+1fSD5bVp0bdbeuHFuTigcmnS+/jKZbb2P/L7vxSuSfebI8XtmGVa1X9RjJxszPz9f1V6W50P99b7+HwZvuRHLpRP+D91E5r3tnD3rueYRdS+9/VK19XO5T6Uem3X9XgO0u9ZM2kCbXCaE349J7GlhOziNnfE3XRlb6YXp+dLgh+VU833RL3WdI+/FKpB/qxaTH13B1p3IuZdlltuz9UKGlk3517CLZD1PyQmv9ftL2Ce7ecjf+3f7v0HUbz9ZXv2xRNQeYdQ1QdnywrCjXMcGKna+zLimWEgl7Vu2LFn3pIJreK4Arwwe40tmDG3fpBBRdfSWa7vm1fF/V6uXIPrIwaO/23fNT3I1XnOGNhP52eP5wlT84qyrGGfDQt3SCP6h79GxjHunpZhO6QKl/fcO+hHowGvbAbSIQJBZAm07atGnctyhosQ/t3q1rqFPwNuu0ucg8/vig9uiN3L5/bUbxksVB7dnpN9SxCgD7X3wJFXNmyzRU0lRNQz2aOhW4XbZtGba3b2fVbE/YM4y64iR05rkwu2w21p671jR/F726CFubtgZUNiUjUN2O6Q3zovPk6r2U4N2fK0ixMWUnAVPmA1e8GBZnwyn6qmG2vVDoqV6nyrborY2q1hHsOMJtizd4+mJg7wbtiqT0+Q2HLwICsYR42OtCDtxGijY8nZKAeiQeZC4WaOekrRLPiGZbymmdZkeuKHhLyL/wQlvr1Q59eeOhdjzvvIuSqVMw3NUV1KfRWEK1VaMBsa47ndJJjuo2I1s/AfZptS9aMK0P7buy4B2grzvcqH7pH0zmSP6S8/J019/NQ+ud33dGeeTpHQl4sSnAZ1aMUyAyEIHbEAgiIBBtiCZjuL6vHldvuVrzmlcuesXUkzL6vOeCly8IuR0ZbV/As/oUeSOSoHyaKONH2wKevkYTfXkw056T9HSUN/SJ1KPmcpyp+SIgIBAfgVtHYaRThB4RcBCO2ypxjGi2paJap8UBfUNpT2B8dNK42frxvE8b+aK3vm9q3mHZd1Q8Un6VanecApGPU1qrwiUgIJDQaBrgFwCTQJ87mAHlJHKiHRkd+1hwlp4aKkG/A4K20qcdcQYn6ekobyhvklnEIV8EBAQchpFOEXpEwEE4bqsICAgIRIFOGjdbP573aSNf1OS8w7LvqHjkxDgFIg8RuBUQEDCNsgz9TyekHFtGMCpCYLYdGQWT2dND+tRDCf+nHyo1J+XmiSM4SU9HeVMw2fy1ccgXAQEBh2GkU4QeEXAQjtsqAgICAlGgk8bN1o/nfdrIFzU577DsOyoeOTFOgchDBG4FBBIMlFuHcujwQMfpvBYqsyoxs3AmklWqg/LuUNJ0s59u1OTVsOvpPnU7Czqmo7wvlXsf5QiT8oQFjNuThf1vVY7ltD2HqsRSlU7X6Kcgyf7cSlTAJoo+/1DygnKODbe0yOfo/3TMDF/06GmFL1baMiVHRUf4aa5qKwBRyBcBAad0poDD0NIpQo8IhAFO7q0CAgIC0aKTIm7rR9E+HS6bjnxRVuCL54vS8cOZ47fvKHgUVLzb5jgFIg9RnExAIAFA+XLo04uKTw5i6Ka7xqqpFuT7P5+wUInaqQrLvHYoaHvh7z6Du2y02rO7Tx5f56YdY1V5770H+acdy855PDnYf93P4G30b8Jlp3Ygf8pA8Mb0velwf++pqKk6rqxsm37Nd+BZ+SDcxfmskidGRlC75BZ4WjtRtmol2h5bEzG+mGlLGrs0XndJkcynvYfa0f/DG1lV4iqqynvKDOD57wdWmlUiwarBR4MeSMSqrXbBZH3xkrFK4QqdZFZnCjiMgY5gnSLpkb5DMn/G2zl0DJSbLt7mFEPQ2w87BjtiXqeKfUFAIHLrjIJvVHBK0hl21l84/TBHbf2MQmCgParsfT2bTjmn1PvuROOMUtN8YYXJyB6sq/MX+Dq9jqUdYL4oBUOpYFlVlb9PE4W/TPNYaR+MjMj/3+9KCZargQ54fn8lan+/M7B4dwjjFIji4mQrV640dd3tt9+OaIIoTiYwHlAWAJCKCkS6+Ita8U/oHsGd63wo7QR8OT4cOb9VVtj7N1bC2+U1rbBru2tZnp1QHRZlO/SmrdbGR2/Ueru97B5Xrgs1Z9Szc51709H0bqG/sZIS5K28GeVl6f7AysFW1C6+mQVAmWER4cCKVhGH7KJs3P2X63H+A+/4eZHlQ9rICLz9LrgyvcAIWBVSV4YXcKezeUeaL5pt9bfD84erNDf+XRuKkdKTguZ84G83zsLy//ew38g4tMefN0n6BEf6fxgDETFRbCkCcDKoH66CKdEKp43xWETU8lipU8gRfOFqfjBXx0mM2rkR+tttzUkgPFDuh/lp+WHTqeO5L9AXVQ9/5WH0tvWy39GyNmK54JjaF4jGOYiCY5FdZxJIj3QOddrWIeHwwxy19cm+V/+OYptOOacVC1NwKDfJNF+CAsKp/fK8Q3nIr8ljnn2gwL8y0rG0uAjdKcn6wfiJxY6MUyAKA7eVlZWmrquvr0c0QQRuBRI1cLvo1UXY2rSVPd2VQMHbR586zDYn2ogpGbk/r40LrjwXal7+57gGINimetF5/oCIeny5/uqXLJCpOpeSk4KMJ55GcklJAH2pvaHdu8dlA9Iyhu/+992ML/ldXpkXLEiLJHgH/J/GpKR7kZQMFsxlxsVL/4iOwNDTFwN7N8DTOxLwRrOSF/RQ4LorU9GZ58LsstlYe+7acRmqCNxq6wF668MJ3kR14CsCOimq1maYEBM8HtVLUMg4+yxzynzgihdjc2425yQQ2zp1POdAabDmlM/B8qOXs9/RsjZE4Dby9I0m/scyeOtMC1GlQ2LI1nfSppPmJAVtrfCFgqJp06Zx7UHHfVGefaAAeZRbM9JxbWlJ0PgjOk4BR+OUlnLcUkDWzJ+AgMD4gz6/oSe8amMhO8M7+qbtaD6b14rlDblmXr3/KeE4gj5b8T8B5YzvjPrRN22Dz02e3wB3WvDYaWOKpg2ovq9e5ouSF/SGrRS0JfgGXf6gLT3lJnqMM1/kT3Lo6e6Ijz2h9j99D+YFzSkrw8vmSHOlJ8YC0aUHBG+c0UlRszYTGQq9FAD6TcfpjZ9YQzzOKU4QDzpVaw7DGGbHG/r5gTwBAYHQ15kWokaHxLmtr2fTSXNSwuz8yNfUeojvqC+qZR8oQK85nTYwiEkeT9D4IzZOAcchipMJCMQpKLcND1Ueelt1mD1hVIJ+03H2ycR4omOf7vj0zrm6DyDa0TTQZMiLqOQLgXInKaDHi0meMcOHPvMRiC49IEHwJnSdFBVrM5Gh0ktBiEX+xOOc4gTxoFON5tDY3xixsQgIxCuM1lnU6pB4t/UNbDrlnJSImvkZ2QcKxCR/BJwN3L7//vtYtmwZFixYgIULF7Kcttu2bbPTlICAQJhQlVPFPV7ndrFcPvRZiBL+z0SSx3KQjhcKJuuOT++cN3cSoh1lGWWGvIhKvhAKJgf81OPFAbc/rQWBcjMJRJcekCB4E7pOioq1mchQ6aUgxCJ/4nFOcYJ40KlGcyjPLI/YWAQE4hVG6yxqdUi82/oGNp1yTkpEzfyM7AMFYpI/As4Fbn/605/ilFNOwWOPPYbPP/8cu3btwqOPPoqZM2fiZz/7mdXmBAQEwoSavBqWkJxy2yjRO+BiCdjlT33PGUubQAXKKDn5eMLjyfIX/eGN761K9qc59qHxHbsZVGZVynxR8oJy3LoyFPny0r2sUBn7jIfoMc58YSg6wl8cJynFX5xJkfdKyQuaU9+Ai82R5hqr1bbjWQ8I3jijk6JmbSYyFHopAPSbjkdBQRTLiMc5xQniQadqzYFy3NLxikyR21RAIFzrTAtRo0Pi3NbXs+mkOSkRdfPTsg8U8I4WKDvgdkff+AVsw1Jxsr///e+45JJL8Jvf/Ia9aZuc7I/7+nw+PPPMM1i0aBFefvllnHfeeYgmiOJkAokKdTVTKkx25zofSjvBErD786tGT4V0vWqfFLClomQEKlLmz3UbPWM3ynNFn0xJVUGJL3f/5Xqc/8A7fl5k+ZA2MsJy2lKwFiNgOW9ZwTJ3ur8YW7TMbaADnt9fab3SrEBUVTWOtQro4wU9nRTteiehMNABPP/9wArL5Nhc8iSQof1FQ1QjHucUJ4gHnRoPcxAQiMV1JiE/LR+dQ53Ruf7iyNZX+mDlfamaNp1yTisWpsgFyqKKL3r2gQIUtF1aXITulOToHL+ArTilpcDtxRdfjFNPPRW33nor9/yqVauwfft2PP/884gmiMCtQKKDEpJTbpuKTw5i6Ka74CspQNYTD2JyYYE/V17hFPbWGNvMmppQteaxcUlOTpUu6xYvYQEQFghJ7Ufb7ndZCoT0z1vRdOtt7Lqye+9B/ukzomrsVh2jlHc+Qt21ixkv0q/5DjwrH4S7OB/Va+5j19Uuvhme1k6UrVqJtsfWRNXcGJ+uXSyP1z2xWObFvvYO9F1zA1JaOlD1+JqoGK9AoB6QHiAI2NNJ0a53EhpUtGuUP3HzVmo8zilOEA86NR7mEA1oaPAXdKuoEG8rC2ivs5TkFPiGffJ6i+b1F+u2Ps8HW9AxHRf+7jO4y8qDbDrlnNLuvwsNx06MSr5o2geE0f/Xul1RK1cCEQrcVlVV4a233sKUKfzcWl988QXOPvts1NZGV1VBEbgVEAjciNOmTeO+HUYBiKHdu8d1A1aPT2kMd778Mvt//oUXRuXY1Vj06iJsbdoaUFGWPlmZXTYba89dGzBX9byV84nGuUW7HAkIOAUh6wICAgICehCBW4F4RCzbP1o+2Lfap+Hmb/06JuckEH8IW+A2LS0Nvb29cLvd3PMej4d1ODAwgGiCCNwKCMQuYtUYpk9zLnj5As3zr1z0ingKKiAgICAgICAQ44hVW1VAIB4hfDCBWIGVOKWl4mSHDx/WDNoS6Nzg4KCVJgUEBATiEpRPSQ/0CYuAgICAgICAgICAgICA8MEEBLQQWDbPBFauXGn1FgEBAYGEQ1VOle55yjskICAgICAgICAgICAgIHwwAQFHArf0+cfatWsNr4klhCOXiVY+GDqenJeH4a6uoP4imVMllvPVJOK8BGITNXk1rBCZVo7bWE8WL9abgICAgIDYTwRiDdHupwkICITmh1T0p7FCZM8V7o5LHyzW/D7hM45D4La+vh6xDE9zM3C4BejYx60I3Xb8JPZ5M68CH+VKMXNuwoe1YxWoH1oBd2ov66v30wZWmVFC1b23I3v6BH5l6qPL5TEGVRBu+0LznJkxVnxyEEM3rxirkO3us0WPSMLpeenSVyC60PYFKvpHeRXBPp1af6vnrQ6qaHpkwZG47sTrEMsIqjRbUqS93px2crR4YJNvYQWvX7Pjj9SYnegnjGPlrSutPYF7PAJ01NujLGG85DSSc4yVvjm8MC2LkeCjA32MJ0+D9hPJbjay38IdNIvgGjQrT+MhY9EiG47u96PHe3e1o+7WlWPy1vtvoOF99B7MQt3P1xr7afcsk48r26cXl4hum+o3WfMjbcxxvPljeS5W922B6IGCp/U9dehs2o6C8pmoqA6jLjaxJlhcRblvKIKJtE5pvV7Y1AhcfRTWF+yUzx1ffDwuPOJC1HbXxoe8KWlFZavCtC9Y2afV8aOo2uNjHJaKk8V60t9tM4/GsWc2wp01DE9fMmo3VcHT6UFKZQUeuaoYG4Y/ke+hN+V+Mu0n7P/37rgXH7R/IJ+bWTgTt864lf3/od0PBQRl5icfix/910H4GprgzvKi+qw21l9n8kw0PdcC+Hwss3D1WS3ILPIGjCO5dCIq/iMV2d3vyO0NVs5F+1n3scVY/vZyYM/rQeeyKiqCgkM0fgoaEa7/5/Xy+Cd0j+Ce59zIbxuEO9+N6tPruPR44ppK/GPgg6D28tLyTCXnV799bXScd6zH0xNEX+W8lHM2My93ZTmqL8qEu3Xj2ICnng1c8iSQUYBEhJIv4XprXo/HWscrCjKAF64Okvf0hevCx6v+9qA+ZfkgNalxris5WXP99bb1st+7unbhkZ2P4IueLyyvKSPwaGl1HVpCfzs8f7gKtb/fCU+fK0DPKddbUlkZsh64H1Unnhg0XnXfpop6aPHn/PuBv91kiW+Nc1eifMrRcBry3HhjrZkHJAHYt9F4/BmFwEC7oexr0ZJHx6DjevJudo1ptOEEfbuGuoLW1fH5xyMtPQ3vNr8bsI6WzVqGVe+sCrj23OKTsbr1ENz73rI/PxtjtLWuneBFmAroODFHrT6NdIFj9LWC/nYMrr8c6fVjfXomn4GlJUV4teU9+dippaeyf5WyaCRzVmivpasb93yKwjdvCRifZAuOpOUZ6ns6RraVll2b486xtY+Ecq3kZHvq6uDKc6FmXn2w/VZVFeSch4oAfhisQSt2jBF4cs2TJzsyFirGZc3xwOEHyXnH3OUo2Hx3gPwb2mmqPTZArnKGUT2/hclbf5sLta8XAyNJQX7a/o2V8HZ5A/wLs3YggXful6fchty/3mDJptHrJxT+8HwBnlxryYfmXHTGTPv28reWB+ghte3MG4NAhMDTiQp8ml+Kyu/8E3n5DgY/Ldj5nuJ5qH2pH576xoD9QbmfSMcbsw7js0Of4U87/4RtLdvGV7dFiD9O25ABdLUYP/rFl25E+9XX6d4bjj0+HouTWQrcms1ve/vttyMaCfLuEdNQkDuC8jkdaNxS4A865Lux9saT8OrAdgxjOOBV+hMKTmD/394eeC4ZyTix0B+M+LDjw4BX8Onc+rpBpL08DE+vP6jB+ttawH6zcnDDGDuuGEfppaXI6nkfSYr2RpJSMFQxm/0/vWErwDn3k+pJmp9jE7Y0bgkYf3FPMh59aghJ3UnccRA9Xhv8kNve2nPXRixwu2zbsiD6KuelnrPRvKqvqIa7bXMADZGUAkyZD1zxIhIRURu4feNHwN4NQfKeFE5ePX1xUJ+yfBA0zi0qLdFcf8uPXi7LslqPmF1TURe4HaWTp3cEtW8UycFb9XpLe/yPSC4psRysMeo3iAfpecBglyW+DVbMRvrVf4fTkOfGGysPWuNXQUv2Qwrc6sm72TWm0YYT9F306qKgdcUDraOc1Bz0HO4JuHZtcytmDwwiBSNh0/e8Mdpa107wIkyBWyfmaDdw6xh9reDpizGyd0OAHeZDErZmpGNRabHurUYy50TgdvB3X0daw1aunXjoa781FTTl7UeSXbvqpFURD9xKTuH+C7/iD47x7LeX/uG4QxfAD4M16GTg1qxu4yHcem1c1hwPHH6QnA+n5iD5cE+A/BvaaZw91tPvRu0bhfD0pgTJG5JGWPA2SA4pyHvmIbgzPZbsQALv3Lr2ARzT1WrJptHrJxT+mA3casmH5lx0xkz7dvdQN9culmxn3hgEIgQDO9ZLL6Xkl+KYn+wKb586a8JTNBe1Lw/JQdry1avRuHRpQNBW2jeiRrc5BSM/Iww2JAveXnSeP9BqMX70yLF36t4bjj0+HgO3llIlGOW3jdbArQRXlg+evlTUvuY3vtmbYqc347Puwxh2uwOuJaFTPgVUgjYZrXNVniEc422G58xkOaih7K98TjsatxQGHadxuLtrg9oj4yTgyTLnXB32wscZv/LpphKZ6UOYdsZB7vgkemi1F6lPC+r76rk0DmVe7tZg+jKFR0+rDu2Juk9TExWuzv3cJ4hJ4eQVfWrCe2op9cnD6Lm6oTLN9dJQ0wB6NqYnyzH1uY6CTu4seiOlTXO9HUrrhy8M/QbxQPFmqlm+MZ0arjWvNVaNsXDHH27ZN5J3M/3otBEqfenTSS09z1tHnUOdAceqPR7MHRjgjs0pOmqN0fK6doIXYYJjc4yVvkd5QS/GK0FBMpKnSR4PDqh0vSWZQ3rI4+PZg5ItmNK1n6Ictmwrya5t6G9ABSIfKKFPJ+lNW037LbU/fJ2bWYOh8s6GblMj3HptPNe7GX6QnKeodL0pO42zx1LwtfrMVmt+2vw2uDOHLduBPFQeHsQxnc2mx2umn3DzR0s+dOeiM2b1vq22nSsyRcB23GDCjqUAEvG9oXajM2kTbNj59AVt9cP/QO31d7Bgbe2CBeyUOmgbNbotkn5GGGxI2qfpbVk78aOWL32ue29Y9/g4Ar3/aSnHrZm/aEXZqR0BvynaT69qT/LQcyNnUDXaFrVL7av7o89ueMflz25swOr4aYxa4zOix4HuA4gEmgaaLN9jNC9dtO+13J9AeJDScyDyvKJcOzaht14a+xsNZTlSayocdNJbby4n5xUCfyK+5sMxVqfHbDRGM/040YYGKDeWE/twOOloNEbT6zqMdIyaOcZK3wa80NP1kZA5o/GZ0blG+xHtWeOCjn369ls410EE12Aoui3cMjae6z1ie6gCTvppdvwwOxhP/0xLPozmYsfHHjc9JGB5DXY08l9ki9S6d7t72Ju2StBv5dubUaPbnIIVWjm5dxrs03prvaNx2/jt8YkauI11NL0bmOfD/4p2Mg64Lb14rIu60baoXWpf3R/lUOIdp+vtwur4aYxa4zOiByWajgTKMqy/Lm80L11EsviVgC58OZMiz6uCybZv1Vsv5ZnlhrIcqTUVDjrprTevk/MKgT8RX/PhGKvTYzYao5l+nGhDA1U5VXBiHw4nHY3GaHpdh5GOUTPHWOnbgBd6uj4SMmc0PjM612g/oj1rXFAwWd9+C+c6iOAaDEW3hVvGxnO9R2wPVcBJP82OH2YH4+mfacmH0Vzs+NjjpocELK9BKlQ2nuve48lh6RGUYOkSmpqiT7c5BSu0cnLvNNin9dZ6QflJ47fHxxEsRQvvu+8+U3/RCm+fP5dRyfFdcGVSwnkXS4p8VO5slt9LKnJ1wp5hlpODijXQH52jY3SOQL+lc3SddA+hzp2GT12lqH1zLPdj9TmtcGeP9vdGiXxcPY7e3FkshxOBhLi3MY39poT89MfylVBlv8Y0dl46V1U9n41DPX5KCE1/0twk9A+mYfdbEzXHQfTQai9SnxJUZlXK9FVCOS/1OfW8GN2zxuZFicwlGsqg35TAW6RJsAWqFKncHJWg43TeKrz5NX6eqHhF8t47MotVoHSyP4aiIwL6lNaYLB+Kc51709G5N0M+J60/WidHNAwHrBf61ItkmYoWnkRfWyoQ6TXlCB8VdGJJ5RU5boPW2xCfT07wRwb9pkJeWutag6dMnyrWfEiyY3asPGiNX4URp/WUHj3N9qPThpq+VlGTV8PV8TzQNflp+QHX1rrd2JyRwXKTqsfmFB21xmh5XTvBizDBsTlGYd9cnTfKi8P9bqYvJJAckTxppUmIlMzR+GhtSXaiBMkW9OXVmLat1HahZNeO1+fJHk8WKwCluZ9o7PuOIIJr0Ipui7SMjed6N8MPknNfWn6Q/Kv3e3mv5+yxkm/Fcty+WcyXt9c1jm8oYffx+lb6YVr+itKPrE9NZ8Wd1OMysmn0+gk3f7TkQz0Xtf2s5aPSvq3WQ0rbWWAc7X0Tdiy9W0l8dyRNgk07nxUoG02TwNIjrF/P/mVpE6iQ1ug8x0u3adGajvd/9BHX7zDlj5jxM8Kwf9E+zfZjE/GjeTuGcfoOn0zjktQvyffSPXRvxPb4OIKl4mRJSUnIz89HWtqYQctDczMn100UJP3dNvMoTD66HU3v5cOV7gPc6fB2e+GaWIgXvl+O/+v6DHeu86GoG3jx6i9h1kXXsqc0//PMXfjGE5+gLRdYsTAF86qPw9IjLsVwQQ1WbHoI5z/wDrvnl5ckI2/6DPzwNwfgOtgRUG29P30Oap+uB3w+Fi4vmdGJlo/zAsbhrixH9UWZwP5/+QMi/SmounQS2hffh4beBpyw8dfwbfkAdZsK4c70ofryarjPXYyeCVNwyydrsXPnv7jjf2T7I3JuF9o0f/mnFOS0H4Y734WiL7Waoscr1xyLxdf+LiKVFykXDX3WUJheGDB2wkklJ+Gy6Zdx5/WLZ10oODQEV74LNacHVyX2lU5A9UUZyOl4N+yVuxMBtLHULV7CPkdhuYTcff7PNwqnMAUsbZqpv7oDjTNK2RNN5eYo8XmKbxgVhw/7n7ZNmMqON7V9xuQ9o/btsf5GZqHuhUa4y8p1+6ta8xiy582zni+oeQfw7m/Qu3Xb2Br73nS4L7wL6DjAznVu2C6/uV92QTnyf/4Cq5i75vGrccETn/jLhYwA//ruFFz5jR8ga+IMNrZ9V1yBw42NWH1JEj6cmizL8q/P+nXY15REZy36V3xyEEM3r+Dyce+hdvT/8EaktHSg6vE1aDt+EuPNsa88gINP7R9zbkb1HFtv5HxTgZnyUlSvWx96snniDY0nswh44+7AvE5Vc4ATFwIfrgMObAlc12ctk/nG5SlVZLUjO9J4RuWVezyzEHj++4FjraEHRwD2bQwc5zceAF65MfBaMlKVOb2UeorXv9ljSgx0BI/Rqj40akNjDEYyScfJqVNXop5VOgsjGAmoss6qU89ehlVbVwVce27JKVjd0satvr7/cFdQ/7wxaY3T8QrsenTsO6TPxzBDa46/OuaHyOlrC21cBjIajgr3xNPm1/+OvOVr4C7n7CXdHtRedik8XYdRdXo7ssuH4Jl8BpaWFOHVlvd0ZVFP5rh8HKd1q0fXjsEOXZkPB8xUq06qKMWhX/0ElUecYHtcuuvZCX1o0JeebcuTJ6syptZr46rTeDCSbY49FrCva+33dHzWNextsN4nlqLuzwf8ez3ZJcedKe+xno/fHH3YnILkrBQM9w37C47Nb/H7aW3+oC0VJiM/rfqsFpY2IcCuyXehetS/kGh/4Ct34bP+JqzfuT6gYr3SX1H7kdOnn4bVp9yGvL/eMDau/hSU/OdE7FlwHWa88/sA+1evH8f4YwCSnV3tu4L6v6hgBpZOOh9Z254OtrW+9xSzkUmm1D7qjG9chb/s+cu4zCVuYWBzBdn7ihywDXs+woRbHsbIwVbmt7V8KTvID1Pi47wStHztHkwrP8WZvcLiuqeg7Z4XejDSeBBJFWWY+sw6Np+A/YT8/jX3wX30bK5uC6cfpuUj9+5qR93Su8emd+/tyJ4+QdN3psAn5YkN0NlatFJiVGfUeroc2c8ZXRcugKex2TB+VPfxv7HkFb+O/OflX8JV3/gRDv3kLniaD8GV6wI8g/AOpqDslE60fV7i2B6fCMXJLAVuTzrpJOzduxeXXXYZvv/97+Pkk09GLBFk7urp+EbpsfjPJxvhPdiOlJwUJPmG4B0g4elA6+cl8HZ60V6YimWX+XAo1/9U+7yMmbjqsT1wHWxDSl4KJs9rkI1KekOAquB6SifgiWsq0bVzB376/DDbnDde/SXc/OXvsgDvo8+tCgjsvHdpNU7f0MPGgZIS5s+PtLejbOXdaPv1w2xhYOIEPLnkCPxjYCx/zCWeKfjWfzVjpK07IGBCb+vWv3KYKbCOCWn42be98vhpI7zuxOvQ03kAX/qv1Wh54aBs1KB8JmpfHoDnYLt/MXkHg+ihVIhasFJxV6vKcI+nB/fuuDegcIY09vqeevxp55+CNnjp3DsvPY6Lf/e5Irh+PJYe8U1ucH3yySewcxRUi8U3be3Smget6vN65804W9K66ChMw88uG5NFeqNnyfQleHL/k9hRtxGrWw/htIFBuU16entNrgvdKf7g5tczj8Qdx1wuB0D3LFiIkaYmuPJcrJCJ2rlTJ6Q3BAXvXrg6wFnryzwe9X89jOHWrqB57dtQDl+PfzNylZch/f77kVxSAu9nn6Hv+uuR5BsONvhHx5ZSWYFff68Ib3k+DdlINeI3HdNywJbNWoZV76wKeOhxz3Nu5LcNBs1314ZipPSkoDkfePC7BdiX3sPuoTcmbn1+GKnFhah5/H64JxYDTR8B7z4Bz2fvBDx4yr71OXsPRvrbMbj+8sBiPFPPxsEZP4Sruw4Tvng+wGAZmngS0k7/EVBQA7yxMoCnnrxTZD2nFRgwlB2OrDCH+fz7gb/dxHf4+9vRtvtd9glz6dFz/eeoUED7XjR7MseOjR4PuHb0OtkA5/XPCwZrBYg5AQiSl4r0wcB+pOOqtc87phy74Vinno2uCx7E0vfuMZRJ5dqgAiaUf0xpeFIhCeUxaWzq4+qxdWUXBa2JU0tPZf8qgyO8Y3prlduvhq7WpaWSjsQrnrxp8JHXj5OQ5ljjzkPVP+/SHJeunBitJY0gmRP03bl/p2xfkM6jAEJpJ+DKd6OGpw/owdPKa+E+cqa8LnjjMJI5Lh9r5mHw8BDSG98xtW4b2seKdsjzUusLFU3UdOHR6t3d77JckvRZ8pGTjtQN2Flp1+q1nnfexcDy5WOBdCpSMkq/9l4fdi38FrLbB5n9Rg8+re6bvL2QbJGHv/Jw8NwU+rBhMJ1rq/LmpteXlj4h+5UC5UbyZEev0YMvZfEnp3SaFcj8LsgIWgP0hnj6wnXyeqdrkwY7UfjmLQF7Pu3rfUdehML9fw8K5rRP/joyd72E9INjfgF7A+/5bniaWgIeHiuDDv3ZLqT1e9E66i98vaAKCzKmYcNn/8YJ6xtkP+3Nyyfh+1+/Alnbnwmwa0r/oxAFV9zMXpr50fsPBfgsFAj6f1P/H/782Z/xaeeYrUd+5PfW7kcSL8g0Oq6ewlT8VOF7XlRwnOzH0Ms56oDTV0u+iqm5U9nbqUbyaccXUPpmD+1+KKj/KydfgC9veTIgsBdga1VVIe2Xq5mNPNzSgr6bb8JIQ1OQjyoFuAu9hfKbtkZjD+deF5MwaXMF2PtVVSj83aP42ecPyEF12hNbC5Jxx4KkIDkkP4xeIms6sBl/bnsf/9v9uTMBd97YWbB2AfDheu5DGlp7FFcJehhCY/D54PnDVaj9/U6/H0IPYE8/je2l9BDhx2/8OCIPC7R8ZPaA6I0Sqgiq6S92FqXjtm95ZB5IOLf4ZOY3Bzy4kx5clR3v/92+Fz3ZxUE6I1Qe9a7+dsBDMb34UcvOYvi6/eWp6Vzx9BY0vVcAV4YPcI2+rDgaVO/JKgx5j491hC1wS9i2bRt+97vfYf369aiurmYB3MsvvxyFhYWIdoIc9fhRSM1MxTnpJ2DRA9vY4nBleFF4ZB86dmext8dGckdw3RWpaM0dIws97Tg96Sj8+NFPWDDKX3W0YzQvh4sFkX5z00y8NvgheypCQY264iR05KZgTvkc1sbWpq2Y8YUHvelA9iCw4wg3G8fVjx9ggSgK3pZ85zvo+NOf2CJPKivDY9+biE0jn7E2JdBnJc/U9iPjfwBPr2tsLFsL2G9a7Mu+PYzWnOGA8c8um421zS3A3g3obXAhLc/jf2JMnxoUzUXt07VcejBl89I/DANhTgRul21bhu3t21l1Y/XYJRoqaaE+R/QlupOiU5/L7/KiqnWEKQSZHueuRSwiWgK38sZ00Xl+R1e1LkZyRnDdlYFrieQ325WNPl8fHm1qwuyBQVaZVPnpzdaMdFxbWiJfT2tI4lXd9u0YXHQldx2aldUAPH0xWxOs+uYo6DO8vpyT0fRcE3+90waVXQxvYxNbpxm33YqBe+71r2OKNw+DO7a1N54k6wgJdmXRjGO86NVF3DWTk5qDnsM9AceLe5Lx6FNDSOpOChq7L8fH+Kg2IE7aCxQfMxP3XfpfQbQkA2Soy43sCi8wZT5wxYuW5ie1N7J3A6skLYM+Ca7wr+v0hq1BfEuivggqnqr1nC3Z4cgK+xQpPQ8Y7Ao+PjpvK8El3QAcr3+zUIzHaAx6YzPtLGnQ6tO8YiwszDAlk1bWhtmx8daEWdhZq5YDt2bkTYOPvH7CAoNxOT03p+j7nb9+J8C+IAf20acOswdTjuwlWtBYt7Qrqj54D8YoTRrOeiRsQVMJd//7bk0bi2Q+nIFbAgVvK+bMDqI5rdnPd21BRYtX/lrF6lrkrXulbWHVVuVdq9cXD07aoXb1WrhtYZnfb/yIa2clKdY7XTvhf3+AtIatAXs+XTecmoOUwz1B+sKXmoPkwz1BNgLb618ekj+fpkJFLOdlXZ3sI5E8Sf6C0i5V+mkfT3VhfccgjulqDbBrsiq8bOyLSkuwpXFLkM9C+1n3UHfQceZ7/rZRd1w8343AW5snFJyAVSet8tM4jIFb8s0+7Ai2Xde1D8i00aK/2kbWm+fyo5ebHrsI3Nq3ucjeX/VsMgveEj8e+LoPi//Hw4K29JIGBUGV9r5aT2j5FrZ1iQ3bmtaelt8vxTo8vSN+P6R8KOg+x8ZuAD0fGUkj7O3+IN+Z4lBXpgWsEQlrm1uZ35zif7ykaTuFi0dW4kfMVya/noK0WV4UTOtD+64seAcCbSwn9vhECtxarohFb92uWbMGTU1NuPnmm/HCCy8wBfrtb38bsQAS4p3dW0effHiZALV8mCd/8jvtjIPIyDgcdM++oe2jb/iN5uN4bSwHEh3/rHtsgZDgkRKhTZuedtAfnaPjX1T4nybQb3qTtmfVLWxjQ0sLWn75S3lD7155MzYMfxJkiFV5hjBjuAXVZ7YFjmU0iPvIxQNBi53aqK/d4H+aNeJjSkz+zGfEB3frRk16sOP09kOYUd9Xz55aKw0daexKGuqdk+jOO0fHJYUgnaM3CgRCA336IcmOel1Mmx+8loi/3d5uVB4eZG/aqtOY0286Psnjka9X8io1tV9zHVqWVfrUZHRNKEFOQHb3O9rr/Yx61Dz6C7ZOyRDt//H17N/kicXsyanW2JQ6QkK4ZJE+e9JaM/QWjvp4ZvoQ0328sR85vxVZGcGVQrdNAdNhbOwqWpJ+YcYS/abj9HaQFYy2F+CQEUZ87G0c9kYOh2+sLw5P1XrOsuxoyAr7TSkNeMftzNtq/2bh9Hj0oEOrYzqbUXF47A17PZl0em1orQmziOi+oSdvkeJjuMY1DnMj3qvti+wMv25zZC+xsW4Ng7YKmqR07Ue47S89GysSMu+edWpQ0FZas2TTSvab1XFprXu1bRFpHeMUbUPRa5Hgr6tzv6adpVzvdB3t6+o9n36n0JvDHH1Bx5O09vqHfz6W63LBAn8QsaKUvcUmyZPkL0h2qdpPI3+L9iy1XSONva52A9dnof2Md5zspZQ1v9AdlxX/h3RaQ79+QNYp30zdP9nwStpo0V9pIxvNM9xziVtYtLmI/sQHX1kxC97+/CntoK1aT+j5FrZ0iU3bmtYez+9XxjpkP4RznyNjD9FHrj6bb3+wOFT66LgVqPZ4MHdgIDBoq5ibpEvDySMr8SPmK58x5kfTORa0VdhYTuzxiQbLgVsJGRkZWLBgAW699VYce+yx+POf/4xYQZWHov/D7AmHEvSbjk/yeB25xwwasobY08iANukpbHZgwEs5DoLWWPI4ARblfVrQmxv7HCvMaBrgJ0oPJ+gzMIEQ0bHP1rowkkf1fRKvUnoOOCerlAdKB3r9uN09bJ0qkXv1N9jnLnZ0hNOySLmsrCAU/cbGbkBLyzrEqD2bsC07dsfjlO50ih4R0OVGY7W6Vzq1NqyuiXHdN5xeT9E0rnGYG4/3RjrPkXE4tG5dYZY5I/trvGwlozVrZlxOtBFOHRNq/07otXDyl2w2XYyuM8PrLIJno3X89KqgoJQerNqpZlCfMRDyuJSgVCfjoRsMfUob9A/3XOIWNmwu4sPHi0a/UBvFoxcEB23VesJxfWpzj3TKtwy7HtTxka36i0Zzk3RppHmk6ysb2FiR3J8TOnC7Z88e3H777SxVwqJFi3D++edj377wONrhQJ3bxT55odfSlfC/pp6MA26XI/eYQUVfGvuEJKDNpUtR3puqOXaC1li6Bly692lBb24sT1uYUZbhwCeJFkG5uwRCRMFkW+vCSB7V90m88uVMck5WCybrntbrx+PJYetUie7fvcJyF9nREU7LIhXDsIJQ9BsbuwEtLesQo/Zswrbs2B2PU7rTKXpEQJcbjdXqXunU2rC6JsZ133B6PUXTuMZhbjzeG+k8R8bh0LqlPLbjaX+Nl61ktGbNjMuJNsKpY0Lt3wm9Fk7+ks2mi9F1ZnidRfBstIJf/pGlSDELq3aqGVQOZIQ8LiUoP/V46AZDn9IG/cM9l7iFDZuL+HDc2g0Bx677q0+XP6QnHNenNvdIp3zLsOtBHR/Zqr9oNDdJl0aaR7q+soGNFcn9OSEDt08//TTOPPNM9obt7t278fvf/54FbO+66y4WxI0FUN6M6bmz/QmgpdfVzxl7XX33WxMxMJAadM/ktBNZwSXePXT8qNzZ7DolKG8SJVimP/U5+k3J6nOW/cr/CQlVHVy/Xv6EJvf2+zA/+dig++rcadiRXILaN4sCx5LtH8uPXsxg+WvUfVVWz/cXulC1x3KUUDJ/DXqw44czEW5UZlWyYhFEM/XY9WgYyrlEq1oYDng8WdpraUPwWiL+5rpyUZ+ajn9lpLOctkrQbzp+wO2Wr1fy6vDhTM11aFlWi47grgnKqUbF/jTX+1uV2H/dz+T8XZm/fpj9O3ywlSWc1xobT0eESxZr8mo0ZZ+KlqiP9w+mMd3HGzsVKOvjPBAKGLsGLdlvOm61COBoeyOc9qioCf3x+Mb6Coee05tfRqFz87bav1k4PR496NCKig82pI4V/NGTSafXhtaaMIuI7htOr6doGtc4zI14r7Yvegf8ui2sdo/OujUVphmliS+vBuG2v6LRVtLbx8yOS6sNtW0RzvHy4BRtQ9FrkeCvN79G085Srne6jvZ19Z5Pv31p+Vx9QcdHtPb66++QU89JvtVIQzMrzKT2kSS7lOdv0Z6lNfaq6vlcn4X2M95x8vl8i39melxGfgzpNKmYV7h9M3X/ZMPzaKOmv9JGNppnuOcSt7BocxH9iQ8pTa0sx+0dV7pZmgRKl0AFytTBW6WecEInO2Fb09rjjUEv1qF3X7j0oJ6PXPt6sXYcajAtqK1atxubMzLgUydaUunSSPJIz69ivvJb+v6642NNAFgqTpaUlMQCtJdddhkmTJigeR3lvo3W4mRfLzwV1zxRD199g2Z1cXU1P9psje6hivFPXFPJchhJkKriEdQVXwPaVFQzV1Yh5LXJ7vvNAfgamvwL4SzKdTvsXzwv9cNT3xg0frk63/Aw8Pz3A6utK+6zXW3dIfCq8erRMNRziVKtMFzQqpipt5ZYBfnZy7Bq6yrsqNuE1a1tLKetBDIyrsl1oTslOYhXZvqzLKsDHZbWBG1ClGid4CovQ806f2Xg/o8+Qu2ChYDPp1kl1FM6ga3ntzyfRkQWtdaTRH9ulVnVfCmwQcV7yKh78LsF2Jfeoz12Di31KsUbQq89goVzjug5rfF84wHglRudm7cWeP3zKtHrVKe3PB7KbUWfSdGTfCsBNQ1aqSsca8lkuNYGb03MKp2FEYwEVFvnHbM9Hodp6LRcUY4x+lwtoHq83pidGFeE5qbFe9J5UgVtV74bNeG0e2Jk3erZX06uQbW8ceXP4XFFam5afTmqT0z2ScFDyrUaljFYlS+z693qHss5HrDXa/hWenZpkIycchvy/noDd+xUod6MjWXW59Py3cbbj9FcPxza2KW/8MlChEmbK8Der6pC4e8exc8+fwA7d/5L3hNbC5Jxx4IkTf44rk9t2NZaa08r1tFf/WV8NO/HyM2bhEe2PxKRtaTls9KbtvSSD0uDreEvqteIhHNLTsHqlja4972lq0sjwSPTvnKuazTXLd/G6i/MTPg4TbeF4mSWAreVlZWmrquvr0c0EmRH3Q7U7G1H3eIlbCNhGwoVoKC8IIVTWPSfLbKmJqTddycajp3IjMkJH9aauqdqzWM4dEI1y8nBM0IpybJ0LqhNhXMgL3ZOmwH3UfJ3d49sPCnvU44/yBimBNaj4+/9tMH03LLnzYsIv5R00qOhU+cE7KF340bLa0lJe4knU3wjqDg8JMuxFq/M9mdLVk2uic5NO9B0623slrJ770H+hReOje3axXJzVauXI/vIQvRkF2PFpodw/gPvoKgb+OUlyUieczIum34ZjppwVERkUYue0vGKTw5i6OYV3Pnua+9A3zU3IKWlA1WPr9HVbzxaOvL2nF57Js85que0+nR63lb6N3vMLPrbgReuDj2wpjEGI5kMt57m9WP2WLTQMFTwDPtzi0/G6tZDhk6BY+OK1JpRgHja9NrfkLd8Ddzl5ZGze2Jk3YZrDZoJLuo5lk6MK5J2oOP6xEafUafTzMq21T1Wb6/X8K2M7NIg+uiM3cx+Ztbn0/PdxtuPMUObUOkvECIMbK4ge3+UP3S+/osPMeGWhzFysNUUfxyXRxu2te4YDu1B38EdWP3Ff+Oljo8D9pnrTrwOHYMdYZU/LZ+1d1c76pbeHeQvavnOKckp8A37AsdqUpeGk0eWfOXTZxjaWOOt3+IycBsvBKHFlDZtGvdNChKkod27g4x1O/cYwW6bTo8lHHMTSAxEQnYaGvyVZisqKiImq3r9dL78MvtXCtoq70nOy8NwV5c8hkWvLsLWpq3I7/KiqnWEVc2kT0Bml83G2nPXIlqQCDrAzhxJ9kjuEg5PXwzs3RBY0Zc+ZZoyH7jiRce6iUX6KvWR+njAsQjR0C4k3aSsPLy2uRWzBwYDqxZH0ZijTedFnfxGsczx5E2NaNwbBWJDvqLBntHaG8Z7XJFGoswzVpFI/OHtO5HcZ7RozfMXY5EHdnzlWJtjJCACtyEQREBAILpgZAxHK+gT0AtevkDz/CsXvZJwTxVjDVEXmIkE6DPYR2dqn//RNsfejozbwG0EaeiUbqr2ePBKPb+CeDSMORoRVfIbxTJntBeqIfbGKEQUy1e0IFZtVQGBeITwwQTiMU5pqTiZgICAgIA5UN4+PdAnIQICUQfKXagH+txJIKZpyNNNVR51qUgVBN+jG1Esc0Z7oRpib4xCRLF8CQgICKghfDCBeIQI3AoICAiEAVU5VbrnKY+PgEDUoWCy/nnKqSUQ0zTk6aY6t0v/JsH36EYUy5zRXqiG2BujEFEsXwICAgJqCB9MIB4hArcCAgICYUBNXg1Lgk/5lJSg33RcpEkQiIb8VJRrKgBFR7CCM55+N3ob09gfVYJluQypEI3ic1i6l9oQAJeGjGZKcGgYLbqp1u3G5owM+JAUlWMWsC5zbO32u7n8i+Ta1doL1RB7YxQjiuVLQEBAQA3hgwnEIxKyOJmAgEAC5WWjT/wiWLXcqJK2XuXsRMs/RZ8yJWIF0WhAUMVbZfXnfTtRe9ml8HQeBsXx3Jk+VH9vOtzfe0quHq6sEK2sDJvokOS6JjUfVf+4k1+Bve9Q6HqJo9vUa4q3xqRjhemFeGT7IwG66dySU7C6pQ3ufW/ZqxpvY8y2rgml/XiENO/MIuCNu5nMUVCtblMh3HmpqP7Tn+HOccm00arqrAc9WbIrb/lp+egc6gzb3mhmj7G7DyXS/sXTaWblK/VXd6BxRmlC0ElAQCC69mXhg5lAotpNMRqnNPg2TkBAQCAG0d8OvHA1P2jiRADCJMgBpcqltd21LG+fcF6EIRUtkCrBeurqmJMtBW9ZQPaaH8PT6YFrYgkw7IWntQO1Lw+h+uJBuDMUQdu6OrirqlhbiQ4tB+FX57yFnN5Wv1FMuuf574emlzi6zTP5DCwtKcKrLe9pBsVOLT2V/ftu87sB4/vT+X9Cx2BHoG46tMefs9IpQ96MPg5FZ0eJvo84tOb9gzeRVvsF3F88Dk9jM3sIU316HdxZw+zt+dpNVWx9m1m7PLnmyZJdeQvH3mjGWbfr0CdSIEBPp6Xv+9xQvjqL0nHbFytwqCUprukkICAQnfuy8MGimz8C1iHeuBUQELBUMVeJqK2e+/TFwN4NwIhv7Bh94jdlPnDFi4gV+kY1jW1i0auLsLVpK3wK3tAnsrPLZrMgd7RXh4+qSvIhQh2ALV+9Go1Ll8q/KZhLMLpG+bZuqNW2narMrcUn3nGtPq3w2pRcO6CXBn/3daQ3bA1og9IbbM1Ix6LSYlNtaI4vXFXSzcw7FNqEWd+Ph/yagsG82fq+6Dx/kDbLi/I5HWjcUgBPnwvufDeqX/qH4drlybVdRErPm1mLdvehaNy/wgWjuerJ10juCK67Mg2tOcPce8OJmLJVYxCJYqsKJJ4fllAQ/InJN25FjlsBAYH4++yDniCqHU36TcfpbTKBcfvkkt7eUQcB6Dcdp7evBCIHOU1CVZX/zdsFC4ICsmauSXSYkmsn9FLbF0iv3xzURgpGMHdgAJM8Hkvjjsi6MzPvUGiTqPrexLzd7r7RNyG9LJhW+1qxP2ib5fUfT+23Jdd2EQl5M7MW7e5DibR/mZmrnnxNO+MgMtKHNO8VEBCIYyTqvhwrEPyJWYjArYCAQHyBcvXogT4BFhgXUJ48PdAnswKRBQVe6S1aJei3MiBr5ppEhim5dkIvGbQxyeOFHYR13ZmZdyi0SVR9b5Ku9Pk6vQmpBP2m40a0MZJruwinvJlZi3b3oUTav8zqND350tJH8UQnAQEBDhJ1X44VCP7ELETgVkBAIL5QMFn/POVtFBgXVOVU6Z6nPIcCkQV97kqpD5RgqRCamixdk8gwJddO6CWDNg647ZUtCOu6MzPvUGiTqPreJF0p5yh9vq6E/3P2ZEPaGMm1XYRT3sysRbv7UCLtX2Z1mp58aemjeKKTgIAAB4m6L8cKBH9iFuMauD1w4ADef//9gL9PP/2Ue21LSws++eQT9Pfrf9olICCQ4Cg6wp9gnXIpKUG/6biomjluqMmrYQVKKNedEvSbjouq0+Ob47Z6/fqxlAijVcHNXJPoMCXXTuiloiMwWDk3qA3Kcbs5IwMH3G5L447IujMz71Bok6j63sS8PZ4sf6EoKT3COa1jn7XT8cOZtuTaLiIhb2bWot19KJH2LzNz1ZOv3W9NxMBgmua9AgICcYxE3ZdjBYI/MYtxLU62aNEi/PnPf8aUKWNPXo444gg8++yz8u/Dhw/ju9/9Ll544QWUlZWhtbUVDz74IH7wgx+EJemvgIBAHGCgI/Tq7QJhQSJV5Y5mBAVkR/PVKo+7KBVCEuBtbNK8RuS6tSDXTuglThueyWdgaUkRXm15Tz6Wn5aPzqFO+fes0lkYwQjebX5Xe3zhgpl5h0KbRNX3OvP2dA6OrVEqRMZykQ6zNyFZsI0KSpnIU82Ta54sRZO8mVmLdvehRNq/9Oaa2d5vKF+dRem47VseHMpNims6CQgIcJCo+3KsQPAnamAlTjnugdu2tjY8//zzmtfccccd+N3vfod33nkHVVVVeO6553DZZZfhvffew8yZM031IwK3AgIJCkqAT7mU6LMc8YQ3qkAFSijXHX02Kd7AiTx6N25E3eIlY8XHFMEbOTA7Wj3aXVGhfU1TE6rWPIbsefPGYRYxKtdO6CVOG+q+eWMZ13VnZt6h0CZR9T1n3kHrmwqRjV5Db9paXbtmZCna5M1M33bHl0j7F2+uZuUr7b470XDsxISgk4CAAAeJui/HCgR/xh0xFbhtamrCPffcwwZcUVERdE15eTmuvvpq/PznP5ePHXPMMTjjjDOwZs0aU/2IwK2AgICAgEAgyPlOmzaN+8YdOd1Du3ez/xtdI4K2AgKxub7F2hUQ8iUgICAgIDA+sBKntFfJwkH89a9/xWeffcZy2E6YMAG/+c1vcM4557BzFNSlv1NOOSXgnlmzZmHbtm3jNGIBAQEBAYHYh17QhoI9ep9Qm71GQEAgNte3gICQLwEBAQEBgejAuBYnO/vss1FXV4fPP/+c5a694IILcNFFF2H//v3s/KFDh9i/FNBVoqioSD7Hw9DQEIteK/8EBAQEBAQEBAQEBAQEBAQEBAQEBGIF4xq4/eY3vymnR3C73bjvvvuQnJyMl19+WT4mBWKVGBgYkM/xIKVekP4oN66AgICAgICAgICAgICAgICAgICAQKxgXAO3arhcLhQXF6O+vp79rqysRFJSEhobGwOuo9+TJk3SbOe2225jeSKkP3qrV0BAQEBAQEBAQEBAQEBAQEBAQEAgVjBugVuqiaZ+k3bv3r0sTcJRRx3FfmdlZWH27Nn429/+FvC27euvv87SLGghLS2NJfdV/mmh8+WX2R8PVLyBijvwQMfpvJNtRjv05qw1Lzv3RHJ8kWwvlDYjSUeB+EIkZEfIp4CAgICAgEAs2wbR7Oc4OTY63v/RR9x7xnvMkUC0jktAIJoRSuyr9bE1Ys05gHErTubxeHDyySfj2muvxTHHHIMDBw7g7rvvxrHHHosFCxbI19Gxr371qzjyyCMxZ84cPPzww8jPz8cPf/hD6302NwOHW4COfUDhFHRu2oGmW2+Tz+efdqx8znM4E7VXXsWErGrNY2g7fhLqeuowKXcSJnxYi7rFS1hhh+qn/gi3u4/fZs9B5M+ewo5jwlTWltRm6q/uQOOMUtZedW51wDj3d+2X+3LqHNq+kMdIYzFzn/K43py1aFXxyUEM3bzC0j1252ylr+yjyzVpIdGpd1c76m5dGXJ7dmmobLP30wbz92kUI1GOgx6aaMqJQ9CVxXD0cdijzVOT68DMNbbXn4k5hIMvtMmGKjsh9dHcgtolt8DT2omqx9fo92GGN0bXaZ0z27aAgIA98NaYWHcCYQJvv42E3SEQYfvDzp6ucc7I17HjswTdoyj4p/T59HwFJ30cpW+p5XMw/2bp3XIfStvMST/VzpjN+q6a/Zu43o4/q+cfW7X3U5JS4BvxhV1PRdIPm+IbRsXhw874YHrX2fTPdKFoc78rJSJ7SKT2Kls+pgaN9fR5UDztwgvl/9Na2vfNS+Fra0PHn/6Eyf/9Z/6aa2xE192LUXbO+WL/1kHSCHFynEDB2oceegjbt29HQUEBTj/9dCxevJi9MavEG2+8gUcffRQHDx7EjBkzcPvtt7M0CmZBxcko1+22mUfj2DMb4c4ahqcvGfvfqoS328uuSclJweT5DfK52k1V8HR6gLKJWPO9MmwY/kRub37ysVj8+yag6SDc+W5Un14n37dvQwV8PT52nSvTi5qz29i53txZaP67D576RnQUpuFnl3lxKDeJXTe3fC5Wz1uNpqYm3LvjXnzQ/oHc18zCmbh1xq3s/w/tfgibGzcHnSsrK8PSjUsDzklt5vl8GFx/OdLrx85h6tlonLsS3cnJ3P6WTF+Cx3Y+FnBcb877N1bC2+VFSmUFHrmqWKbVhO4R3POcG/ltg3DluVAzr94UfY3m/PBXHmb/V85Z2Zd6fFJfyaUTUfEfqcjufieIFhgZQfnby4E9r7PDynlpzdddWY7qizLhblU8mZ16NnDJk+hKTsb1/7w+gIbnZczENU/Uw1ffoDlGXpue4nmofamfyY7mWKqqgoxGQtdQV5BsKCHLSVqefKyhoYF7rZSPWgn1tT2eHk0Znl4znXufsl3pOK8v3pxyfT6sbj2E0wYGg3iAjIKxY/3twAtXy/zlXse5ZrByLtrPug9ZFRWaa4yguf4UdFVi5/6dQXRSQnk/jx88mimPS8eGW1ow9NOl8NTVacuchuyYhWzo8/p4owiePhfcWV5Uf2863N97KpAvZnljdB1tY7xz598P/O0m47YFBMYRtF7VOs+MLowK8NZlzTyAzJt9wXujWHf6e6DZPVJ5PJGu5dk0p5aeyv59t/ldwz3UyN6I52u1oLYxnNI5SttAywfQtD+09nvOni7ZaWQHFL55S5C/03XBg7h+051cX+fJ/U8G+BG/+JMLBe1Dun7OE9dU4h8DHwT7Hoq5KOeu5XvQuJa+d48pu9KMj6P2LbV8jv42F2rfKAGGyflNQfX6dcg8/viAMXcWpeO2b3nktvT8Mi0f1s6Ypbb66us1ecmj2S9PuQ25f70hSF5415vxxdR8lvpZNmsZVr2zypK9r+eHGd1rB7z+nO5H6mNH3UbnfDC963RsefK3bc2X09e/MtKxtLgI3SnJMcsbrX6U4PZpwCM9X08ZT3OVl6Fm3bogPUi6Bj5f8L2bquDt9KA5H1ixMIXpgnDQJJohxSkpvateloBxD9xGmiDvHjENBbkjKJ/TgcYtBSyg4Mr1v3RMAkfBhYBzeS78+rpjsXH43xhmO5wfyUjGvOSj8eNHP/EHzdT3ZQ0zA8LbnzJ2bmsBPL0uthku+5YPrbljZKenb7PLZmNwcBDb27cH9XVi4Yns/x92fMie0qnPpaenY2vT1oBzUptrm1swsncDkhTnkJSCwYrZWDSxhNtftisbvd5e7pyvf+xTv7GlmjMtxLU3noRXBwLbK+5JxqNPDSGpO4l7z8NLjuHSV2/Oc8rnsP+r52zUV+mlpcjqeZ9LC0J6w1ZAce5wvxsH3pgAT28yt73qK6rhbtsccA+1hynzsai0BFsatwTMi3hyTvoJWPTANk0aarXpKZqL2qdrufcxg/jlf3IDb4teXRREJyVkOTl3rSOB22XblmnK8H9d8F+OOBvKOT3e3ILZA4OBnw6M8gBXvDh27OmLgb0buLySr+NcM5KUgqGK2fhJ9STNNUbQXH8Kuirxnb9+J4hOSijvDyVwSyhJTkbtRedpy9xL/7AdtJXANmetPihoe1Yb3NlJwXwxyxuj6wi8c+l5wGCXcdsCAuOImA7c8tYlD2LdBUAEbu0Fbo1sGqM9NBYCrPESuJVsg/0XfoXrK+naH1r7PWdPl+w0QlrD1iAb/9O8YiwoSOf6On2+vkA/ojsJjz59WNOPID/ntcEPg3yPVc8my8Hb8tWr0bh09IF5VRVKvwau70HjWliYYdqu1PNxRnJGcN2VqUG+pZ7PwZIlDiNozMxP/fYwWnPM+WV6PqzumHNHcN0VwWOmth6qPaDJSx7N1rUP4Jiu1iB50breyBfj8Znuy0nNQc/hHkv2vp7OMrrXDnj9Od2P1MejTU3O+WB61+nY8uRv25ovpy8KPW7NSMe1pSUxyxutfpTg9mmCR3q+HounZRfD29jE1YPl9/0KjT+8gnuvL8fH9Jf0ACccNImXwG1UFScLN1xZPiYgta8VywGFmjPq2R/9P+jcvHrsHQoOrtDvfUPbR58ec+47qwU1Z7cGnuv1n3vk4oGATYpAC4ueitBTSl5fdJz+1AtQOkf3qs/R7/raDezJScDGRxjxsaeYrQe3cvvr9nZrztn/lCR4znT8s+7g9jLThzDtjIOa92jRV2/ONF/enI36oqfdWrRgT3VV51IzPag+s0WzPfZWLKc9onld7YagedF4d3Zv1aWhVpt0XOs+Joep/dxPJHh0Uo+JrqntrkWoqO+r15VhJ/pQzqna42FPeYPyvYzyAIf2jH32Qb81eMWu07iG5IVkg/jJW2NasqhHV5oDj05m77cK+iRFV+Y4suNoHxS0ZQ+zVHwxyxsz12mdG2g3bltAQMAetNYlD2LdCYQIMzZNOPZQgdBsAy1fSdP+0NvvOXu6ZKfRH8/GP6azGZWeIa6vE+RHZBzW9SPIz1HfQwFOejs1qaKMBSlqFyyQgxXVD63Q9D1oXBWHB03blXo+zrT5B5GRcdiaz3FWC9zlpQFjTqooZXNRBm2N/DI9H1Z3zGfwx0y+qx4v1TSrPDzIjsMCjY18MR6f6XfnUKdle19PZzmtp7T6c7IfqQ+iu2M+mNF1Ora8nn+mOV+NvmguNKdJHk9M8kavH90+TfJIz9dj8bRHf8H0XpAefOqPyKzI0rz3yPmtyMrwho0m8YSECtyWndoR8Jui/RRQoD/6P+/cJM+YIClR5fHq3qd1Lk8hmOEGjVEPWnPTa88qrezcYxdGfdmB3fbsyo3tsbTvDbqectmYxYHuAwgVTQNNYe9DOScj+ZZpQnl0jK4zuMaunPLmHGm+0Nysyo7jfSih7M8Mb8xcZwdOzFtAIJFhZ12KdSdgE1b2Tkf3UIHI2h9h2O/N2nB2fRZ6U6zjp1cG3rN6NdypvY6My+7Y9O7JLPKi/CffDjje8dOr5LfenIDdMetBfY8dXzccvmko9r5TesqoPyf9MEd9MDPXaUCPV5rzteDzxRJvzPTD7dMCj3RjX+4epveC9CB9VWFwL4+PYv9O8MBt07uBOQ39r2gnsz/6P+/cATe/flud26V7n9a5roHI1YOjMepBa2567VmllZ177MKoLzuw255dubE9FkoirkJVThXMgpKVh4qyjLKw96Gck5F8yzQpmGx8ncE1duWUN+dI84XmZlV2HO9DCWV/Znhj5jo7cGLeAgKJDDvrUqw7AZuwsnc6uocKRNb+CMN+b9aGs+uzUD7Xgl8+FXgPfSZ8ONuRcdkdm949lOu28aFnA44X/PKPbC5Owe6Y9aC+x46vGw7fNBR73yk9ZdSfk36Yoz6Ymes0oMcrzfla8PliiTdm+uH2aYFHurEvTw7Te0F6sKnJ8F4eH8X+neCBW2+fP+ds9TljaQwooTL9yZ9IKM5RkvKjcmezXBtK0O/pubP9Scw59+1/owT7Xy8OPJftP/ejFzNYzh91e5SImf54fdk9V1k9359YWnWO/Z56Nqqq53Pvy0/LtzxnLVr1D6Zh91sTLdPXzpyN+qIiX1q04NHJ0+9G7YYSW+1p0daIhlptsgJlevcdzgy8h+rD5NVw6cSjp7LCJOU34/3xoDw/60uzdHmm1QevPS0o51TrdrNE8kHP6CSeStUwi47QXQfsOoNrtPhpJKe8yp1W+WLEC95x5TGPJ8uy7FiFbh+sQFlyMF/M8sbMdVrnMgqN2xYQGGfwdJ6RLowKaK1LHsS6C4CVvdXoeKJca2bvNNpDef0ZjSFertWCGVvPLmzZH3r7vd6ernHPp/mlaEhNN+Xr2PFZyKejIlwjDf7cjtXr1499LvyTOzXteq1x2fFx6PjAYJo1n+ONEngamwPGPNLQzObilJ9qZ8x6viuPZvWp6ey4FRrb8Wf1/GO79r7evXag1Z+T/Uh9EN0d88FCWPd6/pnmfDX6ornQnA643THJG71+dPs0ySM9fc7iadf9bCw9glIPXnkV+hv6NO/dtaEYfYoXG52mSTwhoQK3rjw3y7eYWeRh/1IiZSpKRn/0f+U5Sk5OCZSp8iQlMVeCftNxlmA5n9NmXzIrTObK9I6d++50uCvLWfJ6SmKvfKJJCZipeh79SUnpnTrHqgFKxXsk0O9LntS8b/3564OO681Zi1Y0R5orS0pv8h67czbTV+1L/azIF48Wajqx6qIby+HpSbbVHm/sZmjIa5MVJqPjevdR1UZ6oqUCbxw8ejoFXVkMQx9U/ZMSyXN5qoTOOjBzje31Z2IOPDhFM7mipw3ZcaQPZfCW5FrNF7O8MbpO69w1b5prW0BAgA/Kfbb71eDc1NIx3tqrmQdMnhfedccbl0BMg3LzbarfFJDXTn2Mt3fOKp2FU0tPDavdwRubQBjtDzt7usY9ld/5pylfx47PIt0jFSZjuRxPOpH9KwctNHwFrXHZ8XHouNq31PI5yue0y4XJqNI7FQ1SjtkpP9XOmI18Vy2a0XGz19vxZ7VkJmDMNux9p/VUpP0wR30wm+ve9nw5fdFcaE6m24hC3mj1Y9inAY/09LkcT2tsgqu8jK8HFyzk35vvRkpPCu5c55N1QThoEi9IGhkZce6biCiv1ta2axcmTEjx5+oonILOTTvQdOtt7Jqye+9B/ukz5HP0FFgyKKrWPIZDJ1SzXBv02vaED2tRt3gJy9nBhJKS6/PavP0G5M+a7H/FfMLUMaFvakLafXei4diJrD31EwUyDKW+nDrHnJvRMarfNNO6T3lcb85atKr45CCGbl5h6R67c7bSV/YxFZq0kOjUu6sddbeuDLk9uzRUttn7aYP5++apnGXOOAiacuIQdGUxHH1QbhwtnppcB2ausb3+TMyB4DTNejduDFl2QurjYCtqF98MT2snqh5fo9+HGd4YXad1zmzbAgIOgSq1q99es1LpfdzH298OvHC1vzCFMiBL6Q/3bRw7Rm9jkGFP16vXWDjWHW9c0hgyAj/BE4gNdA11YenGpawYiQQpEPtu87vyMXoDh5y5vLQ87n4bDruDNzblOATCbH/Y2dM1zhn5OnZ8lqB7KJfjKJQ+n56v4KSPo/QttXwO5t8svVvuQ2mbOemn2hmzWd9Vs38T19vxZ/X8Y6v2fkpyCnzDvrD6R3bHaLePKb4RVBwecsYH07vOpn+mC0WbtW5X2GkW0lhD6Idgqk8NGuvp86B42oUXyvfRWtr3zUvha2tDSlERJv/3n/lrrrERXXcvRtk55yfcm7bdo3HKrq4u5Obm6l6bUIFbHkE6X36Z/asUMqWwDe3ezTUoSIDTpk0L2KRDbTPaoTdnrXnZuSeS44tke6G0GUk6CsQXIiE7Qj4FBOIscPv0xcDeDcFVhtWgT+norYwrXgz7GDXHFekxCDiKRa8uwtam4CruatDnk/Qmztpz147r2MZjHLGKWLMNotnPcXJsdDw5Lw/DXV1B94z3mCOBaB2XgEA0I5TYV+eLLyH/4ovEmuNABG5DIIiAgICAgICAQMIGbikNwaMzrTXwo23hf5vdaFyRGIOAo6AUBBe8fIGle1656JWIvJFjNLZIjUNAQEBAQEAgPmElTplQOW4FBAQEBAQEBAR00LHPOnnos7fxHlckxiDgKOp66izfQ597RsPYIjUOAQEBAQEBAQERuBUQEBAQEBAQEPCjYLJ1SlA+tPEeVyTGIOAoqnKqLN8j5esb77FFahwCAgICAgICAiJwKyAgICAgICAg4EfREf6CX5Q71gh0DV0biRQFWuOK5BgEHEVNXg0r9kV5Y41A19C1kUpPoDW2SI9DQEBAQEBAQEAEbgUEBAQEBAQEHAQvjy0di8b8toSgcV3ypL/glxI184DJqoItdA1dGynwxhXpMQg4itXzVrNiX0rMKp2FU0tPDThG19C14z228RiHgICAgICAQGIjaWRkZARxDlGcTEBAQEBAQEDAIg7t8eeOpTQE0hutvGORRjSMQcBR1HbXsryxlIJAepuVd2w8EC3jEBAQEBAQEEjMOKUI3AoICAgICAgICAgICAgICAgICAgIRFngVqRKEBAQEBAQEBAQEBAQEBAQEBAQEBCIMojArYCAgICAgICAgICAgICAgICAgIBAlEEEbgUEBAQEBAQEBAQEBAQEBAQEBAQEogwJGbjt3bgRnqYm7jk6TuejGbE+/mimRbTQVm8cnS+/zP54SDT+RxOiRXbiZb6JRs9wQNBQ0FVAIJogdNL4I955MJ7zs2O70z39H33EHVcs2vtO0j/eZTUWQDQmGeTxgY7ROaf5IPguIMCHC4mE9r3o/bAbdYuXwF1Whuqn/gi3uw/o2McqE3sOZ6L2yquYIkr91R1onFGqXUG27Qv5voCKxlrHAezv2o+6njpum2bPTfiwdmz8D62AO7VX7ovGLY2/as1jyD66nDsWvb7sjN/uvLT6skUL4mVZmXydmhZtx08yHEfvrnbU3brSUDbs0tb2vBTj6Ny0A0233ibfl3/asdpjnDcP4YYufx3s49mdz+Lj1o+RlJSECekTcGzxsTiv5jzn+tSRezNyqsezeOQLGVU0X19xAdYtmY7cw3vxjd4B1ORNRnr1Rahd/puQ5iu1b6jn7lmG7OkTLPHN8FycIICGWjLZ2Iiq23+A7K98I27p4PS6an7978hbvgbu8vKoWOtWdID6GFdPJMDaiCnw+BGFPDIjb3v+978xdPNdSC6diKnPrIvatRPPMLsvdN29GGXnnB82u04XVv0rxfHeTxtM+QVaPp6m7WTCN6r45CCGbl5hzna/8MIxfly7WD5ede/tsk1j1t63PBed+djyD8NEf7N2tZ35O+nPRsIP0kM4+2fyuehajAwPo68wAx99rxI17gG0Fk1BTcUFKLrhAXgpoJucjKq1j4emt2344nq+/XjzJZJj2NywGc/tfA4NfQ1IT0kPj59sM/Zlmg5RaNdEI5JGRkZGkCjV2m7NQUblGah9qR+e+ka48lyomVcPd9YwPH3JqN1UBU+nBx2FafjZZV4cyk1i988tn4ufTPsJctw5SBrsRPnby4E9r8vtD1bORfrFjwF/uyn4+MJ16EpOxtKNS7G5cbN8jtpcPW81mpqacO+Oe/FB+wcB56g/wkO7Hwq4b37ysVj8ZAPQ3Ap3lhfVZ7Wx8ffmzkLz331sXu7KcpR+PQXZ3e+MEWHq2ei64EFcv+lObl+5Ph8K37wF6fWbA8bfftZ9yKqoCBr/zMKZuPuMu7HqnVXceRGu/+f1QX3Rub76em5fB06/C7/4fE3APdTPrTNu1aTFj/7YCl99A9xVVUj75Wokl5RguKUFQz9dCk9dHVA2EWu+V4YNw58YzplkYP/GSni7vHDnu1F9ep0sG/LxynJUX5QJd+vGINoufe8eTVpo8Z937ryMmbjmiXr/vFTj2LehAr4eH7vOletCzRnB8ku0UBtMWmhoaAg6VlFRYXhf11CX5pzy0vIC2pXa4x3TOk7Hejw9uH3b7djVs0tzHCeVnIRfn/Vr1qct9LcDL1wdsG6Jn7jkSYBUI+ccj9d6PJNkJ6msDFPXr0PL8LApGutBi5Y79+8M0ifSGiL9xaO7nbHU7v4A+6+8EiUdw0jJ8mLyqB5icvhGETx9LrgrSlH9zHpTcqgGM/CvuNyvzxR6zlM8T9bfSjqb4Zt0bnD95QG6Rz6XUYB4guwk1dUFyWTtpkp4Or0BtLVKBy35oeOhynckwRuvem5KfTehewR3rvOhtBNw5btRE0BX6zrYafB086mlp7J/321+Vz6Wn5aPzqFO+fe5xSdjdeshuPe9FfdrI5rlVx5XQUaQHhssPxXpaenAvo1BdqbEI6N91mjvtXotb885Pv94IAn4qOOjAHlLae2Q185I7gimnXEw2MYaXTu0T4ZjvE7YJrF8rd6+sH9TFbydHjTnAysWpjAfSGnXmYHW2EKyx86/H4MvLgnyGXh+V4CNoNDDynl3FqXjtm95ZB+PbKQl05fgyf1PBvk5vz59BXL/eoOmb6f0c2hvuOc5N/LbBoNp+1YlvN1edp2rvAw169axcdGbtrULFgI+H/sGtvqsFmQWeYPvUdn70npRz4X4tWzWMk3fLI/64dD481Nv5fpfD3/lYc17iC9G9Of5ZVpjXv7W8oD+lXa1lr9udf4Enu/i9D22fRILMPLDnADZ+gcuvwJFXf5QkSvTi5qz29j/975ehOF+V5BMW4Zq3QfYUkG2q/94SmUFnrimEv8YCI4xEMJNl2jgDaGuuw6X/vVS9Hp7w+cnG+hmtQ5Q2o2m6KDnh8eh7akbp+zqQm5uLvSQcIHb3HQXPEVzUft0rV8pZHlRPqcDjVsKWLCBjMnrrkhFa+4YWVKSUnBCwQlYddIqTPjfHyC9YSsw4g+eEUaSUpCUngcMdgUfnzIfi0pLsLVpK3yKc9Tm7LLZGBwcxPb27RjGcFB/hA87Pgy4LxnJeKa2Hxn/A3h6XWPj31rg/02GyoVpcLVtRpLiPiSl4NO8YiwoSOf2tfZgC9IatgbcQ+MfqpiNn1RPCho/jSM3LRc9h3u48yJsadwS1Bede6j2ALevT3Im4PIJmQH3UD8nFp6oSYtzM07Eot82MmOAAmMZt92KgXvuxUhTE6PFw1dOwMbhf5ue8+F+Nw68MQGe3uQg2WCGw5U1cLdtDuCzRNuFhRmatNDiv9a5c9JPwKIHtnFllAw4Ahl06nNsk3vpH6Y3T7uB20WvLtKc09pz1zribCzbtizAiNMCbQLUpy08fTGwd0MQPzFlvv//nHNavNblWZ4L6WufQtWJJzoSGNCi5Xf++p0gfSKtIdJfTgVuT3/2dOaEP/HHIfiUekiSQwoIft0L9937bM/R89gFqP3DTr6eIzmf1wh3psc036RzI3s3BOlGdu6KFxFvYM7qRedxZTIgaGuDDtEa+ApH4Fat78hBf/Spw0jpSQlZBzsNnm42g7XNrZg9MIgUjCTE2ohW+ZXH9caPgvQYccYfnkCQnSnxKNKBW96eowezaydc442VAGs4r9XbF3w5Plx3ZaocCFPadWEP3GrZY+l5GBnsCvIZeH4XXc98vJeH/MHpqiqUr16NxqVjQcNl3x5Ga06gjZTtykafry/Ix1jfMYhjulo1fTu1n1Pck4xHnxpCUncS33bPLoa3sSloXCxx4TAs2fvMX70yLWAuxK+c1BxN32xtcwuXxjs0/K855XM074EJ+qv9Mh79pTF3D3UH+WpkV//w/g/8D3UcmD+B57s4fY9tn8QCjPwwJyDZ+r/5w9BYkDbDBwod+Qb9vw/lJWPWy6/Zt3k4697T70btmxPg6Unm7hNrbzwJrw1+aNnnjgRfIsUbiT/KB/Bh8ZMNdDNPB0h2oyk66PnhcWh7hhq4TbwctyM+9rak/wkOPdV0ofa1YtmRpTcAMjIOB9xCAkcBpEPN7/if+KocImZMDLTzj+95HXW1G4KcKPpNTyCoXbXBK/VHf+r7qjxDmDHcguoz2wLHPxrcqP75D9n8AgITo/M+prMZlZ6hoL7aDm5l81LfQ7/pOG/8NGZSFlrzoj/evOprN2j2NaO7JWh81IYWLegcPW1LWfMLZgSRUdD/4+vloG3yY6vYm7a8cWjNOTXTg+ozW7iywZ720pu2GrStODyoSQs9OvHO7ezeqimj9NTd/+Q9+By7J7Uf4QR98qA3p9ru2pD7qO+rNxW0Jdjukz7LoCd8HH6y4xrntHityzOSnbTw84WnT6Q11NAfHKS3+0kOrf0vuQYxWa2HlAHBlHZgz5v2Omn7wq+ntfQc0VkZtDXBN+lckta5Q3sQb6BPzLRkMiBoG+d0cFrfZWd4ceT81nHTwVbGagbVHg/mDgwEBm0JQibGBa7O/Vw9pg7asmPjyCOtPUcP0bp2Egl6+wLxJivD/5an03adbXtsoJ3rM/D8LtnHe/jnzA+g4GHtggX+IGJFKXs7UxnoI5D8dnu7uf4W2Xt6vp1a9jPTh0bfJNew3R/9RdC43OWl7E1bq/Y+81fTg306Ld+M/C8tGmv5X3r3mKG/0i/Tor80Zp6vRna1/03b0Oev53M5fU+410sk/DClrT/l7Da4RvWCdyBFDtrSG7gv/OcQ3hvZ5+i6J/u+ej5/TZDu+qw7+OG0GZ877HosQrxR8sfUtaH0a6Cbtfyt+v1vGdPByA8X/kgQEi9wOwpyWOkJjhL0m45P8owZLUr0tu2w1ZdWe3ZQNdqW5viHvrA8FqlNK/fYRTj6qs8YYE+ulaDfDZmDtsahJxuRpJPeOHTH2L4X4QTlqdHDge4DIffRNMAvRuBon5RLxya01pEeX1wO0CUUvjT2NzrSz45Wvx48bmjIeK3UjX2abYc3dteiLYR53YwLOvZZp2E80sHhdWW01seLhkY6wO6+LGQiskjpsbFXjIPM2ZG3aF07CQWDfYFn3zhh1xmNyUm43T1BfkHHT6+S3yQOl79iaLtzxlX+k2+z9Ah27H0rfofd+YwX/aX+nZp/JBHu9RIJP0xt61fMDeQBoeLLHZiaOoSPWz52fN2Hg+9h12MR4o2SP2Zhu1+burmzaZvxeIzaFvZAEBI2cEu5Uui1eyX8r+En44CbX7Mtu2iGrb602rODutG2NMefdoTlsUhtWrnHLsLRV+VABvvcSAn6XdGfbmscerIRSTrpjUN3jJTYO4yoyqnSPU/Jx0NFWYa1T25s9VkwGXahtY70+OJ1gC6h8KU8s9yRfmYU+/Xgx2lpxmulyp9b0y5v7K5FWwjzuhkXFEy2TsN4pIPD68porY8XDY10gN19WchEZOHLsbFXjIPM2ZG3aF07CQWDfYFn3zhh1xmNyUl4PDlBfkHBL//IUnWE018xtN0542p86Fn0t7ls2ftW/A678xkv+kv9OzX/SCLc6yUSfpja1m/YHJxvtOHtAuw5nIbjSo5zfN2Hg+9h12MR4o2SP2Zhu1+bujm/7CTj8Ri1LeyBICRe4Jby71DydEpwLb12f87YZ1u735qIgYHUgFsoHwclaZ9QOoslpWe5NxSgfEfIKOQfn3o2qqrnszbUbVLOEWqX8gjx+qM/9X117jTsSC5B7ZtFgePPHv2c4I7fsPmxvlXz/jS/FPXutKC+iibOZvNS30O/6Thv/DRmVmxCY170x5tXZfV8zb525JYEjY/a0KIFnaPk9b7FP5NzKWX++mH2L/0eXrKMFTDjjUNrzpTjtvbNEq5sUDEAoi00aNuQmq5JCz068c5Nz52tKaNUsID+eOfYPYczEU7U5NXozsmJCpaVWZWM52Zgu8+iI/wJ0Dn8ZMc1zmnxWpdnJDtD4ecLT59Ia6gi05m8jXMr5rK1/7k3HfvUekiSQypQ5isEpp5pr5OiI/x6WkvPEZ373Zb4Jp0b0ToXh1VMPZ4sTZn0F5FLTgg6OK3vegdc2LWheNx0sJWxmkGt243NGRnwqT/GFzIxLvDm13D1GC/kMTKO61Zrz9FDtK6dRILevkC86RtwhcWus22PZRRyfQae3yX7eNffIee4rV6/3v/ZfkMzKx5GeWiVYDU7XLlcf4vsPT3fTi37/YNpzI/UtN2v+1nQuDyNzah9o8Syvc/81cFgn07LNyP/S4vGWv6X3j1m6K/0y7ToL42Z56uRXc3sZwfmr+dzOX1PuNdLJPwwpa1Phci8A2M5blPSR9Mm9Lvwny+k4ZSkyY6ue5bjdgN/TZDuOip3tmV+RUSPRYg3Sv6YujaUfg10s5a/VVlzhjEdjPxw4Y8EIeECtyxpOlW8lKoVntWGzCKPP99fvpsllF/1XErAU0FKokyVNSnRPqveKxXAGQUrDHHNm/zjlzzJqudJCbOVbdJxapeSv6vP0XH6U99HxbjS3qoay/Uojf+70+GuLPfnTXqpH96iuYETnzIfld/5p2ZfNK8kzvjpOG/81M7689drzov+eH3Rca2+Jn3vtaB76LceLajiqGQETV2/DtVf+Qr7V8oh9aM/trLrzMyZPcXbWO4vTMaRDUqOz2RHg7Z6tLByjpLxs3lxZJQKFVCRAvqj/6vHSPewyrlN5lINkEyr/8xAb07qdnl9aY1BeYx4dHzR8brjoGqZUp+2QFUrVbLIftNxjXM8XuvxTJKdrht/jH2733ek8I0WLXn6RFpDWnS3g2dmPoQV60dYYbIUpR5ieVNHjau3a0zLoRqseAqtNS09R3JOa1UZeDTgm3QuSetcnEGuos2VSVdw8NYiHbTkJ5YKkxG05qA8rtR3ZBvcuc7Hiiu5OGvdqg52GjzdPKt0Fk4tDXz7XW3wv3Dc1zE8eV5CrI1oll95XBw9llQzD1DxSLIz1feb0fdOXMvbc7TkTbl2qLCQ3toJ13idsE1i+Vq9fYH0GfGGeCT5QEq7zgys2pIB0Nq7r3mT6zPw/C7Zx5OCo0/9EZknncj+pd/5bYNY9WxygI9H8vvsfzzL9XPI3tPz7ZSyT21S26wwmZbt3tgEV3mZPK7y+34FpKSwwmTkkZfPaTdt7zN/VTUXmoOeb6ZFYy3/S+8eM/RX+2U8+ktjVvcv2dWsMJmWv25x/lq+i9P3RAJGfpgTIFv/7meGxwqTZXpRc04rJp/bhuRMf/B2Qtcw9i9caN/mUckX2aTMvqfCZBo2FskFyYdZfh1ffDwuPOLCiOS4jRRvCCSDVFgxrH4ywYIOUNqNpuig57MJBCFphEoDJkq1tn3bkXygG3WLl7Dqh2wjp0IIlEOjcAp70i8ZjWn33YmGYyeyV7m5TykoYfLofQFPBLSO01st3bUspwevTbPnJnxYOzZ+Sv7u7pH7kg2ypiZUrXkM2cdUcMei15ed8dudl1ZftmhBvFRUtFTT4tAJ1Ybj6N3VjrpbVxrKhl3a2p6XYhydm3ag6dbb2D1l996D/NNnaI9xnsoJDwN0+esQ3t39Ll6pewV7+vcgKSkJhRmFOLboWJxXc55zferIvRk51eJZT3YxVmx6COc/8A6KuoFfXpKMrNNPZ5tWXloeYpUvvRs3svn6iguwbsl05Bzeh2/0DmByXg3Sqy9C7fLfhCSHUvuGeu7e25F9ZKElvhmeixME0FBLnzU2our2HyD7K9+IWzo4va6aXvsb8pavgbu83HifiIAOtqID1Me4eiIB1kZMgcePKOSRGXnb+3/PY/CmO5FcOhFTn1kXtWsnnmF2X+i6ezHKzjk/Im+ohexfKY73ftpgyi/Q8vE0bScTvlHmpp3IXPkwd28Ist0vvHCMH9cultuqWr1ctmnM2vuW56IzH1v+YZjor+cLhTp/J/3ZSPhBeghn/0w+F12LkeFh9BVm4KPvV6HG3Y/WCVNQU3EBim54AF4K2CYno2rt46HpbRu+uJ5vT3T57NBn+NPOP2Fby1i+VXrbM9w+WKRl4+3Gt/HszmfR2NuI9JT08PjJNmNfpukQhXZNxOOUXV3Izc3VvTaxArejBCFFlDZtWsCGIoGUwdDu3VFtNMb6+KOZFtFCW71xdL78MvtXMvzGa4yRQkNDQ1S8CWWHZ4teXYStTVuR3+VFVesIPpyazD4ToSeOa89di1hGuNdKtKzFWIagoaCrgEA0Qeik8Ue882C85ke2quedd1ExZ7Yl253Gm5yXh+GurqBxxaK97yT9411WYwHEA297O7JmzQriA/Gg75134CosdJQPTvFd8sF8Iz75WLz4YALxAxG4DYEgAgIC0YVYCdyqsb9rPy54+QLN869c9Mr4vM0iICAgICAgICCARLdVBQTiEcIHE4jHOGXC5bgVEBAQiATqeup0z9NnIwICAgICAgICAgICAgLCBxMQ0III3AoICAiEAVU5VbrnKdePgICAgICAgICAgICAgPDBBAS0IAK3AgICAmFATV4NS4JP+ZSUoN90XKRJEIh2UJ4xrUrBdJzOCwjEGoRcCwg5E+tHQEAgMXywE/YMY0L3CNcHi3VbVtgziYWELE4mIBCv+Xzo8/xQqlc60YaT2NywGZvqN6HncA8O9h1Ev68fxenFyErNYpuy4xUzHUbXUBeWblyKzY2b5WNHFx6N5XOWs4qfscgTaUzvH3wfX3R8wf5y+1rw/wZHcJIrF56CGuypmIGSqjlRM15TaPsC6NinX9FU6xrecbPHeMct9MOVDzNzsVp93N0ntxlQ2feeZciePsHcPHXkmXfc7DErdAi1n5BhRYackC2BAH5WfHIQQzev0JfrxkZ03b0YZeecb11GQuFXGHgY6nqxNHcBmTbNr/8decvXwF1ejuqHVsCd2ivTlfTmnssXYLi5Ben3r8CUr14SkoxFUqcF7QuK4kE0L2XF9+yjy4VOEkgohLrGok2vSrb+oYFDqO2uZb+TkpIwIX0CKnIqcET+ETi59OSoGKuWD7bm8avxjSc+QVsusGJhCuZVH4elR1yKrIkzAm1Z0lkaBc+ijS9KfXzg2sXoLUzHq9+rQlb6YbSkZ6M3dyKmHs7H/NWvIbW1G1WPrxFF/KIYojhZCAQRELBbkMBqUQK795kJDtLTxNXzViMvLc/UGHo8Pbh3x734oP0Dy22EA3Xddfj2376N7sPdhteeVHISfn3Wr4PG6RR9Q4E0hl1du/DIzkfwRc8XpukbCl/tjFGik1aBDTqeXZSNGzfciHeb32XHcn0+PHSwFacMHQ5q9/20NKw/4Ru486wH5fFSG7x2efwJ9VrT6G8HXrga2PP62LGpZwOXPAlkFOhfc/79wN9uCjxeMw9IArBvo/4xrfszCoGBdvnnYOVcpF/8WNB1veWzcUdVBV5teU8+Ni93Bh7q7oV731sB97efdR/Kpxxtir4EOi474XV1cOW5UDOvHu6sYXj6klG7qQqeTg/c+W5Un17HjuvO85In0ZWczJXnZbOWYdU7qwKOn1p6KvtXkjPC8fnHIy09LeDYzMKZWDJ9CZ7c/2TA/Tw6EL1+WjwBm7p36PbDO+bIurMiQ07IllJ+HYaR7ETTtaRHr//n9fLeRm/d3POcG/ltgwHyS3K9f1MVvJ0eNOf7HbxDuUmmZeTc4pPx8/pGZDdutc6vMPBQa//grTca++rWQwHrhfrvuuBBLH3vHsO1OZ62QjRBSXOSsxXrfCjpBNxZXlSf1cbkrH/Cl7Hj2SbkHhqS5Wzql2aZljE1nz7NL8U1uS50pySHTacpbYSS5GR5X3BXVcnBW+V+4a4sR/VFmXC3OifPyrUd7UXArNoj0WSrqhGtNI42hGqvR8reNwsaj9LWNwLpmAfmPxCVe4D/QdlCjDQ0wZXvQs3pHFtWocuimS9qP3nRum/ip3/sCtpn3vZlwvNqPjtO+8xffjITKy56bNzHLMCHCNyGQBABgVgL3C56dRG2Nm2Fb8QnH6NPQWaXzcbac9eaGsOybcuwvX07hjFsuY1w4PRnT0fnUKfp62kjVY8zmoxhO/QNha92xmgmcHv3v+8OMGAeb27B3IFBFnNQgz7leDsjA0+fdKE83qgM3D59MbB3A6CgMyi9xZT5wBUv6l+TngcMdgUeNwuT948kpSCJc50PSdiakY5FpcXysbXNLZg9MIQURv2x+4cqZiP96r9bDnwxZ/yi8/yGbZYX5XM60LilAJ4+F9w5w6g+8xDcmR7jeU6Zj0WlJVx5zknNYW/UK4+bRTKSke3KRp+vL+B+Hh389Epj47AKR9adFRlyQraU8uswxjsYa+Va0qNbGrcE6N7inmQ8+tQQkrqTguTal+PDdVemsqCtFaxtbsXsgcEAmQsJIfJQa//grTfu2JNS8GleMRYWZhiuzfG0FaIJapr/5os2lPw9268vVXKmfDhgFjw+eQG2D1xrUa9Z4ZnaRggI0lZVoXz1ajQuXToWzL0wDe62zfp7qkWIwG14IQK3oSFUez1S9r5Z0HiUtr5dPyxaoGvL0gPcl/4RFLSNRr7w/GSz+0w08yfR0W0hTily3AoIxDDo8w3aXNWOFf2m4/RpixHq++rZ20hKx9ZqG06nR7AStGX3jMM4zcIOfZ3gazjmoTTkqj0enKYRtCXQ8bkDA6ir3RC1vGGfnNIbjerABP2m44f26F9Db8baCaxZuD9J4zpy3om+kzwemR9zOYEjuj+9frN/LhZBn5H730j0MiOw9rVi2Tisnt9iHLSV5rnndSYHPHmmtW4naEugNdXt7Q64X4sOfnoNyvSygpDXnVUZckK2JPlNYEh6VK17M9OHMO2Mg1y5PnJ+K7IyKBxmHn6ZG3AuaBsiD/X2D/V60xz7iA/HdDaj4vCgYX/juS9FC9Q0J7p+OaV/9A2oYDn77aUjloK2WnxyAWwftqrXQuGZnCahqooFa2sXLBgL2lJaCHrTVm9PFRCII4Rqr0ebvS+NxyqieQ/QtWXpeGp/1POF5ydb2WfGe8wCzkAEbgUEYhiUc0cPB7oPGLbRNNAUchtOYkfr2KfMVhDpcZqFHfo6wddwz6PKYy64McnjjVresDyBemjfa3zNOIPoa4ofNBer6NjHPruiJ/hK0G85PYLFcYYbRnQIZRy25Xi8ZMgOz+MIWnqUZERPrq3KiFldGCkeGu0f4VovUavnIwA1zSW6aslZnsWHA+HSa3Z5RsFbetNWCfrNcvnqIcF1kkD8IVR7PdrsfSv7R8zsAUa2LEcvRRtfeH6y1X0mavkjYBoicCsgEMOoyqnSPU+J1I1QllEWchtOYkbxDFv3RXqcZmGHvk7wNdzzqHPTuz7GOOB2RS1vUDBZ/zwVfzG6ZpxB9DXFD5qLVRRMZnnA6LMrJfyfYSXbGme4YUSHUMZhW47HS4bs8DyOoKVHSUb05NqqjJjVhZHiodH+Ea71ErV6PgJQ01yiq5acdQ24okKv2eUZfXpM6RGUYOkSDmfr35jgOkkg/hCqvR5t9r6V/SNm9gAjW5ajl6KNLzw/2eo+E7X8ETANEbgVEIhh1OTVsLw1lHNHCfpNx81Uv6zMqmRFfihfpN02nMTcirnIT8u3ds84jNMs7NDXCb6GYx7Ut4Ratxv/ykjX/DiYjm/OyEBV9fyo5Q2KjvAXTVHRmf2m41RZXe8aKiSmPm4WJu8f0biOcrYSfQ+43TI/Nmeks+Pq+6lAmZkq9Z533sVwS8vYb0+Wv3hDnwuuTC9Kju8a+xxrQwk8/W5z85x6NpMDnjzTWlcfNwtaU7mu3ID7tejgp1e6TC8rCHndWZUhJ2RLkt8EhqRH1bq3fzANu9+aOPap5Dmtslzv2lCMPotBNb/MZQTJXEiwwEOqLE2BNN7+QUWyTtgzrLneNMdOOW7zS9GQmm7Y/3juS9EC9Z5NdKUCMbVvFHHl7Ad/TmK8MQstPtH7VP+yodes8CxoX1AWriwvQ8nSn46lTfjJnfAUz9PfUwUE4gih2uvRZu9L47GKaN4DlLasWhez44czo54vPD/Zyj4z3mMWcAYicCsgECKoWIP0F4n71KDqlpQoXQn6TcfNjuHhrzyMOeVzbLURDqw/fz2OQRpO6x/L4amFk0pO4o7TKfqGglDoGwpf7YxRopMWzegY9T2r1F8Bm7C0uAjvpaVy2/0gLQ0vHPf1gPFqtas1rlCuNQ2qdE1FU5Sg33Tc6Jpr3gw+ThXiJ88zPqZ1PwXsFEjSuG548jxGXyVeOO58dlx9f/rCdYY0o+DPwB13YOinS5ljLjvnnR64cl0sEt/ycS6KjulhxRw8Pcmo3Vge+Oat1jwveVJTnmmtq4+TjEmV0PWO0Zp69j+eDbqfRwc/vc631Y/ddUc50jbVb/LnFbMiQ07IllJ+HYbW2jS7ZiN5LfFNqXvJiVn1bLK/MBkVJTmrDZlFHvavK9+NlJ4U3LnOJzs7ZmWE1qJa5kzzKwQe0rqtW7zEv1YpeEv5lHe/il8duwjnpJ/A5vLT54dZ8FZrvXHHPmU+Kr/zT1NrczxthWiCUseR/FBVb9mZHpWz8u8cge4JaSjthCxnocjYrvxStg/r3RcKz5T7QklyMjyfbkHtZd+Ug7ZsX7j/ARQtWTwWvH2pH56iuY7qJDM2SrRgvHyBUKAcQzSMJ9YQqr0eKXvfLCRbn9WyMOGHkX6J1j1Aacuq93xmy3Z6xvbPKOeLEmwv73Ih5R95QftM19e60Z07Iu8zZ6cdHxVjFggdSSMjIw5WU4j9am0CArEKCg5Q/hr6FMLuUzUn2ggZ/e3AC1f7C1mM4vPCSXho8gxs79mHXk9vQND212f9GnlpeYgF2KFvVPBkFF1DXVi6cWlA4YJcdy7OSMrBzXU7Udg/lmNpoPrLyPj2eiAj8POdqAUVTaE8V4VT0DDof8ssyHFRXBPw5hDvuNljvOMW+uHKh9b9OlBXCidHvOn25XCVFDPn3NvUBHfpBFSvuQ8oqJYN3ap7b0f2kYXm5jk63u37tqM8sxynThsLJvDmYfaYFTqE2o/ddUJvO5DhnNfbZl6GnJAtgQB+VnxyEEM3rxgrsERFSUbpRm/dMLlubETX3YtRds75mvJAVdi9OV5zay+MPAxYt+SUsgIsw+yBCnuTqNMDX1kRsp54EJOnnSzfZ0VvOL0+4h1Em6bX/oa85WvgLi9H9cM/h9vdI9OVeLbn8oUYbj6I9PtXYMpXL7FGZxWfwqnT1PJV9KWDaHqvAK4MH+BKh7fb6y9K9tQf/X1I+8Kax5B9TIXQSQIJhVD1YtToVQ0/7N7Kadg52IweT0/M+GHSw03dPV/SWfNUDzCjjS8q/vRu+hfqNhXCnemD7wIfNk+ZjNNa9///9u4ETIri/B/4u8fsfbCwwB4su1yKIiIegCKIeCVREjEmnqhJjBI13gqIxhhFwaghnkT/Jp5ojFcUkp/x4hBZUQFFVESEZU+WZe97dnb+z1uzPXTPdPd0z9kz8/08z4rTZ3VVdXVVTU8VHdLW4KoD8Ju4XclU8tcHKes05YseEJ39lOi4BQBref4coh/WKGcl5p9t5g6liwanK2b45J+s8LefK05bEZmwxpn5786n8tpyrzR4sbGLJrTs90oz8ZbNvNcp2nCnDIu3N048O2/zLriAGp9/3tVpO9A458qvtG3Pzp2aFd14jl+t+wRllXUacqnjxrnzspzRfM152Cr5V9y3c89wvVGU2ScmJXGN3Zfs6sx94x3VawXr5zMr5q+8cR3UuCOT+rq881c0XRcAxEc7LFbKYrX0aa9JpdRcO9myEojScom6W9zpxp23Pa2plHXiCVHZFosXrSY6bjFUAgBYB//Mk7/hlVcWmNNBE5rrqLi3W7GYKw/8Vpv4KTKEFP/sm+NaXmFjI3q7RdqopZlIS347CKKC+42EgZ+81t9/v2qnrbRtVFV0I3yfoKyyDs63Wh2Z0ZivbbaOgTdtB8bse2/owZ9O8nJ+wwjCLlbymWf+qt+a6+q0Vclf0XRdABAf7bBYKYvV0ierqEf8ykZ87mpUpBsvzyrsQlsshqDjFgCso2m37uqRdp6Kwxv/hAVCq7KtUnV5iUaauPFPkiBqcCW2aJlyLCz+jDf2ArtPJCirIOiadosGGr9pK8efRYMOZTAgfwGAweeJHrTDrJ0+mlAPiAnouAUA68gbpbt6r019tm8edwhCqyS7RHV5pUaauPE4fBA1+GdjNQsWKJbxZ7WJG8D4fSJBWQVBlzdK/CSSh0eQcw2XkIgyGJC/AMDw80QP2mHWTh9NaIvFhLjvuG1+803xp4YbqjwuSrTisGs1tqP92sJxzaGIP3+PGTdpmT+WaMwprvFR5XhspUEFVJ3imjRKPrYST/pjicHiY1xZbpmIa45zuaqUNJE2amkm0jJCEyXFzT0TxPiRj3GblJ9PpStXHpwpXGPWXTB2n6CsglCx2zNdE5FJwyOcuv/gsAm8vDcDkQ8xnb+C9bz35zjhqmtonYeXd37xhep5It3GtXJ8QnjaYUjP8KUPpQ8OaVtMLy19lTX7H3sc93UQxNXkZA07dtCQwYmu18wHj6bm9duoduEisU3h0vto0IlHuNd5zjTYMGmk+Amk2oyCPKad1joxFsnAMeU3jd4+/hzPc1379mrlLIq2DtPX5m8Y/Vnn7zXL9xuyteLgNS+/i2wp7YpZfKVrTvnzH6hmYoHuudp3NFLlwnsMxV/W4UXmw2ciTczsF1Xj9GjpaiJ69TeK2Uz5odMy5y+04NP71Gdq95jNlOP9nT3v0PaG7dRp76RhmcPorDFnUVFmkXY+89g/mPdErGjpaaEF6xZ4p8Fxiyj37Ru80ozOfZooPU95f21/g6h6M1FiAhGnW2oO0aE/oj35owJKG/k6r5njY/2eCXSWXVsH2b/bTBW3P0H2mjqiJFfFr+SJx8WEDvIJyzzHugXv/Dc4bTA9suURQ2UVaD/r46FMDdQP331KnVfeSEm1Da6JosRYpP2uGaW5U40nlMJ960UtbyG/WTB/adX//Wnv3LeYssYP0WyPmak3SHV0r31kcSBvdwTa1tIKm2irLLjbvT0/s6U6DXeiGGrj+ogXf9t5RuNT3iYz2t7RbMf50eY22sY03GaNBQG2w6Q2WNvatfSjFVupc0gGfbzoxzSzbCIdbcvzWRcPej9CnKQPnfUQ0aobfbfFiGhD9QZa/cNqqu+oF599tZPV2g2a/Wlnn+3ej9N39y9+SY6GBvFCyKh/vYI2WQCTk8VVx+3mYw6nI06ucVc69qwdQX2trvEZk7KTaNSsaq8KCRUOp8d/XUhr+r9yH++YwcfQwokLxf8v37lcUYDxur+e/lfKdTiIXrtctdC7bv2d9Hnj516FXm1tLS3dtlSxTjpXjsNBRR/foThe94jp1Dj7ASoqKPA6l33oTKp4o5PsVTWUnJtMZTOrDF/b1eOvpse+fUw1HIWFhV4dN9I1s+v+d53ha+N1vyn7jea5tK457aIXqSUx0SscZ6QfQ1f8bS85qmtdbwXMbnBdsywumgan0m0X9NGBnAR3GK4fd73XuUT+WDeC+lr6vCqr7uUjiqh0bgbZ9q/zKiA1w/dkFTmqqjUrwEkjiunJK0bQO12fm9ovHA0zaSZ4LVozbKvNIC8/ltpysYwnteIxeTwqUzwAPo8TqfZw5o7Faz+4ljbXb/Z5PWqVDa2OycVTF9OST5aoVlaYamemyU6aQONXvo2v+NU7nhGaaaCRZtTZSPTShUSVGzWP2ZyYQOcXFVK1LdlU2qilwZBWJ933TxsNauhGZ4YK+Zu1UpnS02KjynWDXR3q/aQoUzwboPHY2a1HK29eM/kaaupuCntDQq3MlZYHct+HDJcPHnUYfrPnipxkak1KjMuOb19leJu9TdQ/O9avp1tf7aeGHKLXrz6K7j11AWW37xdlcOXeVuq48SZy1te7O3TMPBticdus/Cyve3VKwRTx76a6Td51+YH85nc9xkLb+lOmyfPX6hun0p0zrnfnr5B+CapSJoj69ZkPEq2+SbO9o1Vfl7eDtDqhuN5w70vJlNfYo3kczzq6oq6h9szkX6+o1OvN1iu16jSdDclU8cEw8czmL1xLV75IGZMmifPvufAiMbkoS85JprKTvNuB8mNpxYtnuDzbefI2sbydZ6Qe5tkmk7d3tNquzflptOg8u6Idd/9xiyhH5eUB3Y5GjXa6VhvOV5rF5LPJZDvMsw3GeeDOFx1U0EzkyHbQobP267ZfzdTz4zZN5LTaXFrLeXi71ko6b9V5og7hizwe1doNav1pyUWFVPbii17loHghxOFAm0wFOm41ImTT2HGUl+MUEza4xv5KFg8zxhmOO/sU63KT6eFrjqB1/V9Tv3gquiRSIk0ePFn8/9amrYrZo3nd8UXH04q6eqIf1ihnZeSfGeQOpQvz0hTH458ZTCucRt3d3bSlcYvquVbsq6e06nLF8ZwJSdRTPI3SUtNUz2XPn04Vz1e4CkYT15aVnEXtfe2q4UhLS6Py2nLVa2YbazYavjZel5mUqXkurWtOGD2L5hcM8woHH+/FvV2U+mY/2duTD15zeZ74zBWE2y/op/05TsU+R+UdpXqu3k4b7f1gCNnbE73iTxRa80rJ1rDBK95JJ3ynph1F8x/arJomfMwVNx5N73VvNb1f6RvvhPwtuLB33Pph/rvzFQ9vPVLeXHHaCsX+aumWnZJNbb1tXst5f6a2j+exY6nj1rTnz1FWjlXwXdmcmEgzS0eYShutNBjalkiPPtdDCa0JEbtnrExUqOaeoShTqj4aTI7uJNX44e17du5Ep60KvbxppgyI245bLh886jDcBChPT6PfFQyLeHxGgq8yfPHmxe7651G7+qlyaAI15SS56p8DccTb9tfXk2P3Hio7Z66h48qXx+K2d399t9e9qsZdl5fFpZEwWHlbf8s0KX815yZ73YMhey6olAmifp2WS9TdYqq9Y8vqp5GzD1BKht2rPXbR4HRlvaE1gR59vlez3qBWR+e6xpKXE92dtzyZpxgXfuBXKit+W6Rarzdbr9Sr04iBDwe+cJWfP5mf4R37Vdu4tux+Kj35ANkMxIs8XJ7tPHmb2LOdpxdmZ46TrpmX4tUm4/bOlQ9+7npBRm2fS1Jpf7ayLfliYxdNaNmv2ubWuhatdrpeG04vzeLl2WS2Dcadt48+10tJbUk+6+Jm6/lIE/NmvDyDmnuaDW3rmbfV2g2K/rSsodRXU6taDhY98GequXJeRPsxYqHj1sesMrElOdNB9o4UqnhvqPgs3so8qU78f8UH+a7xmuTrZtbRDz126rfZFMfhh5L820bPdVUVa4iqVMYAcTpoQnMdjcgqpL2yY3IhpNXZxMdr2FdOaSrHS3A6KK1Ko5PK6RBvgpbOSDR9ba19rarh0LtmrfDrXRuv0zqX3jVzB1BlTyE5PMI+orebJvTVkf1k9Wt+9Fwn7c+xeYVB61xcySs9uV49/mbUkW1/hfdF6YSPz/VtazmVztinecxvWnv92s+W0knxjn8mY7TTVp43+Ztj/sZYa3/eTu0h5ytvy48d1/hnaD46bRm/O5HX30/TOruoPCPdcNpopUFGWg+NOwn3jBb+mRN/Y260TOEKVbxWqvT4ypsoA/wrH7hyemJXN42020V9CfF5UFVHlaI+tnWMNF1Fv1eeSxw2TPyBK96M1hGkem083r+eZZo7f6mUaSF5LmjVGbh+3dVovr1zcgPZMvpV22PFHu2xjPRe3XqDWh2dOxEXneegFW8WusaDv/BC1z4lJZT42BJ6Z+Ovg1Kv1KvTFB3fSDVfH+l1fh42jp75ifr1zDIeL77aqVrtQ9162EkNlJ4+nD95tXfKZursk6bcR7T9ml1teaPXotdO12vD6aVZPJYVRupCWel94k1bX3VNf+r5SBNzeHgEo522anlbt93A/Wm/eooqrvuDdzk0MLSCmTYHqIuryckKpzQpPnNvP7/mzX/8/2rrRtpdr36bUeJjH7PH9HU8PcG+tnDxJw6lfbSuOTe9z/S59OLPbPikc/mTJr72Ez+JiHM8Jo8/+Oc+gexv5NhxjcdAMmFST09Q0gb3jO90QZkSOF95E2WA73yox/OZiPgkqu3SnyQQceRfvCEuLVKmmawzBFJf9yxf/K2j88/2m269RLnPsmVUndFNwaIXtoz8Piq6/nyv8/NcH8GIl1CEWSs+ze4TSHvRzD6+xHu5q1VuGK2Low0Wetv2b/NrP3fe9tFusNnaRLnjVQ7xl3tocwRFXHXc1m5SDszsekU7Ufzx/6ut22sz/1JypY99zB7T1/H0BPvawsWfOJT20brmlq5k0+fSiz+z4ZPO5U+a+NpPjGMT50qyS/zaj8doCmR/I8eOa3mjTG3+RWpqUNIG94zvdEGZEjhfeRNlgO98qMfzmYj4JCpM13/DEXHkX7whLi1SppmsMwRSX/csX/yto/PPwfPuf065z4IFVNyZRsGiFzYe67Zm+cte57f3ZgUlXkIRZq34NLtPIO1FM/v4Eu/lrla5YbQujjZY6E0cOtGv/dx520e7wW7PFuWOVznEY22jzREUcdVx29fhGl9lyGGtlJTKwyYkiwGV+U+MsZHZR8XTD1BS2sC6dSNodOpkMX4Pj/HED2bGn3kwdv7j8T94Oa+X1o0oneUa4JzHypHj8XYGFVCV7WDHBONj8ADQfDzeX44/5w+fJiblko7XXpMqbhAe71Usl52Ll/N6MeYTD9jPg393JItr4mvja/S8Ns/w5yTnuMMhXbd0zRxODi+T9uN1vJz/PMOvd228Tn4uvWuW8DXz9ZaUznKNTytLl6qUNNqeXEAVH7pew0/O6KNhk1rIluW65mteTRPjV8mvi48hPxfHHVeA+F8e47biw2HuvCHPNyJeh85UjXd5+Dyvd3zONHeaiJ8HnMoDtbvCx8sPy5nmdV2e+3EYOCzy/XiSCBGO2lox86OV2D/ZJMbZU8PLgxXestwykdeMkvKm9NMmaX+1+B+UOsidnjO39dOMbQ73/vJ9xlb3i389j23FdOHwiIepimCFV5zDnukqozzPId0zAzi2mxITxTAJemkj55kGcp3dqbRz7XDNe026Z+IVp4teWRTv8WOUr7wZzz+dNCR/rGp9id93+ig9zf0TV8TnQSMyR7jrn3JSXQx5Tjve1O5VNfEclxEv0zTKBPE5fbDqcr32DtfjuT7vWVfn9lh1irJjtbMrRbfeINXR5XgcV56Ey1ldK2ZNL37kYfHzYP65cP/Vi8WEW7yPVHf0rFeq1R2l+Je3z3TrNB8MI3tNnetnyStXus+/55rFijauYp81w8g+EC/yuJTHi7yt5NnOk7cBpTaxZ3vZM8zyNjYv7+pKUZxeau9wG1UtLcU+3amK84u236ACdxvO3SHtcS3S9nwOvXa6XhtO3haQL7d6WRGO+r5UbnAcc16X8kB7VzLtWDPU3S4XbemBPCCva/pTz4/mNAln2kimF08X8WWUV1tWp90g+tOuuc09pq28HOIJyjqrO7z2VcsHobr2WJHgdDoPjgoe44P+bj7mMBo5ponqv3DNMpiYlkT93a4HYnJOEhVPqaWajYPJ3pFEiZlJ1N/RT86i4fS/2bl0ysrvxKyqd12URDNLj6QFY39J/XlldNf65XTmQ59QfivR/ecmUuLxx9IF4y+gCZnFVPLOnYpxmjpLT6CNx19Oz+1+SzHrPd8UVx91NdW019DKb1eqrmtvraKj1j1Mjo2fU+X6wWTLcFDpxaVkO+0q17fTH9xN9i8/dI0d0plEhWcVUcNnDrLX1lNSZiI5Ohxky3S4xkH6tIjsPDtq0TB68YIimvX0Vnf4Rx07iX5fdhY9smc17f5sq3I22ROvo8TmClr2/b9oXcUXYqZI3u/1yw+hqXN/J74te2TLI4qxaI4edrSID611PxvzM/r3rn8rrlnaRysOv5h5LeXkjqS3XvgjnfXkV+50OXb4sXTF3/aSo7pWPBy4J6iv2xUXdZ/3k7NmHzUNSaUXju+l3/3Hqbiu7MY91P7ycqp8bZ/rRE6ipMwkcnT0k21QMuWVNrjzTVJGMjk6HWQbPphK56YTVX/ujvdhPx9O3/1qoQif5/WeZJsgZku11R3wnpFx/Qjqa+5TzW8/yh1PP3+6lvr2NVJSRhI5Ol0/4eFO6aY9+SItbUUFVPSX5VRz8y2WmvmdC93Kq64WP5OQZg2VhGKm+q/2f0W3rr/V0E9upHx22JDDiItB3mdw2mCvfMX3u3RPHFi3la5e5Soz/nfxIfSrW1xvWDz+xOU058mvROcj/+ejX42mS876LWUOnxjamZctnC6Kcyy7hWzrbyVqcv0kSswoO3DPlMxopKyiHuLvVH5ZXETVtmSamzdRlLEcf3uSk9xpY7R8EbNDv5xMeQd6KHlQMpXN8J6Z2FGYT5lP/oVGjTuW4o3e7LBqM/2CPr2ZkGN+VmM/8Xh2fF+LWaltOUSv/kbxrOcG9xU5ydSalOgdnzwGJv+cWmXG5HiBPOdfflN7jkwtmEpOctKmuk3uZfL8psirUdAREPX5i+/vum1Em/5GtHfjweUlxxNNvoho64uK5fbc46jizS6ycx1Z1Nv7DrZ3Pit2Pc8G2ajo2GpXG4/rHecMp4Rzf0vLKv9LbzR96bvewB2JA+2mJ+ePone6Pvfah4bkETW3kq2oyDURzw3Xi85U3ueDqVk08w1X/efRsxKo/4yZtHjaYnrh6RtV646e7csnfpJAl31so+zGXtEmKR0IG79owp22Yj6wpCQqXfkiZUyaJJ7x359/HtG+/eKcPHFQ2UlVXm0O+XV6tlP7N35mug0orz8//NNEuvU/Ka5J2+TxL2tjc5vstvP7xFATjDu5r3hiNzl02q5tg1PovrP66Nq3+t3nn/6TeTT5hUeo/rV9rvbx7AayjZ9KHUdfLNJYHt5VVxxBZ130B3e7Or3iY9U2ptk2K7clrFg+hLMdtu/9/9L+a26kRCeJPPDSWU769SonZbUmUn+mg2x9TnL0uDpZpTyQUFxIY154kaozemhH4w6vfhB/+hGkvhMez9XK5Xa428isvKacrnn/GurpPzgsnRZ53i7qSKFdF19Izuo6r/KRO215AkSWUDCMxrz0srgWeVvDmZRICY5+d5ujp8VGlesGEyUmuCZWLCqg0nt+R7ZDj7Fku9kqk5PFVcdt7W/zqG1LEfW1ub7xTEhNIGdPv1cnWGuejZbOcbgfCu9feCid8u4BSt7XQEm5STRqZvXBzLpuhJj50l4whJ68YgSttW93n5e/fXzk2OspsWmPeNBJlQP5zZDSlULP7XpOMbA7r/vRsB/R8PTh9Er1K4pC6lz7aDrvmTpyNrjeuBQPp8x+as+ZSnVv94iOWlvhMMqd1E8N7+wXDzD5Ay85N4mKjxvooO5McnVCdjjIPmwQFf80lYa0b3Gf60DWZKp+q4ds9c1ejfvd64rJ0eLweuhyYXnN5Guoqq2KnvnyGdrefDA+pHVfVnxJb1W9pVg3IXcCnV54Ov2v7n+K5RyHd5ReSCmtVfRU4/uKOJyVeAT97h+1lFCzTxT8qRfPo+4HHqSEIUMoKcFJfVxhGT6EHr+8hLY1b3M/tP973ig6/f1mSq1vouTcZCqbqVIBEpfjdMdf1ScF5Ghz5ZWkrCRK6O+hvq4kKjyuiRp2DHNXJm69wKEaFy99+5J3JWjGwbwhr1Rwfjv9g2ZxXVw4Dj2knmo/zaPkdAc5E1PJ0e7w/rJBlpYJhYWU+dCDYkKSYM4gLp+lWG92Y/l6fqO251bXrJLycA1LTHQX5mrh1ZopWa+RceOaGxUNLz1Ztixqt7erruOK469X7HHHv/zhtHttMTlaB8qPYcNozD9dD6fOL76gigsvInI4xO8YSmfXizHHzHSEya9ZzjNO5PErX++5jdpxpeX8MNx14UXkrK1VhEvRoRdgx5041ryLyV5Voyir3J220reuA8vZ11mDaOSgMZRVdbA85LfuFgzNFx04nveUZ0VNWvfJG0/QOf9P/mXbJFow9heqX7ZlzpgRdx1sXpVFnhSAxxkbPDruKkzBxBM48Fhg4Wwo8P0dSDkf6OzzQe0Qam9w50PukPWKz85GotcuV05cxG9Nnfs0Ubryp3vRymgZLi2v7qymnvQedxwF+myItW3b7G20fOdyRX7jeuWlYy4VnVkcb8ltrp9Fe8blt3u+paXblirq57zvwokLKduW7TNsvsIbyW2tUKZ5hU3t/hadtRcSbV2p0onrWt5evtn9QkvRL8ZQ5X/aqb/+gOgIsN22mLqW3EvO+npKys0hRyPX+5OobKAdx5oLj6V3jzyfvt64SlFvmFownv4w9mzK3PIC2b/5xP1l8/C5BdRx/SO0vaOaPnrlYTrv2d36HZWdSa4Xhbr6vTo2jNQdHQVDaNjRRAf+2+DVKfncuy/TjGd2uTt+V115BF3864doySdLKOmd9YoXDX7zm7tU2xwfXTaGzj3jQnqkYrWijaX3sonUBmwcnEKLZe0erj9f+tgu0V6WOrK5rpy++Day37tEdGSrdbxyx+/QjmQatfIe2vdGnc+2a2KGd8cvd6Lf/1KSV+e258sxf59f5u54Z3PzjqTfl57pdf169U29DkOr1Se16vXBrO9Lz/Y737ia5j70OQ0bmHc8MdVB/b2Jrvarg8jR4yprm3MS6P6zE9x54PELcuijkZ2G4lcvTbhfZUT2CHp0y6NR8UV6uNLGbDs5LSmNxgwaQ9sPHOyPOXFvBl31Uqtqu+rpp/9Ip7+wQ/HFFMc3u/vf19HcZZ9QXgdRcybR6wtc/R9pu7+jitufEGWCeInf6RQv8qTm2uPuBZJWdNyqR8jmSaMprTtFVNYYfzuQmOKgvLEd1FqRIToSnDlOumZeCu3PcYqHQMl+J20ba6MZCYfRtY9+JTppXTN4Ng2M6ZEsOv/+dtMx9F73VjEDn4R/MnJ80fHi/8tryxXr+PXzaYXTqLu7m7Y0bhGzcsrXHZV3lPj/rU3ex3yhopPS3yKytycfDEt5nuszZ/KzUym5YQN1VCeLG0A8uMTP/oeQvS1R7DNscgvVfTpIfPPF4S88r5Ay2z6jBNm5eFiCjuxjqe6VOtcN5HHdIq4uSRUzqnpeF9tYs9HrurSuma8rKzmL2vvavZZPHjxZMy5OS59M85+qcXcApvzsp9Tz+htE9fUiLv56yRBa1/+1OKbR9KQEJ1+8GJKg4Lhmqt+S6/rJTnYSJSQkiHyTnN5Hgw/toKadmYbiQkp//glJ5dAEas5N9lonD9+paUfR/Ic2u+M9b1wHNe7IpL6uZEX+9UxLrlylPvGsexbpSHfcMs9O2vRFC8n+5wfcD6TU+5d5hddsY2P+u/MNzxbtC6ebZ/zL8wfng/70IaIhwOHngdfFGD6Vla6HD39z6LGPqPS+8Y7uwyecHbescssW6rjxJnfnrfw6gvWwtD82hyr+/q27k1YRJx6dtkz6FtHVBHDh73DL09PodwXDVO8piee6id/bxb3GFXrPdYNa+sS9xrNlS+tWnLaC4gl33qaOG6eaxlxp7Nm5E522USDaOm65rNa6d33eg8+fQ/TDGtfM3xL+ieToWUTzXqdYYPWO0GjbdvHmxap1R65XPjPnGd3jXvb2Zap1Vd53ydFL4qLjNpS8wqZ1f6flEnW36C7nn8iL9k5WArVnH0tVb/eJug3XOVPnX0n2Rx4lR0ODqIuNnFlDKRl2RVvnq+whNC8/U1Fv4LRe2dRNE1r2i3NwByC/JZZZ3EcJo2fR/IJhop1z5K4+9z48bMKjz/VQQmuCd1uL6+5ZQ6mvptZc3XFeKdkaNlC7rE0nhgHIHUoXDU4XYW5PI8rqJtF+yE7JprbeNpHn+Sfr/Db5x0em6LY55PsYrQs7s7nd42ovy/fh9tXVf98n4p/y8ijt+uuoZ8XfxOfm/DRafH6/aCvJz89hW16xl1Kry73brh8MIXu7d9tVrd3lGf+ecbnixqO92uocZq3r16tv6u1jtfqkZ0dgKOr7UjuM0/XRZ3opqWPgzdoUh2hX9w902joyHXTNZSnuznapLi7nb5r4Wme1dAlX2gSrnSz1Yai1q6Z/aRdlzfqJSaptrpO+7Ke1Rya6+z84LcS1zz1DlC/c5zJiRqOpdnOsQMetRoRsGjuO8nKcorOASW99SaSOhLMPHe4eV01SarfTGzv2mdonFDgcq6pqFW+tKcLy6FKyvfNr1X019/HoPDGzX7iuW89bJ/yDHFe5xlVxh6+khBIfW0I/26geF3rpKb7lFT/n8b5eFo648JXf9MJx4FeryJFbZpmOW/4sf8PTHd6BB1J9f39AjQ3+GeOcN+dQMPmK//1znqPWO/+ivB4eruLwLzXzjm3hZ7o/6w13xy0vl78R7Q5vsCoK/HPHR4/xu9zxdOaIwpCWNavmrrLsz6kAYqHj1ldZrXsPDpQnmn6/OSaGTbB6R2g0bVvVUUWXb7ycfOU3teP6yqtPn/A0TRk3RTds6LjVp4if1C79+9ukmtkvUOfdjyjrNvxT3GO2atY7POsYUnvLTJ3EZ939V/+liuv+4BUu3bqjTj0p1PUif9u+fx93P+Xd+RfFdSYUF9D8s/e73871PI9WXJtpg1qlrW7F+qS8gzDY9X15eSmlwZ738sXLRnI8jGHZKZHrO7BiuoQ6bULVTg5KWvTayb7sOO02oo92czx23MbV5GSMv33jhyD/8f+rrRtpd43TIVdi7zO9TyhwOJhmWHq+19xXL/x6rHDdeqrSu8Q3VHL8uTqjW3MfvfTknylpXW+44sJXftNbl9y6l6zGNvCmrWcaBeOBZGQ822DHf0pKu/f1XH++bt4RPwG2GH7TWe3eCco3nDwGZQDljqdQlzX8c1AACB1fZbXuPThQnmiyYPkKkVXbpd3p5iu/+cqrNZ01focL/Li/TeI6mlfd5vrzdesdnnUMqb1ldHtDdXdbm2q4dOuOJsMQTP62fasze7yus+nWS1U7baXzaDFzfqu01a1Yn+R6fajq+/LyUkqD4unKNGDFJ0S278CK6RLqtAlVOzkoadG0W7+NiHqdl7jruHW9gp0o/vj/1dbttSm/IWKVtmTT+4QCh4NphiV1rOa+euHXY4Xr1jOiK138rECOPxd3KmeLNZqePNat1vWGKy585Te9dX05I8mK3yZ23bdUsUz8FERjJk0zeMD6YPMV/729Wd7Xs/xl3bwjxm20GH7jVu3eCUa6iEkTAyh3PIW6rOEx/AAgdHyV1br34EB5osmC5StEVmF6od/5zVdeLcoo8jtc4Mf9bRLX0bzqNstf1q13eNYxpPaW0e0N1d3t2arh0q07mgxDMPnb9i3uSPW6zrz7nxU/jdc6jxYz57dKW92K9Umu14eqvi8vL6U0qN7gPe589ceR7TuwYrqEOm1C1U4OSlrkjdJvI6JeZ92O27q6Onr11Vfpiy++8FrH86d9+umntGrVKtqzZ4/f50jO5DGKksXsd/znHmfx1P3iX/68c+1w6upKUezHY3WMSp0sJiJT24eXH5YzTWwnx+Mj8YDY/Oe5jj/zcp7ogLfzXMfL+c9zv0pbKm1LHEYVH+Yrw5LlCkvFH/5G9qEzxZhNcmKcoDXDNMPPk5t57sOfebkYJForrrpTVa+L/9SuS+ua+XNOco7qcq244HU8EL40TAKPZZXx8F/Fv/y5/+rFYgIztXDopWfF+0NVl+9eU6yfb3TiQiv9tdaNz5mmGe96+Vfs05NBVvwJiDTeGKcR/wSE04iXc+dhIMpyy0Q8Bouv+Od80Hr7Awcn9lq50nU9NXVicjvNdOm1VrpwvPMYt+5xlKTrGEiXgCsM+WNFWaSYiEweJ2K58t7kKr1ntb5vYIIy/llVIPeUr3VW/PkUQCyRymq/7sH8sa6JyDz2FZ95eRz8nA7MGZE5QrPuyMv18hvnVa26Ki8vzrDWeLFRT+/+Th9sajm3W3goK3m7wF1HW19CvZ02r7bOtpxhVJ2S5tXe2j6owOscoq005hQqKZ3llT86u1NFe0Cz7n6Nq71iqu44dKbqdXLYPMPMeX1Q6iDTdSCtffTqwjvXqLeXud2VvfjPXu0yno3+vn/axDi0nvuMKJ1F3SOmq7ddPxxmuN2lF/98HWpt9WDHmRXrk16TXQW5vi9vh7V3JdOOD4a6h0ngicmS0lxv2PZ1utZ1eAyh4MnfNInGen6o0yYU7eRgpYXdnqlZvlix3WwFCU7uFY0wh8NBs2bNok8++YSuuuoqWr58uWLch5/85Ce0a9cuGj9+PG3atIluuOEGuueee0yPHdHw+1xqLC8RkzoxHii+7KSDs05Ks9jx4OmLzrMrZsnkmTUdVdVeM2tK+ySNKKYnrxihmKlSmsWQac2ibHadCMvf9pKjulY5Uzt3kLzR6ZrBfUQRlc7NINv+dWIfxcz2GuH33EfsJz+mxn6ecRXINS+etpiWlC8xFxdSumjMxqiWLlrpyd94c+VJzEPhMbsrV7jM5puA0lgjvxkJh5VmYQznbKY3rbmJ6io/Ej/T4W9zPce9ki/PScmh1t6BaU89GI7/okIqe/FFwzMDx1u6iGPNu9hVfsjLKtl4ZZ5juO3IGUqj88aRreJj93G403bB0HxqTUoMSZlq1dlmAWIRl9V+34NdTUSv/kY56zx39pz7NFG699s9AIHkt4DyKpindX+f9RDRqhsNLVe0W7TqNrJ6nXSsljl/oQWf3ued1sctoty3b1Atc1oSExX5g98m5Y7JQQ3dwa07qrTP9MJsti2ltY+Rtq9ue1kj/rXaSrk834Us/Y20XeXH0ot/X231YMWZFcuHcLbDHn3pMjr1sW8opy1BMaYtk495eyA3kW6/2DXJFeNO2uae5oDTJNrq+eFKG3k7+ZO6T1TXy9vJTRmDaVzeONpcv9m9Xi2NAkmLjMZO1TLZqu3mUIq6ycnuuOMO+v777+mbb74RHbjyjttrr72W/vvf/4o3bgcNGkRr164V27z//vs0e/ZscxGyewv1b91DtQsXieWFS++jQTMmusbQGDxa9OxL326kPnAnVR8xXLzKPWRrBVVedbXIOCIDpXSq7lPy+GN04KhSMW4H7+f5zU5Fa0XA6xRh+eufxHhJ4lXyIWMO3ugDYcmaUCzC2b6jkSoX3mMo/NI+vK59e7Wh65bHVTCu2a+48LixPeNCni5a6SniacHd7mOULLuDsg4dLK63ef020/kmKGnsEe9GwyHScuZMssLM9UbTKKDwdjYSvXa5onL9Q/5oenXcCXT2jnV0SOPBcY26Sk+g9PNXUoW91R3nzK/4P/vsg9f5u6tU8068povrHFeRLStR2UjKP4ya0sdS/VNfUH+LnUpmNFJWUY+yE4bTcyDOK7izPYRlqhW/gQeIdQHdgwd2ucsHvGkLoc5veF6Emdb9bWC5V7tFq26z9HZ3HU1+LM201ilzpH2Kv9pHPTffFbq6o6x9ZiTMgba/jLZ9ddvLGvGv11aS4tpo21U6ll78G22rB7vNagVhaYcNtMHa139ElesGu5ZlJdBH1x9Do5K7aFLVVhq0r3Gg8zaJKCGB0pYtoqqjR7njLZj52Nc6qwhbG1nji8i8lDw6MruErtq1mQ5vko3bPtAWk7eT9dKImV3nde16fVMWaDeHUlR13H744Yf061//mrZs2SI6ZOUdtxy0IUOG0K233koLFx6cCGjKlCl0xBFH0N///ne/IqT5zTfFcunBKceZpGfnTq9Mwhksddw41V5/rX1CxZ+w+Bt+K123mmBfFy9PzM2l/pYWr/38yTehuK5whiMYwpKHnj+H6Ic1RE7HwWX884y0XKLuFu/lo2cRzXs9qPGvl3fiNV3a7zydUrs2ky3D7hX/9lY79Xz5KWUVdplKGwAAAADVekcE2y2xVncMZ3szGOe3eps1UkIeL7I2WHtNKvV1J1BmgYNsEweOObCO36bs2JdKyelEWSeegLp+GPPs/HfnU3ltOTlk7WEetuDFxi6a0LLfdDs5GHC/RlnHbUNDA02ePJmef/550WF71FFHKTpuKysraeTIkbR69WoxXILk8ssvp61bt9Jnn32metyenh7xJ4+QkpISQxECAFGm4XuiR48xv9/vN+NtLSumC0PaAAAAAABYF+r6lrenZQ/NeXOO6vAIq6p0xtBFW8xyHbcRm5yM+4svvfRSuvjii0VnrRq+AJaXpxy7jN/CbW4+OM6Gp/vuu09EgPTHnbYAEKOadvu3H/8kA6yXLgxpAwAAAABgXajrW15lW6Xqch7TVhfaYpYTsY7bV155hTZs2EBHHnkkvfrqq+KPO2p5rFv+f+7YTU11zRbZ2dmp2Le9vZ3S0pQzacotWrRIHEv64zd3ASBG5Y3ybz8eJwysly4MaQMAAAAAYF2o61teSbb6C4yVNtdkcZrQFrOciHXc8puwp556Kr322mv08ssvi7+mpibasWOH+H/uuOU3ZZOTk2nv3oMTCzH+PHq0dqcLd/jyq8byPwCIUfljXQOp85g8cvw5fbD6ct7eY4IJCGO68HK9dUgbsCAej4vHHFPDy3k9AEA0QbkGAH5DXd/yynLLaHrRdDGmrVxVShptH1SAtlgUiVjH7Y9+9CP3m7bSX1lZGZ155pni/xMTE8VbtSeffLL4LDlw4AC9//779OMf/zhSQQcAqzn3addA6nL8+YoP1Zfz9hC5dOHleutiYcyvne+6ZkfWWxYpVgqLxnhc66vWi5lojSwPB2kGXGmmW3kcSrP/8np03kYozwXj+FrHsMr9YpVwRIFQlhWBHltt/0iVbSjXACBg8VrXjyLLZi6jaYXTFMv484jL/of0iSIRnZzMk+fkZGzz5s00Y8YM+uUvf0nHH388PfXUU2S32+mTTz5xD6UQzEF/ASCKcYOWx+Thn3fI39rUWg6RTZdYS5vORqLXLifa9f7BZWUziRKIaLfsbUx+q5grrel5kQ9fpMKioqWnhRasW0Abaja4l/FbAounLqYlnyzxWs4V0dzU3LCETeqctVdWkm2QjUpnVJIts1/MklyxvoTszXaylZRQ6XPPqs4OHLdCneeCcXytY5z5INHqmyJ/v1j8vrUSrTIkGGVFoMdW239KwRTx76a6TUEPrxEo1wAgaOKlrh/F+MvBva17aWTOSCrNKT24AukTMWb6KS3VcXvLLbfQhAkT6LLLLlMs//bbb+nJJ5+kffv20cSJE+nqq6+m7Oxsw8dFxy0AQHBVV1dTcXGx4eWRxuFinmELanifP4fohzVETof+dvxzJX7jYN7r4Q2vWvgMhMWIYIR3/rvzqby2nByy8PFPu7JTsqmtt81rOb8tsOK0FRQuopNj7hmuTtrMPio6volqNuaRvSPZ1Zn7xjsh67TVit9QCcZ9IY7xwe9DlueClqe1jpGWS87uFkoIVdgtcN/GGq0yJBhlRaDHVttfTbjLtkiWawAAAPGs1UTHrY9RicPrz3/+s+ry8ePH00MPPRT28AAAABj+GbP8jTg93HDnbfkb7nC9eaAVvkiERQX/VFj+JpqEOzmae5pVl/P2/PaA4q2BELLZOsSbthUf5ItOjYr3hrqWZ/ZR6Yw6sqUoJ1KNd8nNe0Kb54KRp/WO0dUoXpYPSdhj5L61Er0yJNCyItBja+2vJtxlG8o1AAAA64vYGLcAAAAxo2m3+X34Z2NWCV84w6Kisq3Sr/34J19h07RbDI/Ab6TJ8WdeHuk4tJqkNh9pE2h8BSNP+3PfGj12HNy3VuKrDAmkrAj02P6Ub2Er21CuAQAAWB46bgEAAAKVN8r8PjzWl1XCF86wqCjJLvFrPx6nK2zyRokxbflnxHKunxUnRjwOrcaR7SNtAo2vYORpf+5bo8eOg/vWSnyVIYGUFYEe25/yLWxlG8o1AAAAy0PHLQAAQKDyx7omDOKxJ33hbXjbcP7EWSt8kQiLirLcMjEpD4/vKMefB6UOUl3O24drmARmt2e6JiLjsR95eIRT94t/xbAJvLw3I2xhiQZ9g8pCm+eCkaf1jpE+mJyRvl8sft9aiV4ZEmhZEeixtfZXE+6yDeUaAACA9aHjFgAATNOauMiKE5NJ4VILW1DDy7O884RBcmUziUbNVC7jbXjbcIdXLXwGwmJEMMLLM6nzpDxy/HnlmStVl/P24eKefZ0n8OEJe2Y3UEa+XfzLn3m5WF9bG5Lza8VvqATjXOIYIcxzQjCOr3WMKz6khFCG3ahQx2EM0SpDglFWBHpstf2nFkylKQVTQhLeaCjXAAAAwJgEp9PppBhnZrY2AACAgPCEQTz2JP+MWXojTm1ZpFgpLCp4Uh4e35F/Kix/60xreTi0r1tHlVddLWZXL33uWddEZANxyG/aSp0bJY8/RlkzPTrqIfR5LhjH1zqGVe4Xq4QjCoSyrAj02Gr7R6psQ7kGAAAQHf2U6LgFAAAAMNDJkTpunOi89cSdtj07d6LTFgCiCso1AACAyEDHbQARAgAAAAAAAAAAABDpfkqMcQsAAAAAAAAAAABgMei4BQAAAAAAAAAAALAYdNwCAAAAAAAAAAAAWEzcd9zyhCI8MH8s4uvi6wvWdft7PH/2s0LYgx2GUB0TAHkuvHAfAwAAxA+rtCPM0Do/L+/84gvV84ezfRgp0RbeeBCN9xdAuCVTHLHX1RH11hM17SYaPJrsvRlUccml4uYuefwxyjq8yL2OhoxR7tzwfVDX7WnZQ5VtlTQyZySV5pQqdvF3nfxc7durqfKqq8Xs16XPPUs2W4f569Y7nmxWbT6O1vGM7pfy5z9QzcQCcV1DtlYEHnZZ+Nt3NFLlwntMHc+ffdTCIE8vM9fVMGmkoXT2umZ/tgsB3XxqJWpxpBNvRu8/vfgOZ9xwpcXwvTRzJlmKVnwGuSw2GxZTZWuw4tREXET63tM8vx/PwnCES215pOMw0uW3WarxZaXwmyznY+bcZlgxTBose8+qhCPU4TIaF1aJH9P8eaabXG7kmWX0ue9PW0a+j+E2n4Fr0Tq/aN8suNu9T8kTj7vrKlrtsqC0N+9bTFnjhwQlLUMRL2baYobvJ7TDfGv4ntrfXU2V9zxFtqIiw2lS/NU+6rn5Lr/q4pEuDyN9/oi2lX3sC/oSnE6nk+JktrbNxxxOR5xcQ7bMfrJ3JFLF+hKyN9vJNqKICn6SRFmtnxzcacwpVDP9HiKnkwZ/eAulVW1wr+oeMZ0aZz8g1hV9fAfRrvcV69IuelGso9cuV6zjY7bM+Qtdt/5O+rzxc/fi6UXTadnMZVRbW0tLty1VrDtm8DG0cOJC8f/Ldy6nDTUbvNaNHzbM61z2oTOp4o1OslfVUHJuMpXNrPK67sSC4VT80xTFdXP4m6bfQXkb7lZcc3vOVKp+q5f66/aRraREFJL1/f3UX19PHTfeRM7aWtV4lIcjobCQMh96kEomTz5YOaispKbBqXTbBX10ICdB7DMr8Qi66u+1RLX7yDbIRqUzKt1h37NuBPW19Ilzlc7NINt+2bdpY04hOvdpEffdKy92h19+zXpxMfoX2e7jKc5lIgxS3mhNTPRKrzPSj6ErnqwiR1W11zGlcCSNKKYnrxhB73R5549ch8MrnaXzFY0+/GA8dDaq5j0RN+l5FEotPS103f+uU83D48vGu5dVV1e7/7+4uNhruXyZnNZ+pqnFUdlMIs6Cu73TM7O4mBasW6BIT710UYtvjhvNY6TmGg46x4FanDFpubSM78+eWxeI+0wrz0n3M1d+PI+tdc5Qqtm13avM5fisPfYWKvzsz+r5v6BAOw00ymJD94NKPpGXaXpxmnr/MlHWyWnlb8341bqXz3yQaPVNynCNOokWDMund+s/1c1fZsLg636U08rf9x+3iHLevkH1Wbjg0/v8vh+M5kmtcC2eupiWfLJEsXxKwRTx76a6TX6FKSg6GxXPr3CW37545ge1uD1t6LG0bP8Bsu1eG7Lwa+VVr/ygFpcq5XxI4tfgM0br3L6ekfLlAW2rU1+obuwMTxgMbpuVn+WV3yYNmkSpaamKe5brHH89/a/ue9ZMfcPfbdXCNih1EDX3NBuqC5kNg9q9p1Z+aZVp14+7nrJt2V7nCAW/6m169VitZ7rKc1Fv+XdTFtJDe//u1a56eMZdXs8sbgPV/ceh+9xvzk+jRefZDbVltPaRnjfMzPOUr+Xe7x5X1L212hydDclU8cEwon4iSkqi0pUvUsakSYp2mVa41Oq7hupEsuXytKypqdGs73m2Q/XqDWbqGYG0xdTqDap1hAi3w4LR1gg5WRyJuP8gn+wdyWQblEylM6p8psmQVifd908bDWroNtS+sULcRPr8oa7HtCQm6l9fBO+LaOinbGlpoZycHN1t46rjdtPYcZSX46Si45uoZmPeQAFho9J5pZTcsIESnI6DOyUkUXfxNPG/qdXlinXOhCTqGViXVl1O5LEuYfQs14cf1ijW8TG35w6lC/PSqF88MV2SEpJoWuE06u7upi2NWxTrEimRJg92Nf63Nm0lh+x40rpnmltVz2XPn04Vz1e4Cq/MPsV1cwdm4XmFlNn2mde19adkU2Jvm9fyjuxjqe6/5OoEKikh2y03U9d9S12dtlw4np2qGo8cjl2vdojtuPN25IMPUs2CBe5O29sv6Kf9OU7Fdc1MPJyuffQrVwepR9ilNLM1bPC6ZhqIe+cPaxThsHfaqOKDIWRvT9SMi6y2zxTH6+200V6NfbTCIOWN+cOHeaUXp/OpaUfR/Ic2q6YJH3PFjUfTe93e+3H+WFFX75XO0vnSLv/PwXh4/hzV/CDiZt7rFErz351PG2s2qubhZ+Y8Y52OW7U4UiHF7/WlI6m8ttxwuqjFN8eN5jFOWxGSjls2LDGRKuaeoZnnSt94x12psULHbff/+4lXmcvx6UjJpqTeNvX8n5qmnQbM3/tB417SK1ulOOUvtox2hGrGr9a9nJZL1N2iWO6gBCpPT6P5BUN181eoOm618veLjV00oWW/6rPwosHpft8PRvOkVriyU7KprbdNsVyNP/doQJ4/x+v5Fa7y2xfP/KAWtyvq9tO0rm5KImfIwm+441YtLtWEIn4NPmO0zh22jlud+kL17EfCEwaD29799d1e+U0N1zmOLzrefc+Go+PWSNj06kJmw6B27xnFZdpReUfRkqOXeJ0jFPyqt+nVY5nB56Le8m3ZQ2hefqZXu2plU7fXM4vrGn06z31njpOuuSSV9mf3e7Vlrntsu+F9pOcNM/M85Wu5eEiGV9tSr80hBkvsJ9F+K1q2zN0u407bxef3q4ZLq76rWyfK7qfSkw+QLcPulZbdPd2a9T3PdqhevcFsPcPftphavUG1jhDhdlgw2hoh5xFHis5bg2kytC2RHn2uhxJaE3y2b6wQN5E+f6jrMfMLhulfXwTvi1jpuI2rMW6TMx3iZq54b6i7YBDf0Oxf512xdzrEN33857kuQbbOMzOLbfmbBP5TOeaE5joaYe9RLOYMzt9O8Del8ocu48+8nP88K2i8rmFfuea5+Lpc30D1eV03v3XKb8aqXVtST7Pqct6+9K9/Eg95frh3XnuduzO2dPldmvHIy3PuukFsx9tXXHih2D+huEC8aSvvtJWua3fPloE3Y/s000ztmqW49wwHVxhKT67XjQvP46Xo7KMVBilv7N/nXaHmz9+2lmumCS//plV9v6qKNarpLJ2PDuw6+PMDjfwglkvbhQD/NILzsVYermitIEvQiiMVUvxWVqwxlS6e8S3FjdoxeHko44Z/PqSX52wpB9+qiriG71XLXBoolzTzv14a+Hs/6NxLemVr0OJU717uavRazh1l07u6aKTdHtb8pZe/R/R2i2ee1rOwuLdbsTjY4dW77/hNOCOdHuGKQ3maJ0Sg/A5G3Jba7SIPKjptIxV+rbhUE+zwmXjGRDRtfdQXklr2kFVUdVSp3stquM4RtnvWRNiCVRfSKteM4v04HNWdBztULcVXPdbEc1Fv+cTWeq9nUIm9R/WZleDjuT/upH2Untaj2pYxs4/0vDH7POVrUWtb6rY5ZteTrahAtMfk7TJ+01beaWukvqtbJ5pVr+y0ldJl1/u69b0Eg/UGf+oZ/rbF1OoNXnUEC7TDItHWMEUljvht2dLZDabSJCOtR9xHRurikY6bSJ8/HPUYrbYyX191xdqI3RexJK46bgunNCk+8zcz7p9thNFIe1/QjlXi41h8fXydwbpum61NfDMrl75oIdlS2nX3S0lpF9vJNd16qftnOGrXFfSw+3E8f8Oglca+rktvP12NP7j+5TFjjGwXAjyejZ69rXvJEnzFkYpA0yWicdO0Wz8fhzBPhCNtAqJ37T7CEvI49TMu1PJqqO89rfzt6/7Quq+CFV5f950ZYSm/Ilh+ByNuDT+nwsGf+ydY4YvkuYMYzmSrPLOJqLZLfQIaK9Q5zIYt0HAFq1yr6awhSwpjPcDzGRRIm0rteeZvnT9Yz1O982fk91HR9eebapfpCXabLVhpFup08bqv0Q7zTSOOgn1/yZ+rkW6jRvr8kWwri8PVbI6a+q2VxVXHbe0m5fgZrtfpwx8Fe23BmxOu0sex+Pr4OoN13XZ7tvg5jRwPl2DvzdLdr7c3S2wnl3f/s2KMGq3rCnrY/Tiev2HQSmNf16W3ny4e4JvljTK2XQiUZJforudByi3BVxypCDRdIho3eaP083EI80Q40iYgetfuIywhj1M/40Itr4b63tPK377uD637Kljh9XXfmRGW8iuC5Xcw4tbwcyoc/Ll/ghW+SJ47iOHss8ozm1+8SD/4c1ejwlXnMBu2QMMVrHKtKKOILCmM9QDPZ1AgbSq155m/df5gPU/1zs9j3dYsf9lUu0xPsNtswUqzUKeL132NdphvGnEU7PtL/lyNdBs10uePZFtZHK7o6Kip31pZXHXc9nUkuV6fP3X/wdfqeQDroTzQcpJyY/7MAybzXxDXbR9UQNUpaYrFPP4HD97Mf/z/ZtaNKJ2leS4xYDxfn/SzAaPXnT5Y+3jX/cE9xm3pypXiXzH8wfV3ah6Pl3fe/cjBsXCl/arrxMDiPEaN53WNz5nmX9g14l6McbtmmKnj+bOPFIaS0lmq6eXrug7LmWY6ncVyaVbG/LHGtguBstwy3Twsn1mSxzmT/uTUlqmtD2hMNq04UuMjPY2mi5m48UUrzuTL5cvs9kz9e6k3Q/PYvpYHnV7+1SiXAiqn9e4HnbD4LFt7M1TjTCvvqsavybjgMW43pKfTXpvN0L1nJAxG7zWt/F2VkiaeeWafhUbuh0DCxZ954iDP5Wr8uUf9FsHy2wh5flCL2wqbTeRBzouhDL+h/OtHOR+0+A3CuX09I7XKe1Pb+shvBYdPD30YDG479ZCpqveykXvWTH3Dn22Nhs1oeewrDFrlmlFSOKaMmxKWZ7vpepuvctBsHUFjudozSO+Zpffc37l2OHV1p5qq82vto9Xm8+d5qtvm+GAY2WvqDLfL/G5vcjuq0xaUtDSbZn7Fi05bTK3e4HVfR0k7LKJU4shzjFsjadLZnSruIyPtm0jHTaTPH8m2Ml9fcelJlq7fRou46rhNzrWJ8VMy8u2ucVQG2cTA5GI2zPzpyo15oGSe5Y7/pMHwg7BuxGX/cw88L+HPPOMe//mzTu1cYqB4vi5pNk8z133Fh9rHkzptn3uWMo6e7JqtcWDMW7Xj+dqPZ4Nc8nKi4hteHjSeZ/z0K+wqcS8eBuuKyN6WaPh4/uwjD4Naehm5Ll7P2xlNZ/c1yxndLgR086mVqMURz5Q5aqbh9DSbLpGIG/cswXr3Eq+vNf8z1JDRik+VcingctqPsBgqW4MVpybion/UTHrtyJ9E7N7Tyt/8zDP7LAxHuFaeudJr+dSCqe5Z2EMZJl0RLL9Vxzjb+a5y7DHZMrW45TzIedES4TdZzsfMuaMwv/H4f+ur1ivG+fNcppbfLHHPaoSNO3lCFS6jcWGV+DHNn2e6Vh1BY7nWM0jtmeXruc+TI5lty6jt46vNZ+Z5qnX+ouMb3ROTUVISFT3wZ5/tsoDam20D7Sn5m7d+pqWZNDMbL0baYmr1BtX7Ce0w32RxpOi0HZRsKE04f3I+FROTGayLR7qNGunzR7StrHW8SNeBokyC0+lU/01EDM7W1rBjBw0ZkuQaR2PwaPFNjHRTlzz+GGVNKHav8+r554ZLENdxRZTHM+FX4z2/ZfF3nfxc7durqfKqq8VsiuJhzAN0m71uvePJZml0dw6pHM/ofqkP3EnVRwwX1zVka0XgYZeFv31HI1UuvMfU8fzZRy0M8vQyc10Hjio1lM6631AZ3S4EdPOplajFkU68Gb3/1OK7paeFFqxbIAZpl/C3kPxAy03NDcnlta9bZ/xemunxII40rfgMcllsNiymytZgxamJuIj0vad5fj+eheEIl9ryQMKkNku8tNzU220RLL+ps5HotctdE0bIK+v8Iu3udQeX8VsS5z5NFfZW7/iKZPgDLOdj5txmRChMas9FqaNxU90m1WdlsO/ZYPIMR6jDZTQurBI/pvnzTDe53Mgzy+hz35+2jHwfw20+A9eidX7Rvllwt3ufkiced9dVtNplQWlvLr2dsg4dHJS0DEW8mGmLGb6f0A7z7cAuav/fKqq85ymyFRUZTpPir/ZRz813+VUXj3R5GOnzR7St7GPfeNQ60E/Z0tJCOTk5utvGVcetWoTwTd2zc6f1Oi2C1GmTOm6corM0kOv293j+7GeFsAc7DKE6JkSH+e/Op/Ja5ayo/BMS/jZyxWkrQnZe5DnEKYRP0DpuI+n5c4h+WON7NmH+iRu/LTHv9XCFDOLguagmHM9KgGhoR5ihdX5enpibS/0tLV7nD2f7MFKiLbzxIBrvL4BgQMdtABECABBs/JPPOW/O0Vy/au6q6PjWFQBiu+OWh0J49Bhz+/x+M96agKA/F9XgWQkAAADx2E8ZV2PcAgBEQmVbpe56/kkJAEDENe02vw//5A0gyM9FNXhWAgAAQDxCxy0AQIiVZJforudxgAAAIi5vlPl9eJwygCA/F9XgWQkAAADxCB23AAAhVpZbJiZX4XH65PgzL8cwCQBgCfljXZOOeZRVqngb3haTS0AQn4tq8KwEAACAeIaOWwCAMOAZsXlyFTn+zMsBIDbwOLZqY9lGxfi2knOfdk06Jlc2k2iUxyQfvA1vCxDE5+LUgqk0pWCKYhmelQAAABDPEpxOp5NiHCYnAwCrqGitEOP08U8+8aYtAFjWgV2u8Wt5KATprVq1ZQAheC7iWQkAAACxrNXE5GTouAUAAAAAAAAAAACwWMcthkoAAAAAAAAAAAAAsBh03AIAAAAAAAAAAABYDDpuAQAAAAAAAAAAACwmLjtu29etI3ttreo6Xs7rI3m8UPA3jFa/tlhNS71wNL/5pvizapoABEOs3tsQHZBfAACir8yNdNlt9vyRDq8ef8KmtQ8v7/ziC9V9zF5nOM4BAKHtj7By2WdVyRRPGn+g9q2tVHnV1WQrLKTS554lm62DqGm3mCHZ3ptBFZdcKjJLyeOPUdbhRe51XrMnN3wv1rXvaKTKhfcYOl7DpJFU2VapOpv8npY9muukc+mFI1hhlF9z+/ZqQ3GV8uc/UM3EgrBcl/x4Q7ZWKMNXWOjejsMVyrTUOl4wrlcv3pvXb6PahYvcuw068QjtMM6cSaGme73RxM97TPP6/dkniKI9Xfhh7b4Hlt9FtpR2d1wq7u37FlPW+CEB3VORuG8sk7fBd/6Lxvyiluaey4xsA5ETRWmh9rzxXObrM1gj3aL2ma9Vj9dqF2gcJ9A6naJdYuA6Qtnu0EtfI/t4tbEMtAG12mUifX93lfs8JUtvd8e/0fakr7aaWL7gbp/n0GuP+9OGi/R9FOnzB40f8evvunCxQhhCVRc20x+hdr9FfT07QhKcTqeT4mW2toXZlD7iJKp4o5PsVTWUnJtMZTOryJbZT/aORKpYX0L2ZjslFgyn4p+mUFbrJ+5jdI+YTo2zHyByOqno4zuIdr0vlvN+e9aNoL6WPrINslHpjEr38aTlSSOK6ckrRtA7XZ+7jze9aDotm7mMamtraem2pfR5o3Ld9eOupxyHQ3EueTiKCgqIXrtcsY7GnEJ07tMijN0rL6a0qg3uMErXZuaa23OmUvVbvdRft0/z2prz02jReXY6kJOguC523f+u87ouXtdRVUWDP7zFHT69+JXW7Z3xR3po799pQ83BfWYlHkG/f3Y/OaqqyVZSQqn3L6PEYcOov76eem5dQPbKStXr4niqmX6POJc8HEbS0jaiiErnZpBtv+xboDGnUMucv9CCT+9ThE+63lyHQzetPNfZh85051HPcOxeU0yONofYLjknmcpO8k5LjgvPCmuwtfS00IJ1C9SvNzWXqqur3cuLi4vFv2rLtJZrbRt0nY2m0kZa15KYqHr99x+3iHLevsHUPlKcmaEVP9/u+darPDlm8DG0cOJCyrZlq8Z7SOPXT6KBM+9i1z2Q2Uelsxtc+Vzj3tAr/3zdU+G8b8JKL2+n5wV0aM47Vsw3Zmjlf14+LDHRVWmsrNTNL/zMKZk8OehhCGqal80k4sfzbtkzK30wUVej/jZByitWZOX8W7Nru2b9yJma67MM9/WcDea2XA/wrOdNKZhCPT099EXTF+5lg1IHUXNPs+Zn6Rk1vmx82K5NvjzetvVVfwuEv3U3v5/5Zz5ItPomRZtM7Znu7jT1KNP12hicLx+ecZdXnY63T7voRVGn88z/Z6QfQ1f8bS85qmsNXYcivFrLVdodHIaec59QbXcsnrqYlnyyRDV9mVraq+0jruXJKlcbSyNsnm1AxT6ydlnfN99Q53XXE3F7KJGodHY9ZeT3KY7VNDiVbrugz6s96dlm1IqzzoZkqvhgGBEnq8Y5tNrjenGm1YbTbfcFeB8ZEcr72Ap1Vb34ZVrXrrcuXPESK2mjVW6KfpG1I6ivtU+3P0Kv/yujsdNQPTum2mW++ilbWignJ4f0xF3HbU5aMtnzp1PF8xWuTJHZR0XHN1HNxjyydySLzFPwywLKbPuMEpyuDjLmTEiinuJp4v/TqsuJZOt6O22098MhZG9LVD3eihuPpve6t5JDtk9SQhJNK5xG3d3dtKVxC/WLJ83BdUflHUUr9tV7nUsKR1pqGtEPaxTrKCGJaPQs13Y/rFGE395powqNMHJnbuF5harX3JF9LNW9UqcaV84cJ11zSSrtz+73ui62sWaj13XxuuUVeym1utxw/PK6r7KH0Lz8TEUcJlIinZY+meY/VSNu/IT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+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "for _, row in worst_df.iterrows():\n",
+    "    result = all_results[row[\"ResultIndex\"]]\n",
+    "    onset_offset = result[\"response_onset_offset\"]\n",
+    "\n",
+    "    # ---- Reload ground truth and my_pairs for this piece ----\n",
+    "    tsv_path = os.path.join(ASAP_PATH, result[\"metadata_row\"].get(\"note_alignments\", \"\").strip())\n",
+    "    ground_truth = load_ground_truth(tsv_path)\n",
+    "    my_pairs = convert_pipeline_output(\n",
+    "        result[\"event_details\"],\n",
+    "        result[\"response_events_normalized\"],\n",
+    "        result[\"ref_events_normalized\"],\n",
+    "    )\n",
+    "\n",
+    "    # ---- Build GT lookup counters for tagging TP vs FP ----\n",
+    "    gt_paired = Counter(\n",
+    "        (int(r[\"pitch\"]), round(float(r[\"onset\"]), 1))\n",
+    "        for r in ground_truth if r[\"label\"] == \"paired\"\n",
+    "    )\n",
+    "    gt_insertion = Counter(\n",
+    "        (int(r[\"pitch\"]), round(float(r[\"onset\"]), 1))\n",
+    "        for r in ground_truth if r[\"label\"] == \"insertion\"\n",
+    "    )\n",
+    "\n",
+    "    # ---- Tag each of my paired/insertion notes as TP or FP ----\n",
+    "    seen_paired = Counter()\n",
+    "    seen_insertion = Counter()\n",
+    "    tp_x, tp_y = [], []\n",
+    "    fp_x, fp_y = [], []\n",
+    "\n",
+    "    for pair in my_pairs:\n",
+    "        if pair[\"onset\"] is None:\n",
+    "            continue  # deletions have no note position to plot\n",
+    "        absolute_onset = pair[\"onset\"] + onset_offset\n",
+    "        key = (int(pair[\"pitch\"]), round(float(absolute_onset), 1))\n",
+    "\n",
+    "        if pair[\"label\"] == \"paired\":\n",
+    "            seen_paired[key] = seen_paired[key] + 1\n",
+    "            is_tp = seen_paired[key] <= gt_paired[key]\n",
+    "        else:\n",
+    "            seen_insertion[key] = seen_insertion[key] + 1\n",
+    "            is_tp = seen_insertion[key] <= gt_insertion[key]\n",
+    "\n",
+    "        if is_tp:\n",
+    "            tp_x.append(absolute_onset); tp_y.append(pair[\"pitch\"])\n",
+    "        else:\n",
+    "            fp_x.append(absolute_onset); fp_y.append(pair[\"pitch\"])\n",
+    "\n",
+    "    # ---- Find FN: GT paired/insertion notes my pipeline never produced ----\n",
+    "    my_paired_counter = Counter(\n",
+    "        (int(p[\"pitch\"]), round(float(p[\"onset\"] + onset_offset), 1))\n",
+    "        for p in my_pairs if p[\"label\"] == \"paired\" and p[\"onset\"] is not None\n",
+    "    )\n",
+    "    my_insertion_counter = Counter(\n",
+    "        (int(p[\"pitch\"]), round(float(p[\"onset\"] + onset_offset), 1))\n",
+    "        for p in my_pairs if p[\"label\"] == \"insertion\" and p[\"onset\"] is not None\n",
+    "    )\n",
+    "    fn_x, fn_y = [], []\n",
+    "    for r in ground_truth:\n",
+    "        if r[\"label\"] in (\"paired\", \"insertion\") and r[\"onset\"] is not None:\n",
+    "            key = (int(r[\"pitch\"]), round(float(r[\"onset\"]), 1))\n",
+    "            found = my_paired_counter[key] + my_insertion_counter[key]\n",
+    "            expected = gt_paired[key] + gt_insertion[key] if r[\"label\"] == \"paired\" else gt_insertion[key]\n",
+    "            # crude check: if GT count for this key exceeds what I produced, it's an FN instance\n",
+    "            if found < (gt_paired[key] if r[\"label\"] == \"paired\" else gt_insertion[key]):\n",
+    "                fn_x.append(r[\"onset\"]); fn_y.append(r[\"pitch\"])\n",
+    "\n",
+    "    # ---- Plot: piano roll of reference vs response, overlaid with TP/FP/FN ----\n",
+    "    fig, ax = plt.subplots(figsize=(14, 6))\n",
+    "\n",
+    "    for ev in result[\"ref_events_normalized\"]:\n",
+    "        for note in ev[\"notes\"]:\n",
+    "            ax.barh(note[\"pitch\"], note[\"duration\"], left=note[\"start\"] + onset_offset,\n",
+    "                    height=0.8, color=\"lightgray\", alpha=0.6)\n",
+    "\n",
+    "    ax.scatter(tp_x, tp_y, color=\"tab:green\", s=20, label=\"TP (agrees with GT)\")\n",
+    "    ax.scatter(fp_x, fp_y, color=\"tab:orange\", s=20, label=\"FP (I predicted, GT disagrees)\")\n",
+    "    ax.scatter(fn_x, fn_y, color=\"tab:red\", marker=\"x\", s=40, label=\"FN (GT expects, I missed)\")\n",
+    "\n",
+    "    ax.set_title(row[\"Piece\"] + f\"  (F1={row['F1']:.3f}, P={row['Precision']:.3f}, R={row['Recall']:.3f})\")\n",
+    "    ax.set_xlabel(\"Time (s, absolute)\")\n",
+    "    ax.set_ylabel(\"MIDI Pitch\")\n",
+    "    ax.legend(loc=\"upper right\", fontsize=8)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2500x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, axes = plt.subplots(1, len(worst_df), figsize=(5 * len(worst_df), 5))\n",
+    "\n",
+    "for ax, (_, row) in zip(axes, worst_df.iterrows()):\n",
+    "    result = all_results[row[\"ResultIndex\"]]\n",
+    "\n",
+    "    ref_idx_list = []\n",
+    "    res_idx_list = []\n",
+    "    for op in result[\"event_details\"]:\n",
+    "        if op[\"reference_index\"] is not None and op[\"response_index\"] is not None:\n",
+    "            ref_idx_list.append(op[\"reference_index\"])\n",
+    "            res_idx_list.append(op[\"response_index\"])\n",
+    "\n",
+    "    ax.plot(ref_idx_list, res_idx_list, \".\", markersize=1.5)\n",
+    "\n",
+    "    max_idx = max(max(ref_idx_list), max(res_idx_list))\n",
+    "    ax.plot([0, max_idx], [0, max_idx], \"r--\", alpha=0.3, linewidth=1)\n",
+    "\n",
+    "    ax.set_title(row[\"Piece\"] + f\"\\nF1={row['F1']:.3f}\", fontsize=10)\n",
+    "    ax.set_xlabel(\"Reference event index\")\n",
+    "    ax.set_ylabel(\"Response event index\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step is centered around reference index: 290\n",
+      "\n",
+      "--- Reference events around the step ---\n",
+      "ref[275]  start=186.283  type=chord  pitches=[51, 55, 58, 61]\n",
+      "ref[276]  start=186.883  type=chord  pitches=[51, 55, 58, 61]\n",
+      "ref[277]  start=187.483  type=chord  pitches=[51, 55, 58, 61, 67]\n",
+      "ref[278]  start=188.083  type=chord  pitches=[52, 55, 58, 61, 70]\n",
+      "ref[279]  start=188.683  type=chord  pitches=[52, 55, 58, 61]\n",
+      "ref[280]  start=189.283  type=chord  pitches=[52, 55, 58, 61]\n",
+      "ref[281]  start=189.883  type=chord  pitches=[51, 55, 58, 61, 63]\n",
+      "ref[282]  start=190.483  type=chord  pitches=[51, 55, 58, 61, 63]\n",
+      "ref[283]  start=191.083  type=chord  pitches=[51, 55, 58, 61, 63]\n",
+      "ref[284]  start=191.683  type=chord  pitches=[56, 59, 63, 68]\n",
+      "ref[285]  start=193.483  type=chord  pitches=[35, 47, 59]\n",
+      "ref[286]  start=195.283  type=chord  pitches=[34, 46, 58]\n",
+      "ref[287]  start=197.083  type=chord  pitches=[39, 51, 63]\n",
+      "ref[288]  start=198.883  type=chord  pitches=[32, 44, 56]\n",
+      "ref[289]  start=200.683  type=chord  pitches=[32, 44, 56]\n",
+      "ref[290]  start=202.483  type=chord  pitches=[32, 44, 56]\n",
+      "ref[291]  start=206.083  type=note  pitches=[56]\n",
+      "ref[292]  start=206.745  type=note  pitches=[61]\n",
+      "ref[293]  start=207.407  type=note  pitches=[58]\n",
+      "ref[294]  start=208.069  type=note  pitches=[63]\n",
+      "ref[295]  start=208.730  type=note  pitches=[60]\n",
+      "ref[296]  start=209.392  type=note  pitches=[65]\n",
+      "ref[297]  start=210.054  type=note  pitches=[63]\n",
+      "ref[298]  start=210.495  type=note  pitches=[61]\n",
+      "ref[299]  start=210.716  type=note  pitches=[60]\n",
+      "ref[300]  start=211.377  type=note  pitches=[63]\n",
+      "ref[301]  start=211.598  type=note  pitches=[56]\n",
+      "ref[302]  start=211.819  type=note  pitches=[55]\n",
+      "ref[303]  start=212.039  type=chord  pitches=[53, 68]\n",
+      "ref[304]  start=212.260  type=note  pitches=[55]\n",
+      "\n",
+      "--- Response events around the same time ---\n",
+      "start=200.930  type=chord  pitches=[59, 35, 47]\n",
+      "start=202.503  type=chord  pitches=[58, 34, 46]\n",
+      "start=203.988  type=note  pitches=[63]\n",
+      "start=204.040  type=chord  pitches=[51, 39]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Beethoven Piano_Sonatas_31-3_4 has a very clean single step around ref index ~700-900\n",
+    "result = all_results[383]  # ResultIndex for Beethoven from worst_df\n",
+    "\n",
+    "# Find the reference-index range where the alignment path deviates most\n",
+    "# from a straight line, so we zoom into the step itself.\n",
+    "ref_idx_list = []\n",
+    "res_idx_list = []\n",
+    "for op in result[\"event_details\"]:\n",
+    "    if op[\"reference_index\"] is not None and op[\"response_index\"] is not None:\n",
+    "        ref_idx_list.append(op[\"reference_index\"])\n",
+    "        res_idx_list.append(op[\"response_index\"])\n",
+    "\n",
+    "ref_arr = np.array(ref_idx_list)\n",
+    "res_arr = np.array(res_idx_list)\n",
+    "\n",
+    "# Rough diagonal for comparison, scaled to the same total range\n",
+    "diagonal = ref_arr * (res_arr[-1] / ref_arr[-1])\n",
+    "deviation = res_arr - diagonal\n",
+    "\n",
+    "# The reference index where response gets furthest ahead of \"ideal\" pace\n",
+    "step_center = ref_arr[np.argmax(deviation)]\n",
+    "print(\"Step is centered around reference index:\", step_center)\n",
+    "\n",
+    "# Zoom into a small window around that step and print the real notes\n",
+    "window = 15\n",
+    "ref_events = result[\"ref_events_normalized\"]\n",
+    "res_events = result[\"response_events_normalized\"]\n",
+    "\n",
+    "print(\"\\n--- Reference events around the step ---\")\n",
+    "for i in range(max(0, step_center - window), min(len(ref_events), step_center + window)):\n",
+    "    ev = ref_events[i]\n",
+    "    pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
+    "    print(f\"ref[{i}]  start={ev['event_start']:.3f}  type={ev['event_type']}  pitches={pitches}\")\n",
+    "\n",
+    "print(\"\\n--- Response events around the same time ---\")\n",
+    "ref_time = ref_events[step_center][\"event_start\"]\n",
+    "for ev in res_events:\n",
+    "    if abs(ev[\"event_start\"] - ref_time) < 2.0:  # +/- 2 seconds window\n",
+    "        pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
+    "        print(f\"start={ev['event_start']:.3f}  type={ev['event_type']}  pitches={pitches}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Operation type breakdown for this piece:\n",
+      "  match: 1494  (76.6%)\n",
+      "  replacement: 196  (10.0%)\n",
+      "  extra: 153  (7.8%)\n",
+      "  missing: 108  (5.5%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from collections import Counter\n",
+    "\n",
+    "result = all_results[383]  # Beethoven, from worst_df\n",
+    "\n",
+    "op_counts = Counter(ev[\"operation_type\"] for ev in result[\"event_details\"])\n",
+    "total = sum(op_counts.values())\n",
+    "\n",
+    "print(\"Operation type breakdown for this piece:\")\n",
+    "for op_type, count in op_counts.most_common():\n",
+    "    print(f\"  {op_type}: {count}  ({100 * count / total:.1f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Reference events in 105-132s ---\n",
+      "ref[138]  start=105.133  type=note  pitches=[71]\n",
+      "ref[139]  start=105.283  type=chord  pitches=[51, 55, 58, 70]\n",
+      "ref[140]  start=105.883  type=chord  pitches=[51, 55, 58]\n",
+      "ref[141]  start=106.483  type=chord  pitches=[51, 55, 58]\n",
+      "ref[142]  start=107.083  type=chord  pitches=[49, 51, 55, 58]\n",
+      "ref[143]  start=107.683  type=chord  pitches=[47, 51, 56, 71]\n",
+      "ref[144]  start=108.283  type=chord  pitches=[46, 51, 55, 73]\n",
+      "ref[145]  start=108.883  type=chord  pitches=[44, 51, 56, 71]\n",
+      "ref[146]  start=109.483  type=chord  pitches=[44, 47, 51]\n",
+      "ref[147]  start=110.083  type=chord  pitches=[44, 47, 51]\n",
+      "ref[148]  start=110.683  type=chord  pitches=[44, 47, 51]\n",
+      "ref[149]  start=111.283  type=chord  pitches=[44, 47, 51]\n",
+      "ref[150]  start=111.883  type=chord  pitches=[44, 47, 51]\n",
+      "ref[151]  start=112.483  type=chord  pitches=[42, 47, 52, 73]\n",
+      "ref[152]  start=113.083  type=chord  pitches=[42, 47, 52]\n",
+      "ref[153]  start=113.683  type=chord  pitches=[42, 47, 52]\n",
+      "ref[154]  start=114.283  type=chord  pitches=[42, 46, 52]\n",
+      "ref[155]  start=114.883  type=chord  pitches=[42, 46, 52]\n",
+      "ref[156]  start=115.483  type=chord  pitches=[42, 46, 52]\n",
+      "ref[157]  start=116.083  type=chord  pitches=[42, 47, 51, 75]\n",
+      "ref[158]  start=116.683  type=chord  pitches=[42, 47, 51]\n",
+      "ref[159]  start=117.283  type=chord  pitches=[42, 47, 51]\n",
+      "ref[160]  start=117.883  type=chord  pitches=[47, 54, 59]\n",
+      "ref[161]  start=118.483  type=chord  pitches=[47, 54, 59]\n",
+      "ref[162]  start=119.083  type=chord  pitches=[47, 54, 59]\n",
+      "ref[163]  start=119.683  type=chord  pitches=[49, 52, 59, 76]\n",
+      "ref[164]  start=120.283  type=chord  pitches=[49, 52, 59]\n",
+      "ref[165]  start=120.883  type=chord  pitches=[49, 52, 59]\n",
+      "ref[166]  start=121.483  type=chord  pitches=[49, 52, 58, 80]\n",
+      "ref[167]  start=122.083  type=chord  pitches=[49, 52, 58]\n",
+      "ref[168]  start=122.683  type=chord  pitches=[49, 52, 58]\n",
+      "ref[169]  start=123.283  type=chord  pitches=[51, 54, 59, 78]\n",
+      "ref[170]  start=123.883  type=chord  pitches=[51, 54, 59]\n",
+      "ref[171]  start=124.483  type=chord  pitches=[51, 54, 59, 83]\n",
+      "ref[172]  start=125.083  type=chord  pitches=[52, 59, 64, 80]\n",
+      "ref[173]  start=125.683  type=chord  pitches=[52, 59, 64]\n",
+      "ref[174]  start=126.283  type=chord  pitches=[52, 59, 64, 92]\n",
+      "ref[175]  start=126.883  type=chord  pitches=[54, 59, 63]\n",
+      "ref[176]  start=127.483  type=chord  pitches=[54, 59, 63]\n",
+      "ref[177]  start=128.083  type=chord  pitches=[54, 59, 63, 90]\n",
+      "ref[178]  start=128.683  type=chord  pitches=[54, 58, 61, 64]\n",
+      "ref[179]  start=129.283  type=chord  pitches=[54, 58, 61, 64, 87]\n",
+      "ref[180]  start=129.883  type=chord  pitches=[54, 58, 61, 64, 88]\n",
+      "ref[181]  start=130.183  type=note  pitches=[82]\n",
+      "ref[182]  start=130.483  type=chord  pitches=[55, 58, 61, 64, 85]\n",
+      "ref[183]  start=131.083  type=chord  pitches=[55, 58, 61, 64]\n",
+      "ref[184]  start=131.683  type=chord  pitches=[55, 58, 61, 64]\n",
+      "\n",
+      "--- Response events in 105-132s ---\n",
+      "res[117]  start=105.139  type=chord  pitches=[44, 47, 51]\n",
+      "res[118]  start=105.443  type=note  pitches=[75]\n",
+      "res[119]  start=106.060  type=chord  pitches=[47, 51, 44]\n",
+      "res[120]  start=106.486  type=chord  pitches=[47, 51, 44]\n",
+      "res[121]  start=106.889  type=chord  pitches=[47, 51]\n",
+      "res[122]  start=106.940  type=note  pitches=[44]\n",
+      "res[123]  start=107.328  type=chord  pitches=[44, 47, 51]\n",
+      "res[124]  start=107.828  type=chord  pitches=[47, 44, 51]\n",
+      "res[125]  start=107.936  type=note  pitches=[73]\n",
+      "res[126]  start=108.323  type=chord  pitches=[47, 44, 51]\n",
+      "res[127]  start=108.794  type=chord  pitches=[47, 44]\n",
+      "res[128]  start=108.862  type=note  pitches=[51]\n",
+      "res[129]  start=109.273  type=chord  pitches=[47, 44, 51]\n",
+      "res[130]  start=109.371  type=note  pitches=[71]\n",
+      "res[131]  start=109.798  type=chord  pitches=[47, 51, 44]\n",
+      "res[132]  start=109.922  type=note  pitches=[70]\n",
+      "res[133]  start=110.249  type=chord  pitches=[51, 47, 44]\n",
+      "res[134]  start=110.392  type=note  pitches=[71]\n",
+      "res[135]  start=110.703  type=chord  pitches=[44, 47, 51]\n",
+      "res[136]  start=110.783  type=note  pitches=[68]\n",
+      "res[137]  start=111.177  type=chord  pitches=[46, 49, 51, 68]\n",
+      "res[138]  start=111.844  type=chord  pitches=[49, 46, 51]\n",
+      "res[139]  start=112.216  type=chord  pitches=[49, 51, 46]\n",
+      "res[140]  start=112.668  type=chord  pitches=[46, 49, 51]\n",
+      "res[141]  start=112.776  type=note  pitches=[67]\n",
+      "res[142]  start=113.236  type=chord  pitches=[46, 51, 49]\n",
+      "res[143]  start=113.633  type=chord  pitches=[49, 46, 51]\n",
+      "res[144]  start=114.056  type=chord  pitches=[51, 49, 46]\n",
+      "res[145]  start=114.479  type=chord  pitches=[51, 46, 49]\n",
+      "res[146]  start=114.938  type=chord  pitches=[51, 46, 49]\n",
+      "res[147]  start=115.026  type=note  pitches=[76]\n",
+      "res[148]  start=115.375  type=chord  pitches=[51, 49]\n",
+      "res[149]  start=115.443  type=note  pitches=[46]\n",
+      "res[150]  start=115.841  type=chord  pitches=[46, 51]\n",
+      "res[151]  start=116.395  type=chord  pitches=[49, 55, 46]\n",
+      "res[152]  start=116.456  type=note  pitches=[51]\n",
+      "res[153]  start=116.516  type=note  pitches=[75]\n",
+      "res[154]  start=116.999  type=chord  pitches=[51, 75, 47, 56]\n",
+      "res[155]  start=117.625  type=chord  pitches=[51, 47, 56]\n",
+      "res[156]  start=118.059  type=chord  pitches=[51, 56, 47]\n",
+      "res[157]  start=118.164  type=note  pitches=[80]\n",
+      "res[158]  start=118.539  type=chord  pitches=[56, 51, 48]\n",
+      "res[159]  start=119.038  type=chord  pitches=[51, 56, 48]\n",
+      "res[160]  start=119.456  type=chord  pitches=[51, 56, 48]\n",
+      "res[161]  start=119.565  type=note  pitches=[78]\n",
+      "res[162]  start=119.882  type=chord  pitches=[56, 49, 52]\n",
+      "res[163]  start=120.337  type=chord  pitches=[56, 52, 49]\n",
+      "res[164]  start=120.725  type=chord  pitches=[76, 56, 49, 52]\n",
+      "res[165]  start=121.081  type=chord  pitches=[52, 56, 61]\n",
+      "res[166]  start=121.182  type=note  pitches=[75]\n",
+      "res[167]  start=121.686  type=chord  pitches=[61, 52, 56]\n",
+      "res[168]  start=122.082  type=chord  pitches=[56, 61, 52]\n",
+      "res[169]  start=122.210  type=note  pitches=[73]\n",
+      "res[170]  start=122.852  type=chord  pitches=[59, 71, 56, 51]\n",
+      "res[171]  start=123.441  type=chord  pitches=[56, 59, 51]\n",
+      "res[172]  start=123.859  type=chord  pitches=[59, 51, 56]\n",
+      "res[173]  start=124.320  type=chord  pitches=[51, 56, 59]\n",
+      "res[174]  start=124.757  type=chord  pitches=[73, 56, 59]\n",
+      "res[175]  start=124.815  type=note  pitches=[51]\n",
+      "res[176]  start=125.337  type=chord  pitches=[75, 56, 59, 51]\n",
+      "res[177]  start=125.832  type=note  pitches=[73]\n",
+      "res[178]  start=125.943  type=note  pitches=[71]\n",
+      "res[179]  start=126.073  type=chord  pitches=[58, 51, 55, 70]\n",
+      "res[180]  start=126.618  type=chord  pitches=[55, 51, 58]\n",
+      "res[181]  start=127.046  type=chord  pitches=[55, 51, 58]\n",
+      "res[182]  start=127.486  type=chord  pitches=[58, 49, 55]\n",
+      "res[183]  start=127.947  type=chord  pitches=[51, 56, 47]\n",
+      "res[184]  start=128.039  type=note  pitches=[71]\n",
+      "res[185]  start=128.654  type=chord  pitches=[55, 51, 46]\n",
+      "res[186]  start=128.707  type=note  pitches=[73]\n",
+      "res[187]  start=129.322  type=chord  pitches=[56, 51, 71, 44]\n",
+      "res[188]  start=130.033  type=chord  pitches=[44, 47]\n",
+      "res[189]  start=130.422  type=chord  pitches=[47, 44, 51]\n",
+      "res[190]  start=130.822  type=chord  pitches=[47, 44]\n",
+      "res[191]  start=131.220  type=chord  pitches=[44, 51, 47]\n",
+      "res[192]  start=131.629  type=chord  pitches=[51, 44, 47]\n"
+     ]
+    }
+   ],
+   "source": [
+    "window_start, window_end = 105.0, 132.0\n",
+    "\n",
+    "print(\"--- Reference events in 105-132s ---\")\n",
+    "for i, ev in enumerate(result[\"ref_events_normalized\"]):\n",
+    "    if window_start <= ev[\"event_start\"] <= window_end:\n",
+    "        pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
+    "        print(f\"ref[{i}]  start={ev['event_start']:.3f}  type={ev['event_type']}  pitches={pitches}\")\n",
+    "\n",
+    "print(\"\\n--- Response events in 105-132s ---\")\n",
+    "for i, ev in enumerate(result[\"response_events_normalized\"]):\n",
+    "    if window_start <= ev[\"event_start\"] <= window_end:\n",
+    "        pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
+    "        print(f\"res[{i}]  start={ev['event_start']:.3f}  type={ev['event_type']}  pitches={pitches}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Windows with the highest response/reference event density ratio:\n",
+      "  582.5s - 587.5s : ref=3 events, res=38 events, ratio=12.67\n",
+      "  192.5s - 197.5s : ref=3 events, res=12 events, ratio=4.00\n",
+      "  65.0s - 70.0s : ref=4 events, res=13 events, ratio=3.25\n",
+      "  445.0s - 450.0s : ref=2 events, res=6 events, ratio=3.00\n",
+      "  62.5s - 67.5s : ref=4 events, res=11 events, ratio=2.75\n",
+      "  190.0s - 195.0s : ref=4 events, res=11 events, ratio=2.75\n",
+      "  442.5s - 447.5s : ref=4 events, res=11 events, ratio=2.75\n",
+      "  195.0s - 200.0s : ref=3 events, res=8 events, ratio=2.67\n",
+      "  347.5s - 352.5s : ref=8 events, res=20 events, ratio=2.50\n",
+      "  412.5s - 417.5s : ref=8 events, res=19 events, ratio=2.38\n",
+      "  350.0s - 355.0s : ref=9 events, res=21 events, ratio=2.33\n",
+      "  395.0s - 400.0s : ref=8 events, res=18 events, ratio=2.25\n",
+      "  440.0s - 445.0s : ref=7 events, res=15 events, ratio=2.14\n",
+      "  365.0s - 370.0s : ref=8 events, res=17 events, ratio=2.12\n",
+      "  370.0s - 375.0s : ref=8 events, res=17 events, ratio=2.12\n"
+     ]
+    }
+   ],
+   "source": [
+    "def compute_event_density_ratio(ref_events, res_events, response_onset_offset,\n",
+    "                                 window_size=5.0, step=2.5):\n",
+    "    \"\"\"\n",
+    "    Slide a fixed-size time window across the piece and compare how many\n",
+    "    response events fall inside it versus how many reference events do,\n",
+    "    in the same absolute time range. A ratio much greater than 1 flags\n",
+    "    passages where the response is systematically split into more events\n",
+    "    than the reference expects (e.g. broken/rolled chord accompaniment).\n",
+    "\n",
+    "    Args:\n",
+    "        ref_events: list of reference event dicts (normalized)\n",
+    "        res_events: list of response event dicts (normalized)\n",
+    "        response_onset_offset: float, to convert response's normalized\n",
+    "            time back to the same absolute clock as reference\n",
+    "        window_size: float, seconds, size of each sliding window\n",
+    "        step: float, seconds, how far the window moves each step\n",
+    "\n",
+    "    Returns:\n",
+    "        list of (window_start, window_end, ref_count, res_count, ratio)\n",
+    "    \"\"\"\n",
+    "    ref_starts = [ev[\"event_start\"] for ev in ref_events]\n",
+    "    res_starts = [ev[\"event_start\"] + response_onset_offset for ev in res_events]\n",
+    "\n",
+    "    end_time = max(ref_starts[-1], res_starts[-1])\n",
+    "    rows = []\n",
+    "\n",
+    "    window_start = 0.0\n",
+    "    while window_start < end_time:\n",
+    "        window_end = window_start + window_size\n",
+    "\n",
+    "        ref_count = sum(1 for t in ref_starts if window_start <= t < window_end)\n",
+    "        res_count = sum(1 for t in res_starts if window_start <= t < window_end)\n",
+    "\n",
+    "        if ref_count > 0:\n",
+    "            ratio = res_count / ref_count\n",
+    "        else:\n",
+    "            ratio = None\n",
+    "\n",
+    "        rows.append((round(window_start, 1), round(window_end, 1), ref_count, res_count, ratio))\n",
+    "        window_start = window_start + step\n",
+    "\n",
+    "    return rows\n",
+    "\n",
+    "\n",
+    "result = all_results[383]  # Beethoven, from worst_df\n",
+    "density_rows = compute_event_density_ratio(\n",
+    "    result[\"ref_events_normalized\"],\n",
+    "    result[\"response_events_normalized\"],\n",
+    "    result[\"response_onset_offset\"],\n",
+    ")\n",
+    "\n",
+    "# Sort by ratio to see the worst mismatched windows first\n",
+    "sorted_rows = sorted(\n",
+    "    [r for r in density_rows if r[4] is not None],\n",
+    "    key=lambda r: r[4],\n",
+    "    reverse=True,\n",
+    ")\n",
+    "\n",
+    "print(\"Windows with the highest response/reference event density ratio:\")\n",
+    "for w_start, w_end, ref_c, res_c, ratio in sorted_rows[:15]:\n",
+    "    print(f\"  {w_start}s - {w_end}s : ref={ref_c} events, res={res_c} events, ratio={ratio:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Reference events in 580-590s ---\n",
+      "ref[1773]  start=580.035  type=note  pitches=[32]\n",
+      "ref[1774]  start=580.148  type=note  pitches=[36]\n",
+      "ref[1775]  start=580.262  type=note  pitches=[39]\n",
+      "ref[1776]  start=580.376  type=note  pitches=[44]\n",
+      "ref[1777]  start=580.489  type=note  pitches=[48]\n",
+      "ref[1778]  start=580.603  type=note  pitches=[51]\n",
+      "ref[1779]  start=580.716  type=note  pitches=[44]\n",
+      "ref[1780]  start=580.830  type=note  pitches=[48]\n",
+      "ref[1781]  start=580.944  type=note  pitches=[51]\n",
+      "ref[1782]  start=581.057  type=note  pitches=[56]\n",
+      "ref[1783]  start=581.171  type=note  pitches=[60]\n",
+      "ref[1784]  start=581.285  type=note  pitches=[63]\n",
+      "ref[1785]  start=581.398  type=note  pitches=[56]\n",
+      "ref[1786]  start=581.512  type=note  pitches=[60]\n",
+      "ref[1787]  start=581.626  type=note  pitches=[63]\n",
+      "ref[1788]  start=581.739  type=note  pitches=[68]\n",
+      "ref[1789]  start=581.853  type=note  pitches=[72]\n",
+      "ref[1790]  start=581.966  type=note  pitches=[75]\n",
+      "ref[1791]  start=582.080  type=note  pitches=[68]\n",
+      "ref[1792]  start=582.194  type=note  pitches=[72]\n",
+      "ref[1793]  start=582.307  type=note  pitches=[75]\n",
+      "ref[1794]  start=582.421  type=note  pitches=[80]\n",
+      "ref[1795]  start=582.535  type=note  pitches=[84]\n",
+      "ref[1796]  start=582.648  type=note  pitches=[87]\n",
+      "ref[1797]  start=582.762  type=chord  pitches=[32, 36, 39, 44, 80, 84, 87, 92]\n",
+      "\n",
+      "--- Response events in 580-590s (absolute time) ---\n",
+      "res[1658]  start=580.057  type=chord  pitches=[43, 85, 73, 82]\n",
+      "res[1659]  start=580.167  type=note  pitches=[55]\n",
+      "res[1660]  start=580.296  type=note  pitches=[51]\n",
+      "res[1661]  start=580.397  type=note  pitches=[55]\n",
+      "res[1662]  start=580.518  type=note  pitches=[51]\n",
+      "res[1663]  start=580.624  type=note  pitches=[55]\n",
+      "res[1664]  start=580.814  type=chord  pitches=[44, 84, 72, 75]\n",
+      "res[1665]  start=580.956  type=note  pitches=[46]\n",
+      "res[1666]  start=581.078  type=note  pitches=[44]\n",
+      "res[1667]  start=581.208  type=note  pitches=[42]\n",
+      "res[1668]  start=581.346  type=chord  pitches=[41, 80]\n",
+      "res[1669]  start=581.440  type=note  pitches=[39]\n",
+      "res[1670]  start=581.589  type=chord  pitches=[37, 85, 80, 77]\n",
+      "res[1671]  start=581.751  type=note  pitches=[44]\n",
+      "res[1672]  start=581.818  type=note  pitches=[41]\n",
+      "res[1673]  start=581.975  type=note  pitches=[44]\n",
+      "res[1674]  start=582.090  type=chord  pitches=[38, 82, 77, 80]\n",
+      "res[1675]  start=582.242  type=note  pitches=[44]\n",
+      "res[1676]  start=582.405  type=chord  pitches=[39, 84, 80, 75]\n",
+      "res[1677]  start=582.596  type=chord  pitches=[44, 51]\n",
+      "res[1678]  start=582.750  type=chord  pitches=[82, 37, 77, 80]\n",
+      "res[1679]  start=582.839  type=note  pitches=[39]\n",
+      "res[1680]  start=582.984  type=chord  pitches=[49, 44]\n",
+      "res[1681]  start=583.112  type=chord  pitches=[39, 79, 73]\n",
+      "res[1682]  start=583.250  type=note  pitches=[51]\n",
+      "res[1683]  start=583.520  type=chord  pitches=[80, 32, 72, 75]\n",
+      "res[1684]  start=583.717  type=note  pitches=[44]\n",
+      "res[1685]  start=583.833  type=note  pitches=[36]\n",
+      "res[1686]  start=583.956  type=note  pitches=[44]\n",
+      "res[1687]  start=584.082  type=note  pitches=[39]\n",
+      "res[1688]  start=584.211  type=note  pitches=[44]\n",
+      "res[1689]  start=584.307  type=chord  pitches=[85, 32, 80, 77]\n",
+      "res[1690]  start=584.448  type=note  pitches=[44]\n",
+      "res[1691]  start=584.562  type=note  pitches=[37]\n",
+      "res[1692]  start=584.650  type=note  pitches=[44]\n",
+      "res[1693]  start=584.793  type=note  pitches=[41]\n",
+      "res[1694]  start=584.936  type=note  pitches=[44]\n",
+      "res[1695]  start=585.068  type=chord  pitches=[32, 82, 73, 79]\n",
+      "res[1696]  start=585.206  type=note  pitches=[44]\n",
+      "res[1697]  start=585.339  type=note  pitches=[37]\n",
+      "res[1698]  start=585.444  type=note  pitches=[44]\n",
+      "res[1699]  start=585.551  type=note  pitches=[40]\n",
+      "res[1700]  start=585.695  type=note  pitches=[44]\n",
+      "res[1701]  start=585.814  type=chord  pitches=[32, 87, 75, 80, 84]\n",
+      "res[1702]  start=585.975  type=note  pitches=[44]\n",
+      "res[1703]  start=586.095  type=note  pitches=[36]\n",
+      "res[1704]  start=586.202  type=note  pitches=[44]\n",
+      "res[1705]  start=586.352  type=note  pitches=[39]\n",
+      "res[1706]  start=586.462  type=note  pitches=[44]\n",
+      "res[1707]  start=586.592  type=chord  pitches=[32, 84, 75, 78]\n",
+      "res[1708]  start=586.651  type=note  pitches=[35]\n",
+      "res[1709]  start=586.720  type=note  pitches=[44]\n",
+      "res[1710]  start=586.858  type=note  pitches=[39]\n",
+      "res[1711]  start=586.966  type=chord  pitches=[37, 44]\n",
+      "res[1712]  start=587.105  type=note  pitches=[42]\n",
+      "res[1713]  start=587.225  type=note  pitches=[44]\n",
+      "res[1714]  start=587.368  type=chord  pitches=[89, 32, 77, 85, 31, 34, 86]\n",
+      "res[1715]  start=587.540  type=note  pitches=[43]\n",
+      "res[1716]  start=587.651  type=note  pitches=[37]\n",
+      "res[1717]  start=587.741  type=note  pitches=[44]\n",
+      "res[1718]  start=587.879  type=chord  pitches=[41, 42]\n",
+      "res[1719]  start=587.971  type=note  pitches=[36]\n",
+      "res[1720]  start=588.023  type=note  pitches=[44]\n",
+      "res[1721]  start=588.117  type=chord  pitches=[32, 86, 77]\n",
+      "res[1722]  start=588.254  type=note  pitches=[44]\n",
+      "res[1723]  start=588.379  type=chord  pitches=[38, 36]\n",
+      "res[1724]  start=588.493  type=note  pitches=[44]\n",
+      "res[1725]  start=588.615  type=note  pitches=[41]\n",
+      "res[1726]  start=588.732  type=note  pitches=[44]\n",
+      "res[1727]  start=588.973  type=chord  pitches=[32, 90, 78, 84]\n",
+      "res[1728]  start=589.264  type=note  pitches=[44]\n",
+      "res[1729]  start=589.469  type=note  pitches=[39]\n",
+      "res[1730]  start=589.638  type=note  pitches=[44]\n",
+      "res[1731]  start=589.788  type=note  pitches=[42]\n",
+      "res[1732]  start=589.911  type=note  pitches=[44]\n"
+     ]
+    }
+   ],
+   "source": [
+    "window_start, window_end = 580.0, 590.0\n",
+    "\n",
+    "print(\"--- Reference events in 580-590s ---\")\n",
+    "for i, ev in enumerate(result[\"ref_events_normalized\"]):\n",
+    "    if window_start <= ev[\"event_start\"] <= window_end:\n",
+    "        pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
+    "        print(f\"ref[{i}]  start={ev['event_start']:.3f}  type={ev['event_type']}  pitches={pitches}\")\n",
+    "\n",
+    "print(\"\\n--- Response events in 580-590s (absolute time) ---\")\n",
+    "onset_offset = result[\"response_onset_offset\"]\n",
+    "for i, ev in enumerate(result[\"response_events_normalized\"]):\n",
+    "    absolute_start = ev[\"event_start\"] + onset_offset\n",
+    "    if window_start <= absolute_start <= window_end:\n",
+    "        pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
+    "        print(f\"res[{i}]  start={absolute_start:.3f}  type={ev['event_type']}  pitches={pitches}\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/Alignment_on_nASAPdataset.ipynb b/notebooks/Alignment_on_nASAPdataset.ipynb
new file mode 100644
index 0000000..b2b64a3
--- /dev/null
+++ b/notebooks/Alignment_on_nASAPdataset.ipynb
@@ -0,0 +1,724 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluate the Alignment Algorithm on Real Piano Performances\n",
+    "\n",
+    "This notebook evaluates the edit-distance (ED) alignment algorithm in `compareMusic` on real piano performances from the [(n)ASAP dataset](https://github.com/CPJKU/asap-dataset) (Peter, 2023).\n",
+    "\n",
+    "(n)ASAP is a dataset of aligned musical scores and performances built by extending the ASAP dataset with note-level annotations. The ASAP contains 236 distinct musical scores and 1067 performances of Western classical piano music from 15 different composers, the piece directory contains the XML and MIDI score, plus all of the performances of a specific piece, including: \n",
+    "- `midi_score`: the MIDI score (what should be played) — used as **reference**\n",
+    "- `midi_performance`: what the pianist actually played — used as **response**\n",
+    "- `note_alignments.tsv`: official note-level alignment annotations — used as **ground truth**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "bib citation:\n",
+    "\n",
+    "@article{Peter-2023,\n",
+    " title = {Automatic Note-Level Score-to-Performance Alignments in the ASAP Dataset},\n",
+    " author = {Peter, Silvan David and Cancino-Chacón, Carlos Eduardo and Foscarin, Francesco and McLeod, Andrew Philip and Henkel, Florian and Karystinaios, Emmanouil and Widmer, Gerhard},\n",
+    " doi = {10.5334/tismir.149},\n",
+    " journal = {Transactions of the International Society for Music Information Retrieval {(TISMIR)}},\n",
+    " year = {2023}\n",
+    "}\n",
+    "\n",
+    "relevant paper: https://transactions.ismir.net/articles/10.5334/tismir.149#5-alignment-of-the-asap-dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the (n)ASAP Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "asap-dataset already exists, skipping download.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Note: use CPJKU version (not fosfrancesco) because only CPJKU has\n",
+    "# the note_alignments TSV files are needed for ground truth comparison.\n",
+    "if not os.path.exists(\"asap-dataset\"):\n",
+    "    os.system(\"git clone https://github.com/CPJKU/asap-dataset.git\")\n",
+    "else:\n",
+    "    print(\"asap-dataset already exists, skipping download.\")\n",
+    "\n",
+    "ASAP_PATH = \"asap-dataset\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the Ground Truth (GT)\n",
+    "\n",
+    "The `note_alignment.tsv` file in each performance contains the official note-level alignment annotations. Each row represents one note, with columns: `xml_id`, `midi_id`, `track`, `channel`, `pitch`, `onset`.\n",
+    "- If `midi_id == \"deletion\"`: the score note was not played → label `deletion`\n",
+    "- If `xml_id == \"insertion\"`: an extra note was played → label `insertion`\n",
+    "- Otherwise: a matched pair → label `paired`\n",
+    "\n",
+    "For `match` and `insertion` rows, the TSV provides `onset` (onset time in seconds) and `pitch` (MIDI pitch) for the performance note."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "\n",
+    "def load_ground_truth(tsv_path):\n",
+    "    \"\"\"\n",
+    "    Read a note_alignments TSV file from the CPJKU nASAP dataset.\n",
+    "\n",
+    "    Args:\n",
+    "        tsv_path: str, path to the note_alignments.tsv file\n",
+    "\n",
+    "    Returns:\n",
+    "        list of dicts, each with keys:\n",
+    "            label  -> 'paired', 'insertion', or 'deletion'\n",
+    "            onset  -> float (seconds) or None for deletions\n",
+    "            pitch  -> int (MIDI pitch number) or None for deletions\n",
+    "    \"\"\"\n",
+    "    rows = []\n",
+    "    with open(tsv_path, \"r\", encoding=\"utf-8\") as f:\n",
+    "        reader = csv.DictReader(f, delimiter=\"\\t\")\n",
+    "        \n",
+    "        for row in reader:\n",
+    "            xml_id  = row[\"xml_id\"].strip()\n",
+    "            midi_id = row[\"midi_id\"].strip()\n",
+    "\n",
+    "            if midi_id == \"deletion\":\n",
+    "                rows.append({\"label\": \"deletion\", \"onset\": None, \"pitch\": None})\n",
+    "            elif xml_id == \"insertion\":\n",
+    "                rows.append({\n",
+    "                    \"label\": \"insertion\",\n",
+    "                    \"onset\": float(row[\"onset\"]),\n",
+    "                    \"pitch\": int(row[\"pitch\"]),\n",
+    "                })\n",
+    "            else:\n",
+    "                rows.append({\n",
+    "                    \"label\": \"paired\",\n",
+    "                    \"onset\": float(row[\"onset\"]),\n",
+    "                    \"pitch\": int(row[\"pitch\"]),\n",
+    "                })\n",
+    "    return rows"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Score (reference)/Performance (response) pairs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Helper functions for format conversion: These functions convert a MIDI file into the `{pitch, start, duration}` format used by `compare_MIDI.py`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pretty_midi\n",
+    "\n",
+    "def midi_file_to_notes(midi_path):\n",
+    "    \"\"\"Parse a MIDI file into the format used by compareMusic.\"\"\"\n",
+    "    midi_data = pretty_midi.PrettyMIDI(midi_path)\n",
+    "    all_notes = []\n",
+    "\n",
+    "    for instrument in midi_data.instruments:\n",
+    "        if instrument.is_drum:\n",
+    "            continue\n",
+    "        for note in instrument.notes:\n",
+    "            all_notes.append({\n",
+    "                \"pitch\": int(note.pitch),\n",
+    "                # Keep full precision. Rounding here can change tolerance-based\n",
+    "                # evaluation close to the matching boundary.\n",
+    "                \"start\": float(note.start),\n",
+    "                \"duration\": float(note.end - note.start),\n",
+    "            })\n",
+    "\n",
+    "    all_notes.sort(key=lambda note: (note[\"start\"], note[\"pitch\"]))\n",
+    "    return all_notes\n",
+    "\n",
+    "def build_sample(ref_path, response_path, composer, title, metadata_row):\n",
+    "    \"\"\"Build one score/performance sample for compare_performance_ED.\"\"\"\n",
+    "    score_notes = midi_file_to_notes(ref_path)\n",
+    "    performance_notes = midi_file_to_notes(response_path)\n",
+    "\n",
+    "    if not score_notes or not performance_notes:\n",
+    "        return None\n",
+    "\n",
+    "    return {\n",
+    "        \"composer\": composer,\n",
+    "        \"title\": title,\n",
+    "        \"reference\": {\"notes\": score_notes},\n",
+    "        \"response\": {\"notes\": performance_notes},\n",
+    "        \"metadata_row\": metadata_row,\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the (n)ASAP dataset, the `metadata.csv` lists every score/performance pair in the dataset, check that both MIDI files exist on disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jz7125/compareMusic/.venv/lib/python3.13/site-packages/pretty_midi/pretty_midi.py:122: RuntimeWarning: Tempo, Key or Time signature change events found on non-zero tracks.  This is not a valid type 0 or type 1 MIDI file.  Tempo, Key or Time Signature may be wrong.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded 169 score (reference) /performance (response) pairs.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def load_samples(asap_path, composer):\n",
+    "    \"\"\"\n",
+    "    Read the ASAP metadata CSV and return a list of sample dicts\n",
+    "    for a specific composer only.\n",
+    "\n",
+    "    Args:\n",
+    "        asap_path: str, path to the cloned ASAP repo\n",
+    "        composer: str, e.g. \"Bach\"\n",
+    "\n",
+    "    Returns:\n",
+    "        list of sample dicts (see build_sample)\n",
+    "    \"\"\"\n",
+    "    metadata_path = os.path.join(asap_path, \"metadata.csv\")\n",
+    "    samples = []\n",
+    "\n",
+    "    with open(metadata_path, \"r\", encoding=\"utf-8\") as csv_file:\n",
+    "        reader = csv.DictReader(csv_file)\n",
+    "        for row in reader:\n",
+    "            if row.get(\"composer\", \"\").strip() == composer:\n",
+    "                ref_path = os.path.join(asap_path, row.get(\"midi_score\", \"\").strip())\n",
+    "                response_path = os.path.join(asap_path, row.get(\"midi_performance\", \"\").strip())\n",
+    "\n",
+    "                if os.path.isfile(ref_path) and os.path.isfile(response_path):\n",
+    "                    sample = build_sample(\n",
+    "                        ref_path, response_path,\n",
+    "                        row.get(\"composer\", \"Unknown\"),\n",
+    "                        row.get(\"title\", \"Unknown\"),\n",
+    "                        dict(row),\n",
+    "                    )\n",
+    "                    if sample is not None:\n",
+    "                        samples.append(sample)\n",
+    "\n",
+    "    return samples\n",
+    "\n",
+    "samples = load_samples(ASAP_PATH, \"Bach\")\n",
+    "\n",
+    "print(\"Loaded\", len(samples), \"score (reference) /performance (response) pairs.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run Alignment on Each Pair\n",
+    "\n",
+    "Each score/performance pair is passed through `compare_performance_ED`. The current pipeline normalises the first onset to zero and groups nearby notes into events. Because `event_details` stores event indices, the same normalisation and grouping steps are reproduced here tso that the correct onset and pitch for each event during ground truth comparison can be looked up.\n",
+    "\n",
+    "The original response onset offset is retained and added back only during comparison with the absolute performance times in the TSV file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alignment complete for 169 pieces.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from evaluation_function.compare_MIDI import (\n",
+    "    compare_performance_ED,\n",
+    "    normalize_start_times,\n",
+    "    group_notes_into_events,\n",
+    ")\n",
+    "\n",
+    "def run_alignment_on_samples(samples):\n",
+    "    \"\"\"Run compare_performance_ED for every loaded sample.\"\"\"\n",
+    "    results = []\n",
+    "\n",
+    "    for sample in samples:\n",
+    "        result = compare_performance_ED(sample[\"response\"], sample[\"reference\"])\n",
+    "\n",
+    "        response_notes_normalized = normalize_start_times(\n",
+    "            sample[\"response\"][\"notes\"]\n",
+    "        )\n",
+    "        response_events_normalized = group_notes_into_events(\n",
+    "            response_notes_normalized\n",
+    "        )\n",
+    "\n",
+    "        # Also rebuild the normalized reference events. convert_pipeline_output\n",
+    "        # needs these to look up how many notes are in a missing (deleted)\n",
+    "        # chord, since the pipeline's event_details does not carry that\n",
+    "        # information for missing/extra events.\n",
+    "        ref_notes_normalized = normalize_start_times(\n",
+    "            sample[\"reference\"][\"notes\"]\n",
+    "        )\n",
+    "        ref_events_normalized = group_notes_into_events(\n",
+    "            ref_notes_normalized\n",
+    "        )\n",
+    "\n",
+    "        response_onset_offset = float(sample[\"response\"][\"notes\"][0][\"start\"])\n",
+    "\n",
+    "        results.append({\n",
+    "            \"composer\": sample[\"composer\"],\n",
+    "            \"title\": sample[\"title\"],\n",
+    "            \"stats\": result.stats,\n",
+    "            \"event_details\": result.event_details,\n",
+    "            \"is_correct\": result.is_correct,\n",
+    "            \"response_events_normalized\": response_events_normalized,\n",
+    "            \"ref_events_normalized\": ref_events_normalized,\n",
+    "            \"response_onset_offset\": response_onset_offset,\n",
+    "        })\n",
+    "\n",
+    "    return results\n",
+    "\n",
+    "all_results = run_alignment_on_samples(samples)\n",
+    "print(\"Alignment complete for\", len(all_results), \"pieces.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute Precision, Recall, and F1 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pipeline output vs ground truth**\n",
+    "\n",
+    "he GT has three different labels, while our pipeline has four operations:\n",
+    "\n",
+    "| Pipeline operation | GT label | Explanation |\n",
+    "|---|---|---|\n",
+    "| `match` | `paired` | Same pitch — GT still calls this `paired` |\n",
+    "| `replacement` | `paired` | Different pitch — GT still calls this `paired`, because the score note *was* played at approximately the right time |\n",
+    "| `missing` | `deletion` | Score note not found in response |\n",
+    "| `extra` | `insertion` | Response note has no score counterpart |\n",
+    "\n",
+    "**Evaluation metrics design**\n",
+    "\n",
+    "Evaluation is performed at **note level** after expanding chord events. Matches are one-to-one, so three notes at the same chord onset remain three separate observations.\n",
+    "\n",
+    "| Label | Matching key | TP | FP | FN |\n",
+    "|---|---|---|---|---|\n",
+    "| `paired` (pipeline `match` / `replacement`) | onset-only matching within `ONSET_TOLERANCE`; pitch is intentionally ignored because this evaluates alignment rather than pitch correctness| Pipeline and ground truth both contain a paired label at matching performance onsets within the tolerance | Pipeline predicts a pairing at this onset that the ground truth does not recognise | Ground truth expects a pairing at this onset that the pipeline fails to find |\n",
+    "| `deletion` (pipeline `missing`) | count only as no onset or pitch available for deletions | Overlap between the pipeline deletion count and the ground-truth deletion count | The pipeline predicts more deletions than appear in the ground truth; the excess count is treated as FP | The ground truth contains more deletions than the pipeline predicts; the missing count is treated as FN |\n",
+    "| `insertion` (pipeline `extra`) | pitch must be equal and onset must be within `ONSET_TOLERANCE` | Pipeline and ground truth agree an extra, unexpected note was played at this pitch and onset | Pipeline flags a note as extra that the ground truth does not agree with | Ground truth marks a note as extra that the pipeline fails to identify |\n",
+    "\n",
+    "- Precision: TP / (TP + FP), i.e. of all labels the pipeline predicted, how many were correct  \n",
+    "- Recall: TP / (TP + FN), i.e. of all GT labels, how many the pipeline found  \n",
+    "- F1: the harmonic mean of precision and recall "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_pipeline_output(event_details, response_events, ref_events):\n",
+    "    \"\"\"\n",
+    "    Convert event-level pipeline output into note-level labels.\n",
+    "\n",
+    "    Note: ref_events is required here so that a missing (deleted) chord's\n",
+    "    true note count can be looked up. The pipeline's event_details does\n",
+    "    NOT carry pitch information for missing/extra events (correct_pitches\n",
+    "    and missing_pitches are always None in that case), so we must go back\n",
+    "    to the original reference events ourselves to know how many notes\n",
+    "    were actually deleted.\n",
+    "    \"\"\"\n",
+    "    predictions = []\n",
+    "\n",
+    "    for event in event_details:\n",
+    "        operation = event[\"operation_type\"]\n",
+    "        event_type = event[\"event_type\"]\n",
+    "\n",
+    "        if operation == \"missing\":\n",
+    "            # A reference note/chord that the student did not play at all.\n",
+    "            if event_type == \"note\":\n",
+    "                number_of_notes = 1\n",
+    "            else:\n",
+    "                # reference_index is 1-based, so subtract 1 to index into ref_events.\n",
+    "                reference_index = event[\"reference_index\"] - 1\n",
+    "                missing_ref_event = ref_events[reference_index]\n",
+    "                number_of_notes = len(missing_ref_event[\"notes\"])\n",
+    "\n",
+    "            for i in range(number_of_notes):\n",
+    "                predictions.append({\n",
+    "                    \"onset\": None,\n",
+    "                    \"pitch\": None,\n",
+    "                    \"label\": \"deletion\",\n",
+    "                })\n",
+    "\n",
+    "        else:\n",
+    "            # response_index is 1-based in the pipeline output.\n",
+    "            response_index = event[\"response_index\"] - 1\n",
+    "            if response_index < 0 or response_index >= len(response_events):\n",
+    "                raise IndexError(\"response_index is outside the response event list\")\n",
+    "\n",
+    "            response_event = response_events[response_index]\n",
+    "            onset = float(response_event[\"event_start\"])\n",
+    "\n",
+    "            if event_type == \"note\":\n",
+    "                pitch = int(response_event[\"notes\"][0][\"pitch\"])\n",
+    "                if operation == \"extra\":\n",
+    "                    label = \"insertion\"\n",
+    "                else:\n",
+    "                    label = \"paired\"\n",
+    "                predictions.append({\n",
+    "                    \"onset\": onset,\n",
+    "                    \"pitch\": pitch,\n",
+    "                    \"label\": label,\n",
+    "                })\n",
+    "\n",
+    "            elif operation == \"extra\":\n",
+    "                # Extra chord: every performed note in it is an insertion.\n",
+    "                for note in response_event[\"notes\"]:\n",
+    "                    predictions.append({\n",
+    "                        \"onset\": onset,\n",
+    "                        \"pitch\": int(note[\"pitch\"]),\n",
+    "                        \"label\": \"insertion\",\n",
+    "                    })\n",
+    "\n",
+    "            else:\n",
+    "                # Matched or replaced chord. correct_pitches / missing_pitches /\n",
+    "                # extra_pitches already hold real MIDI pitches (see\n",
+    "                # compute_chord_accuracy in compare_MIDI.py), so we can use\n",
+    "                # them directly with no pitch-class fallback needed.\n",
+    "                for pitch in event.get(\"correct_pitches\") or []:\n",
+    "                    predictions.append({\n",
+    "                        \"onset\": onset,\n",
+    "                        \"pitch\": int(pitch),\n",
+    "                        \"label\": \"paired\",\n",
+    "                    })\n",
+    "\n",
+    "                for i in range(len(event.get(\"missing_pitches\") or [])):\n",
+    "                    predictions.append({\n",
+    "                        \"onset\": None,\n",
+    "                        \"pitch\": None,\n",
+    "                        \"label\": \"deletion\",\n",
+    "                    })\n",
+    "\n",
+    "                for pitch in event.get(\"extra_pitches\") or []:\n",
+    "                    predictions.append({\n",
+    "                        \"onset\": onset,\n",
+    "                        \"pitch\": int(pitch),\n",
+    "                        \"label\": \"insertion\",\n",
+    "                    })\n",
+    "\n",
+    "    return predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ONSET_TOLERANCE = 0.05  # 50 milliseconds\n",
+    "\n",
+    "def count_onset_matches(gt_onsets, predicted_onsets, tolerance):\n",
+    "    \"\"\"Count one-to-one onset matches while preserving chord notes.\"\"\"\n",
+    "    gt_onsets = sorted(gt_onsets)\n",
+    "    predicted_onsets = sorted(predicted_onsets)\n",
+    "\n",
+    "    gt_index = 0\n",
+    "    predicted_index = 0\n",
+    "    matches = 0\n",
+    "\n",
+    "    while gt_index < len(gt_onsets) and predicted_index < len(predicted_onsets):\n",
+    "        difference = predicted_onsets[predicted_index] - gt_onsets[gt_index]\n",
+    "\n",
+    "        if abs(difference) <= tolerance:\n",
+    "            matches += 1\n",
+    "            gt_index += 1\n",
+    "            predicted_index += 1\n",
+    "        elif difference < -tolerance:\n",
+    "            predicted_index += 1\n",
+    "        else:\n",
+    "            gt_index += 1\n",
+    "\n",
+    "    return matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_metrics(ground_truth, predictions, onset_offset=0.0):\n",
+    "    \"\"\"Compute note-level precision, recall, and F1.\"\"\"\n",
+    "    total_tp = 0\n",
+    "    total_fp = 0\n",
+    "    total_fn = 0\n",
+    "\n",
+    "    # 1. Paired notes: compare onset only.\n",
+    "    gt_paired = [\n",
+    "        row[\"onset\"]\n",
+    "        for row in ground_truth\n",
+    "        if row[\"label\"] == \"paired\" and row[\"onset\"] is not None\n",
+    "    ]\n",
+    "    predicted_paired = [\n",
+    "        row[\"onset\"] + onset_offset\n",
+    "        for row in predictions\n",
+    "        if row[\"label\"] == \"paired\" and row[\"onset\"] is not None\n",
+    "    ]\n",
+    "\n",
+    "    paired_tp = count_onset_matches(\n",
+    "        gt_paired, predicted_paired, ONSET_TOLERANCE\n",
+    "    )\n",
+    "    total_tp += paired_tp\n",
+    "    total_fp += len(predicted_paired) - paired_tp\n",
+    "    total_fn += len(gt_paired) - paired_tp\n",
+    "\n",
+    "    # 2. Insertions: compare both pitch and onset.\n",
+    "    gt_insertions = {}\n",
+    "    predicted_insertions = {}\n",
+    "\n",
+    "    for row in ground_truth:\n",
+    "        if (\n",
+    "            row[\"label\"] == \"insertion\"\n",
+    "            and row[\"pitch\"] is not None\n",
+    "            and row[\"onset\"] is not None\n",
+    "        ):\n",
+    "            pitch = int(row[\"pitch\"])\n",
+    "            if pitch not in gt_insertions:\n",
+    "                gt_insertions[pitch] = []\n",
+    "            gt_insertions[pitch].append(float(row[\"onset\"]))\n",
+    "\n",
+    "    for row in predictions:\n",
+    "        if (\n",
+    "            row[\"label\"] == \"insertion\"\n",
+    "            and row[\"pitch\"] is not None\n",
+    "            and row[\"onset\"] is not None\n",
+    "        ):\n",
+    "            pitch = int(row[\"pitch\"])\n",
+    "            if pitch not in predicted_insertions:\n",
+    "                predicted_insertions[pitch] = []\n",
+    "            predicted_insertions[pitch].append(\n",
+    "                float(row[\"onset\"]) + onset_offset\n",
+    "            )\n",
+    "\n",
+    "    for pitch in set(gt_insertions) | set(predicted_insertions):\n",
+    "        gt_onsets = gt_insertions.get(pitch, [])\n",
+    "        predicted_onsets = predicted_insertions.get(pitch, [])\n",
+    "\n",
+    "        insertion_tp = count_onset_matches(\n",
+    "            gt_onsets,\n",
+    "            predicted_onsets,\n",
+    "            ONSET_TOLERANCE,\n",
+    "        )\n",
+    "        total_tp += insertion_tp\n",
+    "        total_fp += len(predicted_onsets) - insertion_tp\n",
+    "        total_fn += len(gt_onsets) - insertion_tp\n",
+    "\n",
+    "    # 3. Deletions: count-based because the pipeline does not return xml_id.\n",
+    "    gt_deletions = sum(\n",
+    "        row[\"label\"] == \"deletion\" for row in ground_truth\n",
+    "    )\n",
+    "    predicted_deletions = sum(\n",
+    "        row[\"label\"] == \"deletion\" for row in predictions\n",
+    "    )\n",
+    "\n",
+    "    deletion_tp = min(gt_deletions, predicted_deletions)\n",
+    "    total_tp += deletion_tp\n",
+    "    total_fp += predicted_deletions - deletion_tp\n",
+    "    total_fn += gt_deletions - deletion_tp\n",
+    "\n",
+    "    precision = (\n",
+    "        total_tp / (total_tp + total_fp)\n",
+    "        if total_tp + total_fp > 0 else 0.0\n",
+    "    )\n",
+    "    recall = (\n",
+    "        total_tp / (total_tp + total_fn)\n",
+    "        if total_tp + total_fn > 0 else 0.0\n",
+    "    )\n",
+    "    f1 = (\n",
+    "        2 * precision * recall / (precision + recall)\n",
+    "        if precision + recall > 0 else 0.0\n",
+    "    )\n",
+    "\n",
+    "    return {\n",
+    "        \"precision\": precision,\n",
+    "        \"recall\": recall,\n",
+    "        \"f1\": f1,\n",
+    "        \"tp\": total_tp,\n",
+    "        \"fp\": total_fp,\n",
+    "        \"fn\": total_fn,\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate all pieces\n",
+    "\n",
+    "The evaluation is note-level. Notes inside a chord are kept as separate notes. Paired notes use a 50 ms onset tolerance, while insertions require both the same MIDI pitch and a matching onset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  Piece  Precision  Recall      F1    TP  FP  FN\n",
+      "0  Bach (Fugue_bwv_846)     0.9565  0.9739  0.9651   747  34  20\n",
+      "1  Bach (Fugue_bwv_848)     0.9945  0.9911  0.9928  1442   8  13\n",
+      "2  Bach (Fugue_bwv_848)     0.9809  0.9856  0.9833  1439  28  21\n",
+      "3  Bach (Fugue_bwv_848)     0.9788  0.9828  0.9808  1429  31  25\n",
+      "4  Bach (Fugue_bwv_848)     0.9863  0.9870  0.9866  1441  20  19\n",
+      "5  Bach (Fugue_bwv_848)     0.9945  0.9917  0.9931  1438   8  12\n",
+      "6  Bach (Fugue_bwv_848)     0.9897  0.9883  0.9890  1441  15  17\n",
+      "7  Bach (Fugue_bwv_848)     0.9689  0.9782  0.9735  1434  46  32\n",
+      "8  Bach (Fugue_bwv_848)     0.9945  0.9904  0.9924  1437   8  14\n",
+      "9  Bach (Fugue_bwv_848)     0.9910  0.9903  0.9907  1436  13  14\n",
+      "Mean Precision: 0.9644\n",
+      "Mean Recall   : 0.9743\n",
+      "Mean F1       : 0.9691\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "evaluation_rows = []\n",
+    "\n",
+    "for sample, result in zip(samples, all_results):\n",
+    "    metadata_row = sample[\"metadata_row\"]\n",
+    "    tsv_relative_path = (metadata_row.get(\"note_alignments\") or \"\").strip()\n",
+    "    tsv_path = os.path.join(ASAP_PATH, tsv_relative_path)\n",
+    "\n",
+    "    if not os.path.isfile(tsv_path):\n",
+    "        print(\"TSV not found:\", tsv_path)\n",
+    "        continue\n",
+    "\n",
+    "    ground_truth = load_ground_truth(tsv_path)\n",
+    "    predictions = convert_pipeline_output(\n",
+    "        result[\"event_details\"],\n",
+    "        result[\"response_events_normalized\"],\n",
+    "        result[\"ref_events_normalized\"],\n",
+    "    )\n",
+    "\n",
+    "    metrics = compute_metrics(\n",
+    "        ground_truth,\n",
+    "        predictions,\n",
+    "        onset_offset=result[\"response_onset_offset\"],\n",
+    "    )\n",
+    "\n",
+    "    evaluation_rows.append({\n",
+    "        \"Piece\": f'{result[\"composer\"]} ({result[\"title\"]})',\n",
+    "        \"Precision\": metrics[\"precision\"],\n",
+    "        \"Recall\": metrics[\"recall\"],\n",
+    "        \"F1\": metrics[\"f1\"],\n",
+    "        \"TP\": metrics[\"tp\"],\n",
+    "        \"FP\": metrics[\"fp\"],\n",
+    "        \"FN\": metrics[\"fn\"],\n",
+    "    })\n",
+    "\n",
+    "df_eval = pd.DataFrame(evaluation_rows)\n",
+    "\n",
+    "if df_eval.empty:\n",
+    "    print(\"No pieces were evaluated. Check the paths above.\")\n",
+    "else:\n",
+    "    print(df_eval.head(10).round(4))\n",
+    "\n",
+    "    print(\"Mean Precision:\", round(df_eval[\"Precision\"].mean(), 4))\n",
+    "    print(\"Mean Recall   :\", round(df_eval[\"Recall\"].mean(), 4))\n",
+    "    print(\"Mean F1       :\", round(df_eval[\"F1\"].mean(), 4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Current limitation\n",
+    "\n",
+    "Deletion matching is still count-based rather than identity-based. The ground-truth TSV identifies deleted score notes with `xml_id`, but the current pipeline output does not return that identifier, so we cannot yet confirm that a *specific* predicted deletion corresponds to a *specific* ground-truth deletion.\n",
+    "\n",
+    "This version does fix an earlier bug where a missing (deleted) **chord** contributed 0 notes to the deletion count, because `event_details` leaves `correct_pitches`/`missing_pitches` as `None` for `missing`/`extra` events (this is true for both notes and chords, confirmed in `event_level_feedback()`). We now read the true note count for a missing chord directly from `ref_events` via `reference_index`, so the total deletion count is accurate again — only the note-to-note identity link is still missing.\n",
+    "\n",
+    "A future improvement would be to keep the reference-note index or `xml_id` in each alignment result so that identity-based deletion matching becomes possible."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "compareMusic",
+   "language": "python",
+   "name": "comparemusic"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 9d7d7d749a9132beeae6e5d8d393c2f9153ee543 Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Sat, 11 Jul 2026 23:11:17 +0100
Subject: [PATCH 12/15] eevert "Merge branch 'chord_comparison' of
 https://github.com/lambda-feedback/compareMusic into chord_comparison"

This reverts commit cd7eecc7ef70c12fb36d5e7359234dc7bdc34c11, reversing
changes made to a2405c7042b2df41cd1e0461b030ca5490e54a3e.
---
 .DS_Store                     |  Bin 8196 -> 0 bytes
 data/referenceMIDI.json       |   30 -
 data/referenceMIDIchords.json |   16 -
 data/responseMIDI.json        |   25 -
 data/responseMIDIchords.json  |   16 -
 data/test_cases.json          | 1610 ---------------------------------
 data/test_cases_chords.json   |  178 ----
 debug.ipynb                   |  157 ----
 evaluation_function/.DS_Store |  Bin 6148 -> 0 bytes
 notebooks/.DS_Store           |  Bin 6148 -> 0 bytes
 notebooks/DTW.ipynb           |  809 -----------------
 notebooks/asap-dataset        |    1 -
 notebooks/asap_worker.py      |   50 -
 notebooks/vienna4x22          |    1 -
 poetry.lock                   |  370 +-------
 pyproject.toml                |    1 -
 16 files changed, 1 insertion(+), 3263 deletions(-)
 delete mode 100644 .DS_Store
 delete mode 100644 data/referenceMIDI.json
 delete mode 100644 data/referenceMIDIchords.json
 delete mode 100644 data/responseMIDI.json
 delete mode 100644 data/responseMIDIchords.json
 delete mode 100644 data/test_cases.json
 delete mode 100644 data/test_cases_chords.json
 delete mode 100644 debug.ipynb
 delete mode 100644 evaluation_function/.DS_Store
 delete mode 100644 notebooks/.DS_Store
 delete mode 100644 notebooks/DTW.ipynb
 delete mode 160000 notebooks/asap-dataset
 delete mode 100644 notebooks/asap_worker.py
 delete mode 160000 notebooks/vienna4x22

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diff --git a/data/referenceMIDI.json b/data/referenceMIDI.json
deleted file mode 100644
index f7c9254..0000000
--- a/data/referenceMIDI.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "performance_type": "reference",
-  "notes": [
-    {
-      "pitch": 60,
-      "start": 0,
-      "duration": 0.5
-    },
-    {
-      "pitch": 62,
-      "start": 0.6,
-      "duration": 0.5
-    },
-    {
-      "pitch": 64,
-      "start": 1.2,
-      "duration": 0.5
-    },
-    {
-      "pitch": 65,
-      "start": 1.8,
-      "duration": 0.5
-    },
-    {
-      "pitch": 67,
-      "start": 2.5,
-      "duration": 0.5
-    }
-  ]
-}
\ No newline at end of file
diff --git a/data/referenceMIDIchords.json b/data/referenceMIDIchords.json
deleted file mode 100644
index 45f9074..0000000
--- a/data/referenceMIDIchords.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-    "performance_type": "reference",
-    "notes": [
-        {"pitch": 60, "start": 0.00, "duration": 0.50},
-        {"pitch": 64, "start": 0.00, "duration": 0.50},
-        {"pitch": 67, "start": 0.00, "duration": 0.50},
-
-        {"pitch": 65, "start": 0.60, "duration": 0.50},
-        {"pitch": 69, "start": 0.60, "duration": 0.50},
-        {"pitch": 72, "start": 0.60, "duration": 0.50},
-
-        {"pitch": 67, "start": 1.20, "duration": 0.50},
-        {"pitch": 71, "start": 1.20, "duration": 0.50},
-        {"pitch": 74, "start": 1.20, "duration": 0.50}
-    ]
-}
\ No newline at end of file
diff --git a/data/responseMIDI.json b/data/responseMIDI.json
deleted file mode 100644
index 0f1948a..0000000
--- a/data/responseMIDI.json
+++ /dev/null
@@ -1,25 +0,0 @@
-{
-  "performance_type": "response",
-  "notes": [
-    {
-      "pitch": 60,
-      "start": 0,
-      "duration": 0.5
-    },
-    {
-      "pitch": 63,
-      "start": 0.6,
-      "duration": 0.5
-    },
-    {
-      "pitch": 64,
-      "start": 1.35,
-      "duration": 0.5
-    },
-    {
-      "pitch": 65,
-      "start": 1.8,
-      "duration": 0.7
-    }
-  ]
-}
\ No newline at end of file
diff --git a/data/responseMIDIchords.json b/data/responseMIDIchords.json
deleted file mode 100644
index 0a10df7..0000000
--- a/data/responseMIDIchords.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-    "performance_type": "response",
-    "notes": [
-       
-        {"pitch": 60, "start": 0.00, "duration": 0.50},
-        {"pitch": 64, "start": 0.00, "duration": 0.50},
-        {"pitch": 67, "start": 0.00, "duration": 0.50},
-
-        {"pitch": 69, "start": 0.62, "duration": 0.50},  
-        {"pitch": 72, "start": 0.62, "duration": 0.50},
-
-        {"pitch": 69, "start": 1.21, "duration": 0.50},
-        {"pitch": 72, "start": 1.21, "duration": 0.50},
-        {"pitch": 76, "start": 1.21, "duration": 0.50}
-    ]
-}
\ No newline at end of file
diff --git a/data/test_cases.json b/data/test_cases.json
deleted file mode 100644
index e2b76c7..0000000
--- a/data/test_cases.json
+++ /dev/null
@@ -1,1610 +0,0 @@
-[
-  {
-    "case_id": "perfect_performance",
-    "purpose": "A fully correct performance, verifying is_correct=True and all the positive feedback wording",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "single_pitch_error",
-    "purpose": "One note has the wrong pitch (off by 2 semitones), everything else is correct",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "multiple_pitch_errors",
-    "purpose": "Three notes have the wrong pitch, verifying plural wording and multiple detail messages",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 66,
-          "start": 3.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 67,
-          "start": 4.2,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 71,
-          "start": 3.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 67,
-          "start": 4.2,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "missing_note",
-    "purpose": "Student skips one note, testing the missing (deletion) branch and its feedback wording",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "extra_note",
-    "purpose": "Student plays one extra note, testing the extra (insertion) branch and its feedback wording",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 80,
-          "start": 1.5,
-          "duration": 0.2
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "missing_and_extra",
-    "purpose": "Both a missing note and an extra note occur, verifying the two gap types don't interfere with each other",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 66,
-          "start": 3.6,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 90,
-          "start": 0.3,
-          "duration": 0.2
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 66,
-          "start": 3.6,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "global_tempo_slower",
-    "purpose": "Performance is clearly slower overall (scale~1.4 > GLOBAL_SLOW_THRESHOLD), but local rhythm stays consistent",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.84,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.68,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 2.52,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 3.36,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 4.2,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "global_tempo_faster",
-    "purpose": "Performance is clearly faster overall (scale~0.6 < GLOBAL_FAST_THRESHOLD), but local rhythm stays consistent",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.36,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 0.72,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.08,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 1.44,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 1.8,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "global_duration_longer",
-    "purpose": "All notes are held noticeably longer overall (duration_scale~1.8), testing the global duration trend estimate",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.4
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.4
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.4
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.4
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.4
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.72
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.72
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.72
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.72
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.72
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.72
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "local_timing_anomaly_only",
-    "purpose": "Global tempo is normal, only 1 note is noticeably rushed, testing the separation of local residuals from the global trend",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 66,
-          "start": 3.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 67,
-          "start": 4.2,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.05,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 66,
-          "start": 3.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 67,
-          "start": 4.2,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "local_duration_anomaly_only",
-    "purpose": "Global duration_scale is normal, only 1 note is noticeably too short, testing the local duration residual judgement",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 66,
-          "start": 3.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 67,
-          "start": 4.2,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        },
-        {
-          "pitch": 65,
-          "start": 3.0,
-          "duration": 0.1
-        },
-        {
-          "pitch": 66,
-          "start": 3.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 67,
-          "start": 4.2,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "first_note_no_timing_penalty",
-    "purpose": "First note has the wrong pitch, verifying the special case where timing is always correct when ref_idx==0",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 64,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 2.4,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "too_few_matches_for_global_fit",
-    "purpose": "Only 2 matched notes, verifying the <3 early-return branch in the global trend estimation functions (falls back to scale=1.0)",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 0.6,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 64,
-          "start": 0.9,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "all_notes_missing",
-    "purpose": "Extreme case: response is completely empty, testing the boundary conditions of alignment and feedback generation",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": []
-    }
-  },
-  {
-    "case_id": "all_notes_extra",
-    "purpose": "Extreme case: reference is completely empty, testing the other boundary condition of alignment",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": []
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 0.6,
-          "duration": 0.5
-        },
-        {
-          "pitch": 62,
-          "start": 1.2,
-          "duration": 0.5
-        },
-        {
-          "pitch": 63,
-          "start": 1.8,
-          "duration": 0.5
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "repeated_pitch_ambiguous_alignment",
-    "purpose": "Reference contains a repeated pitch, testing whether edit distance still aligns repeated pitches correctly",
-    "length_category": "short",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.3
-        },
-        {
-          "pitch": 60,
-          "start": 0.35,
-          "duration": 0.3
-        },
-        {
-          "pitch": 60,
-          "start": 0.7,
-          "duration": 0.3
-        },
-        {
-          "pitch": 62,
-          "start": 1.05,
-          "duration": 0.5
-        },
-        {
-          "pitch": 60,
-          "start": 1.6,
-          "duration": 0.3
-        },
-        {
-          "pitch": 60,
-          "start": 1.95,
-          "duration": 0.3
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.3
-        },
-        {
-          "pitch": 60,
-          "start": 0.35,
-          "duration": 0.3
-        },
-        {
-          "pitch": 60,
-          "start": 0.7,
-          "duration": 0.3
-        },
-        {
-          "pitch": 62,
-          "start": 1.05,
-          "duration": 0.5
-        },
-        {
-          "pitch": 61,
-          "start": 1.6,
-          "duration": 0.3
-        },
-        {
-          "pitch": 60,
-          "start": 1.95,
-          "duration": 0.3
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "long_stress_test",
-    "purpose": "24-note long-piece comprehensive stress test: mixes pitch errors, missing notes, extra notes, mild global tempo/duration drift, and local timing/duration anomalies, triggering almost every feedback branch in the pipeline at once",
-    "length_category": "long",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 62,
-          "start": 0.5,
-          "duration": 0.45
-        },
-        {
-          "pitch": 64,
-          "start": 1.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 65,
-          "start": 1.5,
-          "duration": 0.45
-        },
-        {
-          "pitch": 67,
-          "start": 2.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 69,
-          "start": 2.5,
-          "duration": 0.45
-        },
-        {
-          "pitch": 71,
-          "start": 3.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 72,
-          "start": 3.5,
-          "duration": 0.45
-        },
-        {
-          "pitch": 72,
-          "start": 4.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 71,
-          "start": 4.5,
-          "duration": 0.45
-        },
-        {
-          "pitch": 69,
-          "start": 5.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 67,
-          "start": 5.5,
-          "duration": 0.45
-        },
-        {
-          "pitch": 65,
-          "start": 6.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 64,
-          "start": 6.5,
-          "duration": 0.45
-        },
-        {
-          "pitch": 62,
-          "start": 7.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 60,
-          "start": 7.5,
-          "duration": 0.45
-        },
-        {
-          "pitch": 60,
-          "start": 8.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 64,
-          "start": 8.5,
-          "duration": 0.45
-        },
-        {
-          "pitch": 67,
-          "start": 9.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 72,
-          "start": 9.5,
-          "duration": 0.45
-        },
-        {
-          "pitch": 67,
-          "start": 10.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 64,
-          "start": 10.5,
-          "duration": 0.45
-        },
-        {
-          "pitch": 60,
-          "start": 11.0,
-          "duration": 0.45
-        },
-        {
-          "pitch": 67,
-          "start": 11.5,
-          "duration": 0.45
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.473
-        },
-        {
-          "pitch": 62,
-          "start": 0.55,
-          "duration": 0.473
-        },
-        {
-          "pitch": 64,
-          "start": 1.1,
-          "duration": 0.473
-        },
-        {
-          "pitch": 66,
-          "start": 1.65,
-          "duration": 0.473
-        },
-        {
-          "pitch": 67,
-          "start": 2.2,
-          "duration": 0.473
-        },
-        {
-          "pitch": 69,
-          "start": 2.75,
-          "duration": 0.473
-        },
-        {
-          "pitch": 71,
-          "start": 3.3,
-          "duration": 0.473
-        },
-        {
-          "pitch": 72,
-          "start": 3.55,
-          "duration": 0.473
-        },
-        {
-          "pitch": 72,
-          "start": 4.4,
-          "duration": 0.473
-        },
-        {
-          "pitch": 50,
-          "start": 4.6000000000000005,
-          "duration": 0.15
-        },
-        {
-          "pitch": 71,
-          "start": 4.95,
-          "duration": 0.473
-        },
-        {
-          "pitch": 67,
-          "start": 5.5,
-          "duration": 0.473
-        },
-        {
-          "pitch": 67,
-          "start": 6.05,
-          "duration": 0.473
-        },
-        {
-          "pitch": 65,
-          "start": 6.6,
-          "duration": 0.08
-        },
-        {
-          "pitch": 64,
-          "start": 7.15,
-          "duration": 0.473
-        },
-        {
-          "pitch": 62,
-          "start": 7.7,
-          "duration": 0.473
-        },
-        {
-          "pitch": 60,
-          "start": 8.55,
-          "duration": 0.473
-        },
-        {
-          "pitch": 60,
-          "start": 8.8,
-          "duration": 0.473
-        },
-        {
-          "pitch": 64,
-          "start": 9.35,
-          "duration": 0.473
-        },
-        {
-          "pitch": 70,
-          "start": 9.9,
-          "duration": 0.473
-        },
-        {
-          "pitch": 72,
-          "start": 10.45,
-          "duration": 0.473
-        },
-        {
-          "pitch": 64,
-          "start": 11.55,
-          "duration": 0.473
-        },
-        {
-          "pitch": 60,
-          "start": 12.1,
-          "duration": 0.473
-        },
-        {
-          "pitch": 67,
-          "start": 12.65,
-          "duration": 0.473
-        }
-      ]
-    }
-  },
-  {
-    "case_id": "long_perfect_performance",
-    "purpose": "A 20-note long piece played completely correctly, the zero-error control group for long sequences",
-    "length_category": "long",
-    "reference": {
-      "performance_type": "reference",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 62,
-          "start": 0.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 64,
-          "start": 1.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 65,
-          "start": 1.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 67,
-          "start": 2.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 69,
-          "start": 2.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 71,
-          "start": 3.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 72,
-          "start": 3.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 71,
-          "start": 4.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 69,
-          "start": 4.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 67,
-          "start": 5.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 65,
-          "start": 5.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 64,
-          "start": 6.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 62,
-          "start": 6.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 60,
-          "start": 7.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 62,
-          "start": 7.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 64,
-          "start": 8.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 67,
-          "start": 8.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 64,
-          "start": 9.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 60,
-          "start": 9.5,
-          "duration": 0.4
-        }
-      ]
-    },
-    "response": {
-      "performance_type": "response",
-      "notes": [
-        {
-          "pitch": 60,
-          "start": 0.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 62,
-          "start": 0.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 64,
-          "start": 1.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 65,
-          "start": 1.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 67,
-          "start": 2.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 69,
-          "start": 2.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 71,
-          "start": 3.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 72,
-          "start": 3.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 71,
-          "start": 4.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 69,
-          "start": 4.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 67,
-          "start": 5.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 65,
-          "start": 5.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 64,
-          "start": 6.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 62,
-          "start": 6.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 60,
-          "start": 7.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 62,
-          "start": 7.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 64,
-          "start": 8.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 67,
-          "start": 8.5,
-          "duration": 0.4
-        },
-        {
-          "pitch": 64,
-          "start": 9.0,
-          "duration": 0.4
-        },
-        {
-          "pitch": 60,
-          "start": 9.5,
-          "duration": 0.4
-        }
-      ]
-    }
-  }
-]
\ No newline at end of file
diff --git a/data/test_cases_chords.json b/data/test_cases_chords.json
deleted file mode 100644
index 8484dc5..0000000
--- a/data/test_cases_chords.json
+++ /dev/null
@@ -1,178 +0,0 @@
-[
-  {
-    "case_id": "perfect_melody",
-    "length_category": "short",
-    "purpose": "Pure melody, all notes correct. is_correct should be True.",
-    "reference": {"notes": [
-      {"pitch": 60, "start": 0.0, "duration": 0.5},
-      {"pitch": 62, "start": 0.5, "duration": 0.5},
-      {"pitch": 64, "start": 1.0, "duration": 0.5},
-      {"pitch": 65, "start": 1.5, "duration": 0.5}
-    ]},
-    "response": {"notes": [
-      {"pitch": 60, "start": 0.0, "duration": 0.5},
-      {"pitch": 62, "start": 0.5, "duration": 0.5},
-      {"pitch": 64, "start": 1.0, "duration": 0.5},
-      {"pitch": 65, "start": 1.5, "duration": 0.5}
-    ]}
-  },
-  {
-    "case_id": "perfect_chord",
-    "length_category": "short",
-    "purpose": "Single C major chord, played perfectly. is_correct should be True, total_chords_correct = 1.",
-    "reference": {"notes": [
-      {"pitch": 60, "start": 0.0, "duration": 0.5},
-      {"pitch": 64, "start": 0.01, "duration": 0.5},
-      {"pitch": 67, "start": 0.02, "duration": 0.5}
-    ]},
-    "response": {"notes": [
-      {"pitch": 60, "start": 0.0, "duration": 0.5},
-      {"pitch": 64, "start": 0.01, "duration": 0.5},
-      {"pitch": 67, "start": 0.02, "duration": 0.5}
-    ]}
-  },
-  {
-    "case_id": "imperfect_chord_missing_one_note",
-    "length_category": "short",
-    "purpose": "C major chord in reference, student plays only C and E (missing G). chord_accuracy should be 0.83, total_chords_imperfect = 1.",
-    "reference": {"notes": [
-      {"pitch": 60, "start": 0.0, "duration": 0.5},
-      {"pitch": 64, "start": 0.01, "duration": 0.5},
-      {"pitch": 67, "start": 0.02, "duration": 0.5}
-    ]},
-    "response": {"notes": [
-      {"pitch": 60, "start": 0.0, "duration": 0.5},
-      {"pitch": 64, "start": 0.01, "duration": 0.5}
-    ]}
-  },
-  {
-    "case_id": "wrong_chord_no_overlap",
-    "length_category": "short",
-    "purpose": "C major chord in reference, student plays D F A (no common pitch classes). total_chords_wrong = 1.",
-    "reference": {"notes": [
-      {"pitch": 60, "start": 0.0, "duration": 0.5},
-      {"pitch": 64, "start": 0.01, "duration": 0.5},
-      {"pitch": 67, "start": 0.02, "duration": 0.5}
-    ]},
-    "response": {"notes": [
-      {"pitch": 62, "start": 0.0, "duration": 0.5},
-      {"pitch": 65, "start": 0.01, "duration": 0.5},
-      {"pitch": 69, "start": 0.02, "duration": 0.5}
-    ]}
-  },
-  {
-    "case_id": "chord_with_extra_note",
-    "length_category": "short",
-    "purpose": "C major chord in reference, student plays C E G plus an extra Bb. total_chords_imperfect = 1 (A > 0 but < 1 because of extra note).",
-    "reference": {"notes": [
-      {"pitch": 60, "start": 0.0, "duration": 0.5},
-      {"pitch": 64, "start": 0.01, "duration": 0.5},
-      {"pitch": 67, "start": 0.02, "duration": 0.5}
-    ]},
-    "response": {"notes": [
-      {"pitch": 60, "start": 0.0, "duration": 0.5},
-      {"pitch": 64, "start": 0.01, "duration": 0.5},
-      {"pitch": 67, "start": 0.02, "duration": 0.5},
-      {"pitch": 70, "start": 0.03, "duration": 0.5}
-    ]}
-  },
-  {
-    "case_id": "missing_chord",
-    "length_category": "short",
-    "purpose": "Reference has a melody note then a chord. Student plays only the melody note. total_chords_missing = 1.",
-    "reference": {"notes": [
-      {"pitch": 60, "start": 0.0,  "duration": 0.5},
-      {"pitch": 60, "start": 0.8,  "duration": 0.5},
-      {"pitch": 64, "start": 0.81, "duration": 0.5},
-      {"pitch": 67, "start": 0.82, "duration": 0.5}
-    ]},
-    "response": {"notes": [
-      {"pitch": 60, "start": 0.0, "duration": 0.5}
-    ]}
-  },
-  {
-    "case_id": "mixed_melody_and_chord_perfect",
-    "length_category": "short",
-    "purpose": "Melody note followed by C major chord, all correct. is_correct should be True.",
-    "reference": {"notes": [
-      {"pitch": 60, "start": 0.0,  "duration": 0.5},
-      {"pitch": 60, "start": 0.8,  "duration": 0.5},
-      {"pitch": 64, "start": 0.81, "duration": 0.5},
-      {"pitch": 67, "start": 0.82, "duration": 0.5}
-    ]},
-    "response": {"notes": [
-      {"pitch": 60, "start": 0.0,  "duration": 0.5},
-      {"pitch": 60, "start": 0.8,  "duration": 0.5},
-      {"pitch": 64, "start": 0.81, "duration": 0.5},
-      {"pitch": 67, "start": 0.82, "duration": 0.5}
-    ]}
-  },
-  {
-    "case_id": "mixed_melody_wrong_pitch_imperfect_chord",
-    "length_category": "short",
-    "purpose": "Melody note has wrong pitch (1 semitone off) AND chord is imperfect (missing G). Both pitch_all_correct and is_correct should be False.",
-    "reference": {"notes": [
-      {"pitch": 62, "start": 0.0,  "duration": 0.5},
-      {"pitch": 60, "start": 0.8,  "duration": 0.5},
-      {"pitch": 64, "start": 0.81, "duration": 0.5},
-      {"pitch": 67, "start": 0.82, "duration": 0.5}
-    ]},
-    "response": {"notes": [
-      {"pitch": 63, "start": 0.0,  "duration": 0.5},
-      {"pitch": 60, "start": 0.8,  "duration": 0.5},
-      {"pitch": 64, "start": 0.81, "duration": 0.5}
-    ]}
-  },
-  {
-    "case_id": "type_mismatch_note_vs_chord",
-    "length_category": "short",
-    "purpose": "Reference has a chord at position 2, student plays a single note there instead. Alignment should treat them as unmatched (gap penalty). total_chords_missing should be 1.",
-    "reference": {"notes": [
-      {"pitch": 60, "start": 0.0,  "duration": 0.5},
-      {"pitch": 60, "start": 0.8,  "duration": 0.5},
-      {"pitch": 64, "start": 0.81, "duration": 0.5},
-      {"pitch": 67, "start": 0.82, "duration": 0.5}
-    ]},
-    "response": {"notes": [
-      {"pitch": 60, "start": 0.0, "duration": 0.5},
-      {"pitch": 64, "start": 0.8, "duration": 0.5}
-    ]}
-  },
-  {
-    "case_id": "chord_timing_off",
-    "length_category": "short",
-    "purpose": "C major chord is correct in pitch but played noticeably late (0.4s late relative to a 0.8s IOI = 50% off). timing_correct for the chord should be False.",
-    "reference": {"notes": [
-      {"pitch": 60, "start": 0.0,  "duration": 0.5},
-      {"pitch": 60, "start": 0.8,  "duration": 0.5},
-      {"pitch": 64, "start": 0.81, "duration": 0.5},
-      {"pitch": 67, "start": 0.82, "duration": 0.5}
-    ]},
-    "response": {"notes": [
-      {"pitch": 60, "start": 0.0,  "duration": 0.5},
-      {"pitch": 60, "start": 1.2,  "duration": 0.5},
-      {"pitch": 64, "start": 1.21, "duration": 0.5},
-      {"pitch": 67, "start": 1.22, "duration": 0.5}
-    ]}
-  },
-  {
-    "case_id": "multiple_chords_mixed_accuracy",
-    "length_category": "long",
-    "purpose": "Two chords: first is perfect C major, second is imperfect A minor (missing E). total_chords_correct = 1, total_chords_imperfect = 1.",
-    "reference": {"notes": [
-      {"pitch": 60, "start": 0.0,  "duration": 0.5},
-      {"pitch": 64, "start": 0.01, "duration": 0.5},
-      {"pitch": 67, "start": 0.02, "duration": 0.5},
-      {"pitch": 69, "start": 1.0,  "duration": 0.5},
-      {"pitch": 72, "start": 1.01, "duration": 0.5},
-      {"pitch": 76, "start": 1.02, "duration": 0.5}
-    ]},
-    "response": {"notes": [
-      {"pitch": 60, "start": 0.0,  "duration": 0.5},
-      {"pitch": 64, "start": 0.01, "duration": 0.5},
-      {"pitch": 67, "start": 0.02, "duration": 0.5},
-      {"pitch": 69, "start": 1.0,  "duration": 0.5},
-      {"pitch": 72, "start": 1.01, "duration": 0.5}
-    ]}
-  }
-]
\ No newline at end of file
diff --git a/debug.ipynb b/debug.ipynb
deleted file mode 100644
index 24bc0e7..0000000
--- a/debug.ipynb
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "09df5154",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "from evaluation_function.evaluation import (\n",
-    "    basic_comparison,\n",
-    "    evaluation_function\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c03146c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"./data/referenceMIDI.json\") as f:\n",
-    "    reference = json.load(f)\n",
-    "\n",
-    "with open(\"./data/responseMIDI.json\") as f:\n",
-    "    response = json.load(f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "21acff2a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n",
-      "Note 1 with pitch 60 is correct.\n",
-      "Note 2 is wrong: expected 62, but played 63.\n",
-      "Note 3: difference in start time: 0.15s.\n",
-      "Note 4: difference in duration: 0.20s.\n",
-      "Note 5: difference in start time: 0.10s.\n"
-     ]
-    }
-   ],
-   "source": [
-    "is_correct, feedbacks = basic_comparison(\n",
-    "    response,\n",
-    "    reference\n",
-    ")\n",
-    "\n",
-    "print(is_correct)\n",
-    "\n",
-    "for feedback in feedbacks:\n",
-    "    print(feedback)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8bb565bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "========================================\n",
-      "case: all correct\n",
-      "is_correct: True\n",
-      "feedback:\n",
-      "  - Note 1 with pitch 60 is correct.\n",
-      "  - Note 2 with pitch 62 is correct.\n",
-      "========================================\n",
-      "case: wrong pitch\n",
-      "is_correct: False\n",
-      "feedback:\n",
-      "  - Note 1 is wrong: expected 60, but played 61.\n",
-      "========================================\n",
-      "case: timing out of tolerance\n",
-      "is_correct: False\n",
-      "feedback:\n",
-      "  - Note 1: difference in start time: 0.50s.\n",
-      "========================================\n",
-      "case: missing note\n",
-      "is_correct: False\n",
-      "feedback:\n",
-      "  - Note 1 with pitch 60 is correct.\n",
-      "  - Note 2 with pitch 62 is missing in your performance.\n",
-      "========================================\n",
-      "case: extra note\n",
-      "is_correct: False\n",
-      "feedback:\n",
-      "  - Note 1 with pitch 60 is correct.\n",
-      "  - You played an extra note 2 with pitch 64.\n"
-     ]
-    }
-   ],
-   "source": [
-    "def make_midi(notes):\n",
-    "    return {\"notes\": [{\"pitch\": p, \"start\": s, \"duration\": d} for p, s, d in notes]}\n",
-    "\n",
-    "cases = {\n",
-    "    \"all correct\":   (make_midi([(60, 0.0, 0.5), (62, 0.6, 0.5)]),\n",
-    "                   make_midi([(60, 0.0, 0.5), (62, 0.6, 0.5)])),\n",
-    "\n",
-    "    \"wrong pitch\":   (make_midi([(61, 0.0, 0.5)]),\n",
-    "                   make_midi([(60, 0.0, 0.5)])),\n",
-    "\n",
-    "    \"timing out of tolerance\":   (make_midi([(60, 0.5, 0.5)]),\n",
-    "                   make_midi([(60, 0.0, 0.5)])),\n",
-    "\n",
-    "    \"missing note\":   (make_midi([(60, 0.0, 0.5)]),\n",
-    "                   make_midi([(60, 0.0, 0.5), (62, 0.6, 0.5)])),\n",
-    "\n",
-    "    \"extra note\":   (make_midi([(60, 0.0, 0.5), (64, 0.6, 0.5)]),\n",
-    "                   make_midi([(60, 0.0, 0.5)])),\n",
-    "}\n",
-    "\n",
-    "for name, (response, ref) in cases.items():\n",
-    "    ok, feedbacks = basic_comparison(response, ref)\n",
-    "    print(f\"{'='*40}\")\n",
-    "    print(f\"case: {name}\")\n",
-    "    print(f\"is_correct: {ok}\")\n",
-    "    print(\"feedback:\")\n",
-    "    for f in feedbacks:\n",
-    "        print(f\"  - {f}\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "compareMusic",
-   "language": "python",
-   "name": "comparemusic"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
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diff --git a/notebooks/DTW.ipynb b/notebooks/DTW.ipynb
deleted file mode 100644
index a20e62f..0000000
--- a/notebooks/DTW.ipynb
+++ /dev/null
@@ -1,809 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "96c2775c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from typing import Any\n",
-    "from lf_toolkit.evaluation import Result, Params\n",
-    "\n",
-    "from evaluation_function.compare_MIDI import compare_performance_ED"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a6822f11",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import json\n",
-    "\n",
-    "cwd = os.getcwd()\n",
-    "dir = os.path.dirname(cwd)\n",
-    "reference_path = os.path.join(dir, \"data\", \"referenceMIDI.json\")\n",
-    "response_path = os.path.join(dir, \"data\", \"responseMIDI.json\")\n",
-    "\n",
-    "with open(reference_path) as f1:\n",
-    "    reference = json.load(f1)\n",
-    "\n",
-    "with open(response_path) as f2:\n",
-    "    response = json.load(f2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8d3bba8e",
-   "metadata": {},
-   "source": [
-    "# Note Alignment techniques"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "519ed38f",
-   "metadata": {},
-   "source": [
-    "## DTW"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a900434b",
-   "metadata": {},
-   "source": [
-    "The goal of the note alignment is to find if the student played any missing or extra notes, and which note is missing/extra.\n",
-    "\n",
-    "\n",
-    "Based on the findings during the project plan phase, DTW is commonly used for alignment. The algorithm can be found in this book (Chpater 3.2 Dynamic Time Warping):\n",
-    "M. Müller, Fundamentals of Music Processing. Cham: Springer International Publishing, 2021, ISBN: 9783030698072. DOI:https://doi.org/10.1007/978-3-030-69808-9.\n",
-    "\n",
-    "In general, this DTW algorithm finds an optimal possibly nonlinear alignment between response MIDI sequence to reference MIDI sequence.\n",
-    "\n",
-    "Basic approach:\n",
-    "- Evaluating the local cost measure for each pair of elements in the response(X) and reference(Y) sequences. \n",
-    "- Dynamic programming to find an alignment path between X and Y having minimal overall cost, i.e. DTW distance. The algorithm computes a cumulative distance path, the timestamps of the target MIDI are warped so they perfectly align with the anchor points of the reference MIDI.\n",
-    "\n",
-    "\n",
-    "However, this basic approach will not correctly handle the missing note case as expected, because it allows a note to match with multiple notes, and each note must be paired. Let's say, there is a note missing in the response, this algorithm tends to match a response note with two reference note, instead of reporting the missing problem.\n",
-    "\n",
-    "! need to modify this algorithm to make it handle the missing/extra problem correctly. Cosider analysing each entry of the warping path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0f6ac5e5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_cost(note1, note2):\n",
-    "    \"\"\"\n",
-    "    Compute the local cost measure for each pair of notes.\n",
-    " \n",
-    "    Only pitch is involved in the cost calculation, since the purpose \n",
-    "    is to pair up notes with similar pitches.\n",
-    " \n",
-    "    Args:\n",
-    "        note1: dict with keys \"pitch\" (int), \"start\" (float), \"duration\" (float)\n",
-    "        note2: dict with keys \"pitch\" (int), \"start\" (float), \"duration\" (float)\n",
-    " \n",
-    "    Returns:\n",
-    "        int: cost value >= 0 (lower means more similar pitch)\n",
-    "    \"\"\"\n",
-    "    return int(abs(note1[\"pitch\"] - note2[\"pitch\"]))\n",
-    "\n",
-    "\n",
-    "def note_alignment_DTW(response_notes, ref_notes):\n",
-    "    \"\"\"\n",
-    "    DTW pipeline: build cost matrix C -> build accumulated cost matrix D \n",
-    "    -> backtrack to find the optimal warping path\n",
-    "    - the Rows of C and D correspond to response notes\n",
-    "    - the Columns of C and D correspond to reference notes\n",
-    "    \n",
-    "    Args:\n",
-    "        response_notes: The student's response MIDI notes to evaluate\n",
-    "        ref_notes: The reference MIDI note\n",
-    " \n",
-    "    Returns:\n",
-    "        path: list of (response_idx, reference_idx) pairs — the optimal alignment\n",
-    "        C: local cost matrix\n",
-    "        D: accumulated cost matrix\n",
-    "    \"\"\"\n",
-    "\n",
-    "    N = len(response_notes)\n",
-    "    M = len(ref_notes)\n",
-    "\n",
-    "    # step1: Build the local cost matrix C of size (N x M).\n",
-    "    # C[i, j] = note_cost(ref_notes[i], response_notes[j])\n",
-    "    C= np.zeros((N, M))\n",
-    "    for i in range(N):\n",
-    "        for j in range(M):\n",
-    "            C[i, j] = compute_cost(response_notes[i], ref_notes[j])\n",
-    "\n",
-    "    # step2: Build the accumulated cost matrix D of size (N+1 x M+1)\n",
-    "    # using small trick for simplifying the initialization\n",
-    "    # D[n, 0] = inf  for n >= 1\n",
-    "    # D[0, m] = inf  for m >= 1\n",
-    "    D = np.full((N + 1, M + 1), np.inf)\n",
-    "    D[0, 0] = 0\n",
-    "    # for all n in [1..N] and m in [1..M]:\n",
-    "    # D[i, j] = C[i, j] + min(D[i-1, j], D[i, j-1], D[i-1, j-1])\n",
-    "    for i in range(1, N + 1):\n",
-    "        for j in range(1, M + 1):\n",
-    "            D[i, j] = C[i - 1, j - 1] + min(\n",
-    "                D[i - 1, j], # vertical step, multiple response notes are mapped to the same ref note\n",
-    "                D[i, j - 1], # horizontal step, same response note is reused for multiple ref notes\n",
-    "                D[i - 1, j - 1]) # diagonal step\n",
-    "\n",
-    "    # step3: Backtrack through the D to find the optimal warping path P\n",
-    "    path = []\n",
-    "    n, m = N, M\n",
-    "    while n > 0 and m > 0:\n",
-    "        path.append((n-1, m-1))\n",
-    "        # Find the minimum cost step\n",
-    "        diag = D[n-1, m-1]\n",
-    "        vertical = D[n-1, m]\n",
-    "        horizontal = D[n, m-1]\n",
-    "        min_step = min(diag, vertical, horizontal)\n",
-    "        if min_step == vertical:\n",
-    "            n -= 1\n",
-    "        elif min_step == horizontal:\n",
-    "            m -= 1\n",
-    "        else: # diagonal step\n",
-    "            n -= 1\n",
-    "            m -= 1\n",
-    "    # Reverse to get the path from start to end\n",
-    "    path.reverse()  \n",
-    "\n",
-    "    return path, C, D\n",
-    "\n",
-    "\n",
-    "def path_classification(path, C):\n",
-    "    \"\"\"\n",
-    "    Classify the each entry of the alignment path into:\n",
-    "    - correct: response pitch matches reference pitch (cost = 0)\n",
-    "    - wrong: response pitch differs from reference pitch, but all the parings are one-to-one (cost > 0)\n",
-    "    - missing: same response pitch is mapped to multiple reference pitches \n",
-    "    - extra: multiple response pitches are mapped to the same reference pitch\n",
-    " \n",
-    "    Args:   \n",
-    "    path: list of (response_idx, reference_idx) pairs from the backtrack warping path\n",
-    "    C: local cost matrix (N x M)\n",
-    "\n",
-    "    Returns:\n",
-    "        list of event dicts, each one of:\n",
-    "            {'type': 'match' or 'replacement' or 'missing' or 'extra', \n",
-    "            'response_idx': int, 'reference_idx': int, 'cost': int}\n",
-    "    \"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "095ae680",
-   "metadata": {},
-   "source": [
-    "Evaluation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f2cc4262",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_note_pair(response_note, ref_note, reference_idx,\n",
-    "                       timing_tolerance=0.1, duration_tolerance=0.1):\n",
-    "    \"\"\"\n",
-    "    Evaluate a single aligned note pair and return feedback.\n",
-    " \n",
-    "    Args:\n",
-    "        response_note: student's note dict\n",
-    "        ref_note: reference note dict\n",
-    "        reference_idx: 1-based display index (based on ref position)\n",
-    "        timing_tolerance: consider as correct if start is within this tolerance\n",
-    "        duration_tolerance: consider as correct if duration is within this tolerance\n",
-    " \n",
-    "    Returns:\n",
-    "        is_correct (bool), feedback (list of str)\n",
-    "    \"\"\"\n",
-    "    feedback = []\n",
-    "    is_correct = True\n",
-    " \n",
-    "    # Pitch check\n",
-    "    if response_note[\"pitch\"] != ref_note[\"pitch\"]:\n",
-    "        is_correct = False\n",
-    "        feedback.append(\n",
-    "            f\"Note {reference_idx}: wrong pitch — expected {ref_note['pitch']}, \"\n",
-    "            f\"played {response_note['pitch']}.\"\n",
-    "        )\n",
-    " \n",
-    "    # Timing check\n",
-    "    timing_diff = abs(response_note[\"start\"] - ref_note[\"start\"])\n",
-    "    if timing_diff > timing_tolerance:\n",
-    "        is_correct = False\n",
-    "        feedback.append(f\"Note {reference_idx}: difference in start time: {timing_diff:.2f}s.\")\n",
-    " \n",
-    "    # Duration check\n",
-    "    duration_diff = abs(response_note[\"duration\"] - ref_note[\"duration\"])\n",
-    "    if duration_diff > duration_tolerance:\n",
-    "        is_correct = False\n",
-    "        feedback.append(f\"Note {reference_idx}: difference in duration: {duration_diff:.2f}s.\")\n",
-    " \n",
-    "    if is_correct:\n",
-    "        feedback.append(f\"Note {reference_idx} (with pitch {ref_note['pitch']}) is correct.\")\n",
-    " \n",
-    "    return is_correct, feedback"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "99c84405",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def comparison(response, ref,\n",
-    "                   timing_tolerance=0.1, duration_tolerance=0.1):\n",
-    "    \"\"\"\n",
-    "    Compare student MIDI against reference MIDI after DTW-based alignment.\n",
-    " \n",
-    "    Args:\n",
-    "        response: The student's response MIDI\n",
-    "        ref: The reference MIDI\n",
-    "        timing_tolerance:   seconds\n",
-    "        duration_tolerance: seconds\n",
-    " \n",
-    "    Returns:\n",
-    "        all_correct (bool), feedback (list of str)\n",
-    "    \"\"\"\n",
-    "    response_notes = response[\"notes\"]\n",
-    "    ref_notes = ref[\"notes\"]\n",
-    " \n",
-    "    # Align using DTW — response first, ref second\n",
-    "    path, C, D = note_alignment_DTW(response_notes, ref_notes)\n",
-    " \n",
-    "    feedback = []\n",
-    "    all_correct = True\n",
-    "    \n",
-    "    for response_idx, reference_idx in path:\n",
-    "        is_correct, feedback = evaluate_note_pair(\n",
-    "            response_notes[response_idx], ref_notes[reference_idx],\n",
-    "            reference_idx=reference_idx + 1,\n",
-    "            timing_tolerance=timing_tolerance,\n",
-    "            duration_tolerance=duration_tolerance,\n",
-    "        )\n",
-    "        if not is_correct:\n",
-    "            all_correct = False\n",
-    "        feedback.extend(feedback)\n",
-    " \n",
-    "    return all_correct, feedback"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a203ee2d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluation_function(response: Any, answer: Any, params: Params) -> Result:\n",
-    "    \"\"\"\n",
-    "    Entry point for Lambda Feedback.\n",
-    " \n",
-    "    Args:\n",
-    "        response: student MIDI dict\n",
-    "        answer:   reference MIDI dict\n",
-    "        params:   optional extra parameters\n",
-    " \n",
-    "    Returns:\n",
-    "        Result with is_correct and feedback string\n",
-    "    \"\"\"\n",
-    "    all_correct, feedback = comparison(response, answer)\n",
-    "    return Result(\n",
-    "        is_correct=all_correct,\n",
-    "        feedback_items=[(\"feedback\", \"\\n\".join(feedback))]\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cd3befd9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n",
-      "Note 1 (with pitch 60) is correct.\n",
-      "Note 2: wrong pitch — expected 62, played 63.\n",
-      "Note 3: difference in start time: 0.15s.\n",
-      "Note 4: difference in duration: 0.20s.\n",
-      "Note 5: wrong pitch — expected 67, played 65.\n",
-      "Note 5: difference in start time: 0.70s.\n",
-      "Note 5: difference in duration: 0.20s.\n"
-     ]
-    }
-   ],
-   "source": [
-    "is_correct, feedbacks = comparison(\n",
-    "    response,\n",
-    "    reference\n",
-    ")\n",
-    "\n",
-    "print(is_correct)\n",
-    "\n",
-    "for feedback in feedbacks:\n",
-    "    print(feedback)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "25bc065e",
-   "metadata": {},
-   "source": [
-    "note5 should be a missing pitch!! Need to check the DTW algorithm!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ded66276",
-   "metadata": {},
-   "source": [
-    "## Edit Distance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adbaf01b",
-   "metadata": {},
-   "source": [
-    "a simplified version of this is applied here: https://www.math.univ-toulouse.fr/~mongeau/music.pdf "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31f3c137",
-   "metadata": {},
-   "source": [
-    "The note alignment algorithm is based on Edit Distance (ED), where the reference note sequence and the student's response sequence are aligned using dynamic programming. Instead of using a constant replacement cost as in the classic ED algroithm, here absolute pitch difference (measured in MIDI semitones) between two notes is used, so that matched note pairs have zero cost while larger pitch deviations have proportionally higher penalties.\n",
-    "\n",
-    "Insertion and deletion operations are represented by a fixed gap penalty, corresponding to extra notes played by the student and missing notes from the reference sequence, respectively. The gap penalty is empirically set to 6. This value is chosen to encourage the alignment algorithm to interpret most pitch deviations as replacement (wrong pitches) rather than decomposing them into separate insertion and deletion operations. Such behaviour is more consistent with common music teaching practice, where a note played at the correct temporal position but with an incorrect pitch is typically regarded as a wrong note instead of a missing note accompanied by an extra note.\n",
-    "\n",
-    "After constructing the accumulated cost matrix, backtracking is performed to recover the optimal alignment path. Each aligned pair is classified as one of four operation types: match, replacement, missing, or extra. These structured operations are subsequently used to generate formative feedback for the learner."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "453b33d9",
-   "metadata": {},
-   "source": [
-    "Proportional tolerances are used instead of fixed absolute thresholds, because a fixed tolerance (e.g. +/-0.5 s) is unfair: it is too tolerant for long notes and overly strict for short notes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "f29a08ef",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overview: \n",
-      "Timing: your overall tempo is within an acceptable range. Good job! The timing is about 3% behind the reference in general while notes are held about 10% longer than the reference.\n",
-      "There is 1 note played with the wrong pitch.\n",
-      "There is 1 note you missed from the reference.\n",
-      "There are no extra notes. Good job!\n",
-      "\n",
-      "Detail: \n",
-      "Note 5 (pitch 67) is missing in your performance.\n",
-      "Note 2: wrong pitch — expected 62, played 63 (1 semitone(s) off).\n",
-      "Note 4: duration is 0.15s longer than the reference (i.e. 30% off) after accounting for the overall duration trend \n"
-     ]
-    }
-   ],
-   "source": [
-    "result = compare_performance_ED(response, reference)\n",
-    "print(result.feedback_message)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2043a53",
-   "metadata": {},
-   "source": [
-    "### Test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "705bee47",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded 18 test cases\n",
-      "  - perfect_performance (short)\n",
-      "  - single_pitch_error (short)\n",
-      "  - multiple_pitch_errors (short)\n",
-      "  - missing_note (short)\n",
-      "  - extra_note (short)\n",
-      "  - missing_and_extra (short)\n",
-      "  - global_tempo_slower (short)\n",
-      "  - global_tempo_faster (short)\n",
-      "  - global_duration_longer (short)\n",
-      "  - local_timing_anomaly_only (short)\n",
-      "  - local_duration_anomaly_only (short)\n",
-      "  - first_note_no_timing_penalty (short)\n",
-      "  - too_few_matches_for_global_fit (short)\n",
-      "  - all_notes_missing (short)\n",
-      "  - all_notes_extra (short)\n",
-      "  - repeated_pitch_ambiguous_alignment (short)\n",
-      "  - long_stress_test (long)\n",
-      "  - long_perfect_performance (long)\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_path = os.path.join(dir, \"data\", \"test_cases.json\")\n",
-    "\n",
-    "with open(test_path) as f:\n",
-    "    test_cases = json.load(f)\n",
-    "\n",
-    "print(f\"Loaded {len(test_cases)} test cases\")\n",
-    "for case in test_cases:\n",
-    "    print(f\"  - {case['case_id']} ({case['length_category']})\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "34952131",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Purpose: 24-note long-piece comprehensive stress test: mixes pitch errors, missing notes, extra notes, mild global tempo/duration drift, and local timing/duration anomalies, triggering almost every feedback branch in the pipeline at once\n",
-      "\n",
-      "is_correct: False\n",
-      "stats: {'pitch_all_correct': False, 'timing_all_correct': False, 'duration_all_correct': False, 'total_notes_in_reference': 24, 'total_notes_missing': 1, 'total_notes_extra': 1, 'total_notes_wrong_pitch': 3, 'total_notes_wrong_timing': 2, 'total_notes_wrong_duration': 1, 'total_notes_correct': 17, 'timing_scale': 1.1044667421912935, 'timing_offset': -0.0248583913254565, 'duration_scale': 1.0131400966183572}\n",
-      "\n",
-      "Overview: \n",
-      "Timing: your overall tempo is within an acceptable range. Good job! The timing is about 10% behind the reference in general while notes are held about 1% longer than the reference.\n",
-      "There are 3 notes played with the wrong pitch.\n",
-      "There is 1 note you missed from the reference.\n",
-      "There is 1 extra note played during practice. You may need to adjust your fingering or hand position to avoid extra notes.\n",
-      "\n",
-      "Detail: \n",
-      "Extra note played: pitch 50 at t=4.60s \n",
-      "Note 21 (pitch 67) is missing in your performance.\n",
-      "Note 4: wrong pitch — expected 65, played 66 (1 semitone(s) off).\n",
-      "Note 11: wrong pitch — expected 69, played 67 (2 semitone(s) off).\n",
-      "Note 19: wrong pitch — expected 67, played 70 (3 semitone(s) off).\n",
-      "Note 8: timing is off by 0.29s (58% of the expected note interval), after accounting for the overall tempo trend.\n",
-      "Note 16: timing is off by 0.29s (58% of the expected note interval), after accounting for the overall tempo trend.\n",
-      "Note 13: duration is 0.38s shorter than the reference (i.e. 84% off) after accounting for the overall duration trend \n"
-     ]
-    }
-   ],
-   "source": [
-    "# Pick one case by id to inspect closely\n",
-    "case_id = \"long_stress_test\"\n",
-    "case = next(c for c in test_cases if c[\"case_id\"] == case_id)\n",
-    "\n",
-    "print(\"Purpose:\", case[\"purpose\"])\n",
-    "print()\n",
-    "\n",
-    "result = compare_performance_ED(case[\"response\"], case[\"reference\"])\n",
-    "\n",
-    "print(\"is_correct:\", result.is_correct)\n",
-    "print(\"stats:\", result.stats)\n",
-    "print()\n",
-    "print(result.feedback_message)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "772e0e46",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[perfect_performance                ] is_correct=True  missing=0 extra=0 wrong_pitch=0 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n",
-      "[single_pitch_error                 ] is_correct=False missing=0 extra=0 wrong_pitch=1 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n",
-      "[multiple_pitch_errors              ] is_correct=False missing=0 extra=0 wrong_pitch=3 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n",
-      "[missing_note                       ] is_correct=False missing=1 extra=0 wrong_pitch=0 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n",
-      "[extra_note                         ] is_correct=False missing=0 extra=1 wrong_pitch=0 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n",
-      "[missing_and_extra                  ] is_correct=False missing=1 extra=1 wrong_pitch=0 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n",
-      "[global_tempo_slower                ] is_correct=True  missing=0 extra=0 wrong_pitch=0 wrong_timing=0 wrong_duration=0 timing_scale=1.40 duration_scale=1.00\n",
-      "[global_tempo_faster                ] is_correct=True  missing=0 extra=0 wrong_pitch=0 wrong_timing=0 wrong_duration=0 timing_scale=0.60 duration_scale=1.00\n",
-      "[global_duration_longer             ] is_correct=True  missing=0 extra=0 wrong_pitch=0 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.80\n",
-      "[local_timing_anomaly_only          ] is_correct=False missing=0 extra=0 wrong_pitch=0 wrong_timing=1 wrong_duration=0 timing_scale=0.99 duration_scale=1.00\n",
-      "[local_duration_anomaly_only        ] is_correct=False missing=0 extra=0 wrong_pitch=0 wrong_timing=0 wrong_duration=1 timing_scale=1.00 duration_scale=0.90\n",
-      "[first_note_no_timing_penalty       ] is_correct=False missing=0 extra=0 wrong_pitch=1 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n",
-      "[too_few_matches_for_global_fit     ] is_correct=False missing=0 extra=0 wrong_pitch=0 wrong_timing=1 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n",
-      "[all_notes_missing                  ] is_correct=False missing=4 extra=0 wrong_pitch=0 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n",
-      "[all_notes_extra                    ] is_correct=False missing=0 extra=4 wrong_pitch=0 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n",
-      "[repeated_pitch_ambiguous_alignment ] is_correct=False missing=0 extra=0 wrong_pitch=1 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n",
-      "[long_stress_test                   ] is_correct=False missing=1 extra=1 wrong_pitch=3 wrong_timing=2 wrong_duration=1 timing_scale=1.10 duration_scale=1.01\n",
-      "[long_perfect_performance           ] is_correct=True  missing=0 extra=0 wrong_pitch=0 wrong_timing=0 wrong_duration=0 timing_scale=1.00 duration_scale=1.00\n"
-     ]
-    }
-   ],
-   "source": [
-    "for case in test_cases:\n",
-    "    result = compare_performance_ED(case[\"response\"], case[\"reference\"])\n",
-    "    print(\n",
-    "        f\"[{case['case_id']:35s}] \"\n",
-    "        f\"is_correct={result.is_correct!s:5} \"\n",
-    "        f\"missing={result.stats['total_notes_missing']} \"\n",
-    "        f\"extra={result.stats['total_notes_extra']} \"\n",
-    "        f\"wrong_pitch={result.stats['total_notes_wrong_pitch']} \"\n",
-    "        f\"wrong_timing={result.stats['total_notes_wrong_timing']} \"\n",
-    "        f\"wrong_duration={result.stats['total_notes_wrong_duration']} \"\n",
-    "        f\"timing_scale={result.stats['timing_scale']:.2f} \"\n",
-    "        f\"duration_scale={result.stats['duration_scale']:.2f}\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f3765faf",
-   "metadata": {},
-   "source": [
-    "### Visualisation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0216997f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# use ChatGPT to help with the code for the cost matrix and alignment arrows, wrote the code myself\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "def plot_cost_matrix(D, operations, response_notes, ref_notes):\n",
-    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
-    "    \n",
-    "    # Slice off the padding row/column — they are only needed for the algorithm,\n",
-    "    # not meaningful to display\n",
-    "    D_display = D[1:, 1:]\n",
-    " \n",
-    "    im = ax.imshow(D_display, origin=\"upper\", aspect=\"auto\", cmap=\"gray_r\")\n",
-    "    plt.colorbar(im, ax=ax, label=\"Accumulated cost\")\n",
-    "\n",
-    "    # Annotate every cell with its value\n",
-    "    for i in range(D_display.shape[0]):\n",
-    "        for j in range(D_display.shape[1]):\n",
-    "            val = D_display[i, j]\n",
-    "            ax.text(j, i, f\"{val:.0f}\",\n",
-    "                    ha=\"center\", va=\"center\", fontsize=8,\n",
-    "                    color=\"white\" if val > D_display.max() * 0.5 else \"black\")\n",
-    "            \n",
-    "    # Reconstruct path coordinates from operations\n",
-    "    # path coordinates — no +1 shift needed after slicing D\n",
-    "    path_rows, path_cols = [], []\n",
-    "    for op in operations:\n",
-    "        if op[\"type\"] in (\"match\", \"replacement\"):\n",
-    "            path_rows.append(op[\"response_idx\"])\n",
-    "            path_cols.append(op[\"reference_idx\"])\n",
-    "        elif op[\"type\"] == \"extra\":\n",
-    "            path_rows.append(op[\"response_idx\"])\n",
-    "            path_cols.append(path_cols[-1] if path_cols else 0)\n",
-    "        elif op[\"type\"] == \"missing\":\n",
-    "            path_rows.append(path_rows[-1] if path_rows else 0)\n",
-    "            path_cols.append(op[\"reference_idx\"])\n",
-    " \n",
-    "    ax.plot(path_cols, path_rows, \"ro-\", markersize=10, linewidth=2,\n",
-    "            label=\"Optimal alignment path\")\n",
-    " \n",
-    "    # axis labels\n",
-    "    ref_labels = [f\"$x_{j}$\" for j in range(len(ref_notes))]\n",
-    "    response_labels = [f\"$y_{i}$\" for i in range(len(response_notes))]\n",
-    "    ax.set_xticks(range(len(ref_labels)))\n",
-    "    ax.set_xticklabels(ref_labels, fontsize=8)\n",
-    "    ax.set_yticks(range(len(response_labels)))\n",
-    "    ax.set_yticklabels(response_labels, fontsize=8)\n",
-    "    ax.set_xlabel(\"Reference\")\n",
-    "    ax.set_ylabel(\"Response\")\n",
-    "    ax.set_title(f\"Accumulated Cost Matrix D\")\n",
-    "    ax.legend(loc=\"upper left\", fontsize=8)\n",
-    " \n",
-    "    plt.tight_layout()\n",
-    "    # plt.savefig(\"edit_distance_matrix.png\", dpi=150, bbox_inches=\"tight\")\n",
-    "    # print(\"Saved: edit_distance_matrix.png\")\n",
-    "    plt.show()\n",
-    " \n",
-    " \n",
-    "def plot_alignment_arrows(operations, response_notes, ref_notes):\n",
-    "    fig, ax = plt.subplots(figsize=(12, 3))\n",
-    "\n",
-    "    N = len(response_notes)\n",
-    "    M = len(ref_notes)\n",
-    "\n",
-    "    # Draw boxes for ref notes (top row, y=1) and response notes (bottom row, y=0)\n",
-    "    for j, note in enumerate(ref_notes):\n",
-    "        ax.add_patch(plt.Rectangle((j - 0.4, 0.75), 0.8, 0.5,\n",
-    "                                   fill=False, edgecolor=\"black\", linewidth=1.5))\n",
-    "        ax.text(j, 1.0, f\"$x_{{{j+1}}}$\\n{note['pitch']}\",\n",
-    "                ha=\"center\", va=\"center\", fontsize=9)\n",
-    "\n",
-    "    for i, note in enumerate(response_notes):\n",
-    "        ax.add_patch(plt.Rectangle((i - 0.4, -0.25), 0.8, 0.5,\n",
-    "                                   fill=False, edgecolor=\"black\", linewidth=1.5))\n",
-    "        ax.text(i, 0.0, f\"$y_{{{i+1}}}$\\n{note['pitch']}\",\n",
-    "                ha=\"center\", va=\"center\", fontsize=9)\n",
-    "\n",
-    "    # Draw arrows between aligned pairs\n",
-    "    for op in operations:\n",
-    "        if op[\"type\"] in (\"match\", \"replacement\"):\n",
-    "            colour = \"green\" if op[\"type\"] == \"match\" else \"red\"\n",
-    "            ax.annotate(\"\",\n",
-    "                xy =(op[\"response_idx\"], 0.25),\n",
-    "                xytext = (op[\"reference_idx\"], 0.75),\n",
-    "                arrowprops = dict(arrowstyle=\"<->\", color=colour,\n",
-    "                                lw=1.5, mutation_scale=12),\n",
-    "            )\n",
-    "        elif op[\"type\"] == \"missing\":\n",
-    "            j = op[\"reference_idx\"]\n",
-    "            ax.text(j, 1.45, \"missing\", ha=\"center\", va=\"center\",\n",
-    "                    fontsize=12, color=\"red\", fontweight=\"bold\")\n",
-    "        elif op[\"type\"] == \"extra\":\n",
-    "            i = op[\"response_idx\"]\n",
-    "            ax.text(i, -0.45, \"extra\", ha=\"center\", va=\"center\",\n",
-    "                    fontsize=12, color=\"red\", fontweight=\"bold\")\n",
-    "\n",
-    "    ax.text(-0.8, 1.0, \"Sequence X\\n(ref)\", ha=\"right\", va=\"center\", fontsize=9)\n",
-    "    ax.text(-0.8, 0.0, \"Sequence Y\\n(response)\", ha=\"right\", va=\"center\", fontsize=9)\n",
-    "\n",
-    "    ax.set_xlim(-1.2, max(N, M) - 0.4)\n",
-    "    ax.set_ylim(-0.7, 1.7)\n",
-    "    ax.axis(\"off\")\n",
-    "    plt.tight_layout()\n",
-    "    # plt.savefig(\"edit_distance_alignment.png\", dpi=150, bbox_inches=\"tight\")\n",
-    "    # print(\"Saved: edit_distance_alignment.png\")\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "3bd7b6f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_cost_matrix(D, operations, response[\"notes\"], reference[\"notes\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "7b54ee04",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_alignment_arrows(operations, response[\"notes\"], reference[\"notes\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e5a7185",
-   "metadata": {},
-   "source": [
-    "### Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9ea553da",
-   "metadata": {},
-   "source": [
-    "Step 0: Normalise the start times\n",
-    "- Adjust the start time of the first note to t=0 for both the reference and the response MIDI. This action will help eliminate the time gap between student start recording their practice and start playing the first note (if the gap exists).\n",
-    "\n",
-    "Step 1: Align notes using edit distance\n",
-    "- The purpose of note alignment here is to identify if there is any missing/extra notes. Unlike standard DTW where off-diagonal moves has no cost and every note must be aligned to another, the Edit-distance approach allows a note to be explicitly left unaligned at the cost of gap_penalty, i.e. allows for insertions (extra notes) and deletions (missing notes). Each aligned pair or unmatched element is classified into one of the four operation types according to the moving direction during backtracking:\n",
-    "    - diagonal = match (identical pitch)/replacement (wrong pitch)\n",
-    "    - vertical = extra (additional note played)\n",
-    "    - horizontal = missing (note not played)\n",
-    " \n",
-    "Step 2: Estimate the overall tempo trend\n",
-    "- It is common for students to play at a slower tempo by setting the metronome to a slower pace during practice. Then after becoming more and more familiar with it, they may speed up unconsciously especially for the easier parts. Therefore, to avoid prompting the tempo problem for every single note in these cases, the global drift problem must be separated from local deviations. \n",
-    "- `estimate_global_timing` fits a linear regression over all matched note pairs: response_start ≈ scale × ref_start + offset, where scale represents the student's overall tempo relative to the reference (greater than 1.0 indicates playing slower; less than 1.0 indicates playing faster), and offset captures any constant time shift.\n",
-    "- `estimate_global_duration_scale` fits a least-squares regression, this regression line passes through the origin because note duration has no meaningful constant offset term: response_duration ≈ duration_scale × ref_duration, where duration_scale represents the general holding time for notes relative to the reference (greater than 1.0 indicates longer holding; less than 1.0 indicates shorter holding time)\n",
-    "- To ensure the fitting method is statistically and musically meaningful, if the amount of matched pairs available is smaller than three, the functions will assume there is no global tempo trend. \n",
-    "- The slow/fast decision mainly depends on the scale for timing.\n",
-    "    \n",
-    "Step 3: note-level evaluation for matched pairs\n",
-    "\n",
-    "Pitch:\n",
-    "— A note is considered correct if and only if the pitch matches exactly (pitch_diff == 0). \n",
-    "- The absolute semitone difference is recorded for feedback.\n",
-    "Timing:\n",
-    "— The expected start time is predicted from the global trend line, then the relative difference in start time is calculated by ∣response_start − predicted_start∣ / inter-onset interval (IOI) of the reference note, \n",
-    "a note is considered correct if this relative difference is within a threshold.\n",
-    "- In the feedback, the amount of difference will be reported instead of directly indicate the correctness, so that the students will not be discourage to include their expressiveness in certain parts of the practice.\n",
-    "- The first note (ref_idx == 0) is always marked as timing-correct, since normalisation in Step 0 guarantees both sequences start at t = 0.\n",
-    "Duration \n",
-    "— The expected duration is predicted from the global duration scale, then the relative difference in duration is calculated by ∣response_duration − predicted_duration∣ / ref_duration, a note is considered correct if this relative difference is within a threshold.\n",
-    "\n",
-    "Step 4: Compute summary statistics\n",
-    "- Summary counts, including total notes missing, extra, wrong pitch, wrong timing, and wrong duration, as well as boolean flags indicating whether all paired notes are correct on each dimension. The global trend parameters (timing_scale, timing_offset, duration_scale) are also included. \n",
-    "\n",
-    "Step 5: Generate the human-readable feedback messages from those statistics and note-level feedback\n",
-    "- Overview provides a summary of the student's overall performance:\n",
-    "    - Tempo judgement based on timing_scale: explicitly states whether the overall tempo is acceptable, too slow, or too fast, along with the duration_scale as supplementary context\n",
-    "    - Total count of pitch errors, missing notes, and extra notes\n",
-    "- Detail provides actionable, note-level feedback for each issue identified:\n",
-    "    - Missing/extra notes: identifies the specific note and pitch\n",
-    "    - Pitch errors: states the expected and played pitch, and the semitone difference\n",
-    "    - Local timing anomalies: reports the absolute and relative deviation after the global trend has been removed\n",
-    "    - Local duration anomalies: reports the deviation direction and magnitude after the global duration scale has been removed"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "compareMusic",
-   "language": "python",
-   "name": "comparemusic"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/asap-dataset b/notebooks/asap-dataset
deleted file mode 160000
index 4097b45..0000000
--- a/notebooks/asap-dataset
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 4097b45757bed854818cf87e77b92323ebf90615
diff --git a/notebooks/asap_worker.py b/notebooks/asap_worker.py
deleted file mode 100644
index 494acc1..0000000
--- a/notebooks/asap_worker.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from evaluation_function.compare_MIDI import (
-    compare_performance_ED,
-    normalize_start_times,
-    group_notes_into_events,
-)
-
-def evaluate_one_sample(sample):
-    try:
-        result = compare_performance_ED(sample["response"], sample["reference"])
-
-        # Reconstruct response events (already existed)
-        response_notes_norm = normalize_start_times(sample["response"]["notes"])
-        response_events_norm = group_notes_into_events(response_notes_norm)
-
-        # NEW: reconstruct reference events too, using the exact same steps
-        # compare_performance_ED uses internally, so reference_index in
-        # event_details lines up with this list.
-        ref_notes_norm = normalize_start_times(sample["reference"]["notes"])
-        ref_events_norm = group_notes_into_events(ref_notes_norm)
-
-        if sample["response"]["notes"]:
-            response_onset_offset = sample["response"]["notes"][0]["start"]
-        else:
-            response_onset_offset = 0.0
-
-        return {
-            "composer": sample["composer"],
-            "title": sample["title"],
-            "stats": result.stats,
-            "event_details": result.event_details,
-            "is_correct": result.is_correct,
-            "response_events_normalized": response_events_norm,
-            "ref_events_normalized": ref_events_norm,   # NEW
-            "response_onset_offset": response_onset_offset,
-            "metadata_row": sample["metadata_row"],
-            "error": None,
-        }
-    except Exception as error:
-        return {
-            "composer": sample.get("composer"),
-            "title": sample.get("title"),
-            "stats": None,
-            "event_details": None,
-            "is_correct": None,
-            "response_events_normalized": None,
-            "ref_events_normalized": None,   # NEW
-            "response_onset_offset": None,
-            "metadata_row": sample.get("metadata_row"),
-            "error": str(error),
-        }
\ No newline at end of file
diff --git a/notebooks/vienna4x22 b/notebooks/vienna4x22
deleted file mode 160000
index 1033ade..0000000
--- a/notebooks/vienna4x22
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 1033ade0899bfd03a89f370c9ad5d8443ddccd3e
diff --git a/poetry.lock b/poetry.lock
index d74165a..34eb223 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -708,22 +708,6 @@ https = ["urllib3 (>=1.24.1)"]
 paramiko = ["paramiko"]
 pgp = ["gpg"]
 
-[[package]]
-name = "elementpath"
-version = "5.1.3"
-description = "XPath 1.0/2.0/3.0/3.1 parsers and selectors for ElementTree and lxml"
-optional = false
-python-versions = ">=3.10"
-groups = ["main"]
-files = [
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-    {file = "elementpath-5.1.3.tar.gz", hash = "sha256:35e98cc2e26332305a49b32eaaebfb7289d1963c88d3989ff5ccb02c46809820"},
-]
-
-[package.extras]
-dev = ["coverage", "flake8", "lxml", "lxml-stubs", "memray ; platform_python_implementation != \"PyPy\"", "mypy", "psutil", "sphinx", "tox (>=4.0)", "xmlschema (>=4.0.1)"]
-docs = ["readthedocs-sphinx-search", "sphinx"]
-
 [[package]]
 name = "fastjsonschema"
 version = "2.21.2"
@@ -1305,23 +1289,6 @@ files = [
     {file = "kiwisolver-1.5.0.tar.gz", hash = "sha256:d4193f3d9dc3f6f79aaed0e5637f45d98850ebf01f7ca20e69457f3e8946b66a"},
 ]
 
-[[package]]
-name = "lark-parser"
-version = "0.12.0"
-description = "a modern parsing library"
-optional = false
-python-versions = "*"
-groups = ["main"]
-files = [
-    {file = "lark-parser-0.12.0.tar.gz", hash = "sha256:15967db1f1214013dca65b1180745047b9be457d73da224fcda3d9dd4e96a138"},
-    {file = "lark_parser-0.12.0-py2.py3-none-any.whl", hash = "sha256:0eaf30cb5ba787fe404d73a7d6e61df97b21d5a63ac26c5008c78a494373c675"},
-]
-
-[package.extras]
-atomic-cache = ["atomicwrites"]
-nearley = ["js2py"]
-regex = ["regex"]
-
 [[package]]
 name = "lf-toolkit"
 version = "0.0.1"
@@ -1357,156 +1324,6 @@ url = "https://github.com/lambda-feedback/toolkit-python.git"
 reference = "main"
 resolved_reference = "b6c3eac917e0bfa81085638cf8173d5e6b8ff12d"
 
-[[package]]
-name = "lxml"
-version = "6.1.1"
-description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
-optional = false
-python-versions = ">=3.8"
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 [package.extras]
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-]
-
-[package.dependencies]
-numpy = ">=2.0.0,<2.8"
-
-[package.extras]
-dev = ["click (<8.3.0)", "cython-lint (>=0.12.2)", "mypy (==1.19.1)", "pycodestyle", "pyrefly (==0.63.0)", "ruff (>=0.12.0)", "spin", "types-psutil", "typing_extensions"]
-doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.19.1)", "jupytext", "linkify-it-py", "matplotlib (>=3.5)", "myst-nb (>=1.2.0)", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.2.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)", "tabulate"]
-test = ["Cython", "array-api-strict (>=2.3.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest (>=8.0.0)", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "scipy-doctest (>=2.0.0)", "threadpoolctl"]
-
 [[package]]
 name = "secretstorage"
 version = "3.5.0"
@@ -3277,26 +2929,6 @@ cffi = ">=1.16.0"
 [package.extras]
 test = ["pytest"]
 
-[[package]]
-name = "xmlschema"
-version = "4.3.2"
-description = "An XML Schema validator and decoder"
-optional = false
-python-versions = ">=3.10"
-groups = ["main"]
-files = [
-    {file = "xmlschema-4.3.2-py3-none-any.whl", hash = "sha256:cf5c970a30f6ebcb3da35260e694704cc0b6794d8ace46125bb2a95e38ed9307"},
-    {file = "xmlschema-4.3.2.tar.gz", hash = "sha256:f3b33a21a2b2cdc404ddc3fb599d8bc1dee90b155d547370672e6e684746b8d6"},
-]
-
-[package.dependencies]
-elementpath = ">=5.1.3,<6.0.0"
-
-[package.extras]
-codegen = ["jinja2"]
-dev = ["coverage", "flake8", "lxml", "lxml-stubs", "mypy", "psutil", "tox", "xmlschema[docs]"]
-docs = ["jinja2", "sphinx", "sphinx_rtd_theme"]
-
 [[package]]
 name = "zipp"
 version = "4.1.0"
@@ -3433,4 +3065,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.11"
-content-hash = "d8dded14df4d684e5db5332834f7c5be380606f3aee5b568dced52860e800ce0"
+content-hash = "ecdcfccfc009831d6b449c0b55f8f228de58f0537e64a57a2ae1843d238021af"
diff --git a/pyproject.toml b/pyproject.toml
index 8b55f77..979f0bc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,6 @@ matplotlib = "^3.11.0"
 pretty-midi = "^0.2.11"
 pandas = "^3.0.3"
 tqdm = "^4.68.4"
-partitura = "^1.9.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.2.2"

From 1248521e47d32b80389e2e3fb1e9ac136a12c98a Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Sat, 11 Jul 2026 23:20:50 +0100
Subject: [PATCH 13/15] remove outdated notebook

---
 notebooks/Alignment_on_ASAPdataset.ipynb | 2051 ----------------------
 1 file changed, 2051 deletions(-)
 delete mode 100644 notebooks/Alignment_on_ASAPdataset.ipynb

diff --git a/notebooks/Alignment_on_ASAPdataset.ipynb b/notebooks/Alignment_on_ASAPdataset.ipynb
deleted file mode 100644
index 7075a06..0000000
--- a/notebooks/Alignment_on_ASAPdataset.ipynb
+++ /dev/null
@@ -1,2051 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ED Alignment Algorithm on Real Piano Performances\n",
-    "\n",
-    "This notebook evaluates the ED alignment algorithm in `compareMusic` on real piano performances from the [(n)ASAP: the (note-)Aligned Scores And Performances dataset](https://github.com/CPJKU/asap-dataset) (Peter et al., 2023).\n",
-    "\n",
-    "(n)ASAP is a dataset of aligned musical scores and performances built by extending the ASAP dataset with note-level annotations. The ASAP contains 236 distinct musical scores and 1067 performances of Western classical piano music from 15 different composers, the piece directory contains the XML and MIDI score, plus all of the performances of a specific piece, including: \n",
-    "- `midi_score`: the MIDI score (what should be played) — used as **reference**\n",
-    "- `midi_performance`: what the pianist actually played — used as **response**\n",
-    "- `note_alignments.tsv`: official note-level alignment annotations — used as **ground truth**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "bib citation:\n",
-    "\n",
-    "@article{Peter-2023,\n",
-    " title = {Automatic Note-Level Score-to-Performance Alignments in the ASAP Dataset},\n",
-    " author = {Peter, Silvan David and Cancino-Chacón, Carlos Eduardo and Foscarin, Francesco and McLeod, Andrew Philip and Henkel, Florian and Karystinaios, Emmanouil and Widmer, Gerhard},\n",
-    " doi = {10.5334/tismir.149},\n",
-    " journal = {Transactions of the International Society for Music Information Retrieval {(TISMIR)}},\n",
-    " year = {2023}\n",
-    "}\n",
-    "\n",
-    "relevant paper: https://transactions.ismir.net/articles/10.5334/tismir.149#5-alignment-of-the-asap-dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download the (n)ASAP Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "asap-dataset already exists, skipping download.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "# Note: use CPJKU version (not fosfrancesco) because only CPJKU has\n",
-    "# the note_alignments TSV files are needed for ground truth comparison.\n",
-    "if not os.path.exists(\"asap-dataset\"):\n",
-    "    os.system(\"git clone https://github.com/CPJKU/asap-dataset.git\")\n",
-    "else:\n",
-    "    print(\"asap-dataset already exists, skipping download.\")\n",
-    "\n",
-    "ASAP_PATH = \"asap-dataset\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load the Ground Truth (GT)\n",
-    "\n",
-    "The `note_alignment.tsv` file in each performance contains the official note-level alignment annotations. Each row represents one note, with columns: `xml_id`, `midi_id`, `track`, `channel`, `pitch`, `onset`.\n",
-    "- If `midi_id == \"deletion\"`: the score note was not played → label `deletion`\n",
-    "- If `xml_id == \"insertion\"`: an extra note was played → label `insertion`\n",
-    "- Otherwise: a matched pair → label `paired`\n",
-    "\n",
-    "For `match` and `insertion` rows, the TSV provides `onset` (onset time in seconds) and `pitch` (MIDI pitch) for the performance note."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import csv\n",
-    "\n",
-    "def load_ground_truth(tsv_path):\n",
-    "    \"\"\"\n",
-    "    Read a note_alignments TSV file from the CPJKU ASAP dataset.\n",
-    "\n",
-    "    Args:\n",
-    "        tsv_path: str, path to the note_alignments.tsv file\n",
-    "\n",
-    "    Returns:\n",
-    "        list of dicts, each with keys:\n",
-    "            label  -> 'paired', 'insertion', or 'deletion'\n",
-    "            onset  -> float (seconds) or None for deletions\n",
-    "            pitch  -> int (MIDI pitch number) or None for deletions\n",
-    "    \"\"\"\n",
-    "    rows = []\n",
-    "    with open(tsv_path, \"r\", encoding=\"utf-8\") as f:\n",
-    "        reader = csv.DictReader(f, delimiter=\"\\t\")\n",
-    "        \n",
-    "        for row in reader:\n",
-    "            xml_id  = row[\"xml_id\"].strip()\n",
-    "            midi_id = row[\"midi_id\"].strip()\n",
-    "\n",
-    "            if midi_id == \"deletion\":\n",
-    "                rows.append({\"label\": \"deletion\", \"onset\": None, \"pitch\": None})\n",
-    "            elif xml_id == \"insertion\":\n",
-    "                rows.append({\n",
-    "                    \"label\": \"insertion\",\n",
-    "                    \"onset\": float(row[\"onset\"]),\n",
-    "                    \"pitch\": int(row[\"pitch\"]),\n",
-    "                })\n",
-    "            else:\n",
-    "                rows.append({\n",
-    "                    \"label\": \"paired\",\n",
-    "                    \"onset\": float(row[\"onset\"]),\n",
-    "                    \"pitch\": int(row[\"pitch\"]),\n",
-    "                })\n",
-    "    return rows"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Score (reference)/Performance (response) pairs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Helper functions for format conversion: These functions convert a MIDI file into the `{pitch, start, duration}` format used by `compare_MIDI.py`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pretty_midi\n",
-    "\n",
-    "def midi_file_to_notes(midi_path):\n",
-    "    \"\"\"\n",
-    "    Parse a MIDI file and return all its notes in compareMusic format.\n",
-    "    \n",
-    "    Args:\n",
-    "        midi_path: str, path to the MIDI file\n",
-    "\n",
-    "    Returns:\n",
-    "        list of dicts sorted by onset time, each with keys:\n",
-    "            pitch -> int, MIDI note number\n",
-    "            start -> float, onset time in seconds\n",
-    "            duration -> float, note length in seconds\n",
-    "    \"\"\"\n",
-    "    midi_data = pretty_midi.PrettyMIDI(midi_path)\n",
-    "    all_notes = []\n",
-    "    for instrument in midi_data.instruments:\n",
-    "        if instrument.is_drum: # Ignore drum tracks\n",
-    "            continue\n",
-    "        for note in instrument.notes:\n",
-    "            all_notes.append({\n",
-    "                \"pitch\": note.pitch,\n",
-    "                \"start\": round(note.start, 3),\n",
-    "                \"duration\": round(note.end - note.start, 3),\n",
-    "            })\n",
-    "    # Sort by onset time, then by pitch (ensures consistent ordering for chords)\n",
-    "    all_notes.sort(key=lambda n: (n[\"start\"], n[\"pitch\"]))\n",
-    "    return all_notes\n",
-    "\n",
-    "\n",
-    "def build_sample(ref_path, response_path, composer, title, metadata_row):\n",
-    "    \"\"\"\n",
-    "    Convert one score/performance MIDI pair into a sample dict\n",
-    "    ready for compare_performance_ED.\n",
-    "\n",
-    "    Args:\n",
-    "        ref_path: str, path to the score (reference) MIDI file\n",
-    "        response_path: str, path to the performance (response) MIDI file\n",
-    "        composer: str\n",
-    "        title: str\n",
-    "        metadata_row: dict, one row from metadata.csv\n",
-    "\n",
-    "    Returns:\n",
-    "        sample dict with keys: composer, title, reference, response, metadata_row\n",
-    "        Returns None if either MIDI file produces zero notes.\n",
-    "    \"\"\"\n",
-    "    score_notes = midi_file_to_notes(ref_path)\n",
-    "    perf_notes  = midi_file_to_notes(response_path)\n",
-    "\n",
-    "    # If either MIDI file has no notes, return None to indicate that this sample\n",
-    "    # should be skipped (e.g., some ASAP pieces have empty score or performance).\n",
-    "    if not score_notes or not perf_notes:\n",
-    "        return None\n",
-    "\n",
-    "    return {\n",
-    "        \"composer\": composer,\n",
-    "        \"title\": title,\n",
-    "        \"reference\": {\"notes\": score_notes},\n",
-    "        \"response\": {\"notes\": perf_notes},\n",
-    "        \"metadata_row\": metadata_row,\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the (n)ASAP dataset, the `metadata.csv` lists every score/performance pair in the dataset, check that both MIDI files exist on disk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jz7125/compareMusic/.venv/lib/python3.13/site-packages/pretty_midi/pretty_midi.py:122: RuntimeWarning: Tempo, Key or Time signature change events found on non-zero tracks.  This is not a valid type 0 or type 1 MIDI file.  Tempo, Key or Time Signature may be wrong.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Skipped 45 pairs due to exceeding max_notes limit of 8000\n",
-      "Loaded 1021 score (reference) / performance (response) pairs.\n"
-     ]
-    }
-   ],
-   "source": [
-    "def load_samples(asap_path, composer=None, max_notes=8000):\n",
-    "    \"\"\"\n",
-    "    Read the ASAP metadata CSV and return a list of sample dicts\n",
-    "    for a specific composer only.\n",
-    "\n",
-    "    Args:\n",
-    "        asap_path:  str, path to the cloned ASAP repo root\n",
-    "        composer:   str or None.\n",
-    "                    If a string (e.g. \"Bach\"), only that composer is loaded.\n",
-    "                    If None, all composers in the dataset are loaded.\n",
-    "        max_notes:  int, skip any pair where either the score or performance\n",
-    "                    has more than this many notes. Default 3000.\n",
-    "                    Set to None to disable the limit.\n",
-    "\n",
-    "    Returns:\n",
-    "        list of sample dicts (see build_sample)\n",
-    "    \"\"\"\n",
-    "    metadata_path = os.path.join(asap_path, \"metadata.csv\")\n",
-    "    samples = []\n",
-    "    skipped = 0\n",
-    "\n",
-    "    with open(metadata_path, \"r\", encoding=\"utf-8\") as csv_file:\n",
-    "        reader = csv.DictReader(csv_file)\n",
-    "\n",
-    "        for row in reader:\n",
-    "            row_composer = row.get(\"composer\", \"\").strip()\n",
-    "            # Skip rows that do not match the requested composer (if one is given)\n",
-    "            if composer is not None and row_composer != composer:\n",
-    "                continue\n",
-    "\n",
-    "            ref_path = os.path.join(asap_path, row.get(\"midi_score\", \"\").strip())\n",
-    "            response_path = os.path.join(asap_path, row.get(\"midi_performance\", \"\").strip())\n",
-    "\n",
-    "            if os.path.isfile(ref_path) and os.path.isfile(response_path):\n",
-    "                sample = build_sample(\n",
-    "                    ref_path, response_path,\n",
-    "                    row_composer,     \n",
-    "                    row.get(\"title\", \"Unknown\"),\n",
-    "                    dict(row),\n",
-    "                    )\n",
-    "\n",
-    "                if sample is not None:\n",
-    "                    ref_count  = len(sample[\"reference\"][\"notes\"])\n",
-    "                    perf_count = len(sample[\"response\"][\"notes\"])\n",
-    "                    exceeds_limit = (\n",
-    "                        max_notes is not None\n",
-    "                        and (ref_count > max_notes or perf_count > max_notes)\n",
-    "                    )\n",
-    "                    if exceeds_limit:                            \n",
-    "                        skipped = skipped + 1\n",
-    "                    else:\n",
-    "                        samples.append(sample)\n",
-    "\n",
-    "    print(f\"Skipped {skipped} pairs due to exceeding max_notes limit of {max_notes}\")\n",
-    "    print(f\"Loaded {len(samples)} score (reference) / performance (response) pairs.\")\n",
-    "    return samples\n",
-    "\n",
-    "samples = load_samples(ASAP_PATH, composer=None, max_notes=8000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Run Alignment on Each Pair\n",
-    "\n",
-    "Passing each score/performance pair through `compare_performance_ED`, where score MIDI is the reference and the performance MIDI is the response.\n",
-    "\n",
-    "**Why do we need the normalised events? - to fix the onset mismatch bug**\n",
-    "- Inside `compare_performance_ED`, `normalize_start_times` shifts the first note to t = 0, and `group_notes_into_events` groups simultaneous notes into chords. The `response_index` values stored in `event_details` refer to positions in these grouped events, not in the original flat note list. Therefore, need to reproduce these two steps here so that the correct onset and pitch for each event during ground truth comparison can be looked up.\n",
-    "- The `response_onset_offset` (the original first-note onset before normalisation) will also be recorded, and will be added back when comparing normalised pipeline onsets against the original (absolute) times stored in the ground truth TSV."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1021/1021 [09:20<00:00,  1.82it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Succeeded: 1021  Failed: 0\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "from concurrent.futures import ProcessPoolExecutor, as_completed\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from asap_worker import evaluate_one_sample\n",
-    "\n",
-    "\n",
-    "def run_alignment_on_samples_parallel(samples, max_workers=None):\n",
-    "    \"\"\"\n",
-    "    Run evaluate_one_sample on every sample using a process pool, so multiple\n",
-    "    pieces are aligned at the same time on different CPU cores.\n",
-    "\n",
-    "    Args:\n",
-    "        samples: list of sample dicts from load_samples()\n",
-    "        max_workers: int or None. None means \"all CPU cores minus one\",\n",
-    "                     leaving one core free for the OS and other apps.\n",
-    "\n",
-    "    Returns:\n",
-    "        results: list of successful result dicts\n",
-    "        failed: list of (composer, title, error_message) for pieces that failed\n",
-    "    \"\"\"\n",
-    "    if max_workers is None:\n",
-    "        max_workers = max(1, os.cpu_count() - 1)\n",
-    "\n",
-    "    results = []\n",
-    "    failed = []\n",
-    "\n",
-    "    with ProcessPoolExecutor(max_workers=max_workers) as executor:\n",
-    "        future_to_sample = {\n",
-    "            executor.submit(evaluate_one_sample, sample): sample\n",
-    "            for sample in samples\n",
-    "        }\n",
-    "\n",
-    "        for future in tqdm(as_completed(future_to_sample), total=len(samples)):\n",
-    "            sample = future_to_sample[future]\n",
-    "            result = future.result()  # exceptions are already caught inside the worker\n",
-    "\n",
-    "            if result[\"error\"] is not None:\n",
-    "                failed.append((result[\"composer\"], result[\"title\"], result[\"error\"]))\n",
-    "            else:\n",
-    "                results.append(result)\n",
-    "\n",
-    "    print(\"Succeeded:\", len(results), \" Failed:\", len(failed))\n",
-    "    return results, failed\n",
-    "\n",
-    "\n",
-    "all_results, failed_pieces = run_alignment_on_samples_parallel(samples)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Compute Precision, Recall, and F1 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**our pipeline vs GT**\n",
-    "\n",
-    "The GT has three different labels, while our pipeline has four operations:\n",
-    "\n",
-    "| Pipeline operation | GT label | Explanation |\n",
-    "|---|---|---|\n",
-    "| `match` | `paired` | Same pitch — GT still calls this `paired` |\n",
-    "| `replacement` | `paired` | Different pitch — GT still calls this `paired`, because the score note *was* played at approximately the right time |\n",
-    "| `missing` | `deletion` | Score note not found in response |\n",
-    "| `extra` | `insertion` | Response note has no score counterpart |\n",
-    "\n",
-    "**Evaluation metrics design**\n",
-    "\n",
-    "GT does not distinguish `match` from `replacement` because the alignment methods in GT are based on the temporal information. However, the cost function in our edit distance algorithm is based on the pitch correctness. \n",
-    "\n",
-    "Following Peter et al. (2023, TISMIR), label-level F1 would be applied as the main metric where TP, FP, and FN are computed separately for each label type and then summed:\n",
-    "\n",
-    "| Label | Matching key | TP | FP | FN |\n",
-    "|---|---|---|---|---|\n",
-    "| `paired` (pipeline `match` / `replacement`) | onset only (rounded to 1 d.p.) | Pipeline and ground truth agree a score note was aligned to a performance note at this onset (regardless of pitch correctness) | Pipeline predicts a pairing at this onset that the ground truth does not recognise | Ground truth expects a pairing at this onset that the pipeline fails to find |\n",
-    "| `deletion` (pipeline `missing`) | count only as no onset or pitch available for deletions | Overlap between the number of notes the pipeline judges as missing and the number the ground truth judges as missing | Pipeline predicts more missing notes than the ground truth; the excess | Ground truth expects more missing notes than the pipeline predicts; the shortfall |\n",
-    "| `insertion` (pipeline `extra`) | `(pitch, onset)` | Pipeline and ground truth agree an extra, unexpected note was played at this pitch and onset | Pipeline flags a note as extra that the ground truth does not agree with | Ground truth marks a note as extra that the pipeline fails to identify |\n",
-    "\n",
-    "- Precision: TP / (TP + FP), i.e. of all labels the pipeline predicted, how many were correct  \n",
-    "- Recall: TP / (TP + FN), i.e. of all GT labels, how many the pipeline found  \n",
-    "- F1: the harmonic mean of precision and recall "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def convert_pipeline_output(event_details, response_events_normalized, ref_events_normalized):\n",
-    "    \"\"\"\n",
-    "    Convert event_details from compare_performance_ED into a list of\n",
-    "    dicts with keys: onset, pitch, label.\n",
-    "\n",
-    "    Note: for aligned (match/replacement) chord pairs, correct_pitches /\n",
-    "    missing_pitches / extra_pitches inside event_details are now already\n",
-    "    real MIDI pitch multisets (with octave, computed by compute_chord_accuracy\n",
-    "    in compare_MIDI.py), so they are reused directly here instead of being\n",
-    "    re-derived. response_events_normalized / ref_events_normalized are still\n",
-    "    needed for onset lookups, and for the full note list of entirely-missing\n",
-    "    or entirely-extra chords (event_details only stores the chord name, not\n",
-    "    individual pitches, in those two cases).\n",
-    "\n",
-    "    Args:\n",
-    "        event_details: list of dicts from compare_performance_ED\n",
-    "        response_events_normalized: list of event dicts (real notes, grouped)\n",
-    "        ref_events_normalized: list of event dicts (real notes, grouped)\n",
-    "\n",
-    "    Returns:\n",
-    "        list of dicts with keys: onset, pitch, label\n",
-    "    \"\"\"\n",
-    "    my_pairs = []\n",
-    "\n",
-    "    for event in event_details:\n",
-    "        op = event[\"operation_type\"]\n",
-    "\n",
-    "        if event[\"event_type\"] == \"note\":\n",
-    "            if op in (\"match\", \"replacement\"):\n",
-    "                ev = response_events_normalized[event[\"response_index\"] - 1]\n",
-    "                my_pairs.append({\n",
-    "                    \"onset\": ev[\"event_start\"],\n",
-    "                    \"pitch\": ev[\"notes\"][0][\"pitch\"],\n",
-    "                    \"label\": \"paired\",\n",
-    "                })\n",
-    "            elif op == \"missing\":\n",
-    "                my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
-    "            elif op == \"extra\":\n",
-    "                ev = response_events_normalized[event[\"response_index\"] - 1]\n",
-    "                my_pairs.append({\n",
-    "                    \"onset\": ev[\"event_start\"],\n",
-    "                    \"pitch\": ev[\"notes\"][0][\"pitch\"],\n",
-    "                    \"label\": \"insertion\",\n",
-    "                })\n",
-    "\n",
-    "        elif event[\"event_type\"] == \"chord\":\n",
-    "            if op == \"missing\":\n",
-    "                # Whole chord missing -- event_details only has the chord name,\n",
-    "                # so the individual reference pitches still need to be looked up.\n",
-    "                ref_ev = ref_events_normalized[event[\"reference_index\"] - 1]\n",
-    "                for note in ref_ev[\"notes\"]:\n",
-    "                    my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
-    "\n",
-    "            elif op == \"extra\":\n",
-    "                # Whole chord extra -- same reasoning, look up response pitches.\n",
-    "                res_ev = response_events_normalized[event[\"response_index\"] - 1]\n",
-    "                for note in res_ev[\"notes\"]:\n",
-    "                    my_pairs.append({\n",
-    "                        \"onset\": res_ev[\"event_start\"],\n",
-    "                        \"pitch\": note[\"pitch\"],\n",
-    "                        \"label\": \"insertion\",\n",
-    "                    })\n",
-    "\n",
-    "            else:\n",
-    "                # Aligned chord pair (match or replacement). compare_MIDI.py's\n",
-    "                # compute_chord_accuracy already computed correct/missing/extra\n",
-    "                # pitches as real MIDI pitch multisets, so reuse them directly\n",
-    "                # instead of re-deriving with a Counter here.\n",
-    "                res_ev = response_events_normalized[event[\"response_index\"] - 1]\n",
-    "                onset = res_ev[\"event_start\"]\n",
-    "\n",
-    "                for pitch in event[\"correct_pitches\"]:\n",
-    "                    my_pairs.append({\"onset\": onset, \"pitch\": pitch, \"label\": \"paired\"})\n",
-    "\n",
-    "                for pitch in event[\"missing_pitches\"]:\n",
-    "                    my_pairs.append({\"onset\": None, \"pitch\": None, \"label\": \"deletion\"})\n",
-    "\n",
-    "                for pitch in event[\"extra_pitches\"]:\n",
-    "                    my_pairs.append({\"onset\": onset, \"pitch\": pitch, \"label\": \"insertion\"})\n",
-    "\n",
-    "    return my_pairs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import Counter\n",
-    "\n",
-    "def count_paired_matches(ground_truth, my_pairs, onset_offset):\n",
-    "    \"\"\"\n",
-    "    Count TP, FP, FN for 'paired' labels.\n",
-    "    Matching key: (pitch, onset rounded to 1 d.p.) -- pitch is now\n",
-    "    included, and Counter (multiset) is used instead of set, so that\n",
-    "    multiple notes sharing the same onset (chords) are no longer\n",
-    "    collapsed into a single entry.\n",
-    "    \"\"\"\n",
-    "    gt_counter = Counter()\n",
-    "    for row in ground_truth:\n",
-    "        if row[\"label\"] == \"paired\":\n",
-    "            key = (int(row[\"pitch\"]), round(float(row[\"onset\"]), 1))\n",
-    "            gt_counter[key] = gt_counter[key] + 1\n",
-    "\n",
-    "    my_counter = Counter()\n",
-    "    for row in my_pairs:\n",
-    "        if row[\"label\"] == \"paired\" and row[\"onset\"] is not None:\n",
-    "            absolute_onset = row[\"onset\"] + onset_offset\n",
-    "            key = (int(row[\"pitch\"]), round(float(absolute_onset), 1))\n",
-    "            my_counter[key] = my_counter[key] + 1\n",
-    "\n",
-    "    tp = 0\n",
-    "    for key in gt_counter:\n",
-    "        tp += min(gt_counter[key], my_counter[key])\n",
-    "    fp = sum((my_counter - gt_counter).values())\n",
-    "    fn = sum((gt_counter - my_counter).values())\n",
-    "    return tp, fp, fn\n",
-    "\n",
-    "\n",
-    "def count_deletion_matches(ground_truth, my_pairs):\n",
-    "    \"\"\"\n",
-    "    Count TP, FP, FN for 'deletion' labels.\n",
-    "\n",
-    "    Deletions have no onset or pitch (the note was never played), so\n",
-    "    identity-based matching is not possible. Count-based matching is used:\n",
-    "        TP = min(GT deletion count, predicted deletion count)\n",
-    "        FP = max(0, predicted count - GT count)\n",
-    "        FN = max(0, GT count - predicted count)\n",
-    "\n",
-    "    Args:\n",
-    "        ground_truth: list of dicts from load_ground_truth()\n",
-    "        my_pairs: list of dicts from convert_pipeline_output()\n",
-    "\n",
-    "    Returns:\n",
-    "        tp, fp, fn: ints\n",
-    "    \"\"\"\n",
-    "    gt_deletion_count = sum(1 for row in ground_truth if row[\"label\"] == \"deletion\")\n",
-    "    my_deletion_count = sum(1 for row in my_pairs if row[\"label\"] == \"deletion\")\n",
-    "\n",
-    "    tp = min(gt_deletion_count, my_deletion_count)\n",
-    "    fp = max(0, my_deletion_count - gt_deletion_count)\n",
-    "    fn = max(0, gt_deletion_count - my_deletion_count)\n",
-    "\n",
-    "    return tp, fp, fn\n",
-    "\n",
-    "\n",
-    "def count_insertion_matches(ground_truth, my_pairs, onset_offset):\n",
-    "    \"\"\"\n",
-    "    Count TP, FP, FN for 'insertion' labels.\n",
-    "    Same multiset fix as count_paired_matches.\n",
-    "    \"\"\"\n",
-    "    gt_counter = Counter()\n",
-    "    for row in ground_truth:\n",
-    "        if row[\"label\"] == \"insertion\":\n",
-    "            key = (int(row[\"pitch\"]), round(float(row[\"onset\"]), 1))\n",
-    "            gt_counter[key] = gt_counter[key] + 1\n",
-    "\n",
-    "    my_counter = Counter()\n",
-    "    for row in my_pairs:\n",
-    "        if row[\"label\"] == \"insertion\" and row[\"onset\"] is not None:\n",
-    "            absolute_onset = row[\"onset\"] + onset_offset\n",
-    "            key = (int(row[\"pitch\"]), round(float(absolute_onset), 1))\n",
-    "            my_counter[key] = my_counter[key] + 1\n",
-    "\n",
-    "    tp = 0\n",
-    "    for key in gt_counter:\n",
-    "        tp += min(gt_counter[key], my_counter[key])\n",
-    "    fp = sum((my_counter - gt_counter).values())\n",
-    "    fn = sum((gt_counter - my_counter).values())\n",
-    "    return tp, fp, fn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics_label_level(ground_truth, my_pairs, onset_offset=0.0):\n",
-    "    \"\"\"\n",
-    "    Compute label-level precision, recall, and F1 following Peter et al. (2023).\n",
-    "    Args:\n",
-    "        ground_truth: list of dicts from load_ground_truth()\n",
-    "        my_pairs: list of dicts from convert_pipeline_output()\n",
-    "        onset_offset: float, the first-note onset before normalisation.\n",
-    "                      Added back to convert normalised onsets to absolute time.\n",
-    "\n",
-    "    Returns:\n",
-    "        dict with keys:\n",
-    "            precision, recall, f1 -> floats (rounded to 4 d.p.)\n",
-    "            tp, fp, fn  -> ints (totals across all labels)\n",
-    "            tp_paired, fp_paired, fn_paired\n",
-    "            tp_deletion, fp_deletion, fn_deletion\n",
-    "            tp_insertion, fp_insertion, fn_insertion\n",
-    "    \"\"\"\n",
-    "    # Count TP/FP/FN separately for each label type\n",
-    "    tp_p, fp_p, fn_p = count_paired_matches(ground_truth, my_pairs, onset_offset)\n",
-    "    tp_d, fp_d, fn_d = count_deletion_matches(ground_truth, my_pairs)\n",
-    "    tp_i, fp_i, fn_i = count_insertion_matches(ground_truth, my_pairs, onset_offset)\n",
-    "\n",
-    "    # Sum across all label types for overall metrics\n",
-    "    tp = tp_p + tp_d + tp_i\n",
-    "    fp = fp_p + fp_d + fp_i\n",
-    "    fn = fn_p + fn_d + fn_i\n",
-    "\n",
-    "    # Precision: of all labels predicted, how many are correct\n",
-    "    if tp + fp > 0:\n",
-    "        precision = tp / (tp + fp)\n",
-    "    else:\n",
-    "        precision = 0.0\n",
-    "\n",
-    "    # Recall: of all GT labels, how many the pipeline found\n",
-    "    if tp + fn > 0:\n",
-    "        recall = tp / (tp + fn)\n",
-    "    else:\n",
-    "        recall = 0.0\n",
-    "\n",
-    "    # F1: harmonic mean of precision and recall\n",
-    "    if precision + recall > 0:\n",
-    "        f1 = 2 * precision * recall / (precision + recall)\n",
-    "    else:\n",
-    "        f1 = 0.0\n",
-    "\n",
-    "    return {\n",
-    "        \"precision\": round(precision, 4),\n",
-    "        \"recall\": round(recall, 4),\n",
-    "        \"f1\": round(f1, 4),\n",
-    "        \"tp\": tp, \"fp\": fp, \"fn\": fn,\n",
-    "        # Per-label breakdown -- useful for diagnosing which label type is weakest\n",
-    "        \"tp_paired\": tp_p, \"fp_paired\": fp_p, \"fn_paired\": fn_p,\n",
-    "        \"tp_deletion\": tp_d, \"fp_deletion\": fp_d, \"fn_deletion\": fn_d,\n",
-    "        \"tp_insertion\": tp_i, \"fp_insertion\": fp_i, \"fn_insertion\": fn_i,\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Evaluate against ground truth:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_one_piece(asap_path, metadata_row, event_details,\n",
-    "                       response_events_normalized, ref_events_normalized,\n",
-    "                       response_onset_offset):\n",
-    "    \"\"\"\n",
-    "    Run ground truth comparison for each score/performance pair.\n",
-    "\n",
-    "    Args:\n",
-    "        asap_path: str, path to ASAP repo root\n",
-    "        metadata_row: dict, one row from metadata.csv\n",
-    "        event_details: list from compare_performance_ED result\n",
-    "        response_events_normalized: list of event dicts (normalized + grouped)\n",
-    "        response_onset_offset: float, onset of the first response note\n",
-    "                               before normalization — added back to convert\n",
-    "                               normalised onsets to absolute times for GT comparison.\n",
-    "\n",
-    "    Returns:\n",
-    "        metrics dict, or None if the TSV file is not found\n",
-    "    \"\"\"\n",
-    "    tsv_rel = metadata_row.get(\"note_alignments\", \"\").strip()\n",
-    "    tsv_path = os.path.join(asap_path, tsv_rel)\n",
-    "\n",
-    "    if not os.path.isfile(tsv_path):\n",
-    "        return None\n",
-    "\n",
-    "    ground_truth = load_ground_truth(tsv_path)\n",
-    "    my_pairs = convert_pipeline_output(\n",
-    "        event_details, response_events_normalized, ref_events_normalized\n",
-    "    )\n",
-    "    metrics = compute_metrics_label_level(ground_truth, my_pairs, onset_offset=response_onset_offset)\n",
-    "\n",
-    "    return metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Skipped 30 piece(s) due to missing TSV file.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Piece</th>\n",
-       "      <th>Precision</th>\n",
-       "      <th>Recall</th>\n",
-       "      <th>F1</th>\n",
-       "      <th>TP</th>\n",
-       "      <th>FP</th>\n",
-       "      <th>FN</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Bach (Fugue_bwv_846)</td>\n",
-       "      <td>0.9091</td>\n",
-       "      <td>0.9257</td>\n",
-       "      <td>0.9173</td>\n",
-       "      <td>710</td>\n",
-       "      <td>71</td>\n",
-       "      <td>57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9274</td>\n",
-       "      <td>0.9281</td>\n",
-       "      <td>0.9278</td>\n",
-       "      <td>1355</td>\n",
-       "      <td>106</td>\n",
-       "      <td>105</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9440</td>\n",
-       "      <td>0.9414</td>\n",
-       "      <td>0.9427</td>\n",
-       "      <td>1365</td>\n",
-       "      <td>81</td>\n",
-       "      <td>85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9544</td>\n",
-       "      <td>0.9511</td>\n",
-       "      <td>0.9527</td>\n",
-       "      <td>1380</td>\n",
-       "      <td>66</td>\n",
-       "      <td>71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9336</td>\n",
-       "      <td>0.9374</td>\n",
-       "      <td>0.9355</td>\n",
-       "      <td>1363</td>\n",
-       "      <td>97</td>\n",
-       "      <td>91</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9580</td>\n",
-       "      <td>0.9553</td>\n",
-       "      <td>0.9566</td>\n",
-       "      <td>1390</td>\n",
-       "      <td>61</td>\n",
-       "      <td>65</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9533</td>\n",
-       "      <td>0.9520</td>\n",
-       "      <td>0.9526</td>\n",
-       "      <td>1388</td>\n",
-       "      <td>68</td>\n",
-       "      <td>70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9204</td>\n",
-       "      <td>0.9260</td>\n",
-       "      <td>0.9232</td>\n",
-       "      <td>1352</td>\n",
-       "      <td>117</td>\n",
-       "      <td>108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Bach (Fugue_bwv_848)</td>\n",
-       "      <td>0.9203</td>\n",
-       "      <td>0.9291</td>\n",
-       "      <td>0.9246</td>\n",
-       "      <td>1362</td>\n",
-       "      <td>118</td>\n",
-       "      <td>104</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Bach (Fugue_bwv_854)</td>\n",
-       "      <td>0.9367</td>\n",
-       "      <td>0.9379</td>\n",
-       "      <td>0.9373</td>\n",
-       "      <td>695</td>\n",
-       "      <td>47</td>\n",
-       "      <td>46</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  Piece  Precision  Recall      F1    TP   FP   FN\n",
-       "0  Bach (Fugue_bwv_846)     0.9091  0.9257  0.9173   710   71   57\n",
-       "1  Bach (Fugue_bwv_848)     0.9274  0.9281  0.9278  1355  106  105\n",
-       "2  Bach (Fugue_bwv_848)     0.9440  0.9414  0.9427  1365   81   85\n",
-       "3  Bach (Fugue_bwv_848)     0.9544  0.9511  0.9527  1380   66   71\n",
-       "4  Bach (Fugue_bwv_848)     0.9336  0.9374  0.9355  1363   97   91\n",
-       "5  Bach (Fugue_bwv_848)     0.9580  0.9553  0.9566  1390   61   65\n",
-       "6  Bach (Fugue_bwv_848)     0.9533  0.9520  0.9526  1388   68   70\n",
-       "7  Bach (Fugue_bwv_848)     0.9204  0.9260  0.9232  1352  117  108\n",
-       "8  Bach (Fugue_bwv_848)     0.9203  0.9291  0.9246  1362  118  104\n",
-       "9  Bach (Fugue_bwv_854)     0.9367  0.9379  0.9373   695   47   46"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Precision: 0.8391\n",
-      "Mean Recall : 0.8628\n",
-      "Mean F1 : 0.8502\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "eval_rows = []\n",
-    "skipped_count = 0\n",
-    "\n",
-    "for result in all_results:\n",
-    "    metrics = evaluate_one_piece(\n",
-    "        ASAP_PATH,\n",
-    "        result[\"metadata_row\"],\n",
-    "        result[\"event_details\"],\n",
-    "        result[\"response_events_normalized\"],\n",
-    "        result[\"ref_events_normalized\"],       \n",
-    "        result[\"response_onset_offset\"],\n",
-    "    )\n",
-    "    if metrics is not None:\n",
-    "        eval_rows.append({\n",
-    "            \"Piece\": result[\"composer\"] + \" (\" + result[\"title\"] + \")\",\n",
-    "            \"Precision\": metrics[\"precision\"],\n",
-    "            \"Recall\": metrics[\"recall\"],\n",
-    "            \"F1\": metrics[\"f1\"],\n",
-    "            \"TP\": metrics[\"tp\"],\n",
-    "            \"FP\": metrics[\"fp\"],\n",
-    "            \"FN\": metrics[\"fn\"],\n",
-    "        })\n",
-    "    else:\n",
-    "        skipped_count += 1\n",
-    "\n",
-    "print(f\"Skipped {skipped_count} piece(s) due to missing TSV file.\")\n",
-    "\n",
-    "df_eval = pd.DataFrame(eval_rows)\n",
-    "display(df_eval.head(10))\n",
-    "\n",
-    "print(\"Mean Precision:\", round(df_eval[\"Precision\"].mean(), 4))\n",
-    "print(\"Mean Recall :\", round(df_eval[\"Recall\"].mean(), 4))\n",
-    "print(\"Mean F1 :\", round(df_eval[\"F1\"].mean(), 4))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Composer</th>\n",
-       "      <th>Title</th>\n",
-       "      <th>Performance</th>\n",
-       "      <th>Precision</th>\n",
-       "      <th>Recall</th>\n",
-       "      <th>F1</th>\n",
-       "      <th>TP</th>\n",
-       "      <th>FP</th>\n",
-       "      <th>FN</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>666</th>\n",
-       "      <td>Chopin</td>\n",
-       "      <td>Scherzos_20</td>\n",
-       "      <td>Chopin/Scherzos/20/Wong04M.mid</td>\n",
-       "      <td>0.3195</td>\n",
-       "      <td>0.1892</td>\n",
-       "      <td>0.2377</td>\n",
-       "      <td>1606</td>\n",
-       "      <td>3420</td>\n",
-       "      <td>6882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>983</th>\n",
-       "      <td>Scriabin</td>\n",
-       "      <td>Sonatas_5</td>\n",
-       "      <td>Scriabin/Sonatas/5/ChernovA06M.mid</td>\n",
-       "      <td>0.3917</td>\n",
-       "      <td>0.2573</td>\n",
-       "      <td>0.3106</td>\n",
-       "      <td>3548</td>\n",
-       "      <td>5509</td>\n",
-       "      <td>10241</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>442</th>\n",
-       "      <td>Chopin</td>\n",
-       "      <td>Ballades_1</td>\n",
-       "      <td>Chopin/Ballades/1/MunA19M.mid</td>\n",
-       "      <td>0.4603</td>\n",
-       "      <td>0.3122</td>\n",
-       "      <td>0.3721</td>\n",
-       "      <td>2591</td>\n",
-       "      <td>3038</td>\n",
-       "      <td>5708</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>382</th>\n",
-       "      <td>Beethoven</td>\n",
-       "      <td>Piano_Sonatas_31-3_4</td>\n",
-       "      <td>Beethoven/Piano_Sonatas/31-3_4/HuangSW10M.mid</td>\n",
-       "      <td>0.5562</td>\n",
-       "      <td>0.4399</td>\n",
-       "      <td>0.4913</td>\n",
-       "      <td>2307</td>\n",
-       "      <td>1841</td>\n",
-       "      <td>2937</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>692</th>\n",
-       "      <td>Chopin</td>\n",
-       "      <td>Sonata_2_3rd</td>\n",
-       "      <td>Chopin/Sonata_2/3rd_extra_repeat/KaszoS15.mid</td>\n",
-       "      <td>0.4391</td>\n",
-       "      <td>0.5860</td>\n",
-       "      <td>0.5021</td>\n",
-       "      <td>1465</td>\n",
-       "      <td>1871</td>\n",
-       "      <td>1035</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>984</th>\n",
-       "      <td>Scriabin</td>\n",
-       "      <td>Sonatas_5</td>\n",
-       "      <td>Scriabin/Sonatas/5/Ko07M.mid</td>\n",
-       "      <td>0.5720</td>\n",
-       "      <td>0.4490</td>\n",
-       "      <td>0.5031</td>\n",
-       "      <td>5588</td>\n",
-       "      <td>4182</td>\n",
-       "      <td>6857</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>932</th>\n",
-       "      <td>Schubert</td>\n",
-       "      <td>Impromptu_op142_3</td>\n",
-       "      <td>Schubert/Impromptu_op142/3/SunY08M.mid</td>\n",
-       "      <td>0.5796</td>\n",
-       "      <td>0.4831</td>\n",
-       "      <td>0.5270</td>\n",
-       "      <td>4123</td>\n",
-       "      <td>2990</td>\n",
-       "      <td>4411</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>Beethoven</td>\n",
-       "      <td>Piano_Sonatas_3-1</td>\n",
-       "      <td>Beethoven/Piano_Sonatas/3-1/LUO01.mid</td>\n",
-       "      <td>0.6166</td>\n",
-       "      <td>0.4715</td>\n",
-       "      <td>0.5344</td>\n",
-       "      <td>2964</td>\n",
-       "      <td>1843</td>\n",
-       "      <td>3322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>985</th>\n",
-       "      <td>Scriabin</td>\n",
-       "      <td>Sonatas_5</td>\n",
-       "      <td>Scriabin/Sonatas/5/FALIKS06.mid</td>\n",
-       "      <td>0.6252</td>\n",
-       "      <td>0.5111</td>\n",
-       "      <td>0.5624</td>\n",
-       "      <td>5756</td>\n",
-       "      <td>3451</td>\n",
-       "      <td>5505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>604</th>\n",
-       "      <td>Chopin</td>\n",
-       "      <td>Etudes_op_25_1</td>\n",
-       "      <td>Chopin/Etudes_op_25/1/TongB02M.mid</td>\n",
-       "      <td>0.5111</td>\n",
-       "      <td>0.6564</td>\n",
-       "      <td>0.5747</td>\n",
-       "      <td>1471</td>\n",
-       "      <td>1407</td>\n",
-       "      <td>770</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Composer                 Title  \\\n",
-       "666     Chopin           Scherzos_20   \n",
-       "983   Scriabin             Sonatas_5   \n",
-       "442     Chopin            Ballades_1   \n",
-       "382  Beethoven  Piano_Sonatas_31-3_4   \n",
-       "692     Chopin          Sonata_2_3rd   \n",
-       "984   Scriabin             Sonatas_5   \n",
-       "932   Schubert     Impromptu_op142_3   \n",
-       "340  Beethoven     Piano_Sonatas_3-1   \n",
-       "985   Scriabin             Sonatas_5   \n",
-       "604     Chopin        Etudes_op_25_1   \n",
-       "\n",
-       "                                       Performance  Precision  Recall      F1  \\\n",
-       "666                 Chopin/Scherzos/20/Wong04M.mid     0.3195  0.1892  0.2377   \n",
-       "983             Scriabin/Sonatas/5/ChernovA06M.mid     0.3917  0.2573  0.3106   \n",
-       "442                  Chopin/Ballades/1/MunA19M.mid     0.4603  0.3122  0.3721   \n",
-       "382  Beethoven/Piano_Sonatas/31-3_4/HuangSW10M.mid     0.5562  0.4399  0.4913   \n",
-       "692  Chopin/Sonata_2/3rd_extra_repeat/KaszoS15.mid     0.4391  0.5860  0.5021   \n",
-       "984                   Scriabin/Sonatas/5/Ko07M.mid     0.5720  0.4490  0.5031   \n",
-       "932         Schubert/Impromptu_op142/3/SunY08M.mid     0.5796  0.4831  0.5270   \n",
-       "340          Beethoven/Piano_Sonatas/3-1/LUO01.mid     0.6166  0.4715  0.5344   \n",
-       "985                Scriabin/Sonatas/5/FALIKS06.mid     0.6252  0.5111  0.5624   \n",
-       "604             Chopin/Etudes_op_25/1/TongB02M.mid     0.5111  0.6564  0.5747   \n",
-       "\n",
-       "       TP    FP     FN  \n",
-       "666  1606  3420   6882  \n",
-       "983  3548  5509  10241  \n",
-       "442  2591  3038   5708  \n",
-       "382  2307  1841   2937  \n",
-       "692  1465  1871   1035  \n",
-       "984  5588  4182   6857  \n",
-       "932  4123  2990   4411  \n",
-       "340  2964  1843   3322  \n",
-       "985  5756  3451   5505  \n",
-       "604  1471  1407    770  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "eval_rows_detailed = []\n",
-    "\n",
-    "for result in all_results:\n",
-    "    metrics = evaluate_one_piece(\n",
-    "        ASAP_PATH,\n",
-    "        result[\"metadata_row\"],\n",
-    "        result[\"event_details\"],\n",
-    "        result[\"response_events_normalized\"],\n",
-    "        result[\"ref_events_normalized\"],\n",
-    "        result[\"response_onset_offset\"],\n",
-    "    )\n",
-    "    if metrics is not None:\n",
-    "        eval_rows_detailed.append({\n",
-    "            \"Composer\": result[\"composer\"],\n",
-    "            \"Title\": result[\"title\"],\n",
-    "            \"Performance\": result[\"metadata_row\"].get(\"midi_performance\", \"\"),\n",
-    "            \"Precision\": metrics[\"precision\"],\n",
-    "            \"Recall\": metrics[\"recall\"],\n",
-    "            \"F1\": metrics[\"f1\"],\n",
-    "            \"TP\": metrics[\"tp\"],\n",
-    "            \"FP\": metrics[\"fp\"],\n",
-    "            \"FN\": metrics[\"fn\"],\n",
-    "            \"metrics\": metrics,   # full dict, includes per-label (paired/deletion/insertion) breakdown\n",
-    "            \"result\": result,     # full result dict, for event-level drill-down later\n",
-    "        })\n",
-    "\n",
-    "df_detailed = pd.DataFrame(eval_rows_detailed)\n",
-    "\n",
-    "# Sort by F1 ascending -- worst performances first\n",
-    "worst_n = 10\n",
-    "df_worst = df_detailed.sort_values(\"F1\").head(worst_n)\n",
-    "display(df_worst[[\"Composer\", \"Title\", \"Performance\", \"Precision\", \"Recall\", \"F1\", \"TP\", \"FP\", \"FN\"]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Skipped 30 piece(s) due to missing TSV file.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Add ResultIndex so each row in df_eval can be traced back to all_results\n",
-    "eval_rows = []\n",
-    "skipped_count = 0\n",
-    "\n",
-    "for idx, result in enumerate(all_results):\n",
-    "    metrics = evaluate_one_piece(\n",
-    "        ASAP_PATH,\n",
-    "        result[\"metadata_row\"],\n",
-    "        result[\"event_details\"],\n",
-    "        result[\"response_events_normalized\"],\n",
-    "        result[\"ref_events_normalized\"],\n",
-    "        result[\"response_onset_offset\"],\n",
-    "    )\n",
-    "    if metrics is not None:\n",
-    "        eval_rows.append({\n",
-    "            \"Piece\": result[\"composer\"] + \" (\" + result[\"title\"] + \")\",\n",
-    "            \"Precision\": metrics[\"precision\"],\n",
-    "            \"Recall\": metrics[\"recall\"],\n",
-    "            \"F1\": metrics[\"f1\"],\n",
-    "            \"TP\": metrics[\"tp\"], \"FP\": metrics[\"fp\"], \"FN\": metrics[\"fn\"],\n",
-    "            \"ResultIndex\": idx,   # NEW -- lets us fetch all_results[idx] later\n",
-    "        })\n",
-    "    else:\n",
-    "        skipped_count = skipped_count + 1\n",
-    "\n",
-    "df_eval = pd.DataFrame(eval_rows)\n",
-    "print(f\"Skipped {skipped_count} piece(s) due to missing TSV file.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Piece</th>\n",
-       "      <th>Precision</th>\n",
-       "      <th>Recall</th>\n",
-       "      <th>F1</th>\n",
-       "      <th>TP</th>\n",
-       "      <th>FP</th>\n",
-       "      <th>FN</th>\n",
-       "      <th>ResultIndex</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>666</th>\n",
-       "      <td>Chopin (Scherzos_20)</td>\n",
-       "      <td>0.3195</td>\n",
-       "      <td>0.1892</td>\n",
-       "      <td>0.2377</td>\n",
-       "      <td>1606</td>\n",
-       "      <td>3420</td>\n",
-       "      <td>6882</td>\n",
-       "      <td>667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>983</th>\n",
-       "      <td>Scriabin (Sonatas_5)</td>\n",
-       "      <td>0.3917</td>\n",
-       "      <td>0.2573</td>\n",
-       "      <td>0.3106</td>\n",
-       "      <td>3548</td>\n",
-       "      <td>5509</td>\n",
-       "      <td>10241</td>\n",
-       "      <td>1013</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>442</th>\n",
-       "      <td>Chopin (Ballades_1)</td>\n",
-       "      <td>0.4603</td>\n",
-       "      <td>0.3122</td>\n",
-       "      <td>0.3721</td>\n",
-       "      <td>2591</td>\n",
-       "      <td>3038</td>\n",
-       "      <td>5708</td>\n",
-       "      <td>443</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>382</th>\n",
-       "      <td>Beethoven (Piano_Sonatas_31-3_4)</td>\n",
-       "      <td>0.5562</td>\n",
-       "      <td>0.4399</td>\n",
-       "      <td>0.4913</td>\n",
-       "      <td>2307</td>\n",
-       "      <td>1841</td>\n",
-       "      <td>2937</td>\n",
-       "      <td>383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>692</th>\n",
-       "      <td>Chopin (Sonata_2_3rd)</td>\n",
-       "      <td>0.4391</td>\n",
-       "      <td>0.5860</td>\n",
-       "      <td>0.5021</td>\n",
-       "      <td>1465</td>\n",
-       "      <td>1871</td>\n",
-       "      <td>1035</td>\n",
-       "      <td>693</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                Piece  Precision  Recall      F1    TP    FP  \\\n",
-       "666              Chopin (Scherzos_20)     0.3195  0.1892  0.2377  1606  3420   \n",
-       "983              Scriabin (Sonatas_5)     0.3917  0.2573  0.3106  3548  5509   \n",
-       "442               Chopin (Ballades_1)     0.4603  0.3122  0.3721  2591  3038   \n",
-       "382  Beethoven (Piano_Sonatas_31-3_4)     0.5562  0.4399  0.4913  2307  1841   \n",
-       "692             Chopin (Sonata_2_3rd)     0.4391  0.5860  0.5021  1465  1871   \n",
-       "\n",
-       "        FN  ResultIndex  \n",
-       "666   6882          667  \n",
-       "983  10241         1013  \n",
-       "442   5708          443  \n",
-       "382   2937          383  \n",
-       "692   1035          693  "
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "N_WORST = 5\n",
-    "worst_df = df_eval.sort_values(\"F1\").head(N_WORST)\n",
-    "worst_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "for _, row in worst_df.iterrows():\n",
-    "    result = all_results[row[\"ResultIndex\"]]\n",
-    "    onset_offset = result[\"response_onset_offset\"]\n",
-    "\n",
-    "    # ---- Reload ground truth and my_pairs for this piece ----\n",
-    "    tsv_path = os.path.join(ASAP_PATH, result[\"metadata_row\"].get(\"note_alignments\", \"\").strip())\n",
-    "    ground_truth = load_ground_truth(tsv_path)\n",
-    "    my_pairs = convert_pipeline_output(\n",
-    "        result[\"event_details\"],\n",
-    "        result[\"response_events_normalized\"],\n",
-    "        result[\"ref_events_normalized\"],\n",
-    "    )\n",
-    "\n",
-    "    # ---- Build GT lookup counters for tagging TP vs FP ----\n",
-    "    gt_paired = Counter(\n",
-    "        (int(r[\"pitch\"]), round(float(r[\"onset\"]), 1))\n",
-    "        for r in ground_truth if r[\"label\"] == \"paired\"\n",
-    "    )\n",
-    "    gt_insertion = Counter(\n",
-    "        (int(r[\"pitch\"]), round(float(r[\"onset\"]), 1))\n",
-    "        for r in ground_truth if r[\"label\"] == \"insertion\"\n",
-    "    )\n",
-    "\n",
-    "    # ---- Tag each of my paired/insertion notes as TP or FP ----\n",
-    "    seen_paired = Counter()\n",
-    "    seen_insertion = Counter()\n",
-    "    tp_x, tp_y = [], []\n",
-    "    fp_x, fp_y = [], []\n",
-    "\n",
-    "    for pair in my_pairs:\n",
-    "        if pair[\"onset\"] is None:\n",
-    "            continue  # deletions have no note position to plot\n",
-    "        absolute_onset = pair[\"onset\"] + onset_offset\n",
-    "        key = (int(pair[\"pitch\"]), round(float(absolute_onset), 1))\n",
-    "\n",
-    "        if pair[\"label\"] == \"paired\":\n",
-    "            seen_paired[key] = seen_paired[key] + 1\n",
-    "            is_tp = seen_paired[key] <= gt_paired[key]\n",
-    "        else:\n",
-    "            seen_insertion[key] = seen_insertion[key] + 1\n",
-    "            is_tp = seen_insertion[key] <= gt_insertion[key]\n",
-    "\n",
-    "        if is_tp:\n",
-    "            tp_x.append(absolute_onset); tp_y.append(pair[\"pitch\"])\n",
-    "        else:\n",
-    "            fp_x.append(absolute_onset); fp_y.append(pair[\"pitch\"])\n",
-    "\n",
-    "    # ---- Find FN: GT paired/insertion notes my pipeline never produced ----\n",
-    "    my_paired_counter = Counter(\n",
-    "        (int(p[\"pitch\"]), round(float(p[\"onset\"] + onset_offset), 1))\n",
-    "        for p in my_pairs if p[\"label\"] == \"paired\" and p[\"onset\"] is not None\n",
-    "    )\n",
-    "    my_insertion_counter = Counter(\n",
-    "        (int(p[\"pitch\"]), round(float(p[\"onset\"] + onset_offset), 1))\n",
-    "        for p in my_pairs if p[\"label\"] == \"insertion\" and p[\"onset\"] is not None\n",
-    "    )\n",
-    "    fn_x, fn_y = [], []\n",
-    "    for r in ground_truth:\n",
-    "        if r[\"label\"] in (\"paired\", \"insertion\") and r[\"onset\"] is not None:\n",
-    "            key = (int(r[\"pitch\"]), round(float(r[\"onset\"]), 1))\n",
-    "            found = my_paired_counter[key] + my_insertion_counter[key]\n",
-    "            expected = gt_paired[key] + gt_insertion[key] if r[\"label\"] == \"paired\" else gt_insertion[key]\n",
-    "            # crude check: if GT count for this key exceeds what I produced, it's an FN instance\n",
-    "            if found < (gt_paired[key] if r[\"label\"] == \"paired\" else gt_insertion[key]):\n",
-    "                fn_x.append(r[\"onset\"]); fn_y.append(r[\"pitch\"])\n",
-    "\n",
-    "    # ---- Plot: piano roll of reference vs response, overlaid with TP/FP/FN ----\n",
-    "    fig, ax = plt.subplots(figsize=(14, 6))\n",
-    "\n",
-    "    for ev in result[\"ref_events_normalized\"]:\n",
-    "        for note in ev[\"notes\"]:\n",
-    "            ax.barh(note[\"pitch\"], note[\"duration\"], left=note[\"start\"] + onset_offset,\n",
-    "                    height=0.8, color=\"lightgray\", alpha=0.6)\n",
-    "\n",
-    "    ax.scatter(tp_x, tp_y, color=\"tab:green\", s=20, label=\"TP (agrees with GT)\")\n",
-    "    ax.scatter(fp_x, fp_y, color=\"tab:orange\", s=20, label=\"FP (I predicted, GT disagrees)\")\n",
-    "    ax.scatter(fn_x, fn_y, color=\"tab:red\", marker=\"x\", s=40, label=\"FN (GT expects, I missed)\")\n",
-    "\n",
-    "    ax.set_title(row[\"Piece\"] + f\"  (F1={row['F1']:.3f}, P={row['Precision']:.3f}, R={row['Recall']:.3f})\")\n",
-    "    ax.set_xlabel(\"Time (s, absolute)\")\n",
-    "    ax.set_ylabel(\"MIDI Pitch\")\n",
-    "    ax.legend(loc=\"upper right\", fontsize=8)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2500x500 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(worst_df), figsize=(5 * len(worst_df), 5))\n",
-    "\n",
-    "for ax, (_, row) in zip(axes, worst_df.iterrows()):\n",
-    "    result = all_results[row[\"ResultIndex\"]]\n",
-    "\n",
-    "    ref_idx_list = []\n",
-    "    res_idx_list = []\n",
-    "    for op in result[\"event_details\"]:\n",
-    "        if op[\"reference_index\"] is not None and op[\"response_index\"] is not None:\n",
-    "            ref_idx_list.append(op[\"reference_index\"])\n",
-    "            res_idx_list.append(op[\"response_index\"])\n",
-    "\n",
-    "    ax.plot(ref_idx_list, res_idx_list, \".\", markersize=1.5)\n",
-    "\n",
-    "    max_idx = max(max(ref_idx_list), max(res_idx_list))\n",
-    "    ax.plot([0, max_idx], [0, max_idx], \"r--\", alpha=0.3, linewidth=1)\n",
-    "\n",
-    "    ax.set_title(row[\"Piece\"] + f\"\\nF1={row['F1']:.3f}\", fontsize=10)\n",
-    "    ax.set_xlabel(\"Reference event index\")\n",
-    "    ax.set_ylabel(\"Response event index\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step is centered around reference index: 290\n",
-      "\n",
-      "--- Reference events around the step ---\n",
-      "ref[275]  start=186.283  type=chord  pitches=[51, 55, 58, 61]\n",
-      "ref[276]  start=186.883  type=chord  pitches=[51, 55, 58, 61]\n",
-      "ref[277]  start=187.483  type=chord  pitches=[51, 55, 58, 61, 67]\n",
-      "ref[278]  start=188.083  type=chord  pitches=[52, 55, 58, 61, 70]\n",
-      "ref[279]  start=188.683  type=chord  pitches=[52, 55, 58, 61]\n",
-      "ref[280]  start=189.283  type=chord  pitches=[52, 55, 58, 61]\n",
-      "ref[281]  start=189.883  type=chord  pitches=[51, 55, 58, 61, 63]\n",
-      "ref[282]  start=190.483  type=chord  pitches=[51, 55, 58, 61, 63]\n",
-      "ref[283]  start=191.083  type=chord  pitches=[51, 55, 58, 61, 63]\n",
-      "ref[284]  start=191.683  type=chord  pitches=[56, 59, 63, 68]\n",
-      "ref[285]  start=193.483  type=chord  pitches=[35, 47, 59]\n",
-      "ref[286]  start=195.283  type=chord  pitches=[34, 46, 58]\n",
-      "ref[287]  start=197.083  type=chord  pitches=[39, 51, 63]\n",
-      "ref[288]  start=198.883  type=chord  pitches=[32, 44, 56]\n",
-      "ref[289]  start=200.683  type=chord  pitches=[32, 44, 56]\n",
-      "ref[290]  start=202.483  type=chord  pitches=[32, 44, 56]\n",
-      "ref[291]  start=206.083  type=note  pitches=[56]\n",
-      "ref[292]  start=206.745  type=note  pitches=[61]\n",
-      "ref[293]  start=207.407  type=note  pitches=[58]\n",
-      "ref[294]  start=208.069  type=note  pitches=[63]\n",
-      "ref[295]  start=208.730  type=note  pitches=[60]\n",
-      "ref[296]  start=209.392  type=note  pitches=[65]\n",
-      "ref[297]  start=210.054  type=note  pitches=[63]\n",
-      "ref[298]  start=210.495  type=note  pitches=[61]\n",
-      "ref[299]  start=210.716  type=note  pitches=[60]\n",
-      "ref[300]  start=211.377  type=note  pitches=[63]\n",
-      "ref[301]  start=211.598  type=note  pitches=[56]\n",
-      "ref[302]  start=211.819  type=note  pitches=[55]\n",
-      "ref[303]  start=212.039  type=chord  pitches=[53, 68]\n",
-      "ref[304]  start=212.260  type=note  pitches=[55]\n",
-      "\n",
-      "--- Response events around the same time ---\n",
-      "start=200.930  type=chord  pitches=[59, 35, 47]\n",
-      "start=202.503  type=chord  pitches=[58, 34, 46]\n",
-      "start=203.988  type=note  pitches=[63]\n",
-      "start=204.040  type=chord  pitches=[51, 39]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "# Beethoven Piano_Sonatas_31-3_4 has a very clean single step around ref index ~700-900\n",
-    "result = all_results[383]  # ResultIndex for Beethoven from worst_df\n",
-    "\n",
-    "# Find the reference-index range where the alignment path deviates most\n",
-    "# from a straight line, so we zoom into the step itself.\n",
-    "ref_idx_list = []\n",
-    "res_idx_list = []\n",
-    "for op in result[\"event_details\"]:\n",
-    "    if op[\"reference_index\"] is not None and op[\"response_index\"] is not None:\n",
-    "        ref_idx_list.append(op[\"reference_index\"])\n",
-    "        res_idx_list.append(op[\"response_index\"])\n",
-    "\n",
-    "ref_arr = np.array(ref_idx_list)\n",
-    "res_arr = np.array(res_idx_list)\n",
-    "\n",
-    "# Rough diagonal for comparison, scaled to the same total range\n",
-    "diagonal = ref_arr * (res_arr[-1] / ref_arr[-1])\n",
-    "deviation = res_arr - diagonal\n",
-    "\n",
-    "# The reference index where response gets furthest ahead of \"ideal\" pace\n",
-    "step_center = ref_arr[np.argmax(deviation)]\n",
-    "print(\"Step is centered around reference index:\", step_center)\n",
-    "\n",
-    "# Zoom into a small window around that step and print the real notes\n",
-    "window = 15\n",
-    "ref_events = result[\"ref_events_normalized\"]\n",
-    "res_events = result[\"response_events_normalized\"]\n",
-    "\n",
-    "print(\"\\n--- Reference events around the step ---\")\n",
-    "for i in range(max(0, step_center - window), min(len(ref_events), step_center + window)):\n",
-    "    ev = ref_events[i]\n",
-    "    pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
-    "    print(f\"ref[{i}]  start={ev['event_start']:.3f}  type={ev['event_type']}  pitches={pitches}\")\n",
-    "\n",
-    "print(\"\\n--- Response events around the same time ---\")\n",
-    "ref_time = ref_events[step_center][\"event_start\"]\n",
-    "for ev in res_events:\n",
-    "    if abs(ev[\"event_start\"] - ref_time) < 2.0:  # +/- 2 seconds window\n",
-    "        pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
-    "        print(f\"start={ev['event_start']:.3f}  type={ev['event_type']}  pitches={pitches}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Operation type breakdown for this piece:\n",
-      "  match: 1494  (76.6%)\n",
-      "  replacement: 196  (10.0%)\n",
-      "  extra: 153  (7.8%)\n",
-      "  missing: 108  (5.5%)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from collections import Counter\n",
-    "\n",
-    "result = all_results[383]  # Beethoven, from worst_df\n",
-    "\n",
-    "op_counts = Counter(ev[\"operation_type\"] for ev in result[\"event_details\"])\n",
-    "total = sum(op_counts.values())\n",
-    "\n",
-    "print(\"Operation type breakdown for this piece:\")\n",
-    "for op_type, count in op_counts.most_common():\n",
-    "    print(f\"  {op_type}: {count}  ({100 * count / total:.1f}%)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--- Reference events in 105-132s ---\n",
-      "ref[138]  start=105.133  type=note  pitches=[71]\n",
-      "ref[139]  start=105.283  type=chord  pitches=[51, 55, 58, 70]\n",
-      "ref[140]  start=105.883  type=chord  pitches=[51, 55, 58]\n",
-      "ref[141]  start=106.483  type=chord  pitches=[51, 55, 58]\n",
-      "ref[142]  start=107.083  type=chord  pitches=[49, 51, 55, 58]\n",
-      "ref[143]  start=107.683  type=chord  pitches=[47, 51, 56, 71]\n",
-      "ref[144]  start=108.283  type=chord  pitches=[46, 51, 55, 73]\n",
-      "ref[145]  start=108.883  type=chord  pitches=[44, 51, 56, 71]\n",
-      "ref[146]  start=109.483  type=chord  pitches=[44, 47, 51]\n",
-      "ref[147]  start=110.083  type=chord  pitches=[44, 47, 51]\n",
-      "ref[148]  start=110.683  type=chord  pitches=[44, 47, 51]\n",
-      "ref[149]  start=111.283  type=chord  pitches=[44, 47, 51]\n",
-      "ref[150]  start=111.883  type=chord  pitches=[44, 47, 51]\n",
-      "ref[151]  start=112.483  type=chord  pitches=[42, 47, 52, 73]\n",
-      "ref[152]  start=113.083  type=chord  pitches=[42, 47, 52]\n",
-      "ref[153]  start=113.683  type=chord  pitches=[42, 47, 52]\n",
-      "ref[154]  start=114.283  type=chord  pitches=[42, 46, 52]\n",
-      "ref[155]  start=114.883  type=chord  pitches=[42, 46, 52]\n",
-      "ref[156]  start=115.483  type=chord  pitches=[42, 46, 52]\n",
-      "ref[157]  start=116.083  type=chord  pitches=[42, 47, 51, 75]\n",
-      "ref[158]  start=116.683  type=chord  pitches=[42, 47, 51]\n",
-      "ref[159]  start=117.283  type=chord  pitches=[42, 47, 51]\n",
-      "ref[160]  start=117.883  type=chord  pitches=[47, 54, 59]\n",
-      "ref[161]  start=118.483  type=chord  pitches=[47, 54, 59]\n",
-      "ref[162]  start=119.083  type=chord  pitches=[47, 54, 59]\n",
-      "ref[163]  start=119.683  type=chord  pitches=[49, 52, 59, 76]\n",
-      "ref[164]  start=120.283  type=chord  pitches=[49, 52, 59]\n",
-      "ref[165]  start=120.883  type=chord  pitches=[49, 52, 59]\n",
-      "ref[166]  start=121.483  type=chord  pitches=[49, 52, 58, 80]\n",
-      "ref[167]  start=122.083  type=chord  pitches=[49, 52, 58]\n",
-      "ref[168]  start=122.683  type=chord  pitches=[49, 52, 58]\n",
-      "ref[169]  start=123.283  type=chord  pitches=[51, 54, 59, 78]\n",
-      "ref[170]  start=123.883  type=chord  pitches=[51, 54, 59]\n",
-      "ref[171]  start=124.483  type=chord  pitches=[51, 54, 59, 83]\n",
-      "ref[172]  start=125.083  type=chord  pitches=[52, 59, 64, 80]\n",
-      "ref[173]  start=125.683  type=chord  pitches=[52, 59, 64]\n",
-      "ref[174]  start=126.283  type=chord  pitches=[52, 59, 64, 92]\n",
-      "ref[175]  start=126.883  type=chord  pitches=[54, 59, 63]\n",
-      "ref[176]  start=127.483  type=chord  pitches=[54, 59, 63]\n",
-      "ref[177]  start=128.083  type=chord  pitches=[54, 59, 63, 90]\n",
-      "ref[178]  start=128.683  type=chord  pitches=[54, 58, 61, 64]\n",
-      "ref[179]  start=129.283  type=chord  pitches=[54, 58, 61, 64, 87]\n",
-      "ref[180]  start=129.883  type=chord  pitches=[54, 58, 61, 64, 88]\n",
-      "ref[181]  start=130.183  type=note  pitches=[82]\n",
-      "ref[182]  start=130.483  type=chord  pitches=[55, 58, 61, 64, 85]\n",
-      "ref[183]  start=131.083  type=chord  pitches=[55, 58, 61, 64]\n",
-      "ref[184]  start=131.683  type=chord  pitches=[55, 58, 61, 64]\n",
-      "\n",
-      "--- Response events in 105-132s ---\n",
-      "res[117]  start=105.139  type=chord  pitches=[44, 47, 51]\n",
-      "res[118]  start=105.443  type=note  pitches=[75]\n",
-      "res[119]  start=106.060  type=chord  pitches=[47, 51, 44]\n",
-      "res[120]  start=106.486  type=chord  pitches=[47, 51, 44]\n",
-      "res[121]  start=106.889  type=chord  pitches=[47, 51]\n",
-      "res[122]  start=106.940  type=note  pitches=[44]\n",
-      "res[123]  start=107.328  type=chord  pitches=[44, 47, 51]\n",
-      "res[124]  start=107.828  type=chord  pitches=[47, 44, 51]\n",
-      "res[125]  start=107.936  type=note  pitches=[73]\n",
-      "res[126]  start=108.323  type=chord  pitches=[47, 44, 51]\n",
-      "res[127]  start=108.794  type=chord  pitches=[47, 44]\n",
-      "res[128]  start=108.862  type=note  pitches=[51]\n",
-      "res[129]  start=109.273  type=chord  pitches=[47, 44, 51]\n",
-      "res[130]  start=109.371  type=note  pitches=[71]\n",
-      "res[131]  start=109.798  type=chord  pitches=[47, 51, 44]\n",
-      "res[132]  start=109.922  type=note  pitches=[70]\n",
-      "res[133]  start=110.249  type=chord  pitches=[51, 47, 44]\n",
-      "res[134]  start=110.392  type=note  pitches=[71]\n",
-      "res[135]  start=110.703  type=chord  pitches=[44, 47, 51]\n",
-      "res[136]  start=110.783  type=note  pitches=[68]\n",
-      "res[137]  start=111.177  type=chord  pitches=[46, 49, 51, 68]\n",
-      "res[138]  start=111.844  type=chord  pitches=[49, 46, 51]\n",
-      "res[139]  start=112.216  type=chord  pitches=[49, 51, 46]\n",
-      "res[140]  start=112.668  type=chord  pitches=[46, 49, 51]\n",
-      "res[141]  start=112.776  type=note  pitches=[67]\n",
-      "res[142]  start=113.236  type=chord  pitches=[46, 51, 49]\n",
-      "res[143]  start=113.633  type=chord  pitches=[49, 46, 51]\n",
-      "res[144]  start=114.056  type=chord  pitches=[51, 49, 46]\n",
-      "res[145]  start=114.479  type=chord  pitches=[51, 46, 49]\n",
-      "res[146]  start=114.938  type=chord  pitches=[51, 46, 49]\n",
-      "res[147]  start=115.026  type=note  pitches=[76]\n",
-      "res[148]  start=115.375  type=chord  pitches=[51, 49]\n",
-      "res[149]  start=115.443  type=note  pitches=[46]\n",
-      "res[150]  start=115.841  type=chord  pitches=[46, 51]\n",
-      "res[151]  start=116.395  type=chord  pitches=[49, 55, 46]\n",
-      "res[152]  start=116.456  type=note  pitches=[51]\n",
-      "res[153]  start=116.516  type=note  pitches=[75]\n",
-      "res[154]  start=116.999  type=chord  pitches=[51, 75, 47, 56]\n",
-      "res[155]  start=117.625  type=chord  pitches=[51, 47, 56]\n",
-      "res[156]  start=118.059  type=chord  pitches=[51, 56, 47]\n",
-      "res[157]  start=118.164  type=note  pitches=[80]\n",
-      "res[158]  start=118.539  type=chord  pitches=[56, 51, 48]\n",
-      "res[159]  start=119.038  type=chord  pitches=[51, 56, 48]\n",
-      "res[160]  start=119.456  type=chord  pitches=[51, 56, 48]\n",
-      "res[161]  start=119.565  type=note  pitches=[78]\n",
-      "res[162]  start=119.882  type=chord  pitches=[56, 49, 52]\n",
-      "res[163]  start=120.337  type=chord  pitches=[56, 52, 49]\n",
-      "res[164]  start=120.725  type=chord  pitches=[76, 56, 49, 52]\n",
-      "res[165]  start=121.081  type=chord  pitches=[52, 56, 61]\n",
-      "res[166]  start=121.182  type=note  pitches=[75]\n",
-      "res[167]  start=121.686  type=chord  pitches=[61, 52, 56]\n",
-      "res[168]  start=122.082  type=chord  pitches=[56, 61, 52]\n",
-      "res[169]  start=122.210  type=note  pitches=[73]\n",
-      "res[170]  start=122.852  type=chord  pitches=[59, 71, 56, 51]\n",
-      "res[171]  start=123.441  type=chord  pitches=[56, 59, 51]\n",
-      "res[172]  start=123.859  type=chord  pitches=[59, 51, 56]\n",
-      "res[173]  start=124.320  type=chord  pitches=[51, 56, 59]\n",
-      "res[174]  start=124.757  type=chord  pitches=[73, 56, 59]\n",
-      "res[175]  start=124.815  type=note  pitches=[51]\n",
-      "res[176]  start=125.337  type=chord  pitches=[75, 56, 59, 51]\n",
-      "res[177]  start=125.832  type=note  pitches=[73]\n",
-      "res[178]  start=125.943  type=note  pitches=[71]\n",
-      "res[179]  start=126.073  type=chord  pitches=[58, 51, 55, 70]\n",
-      "res[180]  start=126.618  type=chord  pitches=[55, 51, 58]\n",
-      "res[181]  start=127.046  type=chord  pitches=[55, 51, 58]\n",
-      "res[182]  start=127.486  type=chord  pitches=[58, 49, 55]\n",
-      "res[183]  start=127.947  type=chord  pitches=[51, 56, 47]\n",
-      "res[184]  start=128.039  type=note  pitches=[71]\n",
-      "res[185]  start=128.654  type=chord  pitches=[55, 51, 46]\n",
-      "res[186]  start=128.707  type=note  pitches=[73]\n",
-      "res[187]  start=129.322  type=chord  pitches=[56, 51, 71, 44]\n",
-      "res[188]  start=130.033  type=chord  pitches=[44, 47]\n",
-      "res[189]  start=130.422  type=chord  pitches=[47, 44, 51]\n",
-      "res[190]  start=130.822  type=chord  pitches=[47, 44]\n",
-      "res[191]  start=131.220  type=chord  pitches=[44, 51, 47]\n",
-      "res[192]  start=131.629  type=chord  pitches=[51, 44, 47]\n"
-     ]
-    }
-   ],
-   "source": [
-    "window_start, window_end = 105.0, 132.0\n",
-    "\n",
-    "print(\"--- Reference events in 105-132s ---\")\n",
-    "for i, ev in enumerate(result[\"ref_events_normalized\"]):\n",
-    "    if window_start <= ev[\"event_start\"] <= window_end:\n",
-    "        pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
-    "        print(f\"ref[{i}]  start={ev['event_start']:.3f}  type={ev['event_type']}  pitches={pitches}\")\n",
-    "\n",
-    "print(\"\\n--- Response events in 105-132s ---\")\n",
-    "for i, ev in enumerate(result[\"response_events_normalized\"]):\n",
-    "    if window_start <= ev[\"event_start\"] <= window_end:\n",
-    "        pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
-    "        print(f\"res[{i}]  start={ev['event_start']:.3f}  type={ev['event_type']}  pitches={pitches}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Windows with the highest response/reference event density ratio:\n",
-      "  582.5s - 587.5s : ref=3 events, res=38 events, ratio=12.67\n",
-      "  192.5s - 197.5s : ref=3 events, res=12 events, ratio=4.00\n",
-      "  65.0s - 70.0s : ref=4 events, res=13 events, ratio=3.25\n",
-      "  445.0s - 450.0s : ref=2 events, res=6 events, ratio=3.00\n",
-      "  62.5s - 67.5s : ref=4 events, res=11 events, ratio=2.75\n",
-      "  190.0s - 195.0s : ref=4 events, res=11 events, ratio=2.75\n",
-      "  442.5s - 447.5s : ref=4 events, res=11 events, ratio=2.75\n",
-      "  195.0s - 200.0s : ref=3 events, res=8 events, ratio=2.67\n",
-      "  347.5s - 352.5s : ref=8 events, res=20 events, ratio=2.50\n",
-      "  412.5s - 417.5s : ref=8 events, res=19 events, ratio=2.38\n",
-      "  350.0s - 355.0s : ref=9 events, res=21 events, ratio=2.33\n",
-      "  395.0s - 400.0s : ref=8 events, res=18 events, ratio=2.25\n",
-      "  440.0s - 445.0s : ref=7 events, res=15 events, ratio=2.14\n",
-      "  365.0s - 370.0s : ref=8 events, res=17 events, ratio=2.12\n",
-      "  370.0s - 375.0s : ref=8 events, res=17 events, ratio=2.12\n"
-     ]
-    }
-   ],
-   "source": [
-    "def compute_event_density_ratio(ref_events, res_events, response_onset_offset,\n",
-    "                                 window_size=5.0, step=2.5):\n",
-    "    \"\"\"\n",
-    "    Slide a fixed-size time window across the piece and compare how many\n",
-    "    response events fall inside it versus how many reference events do,\n",
-    "    in the same absolute time range. A ratio much greater than 1 flags\n",
-    "    passages where the response is systematically split into more events\n",
-    "    than the reference expects (e.g. broken/rolled chord accompaniment).\n",
-    "\n",
-    "    Args:\n",
-    "        ref_events: list of reference event dicts (normalized)\n",
-    "        res_events: list of response event dicts (normalized)\n",
-    "        response_onset_offset: float, to convert response's normalized\n",
-    "            time back to the same absolute clock as reference\n",
-    "        window_size: float, seconds, size of each sliding window\n",
-    "        step: float, seconds, how far the window moves each step\n",
-    "\n",
-    "    Returns:\n",
-    "        list of (window_start, window_end, ref_count, res_count, ratio)\n",
-    "    \"\"\"\n",
-    "    ref_starts = [ev[\"event_start\"] for ev in ref_events]\n",
-    "    res_starts = [ev[\"event_start\"] + response_onset_offset for ev in res_events]\n",
-    "\n",
-    "    end_time = max(ref_starts[-1], res_starts[-1])\n",
-    "    rows = []\n",
-    "\n",
-    "    window_start = 0.0\n",
-    "    while window_start < end_time:\n",
-    "        window_end = window_start + window_size\n",
-    "\n",
-    "        ref_count = sum(1 for t in ref_starts if window_start <= t < window_end)\n",
-    "        res_count = sum(1 for t in res_starts if window_start <= t < window_end)\n",
-    "\n",
-    "        if ref_count > 0:\n",
-    "            ratio = res_count / ref_count\n",
-    "        else:\n",
-    "            ratio = None\n",
-    "\n",
-    "        rows.append((round(window_start, 1), round(window_end, 1), ref_count, res_count, ratio))\n",
-    "        window_start = window_start + step\n",
-    "\n",
-    "    return rows\n",
-    "\n",
-    "\n",
-    "result = all_results[383]  # Beethoven, from worst_df\n",
-    "density_rows = compute_event_density_ratio(\n",
-    "    result[\"ref_events_normalized\"],\n",
-    "    result[\"response_events_normalized\"],\n",
-    "    result[\"response_onset_offset\"],\n",
-    ")\n",
-    "\n",
-    "# Sort by ratio to see the worst mismatched windows first\n",
-    "sorted_rows = sorted(\n",
-    "    [r for r in density_rows if r[4] is not None],\n",
-    "    key=lambda r: r[4],\n",
-    "    reverse=True,\n",
-    ")\n",
-    "\n",
-    "print(\"Windows with the highest response/reference event density ratio:\")\n",
-    "for w_start, w_end, ref_c, res_c, ratio in sorted_rows[:15]:\n",
-    "    print(f\"  {w_start}s - {w_end}s : ref={ref_c} events, res={res_c} events, ratio={ratio:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--- Reference events in 580-590s ---\n",
-      "ref[1773]  start=580.035  type=note  pitches=[32]\n",
-      "ref[1774]  start=580.148  type=note  pitches=[36]\n",
-      "ref[1775]  start=580.262  type=note  pitches=[39]\n",
-      "ref[1776]  start=580.376  type=note  pitches=[44]\n",
-      "ref[1777]  start=580.489  type=note  pitches=[48]\n",
-      "ref[1778]  start=580.603  type=note  pitches=[51]\n",
-      "ref[1779]  start=580.716  type=note  pitches=[44]\n",
-      "ref[1780]  start=580.830  type=note  pitches=[48]\n",
-      "ref[1781]  start=580.944  type=note  pitches=[51]\n",
-      "ref[1782]  start=581.057  type=note  pitches=[56]\n",
-      "ref[1783]  start=581.171  type=note  pitches=[60]\n",
-      "ref[1784]  start=581.285  type=note  pitches=[63]\n",
-      "ref[1785]  start=581.398  type=note  pitches=[56]\n",
-      "ref[1786]  start=581.512  type=note  pitches=[60]\n",
-      "ref[1787]  start=581.626  type=note  pitches=[63]\n",
-      "ref[1788]  start=581.739  type=note  pitches=[68]\n",
-      "ref[1789]  start=581.853  type=note  pitches=[72]\n",
-      "ref[1790]  start=581.966  type=note  pitches=[75]\n",
-      "ref[1791]  start=582.080  type=note  pitches=[68]\n",
-      "ref[1792]  start=582.194  type=note  pitches=[72]\n",
-      "ref[1793]  start=582.307  type=note  pitches=[75]\n",
-      "ref[1794]  start=582.421  type=note  pitches=[80]\n",
-      "ref[1795]  start=582.535  type=note  pitches=[84]\n",
-      "ref[1796]  start=582.648  type=note  pitches=[87]\n",
-      "ref[1797]  start=582.762  type=chord  pitches=[32, 36, 39, 44, 80, 84, 87, 92]\n",
-      "\n",
-      "--- Response events in 580-590s (absolute time) ---\n",
-      "res[1658]  start=580.057  type=chord  pitches=[43, 85, 73, 82]\n",
-      "res[1659]  start=580.167  type=note  pitches=[55]\n",
-      "res[1660]  start=580.296  type=note  pitches=[51]\n",
-      "res[1661]  start=580.397  type=note  pitches=[55]\n",
-      "res[1662]  start=580.518  type=note  pitches=[51]\n",
-      "res[1663]  start=580.624  type=note  pitches=[55]\n",
-      "res[1664]  start=580.814  type=chord  pitches=[44, 84, 72, 75]\n",
-      "res[1665]  start=580.956  type=note  pitches=[46]\n",
-      "res[1666]  start=581.078  type=note  pitches=[44]\n",
-      "res[1667]  start=581.208  type=note  pitches=[42]\n",
-      "res[1668]  start=581.346  type=chord  pitches=[41, 80]\n",
-      "res[1669]  start=581.440  type=note  pitches=[39]\n",
-      "res[1670]  start=581.589  type=chord  pitches=[37, 85, 80, 77]\n",
-      "res[1671]  start=581.751  type=note  pitches=[44]\n",
-      "res[1672]  start=581.818  type=note  pitches=[41]\n",
-      "res[1673]  start=581.975  type=note  pitches=[44]\n",
-      "res[1674]  start=582.090  type=chord  pitches=[38, 82, 77, 80]\n",
-      "res[1675]  start=582.242  type=note  pitches=[44]\n",
-      "res[1676]  start=582.405  type=chord  pitches=[39, 84, 80, 75]\n",
-      "res[1677]  start=582.596  type=chord  pitches=[44, 51]\n",
-      "res[1678]  start=582.750  type=chord  pitches=[82, 37, 77, 80]\n",
-      "res[1679]  start=582.839  type=note  pitches=[39]\n",
-      "res[1680]  start=582.984  type=chord  pitches=[49, 44]\n",
-      "res[1681]  start=583.112  type=chord  pitches=[39, 79, 73]\n",
-      "res[1682]  start=583.250  type=note  pitches=[51]\n",
-      "res[1683]  start=583.520  type=chord  pitches=[80, 32, 72, 75]\n",
-      "res[1684]  start=583.717  type=note  pitches=[44]\n",
-      "res[1685]  start=583.833  type=note  pitches=[36]\n",
-      "res[1686]  start=583.956  type=note  pitches=[44]\n",
-      "res[1687]  start=584.082  type=note  pitches=[39]\n",
-      "res[1688]  start=584.211  type=note  pitches=[44]\n",
-      "res[1689]  start=584.307  type=chord  pitches=[85, 32, 80, 77]\n",
-      "res[1690]  start=584.448  type=note  pitches=[44]\n",
-      "res[1691]  start=584.562  type=note  pitches=[37]\n",
-      "res[1692]  start=584.650  type=note  pitches=[44]\n",
-      "res[1693]  start=584.793  type=note  pitches=[41]\n",
-      "res[1694]  start=584.936  type=note  pitches=[44]\n",
-      "res[1695]  start=585.068  type=chord  pitches=[32, 82, 73, 79]\n",
-      "res[1696]  start=585.206  type=note  pitches=[44]\n",
-      "res[1697]  start=585.339  type=note  pitches=[37]\n",
-      "res[1698]  start=585.444  type=note  pitches=[44]\n",
-      "res[1699]  start=585.551  type=note  pitches=[40]\n",
-      "res[1700]  start=585.695  type=note  pitches=[44]\n",
-      "res[1701]  start=585.814  type=chord  pitches=[32, 87, 75, 80, 84]\n",
-      "res[1702]  start=585.975  type=note  pitches=[44]\n",
-      "res[1703]  start=586.095  type=note  pitches=[36]\n",
-      "res[1704]  start=586.202  type=note  pitches=[44]\n",
-      "res[1705]  start=586.352  type=note  pitches=[39]\n",
-      "res[1706]  start=586.462  type=note  pitches=[44]\n",
-      "res[1707]  start=586.592  type=chord  pitches=[32, 84, 75, 78]\n",
-      "res[1708]  start=586.651  type=note  pitches=[35]\n",
-      "res[1709]  start=586.720  type=note  pitches=[44]\n",
-      "res[1710]  start=586.858  type=note  pitches=[39]\n",
-      "res[1711]  start=586.966  type=chord  pitches=[37, 44]\n",
-      "res[1712]  start=587.105  type=note  pitches=[42]\n",
-      "res[1713]  start=587.225  type=note  pitches=[44]\n",
-      "res[1714]  start=587.368  type=chord  pitches=[89, 32, 77, 85, 31, 34, 86]\n",
-      "res[1715]  start=587.540  type=note  pitches=[43]\n",
-      "res[1716]  start=587.651  type=note  pitches=[37]\n",
-      "res[1717]  start=587.741  type=note  pitches=[44]\n",
-      "res[1718]  start=587.879  type=chord  pitches=[41, 42]\n",
-      "res[1719]  start=587.971  type=note  pitches=[36]\n",
-      "res[1720]  start=588.023  type=note  pitches=[44]\n",
-      "res[1721]  start=588.117  type=chord  pitches=[32, 86, 77]\n",
-      "res[1722]  start=588.254  type=note  pitches=[44]\n",
-      "res[1723]  start=588.379  type=chord  pitches=[38, 36]\n",
-      "res[1724]  start=588.493  type=note  pitches=[44]\n",
-      "res[1725]  start=588.615  type=note  pitches=[41]\n",
-      "res[1726]  start=588.732  type=note  pitches=[44]\n",
-      "res[1727]  start=588.973  type=chord  pitches=[32, 90, 78, 84]\n",
-      "res[1728]  start=589.264  type=note  pitches=[44]\n",
-      "res[1729]  start=589.469  type=note  pitches=[39]\n",
-      "res[1730]  start=589.638  type=note  pitches=[44]\n",
-      "res[1731]  start=589.788  type=note  pitches=[42]\n",
-      "res[1732]  start=589.911  type=note  pitches=[44]\n"
-     ]
-    }
-   ],
-   "source": [
-    "window_start, window_end = 580.0, 590.0\n",
-    "\n",
-    "print(\"--- Reference events in 580-590s ---\")\n",
-    "for i, ev in enumerate(result[\"ref_events_normalized\"]):\n",
-    "    if window_start <= ev[\"event_start\"] <= window_end:\n",
-    "        pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
-    "        print(f\"ref[{i}]  start={ev['event_start']:.3f}  type={ev['event_type']}  pitches={pitches}\")\n",
-    "\n",
-    "print(\"\\n--- Response events in 580-590s (absolute time) ---\")\n",
-    "onset_offset = result[\"response_onset_offset\"]\n",
-    "for i, ev in enumerate(result[\"response_events_normalized\"]):\n",
-    "    absolute_start = ev[\"event_start\"] + onset_offset\n",
-    "    if window_start <= absolute_start <= window_end:\n",
-    "        pitches = [note[\"pitch\"] for note in ev[\"notes\"]]\n",
-    "        print(f\"res[{i}]  start={absolute_start:.3f}  type={ev['event_type']}  pitches={pitches}\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "compareMusic",
-   "language": "python",
-   "name": "comparemusic"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 0822ec19df11f077dda07d9e9c63dd64b42acbc3 Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Sun, 12 Jul 2026 18:14:42 +0100
Subject: [PATCH 14/15] attempting to interpret the results

---
 notebooks/Alignment_on_nASAPdataset.ipynb | 2409 ++++++++++++++++++++-
 1 file changed, 2295 insertions(+), 114 deletions(-)

diff --git a/notebooks/Alignment_on_nASAPdataset.ipynb b/notebooks/Alignment_on_nASAPdataset.ipynb
index b2b64a3..a84db3c 100644
--- a/notebooks/Alignment_on_nASAPdataset.ipynb
+++ b/notebooks/Alignment_on_nASAPdataset.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -80,47 +80,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "import csv\n",
     "\n",
     "def load_ground_truth(tsv_path):\n",
-    "    \"\"\"\n",
-    "    Read a note_alignments TSV file from the CPJKU nASAP dataset.\n",
-    "\n",
-    "    Args:\n",
-    "        tsv_path: str, path to the note_alignments.tsv file\n",
-    "\n",
-    "    Returns:\n",
-    "        list of dicts, each with keys:\n",
-    "            label  -> 'paired', 'insertion', or 'deletion'\n",
-    "            onset  -> float (seconds) or None for deletions\n",
-    "            pitch  -> int (MIDI pitch number) or None for deletions\n",
-    "    \"\"\"\n",
     "    rows = []\n",
+    "\n",
     "    with open(tsv_path, \"r\", encoding=\"utf-8\") as f:\n",
     "        reader = csv.DictReader(f, delimiter=\"\\t\")\n",
-    "        \n",
+    "\n",
     "        for row in reader:\n",
-    "            xml_id  = row[\"xml_id\"].strip()\n",
+    "            xml_id = row[\"xml_id\"].strip()\n",
     "            midi_id = row[\"midi_id\"].strip()\n",
     "\n",
     "            if midi_id == \"deletion\":\n",
-    "                rows.append({\"label\": \"deletion\", \"onset\": None, \"pitch\": None})\n",
+    "                rows.append({\n",
+    "                    \"label\": \"deletion\",\n",
+    "                    \"xml_id\": xml_id,\n",
+    "                    \"midi_id\": None,\n",
+    "                    \"onset\": None,\n",
+    "                    \"pitch\": None,\n",
+    "                })\n",
+    "\n",
     "            elif xml_id == \"insertion\":\n",
     "                rows.append({\n",
     "                    \"label\": \"insertion\",\n",
+    "                    \"xml_id\": None,\n",
+    "                    \"midi_id\": midi_id,\n",
     "                    \"onset\": float(row[\"onset\"]),\n",
     "                    \"pitch\": int(row[\"pitch\"]),\n",
     "                })\n",
+    "\n",
     "            else:\n",
     "                rows.append({\n",
     "                    \"label\": \"paired\",\n",
+    "                    \"xml_id\": xml_id,\n",
+    "                    \"midi_id\": midi_id,\n",
     "                    \"onset\": float(row[\"onset\"]),\n",
     "                    \"pitch\": int(row[\"pitch\"]),\n",
     "                })\n",
+    "\n",
     "    return rows"
    ]
   },
@@ -140,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -264,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -365,106 +367,150 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "def convert_pipeline_output(event_details, response_events, ref_events):\n",
     "    \"\"\"\n",
-    "    Convert event-level pipeline output into note-level labels.\n",
-    "\n",
-    "    Note: ref_events is required here so that a missing (deleted) chord's\n",
-    "    true note count can be looked up. The pipeline's event_details does\n",
-    "    NOT carry pitch information for missing/extra events (correct_pitches\n",
-    "    and missing_pitches are always None in that case), so we must go back\n",
-    "    to the original reference events ourselves to know how many notes\n",
-    "    were actually deleted.\n",
+    "    Convert event-level alignment output into note-level alignment labels.\n",
+    "\n",
+    "    Alignment labels describe note correspondence, not pitch correctness.\n",
+    "\n",
+    "    Rules:\n",
+    "        missing:\n",
+    "            Every note in the reference event becomes a deletion.\n",
+    "\n",
+    "        extra:\n",
+    "            Every note in the response event becomes an insertion.\n",
+    "\n",
+    "        match/replacement:\n",
+    "            Pair as many response and reference notes as possible.\n",
+    "            Different pitches may still form a paired alignment.\n",
+    "            Remaining response notes become insertions.\n",
+    "            Remaining reference notes become deletions.\n",
+    "\n",
+    "    This implementation guarantees:\n",
+    "\n",
+    "        paired + insertion == total response notes\n",
+    "\n",
+    "        paired + deletion == total reference notes\n",
     "    \"\"\"\n",
     "    predictions = []\n",
     "\n",
     "    for event in event_details:\n",
     "        operation = event[\"operation_type\"]\n",
-    "        event_type = event[\"event_type\"]\n",
     "\n",
-    "        if operation == \"missing\":\n",
-    "            # A reference note/chord that the student did not play at all.\n",
-    "            if event_type == \"note\":\n",
-    "                number_of_notes = 1\n",
-    "            else:\n",
-    "                # reference_index is 1-based, so subtract 1 to index into ref_events.\n",
-    "                reference_index = event[\"reference_index\"] - 1\n",
-    "                missing_ref_event = ref_events[reference_index]\n",
-    "                number_of_notes = len(missing_ref_event[\"notes\"])\n",
+    "        response_event = None\n",
+    "        reference_event = None\n",
     "\n",
-    "            for i in range(number_of_notes):\n",
+    "        if event.get(\"response_index\") is not None:\n",
+    "            response_index = event[\"response_index\"] - 1\n",
+    "\n",
+    "            if (\n",
+    "                response_index < 0\n",
+    "                or response_index >= len(response_events)\n",
+    "            ):\n",
+    "                raise IndexError(\n",
+    "                    \"response_index is outside response_events\"\n",
+    "                )\n",
+    "\n",
+    "            response_event = response_events[response_index]\n",
+    "\n",
+    "        if event.get(\"reference_index\") is not None:\n",
+    "            reference_index = event[\"reference_index\"] - 1\n",
+    "\n",
+    "            if (\n",
+    "                reference_index < 0\n",
+    "                or reference_index >= len(ref_events)\n",
+    "            ):\n",
+    "                raise IndexError(\n",
+    "                    \"reference_index is outside ref_events\"\n",
+    "                )\n",
+    "\n",
+    "            reference_event = ref_events[reference_index]\n",
+    "\n",
+    "        # ----------------------------------------------------------\n",
+    "        # Entire reference event is missing.\n",
+    "        # ----------------------------------------------------------\n",
+    "        if operation == \"missing\":\n",
+    "            for reference_note in reference_event[\"notes\"]:\n",
     "                predictions.append({\n",
     "                    \"onset\": None,\n",
     "                    \"pitch\": None,\n",
     "                    \"label\": \"deletion\",\n",
     "                })\n",
     "\n",
-    "        else:\n",
-    "            # response_index is 1-based in the pipeline output.\n",
-    "            response_index = event[\"response_index\"] - 1\n",
-    "            if response_index < 0 or response_index >= len(response_events):\n",
-    "                raise IndexError(\"response_index is outside the response event list\")\n",
+    "            continue\n",
     "\n",
-    "            response_event = response_events[response_index]\n",
-    "            onset = float(response_event[\"event_start\"])\n",
-    "\n",
-    "            if event_type == \"note\":\n",
-    "                pitch = int(response_event[\"notes\"][0][\"pitch\"])\n",
-    "                if operation == \"extra\":\n",
-    "                    label = \"insertion\"\n",
-    "                else:\n",
-    "                    label = \"paired\"\n",
+    "        # ----------------------------------------------------------\n",
+    "        # Entire response event is extra.\n",
+    "        # ----------------------------------------------------------\n",
+    "        if operation == \"extra\":\n",
+    "            for response_note in response_event[\"notes\"]:\n",
     "                predictions.append({\n",
-    "                    \"onset\": onset,\n",
-    "                    \"pitch\": pitch,\n",
-    "                    \"label\": label,\n",
+    "                    \"onset\": float(response_note[\"start\"]),\n",
+    "                    \"pitch\": int(response_note[\"pitch\"]),\n",
+    "                    \"label\": \"insertion\",\n",
     "                })\n",
     "\n",
-    "            elif operation == \"extra\":\n",
-    "                # Extra chord: every performed note in it is an insertion.\n",
-    "                for note in response_event[\"notes\"]:\n",
-    "                    predictions.append({\n",
-    "                        \"onset\": onset,\n",
-    "                        \"pitch\": int(note[\"pitch\"]),\n",
-    "                        \"label\": \"insertion\",\n",
-    "                    })\n",
+    "            continue\n",
+    "\n",
+    "        # ----------------------------------------------------------\n",
+    "        # Matched or replacement event.\n",
+    "        # ----------------------------------------------------------\n",
+    "        response_notes = sorted(\n",
+    "            response_event[\"notes\"],\n",
+    "            key=lambda note: int(note[\"pitch\"])\n",
+    "        )\n",
     "\n",
-    "            else:\n",
-    "                # Matched or replaced chord. correct_pitches / missing_pitches /\n",
-    "                # extra_pitches already hold real MIDI pitches (see\n",
-    "                # compute_chord_accuracy in compare_MIDI.py), so we can use\n",
-    "                # them directly with no pitch-class fallback needed.\n",
-    "                for pitch in event.get(\"correct_pitches\") or []:\n",
-    "                    predictions.append({\n",
-    "                        \"onset\": onset,\n",
-    "                        \"pitch\": int(pitch),\n",
-    "                        \"label\": \"paired\",\n",
-    "                    })\n",
-    "\n",
-    "                for i in range(len(event.get(\"missing_pitches\") or [])):\n",
-    "                    predictions.append({\n",
-    "                        \"onset\": None,\n",
-    "                        \"pitch\": None,\n",
-    "                        \"label\": \"deletion\",\n",
-    "                    })\n",
-    "\n",
-    "                for pitch in event.get(\"extra_pitches\") or []:\n",
-    "                    predictions.append({\n",
-    "                        \"onset\": onset,\n",
-    "                        \"pitch\": int(pitch),\n",
-    "                        \"label\": \"insertion\",\n",
-    "                    })\n",
+    "        reference_notes = sorted(\n",
+    "            reference_event[\"notes\"],\n",
+    "            key=lambda note: int(note[\"pitch\"])\n",
+    "        )\n",
+    "\n",
+    "        pair_count = min(\n",
+    "            len(response_notes),\n",
+    "            len(reference_notes)\n",
+    "        )\n",
+    "\n",
+    "        # Pair notes positionally after sorting by pitch.\n",
+    "        #\n",
+    "        # A pitch difference does not change the alignment label:\n",
+    "        # it remains paired.\n",
+    "        for index in range(pair_count):\n",
+    "            response_note = response_notes[index]\n",
+    "\n",
+    "            predictions.append({\n",
+    "                \"onset\": float(response_note[\"start\"]),\n",
+    "                \"pitch\": int(response_note[\"pitch\"]),\n",
+    "                \"label\": \"paired\",\n",
+    "            })\n",
+    "\n",
+    "        # Response side contains more notes.\n",
+    "        for response_note in response_notes[pair_count:]:\n",
+    "            predictions.append({\n",
+    "                \"onset\": float(response_note[\"start\"]),\n",
+    "                \"pitch\": int(response_note[\"pitch\"]),\n",
+    "                \"label\": \"insertion\",\n",
+    "            })\n",
+    "\n",
+    "        # Reference side contains more notes.\n",
+    "        missing_count = len(reference_notes) - pair_count\n",
+    "\n",
+    "        for _ in range(missing_count):\n",
+    "            predictions.append({\n",
+    "                \"onset\": None,\n",
+    "                \"pitch\": None,\n",
+    "                \"label\": \"deletion\",\n",
+    "            })\n",
     "\n",
     "    return predictions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -496,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -613,27 +659,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                  Piece  Precision  Recall      F1    TP  FP  FN\n",
-      "0  Bach (Fugue_bwv_846)     0.9565  0.9739  0.9651   747  34  20\n",
-      "1  Bach (Fugue_bwv_848)     0.9945  0.9911  0.9928  1442   8  13\n",
-      "2  Bach (Fugue_bwv_848)     0.9809  0.9856  0.9833  1439  28  21\n",
-      "3  Bach (Fugue_bwv_848)     0.9788  0.9828  0.9808  1429  31  25\n",
-      "4  Bach (Fugue_bwv_848)     0.9863  0.9870  0.9866  1441  20  19\n",
-      "5  Bach (Fugue_bwv_848)     0.9945  0.9917  0.9931  1438   8  12\n",
-      "6  Bach (Fugue_bwv_848)     0.9897  0.9883  0.9890  1441  15  17\n",
-      "7  Bach (Fugue_bwv_848)     0.9689  0.9782  0.9735  1434  46  32\n",
-      "8  Bach (Fugue_bwv_848)     0.9945  0.9904  0.9924  1437   8  14\n",
-      "9  Bach (Fugue_bwv_848)     0.9910  0.9903  0.9907  1436  13  14\n",
-      "Mean Precision: 0.9644\n",
-      "Mean Recall   : 0.9743\n",
-      "Mean F1       : 0.9691\n"
+      "                      Piece  Precision  Recall      F1    TP   FP   FN\n",
+      "153  Bach (Prelude_bwv_885)     0.8413  0.8866  0.8633   477   90   61\n",
+      "110  Bach (Prelude_bwv_858)     0.8456  0.9163  0.8795   449   82   41\n",
+      "109  Bach (Prelude_bwv_858)     0.8482  0.9710  0.9055   436   78   13\n",
+      "134  Bach (Prelude_bwv_874)     0.8578  0.9029  0.8798  1442  239  155\n",
+      "135  Bach (Prelude_bwv_874)     0.8628  0.9084  0.8850  1428  227  144\n",
+      "76     Bach (Fugue_bwv_889)     0.8667  0.8636  0.8651   741  114  117\n",
+      "73     Bach (Fugue_bwv_889)     0.8749  0.8759  0.8754   748  107  106\n",
+      "77     Bach (Fugue_bwv_889)     0.8751  0.8783  0.8767   736  105  102\n",
+      "102  Bach (Prelude_bwv_856)     0.8763  0.9914  0.9303   921  130    8\n",
+      "72     Bach (Fugue_bwv_889)     0.8796  0.8817  0.8806   745  102  100\n",
+      "Mean Precision: 0.9637\n",
+      "Mean Recall   : 0.9701\n",
+      "Mean F1       : 0.9668\n"
      ]
     }
    ],
@@ -675,15 +721,2150 @@
     "    })\n",
     "\n",
     "df_eval = pd.DataFrame(evaluation_rows)\n",
+    "print(df_eval.sort_values(\"Precision\", ascending=True).head(10).round(4))\n",
     "\n",
-    "if df_eval.empty:\n",
-    "    print(\"No pieces were evaluated. Check the paths above.\")\n",
-    "else:\n",
-    "    print(df_eval.head(10).round(4))\n",
+    "print(\"Mean Precision:\", round(df_eval[\"Precision\"].mean(), 4))\n",
+    "print(\"Mean Recall   :\", round(df_eval[\"Recall\"].mean(), 4))\n",
+    "print(\"Mean F1       :\", round(df_eval[\"F1\"].mean(), 4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation of results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Piece: Bach Prelude_bwv_885\n",
+      "Ground-truth labels: 538\n",
+      "Pipeline labels: 567\n",
+      "=== Ground truth ===\n",
+      "Paired: 497\n",
+      "Insertion: 28\n",
+      "Deletion: 13\n",
+      "Performance notes represented: 525\n",
+      "Reference notes represented: 510\n",
+      "=== Pipeline predictions ===\n",
+      "Paired: 482\n",
+      "Insertion: 43\n",
+      "Deletion: 42\n",
+      "Performance notes represented: 525\n",
+      "Reference notes represented: 524\n",
+      "=== Original event data ===\n",
+      "Notes in response_events: 525\n",
+      "Notes in ref_events: 524\n",
+      "=== Conservation checks ===\n",
+      "Every response note represented exactly once: True\n",
+      "Every reference note represented exactly once: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "SELECTED_SAMPLE_INDEX = 153\n",
+    "\n",
+    "selected_sample = samples[SELECTED_SAMPLE_INDEX]\n",
+    "selected_result = all_results[SELECTED_SAMPLE_INDEX]\n",
+    "\n",
+    "ref_events = selected_result[\"ref_events_normalized\"]\n",
+    "response_events = selected_result[\"response_events_normalized\"]\n",
+    "event_details = selected_result[\"event_details\"]\n",
+    "\n",
+    "metadata_row = selected_sample[\"metadata_row\"]\n",
+    "\n",
+    "tsv_path = os.path.join(ASAP_PATH, metadata_row[\"note_alignments\"].strip())\n",
+    "xml_path = os.path.join(ASAP_PATH, metadata_row[\"xml_score\"].strip())\n",
+    "midi_score_path = os.path.join(ASAP_PATH, metadata_row[\"midi_score\"].strip())\n",
+    "midi_performance_path = os.path.join(ASAP_PATH, metadata_row[\"midi_performance\"].strip())\n",
+    "\n",
+    "ground_truth = load_ground_truth(tsv_path)\n",
+    "\n",
+    "predictions = convert_pipeline_output(\n",
+    "    selected_result[\"event_details\"],\n",
+    "    selected_result[\"response_events_normalized\"],\n",
+    "    selected_result[\"ref_events_normalized\"],\n",
+    ")\n",
+    "\n",
+    "# Add the original response onset offset back.\n",
+    "for prediction in predictions:\n",
+    "    if prediction[\"onset\"] is not None:\n",
+    "        prediction[\"onset\"] += selected_result[\"response_onset_offset\"]\n",
+    "\n",
+    "print(\"Piece:\", selected_result[\"composer\"], selected_result[\"title\"])\n",
+    "print(\"Ground-truth labels:\", len(ground_truth))\n",
+    "print(\"Pipeline labels:\", len(predictions))\n",
+    "\n",
+    "\n",
+    "from collections import Counter\n",
+    "gt_counts = Counter(note[\"label\"] for note in ground_truth)\n",
+    "pipeline_counts = Counter(note[\"label\"] for note in predictions)\n",
+    "\n",
+    "gt_response_note_count = gt_counts[\"paired\"] + gt_counts[\"insertion\"]\n",
+    "gt_reference_note_count = gt_counts[\"paired\"] + gt_counts[\"deletion\"]\n",
+    "pipeline_response_note_count = pipeline_counts[\"paired\"] + pipeline_counts[\"insertion\"]\n",
+    "pipeline_reference_note_count = pipeline_counts[\"paired\"] + pipeline_counts[\"deletion\"]\n",
+    "\n",
+    "actual_response_event_note_count = sum(len(event[\"notes\"]) for event in response_events)\n",
+    "actual_reference_event_note_count = sum(len(event[\"notes\"]) for event in ref_events)\n",
+    "\n",
+    "print(\"=== Ground truth ===\")\n",
+    "print(\"Paired:\", gt_counts[\"paired\"])\n",
+    "print(\"Insertion:\", gt_counts[\"insertion\"])\n",
+    "print(\"Deletion:\", gt_counts[\"deletion\"])\n",
+    "print(\"Performance notes represented:\", gt_response_note_count)\n",
+    "print(\"Reference notes represented:\", gt_reference_note_count)\n",
+    "\n",
+    "print(\"=== Pipeline predictions ===\")\n",
+    "print(\"Paired:\", pipeline_counts[\"paired\"])\n",
+    "print(\"Insertion:\", pipeline_counts[\"insertion\"])\n",
+    "print(\"Deletion:\", pipeline_counts[\"deletion\"])\n",
+    "print(\"Performance notes represented:\", pipeline_response_note_count)\n",
+    "print(\"Reference notes represented:\", pipeline_reference_note_count)\n",
+    "\n",
+    "print(\"=== Original event data ===\")\n",
+    "print(\"Notes in response_events:\", actual_response_event_note_count)\n",
+    "print(\"Notes in ref_events:\", actual_reference_event_note_count)\n",
+    "\n",
+    "print(\"=== Conservation checks ===\")\n",
+    "print(\n",
+    "    \"Every response note represented exactly once:\",\n",
+    "    pipeline_response_note_count\n",
+    "    == actual_response_event_note_count\n",
+    ")\n",
+    "print(\n",
+    "    \"Every reference note represented exactly once:\",\n",
+    "    pipeline_reference_note_count\n",
+    "    == actual_reference_event_note_count\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original reference notes: 524\n",
+      "Reference notes after grouping: 524\n",
+      "510 reference notes represented in ground truth\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Original reference notes:\", len(selected_sample[\"reference\"][\"notes\"]))\n",
+    "print(\"Reference notes after grouping:\",\n",
+    "    sum(len(event[\"notes\"]) for event in selected_result[\"ref_events_normalized\"])\n",
+    ")\n",
+    "gt_counts = Counter(note[\"label\"] for note in ground_truth)\n",
+    "print(gt_counts[\"paired\"] + gt_counts[\"deletion\"], \"reference notes represented in ground truth\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No problems with the grouping terminology in my algorithm, so need to insepect if there is anything different between \"reference MIDI\" and \"ground truth TSV\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Piece: Prelude_bwv_858\n",
+      "Path: asap-dataset/Bach/Prelude/bwv_858/midi_score.mid\n",
+      "Number of instruments: 2\n",
+      "{'instrument_index': 0, 'name': '', 'program': 0, 'is_drum': False, 'note_count': 327}\n",
+      "{'instrument_index': 1, 'name': '', 'program': 0, 'is_drum': False, 'note_count': 170}\n",
+      "Total notes: 497\n"
+     ]
+    }
+   ],
+   "source": [
+    "midi_score_path = os.path.join(\n",
+    "    ASAP_PATH,\n",
+    "    selected_sample[\"metadata_row\"][\"midi_score\"].strip()\n",
+    ")\n",
+    "\n",
+    "midi_score_data = pretty_midi.PrettyMIDI(midi_score_path)\n",
+    "\n",
+    "print(\"Piece:\", selected_sample[\"title\"])\n",
+    "print(\"Path:\", midi_score_path)\n",
+    "print(\"Number of instruments:\", len(midi_score_data.instruments))\n",
+    "\n",
+    "total_notes = 0\n",
+    "\n",
+    "for instrument_index, instrument in enumerate(\n",
+    "    midi_score_data.instruments\n",
+    "):\n",
+    "    note_count = len(instrument.notes)\n",
+    "    total_notes += note_count\n",
+    "\n",
+    "    print({\n",
+    "        \"instrument_index\": instrument_index,\n",
+    "        \"name\": instrument.name,\n",
+    "        \"program\": int(instrument.program),\n",
+    "        \"is_drum\": instrument.is_drum,\n",
+    "        \"note_count\": note_count,\n",
+    "    })\n",
+    "\n",
+    "print(\"Total notes:\", total_notes)\n",
+    "\n",
+    "assert total_notes == len(\n",
+    "    selected_sample[\"reference\"][\"notes\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('onset_beat', 'duration_beat', 'onset_quarter', 'duration_quarter', 'onset_div', 'duration_div', 'pitch', 'voice', 'id', 'divs_pq')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import partitura as pt\n",
+    "\n",
+    "score = pt.load_score(xml_path)\n",
+    "\n",
+    "na = score.note_array()\n",
+    "\n",
+    "print(na.dtype.names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The pipeline produced more note-level labels than the ground truth. Inspection of the alignment results showed that this was not primarily because additional notes were detected, but because replacement events were expanded into multiple note-level labels (paired + insertion + deletion). Consequently, one event-level disagreement could generate several note-level false positives and false negatives, reducing the final F1 score.\n",
+    "\n",
+    "The paper also suggests that 'A misalignment of two notes that are unaligned in the ground truth creates one false positive match and two false negatives: a deletion and an insertion'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_note_labels(gt_notes, predicted_notes, compare_pitch):\n",
+    "    comparison = []\n",
+    "    used_predictions = set()\n",
+    "\n",
+    "    for gt_note in gt_notes:\n",
+    "        best_index = None\n",
+    "        best_difference = None\n",
+    "\n",
+    "        for index, predicted_note in enumerate(predicted_notes):\n",
+    "            if index in used_predictions:\n",
+    "                continue\n",
+    "\n",
+    "            if compare_pitch and gt_note[\"pitch\"] != predicted_note[\"pitch\"]:\n",
+    "                continue\n",
+    "\n",
+    "            difference = abs(\n",
+    "                gt_note[\"onset\"] - predicted_note[\"onset\"]\n",
+    "            )\n",
+    "\n",
+    "            if difference <= ONSET_TOLERANCE:\n",
+    "                if best_difference is None or difference < best_difference:\n",
+    "                    best_index = index\n",
+    "                    best_difference = difference\n",
+    "\n",
+    "        if best_index is not None:\n",
+    "            used_predictions.add(best_index)\n",
+    "            predicted_note = predicted_notes[best_index]\n",
+    "\n",
+    "            comparison.append({\n",
+    "                \"status\": \"TP\",\n",
+    "                \"label\": gt_note[\"label\"],\n",
+    "                \"onset\": gt_note[\"onset\"],\n",
+    "                \"gt_pitch\": gt_note[\"pitch\"],\n",
+    "                \"pipeline_pitch\": predicted_note[\"pitch\"],\n",
+    "                \"onset_difference\": best_difference,\n",
+    "            })\n",
+    "\n",
+    "        else:\n",
+    "            comparison.append({\n",
+    "                \"status\": \"FN\",\n",
+    "                \"label\": gt_note[\"label\"],\n",
+    "                \"onset\": gt_note[\"onset\"],\n",
+    "                \"gt_pitch\": gt_note[\"pitch\"],\n",
+    "                \"pipeline_pitch\": None,\n",
+    "                \"onset_difference\": None,\n",
+    "            })\n",
+    "\n",
+    "    for index, predicted_note in enumerate(predicted_notes):\n",
+    "        if index not in used_predictions:\n",
+    "            comparison.append({\n",
+    "                \"status\": \"FP\",\n",
+    "                \"label\": predicted_note[\"label\"],\n",
+    "                \"onset\": predicted_note[\"onset\"],\n",
+    "                \"gt_pitch\": None,\n",
+    "                \"pipeline_pitch\": predicted_note[\"pitch\"],\n",
+    "                \"onset_difference\": None,\n",
+    "            })\n",
+    "\n",
+    "    return comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>status</th>\n",
+       "      <th>label</th>\n",
+       "      <th>onset</th>\n",
+       "      <th>gt_pitch</th>\n",
+       "      <th>pipeline_pitch</th>\n",
+       "      <th>onset_difference</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>10.433771</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>11.362190</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>20.750020</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>24.355792</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>28.185924</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>32.012851</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>40.498970</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>41.455168</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>44.126110</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>47.995772</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>56.575908</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>58.241508</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>59.351552</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>61.246853</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>63.231898</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>67.107970</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>69.113314</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>68.101560</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>70.140024</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>70.113314</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>74.020369</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>80.917812</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>81.835548</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>85.641107</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>88.677435</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>97.756503</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>99.499026</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>100.685993</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>107.358008</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>112.917842</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>113.097330</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>151.923221</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>151.850572</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>154.114463</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>158.082415</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>160.290751</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>6.537399</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>6.607912</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>8.410264</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>8.481846</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>13.960483</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>14.033133</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>15.895314</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>24.533143</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>36.471188</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>36.533154</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>44.005384</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>44.057734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>56.505396</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>FP</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>56.640011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    status   label       onset  gt_pitch  pipeline_pitch  onset_difference\n",
+       "19      FN  paired   10.433771      69.0             NaN               NaN\n",
+       "26      FN  paired   11.362190      70.0             NaN               NaN\n",
+       "50      FN  paired   20.750020      72.0             NaN               NaN\n",
+       "55      FN  paired   24.355792      72.0             NaN               NaN\n",
+       "74      FN  paired   28.185924      77.0             NaN               NaN\n",
+       "85      FN  paired   32.012851      75.0             NaN               NaN\n",
+       "100     FN  paired   40.498970      65.0             NaN               NaN\n",
+       "109     FN  paired   41.455168      67.0             NaN               NaN\n",
+       "117     FN  paired   44.126110      74.0             NaN               NaN\n",
+       "127     FN  paired   47.995772      74.0             NaN               NaN\n",
+       "155     FN  paired   56.575908      68.0             NaN               NaN\n",
+       "156     FN  paired   58.241508      68.0             NaN               NaN\n",
+       "163     FN  paired   59.351552      76.0             NaN               NaN\n",
+       "172     FN  paired   61.246853      76.0             NaN               NaN\n",
+       "176     FN  paired   63.231898      69.0             NaN               NaN\n",
+       "190     FN  paired   67.107970      69.0             NaN               NaN\n",
+       "195     FN  paired   69.113314      66.0             NaN               NaN\n",
+       "197     FN  paired   68.101560      76.0             NaN               NaN\n",
+       "201     FN  paired   70.140024      62.0             NaN               NaN\n",
+       "202     FN  paired   70.113314      66.0             NaN               NaN\n",
+       "218     FN  paired   74.020369      72.0             NaN               NaN\n",
+       "236     FN  paired   80.917812      75.0             NaN               NaN\n",
+       "245     FN  paired   81.835548      68.0             NaN               NaN\n",
+       "251     FN  paired   85.641107      65.0             NaN               NaN\n",
+       "261     FN  paired   88.677435      63.0             NaN               NaN\n",
+       "293     FN  paired   97.756503      66.0             NaN               NaN\n",
+       "295     FN  paired   99.499026      69.0             NaN               NaN\n",
+       "302     FN  paired  100.685993      70.0             NaN               NaN\n",
+       "332     FN  paired  107.358008      77.0             NaN               NaN\n",
+       "342     FN  paired  112.917842      76.0             NaN               NaN\n",
+       "348     FN  paired  113.097330      77.0             NaN               NaN\n",
+       "457     FN  paired  151.923221      54.0             NaN               NaN\n",
+       "461     FN  paired  151.850572      63.0             NaN               NaN\n",
+       "475     FN  paired  154.114463      77.0             NaN               NaN\n",
+       "488     FN  paired  158.082415      75.0             NaN               NaN\n",
+       "495     FN  paired  160.290751      79.0             NaN               NaN\n",
+       "497     FP  paired    6.537399       NaN            62.0               NaN\n",
+       "498     FP  paired    6.607912       NaN            63.0               NaN\n",
+       "499     FP  paired    8.410264       NaN            58.0               NaN\n",
+       "500     FP  paired    8.481846       NaN            60.0               NaN\n",
+       "501     FP  paired   13.960483       NaN            69.0               NaN\n",
+       "502     FP  paired   14.033133       NaN            70.0               NaN\n",
+       "503     FP  paired   15.895314       NaN            66.0               NaN\n",
+       "504     FP  paired   24.533143       NaN            53.0               NaN\n",
+       "505     FP  paired   36.471188       NaN            67.0               NaN\n",
+       "506     FP  paired   36.533154       NaN            68.0               NaN\n",
+       "507     FP  paired   44.005384       NaN            74.0               NaN\n",
+       "508     FP  paired   44.057734       NaN            75.0               NaN\n",
+       "509     FP  paired   56.505396       NaN            69.0               NaN\n",
+       "510     FP  paired   56.640011       NaN            69.0               NaN"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gt_paired = [\n",
+    "    note for note in ground_truth\n",
+    "    if note[\"label\"] == \"paired\"\n",
+    "    and note[\"onset\"] is not None\n",
+    "]\n",
+    "\n",
+    "predicted_paired = [\n",
+    "    note for note in predictions\n",
+    "    if note[\"label\"] == \"paired\"\n",
+    "    and note[\"onset\"] is not None\n",
+    "]\n",
+    "\n",
+    "gt_insertions = [\n",
+    "    note for note in ground_truth\n",
+    "    if note[\"label\"] == \"insertion\"\n",
+    "    and note[\"onset\"] is not None\n",
+    "]\n",
+    "\n",
+    "predicted_insertions = [\n",
+    "    note for note in predictions\n",
+    "    if note[\"label\"] == \"insertion\"\n",
+    "    and note[\"onset\"] is not None\n",
+    "]\n",
+    "\n",
+    "paired_comparison = compare_note_labels(\n",
+    "    gt_paired,\n",
+    "    predicted_paired,\n",
+    "    compare_pitch=False,\n",
+    ")\n",
+    "\n",
+    "insertion_comparison = compare_note_labels(\n",
+    "    gt_insertions,\n",
+    "    predicted_insertions,\n",
+    "    compare_pitch=True,\n",
+    ")\n",
+    "\n",
+    "comparison_df = pd.DataFrame(\n",
+    "    paired_comparison + insertion_comparison\n",
+    ")\n",
+    "\n",
+    "error_df = comparison_df[\n",
+    "    comparison_df[\"status\"] != \"TP\"\n",
+    "].copy()\n",
+    "\n",
+    "display(error_df.head(50))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "START_TIME = 62.0\n",
+    "END_TIME = 69.0\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "response_notes = selected_sample[\"response\"][\"notes\"]\n",
+    "\n",
+    "segment_response_notes = [\n",
+    "    note for note in response_notes\n",
+    "    if START_TIME <= note[\"start\"] <= END_TIME\n",
+    "]\n",
+    "\n",
+    "segment_gt = [\n",
+    "    note for note in ground_truth\n",
+    "    if (\n",
+    "        note[\"onset\"] is not None\n",
+    "        and START_TIME <= note[\"onset\"] <= END_TIME\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "segment_predictions = [\n",
+    "    note for note in predictions\n",
+    "    if (\n",
+    "        note[\"onset\"] is not None\n",
+    "        and START_TIME <= note[\"onset\"] <= END_TIME\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "\n",
+    "# Draw the performed MIDI notes.\n",
+    "for note in segment_response_notes:\n",
+    "    plt.hlines(\n",
+    "        y=note[\"pitch\"],\n",
+    "        xmin=note[\"start\"],\n",
+    "        xmax=note[\"start\"] + note[\"duration\"],\n",
+    "        linewidth=5,\n",
+    "        alpha=0.35,\n",
+    "    )\n",
+    "\n",
+    "# Ground-truth labels.\n",
+    "for note in segment_gt:\n",
+    "    if note[\"pitch\"] is not None:\n",
+    "        marker = \"o\" if note[\"label\"] == \"paired\" else \"*\"\n",
+    "\n",
+    "        plt.scatter(\n",
+    "            note[\"onset\"],\n",
+    "            note[\"pitch\"],\n",
+    "            marker=marker,\n",
+    "            s=100,\n",
+    "            facecolors=\"none\",\n",
+    "            label=f'GT {note[\"label\"]}',\n",
+    "        )\n",
+    "\n",
+    "# Pipeline labels.\n",
+    "for note in segment_predictions:\n",
+    "    if note[\"pitch\"] is not None:\n",
+    "        marker = \"x\" if note[\"label\"] == \"paired\" else \"+\"\n",
+    "\n",
+    "        plt.scatter(\n",
+    "            note[\"onset\"],\n",
+    "            note[\"pitch\"],\n",
+    "            marker=marker,\n",
+    "            s=100,\n",
+    "            label=f'Pipeline {note[\"label\"]}',\n",
+    "        )\n",
+    "\n",
+    "plt.xlim(START_TIME, END_TIME)\n",
+    "plt.xlabel(\"Time (seconds)\")\n",
+    "plt.ylabel(\"MIDI pitch\")\n",
+    "plt.title(\n",
+    "    f'Ground truth vs pipeline: '\n",
+    "    f'{selected_result[\"composer\"]} - {selected_result[\"title\"]}'\n",
+    ")\n",
+    "\n",
+    "# Remove duplicate legend entries.\n",
+    "handles, labels = plt.gca().get_legend_handles_labels()\n",
+    "unique_labels = {}\n",
+    "\n",
+    "for handle, label in zip(handles, labels):\n",
+    "    if label not in unique_labels:\n",
+    "        unique_labels[label] = handle\n",
+    "\n",
+    "plt.legend(\n",
+    "    unique_labels.values(),\n",
+    "    unique_labels.keys(),\n",
+    "    loc=\"upper right\",\n",
+    ")\n",
+    "\n",
+    "plt.grid(axis=\"x\", alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "segment_error_df = error_df[\n",
+    "    (error_df[\"onset\"] >= START_TIME)\n",
+    "    & (error_df[\"onset\"] <= END_TIME)\n",
+    "]\n",
+    "\n",
+    "plt.figure(figsize=(14, 5))\n",
+    "\n",
+    "for _, row in segment_error_df.iterrows():\n",
+    "    if row[\"status\"] == \"FP\":\n",
+    "        pitch = row[\"pipeline_pitch\"]\n",
+    "        marker = \"x\"\n",
+    "    else:\n",
+    "        pitch = row[\"gt_pitch\"]\n",
+    "        marker = \"o\"\n",
+    "\n",
+    "    if pd.notna(pitch):\n",
+    "        plt.scatter(\n",
+    "            row[\"onset\"],\n",
+    "            pitch,\n",
+    "            marker=marker,\n",
+    "            s=120,\n",
+    "            label=row[\"status\"],\n",
+    "        )\n",
+    "\n",
+    "plt.xlim(START_TIME, END_TIME)\n",
+    "plt.xlabel(\"Time (seconds)\")\n",
+    "plt.ylabel(\"MIDI pitch\")\n",
+    "plt.title(\"Alignment errors: FP and FN\")\n",
+    "\n",
+    "handles, labels = plt.gca().get_legend_handles_labels()\n",
+    "unique_labels = {}\n",
+    "\n",
+    "for handle, label in zip(handles, labels):\n",
+    "    if label not in unique_labels:\n",
+    "        unique_labels[label] = handle\n",
+    "\n",
+    "plt.legend(\n",
+    "    unique_labels.values(),\n",
+    "    unique_labels.keys(),\n",
+    ")\n",
+    "\n",
+    "plt.grid(axis=\"x\", alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>onset</th>\n",
+       "      <th>operation</th>\n",
+       "      <th>event_type</th>\n",
+       "      <th>reference_index</th>\n",
+       "      <th>response_index</th>\n",
+       "      <th>correct_pitches</th>\n",
+       "      <th>missing_pitches</th>\n",
+       "      <th>extra_pitches</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>62.054546</td>\n",
+       "      <td>match</td>\n",
+       "      <td>note</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>153</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>62.230828</td>\n",
+       "      <td>match</td>\n",
+       "      <td>chord</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>154</td>\n",
+       "      <td>[50, 65]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>63.063094</td>\n",
+       "      <td>match</td>\n",
+       "      <td>note</td>\n",
+       "      <td>148.0</td>\n",
+       "      <td>155</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>63.231898</td>\n",
+       "      <td>extra</td>\n",
+       "      <td>note</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>156</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>63.282111</td>\n",
+       "      <td>replacement</td>\n",
+       "      <td>chord</td>\n",
+       "      <td>149.0</td>\n",
+       "      <td>157</td>\n",
+       "      <td>[53]</td>\n",
+       "      <td>[69]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>64.091941</td>\n",
+       "      <td>match</td>\n",
+       "      <td>note</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>158</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>64.256471</td>\n",
+       "      <td>match</td>\n",
+       "      <td>note</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>65.107968</td>\n",
+       "      <td>match</td>\n",
+       "      <td>chord</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>160</td>\n",
+       "      <td>[57, 65]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>65.265019</td>\n",
+       "      <td>match</td>\n",
+       "      <td>chord</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>161</td>\n",
+       "      <td>[55, 64]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>66.031046</td>\n",
+       "      <td>match</td>\n",
+       "      <td>chord</td>\n",
+       "      <td>154.0</td>\n",
+       "      <td>162</td>\n",
+       "      <td>[53, 67]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>66.204123</td>\n",
+       "      <td>match</td>\n",
+       "      <td>chord</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>163</td>\n",
+       "      <td>[52, 61]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>66.873995</td>\n",
+       "      <td>match</td>\n",
+       "      <td>note</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>164</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>66.966944</td>\n",
+       "      <td>replacement</td>\n",
+       "      <td>chord</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>165</td>\n",
+       "      <td>[69]</td>\n",
+       "      <td>[53]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>67.037457</td>\n",
+       "      <td>match</td>\n",
+       "      <td>note</td>\n",
+       "      <td>158.0</td>\n",
+       "      <td>166</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>67.107970</td>\n",
+       "      <td>replacement</td>\n",
+       "      <td>note</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>167</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>67.919936</td>\n",
+       "      <td>match</td>\n",
+       "      <td>note</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>168</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>68.101560</td>\n",
+       "      <td>extra</td>\n",
+       "      <td>note</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>169</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>68.152843</td>\n",
+       "      <td>replacement</td>\n",
+       "      <td>chord</td>\n",
+       "      <td>161.0</td>\n",
+       "      <td>170</td>\n",
+       "      <td>[55]</td>\n",
+       "      <td>[76]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>68.822715</td>\n",
+       "      <td>match</td>\n",
+       "      <td>note</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>171</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>68.971219</td>\n",
+       "      <td>replacement</td>\n",
+       "      <td>chord</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>172</td>\n",
+       "      <td>[66]</td>\n",
+       "      <td>[58]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        onset    operation event_type  reference_index  response_index  \\\n",
+       "0   62.054546        match       note            146.0             153   \n",
+       "1   62.230828        match      chord            147.0             154   \n",
+       "2   63.063094        match       note            148.0             155   \n",
+       "3   63.231898        extra       note              NaN             156   \n",
+       "4   63.282111  replacement      chord            149.0             157   \n",
+       "5   64.091941        match       note            150.0             158   \n",
+       "6   64.256471        match       note            151.0             159   \n",
+       "7   65.107968        match      chord            152.0             160   \n",
+       "8   65.265019        match      chord            153.0             161   \n",
+       "9   66.031046        match      chord            154.0             162   \n",
+       "10  66.204123        match      chord            155.0             163   \n",
+       "11  66.873995        match       note            156.0             164   \n",
+       "12  66.966944  replacement      chord            157.0             165   \n",
+       "13  67.037457        match       note            158.0             166   \n",
+       "14  67.107970  replacement       note            159.0             167   \n",
+       "15  67.919936        match       note            160.0             168   \n",
+       "16  68.101560        extra       note              NaN             169   \n",
+       "17  68.152843  replacement      chord            161.0             170   \n",
+       "18  68.822715        match       note            162.0             171   \n",
+       "19  68.971219  replacement      chord            163.0             172   \n",
+       "\n",
+       "   correct_pitches missing_pitches extra_pitches  \n",
+       "0             None            None          None  \n",
+       "1         [50, 65]              []            []  \n",
+       "2             None            None          None  \n",
+       "3             None            None          None  \n",
+       "4             [53]            [69]            []  \n",
+       "5             None            None          None  \n",
+       "6             None            None          None  \n",
+       "7         [57, 65]              []            []  \n",
+       "8         [55, 64]              []            []  \n",
+       "9         [53, 67]              []            []  \n",
+       "10        [52, 61]              []            []  \n",
+       "11            None            None          None  \n",
+       "12            [69]            [53]            []  \n",
+       "13            None            None          None  \n",
+       "14            None            None          None  \n",
+       "15            None            None          None  \n",
+       "16            None            None          None  \n",
+       "17            [55]            [76]            []  \n",
+       "18            None            None          None  \n",
+       "19            [66]            [58]            []  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "segment_event_details = []\n",
+    "\n",
+    "for event in selected_result[\"event_details\"]:\n",
+    "    response_index = event.get(\"response_index\")\n",
+    "\n",
+    "    if response_index is None:\n",
+    "        continue\n",
+    "\n",
+    "    response_event = selected_result[\n",
+    "        \"response_events_normalized\"\n",
+    "    ][response_index - 1]\n",
+    "\n",
+    "    event_onset = (\n",
+    "        response_event[\"event_start\"]\n",
+    "        + selected_result[\"response_onset_offset\"]\n",
+    "    )\n",
+    "\n",
+    "    if START_TIME <= event_onset <= END_TIME:\n",
+    "        segment_event_details.append({\n",
+    "            \"onset\": event_onset,\n",
+    "            \"operation\": event[\"operation_type\"],\n",
+    "            \"event_type\": event[\"event_type\"],\n",
+    "            \"reference_index\": event.get(\"reference_index\"),\n",
+    "            \"response_index\": response_index,\n",
+    "            \"correct_pitches\": event.get(\"correct_pitches\"),\n",
+    "            \"missing_pitches\": event.get(\"missing_pitches\"),\n",
+    "            \"extra_pitches\": event.get(\"extra_pitches\"),\n",
+    "        })\n",
+    "\n",
+    "display(pd.DataFrame(segment_event_details))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bach Prelude_bwv_858\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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EEEIIIaLwxKMP650WWaFdvkII0UAR/fnPf37YaaedCjGdQ0UXLlwY3vOe9xSOdDBH+vrrr7/Sv9tggw1WcqsPh9933HHHVdx6IerHT3/60/DH398QwtrPD162GgohhKi+r9WEWgiRI/fee28451snh77N9g89a66XujlCRJ1jaewXQoiGHCxKx86Boquttlq4/fbbw2WXXRauu+668JWvfCV88pOfLB4zbdq04v9PPvnkSv+Ww0btZyPx4Q9/ODz88MNDX0uWLKn41QhRD+68884wdfpqwQsUdiruhBBCCCFEVXNO9MT+pY/rDRbZoPmVEEI0TEQnqgXR/N/+7d/ClCmDhngc6Pvtt19xeCjMmjUrdHd3h7vuumulf8vf58yZM+rvnj59elhnnXVW+hJCDLpx1n7ahnorhBBCCCFE41l99dWL/w8s703dFCGiIiFdCCEaFOey4YYbFgeK4kJvhb8/4xnPGCp6XvCCF4Sf/exn4W1ve1vxvUcffTQsWLAgnHDCCaGO9HR3hWk9ydYu3LwHIj7s4OD+WXezDcO0p7pdfGaKGBBCiOrRRFoIkSvseqaMnTKw3PEcbKA4TnSw2k43T9IcTQghhHAqolPQHHbYYeEjH/lIeOqpp8Lmm29eHCx6wQUXhPPOO2/ocZ/+9KfDi1/84nDUUUeFXXfdNXzxi18MM2bMCIccckioI0fuPa/4EiI299xzT/H/977qeeHkWbP0AQghhBBCiEYzderUsP2sp4UTD9gxvPCFLwwe6e/vL3aLbrTRRsUubCGEEEL4JOko/YUvfKEQxa+55prw9a9/vfjeFVdcEV7ykpcMPWa33XYLl156aZFt/p3vfKcQ0i+55JKw5pprJmy5EPUV0SnQhRBCCCGEyGEnDuat4WdsCZHL4aJCCCEa4ES3ouaAAw4ovsbiuc99bvjWt74VrV1CNFVEX2+99YozA4QQQgghhMgBIkLZ+SyEEEIIMRm0X0yIjER0O2/AA3JHCCGEEEKIqpETXeSIzkMRQojOIxFdiEzwJqJT2Km4E0IIIYQQVYvocqKL3JBhSQghOo9EdCEyKaK8iegq7IQQQv2tEEJUjeJchBBCCNEJJKILkQEPPfRQWLZsmSsRXQghhBBCiKpRnIvIEe34FUKIziMRXYgMuPvuu4v/S0QXQoj80ERaCJEzcqILIYQQohNM6chvEaImnLZgUTj9wsWj/HQgDCA2FH8e/G9TeOKOa8NjN94VLjz1T6Gra8Xa2RF7zQ1H7j0v5MzY18TK6P0SQgghhKgXcqILIYQQohNIRBdZ0dc/EHr7+ks8Ejm9OSx99MEQVl8nLCteev9K70fulL8m9H4JIYQQQtQNHSwqhBBCiE6gOBeRDffff3+44AdfCQPLl4Xc6Hv8wdCz5nrBE4oXEEIIIYQQVaM4FyGEEEJ0AonoIhsWLlwYHrjnbyFH+p54KHSv+bTghYEBOeCFEEIIIUQcJ/rSpUtDf3+5nYdCCCGEECMhEV1kw9///vew5jrrha4pU0NODPT3hf4nHwk9a6ybuilCCCGEEEJEd6LDU089pXdeZIWMS0II0Vkkoots+Nvf/hbWWf/pITf6nni4iHjvceREF0IIEQ9NooUQuTvR4cknn0zdFCGEEELUGB0sKrJyoq+34TPDtOWjrR0NFMeJdhV/HvxvExh46uHQ3dUVVltng9DTs/Jr7+luzuucKLwH04a9L2M9VgghhBBC1E9ElxNd5IbOoBJCiM4iEV1E5bQFi8LpFy4e93FH7DU3HLn3vI49LzmI9913XzjkwFeGb+2224iPISfx3nvvDRtttFHo7m7OJo3zzjsvnLvmvHDqZ1/nopDiGvivBYtKt6XT18Jw+N1V/n4hhBBCCJEOxbm0Nw+LUX+LeDvRPMz/hBCiKUhEF1Hp6x8IvX39pR7XSe6+++7i/5tssknIjXvuuSc84xnPcFNA8dkuKz7fcp9xp68FIYTIDS/9vxBCpBTRc49zKTsPs8eKZiAhXQghOkdz7LbCPY8//nj4v1/9KAz0LU8S5QIbb7xxyFVE98Dtt98e/vz785TPK4QQQgghoqA4lxDOPvvscO+SW3XFZbiIroV0IYToHBLRRTRuvvnmcOfN14WB5b1JDhVdf/31h4ronPAkol955ZXhr4tvVDEnhBBCCCGiMH369KL2zNmJfsUVV4S/3XpT6mYIIYQQtUYiuojGX//617DaGmuG7ulrJHGiP/OZzwy58cQTT4RHH33UjYhO5vxa662fuhlCCCGEECITENAR0nM+WHSNNdYIvU/lu4gghBBCdAKJ6CKqiL7uBmnEXJzoueahgy8RfYPUzRBCCCGEEJnloucsoq+55pqhd2m+rz/3THQhhBCdQQeLiqhC9tOe/owwrX/8tZue7s4dgtbb2xvuv//+LJ3onkR0Crh//OMfYZ1NZ4dpD5dfv+vktSCEEEIIIfKDSMec41xwoi9f+mSYtk65Glz1txBCCLEqEtFFFJYtW1a4kA/5f/uEb+++e9R3/e677y4E3FxF9PXWW6/Ywpqahx9+uLgODn/5juGL22+fujlCCJEFjH86VEwIkTtyoq8ZdthkjfDzT+yb+qMQkVENIIQQnUNxLiKaC52JfIpIFfLQIdc4Fw8udMCFDk9/+tNTN0UIIYQQQmSEnOhrhMcffzz1xyASoDgXIYToHBLRRTQRHVKJ6E972tOK4jk3PIno7ETACbHhhhumbooQQgghhMgI5gG5Z6I/8cQTElSFEEKISSARXUQ7VBTxNEWsSK6HiuI6QLj25EQnWmbq1KmpmyKEENmgbdxCCKE4F0T05cuXF2dFibxQHSCEEJ1DIrqIJqLPmDEjybuNEz3HPPSHHnqoKJS9iOgI+htttFHqZgghhBBCiMxQnMsaxfuAG13kZ6xSpIsQQnQGiegimhs8hYjOQZY4oHN0ohPlAl5EdD4HiehCCCGEECI2Olh0zeJ9UC56fsiJLoQQnUMiuqgcijVc0SlE9LvvvrtYec/Ric5r7+7uDhtssIGbaBkdKiqEEEIIIWKTuxOdOBeQE10IIYSYOFMm8W+FKB3lAinc4Hag6cYbbxxyw0Trnp6e1E0Jjz32WHGYk0R0IfLktAWLwukXLh7150fsNTccufe8qG0SQggvfWDT8dDHI6IvXbq0MHbk6Mw1EV1OdCGEEGLiSEQXUYRshNwUsSLkoXOY5Rr/zAHMLc7FU5QLKM5FiDzp6x8IvX39Y/5cVEOugpEQdeoDm46HPp44F/pDhHQE9dzg9YOc6HmiOkAIITqD4lxEFCc6cSopHNG5HipqcS5eHPi44kFOdCHyo7+/PyxZeJ0OtRJCZMny5cvDXYtvTN2M7DHhnJ2ROULEI0K6nOh5ooNFhRCiM0hEF1FE9FQHe+KCz/FQUSZs999/vxvnN070ddZZJ0yfPj11U4QQkVm8eHH4w7k/Cv1PPab3XgiRHTfffHO47Owfhv6lT6RuStaYEzv3XHSJ6EIIIcTEkYguKl/1RkRPcajosmXLCvE2Ryc6r5v33lOcixdBXwgRF5uwd/VM1VufAG3hFiK9AxgG+pbpo3DgRM9ZRCfeUnEueaJaQAghOoNEdFEpDz74YLFtMoWITiY4QnKOTnReO3iKc1GUixB5YoJF19RpqZsihBDRmTLln0dQ9eebie6B3ONcQE70fFGcixBCdAYdLCoqBRc6pBCyyUOHHJ3oiOhMFtZee+3gRUTffvvtUzdDCJEAXG/Tpk0L001IGoGebh18KYRoJlOnTg10cVO6BsLUnjz9Sx76eItzyV1Ef+wxRavlig4aF0KIySMRXVSeSY6Yu/766yd57nXXXbfYupijiE6Ui4etewhoxDnIiS5Evk7052/5zPCZT+2builCCJHEif7sGeuG773/BWHOnDn6BBKhOJfBOBeMLSI/PMwJhRCiCeRphxDRsDz0FAM3TvQco1xaRXQveeigTHQh8hXRc1zMFEII6OnpGTqrR6TNpueA+9yd6DpYVAghhJg4EtFF5SJ6KiEbJ3qOUS7eRHRzvMiJLkSesBvFttELIUSOcS6wfPny1E3JHtzouR8sKhFdCCGEmDgS0UVl9PX1hbvvvjvJoaJMVHBA5+hER7B69NFH3YjofA44X+REFSJPJKKnRYeJCeHjYFGJ6D5E9Nyd6Lz+fh1ymyWqB4QQYvJIRBeVOpCZMKQQ0XFiUyDm6EQ357cnEV0udCHyRXEuQoickYjuB3ZF5Syim6GFxW0hhBBCtI9EdFFplAukENGJcoEcRXTc/55EdER95aELkS8S0YUQOSMR3Q+5x7ngRAdFuuSJDhcVQojJIxFdVCqir7vuukMFW+xDRddZZ50kz50aXPjrrbdecXiSBySiC5E3inNJiybNQqRFIrofcneiS0TPG8W5CCHE5JGILioV0VO40M2JnmMeujfReunSpeGRRx5RnIsQGYPrTweLCiFypaenp/i/MtHTk7sTXXEuQgghxOQYPOlGiIpE9Pnz5yd5b3Gib7PNNiFHiHPZfPPNg5c8dPAi6gsBpy1YFE6/cPGIb8YRe80NR+49T29UB11PinMRQuS+GwQ3ukT09OhgUcW55Mx/Xbh41Po3Z1T7CyHaQSK6qMyBfN999yVxojNJwY299957hxwFK177rrvuGjyJ6HU+WJT3VHEIzYFt3Mv7+kNvX/+IP+/rH4jepqaPBdxDcqILIXIGEX3ZsmWpm5E9uce5TJ06tbgWdbBoflCLce0vXd6nec0wVPsLIdpBcS6iMic4pBDREZH7+/uzPFT0oYceCr29vW6c33wWuH7WWmutUFckoDcHJg9HH310uP/vS1I3JRtsoi4RXQiRM4iXcqKnJ/c4F2paIl10sGh+LFmyJPzia58JA8vyXUQSQohOIBFdVBblQqGWQsgmDx1yzETnUFHYeOONgxcnOi50CdHCAw888EDhjBbxRXTLYRXx0UFiQqRHcS6+nOg594scLionen4wF+tbvjz0P/lI6qYIIUStkYguKhPREU+nTZuWxAW/zjrrDJ1An5uI3t3dHTbYYIPgAU+HnArBIbew2hr13RlRN8zxJxFdCJEzEtH9ONHZrZpztA7zIznR82Pdddct/t+/9PHUTRFCiFojEV1UJqKniHIxJ3qOUS4morN40dPTEzwgEV144uGHHy7+LxE9voiuOBchRM5IRPcjokPOuehyoufJ2muvHbq7u0L/UxLRhRBiMuhgUVGZiL7nnnsmeXdxom+11VYhVxH9Gc94RvAALh8y2nWoqPDkRGcCPX216WFaz8hryD3dXdHb1WSUiS6EEDpY1Au2oMsCL7tWc4SdYZhcRH5xLquvuXboXvbEqDVwrqj2F0JEEdHJlb3llluKjNnh7LHHHhP9taIBPProo8VXCic6hzYhJL/4xS8OOXL33XeHHXbYIXjgvvvuKzIn6xznoiz35onoTJqP3Hte8SWqB6ECByaH6ok0qB8TIj06WNSXEz3nw0VTx7mctmBROP3CxR35XUfsNVf1XBu8ZP7m4f8985nhwAP37cj7L4QQOTIhEf2iiy4KBxxwwNAhhsPJ+bAWMehChxQiOgdZknWYY5wLCwj333+/Gyc6nwXU2YkumhfnYpmQIg4IFcpDHx3qJYncQjQfxbn4cqIrzmXw0O8U9PUPhN6+/o79LlGe9dZbbyjaUAghxMSY0F6eI488shDRyZ5mgjz8S+QNIjqThRTiKdckbLLJJiE3EK0RZLyI6GwVxXkl0VJ4c6KLuHEuykMXQuQOZ9VgdhBpkRN9MM4FJ7pMb3mK6A8++GDqZgghRH5O9FtvvTUcf/zxYa211up8i0TtsYM9u7u7k+Shc3BKjtem7QzZeOONgxdRn4UUuSyFF3DfeLk/ckEi+tiofxQiDxTn4gMdLDoY59LX1xd6e3vD9OnTU38kIiJyogshxOSZkMq55ZZbhrvuuqsDTy+a6kRPEeXSKuDnCCI6kwMWEbw40euchw5y6TTPia6dEXFRnEta1IcJ4QPFufjAzujIOc7FItZi56ITt7ls2bKozylWFdH53PU5CCFEBCc6hwS2xrkcdNBB4ZRTTglz585dxUm14YYbTqJJou4TdoTsHXfcMcnz40RnkSdXEZ0oFy/ORpzoXg45nShe3ksxeXBdPfbYY4pzSSCi43oTQoickYjuBwwnOceP2pjMTrH1118/ynMSZXTCCScUUSLr7PyaKM8pVsWMJOzMlF4jhBAVi+gj5VvvtttuIz5Wzqd84WDLpUuXJnGiI5IhJO+5554hZxHdA3wWXAs6VFR4cqGDnOhxYZKuiZoQInckovuBczrkRI/rRGcHO0YneGThdWFaz7yO/N6ebpld2nWiw0MPPaTaTAghqhbRr7766ok+h8gsygVSiOjEhyDe5nioqIno22yzTfAAAjrbNiWi++S0BYvC6RcuDjmx7OF7wgN/XBLO/+pVYeo6S8IRe80NR+7dmUmcGB3cfjpYVIi05Njng6d+HhE91wiFdq6/GJ+ZnOhrRhfRLXv9+r8+HBb/9baw7nM68xlzXdWtb0vZL7WK6ELkQK71Tx3qoixE9Pnz51fbEtEYEZ2svRRuT3M45JiJjtvz0UcfdeNEJ8oF6p6J3lT6+gdCb19/yIneJx4L/QMDoW/KamGgr794D0T1KBM9LYqkErn2+eCpn8/5YNF2rr8Ynxkies5OdBa2GRuYO8RirbXWKv7Px7us98ks+yMP/RLX/rRp0ySii2zItf6pQ12U3cGi5Mr+5Cc/WeX7fI+fiXyxQ0VTTNzJYqdI83KwZmwXPngR0WlPT09PeNrTnhbqSlNjqS6++OKw8KrLQm709z4RQlcIXdNWT92UbOAeYpIuJ7oQ6cBx+LufficM9OXpgvZCrnEud9xxR/jDuWcFT+Qe59Ld3V28BzGd6K1nowwsWxrtecXKMD/HjU4muhBNB12S+qd/Wb79vXAkoh999NFDol0rfO9DH/pQJ9olagpCdoooF3Oi5+hCtygXT85vnOjkIFOo15WmOjivvPLK8MA9g7FLOTGw9InQPRX3VX2vybpBdAERWxLRhUjH3XffHe6+45bQvzSe61SsSq4iOrX5koXXh4H+vuCF3ONcgF3DMUV05gM8Jwz05v3epwYRnQNehWg6LJZS/yx/eFXdUojJMCE14ayzzgpvfOMbV/k+3/vxj388qQaJ+sLkgMlaKhEdAT/XPHTed4oiJgZeRHQvgr5YGQrn1ddaJ7u3BQGpa/rgBE7EwbaK28RZCCFyJVcR3bKwB5b72QmRuxPdnOEx41zsOUGu0LTIiS5yYf311w89U6aEvsceSN0UkWsmeisUgWwD2mCDDVbZMtrOyv7+++8/4uNf+cpXhqOOOmro71dccUX48pe/XLhtt9tuu/CBD3xglecWPoRcDpNMIWTzvFwfL3zhC0OOsAvEk2hNe571rGelboYYIV4DEX2NOVuGcH9+cS7dEtGjYuO7RHQh0tHUXVV1I9eDRc3cMdDXy9+CB+REj+9Eb81FH1i+NAwM9GtnYEIR/fbbb0/19EJE3QGzztM2DH33SUQXDkT0vfbaq4ht+c53vjO0TZvJMjEv/Kws73//+1dyZSxcuDAcfvjh4e1vf/vQ9y677LLw4he/OLzrXe8KL3vZy8KXvvSlsPvuuxeRBK35aiI9OMEhhYiOaEtsQM5O9M022yx4gAWN++67Lzz96U9P3RQxQjYcfe6666wbpj2UV6xJV+8TYcoa64ZpPYOvu6dbwlIsEV1xLmkXziSiCrq7qd1dYco/+79c8NTP53qwKE704vob6Ct1/cX4zHI/WBSYQ8c+x4zn5OPtDl1hal9vtsaG1P3SuuuuWxgfVR+IHFhvw2eEcOeioflf7qTuf7IW0T/72c+GF7zgBYVo99znPrfohHGLc5Dg73//+9K/50UvetFKfz///POLbReve93rhr73kY98pHCmn3rqqcXfX/GKVxRC6Te+8Y3wnve8ZyLNFxUeKspBkilch2QuQo6Z6Nx/LCLsuuuuwQM4nZko1llEj1FYnrZgUTj9wsXjPu6IveaGI/ee15HnfOCBwZX4w18+P5w0e3bIiY997NKw4447hte9bt9kbSj7mTeFpffeHh66Ykm44HOXhJ7V16702hajX3NfvOiW0m+PPpPmwVj27Bnrhp9+ZG9XO9ZyjXPJTbhCROf6O/Oo3d2YPRTnMiho23lKMZ3oXAt8HfeB3cPGG28c9fnFCid6b29vsZCUg8lhIrW3aqHm8P/2nh+e0Xdv+PwJL89q7BUORfQtttgiXHfddeFb3/pWuOqqq4oLkogVHOSI4BOBLY7//d//Hd72trcN5efhYkOU//a3vz30uLXXXjvsvffeheAuEd2fiJ7yUFEKQq6P3MBNQDHkZXJMHjp4ac9EiDHI9vUPhN6+/lKP6xR2kBCLXTmBaEEE2TrrpM2CL/uZN4Xe3qdC/8BAWN49LfSN8Lo7eW2Lzlxz+kyEqE5EL+6xvr6hP+eAzemWLl0avIATnQUNvnL6LFJnolucC8SOkhEri+g2f8xBRJ9I7a1aqDlgsKSve+SRR4pdGEJ0gglVDvvtt184++yzw3/8x3+M+rN24d+wIv7v//7vQ99bsmRJEQ0xc+bMlR7L3y+66KJRfxeFWmuxxk0j4sS5sDMh5aGiOa4wmpPEi6MDVzwZZBNdUGs6TKB/8pOfhN7Vt4r+3IjoTBhzW2xiPGChKWXxxDh0ze9+FQYGtsimnxpY3suKVAg9eYoUqbn88svD3bezqJnH9SbGX0wU6TCxNjfh1jLRPcWntEaR5lYPpcxEN/EWmN+L9CJ603dwP/roo/+svTdXBn+m2DWO4VIiuugUEwoHOuecc0b8PgPir371qwk15Jvf/GbYbbfdwrbbbjv0PUQPGL5KysBvPxuJE088sbhJ7GvWrFkTapMoD8Xx/fffn9SJ3vRCYCwRHdHay2G7ONER0HOaJLZ7rS5YsCA89lD8kz2Jc6F4zkXEHb6QmrJ4+stf/hIWXvWHEPrzycQdWLY0dE2Zlt315oXf/va3Ycmi61M3QyRG958/ET0nvDrRvQn7KZzovH6MHbGwef7OO+8cNt9882jPK1bGamF2aOYwR6b27nv8odRNEYkgXpbx16J/hegEbalcd91114h/NgH90ksvndDBjriIf/3rXxc5561Y5IDl+BqItWPFEXz4wx8O73vf+1YSUCSkVx/lAikO9uTa42DNPfbYI+QIBQIDBGcSeBHR6xzlUjX0X7DG2jhBBg9ejOlEz3GHgE0UUsa5cIDXlKlTQ1fP1JCViD51UEAR8UGs6+7O53oTwjO5iui8boweEtH9iehAzEEsNz7PqShWH4cc81lYxGOTMYNZ/5OPhLBWfvMfEYrxh9366I1CJBHRW4XokUTpadOmhVNOOaXtRpxxxhlFTtob3/jGlb6PqxlxkNx1Dhc1+Dur2GO5Hsz5IOJAx0QnlcINjmjLpCRnJ/oznvGM4AXiXObOnRvqTJUHfyGiU8BOX2NwAhMTCmYvOxZSONFTiuhsKZ2+evzPPCX9y5eG7ikai1PBuNjTs3oI2jUvFOeSnFxFdGop5mRe41xyhV3dsUV0myNgxNt+++21YzUh7ErNwYnO60SfKER0kS1oRHKii2QiOtvRYZttthn6s4EohAu53QMqEKs4oPStb33r0IDeyoEHHlhEvRx66KGFu/Xcc88N11xzTTj99NPbeh5RvRMdITdFhIetLKZwwXsR0efPnx88wP1MgcyWzTpT5fZ3RPQi7qanO0zrGT9Rq6e7q6Mi+rx580KOIjp9U8oDlHCir7bGGqU+86bQ078sdE1fbdTX3Mlru45UuVg3lL08dWqYtrz8NZf7Z9JEFOfig1xFdEBE9+hEz1lENyd6zFx0rn0iVxHuX/e614V99tkn2nOLVcVlMtFzGP/WWvdpoeuJx9qqv1ULNU9EH65dCjEZ2lI8t9566yHn74YbbtixzM5bbrklHHLIISP+/Ljjjgs33HBDIfxsscUWxQ1w0kknhd13370jzy86J6KnErFZWWQBJsfDgShI77vvPjdOdFwNy5YtK3aQiNFFdPrPI/eeV3zFjD1CRB8rCqupcF2SAZlSTMKJvs/8zcLZ79435MJppy0qdqi98535vGZv48Ord5kVvvVqvf9CB4umBrNRriI6orVHJ7qnNqV0osc0E9jzMfcX6aAmzsWZu+9z54XXrraaatHMRXT6H+ZiOepFIqGIfvvttxf/nzNnTnER8jUaPKYss2fPDhdddFHYYYcdRh3kcZ8vXLiwcNzigu+UgC8656ZDRN97772THiqao9uKBS3efy8iOu2BOmeiV+0ORUSn30vhxkZIz1FE57WnjHIBxszcxi5cfikPc/VO1WMWC5pezsoQ6cixNvKInOhLXX0W9I1yosd1ohPdasR8XjGyEz0XZy61t+lYIk8s8hfNSCK6iCqib7bZZkMCk/15NHhMO793vN8HW265ZfElfApUFENk2KeAOJcy11ATIToFPInoTNhzEwvbFdF33HHH6M9rBwjl6kRPLaLjfmhngbkJIFCMFNMm4oDj1dyvQggfIjqLW7nhLc6FOtWbOz42jA18xRSzuQfsWpCI7iMTvWrjkAeYk1555ZWpmyESgrmPbHw0I+mJIqqIvmjRohH/LAQudEgR54Kz9u677659BvdE4bUzEUgtELaK+hRmEm5GxiYOKQ73NBGdPPbcYKKQeqENJ3pu7ge2bafMoc+dIhM9wTklwiftGFxE58nZiU6d6klEB8amnEV0y0WPGedibnSuhbF2tIvqYa7GHBqDh5c5ZJUiOtc5XzJ25Ak7jzAc5hJhJKqn9Oxq7ty5I/5ZCFb1yL1NkYNNHjgTkpwPFWVQ8OIiwIle9zz0qqNcIIWI/sADDxSLGzkWkOyWSRkrwkSFxROJ6CImEtEFeKkPcidnER33MeOwN2E/5zgXoB6M7QhHuKcW5nlzcEF7FtG97NSsGtsdzXWX4xxIrIh0kYguOsWELUqc6Pztb397KE9r2223DQcddNBQpyzyO1Q0RSFknaFlXeUoonvKH8eJvummm6ZuhltSiug40XGh5zZh8eC0sUlqax5o00EoIrZAE5Z01z1fcqIL4YPcDxa1M3O8ICf6oKCdQkQHxidFvqXDjCXMDWbNmhWajM25MN41/bWK0UGr+t3vfqe3SHSE7on8o0svvbTYGn/yyScXIiZfn/vc58Lmm28e/vCHP3SmZaJ2InoqFzwiTa6H1yGib7zxxsEDOEoQ0T2J+t62vCOis6UsxfVKoZxjHjoTRCZrKfsIRPzcRHRz+ElET0NfX1/xf4noQvggdye6tzgXOdHTONFb6yBFuqQDYwmmGpzoTYdrjh3zZmQSeYLh0s7xEyKJE/2www4LBx54YCGctxaFH/jAB4qfXX311ZNumKgHiFMI2c973vOSPD8LOHSKublrgWw3xDkvh4pSDJMvqTiX0aGAS+UGR0T3suASE5sgpHSi20QxpzgXE9G9ZKKftmBROP3CxSEX+pc9Ff7xxyXhV8v/GFb7xQMjPuaIveaGI/eeF71tIg3KRG+vH+j0/ZH7waLe8scR0b1FzMQGVzhnK8V+TiMXMatMvxN7POaQRcwlJAs0HeZcRLrgRBf5YoZPtKPcoqk99kFZiugLFy4MF1100UoOJ/788Y9/PHzta1/rZPuEcxiQmBDMmDEjyfMj4M+ePTvkCK5v8CKi21bdOjvRq85nJJc8RZSLPfc222wTcsMmySlF9Byd6HZYmRcRva9/IPT29Ydc6F+2PPQPDITlA12jvm7eE9F8bEyTiN5eP9Dp+wPRis8iRye614NFrY7OldRO9FxE9DL9TorxOBcRHSSiC/QJxuEcRXSvfVB2cS7z5s0Ld9xxxyrfv/POO4ufibyiXCCFiI4LHgdFzoeKehKtTUS3A1zqSNUOcRadUojo3Cs4snOMc/EiolO4eRGUc4tzQTy8585bQk4M9A/GuYTuntRNEYnJcafeSNx0003FWJjyc8BwlKOIbk50Tws5inMZdIXbgncscotzofZ+6L7B+Zo3OMcuFxGduZec6HnD+Mtu+dwOF2Xsvf/vS1I3o3FMSEQ/4ogjwhve8IZw1llnhdtuuy3ceuutxZ/5Hj+jk7Iv0XwRnYIoRUwB0Ri44HM9VJQFBFwETAQ8gKMHodJLezyCG5w4l9hQJDN5zVFEZwKDkGuHuqWAiSL9ZE5ilqc4l5tvvjn89n+/E/qXxhULkvJPEb1LIroQheP1lFNOCfcuuTXpu5GziM4Chp3V4AEdLLriYNGYixszZ84c+nNKc0MsLr744vB/554VPJKTiI7BC93A00KeiA/GS1IMcuLaa68NF/3om2GgL7/aw12cy6GHHlr8/01vetMqP3vHO95RfBnqrJovouNCTyEOWSeYqxMd0dpLlIs50eueh15lnAsLPriiUzj1yUOHFAJ+anjPU0/UENFzykMHc7d5WFQbctp1T8g3UEvkRBdiVfqWL5vo1Kcj5CyiA5EuXg47lhN9cKcYixt8LrHGamIUDj744OI+2HrrrUMWbv9HH648LnKiInoOB4uaE723tzfLelysAOPlZZddltVbgu5QLGI//mCYsk69dRpPTKiS0cGholVE33bbbZO8IWzHoejDjZ1rnMucOXOCJ1G/7gdXVp2HnkrItufO1YmeWkQnziW3oh3hmv6ZGJvU2IF2XVOmhfyc6Onff+GDnE0t1g8NJIxzAQTkHA8WNYGWvrj1YMnUbeKzwB3f05Nn7JV9FrjRY4no1Nm77LJLyAXmqMuX9YbQtywEZzUIIjr1KQsaXha3qsIMTKQk5FaPi5VFdHZfMEfxEDcZA4tc7nv0PonoHWRCPeb8+fM72QZRUyg+EU5f+tKXJnl+nOi40L2t7EfL+L3nnvD85z8/eIFrYfvtt0/dDLdYvFWKTHSc6EyQPERrpBDRUy+0WZxLThDn4qVARbiZOnVKmO5sklipM61rIHR3dYVpU6aGKT0jC+k93fmNnTmSY400nKHFvIH+MG2U+yHG/YFQ5SnSJIUT3QtWD3kS9mNjrxtBKWZtesUVVxSGvFe84hVJztSKLVTTlfQseyJMmT76QkWK8dhqY+rkFHOTVCL6Zpttlro5IhEWAUwk7uabb57N+LvOeuuH8PgDY9Y/mhO0h68ZpagVdECIAKkKIJzos2bNCjnCKirb0rwcKkoBjpOl7nEuVbvBmcincIMjoufoQrc4l0033TS5iF73XRp1F9GfN3fj8LlP7RtyEdEXLlwYTu67PBz/sZe5GSeESIU5jd+w04xw8uHpzAe5xrm0OtG94NEdn9KJHnPc+973vlfMG7gX3vWud4Umg1D97Bnrhm++ayd38TUI/LmI6CyaUZPqvL68YS5G3Y2GlIuIDq/ZY7uw79Kl4b3v9TUPqjPa5ysmFeWSKpOcIowOMNc8dFzo4CUTnTx0kIg+Ohxog5CdIt4CET3HPHQvmei5xrl42fmASOIhmz2mQ9giI1IeqCt8kXOci4noqV3guca5eHai2yHYOWIL3UPnhkQCExDkIGi2ur29YSJ6boeLinyhJuY6yO1wUQ50vuuuu1I3o1FIRBeTEtFZuU4hTjAIMhGxbTk5iuiIsSkOqRxLRK+z47FqgYFrNpWQjQs+Ryc6fQSTw5RxLlxXuca5SERPh7ldm55zKsZHcS6D7wFfHK6VegKfoxPdo4ju0R0fG8Zo7ouYTnSezxzw1EY5XPu8zx6FahZRqBE8tq0KmDPnsHAjxgYDJkbMnCA1AkMXxjLRGSSii0mJ6KmiXGwFMWcnOq5vL4chkYdOUewlvsGj0ICInmq7ZK5xLlYspHSiIybjfsxRRPfSH3h1oleJRHQxnJyd6EC95MGJnqOI7lGw9timFHUv43RsJ7rVQ4j3OfRLOL49OtH5/GlbLiI6czA50QUGzNxEdJzorSkSYvK0ZVE6++yzSz1uv/32m2h7RI3gRtx1112TPDedHwWwbUXLUUT3EuViTnRFuYwNhVuKPEQm7IjJEtHTYE6rHONcrGhLDSKJOSGzOFRUIrpoQU70Qdi9JxE93QIG778nJ7riXAZBRI/pRAdzorNbkGgXb+Nzp2E3pEcR3bPAX2WcCzuSUkRrCj8iOju0czLYcO1Pmzat0O622Wab1M3JT0Tff//9Sz0uh1Xl3EEgYeU6pROdTjDXySEi+vz584MnEb3OUS4xhOxUB/eYwyTHTHRzoqeMc8lZRPcS54Jwk9tOADnRhVhVyE0d55KrE51aHbHCk4iOoEC7cs5EN0E7tojeOh7z3E0X0RGqvcaI5ORER0hkIZX5WI7GIjGIRQHffffdYc6cOVm8LSwakd6gXPREIrrlHgth20FSieg40b24HGPDBIxizJMTnTiXrbbaKnUz3EKcCouLKUR0Vtshx4KRQpnCwVxPKSCDDnITcb3FuXjbKVP1AjAOP679XBeaxarkbnDhfvAgoucq2iKUehLRTdjPOc4FqI9SxbmYiN50kwdGjsWLFwevInouwprNwZhD5zgnEoNsvPHGQ4bMXER00+zuvPPO1M3IU0T3coih8CGiMyFJ4T5mIoiIvvPOO4ccYTGL98CLiM6kCLHSm0jlKV7BMvhSiOgI+JBjwch1iQM85bZNc6KnFPJjg1CFMOFFREe0arrTbaTFVg4xFEILKX4y0bknbWE1NzwK1uyWynVRw2Ccjn3YXGs9lMPhohaZUnWM20QF/pyc6Caiz5s3L3VzRCKYDzAfzzEX/fLLL1ecUQoRvVWUueCCC8Jtt91WDAabbbZZeMlLXpJtPnWOsHrHSh6umhTOWjL0bDtObuD6Bi8iuu1QqbuIXmVhi4jO708hZNNfM0nKTUQEJoYpo1xsgsj77+UQ4BiYUOIpziWX3MNWET3F+Dyc0xYsCqdf6NOBF4eBgP97cHRZdYw5Yq+54ci9NZnPRUTPNc7FoxPdq7AfGwTt2GJSqxM9FxGd3WGedui1to17IId8aBYx11lnnWSHi3auHhq7rvCKp3on18NFqT+IBM5NQzvtn/de39LO7bpqe4b13e9+Nxx++OGFk4JikFVVilI6pS9/+cvhLW95S8caJ3w70VPmoQPZTjlChheTEe45TyK6MtHHXvhBzE0haiGiN32r7Gjg/El9nzBW5piH7klE9zg5jHGwaGoRvagP+wdCb1/aCA0/rBqnwvsj4qCDRdNC3epNsGaM8tam2CDqpo5zaTpm5sDx7VFEt3rZW51UlRs9RT59dfVQfWoIT/UOGtJVV10VcsJ0OzS83ET0vn/ee/0dvP/a2uN+5ZVXhoMPPjj8+7//e5HtxaoqE7VFixYV3z/ooIPCNddc07HGCZ8wEKQU0Vk5pBjPMZ4CWEFkF4CXLYE44ym8cst8bgcKthRRLibg53qveHCiI6Lndm/Y9ngPk0XGKznR44Mo8573vCc8cv/gzimRDqsVcs9E18GiafHqRM89ziXFwaKtcS6xBfzUQrXXtuUS6cJcLLaIzu759773veGBewbPkxPpQURmR4K3ManqfjenMxCqpi0R/b/+67/CO9/5znDyySeHLbbYYuj7c+fODaeccko45JBDwmmnnVZFO4UjGGgpOlM60en8vIjIKURrT65vnOhEueT6eZQVslO5wXGi5yyip3ais1U5VxHdgxOdyQvioTeHVdX9ZWonOtc9k5Onnmy+y7AuSET3EeeCASlHPIrocqIPLnbzucS8N9ANEHTYHcKfc3Kie8Nz25riRKffpxZ98J7BnfTChxOdmghjYk6g3WGEFZFF9EsvvTS84x3vGPXnhx56aLjkkks60CzhGbv5UjrRc41ysTgXL3noJqJ7EvU9igusdqc6mDlXJzqfKa6f1E50xETFuaTDturndiZA6oNFTZDp7s7nLACvaIHbT5wL92Sumege88flRF/hCo/pCGfx4tOf/nT41Kc+FbbccsvQdBBReZ89CtXURnweHl3yVcBcjM8hZj/M2IOR6rGHH4j2nGJs2NEPOeaiS0RPIKLzpo812G211Vb6YDKA64BBN4WzFmGMDi+3LCeDIpd4COv8vTjjdajo6PT39yfLJcf5wDbdHDPRcUNTJKd2oisTPS3eDjmNtXDHtZ/yMFv6PZs8CuEBD3EutCFXEd2jE92jsJ9KRI8d6cJ7n9rkEBNiFLwK1bTNo8BflYhO/YXBKCbMkx9/+MGozynG7n8wmOUmomOAxdiXe4xZJ2hrry9v+FhuLi7IHLLNcoc4FW7CFO4mBj2K8Fyd6AjW4MX5zbZkBGIv7fEI7w8T9xSZ6Dw35OhEJ8oFUovouca5TJs2LfnBlmACibc4lxh9c8r338RKRMNpPTkL6QPFsV+D1dKqNVNPt2LQYsGCTmoRnXsyVxGdPtibiK44lxVnl8QW0X/xi1+E8847L7zmNa8JL33pS0PT8SxUe25bp7FdwQiJMeeuPO8Tf74jTFuru/K6wive6h20JDSt3JzoZojNIUqr9dpjLtLXwflI2zOss88+u2NPLuoJN96cOXOSPLetGObqRLfsLi9xLuTKsaJfdyd6lZjbQSJ6GhE9pdMJIRPRILc4F0R0L85vr3EuMTLRPcS5HPLCLcIn3jJYtOcIoi2L3/9z/SPhixfdssrPT79wcfH/I/eeV1kbdLBoGFrQUZxLOrw60XN35KVyol922WXFOHXxxRdnIaJTixLH6bVtRHPmAKYixsTYuejMk+eu0x/OPuHlk67/rK5gEUC7/SYOWtK1114bcgL9iGuGw0VjiOinLVg0VOeW4Yi95lZSD/M7+UIbWPfkRCL6/vvv35lnFrWEjhshe7fddkvy/KwYpoqS8SKiU+x4cVVa0SUn+ujgdgCJ6HGxbbMpnehEuUBuTnR2pJm7LTW5OtFTHyxqYmXKSBlP9PUPhN6+/lF/JvIQ0c2Jjvkgt6x6i07x9NpZ7EXYZ26TqxhlY3XsneTsVmutk5oObu+bbropeG3bokWLQi7jAEK6zc1iOtHp/7jPbOFKpBfRFyxYUBieUppOYtcgvO5Yueh9Y9S+oz2+LrQ1w8pllVKMDiufTABSHipKHriXAjyFiO7FhW59AgNPnXMNq57QUajhRE4xQBPnkuq5PYjoLLilFE+JcslRRPfoRJeIni7OJXfOOeecsPA+3gft2Mr9YFFb2KIdHuKuYsJ4TL+QepdMKzYuIKR7GbNiw2eBoB3biU5dxJyG9z71om8MmCdRl3paRBqe1+6xbVUJ2rGd6BYjw/NKRPcBYjLXPP2QxZzkgA4X7QxTJtIBiHyxlatUmeQ40XPNQwc6+lRROmMdKlrnoqvqtiOip3ChW5RMjnnowJat1Hno5rDKMc7FixOdCTr3uBfRxqh6soookXLhwMTKXN2drdx5553hwcemhzD16Y0+zNY7Hg4WNaEwB9FwOBapRZ/spT+2PpLF1lxFdGC8ju1EbzUXIODX2YxTVqhmXMRc4a0mpG30SXwOOZg+0LNiHyhpsaeYz2bPnh31ucXIWDQw10JOIjpG2D//+c/ZLJpVxZQq8tD322+/ibZH1EBER5hKUQBws9PR7bjjjiFHbLV0l112CV6gGFAeul8RHSd6riI6rprUInquTnQm46nfewNxBKEkt0IxtUinOJcVFBOVBi8U1wUvcS6Q4+GirSK6lzHRhHMWfnOtlQBnbGwneqsbl1opBxHdalOPIjpwuKiXe7NKmJNdd9110Req+IrtgBejw+dBv5Pb4aKI6MyN0CdkkJ44U6rIQ8/d7dJ0ET1VlAuCIMV3rk50ipve3l5XcS440efPnx/qyi233BK22GKLSp+DQWrTTTcNqZzoW265ZciR4vCQxJMynOhsk7bsz5xEdC/9lIno3mj6waKKcxlWEycUsnWwaBjaFZFavLZ7MnU7Uru+veCxTSlA0E7tRG86Vo8yl/Pmem0V+L21rQoQDqnP0RRiHjqfIkZGjA2aUuxdCamxexxNTyL6xOlu13Va5ks0F1brUuaht26/yQ1c6OBFnMLRhUBc10NFlyxZEk466aRiIaBK8QQhO6UTPddDeD3EuXjctptbnItXEb1qEOlS5pGbiK44l8FxoKe7O0zrGfmrp1tO8RzjXHJ2onuh1YmeM4zXKTLRh+/aazLUoyxo2qH3HtuGwJ8DJhwyP4v9vNLJfIGmlJuIzoIeC6d33XVX5c/V0901au1b93pYmeiiNBS+dP6pnOB0cjg6UwmSHkR0BAkvq4YI6ExI6xrncvfddxf/r1LkpFhmsSHFNYt4mPMWZd771E50JoY5bI0djkT09CxbtsxFnItE9EFeuu0zwvcO2jfZ5yEGr0UvIjr3Z254FNHlRB8EQSW2kNQa55KDE51FPOpBj0I1bWMu5LFtVWBzMlzhMY15zN+vuuqqaM8nxofP/+KLL04egRgTFswwxNo5h1Vy5N7ziq8m0tbVcsEFF5R63Ete8pKJtkc4hgILR1UqJzoueDq7XPM9EdERrFO6C1ux1fS6OtFpP0V8lYdJsdAAKUR0XOiQo4iOUIKAndqJznbR3JzojBFsC/dySJtXJ3qMg0VTxrkoE30FqYVbI/eoRWWip8WjYG3Cfu5O9BSZ6Lk50S02xatQjenEa9uq+BwQTGNHqzCHx/1OTSCDgQ/Qlfg82JWeU1wwkS433HBD6mbkI6K/9KUvLfW43Av1poKIzaQ/VZwKIn6uUS4monuJcjERmklpXUXaGIeimoieIlLFRPQc41wQrxmHPIjodd2pMVFwGfLeS0RPS2pXjTLRVybl4n+uxoPhSERPi0cnOkIW7fIk7OcS5zL8YNEcQKj2GOfiXeCvYkxkbhRbRMeJTm2EkO5lV3numHCOxpWTiI4h9qKLLirO2svt3K4kmeg46mbPnh0+8YlPhJtvvrkQNUf6Es3EDiCIeQiHgSiTWwfnXURn1Zbroa6r6RRPMUR0JgopnLCI6BSKdmBQbnnooDiX+Jijz1Mmeooxy8PBoopzyWPXgSgHtYrtkEhFzgeL0h+xkOFNsGbBV070wYNFY5rgWmvTXMx3noVq2uZV4K8C5q9mdIr5nKDDRf3AHB19Mzf9Eic6/W5ur7uTtKV+8UYfc8wx4bzzzgs77LBD+OAHPxgWLlwYNt5445W+RHNF9FRRLhQdFN65OtGZcDHoehLRYzi5m+BET5Xhj9MBJ7aX+J+Y2ETAg4ieW5yLiRFenOi4Hj3GuVSNFxFd4rEfET0XoWo0dLBoeljQ9OREt7HKm7AfGxa9ccjG/Gyov1/84heHWbNmhV133TXkgGehmrbZDtYcQNCOLWbjfqcW0OGivsjxcFGLR45xuGhT6W53tebggw8Ol112WfjTn/5UdED/8i//Erbccsvwn//5n9W1UoTcRXRc6JCrE50BlwmwNyd6XfPQOdSLhZmqt9MhZKcS0SmG6xq1M1lskpJSwGZCyvbo3ER03GyeRHSvmehNF9G5/hEtPYjHuYvX+gz8xbnkeLCoVxGd8UFO9MFoldiRLm9+85vDxz72sUJIzynOxcs5GcNFdCIIPbatKU50+v8UMTJibNCWchPRGYtZyIxxuGhTmXAOw7bbbhtOPvnk8Ic//KHoeD/84Q93tmXCFTgqiUhIJaLTubENNpUg6UGwBi8iOkUWRUBdRXQKJ4SNpjvRcxXR6as4tCqlC98yPlsPz8pJRPcU5+JRRK9SWOV3IxamPFhUB2etjIfYs9Rifmo8xLmYiJ5jnAvQF3sU0eVEXyOJiM5i0p///Ofoz5sK9BL6YcRqr22zOMSmw9yMetVq1iY74MX4rmwic1PXBykiXSSiT5wJ2ZQIof/FL34RvvnNbxah9C9/+cvDz3/+80k0Q3jHbrJUTnA7VDRXRxWdO6uGqQ9KNHBxMwmsa5yLbaWrsv0Uo4joqQ72xIn+rGc9K+QITp/U90quIrrHTHRvInrVYqYJdKnjXHKMkvIoXudaN3mOc8ltsm54PMRTcS4rDvmMLSh+//vfL3a3b7HFFuHoo48OTcciBplDpY4bHKttOZylZDuRmafFrFd53hwiNE5bsCicfuHiEX92xF5zw5F7zwteQNtiTEYbyCmWGmPshRde6CZysG60NcO67rrrwre+9a1w5plnFit4RLucccYZbtyxoto4FYr/VJ81z59rHjrcfffdRcfupZMzZ3xdnei4ALieqywUEVFx2aQ4gZ0BMec4F5w0qScoJqLnFueCiI5YlVLAbRWT+fImosc4VBRSithMSDy4r73gZezOGU9O9FzjXLw60b0e9hhbRI/tCLcIhVtvvTWL3Us25/CYi25ty+VesF3CzAdjxglh3rrmmmtC0+nrHwi9ff2j/swTpi/RH+UmotPne5gz15G2Zrnbb799mD17djj88MPDHnvsMSSs89XKS17yks62UrhwotPJpChw7PRgrr9c8ZY/zmotokAql3Un2o+4XaWwYVl7Kd4jhEwmqjmL6KmvTduum5sTHScbrh4PoqGJNd5E9KoxET11nIuc6IN4cfmkdsSnxoMTnRqaayHXOBePmehyog+OkVyXsUV0E+/pm6gdml4vYargffYoVFsEose2VfV66Y9iR6sw9+Q+s1q5iTD/uemK34eBgWe4qH3KXAv0RWhNO+ywQ8gpzsU0Pono7dO2VeyOO+4Ixx133JiPyb1QbyLcYKmiXFixRxTM9VBRi3PZaqutgidRn1V8D27TyYjoVWIieopMdFzokFpITgV9xmabbZa8iEQw8XLAZizoq728ZosN8CaiVy2qKs7FF6lF9DpMYnM5WJTPgropZxHdm0ing0UHr0sEvdhxLq2iObv3mi6iUxPa4aIerwHa5u3+rPL1Mj+LLaJbjGjsGJmYEFdz7SW/Cf1bvCr0rLFuLa4FNCZSD3ICHYQxmc+Lsy5Fe0yZSI6wyG8CiIg+f/78JM9v2/1yjXNBlMJZ62mLEX1BXfPQrf1bb711pc9BgcTkLIWgaCJ6zk50D5noTAhzE7Dor7xMDExEp0jMCYuKSJ2J3vSt+aJeeIhzgZxFdK9xLt5y2lOAEzOVEx1yOVzUs1BNpItHgb9Jh3yagYt5aMwYmZiYXtH3+IO1ENFNYyJWKieYnxLposNFJ0ZbM6wU2b4iPQ888EBR9HKjpYCVQbal53r94UIHT3EuONHnzp0b6rooRNFU9SIAIjouhxQiKvesOV5yg76KCXHq146InlseOuBkkxN9bKruE0woTCmi55BvWxcnuvAT55K7iO75YNHc71MWv2ML2cOd6DmAUO1ZRDcTTg4wR7vpppuiLxyxcBdbvI99HU2ZOi30PfZACE+fE+oionPIcW61K9rebbfdlroZtSSfq0RMGFuhSiWi20EPOXVqI4noXg7wZaLBCronUb8dcFng1IwloqeAIhgROcd7Bhc6pHaiE+ciET0tXuNcqsZDnIsy0VeQWpyz5849atFDnIvdl7keLOoxE53xgXujt7c35AziXuo4lxzwGufivW1VgDmPuVrMsZHxmOdtcrpDEQ20wYah64mHwrSe7lW+erq7XIro1M5NXtwYCbQ9dDYPtVHdqGegsYguouPUsJO7UzjRc89Dp7DxIgQhUjLZqGucixUuMUT0VDn2ONFzjnIBD070pud7jhbnkmrxaDi5HixqAl3Kg0UpyHWwqA8R3chdRPcS58J9mbMT3ZuIbjunGLtyi/4aLqLHdkjnGOfi2YnOvMFr26oAMZv5LKaXmMYb5p9NF2tfv8d24QX33hs++MF9Qx2wyGAE5bqaBCd6uCh1EVpTzlrbRMjPpigmJKKzUpViEsikjw4t1zx0oGPz4kK3KBeou4heZTwQ121qJ3rOh4p6cKLnLKJ7yUSnLR5F9KrFTA9OdGWir4wHJ3ruWJxL6sWEnONcLBM99WfQio0P3mJmYpPiYNFWET0XJzoiOqKthwW94WA+4RrIZaeMzQNT5KI3XUQnQQD9xlNfP961z4JqboeLWsqEctHbRyK6GBdurFSrU7hKGdAloj8jOyd3VVC4UMRW6dLkmmWimFJEz9mJjliSWshVnEt6EEW4z3OLNTKBLqUTXHEuK78XIj3WD6Se1OcsouP05n7w9Poloqc7WLTVaJCLE912SdquSU/YjvNc3OgmomN6iv28zEWbXBug22Bk8Xidj2Y2QOtC+M8J5sroBXfddVfqptSO7slMTs8777zw5S9/eeh7d955Z/LiVHQWCt2777476aGikOsWE+4nj050OtyUUQGTXQSo+pBaK8hSiOhcMzmL6DjRcaGndF/yGeTsRPdysCgLWd5c6FD1tWkCleJc/CA3eHpsUSm1AzRnEd2jYN0a55IzykSPg2eh2nPbquqPuO5ju8IxoSGgN/kQ19Z4lDq1uU7t7RRofBLRI4not9xyS9huu+3Cm9/85nDYYYcNff/oo48OP/nJTybyK4VTEEzp6FMeKsqEo2rR0ysUMuS1eRLREaHr6kIHiqUYeeiQIlIFNw9bMXONc8H1kDoPnck4Qk1uIjrXHeJQ6l0ABkKNRxG9ahTn4ovUmeg6WHRlET21+y/3g0XBUy66R2E/BYzbfC4xF3g4fN3qpFzqJatPPQrVuYnoZnZK4USHJke6MM9mzMWIWTcRPTdDMLnoinOJJKIfddRR4ZWvfOUqnc773ve+cNJJJ03kVwqn2E2V0olOrlZu2/ENXOjgTUSv86EbMRYB6BunTZuWZFJgzoZcneiI6B7y0G2CmBPm5PPiRM9VRDeBLmUmOkJlruO2NxFdDGLXY2oneu4Hi3oV0eVEH8wnj5mLjsj2rne9K+y3337hVa96VcgB5gW8bju/xxPcC9yjHtvWpHxyhHtqgiaL6Iy3aAV1cnYjolM/x15U8SCiox3EPhOj7kxohnPJJZeEY445ZpUJ0rbbbhv+/Oc/d6ptwomIzsp0Kmdh7oeKshOA+8yLEx8xgDbV1YmOqEZWddXv5wMPPDBUJMWG585ZRKf4T+1Ez1VEtwJMIvrYVO1yMZEwtYieMpNdiOEoziU9Hl3fXBcsbEhEXzNJNvncuXPD/vvvn7xuiwXzAl6rV7c3c/4mx4x4ENGpzXif7YyxpoJ+UycnukUH63BRUYYJzbBwUNh2yFaRaMmSJdmJBjmI6Klc6AgNdGTPetazQpM4bcGicPqFi0s99tEbfxd6//FwWHDM+cED/b1Phn/8dmH430e3CKv9vjNC0BF7zQ1H7j0vxMAKpaoXAXielIeKMilM7cbOOc6FhZqcticPF9E9xbmY8zEnqNFYfE3pBEfIl4i+gpSfheJc/MW55Or48uhEt4VfT8J+Cmzcjn1tIiT+6le/Cs9+9rPDjjvuGHIAAdWr25v62WvbqoC5Guaj2LvnmIc22YkOJAlceumloU73JWMUBs7tt98+5AJpB9Ql5KLPmxdHj8lWRN9rr73CqaeeGk444YSh4pwV1fe+971hn3326XQbRWIRfaeddkomRFHMNc2J3tc/EHr7yk3ilj72YOhaY93Sj6+aZY8+GPoHBkL/9LU71ibej1jYqn/VIjoF2WabbVbpc4z13BQCOcYHsPBmB4t6cKJzYFFOmJPPi4iOUONxYb/qe5PtqCld6CaiK85lkNzyNb3iJc4l54NFvYroOORzF9FTOdER0BHarrjiikJIJwqx6Xh2orOL1Xa05uJEZ0zg84h5lhTPW6eok4mAfsOcjLmBlx2q49XmOR4uisGABQ/lorfHhJbcTj755HDGGWeE+fPnF5ODfffdtxCM/vKXv4QTTzxxIr9SOISCklyolHnordtrmgATp4fuG8w5L0Pf4w+F7jUGD3rxQP8Tg+6EnjXque0SEZ1JXNUOYe6bVBE8ONFzPVSUyR9uktQiOguACMm5OXG9ZaLTnhwz0RlnUovoinPxk4kuJ7o/J7pEdF8iOmNW7nEuqZzotqDC/9lJmAMYXbyK6J4F/iYd8pkiRiY2ZoKskyiN5lSn9nYyFx0nuqhYRN9iiy3CddddFw488MBwwAEHFIPBRz7ykSIPfdNNN53IrxQOMRE7lYhOJ8Zko6752yNx2WWXhd/++FulHjvQ3xf6n3w49KzpR0Tve+Kh0D19jdA1ZVqtDxWtUtBgIsYkJJWQjYieax66TcBSx7ngRPfogK4arnvuLS8RKl7jXKp2JnsQ0RXn4kdEF4PIiZ4e+iW+vLm+Fecy+NkwXsZ2oreaWmwXX9PxHJliAn8uO6gsejOFiI7hxltf2OmYEGqfOuWimxM9l+vfQOtD98vtdU+GCc+yEGmOOuqoob9zg+S2db3psK2Dzo8tHingZqYDbtKWcN7T1dYsJ671P/FICAMh9Kz5NFdO9O416pu1bSJ6ldip3qky0dmGufnmm4ccsUmJByd6bnnoYFs2vQiGONvkRE+D4lxWIBHdBzpY1AcItR7jXHJ3opsbXSJ6HKGa95noNQ619dY22sX94CWar0p4/5kz2NwtFjYXRbzHBdxEiGbCUFY3JzrjU267urkGed1cj00yr1bJhNRJHOetAvohhxxSrNwgtv7xj3+c0ARj8eLFY56Ge++994brr78+28N4UsDnsdFGGyUb4Ol0mxTlAvfcc09Y+2nlxNW+JwZPR/flRH8k9KxezygXiDE4WJZgChGdvjR2rp9HJ3pqER03Va4iuqdJFw4fiehpiH1Il3cU55IeT3EuiFQ5i+je3JfKRB8EM1xsEb3VgBf7uVMK1eDRjW5tyy3SJYUTHXKIdKmbEx3qJPx3AlvIUaRLeSY0w3n/+98fXvOa1xR/vvHGG8NZZ50VLr744nDYYYeFD33oQ239rnPOOSfMmTMn7LnnnsXXfvvtt9KBFr29veGtb31rEROz//77F6Lu17/+9Yk0W0zANZ0qygUxEBG/aYeKMpCsu8HTw7Se7nG/up58JPRMnRamr75WqcfH+ApPPhymr/20jv7Onu44rlUmzjgNYjjRmSSnEHJxQOMAzTXOhQkJk+HUB1PxOeQa5+IlD50xxKsTPYeDRZWJvvJ7IdLjJc4FY0qumeheneiKcxmERfDYZrVc41y8i+ge21aloB3bic4cgblKDiL6WCZZb2BC43PJTUTneqQv1uGi5ZnQLAu3+c4771z8+fzzzy8EdQTwHXfcsa0tKeRD82+/8IUvFAI8nHfeecUHaE7KE044IVx00UVh0aJFYdasWeF//ud/ihx2nmunnXaaSPNFSQGCz+HFL35xkveLQgpHQpOc6LY96O2vfnX4xq67jvv47373vnDnnSF89KOvCB6gsD7qvp+FQw55xdD9XydYnEPMqPrATwox+q8UzkNbgMxVRMeJnjoPPfdMdC8iOgvwjGMeRfSqQaBLvUVcmegr4yXiKGcU5+IDr050xbmkd6LnIqJ7dntbDc18NReYF6Izxa4JeF5iRpsuol9wwQUuo4tG+1zqJvx36nXrcNEIIjor1UuWLAlbbbVV4STHKW6DXzu56Mcee2whvpuADvvss89Kj/nGN75RxMUgoMOb3vSm8MlPfjJ885vflIg+AU5bsCicfuHicR/Xt/SJcN+Ffwk/emiz8L4//CrEpvf+u8KDf1wSfrPajWHKj2NtAxoggjwMTnUH/3vEXnPDkXvP68hvt4GSnPey0S9lHxsDa39ds7JitR8RPVUeuhW9OYvoqaNcQHEu6TGRxqOIXnVGNgJ2aie6MtFXoEx0H3iKc8nZiU6fLCe6T5jfxxZPW53oucS5YDZAUPQootM/oeXk5kTns4h9KDvz0aY70Yl6pgZC06hL9rsdLpobpE9cd911qZtRGybUU7z2ta8Nr3rVq8JznvOc8Kc//Sn88Ic/HHKlDxfBx3KJ/e53vwuf/exni9X/W2+9tXAdt4o/XMB8DXe97rLLLuGqq66aSNOzhk6M97q3b/wJxLKH/hH6BwbCwBpPK/X4TvPUI/eF/q6u0Dd97dCf4PmLEz0RAvo7d0qxZYK1I6KzUOVNhCZSqY7QfrZzV50Xjohui34pnOgU5jnmcQNFf2onOm4LBIIcneiML14W/jyL6FWjOBdfpBbR7blpR854iXPJXUT3GOdiTvTU92pqEE9TxrnkIqJzjVGrehWq0WI8CvxVgfGJe585VMw5LuI95/01mdaM8TqJ6Ndcc0124wGfz4UXXliMz4zTooJM9FNPPTUcfPDBxc1P/Iq5LrngcJeXgZU3hPSFCxeGLbbYIrzhDW8oVkAQ563jtnyq4a7O8bKr+PBxJLZ+iRCuvfbacM63Ti41kVr+2P2hq6cndK+RxtXZ99gDoWeN9UJX96BzqAkgilMsltmtQTHPdetFkDIRmrZ7OjiwHehzENDNjVYVFGEpnegUvzkN+t7iXMhDhxwXMjzFuXgW0au+P2O7qUZCcS4rSC1eS0T36URPfV2kwqMTnTZxXeS8uAHU94pziRfp4lWopo722rYqsJjP2LnoONF5ziaPBfQp7BCu2+GizCG8LnJVBTos12KOLvxoIjqrEx/84AfDl770pcIVbpxyyinFIaHtFLO/+MUvwhVXXFEcUHrbbbeFG264IfzHf/xH8TPLThpebCEwjpWrdOKJJxYDgH2lcoV6Y/HixWHa9NVLTeD7Hrs/9Ky1QejqmtAl0hkRfa1qHcOxaSeehceCJxH93nvvrW2Uiy0CVN1++ipE1NQieq5Q8KSOc7FMzxxFdMZmbyJ6jm4KLyK6OX/FChe0SIcXJ7rNX1K3IxUeneg2buWei25O9JiiHteDaQK5ONFNRPcq0nkW+KvAzEexo1UQ76nXmv5eE+lSJ2G21T2fEySCcB/cddddqZtSCyZc1VMAIXxfcsklq3yVATGL1Skc6HZ4JIIhmeccJGrbCvgwh4f78/dNN9101N/94Q9/uBiY7Iv8dhGKg0LX3bCcKNv36KCInorlDRTRWYVlIKmriB5DhK66/VUfKmoHe1YdGTPW8+cqolOIMgFLLaKbEz3XOBcvO1U8O9FzENFxdUo4Fp7wdLAo5Op6pk/2eLAoeGtXChEdAT3m+8A8/9nPfnbxZ3am54Jnt7dngb+qsYF5WwoRHZqei163jHE+F8bp3A4XZYGfOCOJ6OWY0CwLkfuAAw4YEvqGU2YFm8nV3nvvvcpNhdBoLk4G8+c///krHV7KJH3BggXhox/96Jir2jm6z0qJ6BvMDmGc2ojPDyf4tI07c6Bmu/QvfSIM9D7VKBHdDtXYaaedSj2ex1JgeRKAcKJvueWWoa7vPyL68PMVOo1tBaxarB/Lib711luHHDHxOnWcS65OdIQpFte9OdE99aFG1TmLXkT0qqOz6kJuuZpe8RTnYmcXeOyfqkZOdL9Y3CSGhJhj+Tve8Y5ijjqWQa5peHZ7m4ie02I42lMqEZ356bx5aTSXWCL6pZdeWpvriTbWzT3fKTAw0xeL8ZnQLOvII48sRPSjjz56Uq7H4447LrzwhS8s4ld23333cPnll4fvfve74Xvf+97QY44//vjw8pe/vDhgcddddw1f+MIXis790EMPnfDz5gjb8xisn7bp88K0v43dgS1//OHQ1d8XVlv36WFaT/zOrvfJh0J3V1dYfd0Nw5Sozz9QHCc6ONUd/G9Pd1fHspoRddpxontyoSOOUVDV9VBRJgS8/1U76XGCM/imEHIpTviMcnWim2vGgxN92rRpxVdO2DZ4LyI6fRbC5VjRbymIsU3ew8GiykT3I6IrE91XnEvuTnREdG+ObznRB7GdZNTMMc0g3BMI6DktNjJP4D7weIgfOgtzCkwhqWvqWHC9x3YeU5/yXjfdiY72wXjH66yLjkBKRq4i+gUXXJC8bq0DE5pl3XrrrYW4PVmn3Q477BB++9vfhpNPPrlwmzOAclDpXnvtNfQY3Op87/TTTw/nn39+2G677cJXv/rVbDr1TmGrSu9/7a7h5H/G54wGB8R++clZ4aST3pxEDLz44ovDWd1zwn99/s1RnWwUDLit6eA7vVJquzbKDh48vuz5AjGwAb6ucS6x2m+Hl6ZYaTfXSK4iuh0g7cGJnmOUCwu14CnOBWHEWxEYoz1MVlIvHigTfQWajPhAcS6+nOie7guJ6Cs70W08jwW6wpe//OVi3nPYYYe5uS6qBPEUPBqUrI7GfJeL3oIT/brrrksi3jddRG/NGPd2rY/V5uuvv97VOBXrcFEWUemXrI8SHRTRiXQgL6cTsQE77rjjSs7zkUBUbxXWxcREdCYQZdzNPJZCKtXASSdLO5u0FRxRHGG1jIhr0S+th/amhsUFqMvgNxy2ykGMTPRUeehEuUCuIjoDPoVO6hgVRPTUbUjpRPcmoucIAnZqJ7riXIQ3vMS52AJXrk50+mXqXHbMeNmxpYNFV41zicmVV15ZGCGuvfbaYg6KG7LpmECFUO1tbtXatlwidpgfspM09s4Antfm2E3F4mmJbH7Oc54T6iKis5jINZHLQpKJ6IDOKxF9bLonGudy0EEHhT/84Q+FOMUKWuuX8AdFCdtpygjTbGfiJkq18sbz26plU2DgsIMqyoiBvb29ruJcuM8pKuoqDtJ+JgdVR02QiZ4qDz31oaapYQKGAzx13h4FV45OdG9xLl5FdMW55EdqJ5PiXFZ+H1LHuVgdnquIbgIVYpUXqM358hYzk+KzoYaK7URv3Tll59s0HXN7ezzA02ppr5ntVWBzNzvbKhaY65qunTH21i1jnDgXyO1wUXZkMHdSLvr4TMiqdPDBBxf/32233ZJNEkV7cDNYh1DmsSkPJ6STresBlp3IOEdwB08iusXc1HVLEyJ6jCgaiq9tt902pHKiMwHyImKmENFTR7nYBLCusUeTwSbdXq4/ryJ6DDwcLKo4Fz8iuhiEzwBhKLUTXZnoK0R0TwvOjBe2GJzzPcJusthO9FaDjh3O3nS43qjZPYro9JO4b3MU0RG0y+olnXpe5i8es/E7CebIOonozONY8KbNKTWxFGMARlqJ6OMzoVnW1VdfPZF/JhJO4FhJI0++zOQbwTdVfA7FEyJUzAEsBryn22+/fWnBmgImlaM5pQhd5/azNZlimFXcVCI6US65ijW89x623NGHbbbZZiE35ET3Q2oRnZoDoTL1rhAvpBbR5URfAZPi1E50xbkMLm56c31LRB+EXZuxnegWIwOxBfyUEJfgVaj23LYqwIRD3ZTCiQ48b9O0j+EiOvph6nqoLNSvmBnrJPx3CkT0W265JXUz3DOhWdb8+fM73xJRqbiGwGE5R+O5oJn8lnlsFVhn1aQ4FzuRmq1M7US/eMqER9h/3vOeF+oK7/+8efOiZJKnEtGJc8k1D91E9LL3WJXkerAoYwwihBfh1KsTPYeDRW03oqcxLDV1mDTmAP1TahFdTnR/cS62i8qbsJ+CFE70VhE9Fye6CbeeRXSPLvkqx2jmb7GjVcwwh9mr6SI6/WudDqzk88hRROdMiksuuSS5Icc7k5rtLlmyJPzmN78J559/fvFn4RPbklFGGG/nsVVAZ8Ukx9shK5OBgZGFibLxLLjWPYiBBp0oAnFdPxMc4hSpVTvRzb2Q0omeax46sB0ytROd+5zJZ44iOs41L1EuQLHe5K2x4/XZKQVsi8uQiD6IIg79wDXpJc6F2iRHbHHTm2BNu7y1KQUI2injXHJzonsVqnNzokMKEZ15C6aHpueim65RJ1G6bhE0nQINkDrJ4oVFB0V04jbe+ta3htmzZ4eXvexlYZ999in+zPdyWkGuCwjjFIdlBDYey+NSOfiInUFsbtLKF6I4lBXRLX/cCwzsiAB1jXNB3Kb9Vcfj8Dw4GVK5wS3OJUf4fD3Eudj4V9cDeJskouNy9OhEr1pQ5fendo+Y09fLrgQPn7fiXHzgIc5FTnS/TvTcM9FTxbnkmInuXaj23LaqYJ4YW8ymNkjxvLHhNTL21U1ER/PM5bBjw4y0ykUfmwnNcN73vveFa665pnCgM9jxxZ/5Hj8TvkCY5oYoM4njhknlQgc61yZFuZiIjphTRuCz6Bdvh4pCXUV0dgJADCc6RWcK9yXXDU7sXEV0JuO4+lIfLJqziI74wDZwL+Ao9CTqx8IEwpRxLtYGOdFXoDgXHyjOJT3Tpk1zK6LLiZ4+ziUnJzo1KwYQj7uVaBs1LfOLXEDojZ2JbvPTpovodcwYt3id3BzZjIXsypCIXoGI/r//+7/hf/7nf8JLXvKSYuDjiz/zPX4mfNGOMJ5aREfwb1omGCI6A0eZSTSDKFtoPMW5IEIjyNQlw2yk9rP6XXX7KbxSRblYEZ6riG7bYb040XOMc0FE9yRa5xrnYhPelE50xbn4cqILX0703A8WpW/iy5uIroNF08W5INxbH5mbE537wNu9ADZn8ho3UwXM4ahlY+/EQLw3w1eTwSRZJ0GaxQ3E/zoJ/50CLfCuu+5K3YzmiegMriO5hRH+chr86gCTBW7+MsI0AweREKlEbK4r3LRNc6IzYJQVxS36xVOcC050BpK6igAsTFCgVN3+lCI6h4pCrpno9BuQ2oluW/5ydKIz6fDmRPcY51J1P+RBRFecywo8OAztmvPQltQwIU6diU4b+ExyFdGBBU5vrm9log/COB5bROR+MDd6TjqC1aweY1NMRPfYtqqw2M8Uh4tadGqTQQupkyBNHY0JEoNnjoeLyolegYj+vOc9Lxx33HErFYD8+ROf+ETYZZddJvIrRUUgyjKhrcOhotZJNdWJXvaxTC5Si4GtsDpe1yiXmO1PKaKz+AVyoq+TXERHIPHkyM7RiU49wpdHEb1qPIjoJlIqE32FcK33wgceDha1+zPXg0WBvtmb+1ZxLoMgZvf29ka/Pm3ek3qnSEw8C9We29ZEEZ37zQxBTcUyxusU2ZTz4aLc+zktakYR0U855ZTwve99L8yZMye86lWvKr748w9/+MPw+c9/fiK/UlREO8I4IjYTvVRRInRSPL8nF/ZkYaCgA2pHRC8b/RJThK7zZ0L7qz5UlKKfwSaliM4EMEfRECg82SKf+vVzr+NC93T/5piJbuJM6uthJKp2GnkQ0ZWJvgIPzjI50X3FuQDjVe5OdG8iuuJcBrFxPLYb/eUvf3mxm/KlL31pyAVbOPAYmcKcgn4qJxGdBST6ptgiuhm9mp6LbvpSnSJdENFzdKLrcNGKRPSddtopLF68OLz3ve8tRCMEKv7M9/iZ8CWis5rcemjLWI9FwE01+UZER6xNOfnvNBbP0k6ci6dDRZlsMqjXVURHwKD9VTvREbF5rpRxLrm60E1EZzKSWrxGRM8xD90m3F6c6BYT4FFErxpzD6Y8WFSZ6KuSum8Sfg4WBerc3EV0b3EujF+2iylnbL4Y2y3KLvcTTzyxOGMtp0N2WbTwKFQzZqEfeBT4q3zNzONiHy5qc8em56KbSbBOzm5EdOaYdXLPdwLTAxXpMjoTVisRbD7wgQ9M9J+LSNTtUNEm5qFDWREXEX2rrbYKXkCcRRCpa5wLxR+iUtXtt4IrpRM91zx0+5xTHyoKbFPMUURnAQlBxJuInvPBojhuU6FM9BXoYFFfeIpzyVms9RjnYouujB85nmsyXESP7USHO+64o3j+qnePegIDiFehGhHdo8AfI588JtSqzGGa7kTH3MH7WzcR3fScLbbYIuRkOCBeWYeLViCi06l++9vfDn/5y1+Kv2+77bbhoIMOGsrQEj5AmN5xxx1LTfQQ0fkcU0Gnuscee4QmwaGcLDiVEXOIQ2C105MTnfZDXUV0W9WPJaKncoMjos+ePTvkChMQD+cIWJxLbtB3MYZ4iXPx7ETP6WDRlEK+FzyI6Ipz8RfnkruI7tGJLhF9EBvHYzsvb7jhhnDaaacV98anPvWpbPQEz0K157ZVBSKvaVuxn7fpTnQTpesU52LuefS0nER00OGiFcS5XHrppWGzzTYLJ598ciF88vW5z30ubL755uEPf/jDxD4p0XEoUFnVLHNQJyIUroNUTnSemzY00YneTpQLeBLRGdCZdNbV5WwFSdWuFkR0XASpIhQQ0XOPc/HiRM9VRAdvIroXZ3yKTHQPcS46TNOHiC5WoDgXH3jMRLfxwsazXEklopvjkTHstttuC7mAAcSrUO25bVU70WOfZ4LZq+lOdEATqZMTnVqaz6ZObe4UaIIYbD3s3muMiH7YYYeFAw88MNx+++3hl7/8ZfHFn9/2trcVPxM+sIMQygjj7RxAWgXWOTVNRG8n49yjiI4TnYKirmIIIjpOiqoFJWJvUm0/Ja4G8TZnEd1LnEuumegmOngRrRXnIie6ECMhJ7oPPIrorU70nMEJzucTO86l9ewuaqlc8Jw7nqsT3eZVsZ83BxEdnYc5c29vb6gLmFFzFdG5F3K4LifChJSxhQsXhmOOOWal7cL8+eMf/3i4+eabJ9QQ0XkQxnE/lRGmeSxFU6pMZwR/2lrWtV0HWLlDhG7HiY4Q6CmCABG6rlEuEONQUXOip3Lr40KHuu4W6MR9RrGbOs4F10qucS422fYmonvqS2PhIc5FTvRV3ws50X2AIcCDqyr3OBf6Zm9itZzoK7vRYzvRcxbREapjO5/Lto3FLm/3apWYDhJbOERE5zqww+GbCpoI17oZB+sAOlqOIjpxLqBc9A6K6PPmzSsO/xjOnXfeWfxM+ABhfKONNirlwuWxdBKpJnp0TrQ15cS/07DSyiSpHSe6t0UERHQ+l7oSaxEAET3loaKQqxOdyRYFWWonOm5ssnZzFNE9xrkw7nncQaNM9DxRJroPPB0s2nSxZCzkRPcNgnZsJ3pr7RRbwE8JBhDmiikOch0Py6XPyY1uu4pji+g2V22669eMnXUSpWkzc+3cor7YWc3c2tIqxMpMaIZ5xBFHhDe84Q3hrLPOKnLLbr311uLPfI+f0QHYl0gHF33ZeJZ2HluVE71MdnudsIMz2hHRPUW5MNFsghO96pgV3icWTCSip8tDh9ROdHNOKc4lPTincnShe3Oi62BRH5noOljUX5wLi3y5O9G9xbnYwmtOrtuxRPTYQnauIroJ1R4jXayuzklEp2/i+k/hRIema2fs+OGar5OIbvpUndrcKdAG5UTvoIh+6KGHhltuuSW86U1vKg4T5bRa/sz33vGOdxSim32JdBO3ssI4E146hpQiOs/fxDx0ivIyMRt8XkS/eHJ9UzQxyfPUpnZgIkTMR9X9EIUv91AqER0Bn4Jv2rRpIUds4iERPR04qOjrvOwk4t73KqJXvWXb3K0pDxY1kdLjToDYeNyinzM6WNSXE93T/cFiE+NGbm7DkWBXmTLR4+BZqPYs8FctaLPDOPZ7TQ3ddBEd2HVvRsM6gMGR8UEiumhlQjPeq6++eiL/TEQE8ZCV/DLCOG5jxNJUIjqFGsVD05zoiOgI0GUcaBQoTCg8xbkg6kNdF8O4rqFqJ7oVWimd6Lnmobc60VM7wO0QohzjXOjDveShexfRq4axnDEnpYBtIrqc6CtEdC0o+MBTnIvH+IaYIjr3Bot+ngwAjGNyog860S0qMBa5ZqJ7FtG5N1lQ8di2KmE+F1vMpm7jeW3u2mQwTd50002hLnAf8Nnkmot+wQUXFBoV47aYpIg+f/78Vb7HihJCjqdiKGcsv6iMMN7OY6vAOqUmOtHbiXIBT3EuDOQ2qNcRK4CqXgQwET3lwaK55qHbAhSTr9QuaJv0tU4EcwHnnpc8dMhdRE99L+hg0RV4ctoKP3EuOlh0taG+2tO8UU70dJno3JssYlBP5BTnQl/A++3V7W0Hn+YE5qurrroq+vMyX83Fif7b3/62GIvrYrbA6JmriG5aIekjYgUTsir9+c9/DkcdddTQ3w855JBCAOWm+OMf/ziRXyk6DBc727nLuHB5LC7OVE5OOiXEWk8CcidgYamssxwRHada1a7pdkV0BPTUgsxk2s+EqGpnMCI6z5FqhTZ3ER0neupDRc2JjpBcl4KwkzDplRPdz8GiqftsZaKvijLRfeApziX3g0XBWy66RPRBqGVSCNlmQsjJie5dqMYp77VtVcHcl6jM2LuW0ABycaLbuWt1anOOIjo6FnWTDhftkIj+/ve/P7zmNa8p/nzjjTcWh4pefPHF4bDDDgsf+tCHJvIrRYfhYmfVrMwWYg+HirL6mjLDtdMwMaDoaMeJzuDpSYAjzqWuUS7A4Mx7WrV4gYie0q1PoZeziI57J3Ueuk36UkfKpALHmpzoPvAgoisT3dfBosKfEz33g0W9iuiKc1nZiR57J42ZXnhuD7FLMUV0OdH9wNyR6y92pJE50Zu+g82SB+okStNm5vvexqwYtQpalg4X7ZCIjtt85513Lv58/vnnF4L6nnvuGT74wQ+GK6+8ciK/UnSYdoTx1CJ6Uw8VhbJOdFzr3pz4iNB1F9FjtD+liM5gzmQj90x0L070HPPQQU50XweLehHRPS0Kp0Iiui8wlngQ53KPc5GI7l9Ep++KfciqzZk8Lcrn7vb2LPBXhe0Kj324KM/b29s7dMZSU8FwxD1ep8NF7dy+OrW5U6ARyoneIRGdC3/JkiXFn88555yw1157DTnxcsyD9QYTBNzdZYRxJtyIjalF9CYeKgocLFrW9e1JRKd4pk1l2++RHER0c0nk7kT3IKLn7ET3mInu8QCcGO4iBOzUu7oU57ICD44yc8F7aEtqPB0smrOIbmdWeDvEU3Eug9h4HjsX/dWvfnV46UtfGt7xjndkdRiz5zgXa1tO44fN6WLnk5t43/RcdGoSFszq5ES3Bb46tbmTueiI6Dn1AWWY0Aj12te+NrzqVa8Kb3zjG8Of/vSnsP/++w+50vfZZ5+J/ErRYfEQcbyMMI7Yzk2RSkRHfEEIbJoTnZVKW2kdDyZSDJieRHTcvayG11VEZ5JMzEnVIjr3Ds+TWkTP3YnuIc4lZyc6E21Pmejs0PB6sGgOmegI+bxORZisQO+FD7zEueQuosuJ7hszxMXORaeW/Zd/+Zew9dZbh5yghsUQ4lGkom30mTnl1GNE4HWncKLnIKIDuk+dXN2MWcz10c1yA42QeZ7Xhb5aieinnnpqOPjgg4ub/bzzzhsSkK655ppw7LHHdrqNok3sBi8jjNv2jFQitq3oNdGJXjbKhcES0bfs42OACx3qGudiB8JU3X4EXCbCqUR0Xqc5RXKExUIW4uRET4s3ER13o0cRPYaQ6iXOJScX4ViY69mDiO5RoImNDhb1JaLLie4TMwDFFtEZO84999ziK6f+ihqescKjUG07XXOMdIktZlO3YsDLQURH80BEr9N9nuvhojjRQbnoKzNlosUP+efDOeWUUyby60SHQRjHEVkmWgDBnYEi1dZ3OiPb1tM0EX3TTTct/Vjw5Pq2E7PrKqJb+21VvyrMpZDKCY4THQE5tWiWChYxwIMTnclPjk50y031EudCezw70avGgxMdMUAi+sqkFtFTP78XvMS54HT04IhP+TnQT3k7pI1xw5uwn9KJHjvO5aabbgo///nPh4xgz3nOc0IOmBEGp6e3WECrr2mbiWk5gDkqhZjNvNXmsE0GQZr+n3lsXXZT0+arr7465AYLaRilENG322671M1xw4StQhQZuNC//OUvD33vzjvvrNWKUlOxg0LLTJpSHypqIn7qDNdOwj2AMF42noXHsojhQQg0GMAp6ur6udB+RJyqB2YT0VPGueSehw6p7x3cvxSD3iY/MSD2CVHKi4hOe+iDPYroMeojLyK6DhUdRAeL+kJxLn6g7vUmWCMU2JiW+2dDDR3bid56kKkZjHLAaliPbm/ahp6QW5RDCid6yueNjSUg1MnZTZuZ9zNG5AT3vw4X7ZCIfssttxQrEW9+85vDYYcdNvT9o48+OvzkJz+ZyK8UHaQdYTy1iN7EQ0UpghDUyrrrTXD35BRrwqGiCOhVCznEqSAepoqykIg+OOFIHedCHjrk6ES3Sa+XOBdrj0cRPQYeRHQcthLRB/FkLPHUllR4E9Fz/kzooz060YeLuTnCfAQ3emwRvbWGiv3cKaGG9SpUs5iCQcRj26oWs5nPxz67gh3gOTjRmaNj1KtTLjoiuhklcz1cVExSRD/qqKPCK1/5ylUOXHjf+94XTjrppIn8StFBRyQCaBlhnAKFASK1E72Jh4pCO050T4eKAgN4XaNcgFX8GO3neVK50E3Ez9mJTpwLBb5tPU5FziK6bff2IqKbKCMRPR3KRF+BnOi+8JSJDh7aktLt7E1Et3HMm0M+BRhEYse5tNZQHvPBq1zc8yxUszPZa9uqgrkd47edPRVTvOe9bvrB04zFaB91c6JDroeLom81/bqsXES/5JJLwjHHHLNK5uW2224b/vznP0/kV4oOwY1Np1/G3W0rSqlEdIpUnLRNc6IjinNvlM3j9iaic/00wYkeQ0SnuEqZ5VanLLmqRHRz8KTEJns5xrmYY89LnIuJHx5F9FwOFlWcy6qk7qNSP78XqM08RHXYPZrzhNSzE10i+mAuemw3eKshIicnusWmeIxzMRHda9uqwubwsaNVeF7m4cONqk0EUbpOTnQWWbkX6iT8d9KJTu2U42sfjQnNtCj6rAhtLcyXLFmSpYjgCRPGy4rorH6nEkvtRmyaEx1RHAG3zHZ2BCiEQE8iOs4T2lVXEZ3iAxF95513rvy5KHJYPEwBnxETvZyd6BT1qfPQW0X0HJ3o3uJcTPxIdVj2eH1T1WImztbUZ1kozsWnEz1ldMhpCxaF0y9cHFLzxG3XhMcW3RbO/eivkrXhiL3mhhduMGVo0cvjgl+uTnSJ6CtgYTxlnEvVTnQvfZLx0BV/C+H3fwvH3bh+tH7oyL3nlXoswuHtt98ecgKDEouuKUR04Hk9aQNVgP7zl7/8JdQJ9LUchWTTFdEOZ82albo59RXR99prr3DqqaeGE044YWhiwNaT9773vWGfffbpdBvFBA7qLFOUW5RKquxSOiGun7LZ4XWh3UNFwdNAiQsd6hrnQtGPkFZ1+80pkMoJjgsd5ERPm4ducS7Tpk0rvnLDW5yLZyd6Tpnow3cq5krOmdet9PUPhN6+9A7w5f+8PlO2hfdCTvTBPtqb49vGsdwz0c0VHjvKggVgvlhcqlrA99InGf3T1gh9j/wjWpt4/WXBrJJbnAs1DCal2CI6z8lz55CLjgbFYhlzqLqYcGnz9ddfH3Icr9EX77rrrtRNccOEZjknn3xyOOOMM8L8+fOLCcK+++4bNttss2I16cQTT+x8K0UjDxVFxCdzrGnCE1uT2jlUFCSidw4rPKoW0Rn4OaE7VSa6TW5yd6KnPlTUroW6FIBViOgU/F768dxFdC9xLhLRVxbRU78fGBZSCfpMkJf39gYXdHWHMJA+h1wiupzo3knhRG91o1fpRMcA4yHWqZXu6WuE/qd85sAzz6Afz+0MB+Z3sWNVLA42tnifAtNK6hTpgoiO2ZBaOzfQDHW46AomVNVvscUW4brrrgsHHnhgOOCAA4ptPh/5yEeKPPRNN920c5+WaJuywjiTKR6bMo8cJ3rT8tBxATLglhXF6YgRAT0JPojQCIKe2tQOVnhULaKbiJ1KRMeJjjBC/5srRCF5iXPJMcrFHHtMtj3EVQDxALQldaTJSMR4jzw40ZWJvgI50UP42te+Fm7842+DHxE9/edi/VPOmejEuXhzotMm+mk50Qed6LEPFh0uoldxn/I7P/nJT4a/Lr4heKJ7+pqhv/eJMDDgS9wH6mzeN2runEglZuciohMby6JBneJR0K24F8wEmVsuupzoK5gymVXJo446apXvX3bZZWG33Xab6K8VkwDHAM7MMiI6AhzFa2oneozc6pggitO5lhXRWX315EKHJhwqShFe9SKAuRNSiugUtqkdjqmwgt6DiF6nrYidBrHBS5QLMK7RHi+ifmw8iOjKRF+V1NdjyudnYWvZUh+xNl2I6IBQ1ZUmyhDkRPfpROc+8Sju53KwqD2vLcZyfXS6lucz5uuJRzgoczVXInqxwLf0idC1mi9Thpl1iHTJafcrYva1116b5HlvvfXW0HQYBzG81cmJbu55hH9E5Zzg9TLvznnO28qE1BfewOErZDfddFN47WtfG3bfffeJ/EqR4FBRSCWiU6Di5G2aE91WJtuJc/GWCY8IXdc8dGu/HcxStYjOZAsXbioRPadidiTxFsHQQ5wLBUWuTnScaqnugdGuC4+HikIM96sXET3Xxb3h6GDRwe3pbtyVdl0mbo/dozluCfeciQ4swnpsVwoxm+sz9jXaWktVJeLzHEufiu+yH4vu1f65eLDUV7uGi+g5wVySHRGxF/uYg6Ozpd4xFQM0kDo50ekXmXfWqc2dwjRDRboM0tYshzfthS98YeH84wZ/yUteUnSobIvabrvtCov/hRde2M6vFB2Ez4dDQss4m3ksBWwqEc5WHcmWahK8LgrwMoIag6NH1zcitLc2eVwEQETHhZ7K4cciVM4iOrtuwIMTPfc4F09O9Cqca3XCg4iuOBefInoqENG7wkCY1tOd/GtqT0/oJu6pKyRrQ093l5zoTp3owPihOJfBTHSI7UbfZpttiv/jdKwqrpB6bdlTTyTvj1q/pq+xTtE39Sx7Ilo/1I5wSF2Ro4gOsXPReV4W8lLsBIkNOlDdBGkMoHVrcydAWyGKTpEug7Q10/rgBz9YCDdf//rXi7+fcsop4UUvelEhyH7nO98p8tFzniikhs+BzgghvUyUCitKqT4vnh+8ubAnC85yFjHKvK+IgEwgPL0HTBxw1dZdRJ83b140ET0VONFz20o2kojuwYme88GilonuBSYeOYvoHg4WVZyLPxE95fMjou+99QbhzH/bN6Tm6qs3Dl/5yg3h88e+dCg2IgUmyuSeic7h7Nwjqe+PVlgUloi+IlaF3WYxz95hRzu1LUJimfnsREX03dbpDmcelr5Pal18PvzwC8IBB2xXGBY9wf2JYcXq7lywOR6u8Jg7580IxvM23aCDbsV8Fj3E6y7SkdpMAkduUMtxH8iJPkhbMy1c5hdccEHYdttti78///nPLxzov/3tb911+DlS9lBRe+zmm28eUsEKHgVSXTrMsrQTz2LRL54y0RGgoa5xLghIOCVitJ8Fxblz54YUMOnM3YluBxylFtGZ+OAWyVVEjz3BrrOIHutg0dSHqirORQy/7uknPWCiHNdoLgeLnrZgUTj9wsXBG0/9bWF4+Jo7wwUf+mXonhK3zzpir7nhyL3n1SpmJpWIHtsNS38xZ86cSp8DYZKdwN4EKupZr25v6jyvbasKFg4wJcQ+5NMc8MzJq74XUmOaCTv5Z8+eHeoioqN9etj5GRsdLrqCtj55LvCtt9566O8mpusg0fQgquHunj9//riPZfKAiL3HHnuEVNDWpkW5mDDOwlLZx1I0pXQzD8eKyrqK6FboxGg/z7XLLruEVMIlCwbrr79+yFlEZxEu9UIcLnRoultkrGvRU5yLZxE9Rh3A+J66qEcwVSb6ivcCPDhtU+Wrci1IRE9zsCif+fK+/tDb52MRo5XlXT2hf2Ag9PYuDd2RD3nt6x/9XmA8Y1zLHdthFvu94J447bTTCtfzkUceWckciXrN48GNnoVqBGWvbasKxm2uv9giOn0Qi1ixnzf1QZ11EdFxY1PTsMjRRC1rLDDr/vGPf1Sd324mOsVY68TI/px6wiYGXbGIB2Wc6AilTLRTHSpqnWXTOh7ENBwb7TjRWW32dP8wIFA4p9zmPBms4Kj6YFEmFdxvqURs7nfI2YnOBMtLHnrOIrrHOJfUCyupREyKep4j9ZiiTPRVSS2i8/wS0VfMW1KL+rFEdM6suvOma4NHuqZMK/4/sLw3eEJO9LSZ6HfccUe4+eabC+PeH/7wh0qegzmO1W6e8ByZwnwjNxHd5pMpxGye13aHNxk7n8/OyqsDpl/lmIuOdoiJ715nO3nci+h2U7d+jfY9ERfLJyqT2WWPTSWik3tFHmTMfLEYtBvPYvnpnvB40Gk7UHAwOa06XsLyTFPtIiA/LncRHSd66igXsIlYrnEu3kR0xhdPzviYmCCXWkRXJrq/TPTUwnUqAd9rnEssER0B9LGHBxfdvdHVMxjhMtC3LHiC8UNxLoP3CgJXbCd66/jFGU1VgOmB15V6MW04cqL7g3le7INFbUd1Dk50wHxYJ0Ga/oM5n53vlxOmHd51110hd9qaaX32s5+triViUiCMU/iVEdV4LKvdqdzGttrYNCc6r4uJclkRGhF9++23D95E6LpGubS2v2rBwpzgqUR0nt/yE3PFixPdJnk5OtFxI/DlSbRG1M81zsWTiD5t2qDLNHe8iMepDxb1IlZ5EdF5T/iqWkRnV07fcl8itXcRnfFDB4sOwgJ5bCd669y0Krc49Rp9M5+zp523iOhenei0jferTgdAdgLMocRXxD4Amee97bbbQg6gB91www2hbm2uk/DfKVg8YO7917/+NTz3uc8NOdPWTOsDH/hAdS0Rk4KLGWd3mQ6+nQNIq8A6naaJ6IjixHuUOdSNiRMrzN6c6IjQW265ZagrvKcxdsPwPHzOqdzHbKmkoM05dxgnetnopKpFdD4HT0JyLExo8PTaPce5VD0BY0HDg4iuOJdVRfSc++oYYnG7IroHUZ+22D1bFSxm9T3pS6Qe4p+HiQ44E/nlRF8BAnNsEb3VkFCliG6/35OIjjhFTenxwELb4YvIX+cdy+3CnJK6kp0LMa8VnhfDVA47+9CDLr74YpfX/VhtXrRoUcgRDhf96z9TLXIm36q+YbQjjKcW0dn+goPXq9AxGSd6WVEcEZZJnCcRHXcB4mwTnOhVQ2HDgkkqdx/Pn3OUi4noHpzoTMKYkOUY12Aiurc4F69O9KpdyZ6c6E2f9NWRlJnoXhz5tpiR2okOLMRXvbiAiN7f1xum9XS7+5o+bXro7uoKUwaWR3/unu6ucTPRvVyzuYnozA1t/KjquVtFdI9CNfWtN6xtueWi247j2NEqzGXpg2znc5PBEGUHddYFRHTMkx4W5GODhniX4lzac6ILn1CEI+DuueeepQQGBoKUeeRNPFQU6Ey32Wab0o8FTyK6FQh1dRhQbDAAv+AFL6j8ucjHS3n+A5noqQ419QACCJMfD3E2tCPnPHRPTnTGQr68iuhV40lEz9l57TETXXEuvg4Wtfs0hoi+59y1wpn/vm/wBp/Bux7+VTjwwOeG3XbbLXjBxg/PC7KxYIE8diY6fRXiPUJyDCe6J8wYglDtrca3tnmNm6kKm+sx75s9e3b052VuXmdzW7sHddZFH6Kd1LroDp60nFgi+vnnn1/MAb3M/1KgWU4DsJWwMu5yOwQhtRO9aYeK2gpq2XgJPjPcFh6ctIadtFzXwZrCjglpjPZTTKUscHN3orPdFXHKw/1DW3IV0W1y7aWIQvSAXIUPE+TKRIpVPR5KRPcloqeE1+5BtPaUiR5LRKfO7O3tDR6hj6Cvsn7bCzaeKRc9jRM9hshtsRzeRPTWyBRvUFfxlZsTnWuFfjS2E505Jn1kndzZE4U5FO+znZlXB0zHyvFwUeJccn3trUhEbwCWS1RWRGdCk2qlj2IZAbIuK41lYXBlUlZ2NRIRHce3p4k1AzWFQl0FQSs0YonoqQ4VRZTx6FKJiU0wvDjRczxUtFVE9xLnwhZ8yF1ET+1EVya6TxE9ZZyLRPR0TnSvIjpQc1q/7QUbP7y1K5eDRcFqKs4MqOL6pU/itXkT0RESWejzKlRjXPHatqpg7MYVzrwvJlyjzPNii/epqNtBnWgl9CF1anOnwDDa3d2dfS66RPSGiOisXpcRMngsIiOFdQpYZWQi1zQnusWztONE93Ao4nAR2puwPxERveqYFSZWTCpSieh26FDOTnTLi/QgovN55Cqi49Sjv/AiWpvo4fW8jar7Vi8iujLRV6BcZV8iurc4lxgHi3pzerfC2OGtfXKiryzqxo5zsec1qsxF9yaiUyMwn/foRAfmHbmJ6DavTCFmo9fk4ESvo4jOvYqWVac2d7J22XjjjbPPRZeI3gDqdKiodTbeBOTJgijOZMW24pV5vLcMLeJc6hrlAhQavP9VRxnYIS+pRHTy0CF3EZ0CxsOuiZzjXBDREUG8LLyZiO4lXma4mJrTwaKKc1mZ1PcIz6+DRX3FucQ6WFRO9PaQE30FmLMY52MvOrUaE6rMRfcmont3e9M2rwJ/E0X0VM+bAnQhM1rWhboJ/52OdLkr88NFJaI3ACJa6iSisz3Ji3OxU9Dxl3VxI/QgAnoT0RGh6y6ix4pySSmim4ifs4hOEc8EyASRVFDs5R7n4iXKpQ5xLrk40RXnsvJ74UVETwULKh5E61wz0b05vb23TyL6yo5w6pzY+fCtxoSqdmt4FdExA3kV0WmbGXlygvkec7/YAi9z2lxEdARp7vXYsTmTbTP6j4edbbGZMWNGoSnWadGj00hErzmIBnQ4ZYRxHJN8pT5UtGl56O3Gs1j0iycRnYkc4iwLAXUl1gnm3G9MxFMdaklxzeQ7V/czsAjl4VBRc2jlKqJ7O5nde5xL1ZjYkFpElxN9VVKL6CnxFOdiIrqH9igT3beIroNFV8SqxI502XXXXYt6ft68eWH27NlZieie3d4WNZObcIYjnPrKoiRjivfceykilWJjGkrdDhflushloWO4E/2pp54aMvblSNqZloh6qGg7j63SiT5//vzQNOj0KfbKPtabiM4AQFFUZxEdJ/r222/f9r87bcGicPqFi0s//tGbLglL734gXPCxX4cUDD7//eE3bT7/EXvNDUfuXe4a9Q5FvIc8dJuA5bqggcggJ7ofEdWLE12Z6CvQwaKDIroX0cVihnJyonuOc0Gw9naAJ9eIxwNPU2DjO7nkMXeqMhc5/vjjKx07vYro3p3oiIaIuq259U3Hdh4zV45p4LF7jvltVYtJXiClgPgxdKJnP/vZoQ6YKZQ211k/mQgz/qkloi2m2pmfGjnRaw4XLwVfGRc0j6VoTxXZQSHPANQ0J3q78SxkjyMAeoocoE1Q1zgXPgOK4YkcKtrXPxB6+/rLfz3+SAirrd3ev+nk1xOPhjB9zbb/Ha+zKXC/eRDR2dmTs4jORMqbE52c4VzzuL2I6Lh8c/0MhuNFPE6JJye6t4NFY2Wie70OPTrRgfpcTvQVTvSqDvccr74gc7eqaxcRPcXrKiNU89qrPnR4Iti5X16d8lVhc8vYjuNUz5sCFsvQsuqUMc6CCmNFndrcyb5gjTXWyDoXXbOcmoMwzupXmcMUeSwCdqrJLTEmFENNE9EtnqWdOBdPLnRb5eYaKnswqjfs9PIYiwD9Tz4SuldPJ5r2P/Vo6F4tz/gQb3Eu5mLKNc7Fm4iOGONpcbKVGCKWCXKpzwpQJro/J3rK5+e5PYjWuR4synvv4fWOBP21RHS/pIpz4b449thjwwknnBAuv/zySp6Duo3X5aVvMqy29ShUW9u8OuWr7Ke4F2LndSNSUmPnIKK3ZozXBWobIl1yFNG7urqKSBdLucgRieg1p52DQlMfKkoeOtDhNIl241l4vEcRHQE69UR/siL6RJzo7dL/JCJ2ShH9saTP7wFvcS45bWv1HufiVUSPJTzgbk3djyvOZVVSfyaQyo3syYnuSUSP5UQHr5EuXp3oCFeKcxm8frh/Yzu2ydm1nX433nhjZSI6faI3N7qZmTwK1bmK6Da/jC1mUzfwvDbHbTrmRPe6c2o04T9HER3QFOVEF7WETgZhuowwzmO5yVPnoT/taU9rnMiBs9y29JT5HIhO8Sai06a6RrkAhQ3vf9WO4IG+5aG/98nQs3oaAXdgoD/0P/V41k50Jtx8eXCiM8lDRE7t/E2Fx4NFmza+TERET40OFvXnRE8J/aMXEZ3PwYsznnu16sgGO2TZo1ANXrPHJaIPwr2CSSC2E73VmFBVbrnNF7zlonsWqumziC/02LaqIfc5thMdmJvn5ESnr7EFtDqJ6HUS/jvFjBkzCg3MY/RUDJLOtr72ta+F888/f6XvbbrppuHzn//8St+76aabwte//vXig9puu+3C4Ycfnu32+eFuTFbQywjjdPwU0amd6E1zobcbz8JnxufgTUSf6KGcuTnpcaFDqjiXgaVPoMpkLaLbFlcPTnQKvVzz0IFiV050XweLlol2qxrFuazAy8SK6y9VW7yI1q2ivpzoPlCci38QtGO7tVnEsD6rquf2KqLz2hnHPca5mMjvtW1VgiP8qquuyuZ5U2CxuOzY9zDHKwO6Fju92D2T2wGbM2fOHDLpot/mRlIRnU6BiJH3v//9Q98b7i7kMS94wQvCG9/4xuL/3/jGN8IPf/jD8H//939DDotcsRyiMsJ4O4+tCm6yOgu1o0Fnv9lmm1WSnx4DJpOsctf5ZGlE9IlGufR0d4VpPeWSrZb2Pha6u7rCamuuG3pK/ptOsqz38eL5p6+5Tpja5vPzOpuShw4eCiwmX7ku6CKKsSAoJ3o5KDSrFtLlRPeHCdc5H7TqKc7F2iMR3VecS4z+sR0Y16zWyB0WymM70blHeV4E9KpEbnO7exPRuQ+IdPEqVNO2Bx98MOQGc0yE0tgHp2MQS/G8KUCH4DWiF2255ZahDtg5f7Q5NxF9k002KforIl0koicAUfdf/uVfRv35hz/84fCiF70ofPvb3y7+zmNnzZpV/P2d73xnyBmEcfLqyoiHPJaCJFUEAls9EDqbdqgohT/C+K677lrq8TyWAcJTR2uDc91F9B133HFC//bIvecVX2X4/e9/H77f8+fwxZPfkKSYYVHxq32Xh8//5+uzzeG2ia2HOBcmeLk60YlyAW9OdA+LK6lgnE0d58KYKCf6yu+HB1IKlN5EdC/xMspEXxE3g5PPkzEKh7yNcbmTwokOGBSqFNGpXegXvWWim1DtNTKFtuWYg4zWwrjBAkLMObw9L3P1GOd+pR6b0SLqlDFOTDFjF2kLz372s0NOTJ8+vbgmcz1cNPmS1vXXXx/e9ra3hXe/+93hrLPOWulnuBMuvPDClUR2Oq699947nHvuuSF3uGhtFajMY1mwSDWRwq3NZLJpcS4UOQgXZeNZENHpcFILHa3YgSV1zUTHUUZxEaP9xCIxYKZyA/A62ebpSbiMDe4c7h8PDmjiXHJ1opvA4OFzMJSJnj4TXc7rkfHgsk15sKiXxYTcnOh1yET32D5EdI9Z7TmJ6GYU4XOo4n41t7s3J7r3yBTPAn+VmIAdOxfdnjenXHQ0ozrVdjkfLjpz5swsF9WSi+gUkLvsskvhNGfl6bDDDguvfe1rh35+5513FgXm8C0C/P3WW28d9fdSjOFWbP1qIiaMd/qxVWCdi6cYk05gHX07IrrHQ0VZ/V1//fVDHcEVwCp9LBE95fvEa+X5PQgyqaA/Z4Lh4T1g8pWrE922d3sT0T25GWPjIc7FxI5cD9sdjg4WlRM95cGi7FY1p7dH7CBob4K1DhZdQYqDRaHVoFBlLrpHEd2zUG1RMx5288TE5n6xxWyb85nhrenUUZCmzTjRc2TGjBnZOtGTzraOP/74wtVp7LfffmGnnXYKP//5z8OrX/3qIWfCcNclg95YBdeJJ54YjjvuuNBkGLzoZMrEiDCxRrzda6+9QiroXBh4m+ag5X1FLCi7xYrHe8uFZ2Bmh0dds9ZiOukR0VM69nGit/aZOULx7iWyI+dMdJtUe+rTqRlMlMn1YFGJ6L7wIqKnfH4dLDoy7Cqr2oluIro3p3cdnOjstvKW1Z4CyyaPTWttRa1VRd3nVUTHKOJVRKdt3BfsxPQQqxizv0bHiC2iU9MhpOfiRMdsybVfp52liOjEreY4XsycObPoCzC4eZmbxyKpajZcDNphhx3C7Nmzw5/+9Kfi79Y5Dz/AAiGLjmysHHWEFvtasmRJaBoIhxTfZdzluKUR3VNGqSD4Ny0P3d5bOwhjPPi8GAQ9OtHrnofO+x9DXKbvSZlnb070nPEyUOMgZOKfqxPdaya616I7RpyFBxHd3Gl1XZRtqoje2pZcM8hzjHNB+AE50dt3onO/eH3fcopzydWJTj3jbXEJTH/xKvJXCfO/FGI2Rr1cRPTWgzrr1Gbu1RzviRn/1CFzdKO7muXYyqZtA2Z1A2Hs2muvXelx/H0sNy/OBkSW1q+mYRdrGRHdHptaRG9aHro5y8sK0AiwTCS9ieiI0HXNQ7f2IyxXHR/AZJdFudQiupzoD7twv9jEK1cnuonoXkRr6gfPTvQYeBDRFecihqODRUeGe5X7pcrFDRZvcKN7FYM9O9E9xsykgIVyxpaqo4fGEtGrEro9i+jgMRc9ZxE9lZjN8+YS52IaSZ1EdNO3cox0efrTn17UGDnmoicT0RmMv/e97630vZNPPrmIK9h///2Hir+3vvWt4Zvf/OZQZ/3b3/62cKr/67/+a8gZhHEckGVckNzUCG+pHIN81ridm+hER0Qvm/Pebn56DJi8MTDX3YkeYxEAAZv3K5WIzmSbgjp3Ed2LEz13EZ04FwQQL9nXiDDcn7mL6OY8TYWc6CO/H6md6Dx/Kie64lxGxha8Yhwu6l1E9yZWS0RfVcyO7UZvnStVNa5Tv6Vw2Y+HGUU8CtXoDiyMehT4q4b5X+yDRYE5bi5OdMYE3uc6HS6KkY/au07Cfyfru0022SRLET2ZZYmJ9wUXXBA+/vGPh6222qqIXOHiQzB/7nOfO/S4T33qU0XO0DbbbFN8XX755eEjH/lI0nxvD9TpUFGEZiZvTXOiszjAok87h4oyOHhw0RoUaEzg6iyiU1hsttlmlT+PFU6pRHQ+K+6jOovopy1YFE6/cPHkFn1+c334/t82CO//v5SO24Hw1D/uCA//aUlY8IXLQ8/qN5b+l0fsNTccufe80AQnurdDRSF3ET31wapyoo9MahE9Jd6c6J7iXGIsfuES8+b09u5Et7HNdlzFqX8GAstcgz2Fn/5i2YN/Dw/8cUn4zTG/DFPWLnf+Uyc47EWbF2el0XduueWWWTnRbZ7oUajm86B9w6N2cwBHuM2bY+7643lZ7PFWd1cF5sQ6CdLUFOO1ebLzX888ct39YfnDfwknLt64cXPdsZiS8oL79re/XVxwxLMgDD3rWc9aafsW4Di85JJLCvc5IuSzn/3sKIKZdxDGt9tuu9KP3XnnnUMqrFNpmhMddz2iXlknumWPe5pM0yaoa5yLOemf97znRRHR+exSidjmSKlzJnpf/0Do7Zu4mNK/9InQ198f+qasPqnf0wmWPfVk6B8IYXnPtNDXRlt4D5oAxbynPHQTYbyK6DH6fRZ2h9dQsZET3Wcmesrnp97nffBy6JaXjHYTzmMcLurVic61wfvgTUSvwoneXv3jp07o65kW+olLo+ZZI959MxC6hnamVwUiOrvqWFTzsqvOrj++PDrRLdLFo8AfQ8y2+WDMXeU2R8c0NmvWrNB00IuGRzl7B6PoWCL6ZOe/rllzg7D0rpvC0uXLQ1dXd6PmumMxxcONMp64StEdQySrCxTDCIdl3OUUgLilUzrRiZNh1dqT4NIJWNSBsgMpW5M8RbmYiM79lTLnezKwMs81boVNlVA0cR2nyhzmPoa6OtG5X+7/++QOee7vfaL4f9f09H1Jf++ToatnSujqSRufkQomnp4cMd6d6DEERGWi+yNVhIqnttghs15EdI9O9FxFdOuzm+5Ev+GGG8JTj/t6jWXpmjK4W2BgWfz233TTTeHGG28sdp9bFncnsTg+5hIeYgJbYb7hWUT32rYqsbkmYnbM+Xzr8+YgomNOXLBgQWEMSR1RWBa0zOuuu27EOodF+ztuujYMDKzmogbqND1rbxBCf1/oe/yhMGWt+hr9an2wqCgHK13cpO0cKppSRG/qoaKI4rj+ymYit5OfHvtQztSH0U0UO2glhpMeET31oaJMNj0Jl+1w7rnnhusuvWBSv2Ng6eCEttuBiD6AiD6tnp9Fk0X01HEmKfEkoptwmjtenOgpsWvBg/vbkxNdmehhqM/2JqJ3Oqv9v//7v8NtN1wZ6kjXtMGF6f5lcXPruUe/+tWvhvPOOy/84he/qOQ5bP7mMdLFs9s7VxEdExPjWexcdLQG5n+5HC6KIE3tZLvl6wA6F/Oike5ZXsflv/5xWP5QfXLe26FnrUFtpO/RPHL7Dc1yagjCOBOyMvEoPNaymlI60ZsW5WKieNmVaApxDkT0lj1uETN1JScRHSd6XV3oQGExffXJid/9SwcPgOqe5sOJ3p2xiO4tzsW7Ez0XEd3ESU9b4z2Qe5wLeBCu7dqUE90P9NneDhalH8UB2al2FbsBlvp6jWVhe37XlKnxnegDA4UoBVUdMmjxZ15FdK9CtWeXfNVjGfOw2Id8Mn7jRs/lcFHTjOqUiz5Wm9EOKMH6Hh/cUd40uqetFrpXWzP0PRr/0N2USESvIQjjdKZlHHcI2Ai9qSbWTOoROpvqRG/nUFHw6ESvax46UFDgJIkhnCFip3ai1zkPHRF9tTXK7doYK86la+r0IkYlNQPLnsraie7tgCMTOzy1KUcRXQeLroyc6P5E9BzjXLw5vb070YG6slNxLoxLy2oqokPX1NWKmicm3T09Q7V9VSJ3a5yLR6HasxOd94y4jdxIJWYzV89FRGdxa+21165s8ayq64IxfSQRnQXZNdd5Wuh7rLmH8fasvWHoeywvET29EiEmJKKXFaV5bMooF8RjJk5Nc6IzMea1zZ8/vy0R3ZPr2w7lfP7znx/qSqxFAK5hROzUInqds/DYibH6BpuEaY9PfO126bInw9TV1gzTelKv/w6ErmVPhilrrNt2W3q6mxHrgEPMmxMdcSy1iJy7iK6DRf2K6Kkz0b2I6N7iXKoWohDRGX89i+jenOgmfHdKRGes7PvHo2HaGmPVCwPFcaKDPUX6/qKVqTgN+3qj1l7USojcXBtVieh8LvTNnp3oXs6SaMXy6RH5Y5xJ5Qler0Xlxn7eP//5zyEXMB3WyYluqQ+jtXm9DTcKXTfd62D+Wg2rrbNheOrvC4deX1PmumOR72yzxtB577HHHuM+joGXx2699dYhFdaZNE1Ef/TRR4viuqyzHBGdQ2s8uSR5Dbh/6uxER0SPUcAhYDPpTukExwm//fbbhzqC6w/Xyttet2346u67T/j3fOMbfw0PP7xBeP/79w0p4Vr4wAcuCLvsskt405vStiUVHp3ouNa8TTZji+ipD2GSE31lJKKvWEDwcshqbnEuXp3e3tvXyZgZxspd53SFH75n3zHrCotY9HamxCmn3FQI2v/+73HrnRP/uFbhvmXRnven0+8LfROvy6OIjhOdA4G5Bj3VWpC7iJ5CzOZ5iRX1sAAcA3SjW2+9NdStzaOJ6G/ec/swb60rw6c/1cw54x//uEH45je/GU756J6uDFZV4muUFqWETxwlZdzlPA7hKqUTnTgZxGPLnWsK5ixvJ84l5kneZbADOzy547060e0QmVTFIpNs7v26ZqLTdgQU+oLJQJ/GxMIDTLrYbpgjfJYeM9FzPlQU5ET3hxfhWJnoece5IMZ5xWMmeqed6Pwuy/euI8zhUkSe2NzRao4q8Cqim1DtMXvc6nAMRrnBjmSul9gLf8w/Gbc8Xg9VgEmROJc6LRogoqN7jVT38TO0BI8Lxp1gxj+1xhS7NFIhEb1mcHNCGWHcLuSUIjorck3MQ0cUZ1JaVoBuJz89toheVxcBW6ApJmKI6LjAIZUT3Yqmumai21byTojok/0dnYCijgldriI6RSBFoid3FG3K+VBR6xNTx7nIiT4yqXdISET3F+diu0ZiONE9i+g5ONFZcK5KBI4B7U+xCGCZ5VXnonsW0T3molP7sTjnsW1VY3Pm2PnkNtfNJRcd7Yix0UxsdQChnMXGkfoTSy4wE2bTeMYznlHUVhLRhVu4OJkklxFkeSyDXEqRFNG/aVEu1gmyGl1GsEBsQrD2JqLj4qZI4xqpI1ZIxBDReS7E21RRCSbi19WJbiL6ZF3kFOwenOhWILVO8HLCJtMe41y8EkPElBPdH57iXFLhLRNdTnRfeBXRGd86GedSZxE9lRO9tcaq6vl5bR5FdKt1PTqPGc8ssz037Gys2GI2Jired+buOWCic51y0c00OlKbTQur0+tphylTphSvUSK6cAsXJxdpmVw4BGxu6FSTNyb0iMdNdKLjLC+bh47wxwTBo4he9ygXiOVET+kCty2TdRXRza0yGec2/QnirQcneu4iuokB3uJcvIroCKlVx3ogUPIccqL7wkucC+hg0XwPFvUoUhv02x7bR7s6ebBo3eNcUrS/NQo0Nyc6ph2uG69ub0T+HEV0Xjd9d2wRneuBhYtcnOi8Vvpg9Ja6gB5BfWGpEcMXUnlNTRXRLfnirrvuCrmgOJcaiuhlRWkemzLKBQGdiUpTnejt5KGDNxGdz6fOh4pSSFDIxHAms53M3AcpQMSnmK5r5jNOdCZDFBeT+R3gwYlOxjvkGudik2mJ6OWpejHbhDiJ6L5AuM7Zhe7RiZ7bwaKI6DyHl/d/ONQ1XjPRO+lE5zOoesGkKmwRIPY1lLOI7l2oztWJznjOfDBFzAjJArk40XmfMSvWSXSmtkDrGa3NlvPeVGbOnFloj57MI1UiEb1GcFGyulVGGKfQMSd6KmwlrmlOdCZfDGLtiOhMIj1lj1vETJ1FdDtUNIZAkVpEx4le1zz0Th0I2qlc9U4gJ/qgO09xLn4wIS61iG4iS5ndcjngRURXJrq/OBfawVeMTHTwmouO25C2eRP5O+lEt7GyrpEuJmbHbn+MOBfPIrpnodpz26qG+XwKRzhz3lyc6FA3Eb31cNHRfla319MOM2bMKHaV1SnHfjJollMj7FTfMiI6AiOOh9SHiuLUbHUSNAEGMIr9dkR0BtzU4kYrOEoohuse5xJjEQARBCd4ahG9rlEunToQ1La0enGiI354EpFjokx0f3gR0U2clIg+iCdHjuJcfMW52P0aw4nuWUT3KvLbwaKduG9s11bdRfTYuehbbLHF0A7GquaziOh8zlXfhxOBetdrnAsiOm3zNMbFgjl9Kid6TiK6ic51usbGEsr5GQZGj31NJ5jxzz46l0gXP6qeGBcL6y9TSNgqWEoRPbUTviosnqVsJjqP9yZW04mDt3a1A4XENttsU/nzUCQiDKWOc9l8881DXeE9nKyTHiEeN6WHRTlcS7TDg8O0DKctWBROv3Bxx37fE7f/OTx201/DRZ+4IHjhvotvCKtduywcc92vRn3MEXvNDUfuPS809VBRSHX4sWHi5GSim5pG7gsKdv17mQh7iXMBiegrRHRMQp7OtWCRnOuEvnWy/Wrdnei2CBA7Fx3zyLHHHlu8b3PmzKnU7c4CgQeTxnChetGiRcEjtI171tt9GwPmg5dffnn0nWaI6Jh4ysZMdbr2j83Se24ND125KJz9gR+HntXWclPTjwVCOZ8R88Th52bxM2pkTIBNjDped911i7kxeuX8+fND05GIXiO4KClkGLjKPJabN2VmLytxW221VWga5FlR9JctthDRt99+++BRRK9rnAuFC4PQC1/4wsqfy9wGcqJPTgCf7AQIIR43uwdBigKpLoeKMunt6x8IvX2dc14u630qDPRM6+jvnCzLe5eGvq4pY7aJ9yEFMSZa3pzoEtGDK+GY609OdF9xLiARfWUR3RMmDCJYdUpEr+vhoqmc6DHOk7LXhujlUUQ3t7c304a9V+yUbaIgOJ6YTb/A/RzT2GNzdkxktsNoNFh4Wt7X76pOb5f+1dcN/QMD4alH7g9Tpw4u5Hmo6cfCzKNoYPPmrSzwm/mSnzXxnunq6ipy0XNxoqdXI0Rp7KDQMgNpO4+takKPeNxUJzpFXZn3lveBwc7boaII0Cyw1NU9QA4f722MnHkT0VNlkrPFmYlLnTPRTQBP/Ts67UT3DmL/f/zHf4QH7+1sBl//sqWha6qvQ24H+paHriljTyqajBcRHZcNY6O3CX8qPIofsfF4sKiXtiDOxopz8SZSjyRWe6KT7vG6x7mkcqLDBRdcED760Y+GK6+8snInujcQqukfPC6+WMSk17iZKrG5Z+xolXae94QTTgi333hVqDPda6wbQndP6HvswVAX2OFPzTNSpItFHOdwuGgOSESvESaMd/qxVYm0TFKauNJmInpZAbad/PSYn0/do1xiOen5DBn0Ui044PKAumaiczYDk+PJCuCdOJy0UzDZqoOIzuSGCdhAf2ddlwPLl7oSrIvX198XuqakjTJJiRcRHYevXOj+RHQdLJqvE91r5nidnOid+F3cgx7F0LILMfTrKYTm8847r6j5f/Wr0aPaOiGiezxc1HaeezzA0+pxj22LJWbHzkVHhOVeLCOi87hOG2hi09XVHXrWWDf0PV4fEZ0xHW1iJBGdMQA3+mgHjzaBGTNmFGkHXuuNTiIRvSa04+xGtOICTukCt86jiU50VhDbOVQUvInoXB91jXKxRQCI5URPHeVSZxEd8RsmK4B34nDSTjq86yCim+tt6vTOLgANLO915UQfWL6s+L8nYT9nEd1D5JIXvIjooDgXf0507ldq9pyd6DmI6PQB/L66OtHtPJoUIrotylbleGbHAWOWRxHds1DNLhp2KHhsW9XwurmfYzvRuQ+Z99oceCzQHR59sP6HkPas9bTQ99gDoU6gfY11uGiTnegzZswoas0mLxQYmunUBG44iv4y7nI7yTj1oaKsmNYlN7gsuEgQ0MoeKtpufnosGIDrLqLj0IhxiB6HeqY+VLTOIrpNfDoR5+LlPqpLJrq53qZO67CIvqw3dE1xJKL3DToeunp8iugxRFQT4lKL6NQpEtGF9zgXOdH9kEOci/2+uorogIiewknfmsdexUIg4zP1nGcR3WtkCvOwHEV0E7Nji+jA3L2MA57d5o89VC/xeSR61ly/Vk50E8rHE9G9nJdTxQJCV1dXFpEuOli0JtjFWMbZbas/qZ3oTY1yacdZjuObgcyLEw0o4hEB6xznEnMRgCJpu+22Cymd6CxIxVgwqNKJPhkRnWLDS5wLbalLJrpN2FdbfbUwradza+bdfb1hylrrdvR3ToblA8tDd1dXmDZ9epg6Rpt6utP0w7kdLKo4lxV4mSgpziXfOBdzonvdXu3VKd9pcR/3al3jXKz9KZzoZljgnuUaqSJakXrOo4hO/8Dr9ypU5yqiA+aqFCI64v31118/7uOY4z/52MNhatdA6Ooe3M1RR/rX2SAs7X0yTOnvDd1TV3NR048H+hv3Bf29nSdhoI1hemEhJMZu+hTj+UYbbSQRXfgS0XGiDr8ZR3ssnXvKQyMR8rfccsuQu4jeTvRLLGwbWN1F9BiLRAggOMFTHuqJiF5XFzogfiNaTMa5zSSWosNDnAvCNI7KMn2xh7YyCTtqn22Lr07x8Y9fFubPnx9e//p9gwduueWWcNLSS8InPvZyl4u3py1YFL540S2VPsfSe24JD125JCz41IWhe3q6a/PxxX8KT9xxZzj7oyvya4/Ya244cu95IUcU5+LTie6lLTFEdN5/nseriE77mHh7c6LznnGtyIk+SKo4l9baEaG7irmtVye6CdWenejjRVNQ/5x+4eLQNB69cUno/ccd4fR7Rq9tqqh9MJAh3o+3QM8c/9mbrBM+ceROLuvisixZ8uxwwgk3hg++8zlh8803D3XA3m8MpVtsscVKP7MkA37WRBEdSMK46667QtORE70m1OlQUdwCiM177rlnaBq8LooGy3As8/itttoqeBTR6xznQgHxnOc8p/LnwbGPeJtyoEPEr7OITvGPk34yTshOuNk7hU206hLnUoXYj6jgaRHBxJeUC8dj0dc/EHr7qhXtepctD/0DA2FZ6A5dFT/XWCzrWx549tbXy+vPFU8ieirs9Xtx5ecW5wKI1N6c3q1QU3trX6dzzOse58KYn8J527rrDxG/inrcs4jODkyvbm/mwzfddFPy+icF/dPXCr1PPBKWLu8bdYyvovbh+mfMYF40lknPjHLsiK+ziI7ozPuL6FwXEZ3Pxdo8XETHlMd4zM9S7nKvkhkzZoQLL7yw8fWvj73YolEiOh02E5SmHipaNg8dYYdBzpvjm8+HYrgOcRSjva8UuzEWASyPXE70tAeCmgvHQ5wLCyt1EdGZsFuua6egKEKc7/Tv7YSIXnZxMyann356uO36K6t/ov5/ioKpt+0ilHaptGzFwySCNqQSsS3ex4v721ucS9UHiwKTdq9OdK8iOjDOdTLOpc4iuhcnelXP4VVE9xyZYgL/aGPLFVdcES760TdDE+lZY52i7hpYGveesEWk8XLR+WzoV5nz1xmiTElXqNNhnLQZjWKkXHRqsaYfLjpz5sxirPC6g6ZTaKZTAxAsiHQoI4zzWAa0lCK6dRp1Xvkcy1leNp6l3eiXWDThUFGI4Q43103qg0VTiviTpRNZ5h6d6HVYhKpC7EbwQYDyKKJ7dKIvWbIkPPnY4PVbJQMD/xQFUwvYCKWp2+AIL8JxSizOxYtwbXEuHpzxTLZjOdE9i+j03d7iXDotovO7lInePq21Vo4iOvWzVzGKXbL0paO9dywa3fe3O8LAQPPGwe7VB+cjfU8OGmtiYXNfM3mNBmItc/26i+jjHdRZxzZjxqzb62lXRIemHy6qmU4NsINCywjjdsGmFtEpSIhwaBJMuBiM6i6i22GndSVmHA1FCpO7VNEVTN74qnOcS6ec6IgAHpzGdRLRq4hdMSedtzgXxCgT6zzBokN3jMM+caJ39yR3PTNZ7pKIvhIersuU14W3OBf7PDy0J1acC2OnZxHdqxO903EudRbRqXlof+z7ptWJXpUTvg6Z6B76q+GYQWY0pzwGIJrd/1T8HQxV07P64Gvvf7J6k0Qr1LpcE+OJ6MBcXyJ6OhHd9LuRfoZW5vGe7gQbbLBBMaZLRBfJ4Sak6C8TI8IFi8smpUhKe5voQmfAQhBpR0RHPPTk2GyKE52JTQwRM/Xp2exAgTqL6BT/k3Wi8zu4l1ILhBbngoDsQRhLEediIoAnER3xxaMLHRCueqZMrfx5Bvr7Q5eHaxLHmYP71AueJkmp2uLxYFEvzviYmeieRXT6b48iuuJcVkDNzbUaI35orEz0qkR0rr/Yr60MCKb0nRYl6K1tY4noNnfpf8pf2ydL15SpoWvaatFFdGBeOl6cC6BXmKGvzqB/8Xo93p+jgQ7GHH6knUy8HuZntsu6aXR1dWVxuKgOFq0BCON0hBTbnXxsVYx0kEITsPyqspno7US/xIJJFMVOnZ3oRKywCBBDUGXQThml4iGTfbKiDUXCZHeldCISplMwkanLLhuKtE4vApkrz9PiIEWqp/a0Xv8U/atNmxqm9VQrcC8LfYU4WPXzjMdTXQNhYFg7errzFdWbfrBS3UV0XH0p0cGi/p3oncqjNkGe+6AOi/DDsYVzhGwWZWLRWm9VtRBkbndemwnDXrDa18wknjBzy1hOdIb/nt4nktcmVTBtjXVD19LHRn1tVdU+OH1vv/32cR/HXB8hlzo09Vg3WUGaWgodZtasWaEO2LmAtHnOnDkj/gy9zMvcttPMmDEj3HbbbaHJSESvAXU6VJTiEPH4BS94QWgabIliwlNW0OR92HTTTYMnLOO7ziJ6TCc9IvpWW20VUkHxQ4Hqragvizl7OpGJ7mXywJbfOhwqWlUmujnRPYnWCPseon6GYw7Tt+y6RfjCLrtU+ly//OXycMklj4fPfGrfkJIzz7w/LFnytPDhD6dthxe8iOgp2+BNRPfUHh0sOgj9t8fc505mtZsIze/ztJOrXUc4QnPM3ZHMo7bddtvifJEddtihkuewmo76zlu93er29iYg0pdSm48monP/7Dx343DMvluHffbZJzSNr31tSXHNvO99cesd5sBXXjn+gfU212febMJtHTHzYp1EdNpM3UU6w3ARnc+PxXxE9K233jo0NRf9sssuK8wKZlxoGs1bFmzgBKysMM5juVlTiuh01AgHTYxzofNmQCrjIOGz8OhEt2y0use5xIhY4TNERE95qCgiOgVqXQcgmxR3IhPdy2p9nUT0KuJcPGaie41zMddcDAcQ427KHWgGBXMdXZZV4kFET4kn0dpbnAt9Q4x2eHV65xTnYmNxpzLWU4nosXPd6T/f8573hJNOOilsvvnmlYvo3hjP7e0ls300MJ6Vye+uI8xFzZwW+3nZFTvezgwT0ese6cJ8g/ugTodxslsH/WCkNlMT8dlYwkETmTFjRlHb1P3aGwvNdJzDoEnBUkYY57EUZylFdDtEoc4rnqPRjihOQcGEwKOIzmSqLnEUw6FDphiLsQjAfcdnmFJE57XWOQ/d8t464UT3IqJTuNZBRGcRqCoRnQIw5nbu8UDk8CiiW35jjPfKi4iOUFrXRb8qUCa6RPSxUCb6INSlnRKrPR8sCnU9XLQ1ziU2ZhKraiHOs4hOvcWczeNODRPRxxL4mcPY+U5NAzGb1xZ7gdiMZOMJ+Fw39GE6XNTv4aJNZcY/tcgm56JLRHeOnWxbRhhv57FVQYeAW6GuIu1YsGJYNg/dBixvIrpFodTVGYeoTLESQ0S3Q1tSO9HrLKJ3woluByp5inOpQ/+GqMpXpx3jFhHjqQ/xKqLLiS48xbnoYNF841wQqT0fLOrVKd/pg0Xr7ES39qdYBPjBD34Qjj/++PCNb3yjsuuPxV+PInoZoTolGFzGaluTnejMDxlHYi8SlBXRGfdxPDdFRK+bc3ssobzpIvoaa6xR6BcS0UUyWMFicC8j5CGiIySkPISQ9uJC9zBp7CQU9wySZbPE6eiZqMWIHWkHBtI656FbwZCLiE7hWddDRc1BjkgwGYGTSQ3ijxcRvS5O9KoOAK0iZ72pInqOTvQm5x/W3YmeCqsHPYjWYNenh/bIie5bRGdcoR+38y1yjnNhTsNrSOFEX7x4cfH/a6+9trI+CgOYZxHdqxMdoSxXEb2smF3FwgVRYBjjxqMpIjomRhIBPIzbZUEoR0sYaWzjZ8yR67ozqWwuuhl8m4ic6M7h4isrSrfz2KpgVa2Jeeg2AJV1otPRM7h6EDVSHcpZVfsp5GMIywx8iF+WA5lCfKEwrbMT3WJYJtMn2cTBQ5wLTj6+6uBEr+oAUAQAT3noJqJ7PFg0thM9xvOMBxMcZaL7dKKnhGvCy4KCXZ8eMtFjiugeRepWsZr+0ptAYouznXjv6i6iA/VwCtHHag4WNKrauYE5wquIPp7bO3XbMJeM1p8yX2PhxfNOmIlic9HYIjrjOc9d5nmbIqKjLXGNlVk48IJFG4/koDc9qclu9JkzZ8qJLtJR9lDRdh9bBRS/dBRNFNHtYISy8Sw83pvjm4kabgBv7WoHBk8KhxgijR0qmkp8YKLFxK3uInonDhUFD050m2DVyYneacG7ipz1ph4sKie68CBge4Ax24tA6vFg0aoXGBDRPYtYtgjqrY2dFL5ZMOHzrrPzkHoihRO9teaqSujmOVK8trrHudA2GM0pb3OYJuaicz/z+lMcLspc2HZMjwW6BdeO50XUMpjoXKdIl7GEcn5GbVin19MuM2bMKK49r/3qZPFlkxUrwYSDG2+33XYrLWDvscceSQVOhNomHirKe0uBVdaVjIi+/fbbB08w2DJRq7uIHstJbyJ6Kmz7Y53jXCiqJyt+2+GkHtzfuG3qIqJX5UTn93p0onsT9mM70RHsPcSoUIt4cMQP57QFi8LpFw5GAsTksZuvCk/9/e7wq4//Gjk9pOL+SxaGaVc+Fo65bnBHzxF7zQ1H7j0v2vMzWZSIvip2z1a9k8Qy0b3sjBiOLYJ6i+ZqbVcnYJyqs4ieyoneOvdCkKnCXOLdiU796XGnl4noiGUjzVfse8xpvJ0T1gmYJ5YRszsN72sZF7PN+Zk/4wyuK9wD9Me85uc85zmhDtBe+qqRPifGe66d0Q4ebQIz/3m9YfLdcsstQ9OQiO4YhNiyorQ9NvWhotBEJzpbocoO/riKWJX2VizYdq66x7lsvvnmUZ6LoijWc42EuTbq7kSfM2fOpIV4JlAeopFsgoWgH2MLvlcnuodoHQNRyGucS2wneoznKTP+eZvkQ1//QOjti++EXt43EPoGukJvH07jdHEmPP3ylveA9yNXJ7q3g0VjiOj0DfSVXmKfhmP9tze3ZKcjWBiP6x7n0mQnegpHcVmhmvuXmtpE67o40fk5C3dNzkVPcd0gwF533XXjLoyaiI4GUGcRnddYx8M40fDGOly0yU70jTbaqKhxmiqi+5vpiCEsjL+MMG6PTekCZzWNAtFD7EKnoZMrm4fOYMrkzJvjmwGUzsxbAVYWCgXe21iLABR8KQ+GRURnsu9JsEwR52K56h6oW5wLRWenxWVvmegmunhyLw53oud0sChjnwdHfOu4cdZZZ4UnH3skzfOHgYT+cz9wTXgQrT3GuUDVi7LWB3mLS6nK8e3ZiV5nET1VnEurE71KEd2rE73V7e3xmqD2GK1t/AzjSZNF9FROdIwatlt3rHuHz8hiaetMHUXnsYT/Oi4KtFtrPfOZz2xsLrpEdMcgSiNAlYkxsMemFHfsUFGPW0UnOwln8GknDx3Kiu6xo1Dq+vlQ3DKRiSGiM8lhy2rKKBUKTsRjj67OMphrpkkiOttpESM8OH7Hg+uXCXun73f7vV4wccOziB5D3PYiontzojNuLFiwIDxwz6DRIAluxtx0TnjFuYzvRM9ZRPfqRO+0iN4EJ3qKOJfWuW1VIr5nEd1q4NHc3imhbx8vs525VBMz0U1E57VXdeDtaFjc6HgueD6fphwuiq6CiO7lkPIyYG5Ffxnp+kAzY67vbdzrJDNmzJCILuJTp0NFTURvYh46Ih4FdFkRnQ6eCYsX4c9gAPXmjm8HKxRiuMPNVZAyE52Cs85RLkx0cB524mBRL7tbmGB5yGZPeQCoNxHdsxOdohmnaYyFS08iujcnOiRbPK7RZK9KFOcyMnbPVi3AmIjudbLuVUTnfaPv6JTwXfdM9KY70Vlkii2GloG6kz7UoxMdmO+OJ6I31Ylu88TYr8/mh2WiZJoioiM6o8d4vQ9GazN16EgOehYFzKjZVGbOnFkYfeu08FEWP3YhUWsR3Q42bWIeunVuZUV0y0/35viOeShnVe2HGK/BiqHUB4vW+VBR22I42cUkb070OkS5VCWiI9QywfQkont2opuIHgMvIrq3g88sQqSrK2GbHNQCXaEr6STGk4huizwe2hPLiW4itZzo7UEd38kIlrrHuSBm0/7Y904sJzp4dKNzHY4nVKcEQTdXEd2MXbFz0enTMRjZ3DgXER3qFOliqQQjxbbY62lypMvMmTOLuqPMdVo30s+4xIjgxqBDLiOMt/PYquD5EQya6ESns2YCWFa8bSf6JRYUvHxGdXai0wHjxoghluFEZ3Kb0gFNQTp79uxQV2zbqZzoaagiu7yqw0o7IaJ7PFiUwjFW9I+XAwO9ZaKb2NPT0x2m9cQXs5d2hdDX1fXP504npvd0d4cp3bwHg4sJPd1x20IN5cWJZIs8Oca5eHN6G9Z/e8tEB2pOxbmElcZ+3PQxDQWxDha13+9xFygiusc4F2ubnc02EryfiOjjHYJZR3htjCkpDhcte6gpc3/bVe/RcFIWjG2MmYjO22yzTahLn0nc0UhCOYuq/KxOiwLtMuOf2iT9Q501qJGQiO4UbjYGmzKitD02pYhunUNTnegMVGVdfnSG8+bNC56geEFMqLMTnUIh1kGfiOg4J1IVe9zPdY9zMSf6ZER0REiKPi9xLjjR63IPVRG74llE9+SOTyWie3Cie8tENxH9wN03Dye+fevoz/+DHzwUrrpqefjM8S9P+r58+tNXF4uyb33rvkme36MTPaeDRb070e0QbI8if6ed6HWOc7FYldgiOiIt9wpmrarGVM9OdBgvdzx128bKPLdDMLluWqN5mgBjG68vxeGiZUV0M/bhRt90001Dnd9rnN11c26PdYBoHV9PO6y99trFF4eL7rDDDqFJ+JnpiJVgxYaisoyIbo9NKWCTd0Rx6CV2oZO04yxH0EE89OZEt21cdV4FjBlHQzGUMsqFIp6Cs85xLjhmcDxMZsLTqUiYTn4uOce52OTfk2CtOJdB6C+8iOgeneipBGxPzruUTnAdLDoyOlh0BV5F9E460U2Q97Iro11sAT12LjqfwYEHHhh23XXX8LKXvSxbEd2rE522cY+Mdv/aXKbJkS6pnOhl41ygCZEudRSd0efQyUb7Wd1eT7u134wZM8bcqVJXJKI7hYsNwbCMAMWNWfaxVR8q6mWymEpEbzc/PRYMnLZaXlcoFGI60VMfKgp1d6J3IsoFPDnR63KwaE5OdPo2DwLycHB95piJ7lFET7mrSAy6v7040W1BxUN7JKKvLKJ7jHNhHO2kiM5Co8fDK8tgLuIUh4vuvPPO4aCDDqrMDMQcmvvRq4juORMdER1GE/mbLqIzX0wlonNNjNefULNz7zZBREd0rlv8CW1GwxhpxxmLAnwuHnbGVZmL/leJ6CIWdTpU1IT8Jka50OExMNrBEHUV0U2A9rTNvh0oECgUcnGim4he50WPThwI6smJjuCCMF0XEb3KTHRvTnRcah4XcKvceu5VRPca5+KpTSlIfX8oziWtiM7zcA14dHrXwYneqTiX1kzxuse5xIZ68Oyzzw633XZbJb+f+wM3ulcRHaGatlXdV0wEq9FHE/mpm1lIHSvypc6kcqLbnLhMlAy6hGkUdQY9BkNTioW8iYLJlFp0pEWMsX7WFGbMmFHoUB7H98mQ96yiQSJ6ygM9uflZFWzioaLc9Ly+dpzouGY9iUz2Ouoc5WLFSQwRnU6eQjWliI5bg4KzLoJtlU503gcPzmebWCnOZVBU8CSiezxUNGcR3aMTPVWbFOcyiOJc0orovP/0RV4z0Tsdm+LViW61TKdE+diws4prNoWA9bOf/Sz88pe/DF/60pcq20VCfedVnDOh2mOkiznRRxPR6X/scNEmwnyR6yZ2/2W7s8seLtoEodYMm3WKQLE2jxTpYibNOr2edpkxY0ZRC48WaVNXJKI7hBU2vsqI6DwOsSqlE50VUMSCJjrR23WWtxP9EhMGzrociDgSlvkW4zVYkZfaiU5Rmto9OBko9CcropsQ7+F9qJOIzgSTYr7Tiw+4zxA6PLl6WfTyJOqniHPh8+bLg4hOOzxdHx7iXDz0X6nx5ES3z8NDe6x/iBHv4V1Ez8GJbgabuoro3DvUFSmEZua7VhdW5Rb37kT3KqJz73KfjBU3w87aporo7YjZnb4mqPvK5qI3QURHY6EfqpPozA4e5rIjtRmzHH1q3SJq2sHinjlctEn4memIISw3qIyz21Z1Uoro1ik0VUSnMCgrBnoU0ZnExzyUswpoP5PNGLEeti0utRO9zlEunXKidyISptMTuDrsDjA3TBWZ6B52BYwU5+IRBKsYTnTLUvQiont0oud+sChtSJnP7k1E99Ieu1diRDQgdElET3+waJ1FdBOEUsS5WJQMVCXiS0SfODjNcxfRy8SqdHosK5vHjojOAlFdo6QM6lz0jLqJzqMdLspn2PTDRadOnVpoY03LRU8/4xKrwEVGJ1EmfqOdx1YFnQJFpq2SNwk6aVv1HA8mqIjoHH7jCYoaJmh1j3OhSIkhRlAEMcFOKd7ymdX5UFFEPYq1yb6HnXCzd1pEr4MT3YrkKkR0b1FVnkV03KUxnOjmYo11iOlYKBN9VTyI6KlhTPV0yCritYeDvHhf+IohorOg59HpbdCPe3TZdvpgUaizkIWYncKJ3lp7VelE95objYGB+b7Xw0Wp98e6f5nTLFy4MDQR5inUX6ly0cs60QE3+pw5c0KdIQKlbqIzQvnNN9886s/uuOOO0GRmJjxc9LQFi8LpFy4u/ty3tHNjr5zoDuEi44Yq45xq57FVQUdmWzWaBsVU2UNFKR6YoHhzotv2rTqL6DGd9IjoOCZS3lO4Neosopvg3CQnOpM2rglvIvJImMutijgXOdH9xbmYAOfBie4tE92E21T9uQe3M6Suz7w4v1vb40FEt/s2loju3YnuMRMdcZ/avhPXL6+Re7HOTnRqgNRO9KpEdJ7Da5wL1814QnVKMNKNdXAo8yoWADyNA512hMd2ordzqKlpE02IdEH3qqMTHU1ppLrDXo8no0GnmTFjRhHnkuI19vUPhN6+/uJrWV/n+h+J6DU/VBQXeMooF2tDE6Nc2o1nsYHJm4iOAG0DfF2JLaKnfK8YYCg06xzngvgNnThY1IsTnYkVLqXUYlQZbIIuJ3oeB4t6EtEV57IqXvqM1HEuXkRrT050kIi+sljtDRtHOyHwW6Z4nUV0D070HONcTKj26kSnbWMJ/MxpqA+8LgLEErM7DXNjnne88Z3+lTjKJojomBuZq3scL0YDsynX/0jvP6+H+UKKRZhYzJgxo1h89dp/TQSJ6M6gE8TZXUYY57EI7mWy06tub8o2VAVFGsVUWVGcVUQKZMtG8wIdNsWLB4FloteYxbnEcoGnFNERoJnc19mJbiL6ZFzkfO6dyFXvpLu+DnnoVYvocqL7y0T3IqJzz3o7WNSE0twz0VPjMc7FiyOS+zbGwaJeD+703j6LC+tkpEvd41xStD+WiM696HXHBjW1VxHKBP7R+nmb0zQ1F71sNnmnYW5Mv1lm8acph4uacdNr9NJYbR4phsZ+Vjd3fbtxLtCkXHQ/Mx1RQAdMZ1hGRLdVuJROdNpAwdFEJ7p1zmVFdB7PYJZayBhO3Q8VtUz3WK+BezCliG7bIevsRDenyWREZyZpCGCe4lzqIqJXlYnO7/UWZ6NMdD8ieupDPD3GuXgR0VO3QXEuo0Pkk+JcfMe5dFpEr7MTnYX0FE70GHEuJtR7daOP5/ZOCbU6/dhoCyw2pxkr8qUJTvTYi8VmMCuTi46eUSfheTQsZrdOuej0LfRhI7WZewPDTZ1eT7vwGhlLJaKLyrCLq4wwbqf8phTRrQ1NdKLbimDZLHFWd71FuVi76p6HDjGc6CwI4X5OKWCbS6PuTnSKhclkI9tEwVucSx1gEoMw02lR1auIjviSsxPdXKxeRHRPmeiphX1P7uvUIr4X53eucS70k14dttY++jJP1wnYmNcp4bspTvTYfVusg0Wr/P1Nj3OB0drHdY+I1lQnujnCY9/bNjcu44JvihOda4nrrU7ObWogtLKRhHJ+VsfDUtuB12i56E3Bj11IDInorPKXcV/yWOtIUsENz6CYsg1VwWotQmZZgYbO3JuITpFbdyc6hUGsmBwr7lI70RFAW103dQMBfLIO8k5EwnQ6zqUuInpVsSse41yYtJhT0FvfizCW08GiJkpKRF8ZL8781JnonsRRT+1RJvog1o97i3TptBO9CZnojDmxF2Raa+Iq41w8i+jUw4i0MeKfOi2iAwalporoNm+MHelC/8R1W1ZE5/pJsZOk05CAUDfReaw21/H1tEsqEb2nuytM6+kuvqb2dK4e91HZi1UOCi2z9dYOIE25TdcOFU29VbgqEd22DJURDxjAyj4+pvDHhKTuTnSKsxhilAcRnTaweFPne4rrrhOHinpyotctE73TjnEEJ0QETyI6E3m+PIroNsmN4UQ38dqLiO5FNAY50QdhPJGI7jcTPVacizeBuhUzrHhrY6ed6HUX0a0GiO24jRHnYs/hVUQ3odpjpIsZXsYS0ZnbNFVEb8cRXsVzl4lzMS2gKZEudXKiA0502jxS7YGWxs+avHtx5syZxWuMUe+0cuTe88LCT+1bfF19zEs79nv9zHTESsJ4px9bFayaNTEP3QaZss5yBk06RW9itQ2q3trV7muIdaioud5TRqlQgNY5yqWTTnTE0Rgi5HhQ1NQpzqUKx7i58DzFuZjY4lFEN5dejMU/E+xjPNdYKM7FbyZ6ajweLJpbnAtjqfc4F48iurVLcS4rC82x3azcJ3PmzCn+XJVhiXuEcdSriF5GqE7Zp2I0GUvgx4ne1Ex0am5q0RQiOrvNyzrRoQmRLmhP6DRexvGybWasH2nBgz6NhUnbhd1UEb2/v792ix+jIRHdEdxYdAhlhHEey0WYMoucCREiehPz0LnJGWTKFmrtHkIaCxsoY4nQVRAzjgaHBE6PlHEE5kSvMxQBk3WQ8zu8RLkweaZPqIsTvYrs8qoOK50MJmrk7kRXnMv4wn4qIduLcJxayEdE9zTZ9dQeHSxaTWxKJ+8d2qaDRdM60eGII44I73znO8MBBxxQ2WeNWcJr3EWZyJTU7RtLJG9ynIvFjnp2orMgyLyqKSK6aTV1wUynI8W2jPWzprDJP/XCphwuKhHdEdw4dAhlRHRE27KPrQoGQlwtTXSi89oQJsqK4nweiCXesuEZXGiTBzdvHUT0+++/P2mUC1CApjzYtBPgROlEnIuXKBdzJdXJid5psdsEa09xLiZq5O5E9yKip45O8dgmOdH9ZZDn6kSvw8GiHp3owHgqET2tEx0wMuywww6VjqvUeV6d6LY702OcCzDfHM+JznvrMdO9EzB/ZB4ZG+bILKyUGUeacriomRzr5GpmTsscaiShnM+QuqROr2ci4yj3SFMOF/Uz0xFDKzNlhHGyyMs+tiqsE2iiE906sXZEdB6b2u01nLofKopwR6Gei4iOyFD3OBeKYyabTXKik4cOdXGiVymie3Kie45zydmJ7lFE93TYaSpSZ6J7ceV7zESPISrRF9FXeHnddXGiW9s6mYnO2OX1cxgPqwFSiOhLliwJn/70p8NPfvKTLEV05pgI1Z6d6ONlokNTI11SOtEZX8u4/JsiojMfG02Q9gr372gHiFKT8NnU6fVMBHRLOdE7DEXT4sWLR+18GKxZufDiHKkCLipWacsIAjyWwSilKxAhH+dInQW/sURxnA5lHcHt5KfHhIGyznno1h/kIqLj4KAQqrMT3fLcJiuAy4k+cdhm3emxwWOcSx2c6DmJ6B4Faw9xLh4W11O3wZsT3VOcS8xMdPDqRs/JiQ51PVyUe4f6IkWcyyWXXBLuuOOOcP7551fm+PUsoltt7dWJTtvGEtFtbtPUSBfEbK7L2AvGFtlaJtIFrQLNwtOi9mQE6bo5t2mzGWFH+lnTRfSZM2fKid5pDjnkkDBv3rxwwgknrPR9itzDDz+86Hi33377YvvGD3/4w9BEuKnaOVQ0tQPcDhVNPTmrAjplxOeyr82riF53J7oVBDEy3elrKP5SiuhWWNZ5YcqKe8W5pCMXJ7qJGia+5HywKMJG6rHYREmPIrriXNLCtelJRM8xzsVEdI8idWv7mu5EtzE0hQjdKRDRUzjRW8e4qoRk4mo8i+jenegYaUbr621u02QRnb489iIH7ys1TtnDReljPV/jZUETrJvobML/SPdIDiL6jBkzivujCdefiz23Z5xxRrj11lvDs5/97FV+dtJJJ4WzzjorXHPNNUWn+6lPfSr867/+a7j++utD00AYb0dETxnlYqJ/aiG/KtoRxRmM6BC8iegUuBTpdXaiI6IzebEMxipheyEr8ylFdNviWGcR3ZzokxHRKUK5fj3FuTBp9BRTMRpcw1UdLIrIkdrtXBcnusUzxMpEj/E8dY5zyd2J7iHOxZOI7qk9sQ4WtcVGr0507hPa6FHk7+TBorZLrK5OdKAmTyGit55LU5UIUwcnumcRnXHG5gHDoX5kbtBkER1i56IznpWNkjFNoAmRLiZI18lVT5uZH4x0D/Az7p06L7CWcaJDEyJdks90br755vCRj3wknHnmmSO6l7785S8XLvVtttmm+Ps73vGOsPnmm4evfe1roUlwwyCglRHGKeTooFOK6HRY5kRvqohuh1aUeSx4E9HNxV13ER0nfQwRwoqe1CI6k0hPbt92oQCgoJvMIZyWQe7lYFHaU5c8dIozxKFOx7lU4W6fLIyFCFCeRNtUmegeFjc8xrmYiJ1SyPYioqfEk2jtzYlOW+REH8SriF5FnEudhZJUcS6tdWVVIr6J6F6FOc9xLojoMF4uelMz0W3+mCoXvUyci+1ON+2izqBBsShcp0UZM5+OFOlSx8NS2wVNinlbEw4XTTrzpFB685vfHE488cRCGB8ONziHiOy6664rfX/33XcPV1xxRWgSdTtUlA6Lz6+JTnReFwVAO4eKehSrbZW5znEuFCIx89BTu8ApLHn+OosuFPcIzpN5DZ3KVe8UTKgmsygQk6qyy6vIWe9EX+1N2E8R5+JFRPca55KyPV6EGPpjOdHzPljUuxPdHN8eRfROHywKcqK3T+uu1CpFdMZUr/cJQjULOh5jj0xEH0vkJ563TqJnu30s108qEb3M82LsYJ7ZBCd6HUVn7hHGk5FiW3g91GpNjnTp7u4uFj+a4ERPOuv6wAc+UOSgH3jggSP+3DqD4c5QOopLL7101N9LAdZahFlnPtr2Ig8sXLiwGLQRBMZrpz2WQizVa1q0aFFRYDBYeH5f24EJFY5TVnJ5bRRrZV7bbbfdVgxKfCae3ovbb7+9aBevxWsxOB6sVM6fPz/K+8qCnU2UUk1uGFTK9AGeYVGJQnIyr4H3gWuWYsLDe8FYhNBhbbG+guvFmwuawpj3DkGzk+8dkx4vn0frwpe3NrUuiCGMsfhQ9aIYr7/Tn/dEoNbi2kPcSN0Wg3bwOaRqD2MJ74nt0EkFNTFtSfU+pH7+kdqT8rpohXbEuGd4zebai3HOzETBxOLhc2mFBSj6t060i/qBz4G6ovX3ea4rhsOYxnWU4nOy+Qy1TlXPz3MgZNlBmJ6gFqV9zI+8mbe4T6hFaNtmm2024mO4vjkc1ts93inQRXj9Vb6+kfoKNCHmTvRT49Wc7PJtwmeASYVrbvHixWHWrFmhLrCIccstt4z4/nP98LPtttsuNJUNNtig+MxSXH/2nJ0wlXQNJLKmXHDBBeENb3hDccK2OT9f9apXhV122SV89KMfDXPnzg1/+ctfwrbbbhsuuuii8KIXvWjo377nPe8p/h0/H4lPfOIT4bjjjov2WoQQQgghhBBCCCGEEEL4A+Ok5bPXzonO1gtWIg444ICVXhBRJb///e+LrHR7ccO3afD3sV74hz/84fC+971vpRU7Vsx5Pq8xCV/4wheKLR6jufJb+a//+q/CJX3wwQeHVHz/+98vPit2EzQFVqdYyfzWt74VrrrqquIQ2zJ89rOfLa7H1mvZA6ecckoRhcJBvHUEl87xxx8fDjvssLDVVltV/nzcV6zOl7kHq+JjH/tYEVe17777hrrCdUcU0lve8pYJ/w4WSX/3u9+FE044IXiARdmddtop7Lfffiv1FYxZXnLbDRaXv/KVrxSLyZ2MJjr99NOLmJ6U98dwvvvd7xaO7yOPPDJ44ze/+U1hAPj0pz9d+XP9+Mc/Lg5nP/roo0NKqNu+9KUvhWOPPdaNg2/BggXFZ/Gf//mfSZ7/tNNOCx//+MeT9xVf/OIXi7rxoIMOSvL8P/3pT8NNN91U1Ode+g4cz+9+97tTN6UwFV144YWV9xXEP3zwgx8Mb3vb24rxzCNf/epXC6ft29/+9uCJyy67LJx11llFfdOJeeQxxxxTRJW21nqe64rhcL2ed9554TOf+UzU52Uubya55zznOZXMg6kpqJ/e+c53Dp3H5gnv9/F4c4Crr746nHHGGUWUr7eIwE7wi1/8oniN1EFVMVJfgfsdPQL9a/bs2ePWRXb/etXFyvKd73ynGMsx2NaFsd7/n//85+Gaa66p9PpJzcKFC4uaFNN07N00eMfZxdGJOOpkIjrC3nBxj9gGHOennnpq8Xcm7AwQXGhkpwNbmCg4W0Xy4RAlYNl/w3O6PMIHyrb05z73ueMWTvZYioeURRYdFlu1vBd6E80/ZgAq89psi+cLXvACd+8FnQT3j7d2lYWCgDiaWNcZsQsUzKneL8tgZEGmrp+ZbRkn120yr4Ht7SwAeXkf+FwY6Ie3h797aaPB1k7uGyYxbPXsFCxGI4x6er0Un4ztntrUus2UbZkx2sbnzWQ09ftAFBVt8fSZ0Cbug1TtsVo0dV/Be8B7kaoN3AvcE16uC9pDhIqH9tAG+u2q28Jr5v7ky8PrHgnOQaEW89Y+4m+4frmfOzGu0kcyfo30OlP3FWWgPqMm4JqKGT3D+2+HdRMbUsX7xOdrz+Hxc7DrgzrZY/uoPZkHjNY2xF+LQPXY/smy6aabFpHDMe6N1r6CuTLv61jvvcE5hBZEUffPYIsttih0wTq9DqKsf/3rXxf3wPDIaj4brp/WfqhpbLXVVsVrYzGI5JHYdOq8Nd+hayEUKzFnnnlm4dT+4x//WKy88sazQtwUEKTJiixzUCjCKIV/ykNF6XjJimvioaKWs1f2UFEEdAasso+P6VTgWvGWl9euE50CJIaj0XarpMwJpR/g3kp5sOlkof0MiiyATgbuKy+HitqZApN9TbFgLGFyPnwhuRO/15triH6ukwsFnYQJbqwCmOfycLCoHdToKc+XNnlqTypSu814fi8HeQLXhB2Em8vBorxmy1P2CuOW14NFoVOHOTKW1v1g0daDzGPBmGqHdVd1sKgtNGGo8gr18ViHd6aEBSLmM6Nhc7qmHi7KPNLmlDFhkZz7knPdxsO0gaYcLkpfgOZRFzhYE0Y6QJSfMZfmfLGmsvbaaxeLHpgl60y3t9U7Vrdb2X///cOPfvSjYnvDv/3bvxXfY5v/8JWbOmMn1JYRxtt5bFUwOFJIWifQNBiAyoriNgB5E6ttEB1+P9UJXgP3eQwBhGLUnLapYAsp1FlEp19ADJisAI4Q78VVYBMpXCV1wMTuTotmEtHbA6HKJvtVg5sl1nONhYmSPT09wQupRfRExw65awufgTcR3Ut7uHdpS4zPB5Hau4jeKaG60wIVdEr45vfFFqCrENGrErJHg7rGarEqF1t4Ds8i+nhCdeq2jSXw286bporopk9hBEsh4Jd5Xh7HvdQEEd20qOHRz55Ba2ChbjQRHUb6WZOYMWNG7UX09NalYTlSI/Ga17ym+GoqCOMUjmUEPB5LwZ3SMUsWOjTNic5nQM4cAxArm2VgpZCByJtY7VXcb1dEj3WdW9GRcnGuCSK6nXo9WQGcAjzFFq+RMHdDq4hOX8EuqU67vTsBE3Ob8HcKxB3Eg07/3smC2OLxMwCEqlhOdER0D050ieirwoLCK1/5SrfXacz3wYtobe3x5ESPtRhm2/29guM7Byc6Y+lwodFzXTEc25UWW0SHPffcM/zyl78Mz3/+87MW0b2K0LSN6wJDzUj9GQI68xyb8zQNO3+P6N2qGK2vQIsoI6LzuaA5NcHtzGvmmkJ0JialDnB9IJaPJJQzNmBEa7qIPnPmzOLsgDrjyomeKwjjrMiUcQ7yWMTrlM4qbmw64CbtBgAGIw7dYnJV1onOyidCrwcBY7iITpHrLX6hXRE91uKEFaOpneiW31t3EX0yTnSLhPES52ITqdY4F/oKDp7yONmtQuxGEEYA8yaiI7Z4a5Mx2gSyySK64lxWhXriVa96VfK+gvoypRPdW5yLJ1G/VUSvGkR07070XONcPNcVXuJcgMNYObCZneq5iujUx56d6DCWG525ltdFgE7053w+VTrRR+sr0CPKxLmYya4JTnTeb7SCuonOo4no4/2sSSL6fffd53LnWVkkojsS0Tv92Cqd6NzgqTM2q4BVWRYoyjqg28lPTyFA1/UzYrIfU0THMYBImnLyQkFZZxd6a9E8GSc6E2gm+V7iXMyJXpdM9Cqc6DZR9rYohwjhddEpdpyLBxHdnL2eMsg9xLnUdRxuanyKx0z0mCK6R5HauxO9ijiXJmSip3CiAwJ3lYtBvL46xLl4igszzAAzltMcEb2pTnRAQ6jSiT7W8zKXLDO2NUVEr6vojCEWPW2ke5jXU6d4molgWqalW9QRPzOdTKGj40YpI4xzo3GxpRbRm3yoKCI6wm3ZTFce71VEr3OUC8UrE6mYInpKFzpQUKZuw2TBQY5wOBlhsxNu9k5fiyyueMicTpVdbpN9T65vxkP6CK+uvZgHi3oR0U0kVSb6yngQ0VO3wZuI7jXOpWrqkIlO3+npWgGraTolfNf9YFHqIb5SONFvvvnm8OEPfzh85CMfqczFWAcnOveJx2vIzEBjOdF5TFOd6O1kk1fxvNTGZd5bE9E9LsS0Sx1FZ9pM/zXSjhIihdGXvNQoVb3+7u7uWueiS0RPDB0YhXMZYRxhlEEzpYhuQn5TDxWlEy4ritO5tXMIaezryltOu+eDURHRU8cTNcGJbgeCTkawscLbk4hel0NFq4pzsYmyJxHd3IqenegxRXQPizyMidz7qQVbT050T2Jg6oNFPU3WPcW52L1LfZ97nEunY1M6ef3y3nUyE51x1dM9MZGFgBRO9IULFxb3LrsEb7nllkqeg5ovlcu+U5EpKRfC+Borbsac6F764KaI6DZnLvPciOiMOV5jgdoB0Znrydu4MRZjHSDKz7g3mrJTYDTzAPoZCRt1RSJ6YuziKSOM22NTusDpbOmk6upEv+mmm4rDaIZ/3XDDDcXPWfmzQ0V//etfhwMOOCC85CUvCccff/wqnTODFJ2cNxGdCRKfU51FdCsAYh0s6kFEb4ITnYJ+sjEsnTqcdLL87ne/K/qG97///eG73/3uUF8xPG/w4osvDm9729vCi1/84vD6178+fPvb3046MWBiXpUT3VOci/XHXkX0XDPRPbnQrU0xRH2eh3v/1a9+dZHZ+81vfnNIILPnR/x5+9vfXhyMd+CBB4Zrr7025IKc6KMjJ/oKbGeR10iXTjh/eW0///nPw7nnnhv222+/8OMf/3iVGuhzn/tccZYCP2f+4VEsJfIkhRO9tQ6pSug2J7rXRQ4T0T0KoIx342W2YxhizLR6v2kwn+SenezCKNfgf/7nf4Z99tmnmF+cf/75oz72v//7v4tDzNEzyuSim3bRBKHWBOk6udG5RpgjjCai1+31TDQXvc5O9PSzrsxBGGewsXy5scABzuNSikt2s9fVic6AdPnll4ff/OY3K7lLZ8+eXQgRiKkMLJ/+9KfDiSeeGI477riw0047hcsuuyx88IMfDF/4wheG/o2dau1NRDcBus5xLhQAZFDHEMhs61tKEZ1Ci2uzKU70yUDhOdlImE7ANYHIdfjhhxfF/hve8IZVHPIstFG0fvSjHw3/9m//FhYvXhyOOuqosGjRoqIPaZoT3ZOILif6yv2HB/EaJ7qnPHTr36t+b3gO+ocrr7wyfPKTnwyzZs0KP/rRj4p+wzLR77jjjrDrrruGV7ziFeFDH/pQ+OlPfxp233338Kc//SlsvfXWoWpS7w7wlEHurT12fcbKRPfssPUsolOTTNbpyOvCmIM4suWWW4ZDDjmk6CsYW+kbYLfddiv+zM/oP0444YTiMf/3f//nagxmPpriWmqdu1UpotM/8HmlrkVHwupQj4srrZnto2GGIepsWxBoEmYA4/VNVCPgs91jjz2KufDRRx9dXIccqMtcFV2ilRtvvDF87GMfK8aQTTfdtJQTnd9DXYCIvtVWW4U6Y+ZH+tU5c+aEOkANQrtHEtH5zOnr+dkOO+wQmsqMGTPC9ddfX9uzgySiOxDRy7q67VDRlBcaQj4CV2rX7mR57nOfu8rAbQc8IAR+/OMfD9///vfDm970puJnOMeGF8+I6ExIvBUAtqpcdxE9lpOez5vCI+U1bQfsNEFEn2wB04lImE4WOVyHfOFCH84vfvGLsOOOOxbCGeBGJ6/zZz/7WaNEdH4nQo8Ht7MhJ7rPOBcPYn7sOJfvfe97RV9w3XXXDQni9AVco//1X/9V9GUnnXRSMZnGLcbfcatfffXV4VOf+lSx06XpeItz8eSMt3tXB4uuENE9bstnXJ1su0455ZSin8BRym6VXXbZJbz2ta9d6fcilrcKxTvvvHMx97vgggsKd3rucS6tprOqcsvt/ef3exTR6TN4/z060dsR0Zt6uKjNJxGzJyqiH3vsscV7SH9g1/zLX/7yVfog6nO0Ckx+aBf0U2Wc6NTztLMJTnTGDebPdTtcFEPqSAdrUiPW8bDUiTjRuX7ruhPfl2UoQ0wY7/Rjq4IbmpUzb26zdrGYlne/+92Fe7TVWX7hhRcWA4s5T43hhZQdKupB7GuFAZEBhZXMukLhETPKBVJ24E0S0TvhRPeSh44L6etf/3o49dRTwwc+8IFVChoW42677bah7/N4nKXPe97zkrQXUYg2dNqtZhExnvo6m0h4PVg0dia6hwWO1PnjY+W0Vwki+F577bWKo5yawYTjBQsWFLtWWtuCIIYwlksmuhfROueDRb1nont3ok82zoW+glgGdquA/b7W+cXwM1hs7PX2uaWKc2l9f2KI6F4ZT6j23DaEXu71ph4uylyOMW8yuehnnnlmERU5PKlguBbB7ldbjLP3tuzzomGY9lF36ig6W5tHqs3q+HraxTTNuka6+JrtZAZFIh1dGWGc7dqIox5E9LrmoRs4wIheYODh/X/2s58drrjiimIbEMUqojrbZ37yk58UjyWTkEyy4cWzieheXdyeBC/PTnQT0eVEnxy2i6MTmeip89CB++df/uVfwjbbbFPkHLPlbNttty1Ec+Pggw8utlDymPnz5xf982abbRa+8pWvJGmz9VFVONE9HSraKqJ7a5eB4KFMdB/9UtXCPlup2ZFCjjFiOv3GGWecMTQx4vmJcxleO/F36o4Y4hj9WUoRnef3JqJ7aY+J6DEOFkW48ibGjiQQeRTRJ+tE53rjXKbtttsufP7znw+//OUvi7g4op3GgmhJTDHsbvFEKid6rDgX7yI6ZhPvcS6jjTmMB3a4aBNhzOf1TVRER19gbkrMyvve977wohe9KLz1rW9dZdEdnYIFeow+RjsiOjvWm+BEN9G5bhnitJmFSA5JHu31eNrBV8Vi0+qrr17bw0UloifEtnCUEca5kSjAUoro3Mi0ua556EDxymE+b3zjGwtX2A9+8IMic4yMUgYtBhSKd3LTEcKOPPLIcNBBB4XvfOc7xeNbOzPPInqdo1zstPCYTnQmAynFONwYuA1iOVergMkGfVQnnOgeRHS2TeIEob/jUJ+zzz676H85J8G49NJLwyc+8YlwxBFHFJNiskvPOeec8I1vfKNxIrqnLNa6xLnk5kTPNc6Fa/HLX/5yIZB95CMfCS972cuKA4n/4z/+Y6hm4HoYvmvC7tMY4mlq7DPwMiH0lIkuJ3oeTnTuc/ojagjuAxbe2L2CONYqgg2Pijr55JPDt771LXcxmqky0WPEudhzeBbRPTvREfhZrBvrfkFAa6oTHZjDTlREt/oWAZ37/phjjilMPMxLfvjDHxY/Y2H+Xe96V9FHtC4sUVcgzJbZJYJWgGbgZVyeDKQk8Fpi7OjqFGasGCnShdfDmNHke6Srq6vWh4umn3VlDCsvXEBlnN22SpPSBY64xYBYZyf6SFv/iXWhSH3pS19adFqsHlMY/u///u9QrARCGmI7WYbbb799McDxfngU0VlVJmairljREdOJnnpyUtc8sFZsJX2yUSw40T3EudBX0BagQEXowA2CcG7gQqf/QDwHXKgUcIhn73jHO6IviljRXMXBot4c3/TBCGEexOPhIJQg0OXmRPd4sGgMEZ2+m8UDop9sBxi1EovzZJSa6274ZIixB2EuxgJV6p1p9hnweXhYaKENCAceDrSKLaJ7FKhb28fn4TETfbIHi1JTIM5Sn3/2s58tXOivec1rinGCAwPf+973rvR4DhNltxv9ChEw3qDfojaIfQ/xOVg8VFUiN58Jn5d3EX3hwoXBIxZNyTx5tPGNMXHJkiWhqTCvnKg4aPNBdsN/9KMfHZpfsFBPX/HmN7+5OHuJ/gizn3HrrbcWGgD3I/+OQ0bHE9EZd6hNUs+DJws6DX0Cr78uOhU6B+M/KQ/D4wDNsMrP6v7ZjAXmNM4SqyO+ZjuZgTBOB1Zmos1juYlSuu4sm6nOTvSRsBgX/o8oToHL31uFPHvNtvXMMsS8ieg2GMYSoKvADkSJKaKnFrD5zOqeh27bSifjIqcAQoz3IKKDTaDM5UEf0eqC4nMbvjuI4o3CNkVWqLl+Oi3KeY1zYTxMLYCNhDmLYyyiIGDEFOzHwotAGltEp2agRmi9Fvk7YiVuPL6P65TzElphxxsxUB6v4SpFdA/YderBjR77YFGuSa/OQ+4Fr0J/Jw4W3WmnnYoagdfJ72Nspa8YHmtBTMO//uu/hi996UtF/KRHqIW4f2LHA/HeWU1WpROe5/AsolMnjxWZkhKr4cdyysuJPjrEN2255ZariMGtfQWHiXJAMbtY7IufE/u06667lnLB2671JkS6mE5Tp0gX6iJ0pJGc6OgSjIU55KLfc889tdyRKRE9IQjjZVfLuMFS56HTBor9WDEbVUBO6S233DL09z/84Q9F9MLrXve6QvTCif6Wt7ylmOhxABBQoFDI0qGRld464HiLTUEQpr3e2tWuiM51FktIRQhNfU03wYluru3JiOhMiDoRCdMJvvjFLxauDitoyS/F+YEDxHjhC19YTHbNbcKEGNcY5yzgEopNlU50j3Eung8VjSWim/AmJ3o6EZ1dJ5dddlm4+uqrh65N4hc47MuctW9/+9vDeeedVwjncO211xb9Cd/P5WBRjyK6h/bEdqKD5wnrZB3fng8WPfTQQ4vIN85WYUxF6CI2jh1tBhnpzEOIiIrZP7SL1QQpI12qFLm9i+jUmCxipHj/OyGiM+fBNOO5L5oMzCv5bCbal9FXsBvF9Ab+/+Mf/3ior0CveP7zn7/SF7U/8Rhz5swZMqSN10bG5iaI6NyvfNVNdB7tAFHqRj7jur2eduF6pQ6r0+KHkX7/b6YwmUFEJx6gDDyWCVlKuJG5ob1t124HBha2T1IYMXFB/Hr3u99dbJk85ZRTihVBvhioOBWbmAaKZiZciGUm7nGzI6x5E5a8ivvtwMDPwB7Dncd9yCQmtYCNiN4EJzqTzMkIh+Zm9+BEZ4ETwZwi/9e//nXxGR1//PHFFmzjU5/6VNGHzJs3r/jiz2yf/P73v5+kzTllouNU9JqHbiJ6DHe4CW8eHOC5ZqLvueee4TOf+UxRz3H/UyvNnTu3yCr9n//5n+Ixb3jDG8I111xTPHb27Nnh9ttvLybJsUSy1AeLehPRrT0enOh8NrQnhohuC4/0UV7PYKGNnp3ok4kvQRy/4YYbilhIhGBqHhbjWbQ3cJgytn3ta18rvgxqj//3//5f8IIJ2Syyx66hmSPQzzKPqypOxruI3ipUt2Zie4Dah+tjPBEdqK3rPGcdDTNnMcdEKGyX97znPUVcD7UE2gWmHjLROWh4LLgXyuaxM+7w2CaI6GMJ0t7bTEzPaD+ro7jcDmYmZv48a9asUCckoicCYYbBuYy7nAKFQcaDE73uUS5Mcsk1J4eNFeLNNtusKNjJOWbgsYGcgwS5oenYKASY9LaKA2w9YUHBowBNUZnCBdspGPhjRblwD+KCSJk3xmSRe7zuIjpO9E4cKgoenOivfe1ri/uInSocHkpfMdztSztxjbGbgT6FYtS2aqcAsZv+rNNCptdMdK8iujmrYoroXuJcvC2yx2oTuaQsxi9evLioI2xi0CrwsOjGgaMcCMakus6xa00R0b20h/s3phOdusOb8OZdRGe84XqZ7KHR9AOcm4JJhzkVfUIrv/vd70b8d+PlG6cS0VM4oV/96lcX9RhRWlXVW9wfw2N2PGHzPOrmiYi0MdpnNX3OIjo7xCfy+VDHf+UrXyn6CTPojLdYhYGH9/1Xv/pVKSc68N43RURHl8GgUCeoFdEi0AUxZ7bC+IBm5eHslirH1Q033LCWh4tKRE+EHRRaRhi3rKSUIjo3MKt7xBQ0geGrXYjiDE6tQgR/3m677Ub89ww4HosW2sXEvM6dLQP/s571rCjPZQe9pRTRrUhvgojeiUNFvYjoNjmkwMFlPhb0Hal3M1QpdnvORPdIzEx0T3EuXjPRY703iC5knLcyfPKTsq+QE91nJjpwjcZ2onvFc5wL0LbJ9u0IXdQVIy0WEMtQB1KK6Mzh3vnOd1ben3s++NLq5LHc3inhGh8vEx2GH7jdFBBE0RHKOMLHAoGxbOQoO1zs31i83Hiw+/76668PTQDR+f/+7/9cGjpGo/UA0eEiOosCzOsQ2L3Mi6tg5syZQ7ponajHFdZAuFjoXMs4kXisHT6QCsQtbuS6nHjcLmyXKessZyJqh5B6FKDr7G6zeJWYh4p6EdE9iLCpnej8DmJDPAiCMJIzwDNViN0Iwog73uJcPIvoMTPRTbD3cM/kGucyFk12ELWDvQdeDsHzKKLHyAa2PsmziO7ViW5j62Rz0Vt/X4oDyDsF7ee+TiGi068jll1xxRWVPYf3OBf6DOrTsdzenkV0NBDa79ntPxm4N5hbTlZEnwjMoZnfltlphROdNnrZlTUZ0HEYR+u0MMNnRY06UgxNq8DeZGbMmCERXbR/qGiZyR2PpWNIOUm2G7jucS6jgRO97HYyhD4KfI9xLjjR67wtjoILwS7WQZ8UGUzYUgqENtjXOYIHKOQ7EefiIQ/dYAKVu4heVc56DiJ6jIgVEwG9iOje3D+pRXTwIKIrE913nEssJ3odRHT6dY8ieqsTvRMwpnZKkE/Vp6RaCPjLX/4Svv3tbxcHudsB8FWJ6F4W/kaCetmrE71M23Cj10nwbBfmsmbWiv28jG1lFijQDKjdmvA51FF0ZuzHlDlSm8cS2JvmRH/kkUeGdqPXBV+znQxF9DIQ5+IhD50bvc4u59FgoMHBXVYUR3AHb2I1ryOmi7sKLMMtphMdp0BKkYMiB/HZgwjmIc7F05Y1nOhec2NjHQBqE2SJ6OVRnIuvcTFl/+5FpE0t5HsTrT060WNnonslFyc6Y3WdnegW6ZLCid4qzlaVpUvtR3/leaFjPLd3ShDIMcaMtQjBDtwmiLejUfaAzyqeF8rkopuWYdpGneGaY/yom+g82oGo1CmjCexNYsY/Nc66RbpIRE8Ag3JZYZzBh4sqtYjODYzInNrRVQUMcEykysazMNAwIfUmVlOIcG15E/fb/SxsC1xMET3151b3PHTuHxw7nXCiexLReU11EtGryES3CaTiXHw60WMeYlpHJzo1VOqImdQCtgckovs4WLQOmei00XMmeifjXDwLtJ4XAlrrsqoiVyzz3XOkC8YVr3EutI05KWaU0WDu09Q4F7A4l9i7GVicoO4oI+DzWBZxm3C4KK8ZrYrI3TqBqdbOPxxJYK/b62kX9DRqIInoYlzo1Jj4lhHGWWH2kEXOzd3kKBdox4nOKq8357CtONdZROc14KyIJQh5ENEpIOueh25Fcicy0T3FudQtE70KEd0myB5FdBOEvBFT2PZ0sKgy0X1noutg0RXYwoonZ7ziXHw70Tsd58KYameO1JVUTvRWEb2q57fnSPH6ysJ8yauIbhGVYznlc3Ci05fFvoaoB3lvy4joLHDTziaI6Kbl1M25jb7G/Hek66SOr6dduAbrmIvuyzKUCXaRlBHRbWUqpROdiRftSC3kVwWiOFtcy2ZS83iPh4oyANIR1VmQjXkwKte1FxG97k50yzFrkojO9VE3J3oVcS5eM9GZmHjNREcYoS+O4YA2ASa129pL/vhIwn5KEduTiJ4Sb050a09uB4vST/DaPTvR6dc9OtH5jFgY7WQmOtTZjY6InsKJbi7xKp3iVvt5dqKbiO4xt72siM79VOd7oEysSqpIlzJxLma+a4qIbs5tj/fERLLc+Rn3eN2jv8YDnbOqaK6q8DXbyUhEpwAoIzjxWFwZKYU+3JjcvE11otPZIoqXneh6FtEZNL2JGO3AgB/rUFGKNoq3lPcWg3wT4lxMRJ+MAI6AQD/jJc6F6wPBp05O9KoOFqVv9OT6Rjjmy6uIjkCF2BJDPDUR3UOcC/eLBzF/eB+bckz0MpFLLeR7FdG9tCdWnAvXAaYRj05v7050YMzpZJwL1FlAZNE+hVM7hohelziX8SJTUkHtTH8zllPe5j5NdaOnFtHLPm+TRHSc28wlPd4To2Ea1GgiOjQ90mXGjBmFYddLTVaG+qptNcYyzstMauwA0pQTIHPDN9mJXlYUx7WE0OtRRI/p4m7Ca7AT01OK6Ij4TBbrLqJbkTwZwbkTQnwnsYlTXZzoiHVVHSzKZD+1CNeKCSyeRXQ7wC+3OBdvi7ge3PGpn99QnMsKcj1YFOibPDvREdF5L7x8Nq0wFnYyzgXq7DBM6US3mqQqEZ97khrDs4hubm+PkS6Me9Tz4znRoam56PQXXEM23/TqREfTsPPh6s5Yrm6v0NewkDFSm1kUGE1gbxIzZ84sxv06HXDro7L//+2dB5hVxfnGvy3AKiAoGKRJkSaoiKigRo2ARo0tsZcYeyyIxqix/tWowa6xJ9HExBhLjInGSIyKXbGgxoKCIgKCIiAgoJQt/+cdnM3dZe/dc++ec+abOe/veVZw97J37ilzZt555/0yRjGFQrUUFcUN7rtA25wTPQp4yGBSrlFExyqyz3noEAAxGM6SiG4Hjj5H8FgBHGJzS1yodhKgxYluXQy+ONEhiqBvSsKJri0P3Tr3tIro2FWRljPcRkBoEdG1OdFdi+hanOiusYKXluORdRFdq9M7t1/X2EY60XVkoqNPt2OdJEVujGs1i+jWdFJIqHYt8hdqG9qPcxmqEx3PvWIc4XGCuTTuzSg7XaAdYKzkQuxP4nPjmvJNdIb431RxUcwloFNkwYkOfMpFp4ieMpjwQuyMIoyjQ0Mn4FpEx00N0ViLmypO4CiBABhVFLcrZNpEdExM8ZD2eaHDDjLSFNHxcHLpNLYDxxCc6HHkoWsS0X1zoieVXZ5EsdKWYp2A2uNc0sCKgBpEdI1xLhpEdA27ONAGOtH1xrmkKaLD6a3dia5ZRGcm+v/AAjuOh4vFqDQKf2oX0TFeRt+uVURvzomOfhhCe6giOoCI7sqJDqII+NaAF0KkC8ag0Gh8E50houcT/vMJ7CHRrl070xdQRCd5scUOogjj6MwwqHYdo4KbOuQ8dLtdJokipGmBQQoWaHx2otttZ2mK6FjddSlwwImO99d2PZUigMchomNArUW0tk50Le1pDrulOgknujYRXXucC/riNONccN9oWORmnIteEd012pzf2trDOJf/Yft1jcVF44xzsTFpvse5ABefwY7N8N5JLYbhPVw47aOC5z7G3hrjXKxBqDmBH68JXUR3lYkOorw3zgGMHz5FaRQCmo6PTnTcK03tHLDFUkOnu2fFRd3PujKGXWGJIozbVSeXTnRMANEO10J+UhTrLLf56domxWkL0El9Bkye0oqOwKDNZZSLbYPdzugzEMBbmmVu3exa7i24jzBB9OXc2IFXEpno2uJcfHCipyWiQ7DX4EIHjHPRK6K7bgPjXHQUFgV0ouuIc7EFu30vLApc5aJbkhK6tTvRo0SmuATzguYEfsRZhpqJnutET3snGK5d9C9RctHRF0E/CMGJ7qvobHW2fLnouIY07yCLA+iddKKTvODigHAXZfKP10JUcpnJi8EDBiehOtEhiuMhH1WMKaYIaZrgwWez13wvKprWZB+r866zyDFwdN0GTU50LVEu1onuiws9yTgXjZnoVkS32/6znIkO4U2LiO46OsWXNrmCcS5rO9GzGOfiQ2HRLDjRAZ6tITjRXbi1+/btW38Mkxqj+CCiRxGqXQr8GEsX6tswBwrZiQ7NB58/7XNkhfGoLnjsZA9JRM/n6tZKoQKi+DwYv4WyU6BQcVEsFvhy3jizUF5U1LUD3N7MIYvoxYjieL3GyBQ8+DAQ0SKmtERETwsNTnSI6L7noceViR7H74gTTJx8EtGTinNhJrpuJ7o2EZ2Z6Dqd6K7RlkFu26MpzsUWCU4aFhbV4US3z2tfBANtTvRdd91Vjj/+eDn33HMTe+74IKJrdqLbqMpCArJ1omspOu0yViWJ986qiA58cqPDeIPzlc+JDnyLqCkWq4/6kv9OEV25iK6hqCgGJxqF4zhABxs1Dx3uEwwEor4+ZAE6CdIsjIpzCeeMaxEdQr7vIjoEQxzPOJzoLY2EiRO4Z1zuAioWTMQhDMUt3mrMRMf1hgGnVodxVp3ozETXK6K7boNWEV1Le+hEz15hUStC+yyiu3Si4xm79dZbJzo/tSK6ZoG3ueKd2kV0zIEwdsAcIETsPNNVcdEocS4A9xHamNaOqCSxsbu+ic75CojiOYH73LfPUyzQ1zA28yUXXecMNFAwyMCDLoowDmEKHZ9rER03LDojbe6yOLBbY4rJQwca41xwrfi80IEBFATltER0u3XQpYiO6y+EOBc78I0jE12TiI6Jk08iunWMxy2WaYxzgbiiTdjPJatOdGai6xXRXaNNtM5yYVHtmegQR3HPaBTR445zwe/zOc4F5wpfLkR03Lu///3v5eabb07sGGKRAH245nNkI1O09K252DF9IZHfzoFCzUVHf4u5hAsnOubUEMajXBvQNnCtu2hn3GD8jevKRxE9X5t9zHkvZRwEIZ0iOmlRoVDcROjMXIvoIRcVxQMbjsGoorjd5qRNrMZ1grb57ESHqI2HfFqZ7naQ4FJEx6Ac15/vTnQrorfEiY5rWFsmum9xLkmI3ZikahSs8Vm1FhUFEKjScqJrKiyqNc7FpYgdh4iOe/Af//iH3HDDDXLrrbe2qC2usMdAi9CT5cKi2jPRbcFNjZnoeO7gPMV1rnyPc7FCswsRffr06fLKK6/IO++8I5MmTUrkPewY0MXnK0ZEt2NobWBMir4tiogeei66qzgXPOOi5LFbbSOkSBffRGfobbgPmnr2FRLYQ8tFnzNnjviAjplXRsBFgYF7FNEWr8VA0nUWOW7YgQMHSogU6yzH67GarM2VCQcCJtk+i+h2u1maTnTciy6dz3bASBF9jSiKSakmJ7qPhUXjFrvtQE6biI52aS0qaoXtNJ3oaQn2Psa5QFxIS9iHyG3FNYzfIC7NmjWrRUItFlsRWYDPMHr06JL7SLTHpYiurZAnjge+shrnotHlnQv6d41ttM9CPG/j2KmG+YQvgoHm4qhJuZjtGBCmCm0Gqqbc3jY+RQvoY5uLm8H1gz4pZBG9mGzyOLFzasyxm5tr4jzhPIQiosPR/N///ld8zXLv3bv3Wp/n2WefVbnjM24R/e233/ZiFydF9BTBQAk3QZSLH69Fp+tSKICIhK9QnegQxXEuorqf0alpjXIBWgd4UT8DxJe0ok2wvQ3v5bKDtoP+EER0nLuWCM7WJaHFiQ6XHr58inNJwoluJ8baFg4hrmh2oqeZiY4BtRYnusbBPUTStIT9c845x+we/OEPf1g/jvvb3/4mL7zwgmy++ebSt2/fon/nAw88IO+//758/PHH0qdPH/EVbXEuANdqVguLanaiA/TvGkV0+9zBQm4c4wM60Usnd8yZlFM8V0TXSpTccZdgjlNIRMc8DK8JNc4FQGeYMWNG6u9r57kQ8AcMGFDwtXgddARrMPQdCNJPPfVUquPxlpJbQLSxiI7Pg/ET9BKNtfniAmNoPF+xqOa6bl1z6Jh5ZYRii4q6Fq/tthHXbvikgCiOB0bUCTZWZ7FCpg27apxWFEoS4AGPzjItsQMiuuvOGQ8IfF4twnGpYOCOz9CSBYk4ImHixE6YfHKi20z0uH+nRhEdAyzNInqameja4ly0OdEhkqa5WLrpppvKNddcU///mLzdc889MnbsWHnsscdKiiwAvXr1alG7XDt67PtrKtCHa1WTiI77Jw33lVaXdy6a41xAXG3zPRPdjg9cxJ3YoqZJitz2PTSL6Binoi/TWlwUDufmBH6IvSE70THfxOdL22iA8QcWWYopLhqKEx3ala19p1G7yffcw7XSVGyL1eLws5BF9B7fnivkorvWaZpDx8wrA+BGhjC+xRZbRM4i32GHHcQluFHxYPY5JqQQxRQVtR3x8OHDRRt44OEhqTneoDnwgE/zOoOI7rreAFwXcF9oE51KEcBb6siKqzhpXGAHjm8iOpzocR8/m9WqMc5Fy4KLhjgXTSK6Nic6nt0u+1g81yCAT5w4scmfv/baa/LGG2+Y87jVVlvJdtttV/8ziPHPP/+8+ft1111n/sTPc8eGhf49+MMf/lC/wwtj0Ntuu0369+8vY8aMMd+DgAe31ieffGLuKUTG5E44Me65++67ZZ999jFO+n/9618ye/ZsGTJkiOyyyy55+42nn37auOcxxtp5553r71frRG/ufdMA16oWZ7y9h9OIZ0LfhM+tceeIT3EucQnQeJ75sHW9kNDsIns4DREd9wfOkWYRPUpkikswP8UzoxB4Rvkea1QImNxwj2Pel7bhrZgoGYjor776qoSAFZrRN/kiohfKPrdxwvjZsGHDJFQ6dOhQH3M2dOhQ0Yzf6o1H2EIBUdzlWNHHqq1rkQ9CPiZAWiboLkV0iHw4f1rjXHxf6MBnSHNgocGJbkV030Ff1VLxFr8Dk2YtC0F2wpT1OBfNIrp2J3pa20c1iegaM9FdC/u5In6uCxuL39/73veMmA3BGWL43nvvLXvssUe9qxOTv9y/48su8EX59+DKK680QjpiYf75z3/KSy+9ZP4Ejz/+uNky/Itf/MIU58Nr+vXrJzfffHP9v4f4cdZZZ8lzzz0nu+++u3kNhP1dd91VjjrqqLU+74QJE8zvHDdunEyePNnE2UD0//vf/15/PqK8bxad6CCNSBf7nNUoUvviRI9LRMezFf2Cxs+qvbAo7hl7PpJ8f3w+zSJ6VLe3SxG9ubZhLhSyE93Ob13lohcjomNumla0WJLgvsUczrdinNAJob81xtZJ9K1YarHgc2LRA0507eiYeWUAe0NEEcbtaqxrER0dT6hRLnhA4IEddUtMsUVI0xagXZhH7NIAANQtSURBVF8rLQETCHyGESNGpHbuIUS4FtFx/YUgouNYtnRrGRapNDmLrVDlk4ieZJwLRfToQJSDUJimiK4lbkejsxXnwqXDE/38zJkzZaeddmrQjoMOOkimTZsm7777rvTs2dN8D5OGLbfc0mSr33TTTcaJfuaZZ8rrr7/eICIm6r+3TJo0ybjBf/rTn5rfh/EM/i2y2/fff38jslsR93e/+515HXbd5bra4YR/9NFH63Pd8fshlJ9yyimyzTbbmO8hu/1HP/qRca3/6U9/qhdrIUChUBSA8xzie9T3zaITPWnsLhks9mnpOxoDcVSjcGmfhXGJ3vb4J/H8zkJhUQhlOBdJXivYkajxWmwsVGt2ouMcFTI/wImOeYAmU0AS2eQwcLkQ8LFYHVVEt3Ny15HCceCj6Iw24zppKhYSc+3mdnWEQPfu3WXKlCmiHV2WoYCBMI6HR5TCiXgtHiKuC0VC+A+hE20KTCLxoIgqiuP1eABqdHzDkaaxXVHB4BSOqLQ+gx3EuBbRsdqfViFVH5zoWqJc7DUJAciXYjTWGRf3JBy/E88tbe5izU506+DJWpwLnqf40iSiW+d3mtcvIkwgeOPrZz/7mdx///1mUnT77bfXvwau8WeffdaI3VYAB3DfHHfccUZcLiSmFvvvsbgB57gF455bbrnF3EfXX399g+sH/x4TmDvvvLPBe/7gBz9oUBj1iCOOMH/CoW6BkxzX/4033thgVxFEqJEjR5q/P/jgg0W9b5YKi7oQ0bWiNc4F5wnXTZxOdBDX73PpRHdR78BG7iX5/r6I6Fqd6HZsX6h9di6kdSEgjn4D58iFEx0iOoxBUfpTq4mEkosO0dk3J7rNcm9K/Icmh8+jqbZMEmAsi2tQ+44I9zOvjBUVjeKIwmtx47ucjGLAgE43VCd6sc5yvB4PIg1iRS4YOMIB4rOIbguepC2iuxSw8QDEYNF3Jzo+RxwuclucVAvo/3zKQ4cQBEEkCSe6RnecZhHdClNZKyxqHb2aFlysQJpmmzBRxeQH7w23N/5/++23NznkFmSYgw8//FBuuOGG+gUIW0gUz3WMA/MVEy323w8aNMiMJ3MnXvgdEDf+/Oc/m/+3vwNfWDycOnVqg/dEBnoueHZB6MzdcvvWW2/Jxhtv3OS4yo59P/jgg6LeNytxLnbBliL6GtC/a4w4wXUcZ9tCEdHR/6OvS/u5bMdp6D+SiLSz76FdVNSeiQ7Qvnxzbjsfw86ttDPDNWaTx4mdW+O9m9u1jt23eK5rv96jAg0LEXYai97nI7eAKMZTuUAbtEkKro2ASYLrFH06zLz5xsEacD/zygiY0GyyySaRX+s6nsOu3IXsRMfAL6pQVkx+ugsB2vWuhTg+Q1oDJ4joeJi6FLCxQIXJsu8iOiaSeKC3VACHEK+p2jjOj08iup2AJ5GJrk1Ex8AKk3Ut+fn5RPQ041w07JhwIVhrdKJvuummDaJX4ACHGx0xJcgWB9Zdg36vce4jJgw///nPC97Lxf77piZb+B0Q1pvKnUQkS67DHTTVH2LxJtcpVCh/HuIjvnC9FvO+SZL1OBeNTm/tTnSAZ2IScS6+Ygt8YvEubRG9cXHRpER07DDSDIRqfH4tO9Pyiej5sHOhkHPR8Rx2IU7n5rE3py3hGQ09ISQRHfcEPrsvOgn6UNwPTTnocwX2kEX0bt26mWuxkJlEA7p62kDB5BLOJGRiNoddeUGupUtwg2Li6UunUyzFiuJ4/WabbSbasA86n53oeLhh9TutwbfNIncp9iDKBfguokPEAXGI6Jqc6BDRfctDB0k40bVl5lphRasTPe04F4wvNEyarRipKc5Fgzseojr6+4suukgOPfRQs03Vurq///3vy2GHHVb07yzl32NCkutEx+/473//K5dffnlsC1L4nX/84x+NWGKFk1xwHlBQdMaMGbG+b6lkNc7FHnfNcS7o37WK6Ggb41yaXghIW9jJXdxLaueCD3EuuZEp2sQ1jIVwjRQS0fEaLIjYuVGIQMxG3ZC0wVwGx9ca1poDuo/dre87VnSGBueTnoV2N1VcFDs2cC7xeTRqUnGOUaBraS8uqscyFDDojDBQj+LqhsCHgYBrJzpuXnQ4GibnSYAOKKrzFecODx+tTnQ8ILW5RYv9DGkuAkC0dz3ItG4L3zPRbcZhS/LMcX9hgqItE51O9DVOdG0iup0oaxXR03aia4lzsWIkRfS1Qd43rttLLrnE/D+KfA4dOlQuvfTStYQFCN2vvPJKwWNdyr9vHCWIgqAQbC+44IK1XovfidiVYjnppJPM+yOrPVewx/tAPICIvu+++8b+viHEuTATPZtOdJx3PCt8j3OxTvS0QVFjiEqYzyW1cxqfDwsEWnatNIVdtNSci95c2zAfCt2JjmOQds4znv3FRMmE5ETHdYe5go+56E21GecSfV1TAntodO/e3TjRNeN+5pUB7EUQRRi3r3Udo4Kb13UbkgKTOyxsDBs2LHL8BwZPGkV034uKuhDRMUhzfczgtsDkySehNiknOlzfuCc1OdEhovfp00d8wU7Akygsqm23hC9O9DTjXDSI6Bpc3xrbhDYgDx1RJSj4iUiXAQMGyMMPPyw//OEPzc8OOOAAMz6cNWuWPP/88/K9731PRowYkfd34vO05N+DrbbayuSSH3/88aZI6S677GKES2SST5o0SW699VaTpV4MiKz5/e9/LyeeeKK8+eabMmrUKCM2Pvnkk6bIKtrdr1+/2N+3VLLqRPelsCiOhZb+LSknun1u+yyiu4ykQbHja6+91lwjSfXzGKfbzPXc+BjfIlNct6+5toUuottYFWgKacdX4r2LcaLjXOH5kNaOyqSworOPIvrEiRPNfKLxXAI/a6roaGj06NFDnn76adP3Rqkn6QI9s52AgTCOB0iUhy9WlzCgci1coB2hFhWFaIfBUFRR3HZWGkX0tAXoJMDqeJqFZDCAcV24BiI67nGtD4ZiRHQ84FsiaMYVCRMnjHPRW1hUuxM97TgXLSITnehihGKI5Y1BPz9+/Hg5/fTTTbFRgJzHyZMny3333WdywHFdQ/h+9NFH5bbbbqv/txDEkXHemKj//phjjpH99tvP/D3XHQ4OPvhgmTlzppx66qnmesU9deCBBxrX+A9+8APzGkxA8f4DBw5cqw2nnXaajBkzpsH3jjzySBPXcuyxxxpRC7WAHnroIdMOHAcsKkR53zRA+7S4S+1EOQ2Xoi+Z6FrbGHfRUzxjfc5ER/txb7twotv7OMlFCGt20RzpgoUMjAO0OtGjiOiYE2VBRHdVXDTq+1qtIxQ3ej5Xt2ZgYrWGz8bYRYHG47nQ6N69u+lzMR/XivuZVwYoplAoXmsD9V2BgRCErVCd6LZTiiqK40GCSUdTGZ+uQdsGDx4svoIJIwZWaS0EQHDCINN1jIrNZfcdm2Xekv7Kiuha4lwgBmJC61MmepJOdMa5FId1d6YpomsqLJrlOBcI5Y2xLho4xnMLjgJ8f/To0eYrH3vttZf5aooo//7ss882f/7pT39q8ud4Dv34xz8u6AZq3G4Lcs2bAmOrE044oaBo3dz7pkFWC4viukGfod2JbkV0be5fPGfjFJjwjPVZRMf15Ooz4LmDehMYU2NRL4ldLLkiukYzlT0HGENrdqLbBeR8YF4WciY6jgGeOS5EdBvnEsXVa7PD0cfh+R+CiI6dcZodzYUKiDY+B/gZ+lqIy5rMZ3FjdVPkomvVuehEVyiiu85Dtyt2oTrR4Sy3Faijvh6v1db5wgmDTtRnJ7odTKT1GTDQxoPUdSY6Boquhfw4wIJESx/i+B24t7SI1tZN5VPUDsRuiA5xi4V0ouvPRIfopkG41hCdorFNPk3csiRaZznOxS7yaXR5W+xOI41tZJzL2mChw4UTHfMzzCPQr2BnTlad6FHd3q4z0Qu5ZzEnwlg2qQKxrsE4BJ8Ru6FdiOjWRBblekcfF4oTHc5tXFNad2k0BRYlcc805aDPFdhDZsMNNzTjFM3FRfXMdgIFNy46zCjCOAbzGBC4FtER5YLOXuuKe0vBgwEiatQt8HCup51fFgWbb+ZTxel8nyGteBW7VVCDiB6SE72lvwODNg1CYO5EyTcRPW7HOCY7eH4xzkVvnAvOkZY4Fw2CtcY2adpy67otNs5FC1ktLAqw6OqLEz3kwqIAz26fM9HtZ3AhoufGuiUlctudENpF9CjFO10K/OjbCl0j1lgUcqQL5p6u4lxAlFx0PKeh/4QiovsqOqPdTRUQxbnE2MW3z1MsuA61FxfVM9sJFHsDRIlGgViLAb1rER03Jm5SDRPzJMBCRTGiOB4kGoXqUER0ODbTivLAghY6ZpcCNkQEuEVCEdFbeu7icLPHic1f0+KMd+UYh0gA0U1jnAsGkFqfTxCm0L40hFsrADLOpWmsYOvaCa5pYcElmjLItTnjXTjRNYvoVhzV6EqlE71podlFnEuu2SEpER/9FsZB2kV0zU70KIVP7Zwo5EgXGMZcONGtcSyqgA9dIRQRHcccz1ffinHmy3LHuAWLHL59nlJAlA1F9AyDk48JXJRoFHuhuM4ix03rug1JgsWKqC57DODx0NfoyscDDgM7bSJXsSI6HnBpiRwYvED0dSnAQXjGxD0UET2OOBeNIrpvTvS4RXQ7IdbWv6BPhojhWhgt5ERPMw8daFhQYCa6Tve3BfeL67ZoFNG1ONFxfnB80igsCuhELx08f3Ce4rp2fC8s6tKJjmetff4lKXJjPOiDiK7Zid6ciI7XoB8M2Ylus8ld3CeY+xYjojdV1NJH8FzF5/HNuQ0dDjpPUwvrPhZLLQWYivE5tYzTGkN7TMJAGMfNG8UphtfiIeK6iA7c86HmoeNGhHAb1YluV2I1xrmgbdgxoFVMigLORZqZ7hDRXUe52AGi75noEGTicKLH8TviBBMliCvaYkwKgQl43GJ3UsVK4xDR7VZ/jcDdmUURXUN0Sr42uYyK0pKJrqENuDZcC/la41wA5gnMRNfvRLfPxLjaFkKci6tMdPRr1vCQ5Pv7IKJjHI2xoMYdJjDK4FwVEvnRH0MDCV1Ex3Xqol/De0eJcwHQrTA309j/loKPojPajDFsUzsCoEn59nlKFdExJtK6oKNnthMoxRQKhXjtOsoFD2A85EJ1otsCNFGd5fbG1RiZkrYAndT5yJqIbrcq+u5Ex4QC91JLY0/icLPH/bkwYdIgOrl0omsV0ZGRm5uDqg04FNMsKqpFRNfoRLeCrat72fX7a4NO9MLgPmacy/+OBe4bjZnocQv8eMbieatpgamUhQBXbvrcwp9JHUMfRHTr9tboRse4AHOF5uJmMC8KWUQvNlYlTjDXLsaJDqKK7j4I0r7FnxTKcsfPcJ/7vvjaHFYT1Rrp4n7mlQIQeiBQowNPczKDh/mMGTNkp512MkJRc3z88ccydOjQSK9NCrQBq9gYMLhsR1J89NFH5vNh0Brl8+H8wVWIiY224zF79mwZMWKEunYVc3/gvtxmm21S+wx4PywQuTxmttK0xmuq2GNp859b8jkwSMOEWcuxwKo/RFAt7YmCzdiPs804Dji/2q5TuwilqU2Nz4XdpZHGscA5gpjj+nhgQI+2QEhx3ZbGbYLzy0WbcB1oOSaYbOHLZTtwLBCX5fpYWCDQajg3uQtRaEsa7UnzvUoF8zUYH7S1Ec9EXMt2rBDHHBXXIcRDDfUtWvI8cnGuINDifOALImESO9XwHjBVabsWc8FYHMcAcwyNu/Ww+ARBsNAxxNy8udf4jK1F8cknn6RuHsJCF66NKMcW5wHtnD59uqqdwqUCTQt9A4R0bRGVzV0vOAf9+/df6/Pg/EybNk369OkjIbPuuuuazzlw4MDYxuUYh0ILaunO2bI6n5e+I4JOo2fPnq6bQQghhBBCCCGEEEIIISRlIyoKl7aETDjRbdzAzJkz67c7pcEHH3wgt912m1xwwQXNRlZgVfL666+XM88806ng/9BDD8n7778v559/voTIfffdZ26cs846K9Lrr7nmGrOd5NBDDxVNYKX+iiuukHHjxskmm2wivu4KuOmmm+S8885LpXArXD6XXHKJnHjiibLpppvG/vvhJrIRO4VWN3//+98b5+jJJ58sPvP666/L3XffLVdffXXJGdBwBlx66aVyyimnyIABA0QDN998s1nlP+qoo8QXzj33XBk9erSMGTMmtt/53HPPySOPPGL6QE3cfvvtxq137LHHikb+/Oc/G/fkaaedlth72L4GTtprr71WzjjjDOnVq5e4ZOrUqXLrrbfKRRddpKbeA8Z81113nZx99tlOovJwnn72s5/JYYcdZnaNueSBBx6QWbNmmTGmK6666irp27evHHDAAaKBe++91zjTcI40cPnll8uQIUNkv/32U3M9RB3X+H48it1thH7uhBNOMO1rKXAa3njjjamNhZPgnXfekTvuuEMuu+yyFkf8lXItv/jii+bvSc2hn3zySXnqqadk/PjxohV4IvGs23PPPWWXXXYRbdx///2mz2lq/m37GegmdtwZagwariHMd/bff/9U39f2M5gvRKnzhrElXnf44YeL7yBmEdfdIYccIiNHjhRfePDBB41Wcs4556z1s1/+8pcmvWLfffeVkHnkkUdk8uTJRr+J6/mN+VIcz6lMiOi2I8bWmTS3z2ArMYqtYKtFc4NPbK/B9it0rC638+Hi6t27t6qM4jjBccbNE+XzYUCCc7jDDjuoOx42ZgYCura2RQVby3HN4/5II9MXk2Ucs6SubwwCIY7jdxe63/Ea5Jn5et5ytzTjM6BYTalg0IxzAoFLy/HA50IeoJb2ROmnsDUf5yHONuO5iW2cGo8Doms0tis3+zPJ9tm+Bv0M7h8NxwPbf9EWGBVct8WC8Rfa5Oo6Rl+C98einIbzg+30LtuB93fdhsbXB8bbWtqD6wTXSxrtwT2BPr6594o6rkmqjehPtZwfC8atOE8Yt8bRNgjn+H0aP6sPnwELPNbIEeWaLvXzYZyFe1RT8ezGYOxqx+bawLwHsQxNtc32M5gL4PgmdR41gBgJxDel/fkw18Z9Yvvz5oBWAqNTKOcB15+2GlzNAdMBDGtN9Ts4P9CofPo8pQBd9PnnnzfP2ziieNDXgDgW6fQ+CQLA5i9HeeAiNB8PP9d5eHA422IGIQIhNcoKLEBmEh42Gp0hyGLEQD5tx0ec4OEMwSWtonhwhwLXLknkRvpeVBRgMNLSrDxbAElT5h7ue1uoygfgRoaQHnfOHwb5GrMD0SdrLyxa6s6MYrFFCF2PG7QWFrWDZdeihxZHnev0Ro2FRTW1h4VFG4IxblzFO+ME/Tvu6biKutni3T4XicOCFED9ibTJHdMn1ddjTIj+08XnKwaMpZsr3ukKzPcwvi7U59pzaWvfhAgMLy4Ki+LawFixmOKi0BpCAdpWU0U6tbcZY+umzgP0Rd8+TynYyBWNxUUpoicITnjULcQQ3F1sN24smuDhixszRPD58ACPKorbSs5aRXS4L7RMzkvBbhFOC4joWLF1KTjhYQjhOBQRvaUr4DgWEA7sJNI1mCQtW7bMKxHdTrzjPob4vRTRSxPR0+pjrIie1kKkD4K1pjZZ0VrDcxptoIi+tohuF380gPsY/UdaAjUWYDWjtY24l/C8jUvgD0FEt2MFzLPSZttttzVxWTvuuKP069cvkfewY0KMDzUDoVqziG53eOfDzo0QvxmyiI75aNrPY/RbxQj4ENGhmfjcL4UgooOm2g1DKK6jtMYMrujSpYsZq6G+pTb0zHYCA5O3YoTxYgT3pLCicahOdFRWB1Gd6Hg9HjppCr1aBehQRHTXLnQMbjFwct2OOIhjG5kV4jWITACDRfTdPu3wSFJE17K4kQuEC4grWlm1alXqTnQNIjqd6GvjWrTWBhYzNB0TtEebiG7v6aRBH4W+SjNaRXSA3VBxiUv4nHE6212K6C6c2jh+xxxzjBxxxBGJPQt9EdHhNi4kUrvE1qQr1D4cZ5gQQhfR0a+5uJbw3ph7RxXRQShudGhbuK60P/dywVwUu3yaEtHxeTCestpdqFRUVJjPSid6hkAnhdWhKMI4hCSs9rl2gEP0x0Auqsjsq4huHwxRXt+pUycVAkVj8FCL+jk03yMtydMuFjw803y/prBbFOlEX4O2PDf0w8AnEd06v5KIc9EoomPywTiXNVBELwyd6P9Dw0KlNtFaoxM9LREdwiPmKJoWNRqDfl5jnEvcbbPOdhcu7riA8ImFGVdxJ3Apohh6UteLjavRLqJrdqLb2MZC7cO9gPlRyCI6dIXciNE0KdaJHpKIDm0LzzurBfkA7gcIyNDnGmO1utBFdAAtlSJ6hrAnO4qIbm8O1050rHTBGawhXzUJ0NFggBHVxYiOVuOCAiZZGGD47ESH4waD7aw50a2I7rodWjLR4/gdcWInSD7FudiJdxbiXND34UuziA6XC+NcdEARXRfaMtG1tQf9RppOdKDZlafZiR5nnAvAs9ZnJ7r9DC4WArAQdt1118k999wjEyZMSOyzQdDyQUTHPaNx8QnjaixcNifyY34UeiY6cJGLjjk3DGxRFk9xzeOc+SQ6lxqN4mMMDc4P5s9NCewh5qLPmTNH3aI/41wSAicbnU8URyNei8Gza1EUN2KoUS6liOJ4vcY8dJul5rMT3W4nS+uax/HCwoN1ALgCA0MIgJpFwKiTFkwm4opz0YKPIrqdeGfBiW4FFc33T5oius1C1LDw7Vqw1twmDS5w4HoCoi3OJeuFRX0Q0TWKgXHHuQA8a30X0eHWduFEx7jAvu/MmTMT67swxvKhsCjQGOmC52AUp3zoIjquI9zvrpzoGDfaXbdZKi6KY477w0cRHSbQpsYq9meh0717d9PPu1h4KoSK2Q7ErenTpxccQKDT/eijj1QP+HKxeehRJk8Q0XEjuJ7ooWNxHSmTJMWI4hAJIfRqFKrTFqCTwHaEaX0GDChxTl2L6OjrQnCh2wFYHIVFNTnR7efyTUSHGBR37JTGTHQ7RtAsomOCkmYmOsYYrscOAP0r2qFFMAZ20uGqTZoEYw3nRZvzW2OcS1pFwnwQ0dHPZ8WJ7nucixXRXXwGHDv7DExS5Ma40AcnOtAc6RJFRA85zqXYWJU4sXPuYnLRQxHRfRWdocthrN/U9QJjqG+LAqU60YG2SBenM69p06bJyJEjpVevXrLbbruZjvPYY49tMIjEhYPvQfzcaaedzA39pz/9SbRTTKFQDUVFMRjEym+oTnRMZvEgiCqiY4UYkz2NcS74HJhs+ZyrjQc4Br5pxUXYFX/XIjruMZ/Pm8W6XFoiouOe1OZEh4iOiaAGUbLY2JU4RTKcG0yGtcW5WNGCIvr/xkdaanZYEV0Trp3oVkTXdlxcgT5Kk4iuLaM97Ux0oFWk1h7nQif62rhyaqNfSSOz3AcRPUruuEuiONExR7LGp1BxJaLbOXDU94ZmEpKI7qPoXCiGBj/D+Qn5XgHQCdD/ovaFJpyO7E844QQjpMEhDCf6G2+8IQ888IDceuut9a/51a9+JY8++qhMmTLFuLtvvPFGOfroo+Wtt94SrWARABd1FGEckyzcGK5FdJupFKoTHavaOC9RRXGbAaYxzgXXFlaTNTjLWlpUNK3PQBE9XiB+g5a4yDHZgqCiSUTHBMknF3pSsStwJ+LcaBPRtce54Hmedia6FhEd1wucvZqwgq2rdmlyomtoj0Ynuqb2MM6lIejnIQ6ktbDgsuipqzzxEJzowI7bsu5Ex8ITrk2NcS5WRG+ubRDR8azSuhAQl5jtQkTH9YGY4WKc6LintMcYRcWKzpqe+82BeTbmePlEdJucEDJlZWUqi4s6nX1hReHwww+vn6hvuumm0qdPH5k9e3b9a37729/K8ccfL5tsson5/yOPPFLGjx8vd9xxh9x8882iEQjSeABEEcbRiUIYcC1e4+bERapRNI4Du30n6ueDiP7+vK9l26tfViFWjx3VT8aN7m/+js7S5ygXF58BIjoG+FGLyia5mLPFFltIKCJ6lJoP+bADaU1xLj6K6EnErtjYFG1xLtqd6BjM4tmfZpyLFhFdoxPdisaun+Gu319LG3B9aBJENca5MBO9abe8ln7Ogmfji1M/kwHnx1PIcumUqbJq4Wy5Y0kyhTHTYOkHU2XVvBly51cTGsxZ0sA60XGtwDCVxEI2xoY+FPGLIlS7ImomOtBQxyopYCLD58MYJe1nM+beUQV8G2kL4Rn6nO/ASGlFZ1/0LlwfEMub6ntyXeoakxPijnR55513RBNORyXnnHOOXHLJJUZUQqTLf/7zH3Njw6FuhU+sOowYMaLBv9tuu+1k8uTJohW7UhJFGLc3hQYnOjr1tCb/aQNRHIPwqHnUeH3bDhvI6lpMwt27yWpMO9aAzn/zzTcXn8Fn2HrrrVMV0V0PxjA5RlxICJnoENExoWiJwzMON3vc4Py0ZGHAZZxL3L9Ts4juejEsHzZfOM3ColrEJUxMtDrRXYn7rjPZG0MnekOyHOfiQya67efR71uRVFVe+4oVsqomHkdjTUUrqV61Mrbf54La8tayetU35jPkzlnSIPf6gGvWZoNnzYkeVah22TbsVkC/k09vyBXRQwXzUfT1WOxI4lqNK0omNBE9V3T2RUS37c41GFswX8X8D59n2LBhEjLdu3eXiRMnmoVSLXNAp7ahAw880AjiP/nJT+Sggw6Syy+/3AjrAwYMKBjB0Nw2GBxgCDS5X2mL6OikopxkvBYPf9dCEm7AUPPQrSiOh0HUybQZhH1H3/HApBzXvs9OdExaMThK24nuWkS31eZDyUSPo6go0Bbn4puInkSci92SrS3ORbsT3dZzSUtER1+a1ntFeTZpc6JbgdR1uzSI6BraoDXOxfXigoWZ6A2x/bzGXHQIX7il4rp2yirbSF21vs9ZDGWtq6Ru9Uon91PuDsKkhG68hw+xFtATtDrRrdZRqH3QTjD2tHOmEIFGBFxEuuC9o8Z/oA/GHC2UXHR8lnzRKJqBKRdtbty3Wpe6b8VSS3Wi2whsLTidWfzgBz8wAjdO/scff2zc5b/85S/luuuuMz+3DqvGTgkMqApNHBH3go7afvXs2VPSdnUXU1QUN4frCQ7aHLKIjmusmFXHI444QoZ97weiDYjPEAZ8FtEhaKMjzKqIHooTvaXiN34HBjNaREDAOBf9TnS4l1yLos2J6FmMc9GYie66sKcWcVYL2kR0e11oOU94FtKJ7kfx06222kq+/+NTY5u7lVW2lrrq1VJXp+f+KGUhQHAv1ax5DroS0ZMSuvEeMBho2r3imxPdmoiiRLqE7kR3JaJj7o3jb8erzQEDoq0T5zu+is5oM86XNRf7Xiy11GOA86cpF73cpWj74osvyqmnnlr/8Bs0aJD88Ic/lPvvv9/8P4RoHLDGFwf+v5Awfu6555pVTvvV1BaIJMEJLkZEdx3lAmECAp/rXPYkwQOgmLworIK3aq1ju0gudvXYbrHyETtosCvxSYMJsoZsvZCc6BDAW7p7Jg43exJxLr5loifhRNcqokNM0epCz13wT0tEZ5xLYSDYYgzpyqSgJZPd4loshmjtug252EUfLaJYmk50fHacD40CdVNxLtrAsVu3fXw7iMtbrfmsdav1xus0R3mrNc/m2tXpn6/cOJcknehAuxsdY3OIpJr6WoudN0QR0UN2otsCn66c6KApQbYpoDeE4kT3VXTOjaFp6mdYFNB4v8cJ5lW4FlFPU7IuoqMjxcSicQcCkdCKTHhgbbPNNvLYY4/V/xwDvieffFJ22WWXgp0TxJncr7TAwxXiUBRhHINliLuuRXS7IheqEx3XDB7GPuVf5QMPMgzeXQvCLQH3OD5DWo5sCKMQm1wfMwj5GOiHUHcgLie66xirxgIovnyLc0kiEx3CPO5RbdcqPqsPInpauys0OdE1Fha1IrorNInoGtqgzYmuUUSP6g6MA8yVfMhE1yz0xwWc6MDnSJey+oWAlcE60YH2XHQ40dGPWDOEJjB+w33dXNwMdKCQnehWzI4qZMeJ3QVeTC46tIdQRFofRWfcD7hv8onoeI6Hfr8A6KWaRHRnsy+ISYcffricd9555sLo27evKSz6yCOPyF//+tf616Hw6F577SWbbbaZyU+//vrrzb898cQTRSN2m0EUYRw3MSYUrkV07AqwW1xCxK6gFiuiV5SXSesKHaIA2mIFaDx4tYkVxZD2Z8hXWyFtsJATggs9Lhd5HEJ83IstwDcnOiZKSWSiQ5jXILzlAkeiloIyTcE4F11xLlpy2rXdRy6PgyYR3V4bWtoEEd1mtKdxzWCRVLOIrjkTPe45QlmbKikvK5PKumpppWTeUSwVVeus+Qw1q+rnLGnRq1ev+r8nZdCxbncfRHQ7TtdW18a2rzmXOc7ha6+9JiFTTIHPOIF5Cc+aqLno0E4wz/CxZlQ+J7o1V/oSr2o1unwiOsDPXOscaeSiP/XUU6mNkZrDqYXpjjvukFtvvVXuuusuI3ThIThhwgT5/ve/X/+a3XffXR599FG58cYb5YEHHpDNN99cXnjhhdSrGRcjoqNzihK3YQV31zEq9sbT5jqMC5vlVUycCxg3ur/50rYg4HMeOsCgIa0ol1wR3fXDMhQRHZNuiJlxxLm4XkDMxU6MfBLR4USGcBv3RCkJd3uW4lzoRNeBaxFdkxMduHZe0YleGLurBP16Gn2Idic6jofmyJk45wgQtC644GU5Y+wIGThwoPgIxg2nn/60nHDCcBk+vH/q4sppp51mxqZDhgzJtBM9NzJFozkO+k0UJzrMHLj3NRsnWgJ0F9QDTBu7m70YJ7rVH0IQ0a3mBu3LtS5QbLthem0MPgP0OxhzYTgOmR49etQnfmjQgZ2K6OgYf/azn5mvQkBIx5cPQBiHWBulwBZei4vfde4sbkrXQn6SoGNBx69RFCploD1gwADx/TNssskmqYroEN5cn39stcKOG9+BgxyE5kS3EyOfBolJZZcn4W6PA0yQNYvoaTvREUOhKc5FW2FRLSI6WQOuDy2ub41xLlY4Rz+ShoiOfkqrQG0XnzBP1JiJHjf2easxgiMqeDbjnEH8dMHgwYMT/f12d54PmeigOaHaFRC/mouesOImXqdxISAOYCbD53MxdsJ7R3WiW+MeRPQ05+5JgWsLz1doQ0ktuCUB7oPJkyev5cLG37FbwLec91KwxjvopxpEdD/3jCmm2KKiGsRr3HihPqSsE93nQpwWdJx46Pn8WexnSNNNj0FKms73fPi0dSxpER1OO0xENGWi2zgXiujJFCvNgoiethNdU2FR14K1ZhFdw3HBRMu1qM84l+hO9DTQHucCIKJrFvrjwj5vXQnQcd3fEJpdicyTJk0yka/Tp09P7PPBja7diY7xB6Jnmive6QqIX1EKi4KQc54xL8Uz2UUBVczBozrR0QfjnIVSXBTjMR9FZ+h0NoamMdASffs8pYAdFLgeteSiux/ZBwQ6Q7i6o4roxbw2KXBDwqkbuohebJSLRjDogGjic5wLBp+45tIUtTFQcC1eY6KKiUUIcS7W3dISET0uN3vc1yYezmkJoHFgJ9xZiXPRLqK7cKJruV4hWNOJ3hDXonUuGiJlGOdSGIroa5MVJzr6TnxWn53oAOKtKxEd9dQ++OADU1stKXwQ0QEMKlpFdNu2Qs9HiLZ4ZrkQmNPC5le7yEW3eexRxyi2uGgo2OKiPpGbfd4YaFz4vqYxZxKgT4BuauOwXUMRPUawYorBXhRhHAMlvN61iG5vRg2O+CRAhwIRvdiiohqxW698dqLbz5C2E911sQ07EHQt5sfl2IYY0pLscCuia3KiY2LkUx46YJyLLrBYBiEsLcESjlUtTnTGuegWsDWgTUTXWFg0TSe69kx0gEXTLDjRrRvddxEdi++u3PT2Pk7SveyLiB4ld9wVMBPBcFDoWseiEuYHIYvomA9ibOBKREe/GvVahu5g68uFgBWdfQI6Bkwz+YqLot+1O6pDz0X/lE708LArI1GEcVscQIuIHoJTO59Yh4WNED4fBGg8cDVEk7RURE/rM2ARBTsttIjooTjR4SBviTAUh5s9bjD4oIi+Bsa5lAYmhmkW6NYU5wIRXUNsicY4Fy0iumuXEs6F6zZozkSnEz27TnQrovsc5+LaiZ5G4U98Pl9EdM1OdNBc+zBfCjnOBf09zhPmqGljjWxRc9GtE13T87slQHTGfezDvWzBOBLtbqq4qNW4fHPXlwJ0U3zOtMwGhdA14wlARMcgKErYPV6LCYVrcRc3o80YChG7chqCEx0PMKxcaxFNSgEPbGROpxXJYKu7uxbR7UAwBBE9joKg+B02X1KTiO5THjqwE+647yf8Xsa5FA9cnWnGq2hyojPORb+I7hptTnStIrqNhcp6YVFAJ7pfuBTR8d52/JJUP+OLEx1CtVYnutVImmsf5rshi+i5sSou3hdEFfAhouNZEYrTuVA0ivZ2N9VmnB+Mr5oS2EMU0WtqalTsjKCIHiO2UGiUCRNeC2HX9QQ49KKiWK1Cx+KzeztXRPc5Dx1gsJDmZ7ADBA1OdAi0ru/3OJ3oLf0dOB6anKu+xrlAZIj7OGrMRIcgiUG85gXfLIvoGp3ouGY0ONE1oEHIZ2HRwtCJnt3CogDPXN/jXCBku3LTWxEdJCXk+ySiN5c77tqJ3lxUS+hOdDs3dSGiY96AazmqE90aETUIl3EA0RnjEd+c21ZEb3xfwxCAc+Tb5ykFm+ChIdJF14zHcyCMR41nKea1SYJVq1Dz0G2HDwFdi9DQEvCw811ET/szaBLRQ3Chx+lE15SH7rMTPW6xG8IsnJDYVaUJK6RoLyyaZpyLNhGdhUX1CtjAtaCC64NOdF2Z6NoF6qw50X2Pc8F4xHWcC8i6iA63N57Jrs5Fc/0cjmMUJzrmTq6fWyE60Yt9bztvD6W4KK5BfCbfnOjQ67DQ2tS942POe6nPGPQNGoqL6ph9BQAGvVgB2nnnnZt9LR4IEK8HDx4sLsHA9Nm3P5a753SSc9+YICGy+LWJmMHK/ecX//nGjuon40b3Fw3gmsHDa9tttxUN3PjUh3LzxI+K/ncLJr4iVT03kyumx3e9FTpPENEhauU6VFwAN0VIIvqgQYNa/Ds05aH77ESPW+xOqlhpS7G5uNpF9Kw60V3njzcFRASXArYVjLWI6K5hnEthbN+RloiOsZH2wqJZy0T33enp0omeO35LSujGe+B61PTsbS53XOO4NkpmO4QyjKlwLn0zuBQjZGM+lPbYEUBEjupER9swhw1FRLeubt+c27kxNI2jo/Gzl156SbJADyXFRfU+ATwDAx9MmKK4y7GChNVh1050dB61dSJ1664vq2r05FTGycpli6T1d/qU9PlqcHAUuWSx6KHFiY5jU+wxratZLau/WS5t2rSL9XordJ5sUVHXIgbcFAMHDhTfwWJOHAI4+kDX9SAai22Y+Pk2UE8idsVOgLXFufjgRIcgldXCohoz0dFfaWiT6+ePbYNrRx/jXPQ50X0Q0bPiRMcz13cnOkR0nC8XInNacS7292vbTZkvdxyCk68iem4cZojYqFk4wtOO1sV7T58+PfLrERcSkoiOOehrr70mPmGTFSCib7rppg1+husH91QS5iptdO/eXV5++WXXzWCcS1zYbQVRhPFiXpsktjOsaBuGQ7YxdbU1UvvNEqlo23yhV+3Y1WLkePlKzddfmT/L101v4GlFdNeE4kSHAwfCXRwiuiYnup1waXTsFAITbjrRsx3nkrZ7yadMdNfueE2FRTW0gU50nZnorhdXCpElER3Pct8z0e3iu4vFgDSc6Fao1x7pYsfXzQnVrjPbC2HnTCHnohdb4DNOYMrDAkXU5w30h5BEdIjOuLZ8er5gDJUvtsXXYqmlAP0U/YfruCpdMx6PgTCODj+Kew+vxcDQtbgHZ+x2ex4oZZXpTfrTpObrJSJ1YSwSWBHd5wKptd+sEdErMiaiQ3jGl3VV+Axc6KAlArh1s2ty8diK8765XRjnkt3CoriPIFxrcaJrzUR3KR5rFiddoE1EtwssWtrkQkTHZ8e9qz3OJQv3ki0s6vNntSKzC3EjrTiXJH9/nH0JxrPN5Y5rdqKj/fgczRUg9RnMgzBucpGLDj0BfU3URQorovvcPzUlOvsY6YJI6KZ2CvhYLLUU7O4a17noFNFjAicyaoFOXPy4CVw7gyCE9RywuYRK7fI1D+gQRHQ8uPCwxYTC50WNsooKKWudXkwEBgeuRXQ7AAzBiW4H5C0RwONys8eJnRD55kRPQkTXGufiQyZ6miI6xC9MZrSI6BrjXOhEb4jryS9Ea9dtyMVer1pEZBciOtAc6YL+XrvQHxd4luOzaj4fvjjRk3L058a5aCeK29uliA4zTaEFTGgkmDeF7ETHMxEGKxdOdGvKi5qLDhEdfZPWhZlisZGivjm3oTVCR2w8lsLzHHqHb5+nFLBggPGS61x0iugxgQs6ajwLBHfXUS5ZoGb5IimrbCVlrdcJQkT3OcoF1H69RMrX6ZDa4hEG0RjIuxbR7QAwBBE9Die6/R0aneg+iuhJZKLjHtUmVvsgoqcZ52KFNi0iutY4Fw3CvmvDhBZwfWgSQ7WJ6LhO0Cb0I2lgTRmat7PbNmahuKhdEPc50sWlE71Pnz71mcGDBw9O7ByhH9PuRI/q9nbZNjyfmzuOEJhDFtEBrlkXTnTMSXEtR31vq0GEEumCuQSuQ9+c2zDhYp5m56255It6CY3y8nJzHFyL6DpmX56DwR1WEaMI43ho4AIfOXKkaKCivExaV+ia+MbFim+WSOv2G0ibyoqSj40WsFIcdaeD1uvm6xVLpXXbDrFfb/nOk13Zdx2jAic6JseNK2n7CARwOG1bImRaF4M2JzrEC9+KsSSViY7fqU34w3MWAyctorFrEd0Kf1qOhxbBWmOci4Z7SUMbGOfSPLif6UT/H3asAaHft0Xulri4fR0vunSiY2x66aWXmjFMbpHRuPtRXIc+iOgwqriOO8iHvb4xPyo0F4DQG4poW0hEnzlzppPnMUxmUUV0ZKjj+p83b54MGDBAQgBCrG+ic272eeN7Bz978803JQv06NHDef+mY/blOcUUCoUYiom2Fif6uNH9zVeIXHXVO9K58xA55pg9xHcwiNhyyy1FC6VcN//3fy/LZpttJgcdlM75sCK66xx5O0jUJjCVKqLjs7REkNHqRMfESIPQVIxAB2E5KRFdG/isEFQ0n6M041ysW1WLiK7Via6hsChZA+Ncmociun9u+bgIwYmO6xfnzFXcCfqYpPt8X0R0CNXvvfeeaBbRm4sGgQlq6tSpEjIQsidPnuzkvTE/jhrngnsb5yOkRQ2IzlrvkUKLGdATIKKjtmHjz/Pkk0+a+UFacxFXQEfFfeNynK9j9hWAiG63FkR57btzlshed7wn5W1miGvGjuoXrIiOLTpDhgwR38Fg9NUPP5O/LZkmVS+Il9cMxASI2uj80wJbAG1xHZegHSFEudgBb0sd5PgdeLhryvfHhMg3l5sttpZEnItWEV3TNZNPRM9qnItWJ7oGEV3L4oJrUd+K6PjSsBimLc4F4NlIJ/raTnTGufgDxiT3v/yRnDQx/Wz3ZR+8KMs/fkPa9t1K2g3aIZF5LlzuvojoNndcyzPIgvE22tRc3AxEW7xG42eIU8jGuNuFgQVz8hkzomtRtrhoKEC3e+aZZ8wzV8tYOsq4BZngTTno8XkwvoL+1bNnTwndib5q1Sqzk8JV3LEfV4xyIIzbkPsor229TluprqwSqclfUCMtamrDdEpBeMYXzovvYJUYp6m2aj1Z5ek1g0EQHlJpiujoWLHC73qyDie660iZuJ3oLf0dcKG7Pi+5YELkerGlWKxbLQknuraiotaJqDkPHaTp/tAW50Inuj7ROhf0t67bY/t8LSK6bUOh4nYhO9F9cHn70MYQolDiBCLz3K+/llUO7quvP/tIauvqZPnsKdK6/3aJzHN9caJjnG1zxzXFJwII4lEKn2LuhOcFXhfKPKoxtm4XjGYQBtMW8F999dXIz2RoKtOmTZNQQIY47hHoLFGMsNqKi+YrlpoFEb37t4keyEV3JaKHuayXMhDGo+ZV46Lv0Ml/YVc7yOzK7VB8xm61QlFO3z9D2k50DYOu0JzoLY1hiUOITyrOxSfsRDtuwVt7nItm0oxzsUKbli2bENHpRG8aDYKxBqyTUItojfOirdgpRPS0CovaXTPot7SSpcKi6MtxPfoc52LHJCtXuFkIKKtcc03Xrl6zUy8JfBHRbWSK1uKiUUR0O3eCGSlUbOSoi+KimJOjb426cAexEvN51wvySeSLh5Dljr4X82vfPk8p4HPC/OYyF50iegtBRwJhPGrG+dZbby0Dttq+pW9LmsFWW3a1OhUnvXr1ks22Hy3lrXRHGRQCD11MWO2KexpgVd91Hjr6h5Cc6BCb44hz0ZSHbj8Xnej641w0i+i411lYlHEuja8JLSK6hjZoE9EBFn40tYdO9Ow60XGPQgTx3Yk+atQo2Xjg5k7eu6zVt2MExEZVr8y0iG7H2VpFdIj8UZzo1owUKph7YEHThYhu58hRc9GhqWCcG8qiBu5l7JyxmpFPIjrmrU31QzD1ZkFE11BclCJ6C4EghNiQqCL68OHDpWufMKoaa3ei4+GbVj5t0ivFg7fdWXwGD2gMmNKMHoCI7lq8hqMILq8QnOgQhGwUS2giuo+Z6EnFuWACrzHORbuIbp3hWc1E1xjngj5LQya6BgFbAxpFdI1O9LTiXPBeuDY1O9HRRpyjLIjo9nnuuxN92LBh0q3vICfvXdb6f2OEulUrMi2iw/CC+7u54p2uwJywubZhzId7ImQR3RrMXIroUd/bGhNDyUXHsUdigW+icyEHvY+fp1SgvSLOxRW6ZjweYldAooroJD0RPYQ89FDAAzrNKBdMuDDITdP53hR24BeCiI7jCfEjjkx0TXEuELp8FNGtWy0rmejaRXQrRKUd56JFRGdh0bXRtuXZdXu0OtGzKqJDQMCin2YRHW1Ev5+FOBeAZ6/vIrpLyq0T/dtIlyTAWBFzjLRil1rS32Ks7bMT3c6fQhbRrZgN45eL/gZfUZ3oaCeuq1BE9ELRKJrBYgbOQ77iojg/msZZSQHtFdeuq0V2HbMvz0V0bDcsJjaiorxMWlfoWL9AW0IEW3MGDXLjhEhiEuH7NYNOLs2FJjvgci2i2y1vrh3xcQDxG7REAMdDPY5ImDjBhBXt8jHOBYJL3KItM9FLw06o03Kis7BotGOkYTeaBie6hjZoFdE1tSdNER1oF9EB5lhZcqL7HucCXM1ZVlWtK6u+7esqa1aZNsQ9z7WGC+xCt7njWoni9nbZNhhY0N8V2jGG+VMo8SH5gIY0depUJ+8Ng1tUJzqel5hX27pzIQDR+ZVXXlFT8DzqOAFCer7iohj7QncJ3Uza49tCvNBi+/btm/r7U0RvIThxuAGLufHGje5vvkgy2ErLO+/sdwRKrnPM92sG52PLLbdM7f3sir4GEd26QXwnDhEdA2Zc05riXCDqAx9F9Lgd4+g74fhjJnrxWCEqLdHWivZaCotqdKKjr3HZJivO+jIxy6KInuU4F9tfaReosyaih+BEdzVnefrpKrnvvjXuzKOPHiYjR46M/T2siI7xrHYRPUrxTlfYYweRv9BuXYjoM2bMkNBF9JdeesmJkIv3LiZKBuJtSE50iM4YT8N851oziMNBj0x0AIE9dBG967f6qysRXYe11WNw4hjlogsIqJiEhNJ5+D4Bh6sGjo00i3ziGsDk2LVYi4cy2qAtK7gUrJulJSJ6HL8jbmy2pW9xLkk4xu3kXWuciy0yp5G0RW0r/GkRrjVmokOs1dAmLc9wxrnoj3NB/0EnenZFdMa5tIzccVxSueW5Irp2okamaC58ChE99DgXiLc2hjRtMDePGucSooheKF/cRxEdhjA8R3wrllrqeAlan6viou5nFx6DCRouYIrourDbjEIR0X3HrnCnmYlui4q6FlDgRA8hysU60ZFN2hKnrXWzu17cCEFEx+JUUiK6Ric6JhjMRNeZiQ5x1rXrO98YzaWAramwqKY2uBbzc8EYQZMzPm0nOgRq7XEuWcpED8WJ7oq2bdvW/x3mnayL6Bhra45ziSKiw6WOc6m9n2oJ1mTmIhcdc3MsUkRdTIa2gnm9pudmS8AcHfNa30RnOM5xbzeO//K1WGpLIl1cFReliN4CsHKHwS5FdH0iOlanQhEvfceucKctomvYloWBSQhFRa0A3lLx2w7mNUWn2DgXiuh6RXQ8Z/GlrV0uM9Gt0KZBuNbmitfiRNckFmtoj8Y4F21OdIjoaRYsZCa6LkLJRNcgoiclcmPhCf2GDyI6hGqMcTX1cbnXOubqUZzoIORcdDtfLSZWJU4BH2ODqG5/ONEx/gxldwBEZywMNJUv7quDHj/zbVGgVKDBwonuYnxLEb0F2O0DFNF1gY4DHaIG5xVZMyjAYCnNiAgt2WahOdFbGsMCER2THA3uWQsmGGiT610LGjLR7eRdW5yL3cqvOc7FuqTSjHPBfaThOWdFUW33kOucdjrRG2KvD02CDguL+pGJnhUnOp69FNFLB6YV+0xM6tmI34sxoy8iOp5DdheoJnAco8TN2DlUKKJtvvseXy5EdGtwi/reENFBaJEuvonOVufKJ6Lj+66NE2k50fHMdBFbpWvG46GIDkelJlclWeNEDyXKRYNAEocTHQ/pND8LBgOuRXQ8vCCih+JEhwDeUhE9DiE+bjAR8s2FnlQmulYR3TrkNce5uHCia1mMsiK6Ric641z0YEV0TRM7FhZtrT4mAf2+dqE/LvBMx7MkzUifkMD4ct9995WBAwfKzjvvnNj7YMzog4hud49qjnRprm14DZ7jIYvoAHNWFyI65qh4DkZ9b7QTrw9NRPdNdIZhB9pKPhEdz/XQ75lcI7OLXHSK6C2ARUV1EpKI7lOH3pyInhaYgECsde0AxwAbE6FQRPQ4BPA4ImHixmcRPW6xW2uci3UhahbRrRCVpoieluu9OayzWKMTXUObtCyGux5PMM6leVhYdG2yVlgUMBe9dPbYYw8544wzEt0l7ouIHjV33BVRnOgwC8CsGLogiFgVF5noMD9gvhy1uCie45jThySiI0McJiIb7+kLEMubiqHB5wG+uetLAdcu5oYuctGd2phuvfXWJh0Qm222mYwZM8b8feLEifL222+v1dEcccQRokFE33zzzV03gzQSW/BAth2Iz7ie8MYFHsy9evVK7f3sQMsWanGFze9zLeZry0TXdjwwaPJxN1FShUUhWGgQHnOxAooPInpa7nAb56IBZqLrf4ZrEPK1iuhZLyyqXaDOWmFR+yz2cVyiAVwrb775pgwYMCCxHakQ0ZMqXBp3O9HvahbRZ82a1ezrMG8IORMd4FptrHelBebLxbjgEekCw2Io2HxxiM7adks3V1x00qRJTV5LWJCHS33IkCESMmVlZWbB1IWI7nSmPHPmTPnkk0/qv9566y352c9+Jm+88Ub9ax544AG55ZZbGrzOhWW/qQkzxEHmoevCduqhONE1THxbgi0+kqYT3YrorsVaO+ALwYkOkQyum1DjXHycrCYV56LNhe6LEx07YDBoTavPxvtpEdE1Z6JrKCyq7bi4QqOIrjHOhYVF/RP6kxDRSWlAN7jrrrvk5ptvTmwh0xcnOsYjML9ojnOJIvBjPpcFJzo+o4vFd8zRixXRQ3Ki4/NgLNBUNIp28R9aQ+NFZtz3MJP69nlakovuQht2OgO78sorG/z/TTfdJPfcc4/85Cc/afD9oUOHyg033CCasNlJFNF1YTv1UER037EDgjRFdAwE8ABxLV7js8Pl5qNA2xi7xS0OJ7o2ER2fzbc4F5uZmkScy9uffyMDzp8gGhg7qp+MG92/foCoubCoFdHTgk50f0R0Lbhuj0YRHc9obSL6ax8vSK0PXvbhu7Li0w/kvpp871cnuGq+LdUoLvh65juybMqH8q/zHpNTR/c3z4RQsc90FhctHSsGIuYAhrckxg2+iOjFCNUuwJwCC2TN7TSBiP7OO+9I6CI6xvU4V2nPX+Fcnjx5clGiM+4z12OsOMcB+Ey+ic7WQY929+nTZ62f+fZ5WiKiP//886nXitJhY/qWO++8U/bZZ5+1BFBcBL/97W9NZztixAjp3bu3uAYrHhDq7AVMdGC34mh0U2YRm7GWthMdg0bXRe5sUVHfdxMA62JpyYIAJjMYKDMTXW92OX5vZes2sqpGh8hVU1vnjRMd13daeejaCosyzqUwITwD4jwOrsX8XCAAaBL1cU/X1FSn1gfXSLlUr14V8f3cnLea8kqpqa2VVatX1z8TQoVO9JbTtm3b+r9D6KaI3nzxTg2Z7YXEWMylMKfCsyPU56mNIIU4nbaIjjk6Fu7wFcWcA50Oz020FeJzCEDP8y1DHG5z3A/5RPR333036HvGAkMzrkecPwjqaaFm+QgrYP/973/l+OOPb1KIQ+bP73//exk0aJBcffXVBX8XVjURG5D7lYSIjg5PszMui4RUVDSETg8PWAyM0hwQoDCL6zx0K+a7dsPHhe1DWyKA29+hyYkO4RNfvu0WSEpExwC6VWt9QjVEdAjUmh0vcKJnVUTXGueCyYPLNtnjouFZjja4Fq/tudDk/NboRK+tSS8TvayildSl+H6lthHUVa9dQys07EIx41xKJ3dnYVK55XgPO37UDsbtWp3oUQufwomOYx3yDg2b3++iuGiugB8FK5yHFOniY/wJ5hy4bppqN0R0H4ulloJNBUk7F71ckwt94403lt12263B90844QT54IMPjID++OOPmz9/8YtfyCuvvJL3d40fP948NOxXz549ExHRGeWiD6xChVBUFLie8MYBFsDwcE5TyMAAxHUeOoBrQkM74sAK4C0Rm+MQ4uPGDi58i3OxE4m4RXQsQHbqGv/zMg4RXbMLHWCCl9U4F60iupatxlpEdNfY3WGanN/aCouiUFjHDbulN/6rqBSprZG6Oj3HIK+IXrNaQgf9FZ51FNHjcaInKaIn+fuz6EQvhJ1LhZyLDkEUJqNissnjwu4Wj/reMIhh/BmSiA7RGdehb0WsMWZAdFVjrBbmm7u+FPDMxGJC2rno7mcX3664/+Uvf5Fjjz12rQnPVltt1eB7hx12mFkBe+qpp/L+vnPPPdc8MOzX7NmzY28zRXR9YNKBDj0UJ3oIQERPM8rFiuh2RV9DnEsIQADHpKElETl2EK/JiW4zLX11osedif6jH/1IBo/4nmiDIrpuJ7rWOBe0i5noetAa56LJid6/f38Zc+hPU1v0sAK1aHajV2ZHRLeL4yE7bpMm1xSRVG65fQ8fctFhXEE7MWbQBowHGMc2J/LbuVTIIjrA3NWFEx3nAEKkjWCN8tzE3D4kEd1X0Tlf9rmvxVJLBTEumXSiP/jgg6aDP+aYYyKv1hXqcBGxAqEm9ytu9yJEJaz+ED1gBRFRPiGI6JommS0Bq9ppRqvYoiyuRXScPxfFYZIijoKg+B14oOe6hFxjJ0B0ousGIrr26LS041wg/KXpfPdRRHcd52Kf4xpc4BrGFSwsqo8yONGNQK1PYFvbia63jXELWhTRS4cietNub81u9Oac6Jh/YHwBc1LIYL7swomOMQpE8WLeGyJtiCK6b6IzRHQsvED/ygX3C/Qw3z5PqSAdJG0RvVJLlMsee+yxVhg8JmYff/yxcWZYnn76aeMs/+53vyuusNsmGOeiLw8dhCCia5p4t2TCjlXtkSNHpvaeGIjhfV2L6FhkQ/8VUpxLS0V0/A44YjRd1zbOxUcnOo5jEhEnFeVl0rpCxfq6aQvA4FB7nIuLTHQtojXjXPwQ0V2jVUTX1J7U++BWraW8rExaSY1UNPmedaac6Jor2M11XNG6jWljZW11/TMhdCc641z8iHPxTUR3PTcqVUTHMxSmpNCd6BDRp0+f7uy9ozrRrYj+5ptvSijAqIM5u48iunXQ9+rVy/uc91KBhgydAfP6tOb0zkX0jz76SJ577jn5xz/+0eQE5MADD5TBgwfLkCFDZNasWfKnP/3JxL7stdde4gpEuWAbdShibUgiOiZpGopKtpQQJt0YXEL8SjPOxW6Dcz1QtG6JUJzoeDC1dEEgDiE+iWsUAyctjt6oYIKNiXYS/cS40f3NlyZ8iXPJamFRrU50iKManqVsg14RXVucS9p9MIxKV145WS46a6cmd9fiXMFtaLeGuwBC6BlnTJQTT9xWhg3T9WxKyolOEV23Ex3Pejx/fRDRbR0izcVFowh9mIOELqJj7or5I55JaY+nMFcvRhTHMwFzbk1j0TgEad/iXHId9I1FdHyel156SbJA92+Li0KjHTRoUCrv6dxuhg97xhlnyJ577rnWz3BTTp48WQ444AAziR44cKAR3O+44w6nkxK0GRemhoJVpKGIjoeAtol8VrEr2mkualgR3bUD3A70QhHR44pz0VRUFGDF2rcol1wRPStQRF8bTRMXzSI641z0oFFExzWrTURPExuT1XgruCZ8aGOc0IkenxM9KZEbGgTGjj6I6FiUwVhBa5wL5gVR2pYVJzoMpC4+J94bc+ioz2eI6Giriwz3pPDRuQ2DETSPfMVFsXiWhUXZ73znO8YQl2ZxUeczsJ133tl8FRrgotgZvrTAoqI6weqhXZHzHTyYNLjX4hDR03aiY0DmWlyCkwCduab8bw1xLo0ju1yDCZCPIjryUrMmomOApD3OJc0dDZpEdM1xLi6FfdcZ5NraY8c0mkR0XLOa2pM2dvcMdtJoBfcwzhOeA1kAz/Y0hYDQyB3TJRXnYt/HBxEd/W6UyBRX2LY193yCUPjhhx9KyFjTGeayac6d7XvjWYj5a5Td3DaNAQbGUJIZYJCdOHFi6uP5pIqL2t1l+Fnfvn0lZMrLy83nTTMXXdeMxwPQyWO1h3no+gipIw8BiOjIpUqzICAGHq6jXABcBHBN+L4QYifXmLy21EUeh5s9btLMTosTOtH1kXacC9yzFNF1x7lYUUDD4gKOg2sR3S5oaBKt6UTX7/K29T80tzFOGOfS8kUIOw+weeBJiehJivRxgvG7ZhEd45nmjiVEdHwGTc+PuLHzRhfFRa1oHzUXHecNQnNoxUUxTvLtM+UT0aGJ4XryLaKmVKDNprkA7X5k7xm2Am5T2YHEHVg1xLkJxYkeAngQp72SrkVEx0q+60iZuICDHLREAMegxBYW1YSvTnSI6JhoZwUf4lyy7ERnnEvTuBattaE1zkVTe9LGByc6gBkjS070LGy/TwqIRmPHjjU11fbff3/JuhPdCp5a41xyC58WAnMq9NVaP0ccYEyH4+FCRMfxLUbAx2sxx/dNcI5SpNO3SBdokjhvmIc0fr77WCy1VLDbHUbntMZ0FNGLxK5w0ImuT7DFhDUEJ3ooE2906GmL6HCAaxCvIaKHkoceh4iO+BEIbXSix0MW41y0i+gsLKorEx3PUXxpcYG7RlMbNI1xNBYWTRNfRPQsOdGtiK7pPvENiEpjxoxJ1CThm4iu2Ylud4YWws6pspCL7kJEh4CPY1zMe0NzCUlExz2NL9+c2xD/8bxoqt0+FkstFWizWEiIupuipbifXXgoosMBmOQWMVI8toMIQUTHRFPDhNc3JzpWHjG4SrOQaRZEdOv6aIkAbn8HnejxkKU4FwwMIZ6kGQvlgxMdwp+WzEaNmegaolRsG0J4nofsRM+yiI5zAvFEu4iO/j9LIrp97pHS54S33367PPfcc4kdQtQ88kVEj1q80wWYW+AZGcWJbudXIWMLfLp672IESNQqCklELxSN4quDHj9rquhoiHTv3t38mVYuup4ZjyfYoqKcFOnLQ8fiho/RDCGCCRkGRGmK6Hg/TM5dO9HRBjg+XLcjTic6JtotubficLPHDYQTOLp9zERHu7MS52KFBDrRG8I4F/3CvjYR3bWzVauIrqk9rtzo2gXbLDnR7bMdz3lSGk899ZS8+eabcu+99yaWW26d6K771SjA+IfrSeNiGZ4LGIfbeUKhxSX0A3SiJwfm7MU40SGi43w0jhHxGR+d27g3cI/nE9GxKBPSOcoH+hHoDBTRlYvoRGdRUS2T1Zbgw4CsOexDOE1XuH1P15noEPNxDkNxomNga50iLt3scWMnVj4uvGXJiW5zcDWL6LjfIWpnVUS3IqSmOBdNIroGNIyNNIroWY9zAei3NIprWc1Ep4jecuw9nWSGNsaOEKa03zu5u0C1utExX4rSNrwuCyI65l0uRM9SnOgY57iIn0kK1NaDiK5pnBKFfI5zWywVOllWctHnpFRclE70IietuAgpousV0YkO7EM4TSe6HVi5doDbdoQmorf0d0AE1RTJYfMXfXOiY2AHMYFOdD3YyU7WC4tqEGo1iegWTcdFQ1Sdpslp1uNcAJ7L2oXArMW5ABYXbVnUiiWpyBVrwEjK6R4nNoJWay46RP7mMtHt/C70OBdrBHMhTGPOjus5at8DER2EFOkCMRrja1eROi2pA5HPiQ58i6jxQUTXMQPzBLsyhQuV6MEWU9hiiy1cN4V8Cx7+EJTSdB7jgQdB1LVQawd4oYjocIfEIaJ/sLBaBpw/QTQwdlQ/2a17jZdOdOvGoxNdD1aASktExzgEzz0tIjraA7Fak1isQUS3bdByXDQ447WJ6Lg+NLXHBb440Smik6jkjuuSFtHx+12bd6KK6Fqd6BDRH375ffnDhf/GUyLv6756d7ZUL/lCrv+0t4RKzTdLZcGrs+Xf//cPafOd3vVzlnGj+yf+3nb3OObwPXv2jHTe0DeH5HLOFZ3TNALG0e6JEyeuZbCB4Qpz+KyI6N27d5f//Oc/Zq6c9A5mHTMwT7ArGxTRdYEBDLLe6ETXV1Q0TfEAIrqGgSyc6BhUhCJyQgC3g4pSwcC9zbrtZFWNDrGipraufmLlm4huHSKhXF8hxLlYJ3pacS72/bSI6HDyaopy0SKiWzSI6BraoFG0phPdj0z0LMW52Gc7M9FLJ3dcl2QmOvChuCjuH9znWp3oEPm/Wb5UVtVgoTf/Ym9d67ay+mu8Ts8zJG7qWq0jtWVlsnL5Yin79nNizpIGxYroGFdgrh+SEx3XIu4X38yZmKfb2JbGiRk+FkstFfvZodlusskmkiTuZxcegRMCd2lWttH7gl0BRe5TCGiZ7MYhoqcJRHTXeejWiQ4xP4TzGFecC0S/tuutccJoARMfCCi+idF2Yu1bu0MW0dN2osNpok1E1yBWaxPRWVjUDxFdU3tc4IMTPUuFRfEcQd/OOBfdcS72PXwQ0TEfgTio1YmOWJCa6tXN7pYqr2ontau+kbqaNWOgECkrK5eKqvZS83XhQqtJLQxBQC62uGhIIjruFehJvonOhWJbfCyWWir4rBhnphHpomvWoxwWFdUrotvVUKJHRE+zqKg2ET2UKBcMaCGi26JEpXL44YfLljvvKZpA/iIGjL4tdtiJdVYWc30Q0dN2omsT0SFC0omuMz7FoqWfw+RG03FhYVE/olKy5ES3z3eK6LpFdDzvseDhg4gOMI7X6kQfPny47HzAcc0+p8qr1tQwql3hxzEvlfJ11pPab9IX0a2WUoyIjhSAkER0X0Vn9Hn5YlvweaCVZcEwUFlZaRZBKKIrgyK6TtDRwfmbZlG3pNA0uSwVdNIQtNNc1MBxQ4yKBhEdg9RQRHRMWiEQttSJDrG6dZUu5zQmPr5FuWQ1zgVClxbBuCmsizNtEV3LM09jnIsGF7iGNmhDoxMd5ymEsVcWnOhZOU94vlNE1x3ngn4d7+OLiA4nulYRHceybYfm501wooPaFc0XIfWZ8nUhorv5jJhHwwhXjBMd5jHtz5BisE503543iJueO3duk58H4/RizqvvkS6ffvpp4u9DJ3oRW+jRSTTOGSLuwepaKFEuIYDtghB50hTR4ZbGe2oQ0SHma8hmj+u4gpY60TWCiQ8K0fpGFkV0CCiahcisx7nYwqKawIQBuG6XputWw4RQW2FRu/hjr5csApe3dgEEbcT1a/u+0MHznZnouguL2vdJSqSPG4zjtca5RMWK6DXf+LFwUSoV66wnNQ6c6KBYJzpEdBCSQAvnNuZavt0v+bLPC0W9hCyi1yU85tUxA/MAu7JDEV2niD548GDXzSDfYh+kaYrocL4D1yI6JniICQnFiW4HEC11ooOK8jJpXaFDaENbcJ58FNExsbaZqVkR0SGgaMbGuWRVRNecie7SIa9BtNYm5mvLILfXLa5hLfdT2vjiRLfPAy07cJKEInp8cS5Jitx0osf3fFozR8BzqsCzqqK1VFatK+WrlqmZTyRBbdsOsqJ6lVTWrpbyVm3MsUkLRLFiTh3VHGFFdES6hKKRWdEZSQfYweFTu5999tm1dodiDo+IMIjoW265pYROjx49zFgh6YSCbI4YSxTR0ZnQ8awLdPIQbZHJRXSA84EBUZqCthYRHaIzhJNQRHTrRI9DRB83ur/50sIlLyw1W998A+6IrLjQAbbwa85DB8xE15eJriFKBW3QIl5rQWOcC9DUprTxQUS3C6l4Hvi4+F0sEDzoRG9ZP2OPYZLHESI6DBk+ADEQ94/d3aeNY0Z0lXP2HtqscPurtm9Kz5495cc/3kNCZcaMQXLFFVPkglOHm8+aJjDAwaiB+WyUuSz6Y1xPIeWi4xhgbADRedCgQeKTiI6xDM6FXQjwuVhqS0R0G8OdpC4U7jJezOBEQKjNqlNFK9hyhBW3UBY3QphwQ0THYC3NewUiOgbMrgeG1rkdioiOzwPXl+vjmgS+ZqJjQpiVoqIAEz7tiwauMtG1jEc0ZqJbUdSlQ16biK7BGa8tzsVeH5ralDbot3woLAqyUlyUhUVbzlZbbWX+THKnMhzvvmSi21hG3yIqGoO4TDhMQwZucFBMrErc7x01ngXPdLjRkQoQChgX4DP5Vly0UGyLj8VSSwUaFJ6hSRcXpYgeERYV1YntEOhE1wMevGlGuVgR3bULHdiiPaFkosNhAxe6JiEoLjHJVxE9a050X+JczHbklIRk63zXJKJrjXOhiL4GLX24Vid6ljPRfXOiZwEWFm05RxxxhFx++eVy0EEHSVL4FucCtBYXjQrmV6hRFzK4rtAv213WaWLn0sXmoofkRC+UL64Z7ArAtZOvuCg0Mw1mijTGu2kUF9U161EKLjiK6DrByiceND5lVuUjlI4ND96siuhweGD1U7voV8znCbGoKIRoCDk+bgtH27PmRNe+EwICFHZspCVUanOi416iE10/GsYYFNH1FhbVcH1EyUTPAsxEbzl4Hic9frUiuuZ7x2KPhe8iOnb6wonuwzFvybULR7gLJzrGstBUiikUGqKI7mv8ST7xH/GlWIQOfReHBSI6negKwAMHW+hDKZgQmogOF7oWlxVx40RPunhEMX1FKFEuNhM9jjx0bdgMSx9FdDyLsuZE90VETwttIjqd6PrjXLS0AyK6JvGDcS5rnOg4J7Zf0Qid6KRYpk+fLmeeeaaMHz8+sd0vENFx32jfyWHvIYylQohzgRgIQ0nI2AKfLsAcvhgBHzoMrquQdgpBjMYc2LfaFPlEdBt7nJVIlx49ehiN0O7cTQI60SNgVzIoousV0YkO8LBZvnx5qiI6Jn+anOghiej4PCGK6Hb7LeNc9OODiI5BWlp56MCKXWkK94WgE12/iK4FOtH1YfsuzUJg1kR07DZDP5+kABA6b7/9thk/zJw5U2bMmJHIe9gxpE+RLiE40UHojlrMaV040UGxLng40UFIbnSbL+6b6AzHObSxxguHuJ4wZ/DRXV8K0GxxDJI8fxTRI4BsIQzgNIh0pCG4OUIpKhoC9qGbpoiOwSsmfxpyyJHTF5KIHqoT3XcRnXEuunAlotOJnh9moq+NBgc4RHRN+ePMRPdDREdfh3OVFRHd7jYL3W2bJLmxikmJ3D6K6CE40bMgolsh28VzG+9dbJxLaCI6tCWYIHwTnSH+Y47Q+Pzhs9hc9CzQ/dv0kCRz0SmiR3SiY2WHjiJdwGEAkS8UJ3oI15fttG117zSwA6k03zMLTnQM3BB7EmImuo1z8VVEz1qci/YaA67iXLTkkNOJrv+5rqUdWuNcNAn7aeOLyxvtzFImOqCIXjq54zuK6GvAeN53JzqMPei3Qy8uCuMmxpYuFmhghMM8KeozoW3btsbcE5KIjjE9zoFvojP0StCU+A+BvamioyHSpk0bowtRRHcMi4rqxHZsoYjoIQARHQ9SPFDTwmbGuXaiw42KKJtQRHQM3CCOhehEx+AQ16gVUHwB4lPWMtExiNce54KJTtpOdDgztQijECC1CPqanOhJ5fD6jNY4F01tShsfnOgAzwHtQn9c2N1mvuXxaiJ3HoKxeZLv4ZMT3XcRHc8QW1w0ZKwxzEWkS7HvjbEoi4vqALW+8PzIJ6JDO9NkZEg6Fz3J4qI6qlIpBgNrXIjbb7+966aQRiDzKSQRPYT8VDxw03aEQ0TH5Mp1xAVcEVO/+FoO/9O70vqxr8R3qpcukIWvzpb/VLwhrdZv+Xa2saP6ybjR/UUDmPD46ELHQg0Ey6yI6BCL8aX98+K8pO1E1yRaY5ykbUFKg4gOND3T35mzRAacP8FpG758aZpUtv9SLpniPn4NrP5qgXz56mzZ6PnpcsnhG0sW8cmJ/o/XP5GzXnV7DacxprHPPIroLRe4352zRP75m+ek3bPJ7Db5YvJn8q+lE2XdPvpF3a9nTJVl096WB+oeM88mTePyYoBpKnQnup1LY47bp0+fVN/bRrJiTh+1HmCIIjpE5zfffFN8Avc12n33U2/JaS80/NmKz6fLkjc+kEfOekjK27jVTNJg2Yfz5JtZ78it8wfWf69mZXwLqhTRIwi1mLCyqKg+sJqGVXXtLsUohLIqCCd6mnnodoCBAZVrsQKuCJzGmtbtZFWN/6621V8vl9q6OqmurJK6GD5PTa2ea9xXEd1u7Xa9YJQWduu+D3EuaTrRIdprEtHpRPdjYby2ts75s6mmrgwPA+ftsFTXyZrnXHV241x8caLjObBy0UpZVa7j2klyTGOf8YxzKR07xsNpWrXy68T6nLpWbczvr1TSpxWipvW6UlNdLStXfiPlrapUjcuLIQtOdCykoR9w4USHmxnGkGJz0T/44AMJCYjRTz75ZOpGmTgiXZ6Z8rqs2qhhn1S7Tkcz3vnmq4XSagP/tbPmqFt3fale8bWs+HpZ/aJBbU18fZ4u65BC7DYAmzFEdC1whOJCB5om276J6BqK/tpiPeVV/omzTVH77WptiKvViHPBINE3rCtNuzM7bhFd+0KpCye6pgE9RHTXju98C9Mun6uaRHQ15wfHo06P2FRWtua41NZSRNcuouM5UFO9WrIAFgzQd1BEL51co0RddXLXdnmrdaR2lR8FYMvbrHHn165IJt4mLWCcCl1EB5jbuhDR0fdgLl/Me0NER526kOpWoBAnxnE2+cAn8X/p4oVrGTQr1u1gxmA1y8K/d0Bl+zW7OWqWron9jZsWj6pxszT+CgkE8CMT2EfBJXRCE9F9B8IOBjUu4lw0iOibbrqpDBu1j5RVhLHBp3bV11JW2UrKKvSIdXE60X3s07PqRNcuorvIRNfkRNdYWNQWinTZLk07zLbYYgvZZtf9XDfj20UFPcfFiPpoETPR1Yvoe+21l/TfcqRkAdwnWCyniN6yIpq9evUyf2+1frRIilIoa10ldav90D6sKaZupd9Z+3CiI84l9FoWmE+7ENHtexfjRLd6TEiRLhCjQVP54poZOXKkbLnzHmuZOMrKK6Ri3Y6ZEdHL111PyioqpHqZIhH9o48+kl133dVM5PGQb/wVEiwqqhO7MhiKiK7FrdbyOJO6VJ3oeD8tIjoW23oO3FxCoXbl1/WulRCd6D7HuYT2nM2HzejVLqK7cKKjsKgWNDrRtWSiu35/CxYN+wzZynUzMIvTJViXr1lkUdWmlLFFirVnovfv3186de0pWQHPeWailw6u6bPPPlv2+Mk4qeq1RXLv06pK6nxzoq/0oxBqISc6nvFwPocMhGzMcV1QihM9NBEdOifm9r6J6KgH0XezrZv8WUW79aVmud/FhYvZaVjRrpPULE1mIaqkWdixxx5rJrX333+/WQ0MGYjoQ4cOdd0M0gisQEO4wFYbogO7Yp2miA5REW5VDSJ6aNTBid46TMczRXQ/oBO9aehE9yfOhTRCmROdcS5r7hHspNHuRM8adKLHs0DUfn3sjk2uCCWyxVcnFBcQN9gpW9aqTRBxLta8hdpooYvoLoq3Wxd81Fg6CM4Qb0MS0QF0Jt9E9EJUtNtAVs55X7JCRftOUvOVIhF98uTJMmPGjBaLZQceeGCTW9V23313GTt2bP3/v/322/Kb3/zGOI8333xzOe2001LpNOHKKKYyMUkPm08VihNdU3ZqS0R0bJ1Pc2HNrtBrEdErysukdQXOo9/nEpSt/kZar9NOWleUx3ZsNAChAAtwPsa5wJWGfkJ7oc2sFRbF9ZR2nIs2J7q2OBcNTnRtz/U1zye3zvhKOL/LxHk7LLWVFVJeViZlGV/wQB/rg4iu4RpOa0wDUYpxLvqvmVVV60p19Uo112VztFqnnZRjfF9RrmZcXqqIDkNdyEDIxvgKNbfSNq3ivTHWxHtH1dygyfiWHx4l0uXDDz+UUPq92vYbyEoUQq5dLeWtdM+t4qCqw4ay7LNp0qp8jWmixmg08VDSLAyiMm6slgJHe+7vmT59upx++ulyyCGH1H/v1VdflZ133lmOOuoo2XvvvY2Y/te//lVee+21xLe0Y+UJkyCK6Pr4/PPPjYigRTwlYhaccD7SFC20iejHjOgq5+w9VM0W/pZwySWvysCBA+WQQ/aQ0FzowNc4Fzz3NAlzSeKTEz3LhUVduKSagyL62owb3d98ueSWWz42/dfJJ++hpk89/avH5eBtekiW8cWJruEaTgvGufhxzTzzzDrywANfyC2X7e7F2OzXv55mFs1OPFFHH1zqvYHPEHpxUTu3xfw6bRHdGmVhkIsqoiPSJTQnOkT0F154QeU4t5R+b9asIXL55R/IOScNlT59+kjoTJu2iVx77Sdy8albmXO5ePFiWf9ahyL6SSedJGeddZYRtLF1o1TgOM/l3HPPNZ3EAQcc0OB7u+22m9x2223m//fdd1/p0aOH3HnnnQ3c6klFueCBaAsLED1gpRMdvE8dWuhbvvGgTTPKxYro789bLluNf17B4LVOjtoWIvqaXDjfgfsAhZlCLCr67pwlstdtr0urDrPEJ5ZNfUlWfPaZ/Ov8CYm9x9hR/dSIFBDRIe5o7+ddiOivf7pcBl34bxW7XvrP/UKO6N1bNEERXSd4TmsqBmf7Fk1tcgH6We2Z6FkDQqGrooIkOjBkwC2M+0f7gj+AIAojmu/PEbjRs+BEB7f9+y159K6PUn3vuprV8sWrs+Wxy/4p6/TYNNIcASL6u+++K6HFueD+hsYRQvoBPg/uHxiFsyCid/82TQSabtx6bmQRvXfOBAmi36xZs+SBBx4wDWosXn3yySdFNwQX6B//+Ec54ogj6h9CmEA/++yzRjDP7fxHjx4t//73v1MR0SEKprlNm0QDA4CQ8tDdC8AtBw8YFH5KW0Rfp31HWV2LhQj3ixE1ph3+g/54+fLlpqBKaMCJjtNUXdFG6mr8Ek5Wr1ohtRWtZFWC7dZ0DWMM4MOk1EWcCwo0rqpR0u+xsCgpQrSOYydrXNgYIlzDWcYXJ3qWQJwLC4vqx+5qhEHDh/EKzDEffPCB+A5Ml6E70WHOwDxs0eIvZVVN2jWqKkRaryMrly+Wipw5R6E5AkR03Afot9B/hYAVXqE7hSCi41mPBaiQct4LAbM3tONPP/1Utt666WKriYvoF1xwgSTJv/71L3NCjz/++PrvQajHwBbO81x69uwpTz/9dN7fhdXgXEdFqdWbIaIzykUn2C4U983gihAEdCyswTGz3Xbbpfq+GECt2z7cojKuI09CdaKD8tbJxoElQe3qVVJemZ1FXR9EdDhYMU5J24leoSgTva6uVm0musvnK9oQwvM9bhFdk+ubIvoaEI1AJ7ouWFjUPxHdOoc1A0EJO0211ewoFgiBs2fPltDBNfXpgkUiFelHjpWvu57Ufh1dQ4OIbjWaXPOtz2AejHkINMqhQ4dKKAsDvu9GKQZoudB04ybyLOy4446TJIHbfMSIEaZwqMW6IhqvZuH/Czkmxo8fL5dcckmL24QDjjx2os/1B/E0JCd6CKIrJmBpx7kMHz5cPpJu8mbxm19IATDABiE60fv27SuDt91JPvtGjwgZlbrqlVJWGX4hmFwR3YeioiBNJ/rIkSNlWs0MeVtJ9GSdwqxIK2C7Fglcv782tIno9hqhE51OdG2wsKh/IrovoiD6O7S3ffv24rMT/e2335bQQfrCF699KjIz/feuWGc9qflmzXwwCtapHZKIjvEB9KaQnNv4PP/9738lK/To0UNef/312H9vSbMeDIDff//9tb6P75UyOMZqyGOPPdbAhQ5sIYPG23UQ4VCoyAFy1CEC2a9SViohCuKLTnSdeehYQQ9hW00omeg2tzFtEX2bbbaR7psMSvU9s4DdvROiiA6nxGbbjxEfMSJ6BqqpW3zIGLUL+mmK6Oj3NuozQLSAcZ9GJ7rrNvnu9EsCbSI6wHWirU1pg8VKxrnoc6JjITnr16Z2bG04X0R0q59Ys4zPTnTMVTTFgyUBdt336DfYyXuXr9uhKCc6xutYmAmtuGi3bt2Ccm7j80C3sSag0OnevbvRjlFI3rmIfvXVV5v88sbcddddcu21xZc8xe/CYOGQQw5Z60OjMnHj1ZK33nqr4JYKDAYh/uR+FYu1/VNE1ymiAzrRdeWhAx+2MpLoIrrPLpUQqVu9SsoY56IKKzylGeeijdraGrVOdJdQRPdDREeb6ESnE10bmBcDCOlEL3j2Q3fwTURfvHix+C6ig9CLi7oETvTalV+bIqPFGJVCE9GhN0FED8H0aD8PPovV07JUXDROStrPfsstt8hLL7201vdR6BPxJ2eddVZRv+/3v/+9HHroofWruRZMgH784x/LHXfcIT/96U+NoP7EE0/IG2+8Iddff70kCQ50ZWVl6s5a0jy46XGtNL5eiFsRHYtVLqIXKsrLpHWFBgGnzrQlBOBQwRZV107O8K+Z4iivXS2t2qyTaNs1XcMQD7TvhrBOjjRFdAx+11zDOFfuz1cZ2qOsr4BQ61rYD2XCFSc4J9qOC0V0FhbViI0yDalIX6hgvOyLiG7HVL470RHnYtMKQtdqnM1Z2nWUr8vKpGLVcqlst0GkOQJSAubOnSshgQxxzEew8GSvuxCKpSKipnHdyRDZaKONzDgPxUXjNHuWJKJjC0BTE0Z8r9jMoOeff16mTZsm99xzT5M/v/TSS40TfeDAgTJgwAB588035Ze//KXstNNOkrSIjovM9SSMrA1WA0NxoWubTLZERHflQh83ur/50iDahLL6Die6dvEyhGumWMaNmyD77PNdGTPGzziaYsGg1RYq0oqLTHRwzIiucs7eQ1WMUc444wmK6HnQcH40odGJzjiXNf0XC4vqdKLHvQWdZFtER3+HXaZaRfSoO7iy5ER3NWeBaPyLX7wmpxy7uWyxxRaR/g3G7EiMCAmrOUF/CkFEx6Is5vghRdQUAqZoaLrQdrfccktxKqIjjxNudIjZudx0000mu6nYC3PChAl5/x0eTBMnTjTFI+BAHjJkiMnySRocaEa56ATXQRrXQBq43m4ep4geuhMgS0BER/EhogcITxA57MQ6KyI6M9H1gygMbWIxJuKu2xTKInncYx6K6PpgJro+KKL7g08iuo108T3OBcZNHPfGdfNIfGAeCAHS1j2LKqJj98zy5cuDSQyASRDHAUbhTTfdVEIAonJoOwYKAce9ijiXyy67zDjhXnjhBeMIx0ThueeeMxEvTz75ZFG/q3///uarOaKugMUBPg9ulOHDh6f2niT6ucHK2bBhw3jIFIEHbCgPFrJGRLcuD6ID60bL0rZuH0R0F3Eu2tBQxFOjsM9MdD+c6IxzYZyL9jgXohvfxFyIo1pF9GLMZZin+HTcfQPnAgJysSK6NTz27dtXQgBjBMTUFJu2oV1ERxJIVujevbtJNonT3FLSDGPHHXc0gjmcp4hhuffee81Ng+/hZ76DzgKOPzrR9bF06VIjrKAzC4EQnGoorIdtgXSihwPOZ8hxLj6L6HSi6ywsmnaciyY05I9rbBNFdD+iU9AmFhZlYVFt0InuDz460bXGuRQDojUooicLRHTsNi9WRA8l3jRXdA5JREcSCBY6tI3HkgKaLvTDOPuLkmYYF198sXFp33///fLhhx+alQz8Hd/Dz3zH2v0pouvDVhIOJRM9BOwKNUX0cAg9E91HsiaiQ4DEYraLYsWliOhpOtG1xYBBgNTmRNcS56LtXLlGa5wLRfTWUl1dre7cZBlcl1icpRPdDxEd8RW+EEKci3WiZyET3SWY2xfjRMeYHTsdQhPRoTuFlCGORQGMe4pZIPGZHt8WUI0zwqakGcYll1xS0s98EtGR48RMYH2gA8MkjIKtHmwH7KqwKIlfFMRqLfs/XdiJdFZEdFvkzpc4l6w60SEUaxCsNTrRiT9xLtralDZ2sdIuChId4HnPwqL+ONF92V2M8T3MMr73e4xzSc+JXsy1DTd6aCI6RGekIfi0WNbc5wEhueub6/Og7cb5eWOdYUyZMiUIIQ2rFChcSQeRTie6LfAQAiFcY3i4QkCiczkMMLAGPJ+6yFomOhZyfBHRIcJpc2KnhXXwavv8GkR0OtH9EKzpRP/fIqBdvCQ6wPOeIrp+IM6gX/PlXMGJjvb6FEGTT0THWNGX4+4jMC1inAsBOcsiuk1ACEV0xhwfi7ShfJ4oehsSRpw50XEB2YvI/t1+4YZB8c+DDz5YQnCiM8pFr4geSh56KGCbFxY2QlgQIP8T0elE1wWd6DqBczPrRUUBRfS1oYjetIiuza3JwqL/E9HpRNcFRA7GufjhRAe+iNJ2fO97pAsy0QFz0ZPDmmOLif2ATgO9RtuzviXgM0HnCEV0xmeBGz2kiJrmiFtEL8rOe80115g/f/zjH9f/3YJJZO/evWXEiBHiM8gExI0/atQo100hTYCbHYs1RA94sDJeJxxssSE60XUBpw2EDm1iZdad6BCd0oxy0TYpsU50167vxtCJrhOtTnRtbXIV50Inui4Y5+KfiG4LK2p3olsRfeONNxZtRF2AhhMdIBed5sdkRXQY5jbZZJNI/wb3AMbwuB/at28vIQCdE8ciJNEZIvqnn34qWaFHjx7y+OOPuxHRjzjiCPMnLqLdd99dQgQrTBhMszPWucCBTjwUJ3ooLjWI6JtvvrnrZpAYnegQOrA9legS0bMS5QIwAH93zhLZ+doXpbLtGteURpZNmyQr5sySR86fkOJzQ+SobbvKOXu7n6xrdqJreL5qaIMmtBYWffzduXJpSvdwc4wd1U/Gje6f6nvSia4TPPOLiVEgbvDNiQ5hE33xnRPflQn3z3HdHIxsShrTwFGP+Qqd6MkusOJ6KcaJbheSEOkSiohuRedQnOgASSKvv/56MHpUFBE9TiNSScHSoQroNsoFUETXx8KFC83kKxQRPQRwPnBe6EQPS0SHCz0LD1SfwJburBQVtSJ6bZ1IdVmF1NboEt1yQVZkbVmlrEqpjXawW4ODowA60fOjTSzWgEbXN0SY6upVsqpMR7tc3NsU0XWCZz52RxPdWNOJL0UH0edBgF741VeyqqaDun4v6vzDfg6K6MmCOT5MjMW8HqDviupe90V0njx5soS0KIDdZ9jJYXd1hEzXrl1j1TYqS9nO0Vzx0GJuNI0ieqdOndRvIc8idgsNRXQ9YCsgdgiEUFCYNBTRiT4netZEdFBWmV5USknUVkMZlKyiubCohjZxMbIhjHPRCUV0nTDOxQ8qKyuNbuCLEx1AfP50yVIRz/0yEP8gApLkwBy/GG0PzxPk1YdWXBQiLIyDEJ5tBJrvnwfAXZ8FEb1Nmzax6lWRRfSbb765yb+HBkT0bt26uW4GaQKsaOIGCKXgYQiTa/tQpRM9rEx0iuj6yJqIjszO9TfcSOaWuxdCC1FXUy1lFSVt6gviuWFdxRoz0V0fq6xskfU9zgVbfNsvXSDytWQWZqLrjXNhYVF/Il18EtH79+8v06fMF/lGvAbiH53oyQLhcdq0aUX9G0S6hCiiWz1KYy2BYoFpGFnvENGHDBkiWWBIjJ8z8szvkEMOafLvoYGqrb4XRw3ZiY6tNJyU6gEZaTgf6IhJOE50LiTqFNFDyhZsjoEDB8quh58sU576UNSL6OXpieja0OxEdy3sU0T3w4l+4IEHyqdPTJOnlPc1SYKJtC2UTPQ50dmX6Mc3ET2Ufg8i+owZM1w3I2hglMPOc8QX2mdFFBH9k08+kZCABgUgOocgokO/wWcKqVhqc+y3335y9NFHSxy0aOb3/PPPy/vvv2/+PnjwYPnud78rPoPVflZ41gtW/hjlok9Ex5YtbGUk4YjogwYNct0M0sTzyRbrIYqoTdeJrg3NhUU1iOhEv4hO1pwXiCMU0fU50XG/4LyEEB8QMr6J6KGAOSi0Gy40JYeNwECUiRWSmwPzlVdffTWo84JFTeySDUl0xvkMqVhqmpQ085s5c6YccMABJlzfipoQOLfeemt58MEHvV2dYVFR3eAa23TTTSUEQnmoQERnlEs44LpknItOshbnAirKy6R1ha6YkMaU19VKeauqlNtZZ46NBrQWFkVfpqFNGtqgCRwPjYsLmvoaV/c2cmwpouvCPvPx/KeIrl9E960mnJ5+r/QxDZzoGIcsXbqUUZQJkVsXMaqIDn0Q2eEwZoUSwxui6Iyd51OmTAlGl1Ivoh933HGm08I2DSuYz5o1S44//njz9fjjj4uvIjoG+HQ763Rh4gEZtfMm6YnoyBMlYYCJGgrFMhNdZx8IV1qWGDe6v/nSzFVXvWMcN0cdtUcq74eBLr60ZE1qdaJjUu1awNYoFrtGqxPdh74mDREdogfRKaLDAUl0i+gwGfqEln4Pz4RSxzS2ICJy0Tl3SQb0PRjjYc4fFbtzFuc1JBEdueg2hSMEoKstX77c7KLJUmRoHJQ0w3jhhRfkzjvvbOA4x9/xveeee058BSI6LiZGU+h0oQMucOgCq9J0oocDFqpASAOeEIAYl0Unug/AuRk1IzJEtDrRtcS50NnTtIjOBQZ90ImuD7twzuKi+mGcixtyRXSS3HMbtc+K2WkB9zrGP1oMH3EBnRCfyY59QymWGpK7Pi1KmmF07969SScJvuezKxUiOj4b0Suih5IJHMLEGoN6rF5SRA8HRLkAujn0CbV4vmbNie4DKLQE8SmraHWia4hzoYi+NvacUETXB+JC6ETX60Qnumnbtq1xc7JvSxeMi2FkoIieLJjrFyOi45xggcPqN6EA0bklOyc0nleMy0LKeU+LkmYYiGw55phj5OOPP67/Hv6Oaqf4mY/goTd37lyK6ErBzY3iIcwE1IPd1kURPRyQXQfoRNeFnUDTia5TRKcTnU70pqCInl9E1xjpknXoRNcHRXS/nOjo87lrIH1TGsRaFBclyQFnebGZ/zA+hiI2N3ZuhyI6I30D5wkaKEkhE/3WW281GeibbLKJ6bjw0LCd10cffWR+bkFuug8sXrzYCBV0ousEK5mMctGFfZhSRA/LiQ5BkItVuqCIrnuXQJpOdEwYNTnd7JZWbU50iLTa2kT+twtP0zVM1oDnPguL6gLPFiw8UZj1Q0QHcKPDlU7SA1oUnejJi+iTJk0qyhwAcRa6YEggNxy7HxB/MmzYMAllYSCURQH1IvqFF14ooYEoF0ARXSe4uQcMGCAhYCePvke6wImOBwkjJsJyoiPKxfdrMzTsBJpOdH0wzqVWrYjueocAnehrQye6XuhE1wfGYnjuM87FLxGdpq/0RXSr45BkgGEOcV/FFKCEiP7SSy8FNRbC50AuekiiMz7Pyy+/7LoZ2RDRjzvuOAkNdL5VVVX1BSqIHmz21I477iihEMLDBCI6XejhieiMctGHnUBzwUofLCzKwqL5CGniGBcU0XWL6BBIiC7w3KcT3R8RHbWiSLog7vWdd97hYU/YiW53oRcjosNogrQHnKNQgHN79uzZEtLnsYkcNGtFx23VJWUierdu3TjhUQi2aFVXV3NlXxl4kNqHKgnLiU50wTgXnSDKRIPj2SU229p1Ec+m2uVawKaIvjYU0fVCJ7pO6ET3AxvhwoWo9IEBcunSpUYrIMmL6FGxOzJCy0W3TvRQYulCy3lPC12zHsciOqNcdGIrO4eyPc71xD4u6EQPMxOdIrpOER3iU5rZ26R54LABWT4vmjPRNQj7oTzv44Iiul6Yia7Xic44F/3gGYgFD4ro6WPr88FNS5IB1zYWijD3j0qnTp3MGCg0ER2iM3ahhpLDb/U15LyT6LifYSgAk0CsvlBE1yuio3owo3b0gNV+PDwY5xIWjHPRCbZyYwBLQU4XtghfmiK6NueLdaJrFNFdt8keG/I/KKLrhU50neDZzzgXfyJdKKKnj40KCUXU1Arm/MU40aHdQEgPUUQPybmNBXScp1A+j+pM9NDAzQ1RkCK6XhEduVoaXGVE6gcqEHMY5xIOEHywHZJOdL0iOtHpRM9ynAtMCO/OWSKbXfQfKavQM6T88qX3pbL9F3L5tAnO2rDkjXdlh74d5HRnLdAHRXS9UETXCZ792CVI9EMR3a2IvmjRIkctyAaY8z/00hS5bGr0cdXi1xaKTJ4o576xroRCXV2tzJ88V/51+cOybp9Z9d8fO6qfjBvdX3xdGKATvTiKmvFEXaFAVpBP2IrOFNF1guvOt2sq9JxUu52LTvRwQDEkCOkU0fWBrdwsKqoPF050lbnwdSKr6kTKavQ4r2vgAke7HLapurZWlG0ccA5FdL2gH1u5cqXrZpBGMM7FHyiiu3PSImqETvRkwZx/6eLXZVXH6OOquqr1ZPWXnzodiyXCOh1kxdIvpTLnc9VgMOwp0Nn++9//um5GuCK63b7g23bjKCJ6hw4d6ouCEH1O9JEjR0oI+HZvFBLRsVU+pGrbWQdRLoAiuj5YMV23iJ6mEx0LsJqeI/jslebzl6lzCmlYrNbQBk3Y46HpGiYNM9FDMHqEBONc/BLRQ4uu8AVEvlJET15oXbXim6KeEeVtO0rNp1OCe65UtNtAapaFEx8Ejfepp54yO2yzvLs2MRF9wgR322KThEVF9QJXDLZnheRED+EhAhEd+VmM2AlPRMeCItEFneg6YWFRkWHDhsmuh50kv39D2TZqI9I6ftZSKF4LOtH1YnfUoF/L8u4ajSI6C4v6I6J//PHHrpuRSWDqooieLNtuu62MObRG/vBm9AKuFet2EKmtkdoVS6VinfUkJBF99YJZwSwOQETHZ4FxtUePHq6bE56Ivvvuu0uoIjomgkQfdkXfVg72nRA6WoDCIoxyCQubuUknus5M9JAWEkOBmehrCke1X78z0khFFXW1ImXu66iUsZZLA2yxV8QAEX1OdAA3OkV0XXEuOCe4Z1wXSyaFwY52FhZ150T/8MMPHb17dhbB19tgQySdR/43Fet2NH/WLl8Slojedn2pW71S6lZ9I2Vt/M97t2kjyEWniB6NkqtAzZgxw3xBFOzTp4/07t1bfHU6QxDs1q2b66aQAjn8oYjooQAnev/+fhbPIPmd6FVVVdzGpRDGueiEmeiKgQvc+aI1I0sawzgXvVjhHPMiOGqJDmxRcYwDeF50g/MD0wPqC3GnbrowzkUn5euuZ8ZiNV8vllbSU0IS0UHN8kVSHoCIjsVamOii1r8kJYjor7/+uhx//PHy1ltvNfj+VlttJb/73e/Mn8UAIf7888+XiRMnmhN4wgknyFlnnVW/2n7qqaea35vLkCFDZPLkybGcv7lz55o/WVRUJ9hW0r59exbVUwS2+2Dhafvtt3fdFBKzE51RLjphnItOXGSia6SivExaV7h3fedSUSbSqqLCabsqysqk3LmQrwvGuegX0W2/RnRgi4pDnKWIrhucH8yReK7ciOgYK69YscIYgoiW8V65tG7bQcpWfKVunNgSWq23gSwrL5fybxZL64qe9cfGZ7DjGU50koCIPmvWLBk9erQRyu+77z4ZOHCg+f4HH3wgt912m4waNUrefffdyNsAZs+ebQpG7rbbbvLyyy+bgcLNN99s/v7d7363frv0nnvuKX/5y1/q/12cq7uIcoEzhk50vSJ6SC70ELKzli5dapxKnTtjCz8JyYnOKBedYEJm3Wgk25noGgsyjhvd33xp4vzzX5Btttla9ttvD2dtuOmmj0zcDfkfFNH1QhFdvxOd6MYuciDShQse6WeiA+SiU9PRNd7DWAjP/lNOcTceS4L/a/WqDBnSSw4+OIzPhftm2rRprpvhDUWN7q+//nrZeeed5eGHH24gBG655ZZy0EEHyd577y033HCDXHPNNZF+33nnnWeEuLvuuqveeX7ppZeu9TrceEmtKkJE/853vpN5J5lWWOBAZ5QLYCZ6eCI6nej6QA4qnIEU0fWB84KxC7dt60PDdnoseDw9db5cf/4E0cDYUf2cL3ZQRPcjE53ogSK6P1jhfPny5a6bkkknOli0aBFFdGXADAmTbWiE5tzG53n++edVjJ99oKgj9NRTT8kvfvGLJp20ONjnnHOOPPnkk5F+F04QxPhDDz202UIpTzzxhOkckb1+5JFHyqeffipxiuiMctEJJqDIZmJBPV0gygXQiR4WdKLrxLrPKKLrdKJnPcpFK1omARjHrKqpVfFVU+t+FwNFdD8y0YnOOBfijxOdpLv7rmPHjkafghOd6AJmVWgHGJeFBIpxhpQhjs8D45Y1S5LClBebXz506NC8P4cj/eOPP470u3CCEAuBStZjxowx23CQdX7VVVeZE2jp1auX3HHHHSYy5qGHHjIC+o477mj+bT4wAIQglPuVr2OmiK47oxnnMqQ4F9+jXOy9i9gP61oi4dxvjHPRK6LbiTTRAxybaUa5EL9EdDzvyysY59L4mGiNJco6jHPRid2JzTgX/UDTABTR0wfPewjpFNF1iujQ9kI7NxCdsfMBOfyhfB4Q0sJAkhQ1w8D2pEIZXygAGfXBYYXyiy66SMaNGyfTp083MTCXXXaZXHHFFfWvO/fcc+XAAw80N+CwYcPkwQcfNFsnkMmej/Hjx5tYAvvVs2fT1YAhxKO9zM7SG+UC6ETXJ6IzyiUsqqurTf9OEV0f1n1GJ7o+XDjRQ1iITUtEd32s9t9/fxkychenbdCG3Xmaa5YhOqCIrhMbaUoR3Y9zBcMDRfT4KOY5DkMmRE2iC2h4ubpOKFh9KhTRGRoA5ppz58513ZTwRHQbr1HoK6q7BFEQKLh02GGHyT777GPiWvbYYw855phjCgrkeN3GG29cMPgewjtclfYLBUybAi50wDgXnaCzxYCEsSG6oIgeHnZnDzPR9UEnul7oRNeLBic6DBrt12cB7lwY56IXW9+Bmej6gLDBOBc/gNmQIroboBGF5nYO5bxA8/viiy8kJEIT0bFgFVpETZJUlmr1j8PxsPXWW6+Vh47/LyTEI5oFkS6F3MmImYgSNQERHS4yumr1iugQ0JvLzPcFXNeunXFxieibbrqp62aQGLGRV3Si64OZ6Lqd6Ixz0SuihzJ2CAnGueg+N+jPmImuU0SnE90PKKK7FWtnzZrlsAWkKawhMjQRHTuEsPshtOKi1mRMYhTRJ0yYIHFy1llnGef5IYccIttvv728+uqrctddd8npp59ufo6B3FFHHWWc5RDtIJ4j+gWDicMPP7zF74+LBE4h124l0jQsKqoz9gOCKxeewoIiul4oousFjk0WFtWJhjgXUFFeJq0rdIwx0RbX0ImuG4jodKLrAxEhdKL7AUV0d0DQhBM9FNNaaJEuoYnoVnQOSUSHWXry5Mm8h+IW0XfffXeJkx/96EeycOFCOeKII+oF7dNOO03OO+8883O4yQ844AA57rjj5O233zYuSRQVffnll2PJyWZRUf1OdBSrJXrAdqyjjz5attpqK9dNITGL6Bhwoq4F0QUmzngWcrFXHxTR9aIhzgWMG93ffJE1UETXDZ41dKLrg050v0R0xiG4c6LD7IU4Hc5n9Ino0PJCA6Lze++9JyF9HowBUFsA9xOJSUSP+lAoRuA+/vjjzVe+CQ8KM+Er7lVFvB+C87fddtvYfieJDzwEFyxYIF26dAnmsIayKj5y5EjXTSAxg9oRGPgz/kCnE51FRXXiIs4lat2ZrKNFRCcNoYiuGzrR9TrRWTDRD+hEd+tEB3CjU0TXBfQc6DooKh7SXBOi8zPPPGN0KxgNfcfGdsNdTxG9MJVJ5KGXMslrbrITtwCJGxkTYLjfic7cbVxHcew4IIQ070TngFOviI4JNNEHxhCYMKcJxkIU0psHx4giuj4oout3ojPORR9YSGdOrR9QRHeHFf0govfq1cthS0hTTnSYG5BAgb+HArRRfC5E1YSgKeIeQkwljNNDhgxx3RzVOM1Ed4kdjHTv3t11U0iBXQ8hOdEJ0exE79Chg+tmkDxxLnSi6wRiU5pOdOZ8Rj9OFNF1QhFdN3Si64RxLn6J6Bi3cTeUm2MPARAiOtGFFc4hNockoluzJ5zbIYjoGKOFlvMeZCa6axG9bdu2Jmed6MxDx6AxFHcsxQ+imaVLl3LbllIY56KXtONcQokESxqIF4BOdH1QRNcN+jNmousDu9FskXGiG7s7bfny5cHMYX0BYyREujD6SB84L1jggL6z2WabSSjgHoeeGJLoTBE9ARF98eLFkV7XsWNH0Y4tKspJqU7QycKFzvNDSDpO9D59+vBQKwSOJu4S0AkLi+qEIrpeKKLrF9FRlI/odKLTkOOPiM7ilu7iKOhE1wf0nA033NA40UMDkS4hFRPG55kyZQqfN3GK6LZgQ3P4kNcJEX3w4MGum0GaEdFDgYsBRHsmOnfl6AQT56j1SEi6UETXCUV0/WMhH+YJWc1EpwClU0THPYNdAlVVVa6bQwoAVyrgYpQ7ET0kQTMkEOMSqog+Y8YMCenzYCcNFwILU3QZWRRqOProo2X48OHi8xZs3MS77rqr66aQPOABGNJ2H0K0gknZihUrKKIrhYVF9ZJ2nAuJhhVoGeeiDzrRdcNMdJ3YuijYmUYR3R8nOkkfGD7hoiU6RfQ33nhDQgPxJ5MmTQqmDoI1biGihpFUMYnokydPljvuuEOuv/566d27txx77LFy+OGHe5elC4EWkywWFdUJVr/wFZITnRDNeeiATnSdsLCoXiii66Smpsb8GcJkJlQnut0tQHTBTHS9meiAueh+nCv0cxTR3QBNChGVGAdUVFQ4agXJJ6IvXLhQqqurpbKyaB+vatEZ8wHs4urcubP4DmJ3MH6GXjpgwADXzVFLUVfwVlttJbfeeqtce+218te//lXuvPNOOfvss2W//fYzgvqYMWPEBxDlAkKoohsidhsWRXRCkgeDTcDcbX1gsZeFRfWem7TjXJiHGw3GuegF4hK+KKLrhE503SI6FtWJbiA+4XxRRHfnRMdYCXX8OnXq5KgVpCmg6+DcLFiwwLi3Q8F+lmv+Pkke+kTHws3YUf1k3Oj+Jf1bLHBASA+pWGoSlJe6rezII4+UZ599Vt58802ZPn26V9EoENHRsXJLnN48dEyyQhHRmf1JtOehAzrRdUbtoP+wW7mJHuBywrlhnIveZy5rkegE7kCK6Hoz0bE4SHRhxwB0ovsT6UIR3Q02HYG1HXQ60UFouei45jAXWLzgC1lVU6viq6a2ZXVnYDSmiF6Ykve6fvzxx3LhhRca8RyiJ/7uk4jOKBfdTnR0SGk6/AjJsogO54wthkT0YCfM1oVG9GCFJj6n9GEFWm7l1gmd6HphnItOKKL7J6IjlpS4E9EXLVrEw68M7HjGMyY0ER1jGrjRv1q0QEIBn4cieowiOib099xzj4waNUqGDBkiU6dONRnpqEj7y1/+UnyBIrpu0LmG4kIHdMMR7SI6XOi8TvWK6HSi6wP5hyBNJzrv0WgwE103WLSlE12vEx3nxt5DRAdYrMUWe8a5+AGd6G77MBhP6ETXB8awcKPDfBsaiLTeqNcmElLOOyKRVqxY4bopYWSi44B27NhRjjrqKPnd735XnzVl4wAseI1WsDKMi4JOdN1O9EGDBrluBiGZyURnlItOKKLrF9HpRNcH41x0QxFdL3ZREFFi3AGlCyymM87FHxF97ty5rpuRaTc6neg6gYgemhMdjBgxQl76an2R2R9KCEDzBXCj9+nTx3Vz/HeiQ2yZOXOmXHLJJdKvXz9TvKGpL83YhxpFdJ3AATN//vygnOiE+OBEJ/qwrjM60fXGuTATXR8sLKpfRGetGJ3Y/oy56PrAogZFdD+gE90t0KLoRNdJqCJ6aFgdjpEuMTnRJ0yYIL6DKBfkZFKk1cnChQuluro6qKrNhGgX0VFAhOiDmeh6YSa6Xiii64ZOdL1QRNcLnej+QBHdvRN9+vTpjltB8ono2CWA3Zyh7eSsKC+T1hUll5uMvS0tjUVC4gjSIUgMIvruu+8uIYjoEGhZcEonNicrlEUObisnPojojE/S60THsyq0gWYIuMhEJ9GgiK4bFhbVCybOgE50nSI6M9H9EdFhgkBtAeoN6UMnum4RHdoIUgdCM3CNG93ffIUCi4sWRsdySYqwqKhusOIFwUh7LFAxsBgc0QoGMojpQsV0og9MwjBxZh+iDxdxLozAiAZFdN1AVGJhUf2Z6EQXFNH9EtFtHTbixomOBSf2Y/qwJklGuviRi844l/xkSkTHBJQiun4nOjrYUESjUD4HCVekRXwSM9H1nh8Wd9MJC4vqhSK6bhjnohfGueiFmej+0LZtW/PnsmXLXDclsyI6YC66Ptq3b292PFFE90NEX7BgQf18h2RYREcG04oVK1hUVLmIzjx0QtKLcgEU0XUCJw2LiuqEmeh6oYiuG8a56IUiul4oovvnRKeI7lZEh+5D9D3/WVzUHxEdBmQbtUwyLKLbLQmhZTCFBG5UdK6EkOShiO5HnAvRh4tMdO5sigZFdN3Qia4XZqLrhXEu/kAR3S0dO3Y04yU60XWCxAEKs/qxplYWF01ARF+4cKG89tpr4pOIXlVVVb9CSXSBXQKLFy+mE52QlEV0ZqLrhCK6bid6ZWUlhW2F2Ox4iLVEHxTR9WKLWDNLWKeIjjEB8WPXAERcOtHd1d3AvIZOdJ3Qie5PLBV2qjMXvWlKmmFA6Nxvv/2kc+fOsu2229Z/H997+eWXRSu4CLp3785Jr1LsqqQtOhECLARHNIOiopg0W/cZ0QUz0XU70dN0oZPo1NTUmD8pousE54VjI51A+EO/ZuOqiC4RHTVsmE/rx30ENzpFdHesv/76dKIrFtGhJfI544cbnSJ6jCL62WefbVwKU6dObfD9k08+WS699FLRLqIT3SJ6KJnonCQSH5zocGswJkInzETXCwb/1rVJdME4F93Qia4biOh0ouvDFhmnG90PIKIvX77cdTMyC1IHGOeiExvby+KifuSiU0SPUUR/9NFH5Te/+Y0MGDCgwfe32WYbefbZZ0WzSEsRXVSfH2wbQeROCECYpDhJtIvoLCqqF4roeqGIrn8Bm89fvSK63S1A9IGdaXQI6sPWR6GI7k8UAp3obp3ojHPRCUV0v0R06HPWnEL+R6WUADoldE6NJyl4WGjePotBO0V0vaBwwYdLK2TA+RNcN4UUYOyofjJudH8eo0DiXCii64VxLnphnIte7GAfuahEH4xz0Q3jXHSL6FhcJ/phnIsOJzoW1bmgru/eQH9GJ7ofIjr00/nz5wcVt+xMRB8+fLj8/e9/lyOPPLJBx3TllVfK9ttvL5qhiK4XrHS17dBJVtVwtUszNbVrXH4kDCd6nz59XDeDNIHNPrUTZ6ILFyI6J4PRYCa6bhjnohuK6DphnIt/QuGnn37quhmZFtExTkOkDs4F0QO0QxYX9QMbsQyjK0X0GET08ePHy5577inPPfecmdRdeOGF8vjjj8s777yjOs4F2b92EEJ0gesIInr77luJLHbdGkKylYlO9GG3bPOZpRPGueiFcS76J9DcGqwXZqLrhE50v6AT3S02MQFudIro+qCI7gfQCPDsQS760KFDXTdHFSVlr+y4447y/PPPm0l+//795b777jNuxpdfflm23XZb0Uq3bt1cN4HkARMqPOQ27N4rmGPEwqJE+z23dOlSxrkoF9HpRNeJCyc6tyRHg4VFdUMnum7oRNcJ6kXhGcBMdD+giO7eiQ6Yi64TuJphniS6wTOHxUVjdKKDLbfcUu655x7xCVwERCfILsUOh+ufmCby3oeum0NI8GCLI8QmOtF1QhFdN3Si64Uium4oousvLLpy5UrXzSBNiBlYVKeI7o+IvmLFChPNV1lZstxCSoy0a9++vTnucKITnU507IbGPYIFQqI70mXOnDmumxGGEx0TlPfff3+t7+N7Grdo4gYFFNFJmtA1SLQXFbUDTaIPWzyMTnSdUETXC0V03bCwqG7oRNcLxgMsLOoHbdu2rTesEDdzcES6UETXK6IDFKwkuoF+ikx0Jiw0pKSl0auvvtpsj7niiisafP+uu+6Szp07y1lnnVV0ESgI8Mh+7du3b5OvmT17ttn2MWDAgKLjB5DjAyii66eivExaV5S0tkNSPEfEf+AAAHSi64SZ6LpxEedCokERXb+Ibou/En0wE10vdKL7g83hXrZsGcfZjsxpFNH1i+jQ9nr27Om6OaQA0E+xOw3ar41JIiWK6Lfccou89NJLa31/7NixsvPOOxcloj/00ENyyimnGAEdX9gycO+99xox3rrIDz/8cJkwYYL06tVLZs6cKVdeeaWceuqpRYvorCqrn3Gj+5svQkg6Inqxi5IkXRGd2xx1QhFdLxTRdcM4F93Qia4Xiuh+iujEDRD86HTWu1MDX1988YXrppBmsCZk6KkU0Vsooi9YsEBatWq11vfxPStYR+G5556TAw88UH73u9/JMcccY773zDPPmFUpK6Jfcskl8uqrr8r06dPNSfzHP/4hP/zhD00B0xEjRkR6n7lz55o/R175rFS0WbO9iuhj7Kh+FNAJSVFEx4Ssqb6cuAdbtiGgQ3Ai+mCci14oouuGcS66YSa6XmA2Yya6H1BEdw8Ev6lTp7puBingRqeI7sd9BK0AkS5Dhgxx3Ry/RfRtttnGuNF/+ctfNvj+TTfdJFtvvXXk33PxxRfLmDFj6gV08L3vfa/Ba/7whz/ISSedVL8Kst9++8lmm21mvh9VRLfC/uqaOqmp0ZfZTtZQU1uXyeInhLjKRKcLXS+YKDMPXbeInnacC58rxYnofAbrFdGxk4PohE50vWBMACMb8eNcoa+jE92t+Ld48WIzJqAhRR8U0f0A9w7SPIoxSmeBkkT0yy67zIjfL7zwguy0005mYgdXOSJennzyyUi/A9k6+PfXXXedLF26VKZNmybdunVrkFsOBzlc6cOHD2/wb+FCf/PNNwv+bltZHm375JNPSvmYhBAStBOdeei6negU0fUCEZC7OHTCCbNusLhhFzqITic6FgmJPlhY1K9+Dm50iujuQCY6dCAI6Yyh0CmiT5kyxXUzSASgz1JEb0hJ+8R33HFHI5hvuOGGcs8995gMc9wI+B5+FgWspGMS+vbbb8vAgQPl+OOPN39+//vfl4ULF5rX2IrKnTp1avBv8f+Fqi2PHz/eiEP46tixo9xxxx2lfExCSoYOOOKDiE4num4nOrZuE524cKKTaFBE1w0z0XWDfg3zM4hPRBeMc/ELZD4vX77cdTMyixXOURCR6APuZhhpGVGlH4roa1Ny2Crc4ffff798+OGHxkWOvzd2jBfCOrieeOIJeeutt+SNN96QGTNmmK8zzzyzwWtQXDQX3GyFJq/nnnuuiSqwX2gfIYSQ/8E4F90wzkUvEJdcONG5OBsNiui6oYiuGzu/ohtdHyws6hd0ousQ0QsZL4k7YMAFzEX3Q0THgiAWPcgaWlyxDAJ3468ooHAoVmh/9KMf1d9EcJij0Ojzzz9v/r9nz55msD1nzpwG/xb/v/HGGxfcigiHpf3CShchhJD/QSe6buhE10t1dbX5k050vYsczD/VC0V03VBE1+1ExzybcUh+QBHdLVVVVWbhiSK6Tiii+4ON20ZxUdKCTPSPPvrIFPt88cUXm9yCEWULIAbRu+6661oC+aeffmpiYuxgYfvtt5dHHnlEjjjiCPM9rIIgdx1FSYulVUWZVFS0eN2AJERFOQtxEpKWCIi+lJnoujPRUSeE6MM6NJmJrhM60XVDEV03MCIB1JaC2YnowdZJgZDOuDc/RPRZs2a5boZkPRedcS56+7P27dub+odEN9BmMXZDLnr//v1dN8dfEf3YY481q3uIcEHnVCq//OUvZYcddpCLLrrI/PnKK6/IX/7yF3nggQcaFDGF2I6Ilu22205uuukms3J1wgknFP1+ky8YYzLSCUkSLCJx2z3RjN2OxUx0vTDORS+IcgF0ouukpqaGTnTFUETXDZ3o+kV0LLJTRNcPneg6Il3oRNcLND3GueinsrLSCOksLtpCEX3y5Mkmu9w6xktl8803N8VIr7/+ernqqqtMRMuzzz5r3OeWnXfeWZ5++mm55ZZb5NVXXzX/5u677zYPJkIIIaVFuQCK6HphnIt+JzpFdJ0wzkW/iM6ilXqhiK4XK5yzEJ8fUETXIaJDsyJ6RXRGhPgBi4vGIKJ37969PhO0pWy22WZy5513FnwNXOr4IsQH6EInPhQVBRTRdQKBiU50/U50xrnohHEuuqETXTcU0fU70Smi+yOiIxbJRSFysgYkJsD8SfSK6G+//bbrZpCIIvrLL7/MY/UtJQWEIw/9rLPOMpm6hBBC/ItzwWIPsuiIPjDpgpBuJ8xEFy4y0encLU5E52K2XnBuWBjRj0x0oguK6H5hd81TL3HrRMfxt+M2ok9Ex/nhPeKHiL548WJTk4OUKKIjfuWee+4xq3u9evWS3r17N/gihBCi24mOwX1FRYXrppAmQN4poIiuE2ai6wYCLfs2veDcUETXC53ofmSiE39E9GXLlrluSqZFdMDiojrp0qWL+ZO56PrZaKONzJ+M32lBnMuFF15Yyj8jhBCiJBOdUS56sVu1WThMJy4y0emsjg7jXHTDOBfd2H6NTnSdxd2wA4oiuh9QRNcjoqO4qBVsiR5sfUWI6H369HHdHBJBREdx0d40TZcmoh933HG8yAjJs+WeYgfxwYlOEV0vdKLrhpnoumGci24Y56J/pwC+GH+gEyyuMxPdDyiiu6djx471IjrRR1VVlZmP0onuR9Rbp06djIhOSoxzsSxcuFBee+01HkdCCPEIOtF1Qye6blw40Ul06ETXDZ3ofkyWKaLrjXShiO7PfYQFKca5uN29AZGWcS56wQ6BefPmuW4GiehGp4jeAhEdofL77befdO7cWbbddtv67+N7rNpKsgxd6MQXEb1Dhw6um0HyYCfIzETXiRWXMDkj+mAmum4oousHC4QU0XVCJ7pfc8K2bdtSRFcQ6UInuu7ionSi+1NclJnoLRDRzz77bJOVN3Xq1AbfP/nkk+XSSy8t5VcSQghJCTrR9ce52OxTojPOBeeGi6Y6YZyLfhEd0XdELxTR9YLFdWai+xXpQie6Wyii+yGic1zgh4g+f/78+ljLLFOSjerRRx+VSZMmycYbb9zg+9tss408++yzcbWNEEJIzGABdMWKFcxEV+5EpwtdLxg8MspFL4xz0Q2d6PpB/8bCojrB2ICirD9QRHfP+uuvL3PmzHHdDFJARMe8B/1a+/bteZyUi+hY7Jg3b5706NFDskxJTnTkSqFDArlOLFz8GBwTQgjRydKlS82fjHPRC0V03SDmIO1dAnToRIcium4oouuHmeh6YZyLX1BE1+FEh3bFcZReER0w0sWPTHTw+eefS9YpSfEePny4/P3vf19LRL/yyitl++23j691hBBCYmXJkiXmTxTaIXpFdEyUiU7oRNcNJso0dOiFIrp+GOeiF8a5+AVFdB0iOswPjEHSCUV0f0CNB+wW+OyzzyTrlBTnMn78eNlzzz3lueeeM5OVCy+8UB5//HF55513GOdCCCHK89ABRXS9YKDPOBe9uHCiM389OnSi64Yiun4oousFYwNbfJzohyK6DhEdoLgoRECi73nTsWNHOtE9inT5jCJ6aU70HXfcUZ5//nnzEO/fv7/cd9990qdPH3n55Zdl2223jf9sEUIIic2JDhGDA0m9MM5FNy5EdBKdmpoaOtEVgwUhLHQQvTATXS/YpYaFdkZT+COiL1++3HUzMo2NIEakC9FdXJTohyJ6C5zot99+u5x44olyzz33lPLPCSGEOMxEhwudzla9YIJsB/1EH4xz0Q3jXHRDJ7p+6ETX7UTHIhQWc5FdT/SL6DhX+GJBcjdgzlNRUWGc6EQnXbp0kU8++cR1M0hEEf3FF1/M/K7Pkpzo48aNM04fQggh/jnRWVRUN8xE1w1FdN0wzkU3FNH1A3F25cqVrptBmsDWS2Gkiz8iOqAb3R0wDcGYQhFdvxOdO2z8ENGrq6tlwYIFkmVKEtEHDx4skydPjr81hBBCEs9EZx66biii64ZxLvpFdO600S2ic6KsGzrR9WLrpVBE90tEX7ZsmeumZBqK6PpFdCzcYrc00c1GG21k/sx6LnpJcS7HHHOMHHLIIfJ///d/RlBvvD1pyy23jKt9hBBCYnaid+/encdUMSwsql9EtxNjolNEx9ZtoltExxcXO/Q60dHPEX1QRPcLiuh6iosuXLjQdTNIAREdzJs3j0Yv5WA3e1VVlRHRhw4dKlmlJBH9tNNOM38effTRTf6cDhNCCNHrRN90001dN4MUiArBNjk7USY6zxELi+oW0Xl+dIvogIsdeqETXX+cCxbbiX7atm1r/qQT3b2I/uGHHzpuBcnHhhtuaBbVEenSv39/HijF4Dx17dqVTvRSDt78+fPjPyOEEEISBQucENGZia4Xu0WbIrpeXBQIo2s3Ooxz0Q1FdP2gf8PWepqi9EEnun+7OiorKymiKxDRFy9ezJopSoHxAZE7ENGJfrp27Spz5syRLFOSiN65c+f4W0IIISRxgRYuZ2ai6xfRrduM6MOFE52xF9FhYVF/RHSiV0SHgF5TU+O6KaSJc4N7iJnofoBnNyJd6ER3CwRaPHMQaYm/E73FRYkfIvrkyZMzbfApqbAoQEXWO++8U84///z6702aNIkDLkIIUQpc6IAiul7oRPdDRE/biU6iw5gQ3dgJF13Out2zAG50ou/+gRudcS7+QBFdhxMdLFq0yHVTSB4oovtVXHTlypVmd0dWKUlEf+utt0ym7rXXXiu/+tWv6r9/1113yR//+Mc420cIISRmEZ1xLnqxE2PGueiOc2Hmtl4Y56IbOtH1YxcJWVxUJxTR/YIiunus+/zLL7903RSShy5duhgnOhfY9dOtWzfzJ4qLZpWSRPSf//zncsYZZ8iUKVMafP/EE0+U66+/Pq62EUIIiRFsYwR0ouuFcS76cZGJTqLDOBfdUETXD0V03SDujXEu/kARXcfCU1VVFUV05U50jK/tXJXo3tnRqlUriujF8vrrr8vYsWPN33NzcFBNd9q0abGeJEIIIfE50fHQs1u1iU4nOp6rGOwTfcAhg7oCFNH1QhFdNxTR9UMRXb8gSBHdH9q2bctMdMdgXA03OuNcdIvogLnofozjunTpQhG9WCoqKmTp0qVrff+DDz6oz5wihBCiT0RHlEtWi4D4ACbGENB5jvTmoQPGuehe6LBCLdEHRXT9MBNdN3Si++dEX758uetmZB5oVIxz0Uvnzp3N3GfevHmum0IiFhf9/PPPM3usSppl7LnnnnLxxRc3yJ2cOXOmnHTSSbL33nvH3UZCCCExieiMctEvojMPXS82I5giul7oRNcNRXT90ImuG2ai+xnnwqxn9yI6neh6qayslE6dOtGJ7pGIPnfuXMkqJYnoKCj68ssvm4OHycrmm28u/fr1kxUrVsj48ePjbyUhhJAWg5w5FhXVL6LDZUZ0O9HTjnPh5Ds6NTU1dKIrhiK6fuhE1w3jXPwT0TF2YKFet9CJ7kekC+Nc/KBr165mh01T6SRZoLKUf4QMnMmTJ8vDDz9s8tEhpJ933nmy//77MyeUEEIUO9H79u3ruhmkmUx0OtH1YifBaYvojPeJDuNcdEMRXT90ouuGcS7+iegAbnTWJHIHMtEh+GFBg7sJ9YrorK/oBxtttJH5E5Eu7du3l6wR2YkOp7nlggsuMAOsAw88UK688kq5+uqr5dBDD6WATgghyp3ojHPRDeNcdMNMdP0wzkU3dkGIuyt0b6vHeaJzVieMc/FXRCfusHX7GOmiW0SfP38+xweenKvy8vLMFheNLKLPnj1bVq5caf5++eWXJ9kmQgghCQhLGMBTRNcNRXTduHKik+jk1ush+qATXT+4f9DHUUTXK6Lj3CC6iuinbdu25k+K6O6d6IDFRfWCtAuYVbjQ4cdi+4YbbphZET1ynMuWW24pP/7xj2WbbbYx/3/NNdfkfe2ZZ54Z6Xc++uijJg6m8arGySefXNRrCCGEFAa5ZRCXmImuP86lR48erptB8sDCovpBP1dRUeG6GSQP9txQANQNRHRrniK6sJFvWHS3LmeiF3uOMA4n7qCIrh9ofAC56HbnANGdi/4ZRfTC/PGPf5SLL75Y7r//fvP/f/7zn2MR0Z977jk56KCDWvQaQgghzUe5ADrRdUMnum4Y56IfxrnohnEufkAnul5s8XEsulNE9+NeQgY3nehuwTlAdjNdznrp1KmT2a0GEX3QoEGum0MiiOiTJk3K5HGK7ETHhXzffffVD4DfeuutWBowePBgI8639DWEEEIKFxUFFNF1w8KifojojHPRC+NcdMM4Fz9AAUTGuegW0bHoTvQD3QSLHRTR3QN3M+NcdO9U69y5s8ybN891U0hEEX3RokWyYsUKqaqqytQxiyyiNzWJjINZs2bJVVddZSIGdthhB9lss81Keg0hhJD8UETXDwrtYfu8nSATfTATXT90ouuGIrof0InuR5wL8QOK6HpEdDrR9Ue6wIlO9LPRRhuZPz///HPp3bu3ZInIhUUbB8kvWLBA7rzzTjn//PPrvw87f7EZh3g9DvzEiRNl+PDhcsEFF5T0mlwgQkAwyv0ihJCsx7lg4oXtjEQnmBBDSLcTZKIPmAjgKmPmtl6Yia4biuh+wEx0vVBE9w+K6Hpy0elE1w1FdP9E9M8ymItekhMdUS677rqrqcj6/vvvy+WXX26+f9ddd8mUKVPkmGOOifR7Tj/99AZ5R//85z9ln332ke9///uy4447Rn5NY8aPHy+XXHJJKR+NEEKCBIuJjHLRjXWV0Ymu24mOhSib65wGWFhJ8/18h3EuuqGI7gd0ousX0RH/Rvygbdu2snTpUtfNyDw2zoXjKt0iOuohclehH7FvG2ywQSZF9JKc6D//+c/ljDPOMIJ5LieeeKJcf/31kX9P44IBe++9t3Tr1k2eeeaZol7TmHPPPde4Lu3X7NmzI7eJEEJCFdERiUX0i+h0ousX0dOEAnpxcOKlGxYW9QNmouteiML5YZyLX0705cuXu25G5oETHYkFvHd0i+jV1dXcMeBRLvpnGRTRS3Kiv/766/KPf/xjrcld//79Zdq0aS1qEFYGm1tZb+41GFjgixBCyBroRNePfa5RRNcd58KiorqhiK4bG4VUbPwjSRf0c8wO1gt2rFEI9AfGuegArlkANzp3feqkS5cu5k/koqPIKNEvor/99tuSNcpLHQA3tSXpgw8+qO+cmgOD5zfffLPB9xDVgpWMXXbZJfJrCCGENA925dCJrhvGueiHIrp+YLSwkSFEH4xz8QM60XWDxXbGufgnouP5RNxhdSouEOo+R9AaWVzUn1z0+fPnm/lRlijJib7nnnvKxRdfLLfffnu9E33mzJly0kknmbiVKOAhcsopp0jHjh1lyJAhMmvWLHn44YdNVMxuu+0W+TWEEEKiOdHbt2/PQ6UYxrnox0WcCykOOtF1wzgXP2Amun4RnU50v0R0RFQgSqSqqsp1czILakNhIZfFRfWC8wMHOkV0P+jWrZvRbHG+unfvLlmhJBH92muvlTFjxhj7PiYrm2++uXGhb7rppqaoZ6Q3rqyUl156SSZOnGjc5vi3KAaam4Ee5TWEEEIKg4E7shjpRNcNJsQQaPHsI3pFdMa56AUDeYrofjjR7355hvz9XtYs0sqyD9+RTcs+l1NcN4Q0CZ3o/ono4LrH/it3Tf7SdXOUUSfw56+xZSZfRH3B21/Kw/OflHb/aToaeOyofjJudP/E20EKR7rMmzePh8gTJzpAUghF9AgX9uTJk40rHPnomLCcd955sv/++xc9uRw1apT5aulrCCGENI2N34IDg+gFW7OZh64bbFekE10vdqs841z0Ys9NTU2trKqpdd0ckocaKZfqjG3P9gnkOS9evNh1M0iRIvo3y5ez3ytI8nE3da3byaqvv8p7HmpqGbmjobhoFnO2faRt27Zmp3vWiouWbHeDWH7ggQear9zJywMPPCAHHXRQXO0jhBASQ5QLoBNdvxOdIrpu6ETXDUwdgCK6Xuy5qfv2XBGdlFW0kuoVFNE1i+hZEy1CENFXrfjGdVMyT3lVO6n5Zlnmj4N2EX3BggXcWegJXbt2lc8//1yyRHkpsQDvv/++vPXWW6bwp+WZZ56RESNGyGGHHRZ3GwkhhLSwqCigE103dKLrx4UTnYXIij9WFNH1AhMOah2t0447o1RT0Upqa6rrF6aILhjn4hdwaq6//vpS2bqN66ZknoqqdlK7Ys0OXaJXRMezB0I68UNEnzt3rmSJokT0adOmyeDBg83XsGHDZIsttpA5c+bICSecILvssosJln/33XeTay0hhJCSnOgo5sbCovqd6HCXEd0ietqZ6LYQI2kea+6giK4X1Hy48sorpVPXnq6bQgpQVlFZv/uG6IOFRf3r96644grp3G1j103JPOXrtJPalculro4LhJpFdMDiov6I6F988UWmFt2LinM5++yzTXj8jTfeaP7/sssukx122MFMVp599lnZaaedkmonIYSQFojo2EpKYUk3FNH1A0GJmeh6YZwLIfGK6Fg4JHpFdOy+4UIrIdEpr2qPwYLUrfxayqrWxOwQXWywwQZm4Ykiuh9stNFGJq0EOwfsAkjoFCWiv/zyyzJp0iTp06eP+f/+/ftLv3795I033jDOdEIIITpFdEa5+BHn0rlzZ9fNIAVgJrofcS4UlfRTUV4mrSuKTpUkadGqtbmP6ETXCXatob9buXKlVFVVuW4OiQj7vaaoM+VE1+y5S37nXVnbDrK8rEwqVn8trdqu1+Q5Im7Bs2fDDTekiO6REx2gTgdF9CbAapAV0EHfvn3Nn0OHDm3q5YQQQpRkolNE1w8Li+rHRZwLiQ6d6P4wbnR/80V0MmPGDBk//nWK6EqxRcgxbqCI7g/s95p+bkNjgviWxo7Z5cuXyxlnvCAnHLOFDB8+PPH3I6WB64FOdD/o0KGDeQ5BRM+KLlx0T7VixYr6L6x+A7gUcr9PCCFElxMdDziiG4ro+mGcix8iekVFheumEOI1drGQcS66RXTsYCOEFLeLo02bNrJo0SIeNsV06dJF5s2b57oZJOLOga5duxoRPSsUFeeS+9Au9D27nZYQQogOEd3uHCI6wXOTmej6oYjuh4jOOBdCWgZEJnDva5/KEQ8vSCVmgUSnetmXsvDV2fL4Zf+WMw7Yibs6CIkIxgfrr7++fPnllzxmyp3oCxcuNFnbyEcnuunatavMnTtXskJRV+S9996bXEsIIYQkAuNc9INBIr6aWqgmemCci24Y50JIvE70VatWy6oaCOo0SGmitryV1NbVyaqV30hNLc8NIcUWrqSIrl9Eh8EIQjpc6US/iD558uTMFLsuSkQ/5JBDkmsJIYSQ2EHsFr6Yia4buNABRXTdAm1NTQ0z0RVDEZ2QeMCzCEJ6OR2AKimrXLNToK56leumEOIdcKLPmTPHdTNIAWyBSkS6UETXz0YbbWT0hsWLF5v7K3SSr95ACCHEGUuXLjV/MhNdNzbXlCK6Xmw2cKtWrVw3heSBIjoh8YB+7vLLL5fO3XvzkGqkvMJ81VWvqU9GCIkOnej66dixo3kOsbioP050kJVcdIrohBASeJQLoBPdDxEdBY+I3jz03JgDog+K6ITER/v27TOxLdtHcF7KK1tL3WqK6ISUIqKjXhRiFInePm7DDTekiO4JnTp1Mtn1FNEJIYR4DwaJgE503TDOxR8RnU50vVBEJ4RkhbLK1lLLOBdCShLRwaJFi3j0FIMYFzrR/aC8vNxEumRFRGepW0IICdyJXlFRQYezJyI6neh6YZyLfiiiExIvFeVl0roCbnQ60rVR2bpKKmpXm3NECImOzWyGiA63M9Gbi/7666+7bgYpItLl888/z8TxoohOCCGBO9G5JdsPER1bF9u0WVMsjOgV0RnnoheK6ITEyzEjuso5ew81LjOiixtumGoW3k8Y3d91Uwjx0on+5Zdfum4KaUZExznC+Ju7QPWz0UYbyfvvvy9ZgCMiQggJXERnlIsfmegoKsr8Wb0wE10/dXV15k8KfoSQ0MGYwe5iI4REB4Jsu3btKKJ7IKJjXLdgwQLXTSER6NatmyxbtkyWLl0a/PGiiE4IIYGL6Cwqqh9MhDEhJnphnIt+ampqzJ8U0QkhoYMxgy1KTggpPtKFTnT9IjpgLro/TnSQhUgXiuiEEBJ4JjpFdP1QRNcPnej6YZwLISQrIMqFTnRCSo90YWFR3WAnNWIu582b57opJOKiR3l5eSaKi1JEJ4SQgGGcix9gIsyion6I6Mxl1B/nwlgkQkjoMM6FkJaJ6HSi6wZjORR+pRPdDyorK835ohOdEEKI14IS41z8ykQnemFhUf3QiU4IyQpYeGecCyGlQRHdH3czRXR/6Nq1q8ydO1dCh050QggJ2N1cXV3NOBcPoIjuh4iObYoVFRWum0KaEdF5jgghoYOFd4zx7AIvIaS4TPQVK1YwEkk5Xbp0oYjumYj+OTPRCSGE+Apc6ICZ6PphnIsfcS6McvFDRGecCyEkdOzuNeaiE1KaEx0wF12/Ex3nyEYqEv3FRRctWmQWqEKGTnRCCAlcREdhFqIbFhbVDwbwrVu3dt0MUgDGuRBCsoKto0IRnZDSRXTmousX0cH8+fNdN4VEdKKD0N3ola4bQAjxjxuf+lBunviR62Yoo05Q0q7M/H3Nf10ydlQ/2W69JebvdKLrh050/WDLPEV03VBEJ4RkBTrRCSkdGIwQ0UcR3Q8RHbno3bt3d90cEsGJDj777DPp3bu3hApFdEJI0dTU1smqmjXb5klTQE53f47gRIfo16ZNG9fNIc0If9j2xsKiumGci34oohNCsoIdM7C4KCHFAwEdQjrjXHTTvn17qaqqYi66J7Rp08bs8oCIHjKMcyGEkEBZsmSJcaEzH1g3NjeOIrp+Jzoz0XVDEZ0QkhUY50JIy4DYRye6bjCHhRt93rx5rptCItI1A8VFKaITQkigwInOKBf9WBcZRXTdMM5FPxTRCSFZAe5MCEx0ohNSGhTR/QAiOuJciD8i+md0ohNCCPFVRGdRUf3YomDWVUZ0wjgX/VBEJ4RkBQjoENJZWJSQ0qCI7gcU0f3LRZ8/f74xH4UKneiEEBIodKL7gZ0A04muX0RnYVHdUEQnhGQJjBsoohNSGuuvv74sXrxY6urc17Ii+enSpYuJKF25ciUPkydO9Lq6uqB3DzgtLHrNNdfIP/7xjwbf69u3r/zpT39q8L2XXnpJbrnlFpOFtPnmm8s555xjbiZCiBsqysukdQXX4BpSZ8qJlpm/r/mv63NkM9GJbhjn4gdwVLRt29Z1M0gB7ESYdSAIIVkAO9gY50JI6U706upqWbp0KedLyp3oAKJsz549XTeHRBDRASJdunfvLiHiVET/6KOPTJGuSy+9tP57jSeozz33nIwZM0bOOOMMOeigg+Tmm2+WHXbYQd566y1p166dg1YTQsaN7m++SEMHJB7ueNCj4ruG9pz816WMc/EAOtH9caJ37NjRdTNIAWpqaoyAThGdEJIF6EQnpGUiOkBxUZqO9EIR3S/atm0r7du3DzoX3amIDjp16iTf/e538/78/PPPl/3220+uuOIK8/8Q1LG68dvf/tYI64QQQtZm2bJlxpXJQaF+4CLDgnJlpfNHMikAM9H1g8VDDYuYhBCSBhTRCWlZnIsV0Xv37s1DqViUxa6bkONBQqNr167y+eefS6g4n2m8/vrrsttuu8mBBx4oN954Y4MAeggLiHLZZ599GtxEENKfeOIJRy0mhBA/8tABRXQ/nOgsKqofjE+Yia4bLBxSRCeEZAXGuRBSOtCVYGJZtGgRD6NisLuQxUX9Ky76WcBO9HLXq+cHH3ywnH766UZIv/7662X06NFmOy6YPXu2cRU1ztLB/8+aNSvv70XRAQhIuV+EEJIlKKL7JaKzqKh+KKLrh050QkiWoBOdkJaJs4h0gROd6AYiOuojEj/o1q2bOV8Yl4eI073j48ePl6qqqvr/33nnnWXw4MHy4IMPGnHdutJzX2MHDNhWXej3XnLJJQm2nBBCdEMR3R+w64pOdP0wzkU/FNEJIVkCc2IWFiWkdCii+yOiv//++66bQYpwoldXV8uCBQvqM+1DwqkTvbE4PmDAAOnVq5cpGppb7GHhwoUNXof/tz9rinPPPVeWLFlS/wVHOyGEZAn0fRBmsU2R6IZOdD+gE10/FNEJIVkC4zxbnJwQUlouOp3o+oEQu3TpUlmxYoXrppCImegg1EgX55nouSDGBQK5deRhG0CXLl1k8uTJDV736quvyrBhw/L+njZt2pgc4NwvQgjJmhMdlbGJfiii+wGd6PqhiE4IyZoTHaJSqFvmCUkaGDOZia4faIKAkS5+0KFDB2OYDrW4aLnLyei1115bH9kCAf28886T5cuXy/7771//uqOPPlruuOOO+lWMf/zjH/Luu++a7xNCCMnvRMcDjOiHhUX1gzEKRAru7NANzhEyTgkhJAtY4xndmYSULqLDeIToCaIXGwnyxRdfuG4KiQDG4nCjz507V0LEWSY6JqLIyIHbvEePHuYAYyDw97//3eSiWy666CKZOnWq9OvXz0S9fPLJJ/LrX/9aRowY4arphBCiHmx54y4cP0CeKQuL6sYu+Ldu3dp1U0gzInpFRQWPESEkE9ixAxfjCSldRK+rq5PFixdL586deRiVAp2wbdu2FNE9oitF9GRWJ1AA1IrkyKOCmF5e3tAcj20ADz30kMyaNcts30BuOt2VhBDSvBP97S/L5erzJ/BQKWbsqH6Mc/EAW8ycIrpuGOdCCMmiiH7zf96Te95d7ro5pJnx3rjR/XmMlAENCiDShSK6/kgXOtH9Ki76xhtvyK+fnCa3PD3ddXOkZuVy/53ouSL50KFDm33dxhtvbL4IIYQ0D7YmturcQ1Z9xZxMzVTX1NKJ7pETnXEuumGcCyEkm3Eu38iqGo73NFNTW+e6CaSAiM7ion5EulBE98uJvmLFCln21RIVz6famrowC4sSQghpOcj1Q32JqnXb8XAqp6Z6tRH+7ESY6IROdD+gE50QkkUn+uqVK103hRAvadOmjYkJoYjuh4jOwqJ+iejgqy/nS2hQRCeEkMBAJvDIkSPlOz37uG4KaYbVK1eYP5mJ7oeITie6biiiE0KyBMS/7bffXjp0WlN0jxBSWi46RXQ/RHSYxFBLiuinU6dO5vnUdr01uz1CgiI6IYQEBmpOHH300UE+tEIV0elE1w0Li/oBioM1rq1DCCGhgv7uJz/5ibTruIHrphDidaQLMtGJfhEdMNLFr+dT+/U7SWhwpkEIIYQ4YvWqNVuw6UTXDTPR/aCmpoYiOiGEEEIiQye6P4VFASNdiGucFxYlhBCSDBXlZdK6gmulmqlexTgXH2Amuh8wzoUQkkU43vPjHBG9Ijqd6PqpqqqS9u3b04nuGRVK9Iiaivj6YIrohBASKONG9zdfRC+vvfaa3PEa41y0w0x0P2CcCyEki3C8R0jL4lyQs71ixQoj1BLdkS6Mc/GLcUr0iMWLF8v618bzu9wvCRBCCCEZ5ZtvvjHxE61bt3bdFFIAZqL7AZ3ohBBCCCnWiQ7oRvcj0oUiOnENRXRCCCHEoYiOPHQUgyW6negVFRXM21YORXRCCCGEFOtEB19++SUPnCdOdOw8JMQVFNEJIYQQR2D7KIuK+uFEb9WqletmkGagiE4IIYSQYujYsaMxs1BE90NEx9xp+fLlrptCMgxFdEIIIcQRFNH9EdEZuaMfiuiEEEIIKQbsNOzQoQPjXDwR0QEjXYhLKKITQgghjuNciP44FzrR/RDRGY1ECCGEkGJz0elE1w9FdKIBiuiEEEKIQxF93XXX5fH3QESnE90PER2OMkIIIYSQYkR0FhbVT5s2bcyugXnz5rluCskwFNEJIYQQR1BE9wPGufgB41wIIYQQUix0ovtXXJQQV1BEJ4QQQhzBTHQ/YJyLHzDOhRBCCCHFsv7665s4l7q6Oh485VBEJ66pdN0AQgghJMtO9CenLZILzp/guimkAMO+/lS269Wex8gDEb2ykkNbQgghhBTnRK+urpZly5ZJ+/Yc72mmS5cuMnnyZPn1k9Pklqenu24OKcDYUf1k3Oj+EhqcaRBCCCEORfSKDdrIqmW1PAeKYZyLHzDOhRBCCCGlONEB3OgU0fU70VesWCFfL1smq2o4f9JMTW2YOzsY50IIIYQ4EvxWrlwprdpU8fgrp7a6moVFPQDbsMvLObQlhBBCSHFOdCuiE/0iOli6eKHrppCMwpkGIYQQ4khEb9u2rbRfvzOPv3Kqq1dJq1atXDeDNENNTQ1FdEIIIYQURbt27cw4b9GiRTxyHojo6623HqwTrptCMgpFdEIIIcQByG6+9tprpXO3jXn8lUMnuh8wzoUQQgghxVJWVlZfXJToBosdV111lWzYvbfrppCMQhGdEEIIcThoJ/qprl5NJ7oHMM6FEEIIIaVGutCJ7gecPxGXsLAoIYQQ4pCK8jJpXcE1bc3UUkT3AjrRCSGEEFIKcKLPmzePB88TOH/y4xyFCEV0QgghxCHjRvc3X0QvP//5kyws6gHMRCeEEEJIqU70999/nwfPEzh/Iq6g9Y0QQgghpACrV6+miO4BjHMhhBBCSKlO9CVLlpgFeUIIyQed6IQQQgghBYTZVatWycNvz5OTJk5QcJzqpA55kObvYW6TLJVNPp0vAwcOdN0MQgghhHjoRMeY76qHJ8sfJi903RxSgLGj+nEXL3EGRXRCCCGEkDzAkYRJVVlFpayqqVV2nCCnk9xMdBabIoQQQkgpIjpY+tUSheM9kktNLce/xB2McyGEEEIIyQME9FatWknVOm15jJRTV1srFRUVrptBCCGEEA9F9NatW0tN9WrXTSGEKIYiOiGEEEJIHiCgX3rppdKlVz8eI+UwE50QQgghpdCmTRsz3vtOz748gISQvFBEJ4QQQghpptgUY0L0wzgXQgghhJRKx44dpbycEhkhxINMdLiHVq5cKZWVlebLUl1dbb5yQceGrTaEEEIIIYSsGUvWcvJLCCGEEEIICVtE/9nPfia//vWv5bTTTpMbbrih/vtjx46V3/3ud2Y7tWXIkCEyefJkRy0lhBBCSNaoKC+T1hUa3El1ppxomfn7mv+SNZTV1VFEJ4QQQkgA4z1S6BwRkmkR/dFHH5Unn3xSBg4c2OTPf/jDH8qDDz6YersIIYQQQsC40f3Nl4bIki+++EK+853vUDBuxJlnPsVjQgghhBDvx3uEEJ04X2KbM2eO/PSnP5V77rlHqqqqCk4aCSGEEEIIaYqamhqK6IQQQgghhJDwRHQI40cccYScccYZMnTo0Lyve+yxx0y15A022ED22Wcf+fDDD1NtJyGEEEII0Q3GlSwIRgghhBBCCAlORL/00kuloqLCiOj5GDRokDz88MOybNkyk4OOAqQ777yzLFq0KO+/QYHSr776qsEXIYQQQggJF4wRKaITQgghhBBCghLRJ02aJDfddJP85je/MaL3ihUrzOQHW3Hxd8vpp58uu+66q3Gi9+nTx8S+LF68WO6///68v3v8+PHSoUOH+q+ePXum9KkIIYQQQogL6EQnhBBCCCGEBCeiv/XWW8ZdPmTIEOnYsaP5euedd+S2224zf4eY3hTrrbee9OjRQ6ZPn573d5977rmyZMmS+q/Zs2cn+EkIIYQQQohrmIlOCCGEEEIICU5EP/HEE43jPPdriy22kLFjx5q/I+alKb788kuZNWuWEdLzAdc6xPbcL0IIIYQQEi6McyGEEEIIIYQEmYneHIh52XPPPeW5556ThQsXyptvvikHHHCArL/++qYgKSGEEEJI0sIs8eM8UUQnhBBCCCGEZEJEh4O8VatWDf7/zDPPlMsuu8wUGD344INNLvprr70mnTp1ctpWQgghhBCia7GjrKzMdVMIIYQQQgghAVIpinjllVfW+t6oUaPMFyGEEEJI2lCU9aeoKHjwjbly1L8muG4OKcDYUf1k3Oj+PEaEEEIIIcQrVInohBBCCCGEFIstSA8/+qqaNYI60UlNLSOSCCGEEEKIf6iKcyGEEEIIIaRUysubLkxPCCGEEEIIIS2BIjohhBBCCPEa1NE5+uijZaNejAkhhBBCCCGExA9FdEIIIYQQ4j0jR46UytatXTeDEEIIIYQQEiAU0QkhhBBCCCGEEEIIIYSQPLCwKCGEEEIICYKK8jJpXUGPiPZzRAghhBBCiG9QRCeEEEIIIUEwbnR/80UIIYQQQgghcUKrDiGEEEIIIYQQQgghhBCSB4rohBBCCCGEEEIIIYQQQkgeKKITQgghhBBCCCGEEEIIIXmgiE4IIYQQQgghhBBCCCGE5IEiOiGEEEIIIYQQQgghhBCSB4rohBBCCCGEEEIIIYQQQkgeKKITQgghhBBCCCGEEEIIIXmgiE4IIYQQQgghhBBCCCGE5KFSMkBdXZ3586uvvpLycq4bEELip7a2VpYuXSpVVVXsZwghicG+hhCSBuxrCCHsZwghIfDVV1810IZbQiZE9IULF5o/e/Xq5bophBBCCCGEEEIIIYQQQlLUhjt06NCi35EJEX2DDTYwf86aNavFB4wQQvKtbvbs2VNmz54t6623Hg8SISQR2NcQQtKAfQ0hhP0MISQElixZIhtvvHG9NtwSMiGi2wgXCOgUtwghSYI+hv0MISRp2NcQQtKAfQ0hhP0MISQEymOI92ZAOCGEEEIIIYQQQgghhBCSB4rohBBCCCGEEEIIIYQQQkiWRfQ2bdrIRRddZP4khBD2M4SQKMRRwT1uOKYhhLCvIYSEAMc0hBDf+pqyOo0zREIIIYQQx2CIVFZW5roZhBBCCCGEEEIckwknOiGEEEJIsVBAJ4QQQgghhBACKKITQgghhBBCCCGEEEIIIXmgiE4IIYQQQgghhBBCCCGE5KFSAmL16tUydepUExbfp08fqaysbDLfdMqUKVJdXS1Dhgxp8jWEEFKIr776Sj766CPp0qWLdO/evcnXoI95/fXXZf3115eBAwfygBJCimb27Nny5ZdfmjHNeuutt9bPa2trTV+EP/v27SutW7fmUSaEFAXGKx988IHpP9DXtGrVqqTXEEJIIZYuXSoffvihfOc735EePXoUfO2bb74py5cvl+9+97s8qISQovj0009lwYIFZrzSoUOHteZWM2fObPA9aMIjR47MnhP9kksuMZ3xEUccIaNGjZLevXvLo48+2uA106ZNk8GDB5uf77333uY1L7/8srM2E0L8Yu7cuXLooYcaser44483/QkGd+ioLRjw/d///Z95zW677WaqQBNCSDE89dRTsuWWW8r3vvc9+fGPfywbbbSRnHbaaUYst9x4443Sq1cv2WuvvcyYBmOgv/zlLzzQhJDI/OpXvzJ9x+GHHy677rqr6VMeeuihol9DCCH5mDdvnhnLQNDC/GmzzTYzgtUnn3zS5Osff/xx2WabbWTHHXfkQSWEROa5556T4cOHm77jJz/5iZk/nXzyyVJTU1P/mj/+8Y+y5557yjnnnFP/BS25GIIQ0XFQ1llnHfn444/lrbfeklmzZpmB3sEHHywrV66sf90hhxwi/fv3N0IYXoOJ5wEHHCArVqxw2n5CiB+g38BC3fz582Xy5MkyZ84c+frrr+X000+vfw1+VlFRIZMmTZKddtrJaXsJIX6Chbm//vWvMn36dHn77bflhRdekFtuuUUeeOCB+tfAof7aa68ZgwCcXRdeeKEZMGJHHiGERKG8vNz0H//973+NMwsCF8wCMAQU8xpCCCk0ptl///3r50/QYsDYsWPXeu3nn39u+phTTz2VB5QQUrRWc88998iMGTPMmAXzpDvvvFPuvvvuBq+DERJzK/s1YcKE7InoEKzOPvtsadu2rfn/srIy496CuLV48eL6LUH4Ov/8883rwQUXXCCfffaZ/Pvf/3bafkKIH8A18YMf/MD0MaBdu3bGKQEx3YIdLnCfd+vWzWFLCSE+AzEci/6WrbbaSjp27Nigr7n44ouNw8Jy4oknGlPBK6+8knp7CSF+AgdW+/bt6/8f86dVq1aZbdDFvIYQQvIBZ+h+++1XP39ad911ZcSIEQ3GNDZ2F4517LwbOnQoDyghpChgdhw0aFD9/2PXy4YbbrhWX4OIOojsMB7h78USVCA4Vhyw0omDdNlll8m4ceNMZjGAgG4nohZsTezatav5GTp2QgiJArLOkeuHzhdbmu+66y4eOEJIrCxbtszsrsOf9913nxkEYnKZD7i7MAHt168fzwQhJDJwlyMjFO5QRLeccMIJJrKl2NcQQkgh3njjDVNX6p133pF7771XfvOb3zT4+RVXXGHGMWeccYaJXCCEkGKBkRp9DXbLPfjgg2bR7uijj27wGui/hx12mCxZssSkklx//fUF51hBi+j//Oc/zUQT+VqdO3c2kS65255RlKtxIZxOnTqZnxFCSFSuvPJK08+gyBayiLfbbjsePEJIrMAQAAfookWLjHh16aWXmmJcTQGhHdufR48eLdtvvz3PBCEkMtjG/Kc//clsg8Zc6cgjjyzpNYQQUohrr73WRNDB/Ym6UblFQ1Gn7oYbbjDil3WsE0JIKTUYMH9CIgkMAKhVB+O0BSkCiAG3RgD0O0cddZTZARy1uGgQcS4WOM9feuklM/FE7hYKiNrMLYjnTWWff/PNN6bSPCGERAVZxcjYsjtf0N8QQkicDBw40OT0vffee/LMM8+YzHPkojc1jtlnn32MewtGAkIIKQZEQWH+hMU6REmNGTPGTDyLfQ0hhBQCWcWYP2HuBAcoxi4WmB/hBEWyAMY+qMMA8PfGUQyEEJIPFDBGv/Huu++acQt2z1133XX1P//+97/fYCcdattBQIe+E5WgRHQLVi/PPPNMY+FHhVaAA9U4vw/ZoVip2HjjjR22lhDiKx06dDBbmiFwQcgihJAkQBQdXOaNC9/AHIBJKMYyEydONLvwCCGk1PkTYhQwX3r66adLfg0hhBQCNRawMDdp0iSz2w5Aj8H/w0GKLyto4e/PP/88DyghpGg233xz2X333ZstHIoI8GIW64IQ0ZuqDv/RRx/Vx7WAnXbayTjOH3nkkfrXYMKJXONdd901xdYSQkLrazAYbNOmjZM2EULC72tqa2vN1kM7pskV0DHow3gmX9QLIYTkyw1tDPoZ9De2r4nyGkIIKWX+tM4660jbtm3N/8OQBPeo/TrvvPPM9/H3Qw45hAeYEFJ0X4NdutOnT28wXmn8mvnz55saVChCmqlMdHSu11xzjQmH79mzp+mUr7rqKiOcI9IFbLDBBvKLX/xCfv7zn5uBHwLm8f/I9Bs8eLDrj0AI8YBzzz3X9B/f+973pF27dvLiiy/K1VdfbQoZl5f/b00SW4fwOrgrIHShj8Ii3rbbbuu0/YQQP0CuObY2b7HFFmawhyxibHG+++6761/zox/9SF555RX5/e9/b7Y9263PvXv3NoXTCSGkEK+++qpccsklcsQRR5gdu+hjMH9CJugee+wR+TWEEFKIiy++2BgXsaMOxiM4ztGPQChnrC4hJC6g0SBmd+jQoSYl4C9/+YtMmTJFbrvttvrX7LLLLrLffvvJsGHDTEoJ+iIYkU4++eTI71NWB3k+ADCR/MMf/mBWGjbaaCOT1YcJaGXl/9YJ8FHvuusu+dvf/ibV1dUmD2fs2LFrFRslhJCmQAQU8vwee+wxk+UHsQoLcY0Li2KQuHLlygbfwwroww8/zANLCGmWhQsXys0332wELOxygTvipJNOalAYJ7cgVy54XW5hdUIIycfkyZPljjvuMAYkbGeG+Qi5xLlzoyivIYSQfMBYdO+998qjjz5qDEZYkMPC3I477pj33yB+4fLLLzdGJEIIiQL6F8yfoA1jjDJkyBAzL+revXuTr4GxGiZHCOj4e+ZEdEIIIYQQQgghhBBCCCEkboLIRCeEEEIIIYQQQgghhBBCkoAiOiGEEEIIIYQQQgghhBCSB4rohBBCCCGEEEIIIYQQQkgeKKITQgghhBBCCCGEEEIIIXmgiE4IIYQQQgghhBBCCCGE5IEiOiGEEEIIIYQQQgghhBCSB4rohBBCCCGEEEIIIYQQQkgeKKITQgghhDSirq6Ox4Q0yX333Sfz5s3L+/9J8Le//U3mzp1b1L9Jo12F+Oqrr+Shhx6SEIh6/FesWCEPPPCA1NbWptIuQgghhBCSHhTRCSGEEEJIarzwwgtNCrwQKfF97QLkoYceKu+8807e/0+Cn/zkJ/LGG2/kXfDBcfviiy8KtjNtLrroIpk4caKEQKHjn0tVVZXcdtttcscdd6TSLkIIIYQQkh4U0QkhhBBCGlFWVsZjkhDXXHONEXh/8YtfNPg+REp8f9WqVV4d+4MPPlg22mgjZ+9fU1NjjtuUKVPUtOvTTz81YvI555wjWePcc881Cwi+XceEEEIIIaQwlc38nBBCCCGEkFjp06eP3H333fLzn/9cNt9884LxGC+++KIsXbpUttxyS+ndu3eDn//nP/+Rnj17SufOneWVV16Rjh07ysiRI+XBBx+UXXfd1fy79957T77zne/INttsY/7NBx98IFOnTpX+/fvL4MGDG/y+hx9+WL755hspLy83v3fYsGHGXVyI/fbbTzbccMP6/4eTHm1ZsGCBbLbZZuaz5vL111/LSy+9ZETWLbbYQnr06LHW75w5c6a89dZb0qtXL/OaQvz97383f8L1/fnnn8t6660ne+65Z4N2VVdXl3xMorY5l9tvv1123nnnBq+L47jg3EyaNMlcFzjP66+/foOf43fj5zh/22+/vbkeLLnHYPny5fLuu++aY7D11lsXffwLfZYxY8aYRThE2RxyyCEFjxMhhBBCCPEHiuiEEEIIISRVtt12WyM+wo3+2GOPNfmat99+24jBEIW7d+9uBNaf/exnctlll9W/5rzzzpN27drJJ598YsROCLcQ2+HM3mmnnUxkzCabbCLPPPOMHHTQQbLuuuvK008/bURPiM7jx4+X008/vf73TZgwQRYvXmzc3XB2Q6z917/+JYMGDcr7WfBeTzzxhHTp0kW+/PJL0wYIwZtuuqn5HfgMN9xwg3ntk08+KYcddpgRqyHw4jNhIeGCCy6o/32/+93v5NRTT5XtttvO5IrjdRCA84E225icadOmmWOF98xtFz5HqcckSpsb8+ijj5r3s8RxXLBgcvjhhxvhe+ONN5axY8fKb37zGyNagz//+c/y05/+VLbaaitzvLBQgIWafffd1/zcHoO99trLHKcBAwaYBZrdd99d/vKXv0Q+/s19Fgj4+DmOAUV0QgghhJCAqCOEEEIIISQl9t1337qDDz647r333qurqKioe/rpp833//nPf6Kaa90333xTV1tbW7fNNtvUHXTQQXU1NTXm53hdWVlZ3QsvvFD/u4YPH17XpUuXus8//7z+e0uXLjW/50c/+lFddXW1+d5f//pX871DDz20/vf94Q9/qGvXrl39a5rihBNOqNtrr70afA+/54knnmjy/2+++ea6TTfdtMHvfOihh8yf8+fPr1tvvfXq/v73v9f/bOrUqXVt27ate+mll8z/43Osu+66pm2WU045xbwHjk9TrF692vzcHsem2lXqMYnS5qbaU15eXvfII4/Uf6+lx2XevHmmXRdccEH9axYtWlT3zDPP1B83/BzvY7n44ovrOnfuXLd48eIGx2C//fYzbQTvvPOO+d4bb7wR+fgX+iyWyy+/vG7AgAFNHh9CCCGEEOInzEQnhBBCCCGpg9gQFGw8++yzTXHMXD7++GN57bXXTKY2nL3ge9/7nuywww5y//33N3gt3NRwWzfmuOOOk4qKCvN3uIrB8ccfX//78L1ly5bJZ5991uDfffjhh8Ydj2KdHTp0kFdffTXyZ1pnnXVkyZIlxhlv+eEPf2j+RLwH2gNX81//+lfzhcgQxJbAFQ7++c9/Gmf9kUceWf/vG2fHt4Rij0mUNjcGTm3EneRGrbT0uPztb38zbbzwwgvr/z0c4nB8A7i+W7VqJSeeeGL9z3FdwUn+1FNPNWgfPm9l5ZrNuNgNgSggRNlEPf6FPosFnx1RL4QQQgghJBwY50IIIYQQQpxwySWXmFgNCKeIFcnNpAZ9+/Zt8HrEkNifWbp27drk784Vcdu0aZP3e4j5ABBxUYwT0SIjRowwIu38+fPliy++iPx5jjjiCHn55ZdNtEy/fv1M1Mgpp5xiPgdEV2RlI5c7F8TPIIIFzJo1y0SVWFEbQEy2om9LKfaYRGlzY9q3b2/+RO54nMcFefitW7du8j1xTSC/3C4QWLEb10bj62WDDTZo8P/4zPbzRjn+hT6LBZ/dHgdCCCGEEBIGFNEJIYQQQogTIFAifxrZ5ldffXX99+EOtq5muMEt+P/cIp4AAmwcPPLIIyYbHC74Tp06me/BjZ7Pcd0UEHmRqX3TTTeZApfI7EZGNzK4ke0OkRe/Mx9430WLFjX4HpzhhTLRkyRKmxsD8Rri94wZM2I7LljQWLhwYd6f43rBtdEYHEt7LUUhyvEv9FmQ1w7w2QcOHBj5fQkhhBBCiH4Y50IIIYQQQpxx7rnnGgEUYqQF7nQIkoj6sEBEhcj93e9+N5F2fP7550ZwtQI6aOyObo45c+aYP6uqqkz8zG9/+1sT/fHBBx/I97//feNsz/1MAC5oKwAjrgYi/jvvvFP/88avbwxc0rlu6jiJ0uamGD16tCnaGddx2XXXXc3vQOHTXPDvAK6J2bNny+uvv17/s8cff9w4wkeOHBn580Y5/oU+iwWf3RY8JYQQQgghYUAnOiGEEEIIcQZcxnCin3XWWfXfg0AJZzryq5Et3bNnT7n99ttNjnpuXnWcQMw988wzzXtCeIUI2zhPuzkQS/PAAw/IvvvuW78IgJgPOJWRtY3Pedhhh5n4D3wWCLYQ6uHCRszI1ltvbTLef/CDH8jPf/5zk+mNxYXcmJKmwL/79a9/bY4Vfs+ee+4pcTBs2LBm29wUOIb4DF9//bWJ6WnpccHrzjjjDPPvTzvtNHM94PzssssuZicDfn7UUUfJPvvsY84hnONXXHGFeW3//v0jf94ox7/QZwHIV3///fcTu04JIYQQQogb6EQnhBBCCCGpseOOO8r222/f4HsQQiE6IpPcCpb4/yeeeMLEaaC4J4piwomeK2hC+IbomgsKTOL35MZ4wKmN7+Xmf7dt29Z8z2ZXI28dWdf4/vPPP2+KbE6YMMG8Jhf8/0YbbdTk/59++uly3XXXGQf1Cy+8YApfou0QisHll18u//nPf0whVfwc7w13NcRqy913322KYr755ptGDMbrkMOdL4McQGyGiJsr/Oe2q9RjErXNjYEzfPjw4fKHP/whtuNy7bXXGgEbcSsoPIqitLhuLHfccYdZeJkyZYp89NFHxiF+zTXXFLwuAMTwPn36RD7+zX0WxLzgWm2q2C0hhBBCCPGXsjqMVgkhhBBCCCEkJt577z0jbF9//fWZOaaIoDnxxBONyJ7PpU8IIYQQQvyEIjohhBBCCCGEEEIIIYQQkgfGuRBCCCGEEEIIIYQQQggheaCITgghhBBCCCGEEEIIIYTkgSI6IYQQQgghhBBCCCGEEJIHiuiEEEIIIYQQQgghhBBCSB4oohNCCCGEEEIIIYQQQggheaCITgghhBBCCCGEEEIIIYTkgSI6IYQQQgghhBBCCCGEEJIHiuiEEEIIIYQQQgghhBBCSB4oohNCCCGEEEIIIYQQQggheaCITgghhBBCCCGEEEIIIYTkgSI6IYQQQgghhBBCCCGEECJN8/+3dc62jGaa5QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1500x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import ConnectionPatch\n",
+    "\n",
+    "SELECTED_SAMPLE_INDEX = 110 \n",
+    "\n",
+    "selected_sample = samples[SELECTED_SAMPLE_INDEX]\n",
+    "selected_result = all_results[SELECTED_SAMPLE_INDEX]\n",
+    "\n",
+    "ref_events = selected_result[\"ref_events_normalized\"]\n",
+    "response_events = selected_result[\"response_events_normalized\"]\n",
+    "event_details = selected_result[\"event_details\"]\n",
+    "\n",
+    "print(selected_result[\"composer\"], selected_result[\"title\"])\n",
+    "\n",
+    "def plot_alignment(ref_events, response_events, event_details,\n",
+    "                   ref_start, ref_end):\n",
+    "\n",
+    "    # Select operations whose reference event falls in the chosen segment.\n",
+    "    selected_details = []\n",
+    "\n",
+    "    for detail in event_details:\n",
+    "        reference_index = detail.get(\"reference_index\")\n",
+    "\n",
+    "        if reference_index is None:\n",
+    "            continue\n",
+    "\n",
+    "        ref_event = ref_events[reference_index - 1]\n",
+    "        ref_onset = ref_event[\"event_start\"]\n",
+    "\n",
+    "        if ref_start <= ref_onset <= ref_end:\n",
+    "            selected_details.append(detail)\n",
+    "\n",
+    "    # Find the corresponding response time range.\n",
+    "    response_onsets = []\n",
+    "\n",
+    "    for detail in selected_details:\n",
+    "        response_index = detail.get(\"response_index\")\n",
+    "\n",
+    "        if response_index is not None:\n",
+    "            response_event = response_events[response_index - 1]\n",
+    "            response_onsets.append(response_event[\"event_start\"])\n",
+    "\n",
+    "    if response_onsets:\n",
+    "        response_start = min(response_onsets) - 0.5\n",
+    "        response_end = max(response_onsets) + 0.5\n",
+    "    else:\n",
+    "        response_start = ref_start\n",
+    "        response_end = ref_end\n",
+    "\n",
+    "    fig, (ax_response, ax_ref) = plt.subplots(\n",
+    "        2,\n",
+    "        1,\n",
+    "        figsize=(15, 8),\n",
+    "        height_ratios=[1, 1],\n",
+    "    )\n",
+    "\n",
+    "    # ------------------------------\n",
+    "    # Top: response piano roll\n",
+    "    # ------------------------------\n",
+    "    for event in response_events:\n",
+    "        event_start = event[\"event_start\"]\n",
+    "\n",
+    "        if response_start <= event_start <= response_end:\n",
+    "            for note in event[\"notes\"]:\n",
+    "                ax_response.hlines(\n",
+    "                    y=note[\"pitch\"],\n",
+    "                    xmin=note[\"start\"],\n",
+    "                    xmax=note[\"start\"] + note[\"duration\"],\n",
+    "                    linewidth=5,\n",
+    "                )\n",
+    "\n",
+    "    ax_response.set_xlim(response_start, response_end)\n",
+    "    ax_response.set_ylabel(\"Response MIDI pitch\")\n",
+    "    ax_response.set_title(\"Performance\")\n",
+    "\n",
+    "    # ------------------------------\n",
+    "    # Bottom: reference piano roll\n",
+    "    # ------------------------------\n",
+    "    for event in ref_events:\n",
+    "        event_start = event[\"event_start\"]\n",
+    "\n",
+    "        if ref_start <= event_start <= ref_end:\n",
+    "            for note in event[\"notes\"]:\n",
+    "                ax_ref.hlines(\n",
+    "                    y=note[\"pitch\"],\n",
+    "                    xmin=note[\"start\"],\n",
+    "                    xmax=note[\"start\"] + note[\"duration\"],\n",
+    "                    linewidth=5,\n",
+    "                )\n",
+    "\n",
+    "    ax_ref.set_xlim(ref_start, ref_end)\n",
+    "    ax_ref.set_xlabel(\"Normalised time (seconds)\")\n",
+    "    ax_ref.set_ylabel(\"Reference MIDI pitch\")\n",
+    "    ax_ref.set_title(\"Reference\")\n",
+    "\n",
+    "    # ------------------------------\n",
+    "    # Alignment lines\n",
+    "    # ------------------------------\n",
+    "    for detail in selected_details:\n",
+    "        operation = detail[\"operation_type\"]\n",
+    "\n",
+    "        reference_index = detail.get(\"reference_index\")\n",
+    "        response_index = detail.get(\"response_index\")\n",
+    "\n",
+    "        if reference_index is None or response_index is None:\n",
+    "            continue\n",
+    "\n",
+    "        ref_event = ref_events[reference_index - 1]\n",
+    "        response_event = response_events[response_index - 1]\n",
+    "\n",
+    "        ref_x = ref_event[\"event_start\"]\n",
+    "        response_x = response_event[\"event_start\"]\n",
+    "\n",
+    "        # Use the average pitch as the event centre.\n",
+    "        ref_y = sum(\n",
+    "            note[\"pitch\"] for note in ref_event[\"notes\"]\n",
+    "        ) / len(ref_event[\"notes\"])\n",
+    "\n",
+    "        response_y = sum(\n",
+    "            note[\"pitch\"] for note in response_event[\"notes\"]\n",
+    "        ) / len(response_event[\"notes\"])\n",
+    "\n",
+    "        if operation == \"match\":\n",
+    "            line_style = \"-\"\n",
+    "            line_width = 1.0\n",
+    "        else:\n",
+    "            line_style = \"--\"\n",
+    "            line_width = 2.0\n",
+    "\n",
+    "        connector = ConnectionPatch(\n",
+    "            xyA=(response_x, response_y),\n",
+    "            coordsA=ax_response.transData,\n",
+    "            xyB=(ref_x, ref_y),\n",
+    "            coordsB=ax_ref.transData,\n",
+    "            linestyle=line_style,\n",
+    "            linewidth=line_width,\n",
+    "            alpha=0.6,\n",
+    "        )\n",
+    "\n",
+    "        fig.add_artist(connector)\n",
+    "\n",
+    "        # Mark missing and extra events.\n",
+    "    for detail in selected_details:\n",
+    "        operation = detail[\"operation_type\"]\n",
+    "\n",
+    "        if operation == \"missing\":\n",
+    "            reference_index = detail[\"reference_index\"]\n",
+    "            ref_event = ref_events[reference_index - 1]\n",
+    "\n",
+    "            ref_y = sum(\n",
+    "                note[\"pitch\"] for note in ref_event[\"notes\"]\n",
+    "            ) / len(ref_event[\"notes\"])\n",
+    "\n",
+    "            ax_ref.text(\n",
+    "                ref_event[\"event_start\"],\n",
+    "                ref_y + 1,\n",
+    "                \"missing\",\n",
+    "                fontsize=8,\n",
+    "                rotation=45,\n",
+    "            )\n",
+    "\n",
+    "        elif operation == \"extra\":\n",
+    "            response_index = detail[\"response_index\"]\n",
+    "            response_event = response_events[response_index - 1]\n",
+    "\n",
+    "            response_y = sum(\n",
+    "                note[\"pitch\"] for note in response_event[\"notes\"]\n",
+    "            ) / len(response_event[\"notes\"])\n",
+    "\n",
+    "            ax_response.text(\n",
+    "                response_event[\"event_start\"],\n",
+    "                response_y + 1,\n",
+    "                \"extra\",\n",
+    "                fontsize=8,\n",
+    "                rotation=45,\n",
+    "            )\n",
+    "    \n",
+    "    ax_response.grid(axis=\"x\", alpha=0.3)\n",
+    "    ax_ref.grid(axis=\"x\", alpha=0.3)\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "plot_alignment(\n",
+    "    ref_events,\n",
+    "    response_events,\n",
+    "    event_details,\n",
+    "    ref_start=30.0,\n",
+    "    ref_end=35.0,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>status</th>\n",
+       "      <th>label</th>\n",
+       "      <th>onset</th>\n",
+       "      <th>gt_pitch</th>\n",
+       "      <th>pipeline_pitch</th>\n",
+       "      <th>onset_difference</th>\n",
+       "      <th>possible_cause</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>10.433771</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>11.362190</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>20.750020</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>24.355792</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>28.185924</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>32.012851</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>40.498970</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>41.455168</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>44.126110</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>onset tolerance boundary</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>47.995772</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>56.575908</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>onset tolerance boundary</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>58.241508</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>59.351552</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>61.246853</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>63.231898</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>67.107970</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>onset tolerance boundary</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>69.113314</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>onset tolerance boundary</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>68.101560</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>70.140024</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>70.113314</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>74.020369</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>80.917812</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>81.835548</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>85.641107</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>88.677435</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>97.756503</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>99.499026</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>100.685993</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>107.358008</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>FN</td>\n",
+       "      <td>paired</td>\n",
+       "      <td>112.917842</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unmatched error</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    status   label       onset  gt_pitch  pipeline_pitch  onset_difference  \\\n",
+       "19      FN  paired   10.433771      69.0             NaN               NaN   \n",
+       "26      FN  paired   11.362190      70.0             NaN               NaN   \n",
+       "50      FN  paired   20.750020      72.0             NaN               NaN   \n",
+       "55      FN  paired   24.355792      72.0             NaN               NaN   \n",
+       "74      FN  paired   28.185924      77.0             NaN               NaN   \n",
+       "85      FN  paired   32.012851      75.0             NaN               NaN   \n",
+       "100     FN  paired   40.498970      65.0             NaN               NaN   \n",
+       "109     FN  paired   41.455168      67.0             NaN               NaN   \n",
+       "117     FN  paired   44.126110      74.0             NaN               NaN   \n",
+       "127     FN  paired   47.995772      74.0             NaN               NaN   \n",
+       "155     FN  paired   56.575908      68.0             NaN               NaN   \n",
+       "156     FN  paired   58.241508      68.0             NaN               NaN   \n",
+       "163     FN  paired   59.351552      76.0             NaN               NaN   \n",
+       "172     FN  paired   61.246853      76.0             NaN               NaN   \n",
+       "176     FN  paired   63.231898      69.0             NaN               NaN   \n",
+       "190     FN  paired   67.107970      69.0             NaN               NaN   \n",
+       "195     FN  paired   69.113314      66.0             NaN               NaN   \n",
+       "197     FN  paired   68.101560      76.0             NaN               NaN   \n",
+       "201     FN  paired   70.140024      62.0             NaN               NaN   \n",
+       "202     FN  paired   70.113314      66.0             NaN               NaN   \n",
+       "218     FN  paired   74.020369      72.0             NaN               NaN   \n",
+       "236     FN  paired   80.917812      75.0             NaN               NaN   \n",
+       "245     FN  paired   81.835548      68.0             NaN               NaN   \n",
+       "251     FN  paired   85.641107      65.0             NaN               NaN   \n",
+       "261     FN  paired   88.677435      63.0             NaN               NaN   \n",
+       "293     FN  paired   97.756503      66.0             NaN               NaN   \n",
+       "295     FN  paired   99.499026      69.0             NaN               NaN   \n",
+       "302     FN  paired  100.685993      70.0             NaN               NaN   \n",
+       "332     FN  paired  107.358008      77.0             NaN               NaN   \n",
+       "342     FN  paired  112.917842      76.0             NaN               NaN   \n",
+       "\n",
+       "               possible_cause  \n",
+       "19            unmatched error  \n",
+       "26            unmatched error  \n",
+       "50            unmatched error  \n",
+       "55            unmatched error  \n",
+       "74            unmatched error  \n",
+       "85            unmatched error  \n",
+       "100           unmatched error  \n",
+       "109           unmatched error  \n",
+       "117  onset tolerance boundary  \n",
+       "127           unmatched error  \n",
+       "155  onset tolerance boundary  \n",
+       "156           unmatched error  \n",
+       "163           unmatched error  \n",
+       "172           unmatched error  \n",
+       "176           unmatched error  \n",
+       "190  onset tolerance boundary  \n",
+       "195  onset tolerance boundary  \n",
+       "197           unmatched error  \n",
+       "201           unmatched error  \n",
+       "202           unmatched error  \n",
+       "218           unmatched error  \n",
+       "236           unmatched error  \n",
+       "245           unmatched error  \n",
+       "251           unmatched error  \n",
+       "261           unmatched error  \n",
+       "293           unmatched error  \n",
+       "295           unmatched error  \n",
+       "302           unmatched error  \n",
+       "332           unmatched error  \n",
+       "342           unmatched error  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "errors = comparison_df[\n",
+    "    comparison_df[\"status\"] != \"TP\"\n",
+    "].copy()\n",
+    "\n",
+    "error_categories = []\n",
+    "\n",
+    "for index, error in errors.iterrows():\n",
+    "    category = \"unmatched error\"\n",
+    "\n",
+    "    if error[\"status\"] == \"FP\":\n",
+    "        opposite_status = \"FN\"\n",
+    "    else:\n",
+    "        opposite_status = \"FP\"\n",
+    "\n",
+    "    possible_matches = errors[\n",
+    "        (errors[\"status\"] == opposite_status)\n",
+    "        & (errors[\"label\"] == error[\"label\"])\n",
+    "    ]\n",
+    "\n",
+    "    # For insertion, pitch must also agree.\n",
+    "    if error[\"label\"] == \"insertion\":\n",
+    "        if error[\"status\"] == \"FP\":\n",
+    "            pitch = error[\"pipeline_pitch\"]\n",
+    "            possible_matches = possible_matches[\n",
+    "                possible_matches[\"gt_pitch\"] == pitch\n",
+    "            ]\n",
+    "        else:\n",
+    "            pitch = error[\"gt_pitch\"]\n",
+    "            possible_matches = possible_matches[\n",
+    "                possible_matches[\"pipeline_pitch\"] == pitch\n",
+    "            ]\n",
+    "\n",
+    "    nearest_difference = None\n",
+    "\n",
+    "    for _, other_error in possible_matches.iterrows():\n",
+    "        difference = abs(error[\"onset\"] - other_error[\"onset\"])\n",
+    "\n",
+    "        if nearest_difference is None or difference < nearest_difference:\n",
+    "            nearest_difference = difference\n",
+    "\n",
+    "    if nearest_difference is not None:\n",
+    "        if (\n",
+    "            nearest_difference > ONSET_TOLERANCE\n",
+    "            and nearest_difference <= 0.15\n",
+    "        ):\n",
+    "            category = \"onset tolerance boundary\"\n",
+    "\n",
+    "    error_categories.append(category)\n",
+    "\n",
+    "\n",
+    "errors[\"possible_cause\"] = error_categories\n",
+    "\n",
+    "display(errors.head(30))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "possible_cause\n",
+      "unmatched error             84\n",
+      "onset tolerance boundary    20\n",
+      "chord context               18\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "for index, error in errors.iterrows():\n",
+    "    error_onset = error[\"onset\"]\n",
+    "\n",
+    "    near_chord = False\n",
+    "\n",
+    "    for event in response_events:\n",
+    "        if event[\"event_type\"] != \"chord\":\n",
+    "            continue\n",
+    "\n",
+    "        difference = abs(\n",
+    "            event[\"event_start\"]\n",
+    "            + selected_result[\"response_onset_offset\"]\n",
+    "            - error_onset\n",
+    "        )\n",
+    "\n",
+    "        if difference <= 0.10:\n",
+    "            near_chord = True\n",
+    "            break\n",
     "\n",
-    "    print(\"Mean Precision:\", round(df_eval[\"Precision\"].mean(), 4))\n",
-    "    print(\"Mean Recall   :\", round(df_eval[\"Recall\"].mean(), 4))\n",
-    "    print(\"Mean F1       :\", round(df_eval[\"F1\"].mean(), 4))"
+    "    if near_chord:\n",
+    "        if errors.loc[index, \"possible_cause\"] == \"unmatched error\":\n",
+    "            errors.loc[index, \"possible_cause\"] = \"chord context\"\n",
+    "print(errors[\"possible_cause\"].value_counts())"
    ]
   },
   {

From 43f63dce1ea71f212694242f9f6d35726498518b Mon Sep 17 00:00:00 2001
From: ada-3e212e610b <z2720629308@gmail.com>
Date: Sun, 12 Jul 2026 23:37:29 +0100
Subject: [PATCH 15/15] add interpretation and visualisation

---
 notebooks/Alignment_on_nASAPdataset.ipynb | 2515 ++++-----------------
 1 file changed, 436 insertions(+), 2079 deletions(-)

diff --git a/notebooks/Alignment_on_nASAPdataset.ipynb b/notebooks/Alignment_on_nASAPdataset.ipynb
index a84db3c..14bfdfb 100644
--- a/notebooks/Alignment_on_nASAPdataset.ipynb
+++ b/notebooks/Alignment_on_nASAPdataset.ipynb
@@ -8,10 +8,15 @@
     "\n",
     "This notebook evaluates the edit-distance (ED) alignment algorithm in `compareMusic` on real piano performances from the [(n)ASAP dataset](https://github.com/CPJKU/asap-dataset) (Peter, 2023).\n",
     "\n",
-    "(n)ASAP is a dataset of aligned musical scores and performances built by extending the ASAP dataset with note-level annotations. The ASAP contains 236 distinct musical scores and 1067 performances of Western classical piano music from 15 different composers, the piece directory contains the XML and MIDI score, plus all of the performances of a specific piece, including: \n",
-    "- `midi_score`: the MIDI score (what should be played) — used as **reference**\n",
-    "- `midi_performance`: what the pianist actually played — used as **response**\n",
-    "- `note_alignments.tsv`: official note-level alignment annotations — used as **ground truth**"
+    "(n)ASAP is a dataset of aligned musical scores and performances built by extending the ASAP dataset with note-level annotations. The ASAP contains 236 distinct musical scores and 1067 performances of Western classical piano music from 15 different composers, the piece directory contains the XML and MIDI score, plus all of the performances of a specific piece.\n",
+    "\n",
+    "The `note_alignments.tsv` contains official note-level alignment annotations, which can be used as **ground truth**, it uses:\n",
+    "- MusicXML score notes (`xml_id`) as reference\n",
+    "- Performance MIDI notes (`midi_id`) as performance\n",
+    "\n",
+    "Therefore, this notebook deliberately loads:\n",
+    "- `xml_score.musicxml` as the pipeline reference\n",
+    "- `midi_performance` as the pipeline response"
    ]
   },
   {
@@ -40,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -80,13 +85,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "import csv\n",
     "\n",
     "def load_ground_truth(tsv_path):\n",
+    "    \"\"\"\n",
+    "    Read a note_alignment.tsv file from the CPJKU nASAP dataset.\n",
+    "    Returns one dictionary per TSV row. XML and MIDI identifiers are retained\n",
+    "    so that individual alignment errors can be traced later.\n",
+    "        Keys:\n",
+    "            label -> 'paired', 'insertion', or 'deletion'\n",
+    "            xml_id -> score-note identifier, or None for insertions\n",
+    "            midi_id -> performance-note identifier, or None for deletions\n",
+    "            onset -> performance onset in seconds, or None for deletions\n",
+    "            pitch -> performance MIDI pitch, or None for deletions\n",
+    "    \"\"\"\n",
     "    rows = []\n",
     "\n",
     "    with open(tsv_path, \"r\", encoding=\"utf-8\") as f:\n",
@@ -130,37 +146,104 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Load Score (reference)/Performance (response) pairs"
+    "## Load MusicXML (reference) / MIDI Performance (response) pairs"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Helper functions for format conversion: These functions convert a MIDI file into the `{pitch, start, duration}` format used by `compare_MIDI.py`."
+    "The score reference is loaded from `xml_score.musicxml` with Partitura (https://partitura.readthedocs.io/en/latest/).\n",
+    "\n",
+    "The score onset and duration values are kept in symbolic beat units. This is suitable for the current pipeline because it estimates a global timing transformation between the reference and performance. The response remains in seconds because the ground-truth TSV stores performance onsets in seconds.\n",
+    "\n",
+    "\n",
+    "Helper functions for format conversion:\n",
+    "\n",
+    "- `xml_score_to_notes` converts MusicXML score notes into the `{pitch, start, duration}` structure used by `compareMusic`.\n",
+    "- `midi_file_to_notes` converts the performed MIDI into the same structure."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import partitura as pt\n",
     "import pretty_midi\n",
     "\n",
+    "def xml_score_to_notes(xml_path):\n",
+    "    \"\"\"\n",
+    "    Parse a MusicXML score into the format used by compareMusic.\n",
+    "\n",
+    "    Partitura's note array provides score pitch and symbolic timing.\n",
+    "    Beat-based timing is preferred because it remains meaningful even when\n",
+    "    the MusicXML contains tempo-independent symbolic notation.\n",
+    "    \"\"\"\n",
+    "    score = pt.load_score(xml_path)\n",
+    "    note_array = score.note_array()\n",
+    "\n",
+    "    field_names = set(note_array.dtype.names or [])\n",
+    "\n",
+    "    if \"onset_beat\" in field_names:\n",
+    "        onset_field = \"onset_beat\"\n",
+    "    elif \"onset_quarter\" in field_names:\n",
+    "        onset_field = \"onset_quarter\"\n",
+    "    elif \"onset_div\" in field_names:\n",
+    "        onset_field = \"onset_div\"\n",
+    "    else:\n",
+    "        raise KeyError(\n",
+    "            \"Could not find an onset field in the Partitura score note array.\"\n",
+    "        )\n",
+    "\n",
+    "    if \"duration_beat\" in field_names:\n",
+    "        duration_field = \"duration_beat\"\n",
+    "    elif \"duration_quarter\" in field_names:\n",
+    "        duration_field = \"duration_quarter\"\n",
+    "    elif \"duration_div\" in field_names:\n",
+    "        duration_field = \"duration_div\"\n",
+    "    else:\n",
+    "        raise KeyError(\n",
+    "            \"Could not find a duration field in the Partitura score note array.\"\n",
+    "        )\n",
+    "\n",
+    "    if \"pitch\" not in field_names:\n",
+    "        raise KeyError(\"The Partitura score note array has no 'pitch' field.\")\n",
+    "\n",
+    "    notes = []\n",
+    "\n",
+    "    for row in note_array:\n",
+    "        note = {\n",
+    "            \"pitch\": int(row[\"pitch\"]),\n",
+    "            \"start\": float(row[onset_field]),\n",
+    "            \"duration\": float(row[duration_field]),\n",
+    "        }\n",
+    "\n",
+    "        # Retain the MusicXML note ID when Partitura supplies it.\n",
+    "        if \"id\" in field_names:\n",
+    "            note[\"xml_id\"] = str(row[\"id\"])\n",
+    "\n",
+    "        notes.append(note)\n",
+    "\n",
+    "    notes.sort(key=lambda note: (note[\"start\"], note[\"pitch\"]))\n",
+    "    return notes\n",
+    "\n",
+    "\n",
     "def midi_file_to_notes(midi_path):\n",
-    "    \"\"\"Parse a MIDI file into the format used by compareMusic.\"\"\"\n",
+    "    \"\"\"Parse a performance MIDI file into the format used by compareMusic.\"\"\"\n",
     "    midi_data = pretty_midi.PrettyMIDI(midi_path)\n",
     "    all_notes = []\n",
     "\n",
     "    for instrument in midi_data.instruments:\n",
     "        if instrument.is_drum:\n",
     "            continue\n",
+    "\n",
     "        for note in instrument.notes:\n",
     "            all_notes.append({\n",
     "                \"pitch\": int(note.pitch),\n",
-    "                # Keep full precision. Rounding here can change tolerance-based\n",
-    "                # evaluation close to the matching boundary.\n",
+    "                # Keep full precision because rounding may change tolerance-based\n",
+    "                # evaluation near a matching boundary.\n",
     "                \"start\": float(note.start),\n",
     "                \"duration\": float(note.end - note.start),\n",
     "            })\n",
@@ -168,9 +251,15 @@
     "    all_notes.sort(key=lambda note: (note[\"start\"], note[\"pitch\"]))\n",
     "    return all_notes\n",
     "\n",
-    "def build_sample(ref_path, response_path, composer, title, metadata_row):\n",
-    "    \"\"\"Build one score/performance sample for compare_performance_ED.\"\"\"\n",
-    "    score_notes = midi_file_to_notes(ref_path)\n",
+    "\n",
+    "def build_sample(xml_path, response_path, composer, title, metadata_row):\n",
+    "    \"\"\"\n",
+    "    Build one MusicXML-score / performance-MIDI sample.\n",
+    "\n",
+    "    The score reference and ground-truth TSV now share the same MusicXML\n",
+    "    score-note representation.\n",
+    "    \"\"\"\n",
+    "    score_notes = xml_score_to_notes(xml_path)\n",
     "    performance_notes = midi_file_to_notes(response_path)\n",
     "\n",
     "    if not score_notes or not performance_notes:\n",
@@ -182,75 +271,75 @@
     "        \"reference\": {\"notes\": score_notes},\n",
     "        \"response\": {\"notes\": performance_notes},\n",
     "        \"metadata_row\": metadata_row,\n",
-    "    }"
+    "    }\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For the (n)ASAP dataset, the `metadata.csv` lists every score/performance pair in the dataset, check that both MIDI files exist on disk."
+    "The nASAP `metadata.csv` gives the path to the MusicXML score, performance MIDI, and note-alignment TSV for each performance. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jz7125/compareMusic/.venv/lib/python3.13/site-packages/pretty_midi/pretty_midi.py:122: RuntimeWarning: Tempo, Key or Time signature change events found on non-zero tracks.  This is not a valid type 0 or type 1 MIDI file.  Tempo, Key or Time Signature may be wrong.\n",
-      "  warnings.warn(\n"
+      "/Users/jz7125/compareMusic/.venv/lib/python3.13/site-packages/partitura/directions.py:526: UserWarning: error parsing \"(   )\" (UnexpectedCharacters)\n",
+      "  warnings.warn('error parsing \"{}\" ({})'.format(string, type(e).__name__))\n",
+      "/Users/jz7125/compareMusic/.venv/lib/python3.13/site-packages/partitura/io/importmusicxml.py:1112: UserWarning: ignoring direction type: metronome {'parentheses': 'no', 'default-x': '-51.17', 'relative-y': '20.00'}\n",
+      "  warnings.warn(\"ignoring direction type: {} {}\".format(dt.tag, dt.attrib))\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded 169 score (reference) /performance (response) pairs.\n"
+      "Loaded 169 MusicXML-score (reference) / MIDI-performance (response) pairs.\n"
      ]
     }
    ],
    "source": [
     "def load_samples(asap_path, composer):\n",
     "    \"\"\"\n",
-    "    Read the ASAP metadata CSV and return a list of sample dicts\n",
-    "    for a specific composer only.\n",
-    "\n",
-    "    Args:\n",
-    "        asap_path: str, path to the cloned ASAP repo\n",
-    "        composer: str, e.g. \"Bach\"\n",
-    "\n",
-    "    Returns:\n",
-    "        list of sample dicts (see build_sample)\n",
+    "    Read metadata.csv and return MusicXML-score / MIDI-performance samples\n",
+    "    for one composer.\n",
     "    \"\"\"\n",
     "    metadata_path = os.path.join(asap_path, \"metadata.csv\")\n",
     "    samples = []\n",
     "\n",
     "    with open(metadata_path, \"r\", encoding=\"utf-8\") as csv_file:\n",
     "        reader = csv.DictReader(csv_file)\n",
+    "\n",
     "        for row in reader:\n",
-    "            if row.get(\"composer\", \"\").strip() == composer:\n",
-    "                ref_path = os.path.join(asap_path, row.get(\"midi_score\", \"\").strip())\n",
-    "                response_path = os.path.join(asap_path, row.get(\"midi_performance\", \"\").strip())\n",
-    "\n",
-    "                if os.path.isfile(ref_path) and os.path.isfile(response_path):\n",
-    "                    sample = build_sample(\n",
-    "                        ref_path, response_path,\n",
-    "                        row.get(\"composer\", \"Unknown\"),\n",
-    "                        row.get(\"title\", \"Unknown\"),\n",
-    "                        dict(row),\n",
-    "                    )\n",
-    "                    if sample is not None:\n",
-    "                        samples.append(sample)\n",
+    "            if row.get(\"composer\", \"\").strip() != composer:\n",
+    "                continue\n",
+    "\n",
+    "            xml_path = os.path.join(asap_path, row.get(\"xml_score\", \"\").strip())\n",
+    "            response_path = os.path.join(asap_path, row.get(\"midi_performance\", \"\").strip())\n",
+    "            tsv_path = os.path.join(asap_path, row.get(\"note_alignments\", \"\").strip())\n",
+    "            \n",
+    "            if os.path.isfile(xml_path) and os.path.isfile(response_path) and os.path.isfile(tsv_path):\n",
+    "                sample = build_sample(\n",
+    "                xml_path,\n",
+    "                response_path,\n",
+    "                row.get(\"composer\", \"Unknown\"),\n",
+    "                row.get(\"title\", \"Unknown\"),\n",
+    "                dict(row),\n",
+    "            )\n",
+    "                if sample is not None:\n",
+    "                    samples.append(sample)\n",
     "\n",
     "    return samples\n",
     "\n",
     "samples = load_samples(ASAP_PATH, \"Bach\")\n",
     "\n",
-    "print(\"Loaded\", len(samples), \"score (reference) /performance (response) pairs.\")"
+    "print(\"Loaded\", len(samples), \"MusicXML-score (reference) / MIDI-performance (response) pairs.\")"
    ]
   },
   {
@@ -266,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -284,12 +373,16 @@
     "    group_notes_into_events,\n",
     ")\n",
     "\n",
+    "\n",
     "def run_alignment_on_samples(samples):\n",
     "    \"\"\"Run compare_performance_ED for every loaded sample.\"\"\"\n",
     "    results = []\n",
     "\n",
-    "    for sample in samples:\n",
-    "        result = compare_performance_ED(sample[\"response\"], sample[\"reference\"])\n",
+    "    for sample_index, sample in enumerate(samples):\n",
+    "        result = compare_performance_ED(\n",
+    "            sample[\"response\"],\n",
+    "            sample[\"reference\"],\n",
+    "        )\n",
     "\n",
     "        response_notes_normalized = normalize_start_times(\n",
     "            sample[\"response\"][\"notes\"]\n",
@@ -298,10 +391,6 @@
     "            response_notes_normalized\n",
     "        )\n",
     "\n",
-    "        # Also rebuild the normalized reference events. convert_pipeline_output\n",
-    "        # needs these to look up how many notes are in a missing (deleted)\n",
-    "        # chord, since the pipeline's event_details does not carry that\n",
-    "        # information for missing/extra events.\n",
     "        ref_notes_normalized = normalize_start_times(\n",
     "            sample[\"reference\"][\"notes\"]\n",
     "        )\n",
@@ -309,11 +398,15 @@
     "            ref_notes_normalized\n",
     "        )\n",
     "\n",
-    "        response_onset_offset = float(sample[\"response\"][\"notes\"][0][\"start\"])\n",
+    "        response_onset_offset = float(\n",
+    "            sample[\"response\"][\"notes\"][0][\"start\"]\n",
+    "        )\n",
     "\n",
     "        results.append({\n",
+    "            \"sample_index\": sample_index,\n",
     "            \"composer\": sample[\"composer\"],\n",
     "            \"title\": sample[\"title\"],\n",
+    "            \"metadata_row\": sample[\"metadata_row\"],\n",
     "            \"stats\": result.stats,\n",
     "            \"event_details\": result.event_details,\n",
     "            \"is_correct\": result.is_correct,\n",
@@ -324,6 +417,7 @@
     "\n",
     "    return results\n",
     "\n",
+    "\n",
     "all_results = run_alignment_on_samples(samples)\n",
     "print(\"Alignment complete for\", len(all_results), \"pieces.\")"
    ]
@@ -367,23 +461,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "def convert_pipeline_output(event_details, response_events, ref_events):\n",
     "    \"\"\"\n",
     "    Convert event-level alignment output into note-level alignment labels.\n",
-    "\n",
     "    Alignment labels describe note correspondence, not pitch correctness.\n",
     "\n",
     "    Rules:\n",
     "        missing:\n",
     "            Every note in the reference event becomes a deletion.\n",
-    "\n",
     "        extra:\n",
     "            Every note in the response event becomes an insertion.\n",
-    "\n",
     "        match/replacement:\n",
     "            Pair as many response and reference notes as possible.\n",
     "            Different pitches may still form a paired alignment.\n",
@@ -391,9 +482,7 @@
     "            Remaining reference notes become deletions.\n",
     "\n",
     "    This implementation guarantees:\n",
-    "\n",
     "        paired + insertion == total response notes\n",
-    "\n",
     "        paired + deletion == total reference notes\n",
     "    \"\"\"\n",
     "    predictions = []\n",
@@ -406,33 +495,17 @@
     "\n",
     "        if event.get(\"response_index\") is not None:\n",
     "            response_index = event[\"response_index\"] - 1\n",
-    "\n",
-    "            if (\n",
-    "                response_index < 0\n",
-    "                or response_index >= len(response_events)\n",
-    "            ):\n",
-    "                raise IndexError(\n",
-    "                    \"response_index is outside response_events\"\n",
-    "                )\n",
-    "\n",
+    "            if (response_index < 0) or (response_index >= len(response_events)):\n",
+    "                raise IndexError(\"response_index is outside response_events\")\n",
     "            response_event = response_events[response_index]\n",
     "\n",
     "        if event.get(\"reference_index\") is not None:\n",
     "            reference_index = event[\"reference_index\"] - 1\n",
-    "\n",
-    "            if (\n",
-    "                reference_index < 0\n",
-    "                or reference_index >= len(ref_events)\n",
-    "            ):\n",
-    "                raise IndexError(\n",
-    "                    \"reference_index is outside ref_events\"\n",
-    "                )\n",
-    "\n",
+    "            if (reference_index < 0) or (reference_index >= len(ref_events)):\n",
+    "                raise IndexError(\"reference_index is outside ref_events\")\n",
     "            reference_event = ref_events[reference_index]\n",
     "\n",
-    "        # ----------------------------------------------------------\n",
-    "        # Entire reference event is missing.\n",
-    "        # ----------------------------------------------------------\n",
+    "        # for missing event\n",
     "        if operation == \"missing\":\n",
     "            for reference_note in reference_event[\"notes\"]:\n",
     "                predictions.append({\n",
@@ -440,77 +513,59 @@
     "                    \"pitch\": None,\n",
     "                    \"label\": \"deletion\",\n",
     "                })\n",
-    "\n",
-    "            continue\n",
-    "\n",
-    "        # ----------------------------------------------------------\n",
-    "        # Entire response event is extra.\n",
-    "        # ----------------------------------------------------------\n",
-    "        if operation == \"extra\":\n",
+    "        # for extra event\n",
+    "        elif operation == \"extra\":\n",
     "            for response_note in response_event[\"notes\"]:\n",
     "                predictions.append({\n",
     "                    \"onset\": float(response_note[\"start\"]),\n",
     "                    \"pitch\": int(response_note[\"pitch\"]),\n",
     "                    \"label\": \"insertion\",\n",
     "                })\n",
+    "        # for matched or replacement event\n",
+    "        else:\n",
+    "            response_notes = sorted(\n",
+    "                response_event[\"notes\"],\n",
+    "                key=lambda note: int(note[\"pitch\"])\n",
+    "            )\n",
+    "            reference_notes = sorted(\n",
+    "                reference_event[\"notes\"],\n",
+    "                key=lambda note: int(note[\"pitch\"])\n",
+    "            )\n",
+    "            pair_count = min(len(response_notes), len(reference_notes))\n",
     "\n",
-    "            continue\n",
-    "\n",
-    "        # ----------------------------------------------------------\n",
-    "        # Matched or replacement event.\n",
-    "        # ----------------------------------------------------------\n",
-    "        response_notes = sorted(\n",
-    "            response_event[\"notes\"],\n",
-    "            key=lambda note: int(note[\"pitch\"])\n",
-    "        )\n",
-    "\n",
-    "        reference_notes = sorted(\n",
-    "            reference_event[\"notes\"],\n",
-    "            key=lambda note: int(note[\"pitch\"])\n",
-    "        )\n",
-    "\n",
-    "        pair_count = min(\n",
-    "            len(response_notes),\n",
-    "            len(reference_notes)\n",
-    "        )\n",
-    "\n",
-    "        # Pair notes positionally after sorting by pitch.\n",
-    "        #\n",
-    "        # A pitch difference does not change the alignment label:\n",
-    "        # it remains paired.\n",
-    "        for index in range(pair_count):\n",
-    "            response_note = response_notes[index]\n",
-    "\n",
-    "            predictions.append({\n",
-    "                \"onset\": float(response_note[\"start\"]),\n",
-    "                \"pitch\": int(response_note[\"pitch\"]),\n",
-    "                \"label\": \"paired\",\n",
-    "            })\n",
-    "\n",
-    "        # Response side contains more notes.\n",
-    "        for response_note in response_notes[pair_count:]:\n",
-    "            predictions.append({\n",
-    "                \"onset\": float(response_note[\"start\"]),\n",
-    "                \"pitch\": int(response_note[\"pitch\"]),\n",
-    "                \"label\": \"insertion\",\n",
-    "            })\n",
+    "            # A pitch difference does not change the alignment label:\n",
+    "            # it remains paired.\n",
+    "            for index in range(pair_count):\n",
+    "                response_note = response_notes[index]\n",
+    "                predictions.append({\n",
+    "                    \"onset\": float(response_note[\"start\"]),\n",
+    "                    \"pitch\": int(response_note[\"pitch\"]),\n",
+    "                    \"label\": \"paired\",\n",
+    "                })\n",
     "\n",
-    "        # Reference side contains more notes.\n",
-    "        missing_count = len(reference_notes) - pair_count\n",
+    "            # Response side contains more notes.\n",
+    "            for response_note in response_notes[pair_count:]:\n",
+    "                predictions.append({\n",
+    "                    \"onset\": float(response_note[\"start\"]),\n",
+    "                    \"pitch\": int(response_note[\"pitch\"]),\n",
+    "                    \"label\": \"insertion\",\n",
+    "                })\n",
     "\n",
-    "        for _ in range(missing_count):\n",
-    "            predictions.append({\n",
-    "                \"onset\": None,\n",
-    "                \"pitch\": None,\n",
-    "                \"label\": \"deletion\",\n",
-    "            })\n",
+    "            # Reference side contains more notes.\n",
+    "            missing_count = len(reference_notes) - pair_count\n",
+    "            for i in range(missing_count):\n",
+    "                predictions.append({\n",
+    "                    \"onset\": None,\n",
+    "                    \"pitch\": None,\n",
+    "                    \"label\": \"deletion\",\n",
+    "                })\n",
     "\n",
     "    return predictions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -542,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -659,27 +714,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                      Piece  Precision  Recall      F1    TP   FP   FN\n",
-      "153  Bach (Prelude_bwv_885)     0.8413  0.8866  0.8633   477   90   61\n",
-      "110  Bach (Prelude_bwv_858)     0.8456  0.9163  0.8795   449   82   41\n",
-      "109  Bach (Prelude_bwv_858)     0.8482  0.9710  0.9055   436   78   13\n",
-      "134  Bach (Prelude_bwv_874)     0.8578  0.9029  0.8798  1442  239  155\n",
-      "135  Bach (Prelude_bwv_874)     0.8628  0.9084  0.8850  1428  227  144\n",
-      "76     Bach (Fugue_bwv_889)     0.8667  0.8636  0.8651   741  114  117\n",
-      "73     Bach (Fugue_bwv_889)     0.8749  0.8759  0.8754   748  107  106\n",
-      "77     Bach (Fugue_bwv_889)     0.8751  0.8783  0.8767   736  105  102\n",
-      "102  Bach (Prelude_bwv_856)     0.8763  0.9914  0.9303   921  130    8\n",
-      "72     Bach (Fugue_bwv_889)     0.8796  0.8817  0.8806   745  102  100\n",
-      "Mean Precision: 0.9637\n",
-      "Mean Recall   : 0.9701\n",
-      "Mean F1       : 0.9668\n"
+      "                       Piece  Precision  Recall      F1    TP   FP   FN\n",
+      "153   Bach (Prelude_bwv_885)     0.8389  0.8903  0.8638   479   92   59\n",
+      "134   Bach (Prelude_bwv_874)     0.8941  0.9249  0.9092  1477  175  120\n",
+      "135   Bach (Prelude_bwv_874)     0.9012  0.9345  0.9176  1469  161  103\n",
+      "86   Bach (Italian_concerto)     0.9016  0.9330  0.9170  1100  120   79\n",
+      "84   Bach (Italian_concerto)     0.9031  0.9325  0.9175  1146  123   83\n",
+      "110   Bach (Prelude_bwv_858)     0.9111  0.9204  0.9157   451   44   39\n",
+      "76      Bach (Fugue_bwv_889)     0.9126  0.9126  0.9126   783   75   75\n",
+      "152   Bach (Prelude_bwv_885)     0.9129  0.9384  0.9255   503   48   33\n",
+      "77      Bach (Fugue_bwv_889)     0.9134  0.9189  0.9161   770   73   68\n",
+      "108   Bach (Prelude_bwv_857)     0.9143  0.9437  0.9288   587   55   35\n",
+      "Mean Precision: 0.971\n",
+      "Mean Recall   : 0.9759\n",
+      "Mean F1       : 0.9734\n"
      ]
     }
    ],
@@ -737,7 +792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -746,7 +801,7 @@
      "text": [
       "Piece: Bach Prelude_bwv_885\n",
       "Ground-truth labels: 538\n",
-      "Pipeline labels: 567\n",
+      "Pipeline labels: 571\n",
       "=== Ground truth ===\n",
       "Paired: 497\n",
       "Insertion: 28\n",
@@ -754,14 +809,14 @@
       "Performance notes represented: 525\n",
       "Reference notes represented: 510\n",
       "=== Pipeline predictions ===\n",
-      "Paired: 482\n",
-      "Insertion: 43\n",
-      "Deletion: 42\n",
+      "Paired: 464\n",
+      "Insertion: 61\n",
+      "Deletion: 46\n",
       "Performance notes represented: 525\n",
-      "Reference notes represented: 524\n",
+      "Reference notes represented: 510\n",
       "=== Original event data ===\n",
       "Notes in response_events: 525\n",
-      "Notes in ref_events: 524\n",
+      "Notes in ref_events: 510\n",
       "=== Conservation checks ===\n",
       "Every response note represented exactly once: True\n",
       "Every reference note represented exactly once: True\n"
@@ -846,2038 +901,340 @@
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original reference notes: 524\n",
-      "Reference notes after grouping: 524\n",
-      "510 reference notes represented in ground truth\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Original reference notes:\", len(selected_sample[\"reference\"][\"notes\"]))\n",
-    "print(\"Reference notes after grouping:\",\n",
-    "    sum(len(event[\"notes\"]) for event in selected_result[\"ref_events_normalized\"])\n",
-    ")\n",
-    "gt_counts = Counter(note[\"label\"] for note in ground_truth)\n",
-    "print(gt_counts[\"paired\"] + gt_counts[\"deletion\"], \"reference notes represented in ground truth\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "No problems with the grouping terminology in my algorithm, so need to insepect if there is anything different between \"reference MIDI\" and \"ground truth TSV\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Piece: Prelude_bwv_858\n",
-      "Path: asap-dataset/Bach/Prelude/bwv_858/midi_score.mid\n",
-      "Number of instruments: 2\n",
-      "{'instrument_index': 0, 'name': '', 'program': 0, 'is_drum': False, 'note_count': 327}\n",
-      "{'instrument_index': 1, 'name': '', 'program': 0, 'is_drum': False, 'note_count': 170}\n",
-      "Total notes: 497\n"
-     ]
-    }
-   ],
-   "source": [
-    "midi_score_path = os.path.join(\n",
-    "    ASAP_PATH,\n",
-    "    selected_sample[\"metadata_row\"][\"midi_score\"].strip()\n",
-    ")\n",
-    "\n",
-    "midi_score_data = pretty_midi.PrettyMIDI(midi_score_path)\n",
-    "\n",
-    "print(\"Piece:\", selected_sample[\"title\"])\n",
-    "print(\"Path:\", midi_score_path)\n",
-    "print(\"Number of instruments:\", len(midi_score_data.instruments))\n",
+    "The pipeline produced 571 note-level labels while the the GT has 538 labels. Specifically, the pipeline predicted 33 more insertions, 33 more deletions and 33 fewer pairs than the GT. This is because 'paired' can be represented by a single label whereas 'unpaired' requires both an insertion and a deletion. The paper (Peter, 2023) suggests that a misalignment pair (which is unaligned in GT) will create one FP and two FNs (i.e. label as paired instead of a deletion + an insertion). In this case here, it appears as the reverse: a GT paired correspondence is split into a predicted insertion and deletion, such an error can produce two FPs and one FN, which helps explain why precision is lower than recall in this case.\n",
     "\n",
-    "total_notes = 0\n",
+    "As the ground truth is chosen to be the result of another alignment algorithm, the metrics actually measures the agreement between the existing alignment algorithm and mine, rather than absolute musical correctness. This occurrence of FPs and FNs is likely related to the alignment strategy (based on pitch difference) and parameter settings, and it appears that my pipeline with default parameters favours gap operations (insertion/deletion) rather than pairing up 2 notes (match/replacement). In educational applications, pairing a wrong note with its intended score note may provide more informative feedback than treating the same event as an insertion and a deletion. The default parameters are set to be strict, but my pipeline allows teachers to adjust parameters so that the this alignment can be adapted to different musical contexts. For example, the gap penalty and timing thresholds can be adjusted depending on whether the target application is beginner practice, examination recordings or expert concert performances. \n",
     "\n",
-    "for instrument_index, instrument in enumerate(\n",
-    "    midi_score_data.instruments\n",
-    "):\n",
-    "    note_count = len(instrument.notes)\n",
-    "    total_notes += note_count\n",
-    "\n",
-    "    print({\n",
-    "        \"instrument_index\": instrument_index,\n",
-    "        \"name\": instrument.name,\n",
-    "        \"program\": int(instrument.program),\n",
-    "        \"is_drum\": instrument.is_drum,\n",
-    "        \"note_count\": note_count,\n",
-    "    })\n",
-    "\n",
-    "print(\"Total notes:\", total_notes)\n",
-    "\n",
-    "assert total_notes == len(\n",
-    "    selected_sample[\"reference\"][\"notes\"]\n",
-    ")"
+    "Additionally, the nASAP contains performances data from a competition, consequently, the dataset contains expressive timing, tempo fluctuations and ornament that are less representative of beginner practice, makes it differ substantially from the intended application of this project—providing feedback for student practice. (However, this is the most suitable public dataset to use, so the lacking of large-scale students' practice data remains the biggest limitation of this project)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "('onset_beat', 'duration_beat', 'onset_quarter', 'duration_quarter', 'onset_div', 'duration_div', 'pitch', 'voice', 'id', 'divs_pq')\n"
+      "Bach Prelude_bwv_885\n"
      ]
-    }
-   ],
-   "source": [
-    "import partitura as pt\n",
-    "\n",
-    "score = pt.load_score(xml_path)\n",
-    "\n",
-    "na = score.note_array()\n",
-    "\n",
-    "print(na.dtype.names)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The pipeline produced more note-level labels than the ground truth. Inspection of the alignment results showed that this was not primarily because additional notes were detected, but because replacement events were expanded into multiple note-level labels (paired + insertion + deletion). Consequently, one event-level disagreement could generate several note-level false positives and false negatives, reducing the final F1 score.\n",
-    "\n",
-    "The paper also suggests that 'A misalignment of two notes that are unaligned in the ground truth creates one false positive match and two false negatives: a deletion and an insertion'."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compare_note_labels(gt_notes, predicted_notes, compare_pitch):\n",
-    "    comparison = []\n",
-    "    used_predictions = set()\n",
-    "\n",
-    "    for gt_note in gt_notes:\n",
-    "        best_index = None\n",
-    "        best_difference = None\n",
-    "\n",
-    "        for index, predicted_note in enumerate(predicted_notes):\n",
-    "            if index in used_predictions:\n",
-    "                continue\n",
-    "\n",
-    "            if compare_pitch and gt_note[\"pitch\"] != predicted_note[\"pitch\"]:\n",
-    "                continue\n",
-    "\n",
-    "            difference = abs(\n",
-    "                gt_note[\"onset\"] - predicted_note[\"onset\"]\n",
-    "            )\n",
-    "\n",
-    "            if difference <= ONSET_TOLERANCE:\n",
-    "                if best_difference is None or difference < best_difference:\n",
-    "                    best_index = index\n",
-    "                    best_difference = difference\n",
-    "\n",
-    "        if best_index is not None:\n",
-    "            used_predictions.add(best_index)\n",
-    "            predicted_note = predicted_notes[best_index]\n",
-    "\n",
-    "            comparison.append({\n",
-    "                \"status\": \"TP\",\n",
-    "                \"label\": gt_note[\"label\"],\n",
-    "                \"onset\": gt_note[\"onset\"],\n",
-    "                \"gt_pitch\": gt_note[\"pitch\"],\n",
-    "                \"pipeline_pitch\": predicted_note[\"pitch\"],\n",
-    "                \"onset_difference\": best_difference,\n",
-    "            })\n",
-    "\n",
-    "        else:\n",
-    "            comparison.append({\n",
-    "                \"status\": \"FN\",\n",
-    "                \"label\": gt_note[\"label\"],\n",
-    "                \"onset\": gt_note[\"onset\"],\n",
-    "                \"gt_pitch\": gt_note[\"pitch\"],\n",
-    "                \"pipeline_pitch\": None,\n",
-    "                \"onset_difference\": None,\n",
-    "            })\n",
-    "\n",
-    "    for index, predicted_note in enumerate(predicted_notes):\n",
-    "        if index not in used_predictions:\n",
-    "            comparison.append({\n",
-    "                \"status\": \"FP\",\n",
-    "                \"label\": predicted_note[\"label\"],\n",
-    "                \"onset\": predicted_note[\"onset\"],\n",
-    "                \"gt_pitch\": None,\n",
-    "                \"pipeline_pitch\": predicted_note[\"pitch\"],\n",
-    "                \"onset_difference\": None,\n",
-    "            })\n",
-    "\n",
-    "    return comparison"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>status</th>\n",
-       "      <th>label</th>\n",
-       "      <th>onset</th>\n",
-       "      <th>gt_pitch</th>\n",
-       "      <th>pipeline_pitch</th>\n",
-       "      <th>onset_difference</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>10.433771</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>11.362190</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>20.750020</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>24.355792</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>28.185924</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>32.012851</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>40.498970</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>109</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>41.455168</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>117</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>44.126110</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>127</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>47.995772</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>155</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>56.575908</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>156</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>58.241508</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>163</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>59.351552</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>61.246853</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>63.231898</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>190</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>67.107970</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>195</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>69.113314</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>197</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>68.101560</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>70.140024</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>70.113314</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>218</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>74.020369</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>236</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>80.917812</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>245</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>81.835548</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>251</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>85.641107</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>261</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>88.677435</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>293</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>97.756503</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>295</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>99.499026</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>302</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>100.685993</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>332</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>107.358008</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>112.917842</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>348</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>113.097330</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>457</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>151.923221</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>461</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>151.850572</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>475</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>154.114463</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>488</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>158.082415</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>495</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>160.290751</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>497</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>6.537399</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>498</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>6.607912</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>499</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>8.410264</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>8.481846</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>501</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>13.960483</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>14.033133</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>503</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>15.895314</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>504</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>24.533143</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>505</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>36.471188</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>506</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>36.533154</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>507</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>44.005384</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>508</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>44.057734</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>509</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>56.505396</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>510</th>\n",
-       "      <td>FP</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>56.640011</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    status   label       onset  gt_pitch  pipeline_pitch  onset_difference\n",
-       "19      FN  paired   10.433771      69.0             NaN               NaN\n",
-       "26      FN  paired   11.362190      70.0             NaN               NaN\n",
-       "50      FN  paired   20.750020      72.0             NaN               NaN\n",
-       "55      FN  paired   24.355792      72.0             NaN               NaN\n",
-       "74      FN  paired   28.185924      77.0             NaN               NaN\n",
-       "85      FN  paired   32.012851      75.0             NaN               NaN\n",
-       "100     FN  paired   40.498970      65.0             NaN               NaN\n",
-       "109     FN  paired   41.455168      67.0             NaN               NaN\n",
-       "117     FN  paired   44.126110      74.0             NaN               NaN\n",
-       "127     FN  paired   47.995772      74.0             NaN               NaN\n",
-       "155     FN  paired   56.575908      68.0             NaN               NaN\n",
-       "156     FN  paired   58.241508      68.0             NaN               NaN\n",
-       "163     FN  paired   59.351552      76.0             NaN               NaN\n",
-       "172     FN  paired   61.246853      76.0             NaN               NaN\n",
-       "176     FN  paired   63.231898      69.0             NaN               NaN\n",
-       "190     FN  paired   67.107970      69.0             NaN               NaN\n",
-       "195     FN  paired   69.113314      66.0             NaN               NaN\n",
-       "197     FN  paired   68.101560      76.0             NaN               NaN\n",
-       "201     FN  paired   70.140024      62.0             NaN               NaN\n",
-       "202     FN  paired   70.113314      66.0             NaN               NaN\n",
-       "218     FN  paired   74.020369      72.0             NaN               NaN\n",
-       "236     FN  paired   80.917812      75.0             NaN               NaN\n",
-       "245     FN  paired   81.835548      68.0             NaN               NaN\n",
-       "251     FN  paired   85.641107      65.0             NaN               NaN\n",
-       "261     FN  paired   88.677435      63.0             NaN               NaN\n",
-       "293     FN  paired   97.756503      66.0             NaN               NaN\n",
-       "295     FN  paired   99.499026      69.0             NaN               NaN\n",
-       "302     FN  paired  100.685993      70.0             NaN               NaN\n",
-       "332     FN  paired  107.358008      77.0             NaN               NaN\n",
-       "342     FN  paired  112.917842      76.0             NaN               NaN\n",
-       "348     FN  paired  113.097330      77.0             NaN               NaN\n",
-       "457     FN  paired  151.923221      54.0             NaN               NaN\n",
-       "461     FN  paired  151.850572      63.0             NaN               NaN\n",
-       "475     FN  paired  154.114463      77.0             NaN               NaN\n",
-       "488     FN  paired  158.082415      75.0             NaN               NaN\n",
-       "495     FN  paired  160.290751      79.0             NaN               NaN\n",
-       "497     FP  paired    6.537399       NaN            62.0               NaN\n",
-       "498     FP  paired    6.607912       NaN            63.0               NaN\n",
-       "499     FP  paired    8.410264       NaN            58.0               NaN\n",
-       "500     FP  paired    8.481846       NaN            60.0               NaN\n",
-       "501     FP  paired   13.960483       NaN            69.0               NaN\n",
-       "502     FP  paired   14.033133       NaN            70.0               NaN\n",
-       "503     FP  paired   15.895314       NaN            66.0               NaN\n",
-       "504     FP  paired   24.533143       NaN            53.0               NaN\n",
-       "505     FP  paired   36.471188       NaN            67.0               NaN\n",
-       "506     FP  paired   36.533154       NaN            68.0               NaN\n",
-       "507     FP  paired   44.005384       NaN            74.0               NaN\n",
-       "508     FP  paired   44.057734       NaN            75.0               NaN\n",
-       "509     FP  paired   56.505396       NaN            69.0               NaN\n",
-       "510     FP  paired   56.640011       NaN            69.0               NaN"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gt_paired = [\n",
-    "    note for note in ground_truth\n",
-    "    if note[\"label\"] == \"paired\"\n",
-    "    and note[\"onset\"] is not None\n",
-    "]\n",
-    "\n",
-    "predicted_paired = [\n",
-    "    note for note in predictions\n",
-    "    if note[\"label\"] == \"paired\"\n",
-    "    and note[\"onset\"] is not None\n",
-    "]\n",
-    "\n",
-    "gt_insertions = [\n",
-    "    note for note in ground_truth\n",
-    "    if note[\"label\"] == \"insertion\"\n",
-    "    and note[\"onset\"] is not None\n",
-    "]\n",
-    "\n",
-    "predicted_insertions = [\n",
-    "    note for note in predictions\n",
-    "    if note[\"label\"] == \"insertion\"\n",
-    "    and note[\"onset\"] is not None\n",
-    "]\n",
-    "\n",
-    "paired_comparison = compare_note_labels(\n",
-    "    gt_paired,\n",
-    "    predicted_paired,\n",
-    "    compare_pitch=False,\n",
-    ")\n",
-    "\n",
-    "insertion_comparison = compare_note_labels(\n",
-    "    gt_insertions,\n",
-    "    predicted_insertions,\n",
-    "    compare_pitch=True,\n",
-    ")\n",
-    "\n",
-    "comparison_df = pd.DataFrame(\n",
-    "    paired_comparison + insertion_comparison\n",
-    ")\n",
-    "\n",
-    "error_df = comparison_df[\n",
-    "    comparison_df[\"status\"] != \"TP\"\n",
-    "].copy()\n",
-    "\n",
-    "display(error_df.head(50))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "START_TIME = 62.0\n",
-    "END_TIME = 69.0\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "response_notes = selected_sample[\"response\"][\"notes\"]\n",
-    "\n",
-    "segment_response_notes = [\n",
-    "    note for note in response_notes\n",
-    "    if START_TIME <= note[\"start\"] <= END_TIME\n",
-    "]\n",
-    "\n",
-    "segment_gt = [\n",
-    "    note for note in ground_truth\n",
-    "    if (\n",
-    "        note[\"onset\"] is not None\n",
-    "        and START_TIME <= note[\"onset\"] <= END_TIME\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "segment_predictions = [\n",
-    "    note for note in predictions\n",
-    "    if (\n",
-    "        note[\"onset\"] is not None\n",
-    "        and START_TIME <= note[\"onset\"] <= END_TIME\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "\n",
-    "plt.figure(figsize=(14, 6))\n",
-    "\n",
-    "# Draw the performed MIDI notes.\n",
-    "for note in segment_response_notes:\n",
-    "    plt.hlines(\n",
-    "        y=note[\"pitch\"],\n",
-    "        xmin=note[\"start\"],\n",
-    "        xmax=note[\"start\"] + note[\"duration\"],\n",
-    "        linewidth=5,\n",
-    "        alpha=0.35,\n",
-    "    )\n",
-    "\n",
-    "# Ground-truth labels.\n",
-    "for note in segment_gt:\n",
-    "    if note[\"pitch\"] is not None:\n",
-    "        marker = \"o\" if note[\"label\"] == \"paired\" else \"*\"\n",
-    "\n",
-    "        plt.scatter(\n",
-    "            note[\"onset\"],\n",
-    "            note[\"pitch\"],\n",
-    "            marker=marker,\n",
-    "            s=100,\n",
-    "            facecolors=\"none\",\n",
-    "            label=f'GT {note[\"label\"]}',\n",
-    "        )\n",
-    "\n",
-    "# Pipeline labels.\n",
-    "for note in segment_predictions:\n",
-    "    if note[\"pitch\"] is not None:\n",
-    "        marker = \"x\" if note[\"label\"] == \"paired\" else \"+\"\n",
-    "\n",
-    "        plt.scatter(\n",
-    "            note[\"onset\"],\n",
-    "            note[\"pitch\"],\n",
-    "            marker=marker,\n",
-    "            s=100,\n",
-    "            label=f'Pipeline {note[\"label\"]}',\n",
-    "        )\n",
-    "\n",
-    "plt.xlim(START_TIME, END_TIME)\n",
-    "plt.xlabel(\"Time (seconds)\")\n",
-    "plt.ylabel(\"MIDI pitch\")\n",
-    "plt.title(\n",
-    "    f'Ground truth vs pipeline: '\n",
-    "    f'{selected_result[\"composer\"]} - {selected_result[\"title\"]}'\n",
-    ")\n",
-    "\n",
-    "# Remove duplicate legend entries.\n",
-    "handles, labels = plt.gca().get_legend_handles_labels()\n",
-    "unique_labels = {}\n",
-    "\n",
-    "for handle, label in zip(handles, labels):\n",
-    "    if label not in unique_labels:\n",
-    "        unique_labels[label] = handle\n",
-    "\n",
-    "plt.legend(\n",
-    "    unique_labels.values(),\n",
-    "    unique_labels.keys(),\n",
-    "    loc=\"upper right\",\n",
-    ")\n",
-    "\n",
-    "plt.grid(axis=\"x\", alpha=0.3)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "segment_error_df = error_df[\n",
-    "    (error_df[\"onset\"] >= START_TIME)\n",
-    "    & (error_df[\"onset\"] <= END_TIME)\n",
-    "]\n",
-    "\n",
-    "plt.figure(figsize=(14, 5))\n",
-    "\n",
-    "for _, row in segment_error_df.iterrows():\n",
-    "    if row[\"status\"] == \"FP\":\n",
-    "        pitch = row[\"pipeline_pitch\"]\n",
-    "        marker = \"x\"\n",
-    "    else:\n",
-    "        pitch = row[\"gt_pitch\"]\n",
-    "        marker = \"o\"\n",
-    "\n",
-    "    if pd.notna(pitch):\n",
-    "        plt.scatter(\n",
-    "            row[\"onset\"],\n",
-    "            pitch,\n",
-    "            marker=marker,\n",
-    "            s=120,\n",
-    "            label=row[\"status\"],\n",
-    "        )\n",
-    "\n",
-    "plt.xlim(START_TIME, END_TIME)\n",
-    "plt.xlabel(\"Time (seconds)\")\n",
-    "plt.ylabel(\"MIDI pitch\")\n",
-    "plt.title(\"Alignment errors: FP and FN\")\n",
-    "\n",
-    "handles, labels = plt.gca().get_legend_handles_labels()\n",
-    "unique_labels = {}\n",
-    "\n",
-    "for handle, label in zip(handles, labels):\n",
-    "    if label not in unique_labels:\n",
-    "        unique_labels[label] = handle\n",
-    "\n",
-    "plt.legend(\n",
-    "    unique_labels.values(),\n",
-    "    unique_labels.keys(),\n",
-    ")\n",
-    "\n",
-    "plt.grid(axis=\"x\", alpha=0.3)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>onset</th>\n",
-       "      <th>operation</th>\n",
-       "      <th>event_type</th>\n",
-       "      <th>reference_index</th>\n",
-       "      <th>response_index</th>\n",
-       "      <th>correct_pitches</th>\n",
-       "      <th>missing_pitches</th>\n",
-       "      <th>extra_pitches</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>62.054546</td>\n",
-       "      <td>match</td>\n",
-       "      <td>note</td>\n",
-       "      <td>146.0</td>\n",
-       "      <td>153</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>62.230828</td>\n",
-       "      <td>match</td>\n",
-       "      <td>chord</td>\n",
-       "      <td>147.0</td>\n",
-       "      <td>154</td>\n",
-       "      <td>[50, 65]</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>63.063094</td>\n",
-       "      <td>match</td>\n",
-       "      <td>note</td>\n",
-       "      <td>148.0</td>\n",
-       "      <td>155</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>63.231898</td>\n",
-       "      <td>extra</td>\n",
-       "      <td>note</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>156</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>63.282111</td>\n",
-       "      <td>replacement</td>\n",
-       "      <td>chord</td>\n",
-       "      <td>149.0</td>\n",
-       "      <td>157</td>\n",
-       "      <td>[53]</td>\n",
-       "      <td>[69]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>64.091941</td>\n",
-       "      <td>match</td>\n",
-       "      <td>note</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>158</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>64.256471</td>\n",
-       "      <td>match</td>\n",
-       "      <td>note</td>\n",
-       "      <td>151.0</td>\n",
-       "      <td>159</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>65.107968</td>\n",
-       "      <td>match</td>\n",
-       "      <td>chord</td>\n",
-       "      <td>152.0</td>\n",
-       "      <td>160</td>\n",
-       "      <td>[57, 65]</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>65.265019</td>\n",
-       "      <td>match</td>\n",
-       "      <td>chord</td>\n",
-       "      <td>153.0</td>\n",
-       "      <td>161</td>\n",
-       "      <td>[55, 64]</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>66.031046</td>\n",
-       "      <td>match</td>\n",
-       "      <td>chord</td>\n",
-       "      <td>154.0</td>\n",
-       "      <td>162</td>\n",
-       "      <td>[53, 67]</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>66.204123</td>\n",
-       "      <td>match</td>\n",
-       "      <td>chord</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>163</td>\n",
-       "      <td>[52, 61]</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>66.873995</td>\n",
-       "      <td>match</td>\n",
-       "      <td>note</td>\n",
-       "      <td>156.0</td>\n",
-       "      <td>164</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>66.966944</td>\n",
-       "      <td>replacement</td>\n",
-       "      <td>chord</td>\n",
-       "      <td>157.0</td>\n",
-       "      <td>165</td>\n",
-       "      <td>[69]</td>\n",
-       "      <td>[53]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>67.037457</td>\n",
-       "      <td>match</td>\n",
-       "      <td>note</td>\n",
-       "      <td>158.0</td>\n",
-       "      <td>166</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>67.107970</td>\n",
-       "      <td>replacement</td>\n",
-       "      <td>note</td>\n",
-       "      <td>159.0</td>\n",
-       "      <td>167</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>67.919936</td>\n",
-       "      <td>match</td>\n",
-       "      <td>note</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>168</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>68.101560</td>\n",
-       "      <td>extra</td>\n",
-       "      <td>note</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>169</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>68.152843</td>\n",
-       "      <td>replacement</td>\n",
-       "      <td>chord</td>\n",
-       "      <td>161.0</td>\n",
-       "      <td>170</td>\n",
-       "      <td>[55]</td>\n",
-       "      <td>[76]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>68.822715</td>\n",
-       "      <td>match</td>\n",
-       "      <td>note</td>\n",
-       "      <td>162.0</td>\n",
-       "      <td>171</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>68.971219</td>\n",
-       "      <td>replacement</td>\n",
-       "      <td>chord</td>\n",
-       "      <td>163.0</td>\n",
-       "      <td>172</td>\n",
-       "      <td>[66]</td>\n",
-       "      <td>[58]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
+      "image/png": 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       "text/plain": [
-       "        onset    operation event_type  reference_index  response_index  \\\n",
-       "0   62.054546        match       note            146.0             153   \n",
-       "1   62.230828        match      chord            147.0             154   \n",
-       "2   63.063094        match       note            148.0             155   \n",
-       "3   63.231898        extra       note              NaN             156   \n",
-       "4   63.282111  replacement      chord            149.0             157   \n",
-       "5   64.091941        match       note            150.0             158   \n",
-       "6   64.256471        match       note            151.0             159   \n",
-       "7   65.107968        match      chord            152.0             160   \n",
-       "8   65.265019        match      chord            153.0             161   \n",
-       "9   66.031046        match      chord            154.0             162   \n",
-       "10  66.204123        match      chord            155.0             163   \n",
-       "11  66.873995        match       note            156.0             164   \n",
-       "12  66.966944  replacement      chord            157.0             165   \n",
-       "13  67.037457        match       note            158.0             166   \n",
-       "14  67.107970  replacement       note            159.0             167   \n",
-       "15  67.919936        match       note            160.0             168   \n",
-       "16  68.101560        extra       note              NaN             169   \n",
-       "17  68.152843  replacement      chord            161.0             170   \n",
-       "18  68.822715        match       note            162.0             171   \n",
-       "19  68.971219  replacement      chord            163.0             172   \n",
-       "\n",
-       "   correct_pitches missing_pitches extra_pitches  \n",
-       "0             None            None          None  \n",
-       "1         [50, 65]              []            []  \n",
-       "2             None            None          None  \n",
-       "3             None            None          None  \n",
-       "4             [53]            [69]            []  \n",
-       "5             None            None          None  \n",
-       "6             None            None          None  \n",
-       "7         [57, 65]              []            []  \n",
-       "8         [55, 64]              []            []  \n",
-       "9         [53, 67]              []            []  \n",
-       "10        [52, 61]              []            []  \n",
-       "11            None            None          None  \n",
-       "12            [69]            [53]            []  \n",
-       "13            None            None          None  \n",
-       "14            None            None          None  \n",
-       "15            None            None          None  \n",
-       "16            None            None          None  \n",
-       "17            [55]            [76]            []  \n",
-       "18            None            None          None  \n",
-       "19            [66]            [58]            []  "
+       "<Figure size 1500x800 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "segment_event_details = []\n",
-    "\n",
-    "for event in selected_result[\"event_details\"]:\n",
-    "    response_index = event.get(\"response_index\")\n",
-    "\n",
-    "    if response_index is None:\n",
-    "        continue\n",
-    "\n",
-    "    response_event = selected_result[\n",
-    "        \"response_events_normalized\"\n",
-    "    ][response_index - 1]\n",
-    "\n",
-    "    event_onset = (\n",
-    "        response_event[\"event_start\"]\n",
-    "        + selected_result[\"response_onset_offset\"]\n",
-    "    )\n",
-    "\n",
-    "    if START_TIME <= event_onset <= END_TIME:\n",
-    "        segment_event_details.append({\n",
-    "            \"onset\": event_onset,\n",
-    "            \"operation\": event[\"operation_type\"],\n",
-    "            \"event_type\": event[\"event_type\"],\n",
-    "            \"reference_index\": event.get(\"reference_index\"),\n",
-    "            \"response_index\": response_index,\n",
-    "            \"correct_pitches\": event.get(\"correct_pitches\"),\n",
-    "            \"missing_pitches\": event.get(\"missing_pitches\"),\n",
-    "            \"extra_pitches\": event.get(\"extra_pitches\"),\n",
-    "        })\n",
-    "\n",
-    "display(pd.DataFrame(segment_event_details))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Bach Prelude_bwv_858\n"
+      "Selected operations: 45\n",
+      "Missing note markers: 0\n",
+      "Extra note markers: 3\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.patches import ConnectionPatch\n",
+    "from matplotlib.lines import Line2D\n",
     "\n",
-    "SELECTED_SAMPLE_INDEX = 110 \n",
+    "SELECTED_SAMPLE_INDEX = 153\n",
     "\n",
-    "selected_sample = samples[SELECTED_SAMPLE_INDEX]\n",
     "selected_result = all_results[SELECTED_SAMPLE_INDEX]\n",
-    "\n",
     "ref_events = selected_result[\"ref_events_normalized\"]\n",
     "response_events = selected_result[\"response_events_normalized\"]\n",
     "event_details = selected_result[\"event_details\"]\n",
     "\n",
     "print(selected_result[\"composer\"], selected_result[\"title\"])\n",
     "\n",
-    "def plot_alignment(ref_events, response_events, event_details,\n",
-    "                   ref_start, ref_end):\n",
+    "def get_note_centre(note):\n",
+    "    return note[\"start\"] + note[\"duration\"] / 2, note[\"pitch\"]\n",
+    "\n",
+    "def get_event_anchor(event):\n",
+    "    \"\"\"\n",
+    "    Return an anchor located on a real note.\n",
+    "\n",
+    "    For a single-note event, use that note.\n",
+    "    For a chord, use the middle note after sorting by pitch.\n",
+    "    \"\"\"\n",
+    "    notes = sorted(event[\"notes\"], key=lambda note: note[\"pitch\"])\n",
+    "    anchor_note = notes[len(notes) // 2]\n",
+    "    return get_note_centre(anchor_note)\n",
+    "\n",
+    "def event_overlaps_range(event, start, end):\n",
+    "    for note in event[\"notes\"]:\n",
+    "        note_start = note[\"start\"]\n",
+    "        note_end = note[\"start\"] + note[\"duration\"]\n",
+    "        if note_end >= start and note_start <= end:\n",
+    "            return True\n",
+    "    return False\n",
+    "\n",
+    "def plot_alignment(ref_events, response_events, event_details, ref_start, ref_end):\n",
+    "\n",
+    "    reference_positions = []\n",
+    "\n",
+    "    for position, detail in enumerate(event_details):\n",
+    "        reference_index = detail.get(\"reference_index\")\n",
+    "\n",
+    "        if reference_index is not None:\n",
+    "            ref_event = ref_events[reference_index - 1]\n",
+    "            ref_onset = ref_event[\"event_start\"]\n",
+    "\n",
+    "            if ref_start <= ref_onset <= ref_end:\n",
+    "                reference_positions.append(position)\n",
+    "\n",
+    "    if not reference_positions:\n",
+    "        raise ValueError(\"No reference events were found in this range.\")\n",
+    "\n",
+    "    first_position = min(reference_positions)\n",
+    "    last_position = max(reference_positions)\n",
     "\n",
-    "    # Select operations whose reference event falls in the chosen segment.\n",
     "    selected_details = []\n",
     "\n",
-    "    for detail in event_details:\n",
+    "    for position in range(first_position, last_position + 1):\n",
+    "        detail = event_details[position]\n",
+    "        operation = detail[\"operation_type\"]\n",
     "        reference_index = detail.get(\"reference_index\")\n",
+    "        include_detail = operation == \"extra\"\n",
     "\n",
-    "        if reference_index is None:\n",
-    "            continue\n",
+    "        if reference_index is not None:\n",
+    "            ref_event = ref_events[reference_index - 1]\n",
+    "            ref_onset = ref_event[\"event_start\"]\n",
     "\n",
-    "        ref_event = ref_events[reference_index - 1]\n",
-    "        ref_onset = ref_event[\"event_start\"]\n",
+    "            if ref_start <= ref_onset <= ref_end:\n",
+    "                include_detail = True\n",
     "\n",
-    "        if ref_start <= ref_onset <= ref_end:\n",
+    "        if include_detail:\n",
     "            selected_details.append(detail)\n",
     "\n",
-    "    # Find the corresponding response time range.\n",
-    "    response_onsets = []\n",
+    "    response_starts = []\n",
+    "    response_ends = []\n",
     "\n",
     "    for detail in selected_details:\n",
     "        response_index = detail.get(\"response_index\")\n",
     "\n",
     "        if response_index is not None:\n",
     "            response_event = response_events[response_index - 1]\n",
-    "            response_onsets.append(response_event[\"event_start\"])\n",
     "\n",
-    "    if response_onsets:\n",
-    "        response_start = min(response_onsets) - 0.5\n",
-    "        response_end = max(response_onsets) + 0.5\n",
+    "            for note in response_event[\"notes\"]:\n",
+    "                response_starts.append(note[\"start\"])\n",
+    "                response_ends.append(note[\"start\"] + note[\"duration\"])\n",
+    "\n",
+    "    if response_starts:\n",
+    "        response_start = min(response_starts) - 0.5\n",
+    "        response_end = max(response_ends) + 0.5\n",
     "    else:\n",
     "        response_start = ref_start\n",
     "        response_end = ref_end\n",
     "\n",
-    "    fig, (ax_response, ax_ref) = plt.subplots(\n",
-    "        2,\n",
-    "        1,\n",
-    "        figsize=(15, 8),\n",
-    "        height_ratios=[1, 1],\n",
-    "    )\n",
+    "    visible_response_events = []\n",
     "\n",
-    "    # ------------------------------\n",
-    "    # Top: response piano roll\n",
-    "    # ------------------------------\n",
     "    for event in response_events:\n",
-    "        event_start = event[\"event_start\"]\n",
-    "\n",
-    "        if response_start <= event_start <= response_end:\n",
-    "            for note in event[\"notes\"]:\n",
-    "                ax_response.hlines(\n",
-    "                    y=note[\"pitch\"],\n",
-    "                    xmin=note[\"start\"],\n",
-    "                    xmax=note[\"start\"] + note[\"duration\"],\n",
-    "                    linewidth=5,\n",
-    "                )\n",
+    "        if event_overlaps_range(event, response_start, response_end):\n",
+    "            visible_response_events.append(event)\n",
+    "\n",
+    "    visible_ref_events = []\n",
+    "\n",
+    "    for event in ref_events:\n",
+    "        if event_overlaps_range(event, ref_start, ref_end):\n",
+    "            visible_ref_events.append(event)\n",
+    "\n",
+    "    visible_pitches = []\n",
+    "\n",
+    "    for event in visible_response_events:\n",
+    "        for note in event[\"notes\"]:\n",
+    "            visible_pitches.append(note[\"pitch\"])\n",
+    "\n",
+    "    for event in visible_ref_events:\n",
+    "        for note in event[\"notes\"]:\n",
+    "            visible_pitches.append(note[\"pitch\"])\n",
+    "\n",
+    "    fig, (ax_response, ax_ref) = plt.subplots(\n",
+    "        2, 1, figsize=(15, 8), height_ratios=[1, 1])\n",
+    "\n",
+    "    note_colour = \"tab:blue\"\n",
+    "    match_colour = \"0.65\"\n",
+    "    replacement_colour = \"tab:orange\"\n",
+    "    missing_colour = \"tab:red\"\n",
+    "    extra_colour = \"tab:purple\"\n",
+    "\n",
+    "    for event in visible_response_events:\n",
+    "        for note in event[\"notes\"]:\n",
+    "            ax_response.hlines(\n",
+    "                y=note[\"pitch\"],\n",
+    "                xmin=note[\"start\"],\n",
+    "                xmax=note[\"start\"] + note[\"duration\"],\n",
+    "                linewidth=5,\n",
+    "                color=note_colour,\n",
+    "            )\n",
     "\n",
     "    ax_response.set_xlim(response_start, response_end)\n",
-    "    ax_response.set_ylabel(\"Response MIDI pitch\")\n",
+    "    ax_response.set_ylabel(\"Performance MIDI pitch\")\n",
     "    ax_response.set_title(\"Performance\")\n",
     "\n",
-    "    # ------------------------------\n",
-    "    # Bottom: reference piano roll\n",
-    "    # ------------------------------\n",
-    "    for event in ref_events:\n",
-    "        event_start = event[\"event_start\"]\n",
-    "\n",
-    "        if ref_start <= event_start <= ref_end:\n",
-    "            for note in event[\"notes\"]:\n",
-    "                ax_ref.hlines(\n",
-    "                    y=note[\"pitch\"],\n",
-    "                    xmin=note[\"start\"],\n",
-    "                    xmax=note[\"start\"] + note[\"duration\"],\n",
-    "                    linewidth=5,\n",
-    "                )\n",
+    "    for event in visible_ref_events:\n",
+    "        for note in event[\"notes\"]:\n",
+    "            ax_ref.hlines(\n",
+    "                y=note[\"pitch\"],\n",
+    "                xmin=note[\"start\"],\n",
+    "                xmax=note[\"start\"] + note[\"duration\"],\n",
+    "                linewidth=5,\n",
+    "                color=note_colour,\n",
+    "            )\n",
     "\n",
     "    ax_ref.set_xlim(ref_start, ref_end)\n",
     "    ax_ref.set_xlabel(\"Normalised time (seconds)\")\n",
     "    ax_ref.set_ylabel(\"Reference MIDI pitch\")\n",
     "    ax_ref.set_title(\"Reference\")\n",
     "\n",
-    "    # ------------------------------\n",
-    "    # Alignment lines\n",
-    "    # ------------------------------\n",
+    "    if visible_pitches:\n",
+    "        pitch_min = min(visible_pitches) - 1\n",
+    "        pitch_max = max(visible_pitches) + 1\n",
+    "        ax_response.set_ylim(pitch_min, pitch_max)\n",
+    "        ax_ref.set_ylim(pitch_min, pitch_max)\n",
+    "\n",
     "    for detail in selected_details:\n",
     "        operation = detail[\"operation_type\"]\n",
-    "\n",
     "        reference_index = detail.get(\"reference_index\")\n",
     "        response_index = detail.get(\"response_index\")\n",
     "\n",
-    "        if reference_index is None or response_index is None:\n",
-    "            continue\n",
-    "\n",
-    "        ref_event = ref_events[reference_index - 1]\n",
-    "        response_event = response_events[response_index - 1]\n",
+    "        if reference_index is not None and response_index is not None:\n",
+    "            ref_event = ref_events[reference_index - 1]\n",
+    "            response_event = response_events[response_index - 1]\n",
     "\n",
-    "        ref_x = ref_event[\"event_start\"]\n",
-    "        response_x = response_event[\"event_start\"]\n",
+    "            ref_x, ref_y = get_event_anchor(ref_event)\n",
+    "            response_x, response_y = get_event_anchor(response_event)\n",
     "\n",
-    "        # Use the average pitch as the event centre.\n",
-    "        ref_y = sum(\n",
-    "            note[\"pitch\"] for note in ref_event[\"notes\"]\n",
-    "        ) / len(ref_event[\"notes\"])\n",
+    "            response_visible = response_start <= response_x <= response_end\n",
+    "            reference_visible = ref_start <= ref_x <= ref_end\n",
     "\n",
-    "        response_y = sum(\n",
-    "            note[\"pitch\"] for note in response_event[\"notes\"]\n",
-    "        ) / len(response_event[\"notes\"])\n",
+    "            if operation == \"match\":\n",
+    "                line_style = \"-\"\n",
+    "                line_width = 0.8\n",
+    "                line_alpha = 0.35\n",
+    "                line_colour = match_colour\n",
+    "            else:\n",
+    "                line_style = \"--\"\n",
+    "                line_width = 2.2\n",
+    "                line_alpha = 0.9\n",
+    "                line_colour = replacement_colour\n",
+    "\n",
+    "            if response_visible and reference_visible:\n",
+    "                connector = ConnectionPatch(\n",
+    "                    xyA=(response_x, response_y),\n",
+    "                    coordsA=ax_response.transData,\n",
+    "                    xyB=(ref_x, ref_y),\n",
+    "                    coordsB=ax_ref.transData,\n",
+    "                    linestyle=line_style,\n",
+    "                    linewidth=line_width,\n",
+    "                    color=line_colour,\n",
+    "                    alpha=line_alpha,\n",
+    "                    clip_on=True,\n",
+    "                    zorder=2,\n",
+    "                )\n",
     "\n",
-    "        if operation == \"match\":\n",
-    "            line_style = \"-\"\n",
-    "            line_width = 1.0\n",
-    "        else:\n",
-    "            line_style = \"--\"\n",
-    "            line_width = 2.0\n",
-    "\n",
-    "        connector = ConnectionPatch(\n",
-    "            xyA=(response_x, response_y),\n",
-    "            coordsA=ax_response.transData,\n",
-    "            xyB=(ref_x, ref_y),\n",
-    "            coordsB=ax_ref.transData,\n",
-    "            linestyle=line_style,\n",
-    "            linewidth=line_width,\n",
-    "            alpha=0.6,\n",
-    "        )\n",
+    "                fig.add_artist(connector)\n",
     "\n",
-    "        fig.add_artist(connector)\n",
+    "    missing_count = 0\n",
+    "    extra_count = 0\n",
     "\n",
-    "        # Mark missing and extra events.\n",
     "    for detail in selected_details:\n",
     "        operation = detail[\"operation_type\"]\n",
     "\n",
     "        if operation == \"missing\":\n",
-    "            reference_index = detail[\"reference_index\"]\n",
-    "            ref_event = ref_events[reference_index - 1]\n",
-    "\n",
-    "            ref_y = sum(\n",
-    "                note[\"pitch\"] for note in ref_event[\"notes\"]\n",
-    "            ) / len(ref_event[\"notes\"])\n",
-    "\n",
-    "            ax_ref.text(\n",
-    "                ref_event[\"event_start\"],\n",
-    "                ref_y + 1,\n",
-    "                \"missing\",\n",
-    "                fontsize=8,\n",
-    "                rotation=45,\n",
-    "            )\n",
-    "\n",
-    "        elif operation == \"extra\":\n",
-    "            response_index = detail[\"response_index\"]\n",
-    "            response_event = response_events[response_index - 1]\n",
-    "\n",
-    "            response_y = sum(\n",
-    "                note[\"pitch\"] for note in response_event[\"notes\"]\n",
-    "            ) / len(response_event[\"notes\"])\n",
-    "\n",
-    "            ax_response.text(\n",
-    "                response_event[\"event_start\"],\n",
-    "                response_y + 1,\n",
-    "                \"extra\",\n",
-    "                fontsize=8,\n",
-    "                rotation=45,\n",
-    "            )\n",
-    "    \n",
-    "    ax_response.grid(axis=\"x\", alpha=0.3)\n",
-    "    ax_ref.grid(axis=\"x\", alpha=0.3)\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
+    "            reference_index = detail.get(\"reference_index\")\n",
     "\n",
+    "            if reference_index is not None:\n",
+    "                ref_event = ref_events[reference_index - 1]\n",
     "\n",
-    "plot_alignment(\n",
-    "    ref_events,\n",
-    "    response_events,\n",
-    "    event_details,\n",
-    "    ref_start=30.0,\n",
-    "    ref_end=35.0,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>status</th>\n",
-       "      <th>label</th>\n",
-       "      <th>onset</th>\n",
-       "      <th>gt_pitch</th>\n",
-       "      <th>pipeline_pitch</th>\n",
-       "      <th>onset_difference</th>\n",
-       "      <th>possible_cause</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>10.433771</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>11.362190</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>20.750020</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>24.355792</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>28.185924</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>32.012851</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>40.498970</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>109</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>41.455168</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>117</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>44.126110</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>onset tolerance boundary</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>127</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>47.995772</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>155</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>56.575908</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>onset tolerance boundary</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>156</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>58.241508</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>163</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>59.351552</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>61.246853</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>63.231898</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>190</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>67.107970</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>onset tolerance boundary</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>195</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>69.113314</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>onset tolerance boundary</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>197</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>68.101560</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>70.140024</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>202</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>70.113314</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>218</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>74.020369</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>236</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>80.917812</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>245</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>81.835548</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>251</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>85.641107</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>261</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>88.677435</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>293</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>97.756503</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>295</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>99.499026</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>302</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>100.685993</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>332</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>107.358008</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>FN</td>\n",
-       "      <td>paired</td>\n",
-       "      <td>112.917842</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>unmatched error</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    status   label       onset  gt_pitch  pipeline_pitch  onset_difference  \\\n",
-       "19      FN  paired   10.433771      69.0             NaN               NaN   \n",
-       "26      FN  paired   11.362190      70.0             NaN               NaN   \n",
-       "50      FN  paired   20.750020      72.0             NaN               NaN   \n",
-       "55      FN  paired   24.355792      72.0             NaN               NaN   \n",
-       "74      FN  paired   28.185924      77.0             NaN               NaN   \n",
-       "85      FN  paired   32.012851      75.0             NaN               NaN   \n",
-       "100     FN  paired   40.498970      65.0             NaN               NaN   \n",
-       "109     FN  paired   41.455168      67.0             NaN               NaN   \n",
-       "117     FN  paired   44.126110      74.0             NaN               NaN   \n",
-       "127     FN  paired   47.995772      74.0             NaN               NaN   \n",
-       "155     FN  paired   56.575908      68.0             NaN               NaN   \n",
-       "156     FN  paired   58.241508      68.0             NaN               NaN   \n",
-       "163     FN  paired   59.351552      76.0             NaN               NaN   \n",
-       "172     FN  paired   61.246853      76.0             NaN               NaN   \n",
-       "176     FN  paired   63.231898      69.0             NaN               NaN   \n",
-       "190     FN  paired   67.107970      69.0             NaN               NaN   \n",
-       "195     FN  paired   69.113314      66.0             NaN               NaN   \n",
-       "197     FN  paired   68.101560      76.0             NaN               NaN   \n",
-       "201     FN  paired   70.140024      62.0             NaN               NaN   \n",
-       "202     FN  paired   70.113314      66.0             NaN               NaN   \n",
-       "218     FN  paired   74.020369      72.0             NaN               NaN   \n",
-       "236     FN  paired   80.917812      75.0             NaN               NaN   \n",
-       "245     FN  paired   81.835548      68.0             NaN               NaN   \n",
-       "251     FN  paired   85.641107      65.0             NaN               NaN   \n",
-       "261     FN  paired   88.677435      63.0             NaN               NaN   \n",
-       "293     FN  paired   97.756503      66.0             NaN               NaN   \n",
-       "295     FN  paired   99.499026      69.0             NaN               NaN   \n",
-       "302     FN  paired  100.685993      70.0             NaN               NaN   \n",
-       "332     FN  paired  107.358008      77.0             NaN               NaN   \n",
-       "342     FN  paired  112.917842      76.0             NaN               NaN   \n",
-       "\n",
-       "               possible_cause  \n",
-       "19            unmatched error  \n",
-       "26            unmatched error  \n",
-       "50            unmatched error  \n",
-       "55            unmatched error  \n",
-       "74            unmatched error  \n",
-       "85            unmatched error  \n",
-       "100           unmatched error  \n",
-       "109           unmatched error  \n",
-       "117  onset tolerance boundary  \n",
-       "127           unmatched error  \n",
-       "155  onset tolerance boundary  \n",
-       "156           unmatched error  \n",
-       "163           unmatched error  \n",
-       "172           unmatched error  \n",
-       "176           unmatched error  \n",
-       "190  onset tolerance boundary  \n",
-       "195  onset tolerance boundary  \n",
-       "197           unmatched error  \n",
-       "201           unmatched error  \n",
-       "202           unmatched error  \n",
-       "218           unmatched error  \n",
-       "236           unmatched error  \n",
-       "245           unmatched error  \n",
-       "251           unmatched error  \n",
-       "261           unmatched error  \n",
-       "293           unmatched error  \n",
-       "295           unmatched error  \n",
-       "302           unmatched error  \n",
-       "332           unmatched error  \n",
-       "342           unmatched error  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "errors = comparison_df[\n",
-    "    comparison_df[\"status\"] != \"TP\"\n",
-    "].copy()\n",
+    "                for note in ref_event[\"notes\"]:\n",
+    "                    marker_x = note[\"start\"]\n",
+    "                    marker_y = note[\"pitch\"]\n",
     "\n",
-    "error_categories = []\n",
+    "                    if ref_start <= marker_x <= ref_end:\n",
+    "                        ax_ref.scatter(\n",
+    "                            marker_x, marker_y, marker=\"x\",\n",
+    "                            s=130, linewidths=3,\n",
+    "                            color=missing_colour,\n",
+    "                            zorder=10,\n",
+    "                        )\n",
     "\n",
-    "for index, error in errors.iterrows():\n",
-    "    category = \"unmatched error\"\n",
+    "                        missing_count += 1\n",
     "\n",
-    "    if error[\"status\"] == \"FP\":\n",
-    "        opposite_status = \"FN\"\n",
-    "    else:\n",
-    "        opposite_status = \"FP\"\n",
-    "\n",
-    "    possible_matches = errors[\n",
-    "        (errors[\"status\"] == opposite_status)\n",
-    "        & (errors[\"label\"] == error[\"label\"])\n",
+    "        elif operation == \"extra\":\n",
+    "            response_index = detail.get(\"response_index\")\n",
+    "\n",
+    "            if response_index is not None:\n",
+    "                response_event = response_events[response_index - 1]\n",
+    "\n",
+    "                for note in response_event[\"notes\"]:\n",
+    "                    marker_x = note[\"start\"]\n",
+    "                    marker_y = note[\"pitch\"]\n",
+    "\n",
+    "                    if response_start <= marker_x <= response_end:\n",
+    "                        ax_response.scatter(\n",
+    "                            marker_x, marker_y, marker=\"^\",\n",
+    "                            s=110, color=extra_colour,\n",
+    "                            edgecolors=\"black\",\n",
+    "                            linewidths=1, zorder=10,\n",
+    "                        )\n",
+    "\n",
+    "                        extra_count += 1\n",
+    "\n",
+    "    legend_items = [\n",
+    "        Line2D([0], [0], color=note_colour, linewidth=5, label=\"MIDI note\"),\n",
+    "        Line2D([0], [0], color=match_colour, linestyle=\"-\", linewidth=1.2, label=\"Matched event\"),\n",
+    "        Line2D([0], [0], color=replacement_colour, linestyle=\"--\", \n",
+    "               linewidth=2.2, label=\"Replacement event\"),\n",
+    "        Line2D([0], [0], color=missing_colour, marker=\"x\", linestyle=\"None\", \n",
+    "               markersize=9, markeredgewidth=2.5, label=\"Missing note\"),\n",
+    "        Line2D([0], [0], color=extra_colour, marker=\"^\", markeredgecolor=\"black\", \n",
+    "               linestyle=\"None\", markersize=9, label=\"Extra note\"),\n",
     "    ]\n",
     "\n",
-    "    # For insertion, pitch must also agree.\n",
-    "    if error[\"label\"] == \"insertion\":\n",
-    "        if error[\"status\"] == \"FP\":\n",
-    "            pitch = error[\"pipeline_pitch\"]\n",
-    "            possible_matches = possible_matches[\n",
-    "                possible_matches[\"gt_pitch\"] == pitch\n",
-    "            ]\n",
-    "        else:\n",
-    "            pitch = error[\"gt_pitch\"]\n",
-    "            possible_matches = possible_matches[\n",
-    "                possible_matches[\"pipeline_pitch\"] == pitch\n",
-    "            ]\n",
-    "\n",
-    "    nearest_difference = None\n",
-    "\n",
-    "    for _, other_error in possible_matches.iterrows():\n",
-    "        difference = abs(error[\"onset\"] - other_error[\"onset\"])\n",
-    "\n",
-    "        if nearest_difference is None or difference < nearest_difference:\n",
-    "            nearest_difference = difference\n",
-    "\n",
-    "    if nearest_difference is not None:\n",
-    "        if (\n",
-    "            nearest_difference > ONSET_TOLERANCE\n",
-    "            and nearest_difference <= 0.15\n",
-    "        ):\n",
-    "            category = \"onset tolerance boundary\"\n",
-    "\n",
-    "    error_categories.append(category)\n",
-    "\n",
-    "\n",
-    "errors[\"possible_cause\"] = error_categories\n",
-    "\n",
-    "display(errors.head(30))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "possible_cause\n",
-      "unmatched error             84\n",
-      "onset tolerance boundary    20\n",
-      "chord context               18\n",
-      "Name: count, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "for index, error in errors.iterrows():\n",
-    "    error_onset = error[\"onset\"]\n",
+    "    ax_response.legend(\n",
+    "        handles=legend_items,\n",
+    "        loc=\"upper right\",\n",
+    "        framealpha=0.9,\n",
+    "    )\n",
     "\n",
-    "    near_chord = False\n",
+    "    ax_response.grid(axis=\"x\", alpha=0.25)\n",
+    "    ax_ref.grid(axis=\"x\", alpha=0.25)\n",
     "\n",
-    "    for event in response_events:\n",
-    "        if event[\"event_type\"] != \"chord\":\n",
-    "            continue\n",
+    "    fig.suptitle(\"Event-level alignment with one connector per event\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
     "\n",
-    "        difference = abs(\n",
-    "            event[\"event_start\"]\n",
-    "            + selected_result[\"response_onset_offset\"]\n",
-    "            - error_onset\n",
-    "        )\n",
+    "    print(\"Selected operations:\", len(selected_details))\n",
+    "    print(\"Missing note markers:\", missing_count)\n",
+    "    print(\"Extra note markers:\", extra_count)\n",
     "\n",
-    "        if difference <= 0.10:\n",
-    "            near_chord = True\n",
-    "            break\n",
     "\n",
-    "    if near_chord:\n",
-    "        if errors.loc[index, \"possible_cause\"] == \"unmatched error\":\n",
-    "            errors.loc[index, \"possible_cause\"] = \"chord context\"\n",
-    "print(errors[\"possible_cause\"].value_counts())"
+    "plot_alignment(ref_events, response_events, event_details, ref_start=70.0, ref_end=80.0)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Current limitation\n",
+    "## limitation\n",
     "\n",
-    "Deletion matching is still count-based rather than identity-based. The ground-truth TSV identifies deleted score notes with `xml_id`, but the current pipeline output does not return that identifier, so we cannot yet confirm that a *specific* predicted deletion corresponds to a *specific* ground-truth deletion.\n",
+    "- The evaluation uses the nASAP note-alignment benchmark, which is designed to evaluate the alignment results of between music score and expert performance during competitions. In contrast, the objective of this project is to generate educational feedback for student practice, however, there is no large-scale public dataset contains students' practice MIDI and references. \n",
     "\n",
-    "This version does fix an earlier bug where a missing (deleted) **chord** contributed 0 notes to the deletion count, because `event_details` leaves `correct_pitches`/`missing_pitches` as `None` for `missing`/`extra` events (this is true for both notes and chords, confirmed in `event_level_feedback()`). We now read the true note count for a missing chord directly from `ref_events` via `reference_index`, so the total deletion count is accurate again — only the note-to-note identity link is still missing.\n",
+    "- The default alignment parameters (e.g., gap penalty, timing tolerance and chord onset window) were chosen empirically and may not be optimal for all musical styles, therefore these parameters are designed to be allowed for configuration.\n",
     "\n",
-    "A future improvement would be to keep the reference-note index or `xml_id` in each alignment result so that identity-based deletion matching becomes possible."
+    "- My alignment algorithm is event-level, however, the grouping of chords currently relies on a fixed onset threshold, identification of broken chords (Arpeggios), ornaments and other highly expressive performance strategies are left as future work."
    ]
   }
  ],