From 9e596384ff9d4d54c0c981284e8dee8a86a083fb Mon Sep 17 00:00:00 2001
From: Milton Pividori <miltondp@gmail.com>
Date: Thu, 4 Jan 2024 00:17:55 -0700
Subject: [PATCH] intro/relationships: add simulated categorical datasets

---
 .../intro/05-relationships_analysis.ipynb     | 1500 ++++++++++++++---
 .../intro/py/05-relationships_analysis.py     |  360 +++-
 2 files changed, 1616 insertions(+), 244 deletions(-)

diff --git a/nbs/99_manuscript/intro/05-relationships_analysis.ipynb b/nbs/99_manuscript/intro/05-relationships_analysis.ipynb
index c4f8b4c7..0cd57347 100644
--- a/nbs/99_manuscript/intro/05-relationships_analysis.ipynb
+++ b/nbs/99_manuscript/intro/05-relationships_analysis.ipynb
@@ -5,10 +5,10 @@
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@@ -22,10 +22,10 @@
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@@ -39,10 +39,10 @@
    "id": "8b7de09b-03ff-445a-b460-9a32fe7c70ad",
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@@ -57,16 +57,16 @@
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+     "iopub.execute_input": "2024-01-04T08:12:40.710175Z",
+     "iopub.status.busy": "2024-01-04T08:12:40.709812Z",
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@@ -90,16 +90,16 @@
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-     "shell.execute_reply": "2023-12-04T18:41:44.694095Z"
+     "iopub.execute_input": "2024-01-04T08:12:41.552054Z",
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@@ -120,10 +120,10 @@
    "id": "0619629b-09ba-462b-be5b-ad48ff7e8ffa",
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@@ -132,15 +132,40 @@
     "# Settings"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "950a4c5b-b29c-4db3-8fdf-605a412fb069",
+   "metadata": {
+    "execution": {
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+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "PVALUE_N_PERMS = 10000"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a629452d-4419-4114-b7a2-176d99eaad5c",
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@@ -151,20 +176,20 @@
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@@ -178,20 +203,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
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+     "iopub.execute_input": "2024-01-04T08:12:41.654084Z",
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@@ -218,16 +243,16 @@
    "id": "8a2c4b73-af23-44c8-8b30-b297ed3530e2",
    "metadata": {
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+     "start_time": "2024-01-04T08:12:41.667086",
      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "# Generate datasets"
+    "# Numerical datasets"
    ]
   },
   {
@@ -235,10 +260,10 @@
    "id": "c785968f-e38f-4c53-b853-f3d686ad363c",
    "metadata": {
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@@ -249,20 +274,20 @@
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@@ -299,7 +324,7 @@
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@@ -310,20 +335,20 @@
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@@ -399,7 +424,7 @@
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@@ -538,7 +563,7 @@
        "max    19.000000  12.740000"
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+     "duration": 0.009633,
+     "end_time": "2024-01-04T08:12:41.842662",
      "exception": false,
-     "start_time": "2023-12-04T18:41:44.885793",
+     "start_time": "2024-01-04T08:12:41.833029",
      "status": "completed"
     },
     "tags": []
@@ -617,20 +642,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "7b79e345-3dd0-48cf-9dee-2fb45822b074",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:44.901548Z",
-     "iopub.status.busy": "2023-12-04T18:41:44.901223Z",
-     "iopub.status.idle": "2023-12-04T18:41:44.910192Z",
-     "shell.execute_reply": "2023-12-04T18:41:44.909665Z"
+     "iopub.execute_input": "2024-01-04T08:12:41.854966Z",
+     "iopub.status.busy": "2024-01-04T08:12:41.854476Z",
+     "iopub.status.idle": "2024-01-04T08:12:41.870879Z",
+     "shell.execute_reply": "2024-01-04T08:12:41.870191Z"
     },
     "papermill": {
-     "duration": 0.014111,
-     "end_time": "2023-12-04T18:41:44.911192",
+     "duration": 0.0237,
+     "end_time": "2024-01-04T08:12:41.871927",
      "exception": false,
-     "start_time": "2023-12-04T18:41:44.897081",
+     "start_time": "2024-01-04T08:12:41.848227",
      "status": "completed"
     },
     "tags": []
@@ -665,10 +690,10 @@
    "id": "869da4d1-0816-4ded-940f-8eada4532490",
    "metadata": {
     "papermill": {
-     "duration": 0.003497,
-     "end_time": "2023-12-04T18:41:44.918558",
+     "duration": 0.005563,
+     "end_time": "2024-01-04T08:12:41.883484",
      "exception": false,
-     "start_time": "2023-12-04T18:41:44.915061",
+     "start_time": "2024-01-04T08:12:41.877921",
      "status": "completed"
     },
     "tags": []
@@ -679,20 +704,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "c7f7a4aa-d9eb-4917-beb8-59d91947b97f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:44.926919Z",
-     "iopub.status.busy": "2023-12-04T18:41:44.926545Z",
-     "iopub.status.idle": "2023-12-04T18:41:44.929416Z",
-     "shell.execute_reply": "2023-12-04T18:41:44.928940Z"
+     "iopub.execute_input": "2024-01-04T08:12:41.896018Z",
+     "iopub.status.busy": "2024-01-04T08:12:41.895723Z",
+     "iopub.status.idle": "2024-01-04T08:12:41.898787Z",
+     "shell.execute_reply": "2024-01-04T08:12:41.898330Z"
     },
     "papermill": {
-     "duration": 0.008054,
-     "end_time": "2023-12-04T18:41:44.930237",
+     "duration": 0.010359,
+     "end_time": "2024-01-04T08:12:41.899569",
      "exception": false,
-     "start_time": "2023-12-04T18:41:44.922183",
+     "start_time": "2024-01-04T08:12:41.889210",
      "status": "completed"
     },
     "tags": []
@@ -704,20 +729,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "d345cc0c-3e75-4fcb-9556-f8369b734449",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:44.938520Z",
-     "iopub.status.busy": "2023-12-04T18:41:44.938191Z",
-     "iopub.status.idle": "2023-12-04T18:41:44.948149Z",
-     "shell.execute_reply": "2023-12-04T18:41:44.947609Z"
+     "iopub.execute_input": "2024-01-04T08:12:41.911643Z",
+     "iopub.status.busy": "2024-01-04T08:12:41.911286Z",
+     "iopub.status.idle": "2024-01-04T08:12:41.921892Z",
+     "shell.execute_reply": "2024-01-04T08:12:41.921465Z"
     },
     "papermill": {
-     "duration": 0.015181,
-     "end_time": "2023-12-04T18:41:44.949200",
+     "duration": 0.017563,
+     "end_time": "2024-01-04T08:12:41.922686",
      "exception": false,
-     "start_time": "2023-12-04T18:41:44.934019",
+     "start_time": "2024-01-04T08:12:41.905123",
      "status": "completed"
     },
     "tags": []
@@ -760,10 +785,10 @@
    "id": "689579f3-f2cb-40d4-b913-378217f94acb",
    "metadata": {
     "papermill": {
-     "duration": 0.003493,
-     "end_time": "2023-12-04T18:41:44.956712",
+     "duration": 0.005492,
+     "end_time": "2024-01-04T08:12:41.933781",
      "exception": false,
-     "start_time": "2023-12-04T18:41:44.953219",
+     "start_time": "2024-01-04T08:12:41.928289",
      "status": "completed"
     },
     "tags": []
@@ -774,20 +799,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "df0553e7-3d19-4fcb-b294-69c5a2292e9c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:44.964899Z",
-     "iopub.status.busy": "2023-12-04T18:41:44.964532Z",
-     "iopub.status.idle": "2023-12-04T18:41:44.967322Z",
-     "shell.execute_reply": "2023-12-04T18:41:44.966839Z"
+     "iopub.execute_input": "2024-01-04T08:12:41.946007Z",
+     "iopub.status.busy": "2024-01-04T08:12:41.945692Z",
+     "iopub.status.idle": "2024-01-04T08:12:41.948252Z",
+     "shell.execute_reply": "2024-01-04T08:12:41.947852Z"
     },
     "papermill": {
-     "duration": 0.007795,
-     "end_time": "2023-12-04T18:41:44.968127",
+     "duration": 0.009676,
+     "end_time": "2024-01-04T08:12:41.949051",
      "exception": false,
-     "start_time": "2023-12-04T18:41:44.960332",
+     "start_time": "2024-01-04T08:12:41.939375",
      "status": "completed"
     },
     "tags": []
@@ -799,20 +824,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "545070ae-9722-4ed4-a4e7-48faf33c912d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:44.976314Z",
-     "iopub.status.busy": "2023-12-04T18:41:44.975930Z",
-     "iopub.status.idle": "2023-12-04T18:41:44.984832Z",
-     "shell.execute_reply": "2023-12-04T18:41:44.984351Z"
+     "iopub.execute_input": "2024-01-04T08:12:41.961113Z",
+     "iopub.status.busy": "2024-01-04T08:12:41.960725Z",
+     "iopub.status.idle": "2024-01-04T08:12:41.969932Z",
+     "shell.execute_reply": "2024-01-04T08:12:41.969517Z"
     },
     "papermill": {
-     "duration": 0.013807,
-     "end_time": "2023-12-04T18:41:44.985601",
+     "duration": 0.016083,
+     "end_time": "2024-01-04T08:12:41.970729",
      "exception": false,
-     "start_time": "2023-12-04T18:41:44.971794",
+     "start_time": "2024-01-04T08:12:41.954646",
      "status": "completed"
     },
     "tags": []
@@ -852,10 +877,10 @@
    "id": "16c45c61-8ad7-4fea-9853-c41f60baf976",
    "metadata": {
     "papermill": {
-     "duration": 0.003491,
-     "end_time": "2023-12-04T18:41:44.992790",
+     "duration": 0.005457,
+     "end_time": "2024-01-04T08:12:41.981814",
      "exception": false,
-     "start_time": "2023-12-04T18:41:44.989299",
+     "start_time": "2024-01-04T08:12:41.976357",
      "status": "completed"
     },
     "tags": []
@@ -866,20 +891,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "d066318e-1f1c-4032-87c8-cf2557a62230",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:45.000891Z",
-     "iopub.status.busy": "2023-12-04T18:41:45.000544Z",
-     "iopub.status.idle": "2023-12-04T18:41:45.003241Z",
-     "shell.execute_reply": "2023-12-04T18:41:45.002763Z"
+     "iopub.execute_input": "2024-01-04T08:12:41.993927Z",
+     "iopub.status.busy": "2024-01-04T08:12:41.993578Z",
+     "iopub.status.idle": "2024-01-04T08:12:41.996208Z",
+     "shell.execute_reply": "2024-01-04T08:12:41.995804Z"
     },
     "papermill": {
-     "duration": 0.00766,
-     "end_time": "2023-12-04T18:41:45.004040",
+     "duration": 0.009648,
+     "end_time": "2024-01-04T08:12:41.997010",
      "exception": false,
-     "start_time": "2023-12-04T18:41:44.996380",
+     "start_time": "2024-01-04T08:12:41.987362",
      "status": "completed"
     },
     "tags": []
@@ -891,20 +916,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "beb68af6-dd8a-4da3-a532-7477aa0c6fbc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:45.012218Z",
-     "iopub.status.busy": "2023-12-04T18:41:45.011802Z",
-     "iopub.status.idle": "2023-12-04T18:41:45.026217Z",
-     "shell.execute_reply": "2023-12-04T18:41:45.025444Z"
+     "iopub.execute_input": "2024-01-04T08:12:42.009067Z",
+     "iopub.status.busy": "2024-01-04T08:12:42.008919Z",
+     "iopub.status.idle": "2024-01-04T08:12:42.015367Z",
+     "shell.execute_reply": "2024-01-04T08:12:42.014962Z"
     },
     "papermill": {
-     "duration": 0.019741,
-     "end_time": "2023-12-04T18:41:45.027430",
+     "duration": 0.01332,
+     "end_time": "2024-01-04T08:12:42.016147",
      "exception": false,
-     "start_time": "2023-12-04T18:41:45.007689",
+     "start_time": "2024-01-04T08:12:42.002827",
      "status": "completed"
     },
     "tags": []
@@ -937,10 +962,10 @@
    "id": "c51eaf1c-4bff-40ef-9c76-fcea77ca90c5",
    "metadata": {
     "papermill": {
-     "duration": 0.003683,
-     "end_time": "2023-12-04T18:41:45.055546",
+     "duration": 0.005469,
+     "end_time": "2024-01-04T08:12:42.027184",
      "exception": false,
-     "start_time": "2023-12-04T18:41:45.051863",
+     "start_time": "2024-01-04T08:12:42.021715",
      "status": "completed"
     },
     "tags": []
@@ -951,20 +976,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "9328a89b-dbd7-4faf-adeb-8a9a3a60af9e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:45.063643Z",
-     "iopub.status.busy": "2023-12-04T18:41:45.063451Z",
-     "iopub.status.idle": "2023-12-04T18:41:45.068616Z",
-     "shell.execute_reply": "2023-12-04T18:41:45.068217Z"
+     "iopub.execute_input": "2024-01-04T08:12:42.039065Z",
+     "iopub.status.busy": "2024-01-04T08:12:42.038915Z",
+     "iopub.status.idle": "2024-01-04T08:12:42.046013Z",
+     "shell.execute_reply": "2024-01-04T08:12:42.045610Z"
+    },
+    "papermill": {
+     "duration": 0.01402,
+     "end_time": "2024-01-04T08:12:42.046797",
+     "exception": false,
+     "start_time": "2024-01-04T08:12:42.032777",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "datasets = {\n",
+    "    idx: df.drop(columns=\"dataset\") for idx, df in datasets_df.groupby(\"dataset\")\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "555a7d7c-2ea1-471a-8ada-77fce5837bf7",
+   "metadata": {
+    "papermill": {
+     "duration": 0.005533,
+     "end_time": "2024-01-04T08:12:42.057935",
+     "exception": false,
+     "start_time": "2024-01-04T08:12:42.052402",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Create dataset dictionary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1589084-f27a-44b6-a064-e22093d2a351",
+   "metadata": {
+    "papermill": {
+     "duration": 0.005452,
+     "end_time": "2024-01-04T08:12:42.068936",
+     "exception": false,
+     "start_time": "2024-01-04T08:12:42.063484",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Create a dictionary with easier access to datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e6c54042-e751-4fd7-96db-5274cd7d8eb1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:12:42.081143Z",
+     "iopub.status.busy": "2024-01-04T08:12:42.080658Z",
+     "iopub.status.idle": "2024-01-04T08:12:42.087775Z",
+     "shell.execute_reply": "2024-01-04T08:12:42.087366Z"
     },
     "papermill": {
-     "duration": 0.010164,
-     "end_time": "2023-12-04T18:41:45.069340",
+     "duration": 0.014062,
+     "end_time": "2024-01-04T08:12:42.088587",
      "exception": false,
-     "start_time": "2023-12-04T18:41:45.059176",
+     "start_time": "2024-01-04T08:12:42.074525",
      "status": "completed"
     },
     "tags": []
@@ -981,34 +1067,34 @@
    "id": "e9110ac9-5968-405f-9070-eca3f9389a54",
    "metadata": {
     "papermill": {
-     "duration": 0.003585,
-     "end_time": "2023-12-04T18:41:45.076607",
+     "duration": 0.00545,
+     "end_time": "2024-01-04T08:12:42.099904",
      "exception": false,
-     "start_time": "2023-12-04T18:41:45.073022",
+     "start_time": "2024-01-04T08:12:42.094454",
      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "# Plot"
+    "## Plot"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "id": "0109f1d9-22ff-42fd-8bf8-ab84f449cbbd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:45.084391Z",
-     "iopub.status.busy": "2023-12-04T18:41:45.084301Z",
-     "iopub.status.idle": "2023-12-04T18:41:45.089433Z",
-     "shell.execute_reply": "2023-12-04T18:41:45.089056Z"
+     "iopub.execute_input": "2024-01-04T08:12:42.111901Z",
+     "iopub.status.busy": "2024-01-04T08:12:42.111566Z",
+     "iopub.status.idle": "2024-01-04T08:12:42.119638Z",
+     "shell.execute_reply": "2024-01-04T08:12:42.119224Z"
     },
     "papermill": {
-     "duration": 0.009929,
-     "end_time": "2023-12-04T18:41:45.090158",
+     "duration": 0.014956,
+     "end_time": "2024-01-04T08:12:42.120420",
      "exception": false,
-     "start_time": "2023-12-04T18:41:45.080229",
+     "start_time": "2024-01-04T08:12:42.105464",
      "status": "completed"
     },
     "tags": []
@@ -1055,20 +1141,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "id": "8201a1b3-a455-41ac-913f-98b3ba9363d8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:45.098358Z",
-     "iopub.status.busy": "2023-12-04T18:41:45.098020Z",
-     "iopub.status.idle": "2023-12-04T18:41:45.101537Z",
-     "shell.execute_reply": "2023-12-04T18:41:45.101060Z"
+     "iopub.execute_input": "2024-01-04T08:12:42.132228Z",
+     "iopub.status.busy": "2024-01-04T08:12:42.132075Z",
+     "iopub.status.idle": "2024-01-04T08:12:42.135326Z",
+     "shell.execute_reply": "2024-01-04T08:12:42.134911Z"
     },
     "papermill": {
-     "duration": 0.008542,
-     "end_time": "2023-12-04T18:41:45.102363",
+     "duration": 0.010096,
+     "end_time": "2024-01-04T08:12:42.136093",
      "exception": false,
-     "start_time": "2023-12-04T18:41:45.093821",
+     "start_time": "2024-01-04T08:12:42.125997",
      "status": "completed"
     },
     "tags": []
@@ -1089,20 +1175,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "id": "bbd15cd4-c9ea-40a7-a08c-c2a15781ce4e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:45.110622Z",
-     "iopub.status.busy": "2023-12-04T18:41:45.110294Z",
-     "iopub.status.idle": "2023-12-04T18:41:45.114063Z",
-     "shell.execute_reply": "2023-12-04T18:41:45.113590Z"
+     "iopub.execute_input": "2024-01-04T08:12:42.148155Z",
+     "iopub.status.busy": "2024-01-04T08:12:42.147782Z",
+     "iopub.status.idle": "2024-01-04T08:12:42.151272Z",
+     "shell.execute_reply": "2024-01-04T08:12:42.150866Z"
     },
     "papermill": {
-     "duration": 0.008765,
-     "end_time": "2023-12-04T18:41:45.114869",
+     "duration": 0.010335,
+     "end_time": "2024-01-04T08:12:42.152056",
      "exception": false,
-     "start_time": "2023-12-04T18:41:45.106104",
+     "start_time": "2024-01-04T08:12:42.141721",
      "status": "completed"
     },
     "tags": []
@@ -1120,20 +1206,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "id": "55ecad23-cf11-4ed5-a81d-c7faa16ad8b9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-04T18:41:45.123092Z",
-     "iopub.status.busy": "2023-12-04T18:41:45.122673Z",
-     "iopub.status.idle": "2023-12-04T18:42:09.576208Z",
-     "shell.execute_reply": "2023-12-04T18:42:09.575801Z"
+     "iopub.execute_input": "2024-01-04T08:12:42.164056Z",
+     "iopub.status.busy": "2024-01-04T08:12:42.163732Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.098399Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.097965Z"
     },
     "papermill": {
-     "duration": 24.459916,
-     "end_time": "2023-12-04T18:42:09.578416",
+     "duration": 24.943006,
+     "end_time": "2024-01-04T08:13:07.100650",
      "exception": false,
-     "start_time": "2023-12-04T18:41:45.118500",
+     "start_time": "2024-01-04T08:12:42.157644",
      "status": "completed"
     },
     "tags": []
@@ -1141,7 +1227,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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iUZ5KigtUmJ/rd1gAYoAiMQAAAAAAADpVVdugqbOXqL6xSZJUVlmvOYuqNXf6eArFQApgugkAAAAAAAB0qrS8pq1AHFTf2KTS8hqfIgIQSxSJAQAAAAAA0KHmllYtrqiLuK6ssk4tUcxRzDzGQGJjugkAAAAAAAB0KCszQxOL8lRWWd9u+ej8XJ31rT2UmdlxH8Tqugb9+40vmccYSHAUiQEAAAAAANCpkuICzVlUrfrGJg0Z1E/XHD9WVbUNemLpZ/ps7aaIxd91jU2a9uBSrWz2yk/MYwwkLorEAAAAAAAA6FRhfq7mTh+v0vIajcobpBv//kGXN7H7ZE2D1g5tkvpvKz8F5zGeMbmoz98DgI4xJzGQhpgLCsmG7ywAAADgv8L8XM2YXKTquk1d3sSuuaVVK9duirifaOcxBtB36EkMpJGq2gaVltcwFxSSBt9ZAAAAILF0dRO76UcUKjMzQ1mZGfqvXXI1aUy+Pmo0VdY2tG03YXRep/MYA+h7FImBNFFV26Cps5d0ORwISBSdfWf3GDpIWYFGZWurY1gMAAAA0Ec6uomd1L74W13XoE1NLdq8tUWTxuyqCw8fpdue+0gZZiopLujrsAF0gSIxkCZKy2s6HA7EXFBIRB19Z59+e5VqvtqsPYbm6FtDs1T3/mqtfqVSxxxSxAUPAAAAoA+E3sQuaFhOdlvxt6q2QT94cKlOr6rXf4at1fxVX2tYTrb+3/cO0PCdBtJuBxIQRWIgDUQ7HAhIFJ19Z9/+bJ0G9MvUU29+rN2zmnVvXo6ue/ET3f9WLT3jAQAAgD4QehO7sso6TRjdfmq40vIarY3Q4ePdz7/SYXvl+xEygC5QFQLSQHA4UCTMBYVE1Nl3dmzBDlpR590AY21jk9Y2NmlUXu52N8oAAHQfNwoFEAm5AZEEb2L3yPmHaMbkbaP6uuqkxA3rgMREZQhIEyXFBRqWk91uWehwICDRdPSdLdo5t91NL+oatmjPYQMl0egEgJ6qqm3QPQsqNG3OUt2zoEJVIXkWQPoiNyAa4Z2O6KQEJCemmwDSRFfDgYBEE/6dPWCPIdptp4G67bmP2m2Xl9tfn9ZvlkSjEwB6gpvbAojkgy++0rQHl5Ib0CMlxQV68iU6KQHJhCIxkEaCw4GYgxjJIvQ7+8VXm3XKPWVat2lr2/qhOdkampOt6roGDRu6E41OAOgBbm4LIFRzS6tqN36tv7+9ityAHivMz9UjFxyi2pUv6f3RQ3XgvnvSSQlIcBSJgTREgRjJJjMzQy2tTnef9U0tqqjT+6u+0riRQ3VIXrZW3PQvXX7KXjrmkCIanQDQTdzcFkBQVW2DSstrtLiiTt8aOVTf3H0nDRnUr90FeoncgOiNysvVqFFDddA5Bytzpx3blje3tCqL7w+QcCgSAwASXuhQ6NH5uRqZN0gvf1Knk0aP0iH77aqMI0ZLgykQA0B3BeeNLKus324dU/gA6SPStDPDcrJ1zfFj9dOn32+3LbkB3RX8voReiJhYxPSHQKKhSIyEw1VFAOFCh0JX1ja03bjuhWWDdFGG+RkaACS9kuICzVlU3W5YOfNGAumlo2lnqmobNDp/202DyQ3oKea/BxIfReIkkurFU64qAoiks6HQ/6mu14WtTqmbGQEg/ri5LZDeOmtrLVu9QRcePlLPvrda++y6g045YLeoc0Oqn7+ie5j/Hkh8FImTQDoUT7mqCKAjnQ2FHjdqmDLq6EkMAL3FzW2B9NVZW+uAPYbo83WbdNa39tA3d99Ruw/N6XJ/6XD+iu7p7ELEivoGNW1tUXa/zD6OCkA4isQJLl2Kp1xVBNCZjoZCH7vPLtLrPgYGAEkuvKcfBWIgPXXU1jrtgN2059BBUeeGdDl/RWQd9R6PdCFiyKB+uub4sVq1frPOe+h1LigACYAicYJLh+Ipd9UG0JWOhkKPGuD8Dg0AkhI9/QCEyjDppyVj9WHNRn1Us0FjC3bQ3gWDlWHdu3iUDuev2F40x5TwCxHXHD9Wd8xbzgUFIIFQeUtgXRVPW1pa+zii+AheVYyEO+cCCAoOhX7k/EM0Y3IRjUcA6KFgT7/b5y1XWWW9bp+3XFNnL1FV4MZUANLPv96v0dVPvaeFy2s1oF+mFi6v1dVPvad/vV8T9T7S5fwV7QWPKU+/tUqDsjP19FurIh5Tgp0+Zk4ZozMOHqFV6zd3eEEBgD+oviWwdCqelhQXaFhOdrtl3DkXQCSplPsAwA+d9fQDkH5Ci7uVtQ166cM1qgwU+LpT3E2n81dss2D5Gl09ZYwmjcnXpqYWTRqTr6unjNGC5Wu22zbY6eNX3ynWW5+ui7g/LigA/iFLJ7h0KZ6GXlWcWDRMM6eMYZgJAABAjNHTD0C4WBZ30+X8FZ7mllYV7DBAd8xbrgcXVaussl4PLqrWHfOWa9cdB3R4TMnul8kFBSAB+TonsZmNkHSNpIMlfVPSQEmjnHMrQrY5WtL5ksZLGi7pC0nPSZrlnKvt45D7XEfzcKZi8ZS7agMAAMRXpJsHBXFiDqSvjm5c193ibjqdv8I7pnxYszHi6JRlqzfqhJGDO3xurL5zAGLH7xvXFUk6U9Kbkl6WdHyEbaZLypX0K0lVkvaSNEvScWb2TedcY9+E6p90K56mw3tEcuvorr0AACQDTswBhItlcTfdzl/TWXNLa7tpI0bn52pU3iBV123S25+tU8shBcrs4LlcUAASj99F4lecc7tIkpmdp8hF4kvDegy/bGYfSXpV0umSHol7lAmCAyzgL+4EDwBIBV2dmHMxFEhPsS7ucv6a+oKjUz5cvUHXHD9WFWsa9OHqDZo0Jl+Tx+R3+R3gggKQWHwtEjvnupz0rIMpJd4M/NwtthEBSAc9OfkN3rU32OuqrLJecxZVM3c2ACAphZ+YN7e0cjEUgCSKu+iekuIC7TK4v24p/ajdudLf316lp84p1qgo9sF3DkgMfvck7qkjAz8/9DUKAEmlNye/nd0JfsbkoniECwBA3H26dpNKy2s0ctgg/fwfH3AxFADQLYX5uXr2vdURz5VeWPalLvIpLgDdl3SXa8xssKS7JJVL+qe/0QBIFsGewLfPW66yynrdPm+5ps5eoqrahi6fy53gAQCpKHhs/Nvbq/TWZ+s7vBgKAEBHmlta9VrV9jdDlaT/VNertdX1cUQAeiqpisRmliXpCUm7Svqec66lg+1mmZkLffRpoAASTmc9gbsSnGsrknS7Ezz5FQDiw4/8Gjw2jhw2SB+u3hBxGy6GAkhmtF1jo7mT40Bn50rjRg1TRobFKywAMZY0lQ0zy5D0sKSjJX3HOVfe0bbOuVnOOQt99FmgABJOLHoClxQXaFhOdrtl6XgnePIrAMRHX+fX0GNjdd0m7b3rDhG3S7eLoQBSC23X3qmua9DS6rW66NE3dM+Cig5HYXZ0rnTsPrv0RZgAYiSZ5iSeLekMSac65xb6HEvUuDs04L/g1e2yyu2HQUV78tvVneDhn9ZWlzxXPAEgQYQeGytrG3Th4aM0LCe73aibdLwYCgDwVNU26AcPLtXplXX6z9C1mr/q6w7nqu/oXGnUADpuA8kkKYrEZnanpAskneuce9bveKLB3aF7LpEK64kUC3qnpLhAcxZV9+rkN/xO8PBXVW2DXlxaqWFvr1L9K5U65pAi8iwAdEPosfG25z7SNcePVVVtg5at3sDFUCANcK6DzpSW12htN27cHfFcaePGvggVQIz4XiQ2s+8G/nlw4GeJmdVKWuGce8PMrpV0paQ5kqrM7NCQp9c65yr7MNyoBG8Cwt2huyeRCut9EQuNsr4Vy57AFIj9F8yzX69drx+t26Q/vPiJ7n+rljwLAN0Qfmxc29ikaeP31PAdB3KsA1JYIp13ITF1NV1fZ51mOH4Aycv3IrGkuWG/3xv4+bCk8ySVBH7/YeARKrhNQunsBlmRrrihe4X1eBdX413kp1HmH3oCp45gns0JWUaeBYDu49gIpBc6NCEawSmJ3l22crt1zFUPpC7f/7LDJ5EPeZwXWD+pq20SSSxukJWOOiusB1XVNuieBRWaNmdpp5Pm90UsPRVslN0+b7nKKut1+7zlmjp7SdzeCyKjUZPcyLMAEHscG4H0EM9zHaSWkuICDeXG3UBaoTUYY8ErbpFwxS2yaAo+fVVcjXfxiUYZ0HvkWQAAgO7jQju6ozA/V49ccIgmjM7ToaOHauaUMfQ4B1IcZ9JxUFJcoGFccYtaNAWfviquxrP4RKMMiB3yLAAAQPdwoR3dNSovV4eMGqoHzjlYMyZzk2gg1XEUiIPgTUBmThmjiUXDEuqKW3OCFiI7K/j0dXE1XsUnGmVA7ATz7OXH7KXdhw7S5cfslTB5FgAAIFFxoR09wbkqkB4S4cZ1KSnRbgKS6DdLC7+79oTR7WOcWJSnssr67Z4Xj+JqV7H0RklxgeYsqm7XK5pGGdAzhfm5uuiI0Wr9z27KOGK0NDhxchoAAEAiiue5DgAguVEkjrNEKRAnwx1sOyus93VxNV5FfhplQOxlZJjfIQAAACSNROvQhOTS3NKqLL43QEqiSJwGOpvPd8bkIp+i6likhopfxdV4NJpolAEAAADwG+ci6I6q2ga9VlWvVes3a7edBurQwmFdno83t7RSdAKSCH+vKa6r+XyTqVDpd3E11ldMk+VzBwAAAACkr0/rG/RWXbMqaxv14eoN+nprq7IzM5Rh0si87QvFweku3yz/VNOr1yq/rkGjBg/2IXIA3UGROMUFb5bWV/P59oW+jjnR53NG4mEIFgAAAIBU8cW6r3VLaUW7KSyH5WTr7rO+uV2ROHS6y5wtm7R/ZZ3+78GlevjyozmPBhIcVYw0wB1sey54gLt93nKVVdbr9nnLNXX2ElXVNvgdGsI0t7T6HYKqaht0z4IKTZuzVPcsqOB7AgBIe4lwfAYA9Fxrq9OSqvqIU1guqqhTS1iejzTd5drAdJcAEhs9idMAN0vruWSbzzkdJUpP72S5QSQAAH0hUY7PAIDeK//iq4jL31/VfnkqTXcJpCOKxD7wYyi63/P5JqNYH+CYgiD2EqkwywUFAAA8iXR8BgD0TkaG6Vsjh2r+qi+2Wzdu1LB258SpON0lkE74C+1DiTAUnaQcveABLpLuHOAS4f+9M8k8DLSzwmxf6uqCQvgQLAAAUlmiHJ8BbC+Z2/7wz/EdTGH57f133W7bSNNdDmW6SyAp0JO4j9CjIjmVFBdozqLqdic63ZnPOZH/35N9GGgiDWXiijkAAJ5EOj4D2CbZ2/7w16i86KewDJ3u8q0PPtWEtXn6zgWHaBTfNyDhUSTuIwxFT069nc85Uf/fE7l4Ha1EK8z29oICAACpINGOzwBSo+0PfzW3tKowf8eop7AMTnfZckC+MutelfL4ngHJgFZaH2AoenILHuAeOf8QzZhcFHVDKpH/31NlGGikoUx+FWaDFxRmThmjiUXDNHPKGBreAIC0lEjHZwCR2/47DcrWB19s8CkiJIvqugYtrV6rix59o23qxO5c7OPCIJBc6EncB+hRkRq6+/+UqP/vqTQMtLc9veMRDzeIBACkuz2GDkqo4zOQzsLb/kMG9dM1x49VxZoGPbH0M322dhN/n4ioqrZBP3hwqU6vrNN/hq7V/FVf0wMdSHEUifsIQ9HTUyL+vydq8bqnErEwmyhxpLrmllYOYgCQQMLnPD35G7sm1PEZSEfhbf9rjh+rO+YtZ+oJdKm0vEZrE3DqRADxw/l1H/Gzx2NzS6uyaJz7ItF6ugYlYvG6t2JxAsrfSnIILUJMGj5AUxubNMTvoAAgzTHnKZC4gm3/ITnZqljTkJD3TEFiSaXRpwCiR5G4DwV7PP7osFHK7pcZ99fjDraJIRF7uvakeJ3KBVT+VpJHeBHi3WWb5N5ZqePqGjRq8GCfowOA9JWoN+sFsK3t/8EXG/TE0s8ibkPhD6GCPdDfXbZyu3WxGn2ayueXQLKiSNwHgsmvLwtR9OZIPInW4Iq2eJ3qBVT+VpJLpCLE5q0temHZl7po1K4+RQUA6Y0eZ0DiK8zPVWF+rj5buyllpp1DfJUUF+jJl2J/E9JUP78EkhlF4jgKTX6HFg7T8B0H6I+vVmndpq1xL0TRmwPR6qpAnOoFVP5WkkdnRYj/VNfrgpZWTm4AwAepdr8DIJWl4rRziI/C/Fw9csEhql35kt4fPVQH7rtnrwu66XB+CSQzWmxxEkx+t89brrLKev32hY91S+lHuub4sW3bBAtRsdZVb46WltaYvyZSU2cF1FTA30pyCRYhgkbn52rSmHwNGZStcaOGUYQAAB+VFBdoWE7se5wBiK3g1BMzp4zRxKJhmjllDAU6dGhUXq4OGTVUD5xzsGZMLur19yTVzy+BZMcZdZx0lPwq1jRodEhijUchKryQEoreHIhWOhRQ+VtJPiXFBRqdl6NbTttPk8bka/PWFo3Ky9Hhe0X+fwQA9I3wwtOvTy3WM/89kcITkICC0849cv4hMSn8IfXFag7iVD+/BJIdFZA46Cz5fVSzQSPzBrX9Hq9CFL050FvpUkDlbyW5FObn6vdnH6A75i3Xg4uq9Z+qtXrrs3W66JE3VVXb4Hd4AJDWgoWnX568r9Zt2qqZT72nexZUkJ+BBJUq7Xkkh3Q5vwSSGXMSx0Fn87KNLdhBC5fXSopvISrYm6O0vEZllXWaMDo+E8I3t7TyJUph6TBnWV/9rSB2Fi6v3W6kxlrmkQaAhFBV26Az7n+N+SYBIIU1t7QqqwdF3XQ4vwSSGfW9OOko+U0ek6+PajbotAN3i3shKtibIx53lA69Kd+k4QM0tbFJQ3q5z54eaBA/6VJAjeffCmKrq2Fq/B8CgL+4ISwApK7QOsDEou6fG6bL+SWQrCgSx0lnyW98Yd/eYCkeBeLQO5K+u2yT3DsrdVxdg0YNHtyj/fXmQIP46osCaqJcIKC4mPg6G6nBMDUA8BcX8gAgdYXXAXo6UoQOOkDiokgcRx0lv2RPhJF6iGze2qIXln2pi0bt2q19xepAg/iLx/c2/ALBCSNzNCrmr4JUUlXboG+M2FHDcrLb5aGhDFMDAN9xIQ8AUlesR4pwTAASD0XiPpBKya+zHiL/qa7XBS2t3Xq/DElMX5EuEDyZ1axntvR+6hKkpuB3ptU5XXP8WFWsadCKFat1aP0wTZl2EBeWACAB9GS+yUQZUQQAiIyRIkB68PWv2MxGmNnvzazMzDaZmTOzkRG2G2JmD5pZvZk1mtkLZlbsQ8hpr7M7ko4b1b1pNLo60LS0tEZ8DlJDpAsEaxub9Mka7oCOyILfmXWbtuqnT7+vhctrNSArU5kZpl/960NV1fLdAQC/BadcmzlljCYWDdPMKWM6HCFWVdugexZUaNqcpbpnQQV5HAASVGd1AEaKAKnD77/kIklnSlov6eVIG5iZSXpG0rGSZkg6XVK2pAVm1r25DRATJcUFGpaT3W7ZwH6ZOnafXbq1n+4caDiJSC2dXSBYuW5TxAsESG+RvjOVtQ1auLxWn63dpJ0G9lNpeY1P0QEAQgWnXHvk/EM0Y3JRhwXiqbOX6PZ5y1VWWa/b5y3X1NlLaOMBPqJDDjoTqQ7Q1UgRAMnF7+kmXnHO7SJJZnaepOMjbHOypMMkHeGcezWw7RJJ1ZJmSrqyb0JFUPhN+Y7cdTdNzdpdQ/K6P9Q7miGJzFucejqbs3D3IYO4Eo3tdPadycvtr0/rN2s9Q90AIKF0lo+ZcgxIHNxIHNEIrwNMGM13BUg1vhaJnXPRXKo8WdJnwQJx4Hlfmdk/JX1HFIl90e6mfJsapdezu35SB/vp6kDDSURqinSBYGhOtvbKopGByDr6zgzNyVZ1XYMunbAXBWIASALMbQkkDjrkoDva1QHI00DK8bsncTT2lVQeYfkHks41s4HOuc19HBMCYnFg6OxAw0lE6op0geCEkTka8vDbfoeGBBX8zjz73mqtXNeoA3cfqiGtX2vxz57S0IkMdQOAZNHZ6BDmtgT6Fh1y0BPkaSA1JcNf9lBJ6yIsXyvJJO3Up9F0gjmceifSgYYJ8lNb+JyFo3owZQnSS2F+rr69/67aY2iO/vneF1r2xQadUFygP194CL1dACDBhbaVmdsS8F9PbiQOAEhdydCT2CS5DpZHfoLZLEm/iFdA4ZjDKb6imbcYyY1if/Lo6/waLnxI5LvLNimnvEbHRTpKAEAS8Tu/xlNHbWXmtgT8lQ69+lM5t8ZLc0urslLg/x5A9yVDkXitvN7E4YbIKx6vD1/hnJslaVboMjOLSwmBOZzij5MIIHH0ZX6NJNKQyM1bW/TCsi910ahd+yoMAIg5v/Nrd0VbROiqrczcloC/Ur1DTrLlVj9FvKA3wO+oAPSlZCgSfyDpqAjL95FU7fd8xMzh1Dc4iQDQ2ZDI/1TX64KWVvIDAMRZd0fQRdNWJncD/qFDDqSOL+g9dU6xRvkcG4C+kwwtsmck7WlmE4MLzGwHSScF1vmGOZz6HicRSBXMYd59nc1RPm7UMPIDAMRZsIhw+7zlKqus1+3zlmvq7CWqqm2IuH1oW3l0fq6O2XtnjQ4UnmgrA4kj/D4hFIjTT0cX9F5Y9qVPEQHwg+9n1Gb2XTP7rqSDA4tKAsuCvz8jaYmkx83sTDObEljmJN3R9xFvw03VAHRXVW2D7llQoWlzluqeBRUdnlgjskg3OhrYL1PH7rOLTxEBQProrFdwJFmZGTp67511y2n7adKYfG1qatGkMfm65bT9dNTYnWkrAwmGv8n01NVovdZWZucA0kUiTDcxN+z3ewM/H5Z0nnOu1cy+LelOSfdJGiCpTNJRzrlVfRdmZKk+hxPSDzcqiB/mMO+98CGRR+66m6Zm7a4heXx+ABBPXY2g62hKsEMLh2nag0vbHfuG5WTrkQsOiWu8AIDodHYDw3GjhimjznyICoAffC8SO+e6zDjOubWSzu+DcLqNOZyQKro7xyC6jznMY6PdHOWbGqXXs7t+EgCgVzorInQ2gm7h8tqIx76Fy2u17/Ad4xIrkI7o6OGTLVukpqaut4vGxo3e/jZu7LP9BL83J4zM0ZNZzVobkq+H5mTruD1ypEW9iClW7ynRXzPRpPpnkMrvLztb6t/ft5f3vUicCripGpIdPVzjr6c9sNAxPi8A6FvdHUEXq2MfxS+gY3T08NmSJdLChbHZ15Yt3v6k3hWJotjPusYmfbKmQSvXbtLuQwdpr51z9U+TPl7boJXrNmn3IYO0V1auhjz+Wu9iitV7SvTXTDSp/hmk8vubNMl7+IQicQxRsECyoodr/PW0BxYAAImiuyPoenvso/gFdG7l2kb9/qVP9P6qDaqsbaCjhx/Gj5cOOig2+wr2irzsMmnw4Ljtp7quQdMeXKq1Q5ukod6yof29aYAOycvVQS2t2/Jzb2OK1XtK9NdMNKn+GaTy+8v2d5QsRWIgzdHDte8wh3nP0YsMABJDd0fQ9fTYxygnoHNVtQ16+u1VWrNxiyaNydeFh4/Sbc99REePvta/f2x7Mvbv7xW9elv46mQ//37jS61szpL6bysHNTZL/17RqBmjdlVmrGOK1XtK9NdMNKn+GaT6+/MJRWIgzdHDte8wh3n30YsMABJTtO2Dnh77GOUEdCzSRZRhOdm65vix+unT79PRAx2igxCAzlAkRtKjh2Hv0cO17zCHefToRQYAqaG7xz6KGEDnOrqIUrGmQaPzc+nogQ7RQQhAZygSI2nRwzB26OHa92iAdY1eZACQ3MIv5GdmZkR1cZ8iBtCxzi6ifFSzQfvttgMdPdCpU745XPM/WqM3P10nSRqdn6v9dttBJ39jV58jA+A3isRISvQwjD16uCKR0IsMAJJX+IX8E/crUKtTty7uM8oJiKyziygH7DFEZx48QrsPzfEhMiS60Nx85H/la+Zx/6X1m7eq/IsNeuvTdXrm3dV0FALSHEViJLxIPU7oYRg/FN6QCOhFBgDJKdKF/F0G99ctpR916+I+o5yAjnV0EeW0A3ajQIyIIuXmO767f7dzM4DUxlk2ElZVbYPuWVChaXOW6p4FFaqqbZDUdQ/DlpbWmMbRHOP9ITp87igpLtCwnOx2y4blZGvksEHtcgIAIHGEX8gv2jlXH9Zs7PDifmeCo5weOf8QzZhcRNECCAheRJk5ZYwmFg3TzCljKOyhU7HMzQBSFz2JkZC6mk6iL3oYMuexP9Y1NmnuK5Va+MXXfO5pLrwX2T677qDC/Fzd8Pdyrdu0VXMWVeupc4o1yu9AAQCSIl/IHzlskD5cvSHi9tFOH8ToEWB7TBWHaMUrN+P/t3fn8VHU9//AX5M72QRybRJCArkwQQLKITcIBQX8VlQUtR6oRRGlVtsCVm0rra32B7Zf7bdW0aKIR2k9aq1VkLOgXCI3gUgOIISE7OYg2WzO3fn9kWySzV6zu7O7s7uv5+ORh7I7O/uZ2dn3fuY9n3l/iAIPv/WkSPbKSQC2RxjKVafOlKRes7kYe0prsWZzMRa+ttdi5CJHu8qrXKvDPw5W4OWtZ+zudwoephOgO68ZgpKaZmw7dQmJqkgAXTFhS9ElH7eQiIhMTKWC+irX6jF80ACry0/OTYbojYYRBTAm8sgRV2Izjyui4MRvPjnk7USolHIS1m6x+vvDE2UbceooSW2rFAa5Z0vRJbR0GMwe4y1P1GkwIkQActQq6NsNmJGvxgsLRiIhJhz7y2thNDLFQESkFP0v5JdqdBieFmf14v5VGQPZlyIi8gJnYnP/gVfsaxMFD5abIJt8VW5B6oRVphGGt1ydjp3fafCrf52UpZ2OktRzR6Ti9rX7WOBfZp0GI/aV1WKUled4y1NwO1+nx6/+ddLsO5ekisDKuQVo0tQhRCv4uIVERGSSo47FJ8sm40jFZew6U4OspFiMy0owKx80ITsJ6QOj8NjfDqNe38G+FBGRh1mbDLR/bO4/QWiZRoetB0qRdLgStbtKMXs8a8MTBTomickqRzWBPc3WjL19r2p2GoyoutyCm1/ZI2s77SWp/2fkIGw6ecnmKONlM/Ncek/q2u/XZCWizcpzvOUpuH12rMrqd65Mo8N9hWnANz5qGBERWbA2yCAruatPZqqf+veDFVj+4TGz17EvRUTkWbbqWFt7zJQPaK1rwEP1eryx9QzWHtLwYh5RgGPWhaxyVG7B0+zN2Nu31MPfD17A8jn5SIgJl7WdtmoeX3uF2mEpDLkFS93jMo0OgwZEITo81OxxOWtNk/+prNdjX5nlBRsAKKpqxKCB0V5uERER2SJlTgcRXRf/rPFUX4qIiHpZG3zT/zFf5wOIyDc4kjgAdBqMCJNxlKWjcgveuu3f2pVOayOcTbedP/Xxcdnaae12nHmFaRicECOpFIYc+o/EuSFLhWzZ1q48X5yoxvtbi/FaXjIaJmXhWKMBBWkDMDNfHfBXq+X+DgeS/36nwfBBA7zynSMiIvfYSyqYRghLLStGRES+oZR8ABF5H5PEfsJaEslTNYP7dt5z1bHITo5BuVaPUo3OJ533vu9n6+SjpEaHXHUsSrtHqsjRTlu340gpheEua8nwjWGd+LStHQmyvYtymDoiDfoObDt1CbuztEjNSMbOYg1OVzdiUk5SQHZEfFX32190Goz47FgVbrwqHUmqCIvv3NwRqT5sHRER9eVMUkGuvhQvshIRyY8X84iCF5PECmcrieTpmsH/MzINqXGROFXdhFNVjZiRr8Yj1+ZgXJbvUpT2Tj5OVzdiYnYispNjUK/vkDVh2/9H0NYoYzmTe9aS4XXN7ThTp8N42d5FOUwdkaNFFQCAcq0OJ5q6bjddMGZwQHZEfF332x+YjovVm05j5dwClNTocLq6EQVpA3DjqHRkJMQArXpfN5OIiOBcUsHdvhQvshIROb5Q1mkwupzwMV3Ma23r7WuzDCBR4GOSWMFsJZE+WTZZ0u187jCKwAtfnLYo6/DB0klur9tV9k4+rslKhCoiFOW1zbj2CrXH22JrlLEc7CXDK+r1GGswItTqs/5tXmEaNm6zrAMdqB0RT3+HA4Wpg/rUx8cxMz8F1w9PwajMBGw9dQmrN5/GBHU4ftDUihRfN5SIiJwaIexqX4oXWYko2Dm6UFau1UFTXofX3jmIsYVDXbqQZrqYt/VACZJKY/D47GGYPT6PcZYowDFJrGC2kkhHKi57vEaQUhNYtk4+MuKje2bJ3lNai7f3nPXKyYInRrjaS4ZnJsQE5KhaoKsjsmHxeGgqtuF4biLGjHCtQ+MPWOdLuhx1LD56ZBIu1LfgqxItkuOi8Nv/nEJCTDguNbbh9aIKRB2pxPSqyxgeF+fr5hIRBTVXRgg7+3un1D4qEZE3OLpQVqbR4b51B3BrqRb7E+uwvbLV5QtpOepYLJmeC+P+wQiZngvEBd55GRGZYxZCoewlkXadqcGUvGSrz8lRI8hRAsuXs06bTj5WzMnHlLwkrJiTj5fvvBq/+/yU2XL+NPNqp5X9Oa8wDUkq81G1iaoIDEsJ7B/m7ORYjM9OxOv3jMOymYF7pdp0IcAa1vmyZBSBxzcewYHyOsREhOHqzHjo2w2YNTwF/3vn1RgYE44viy75uplERITeEcIbHhgv+2+5kvuoRETeYO9Cmen5OjvPuyIkRHD5tUTkX5iJUCh7SaSspFirSUS5bs1XegKr78nHw9Nz8JedpajXd1gsp/SThTKNDq/sKMGiNw/glR0lKOuedA+wngzfsHg8Evp95oHK18eYN3jyOxxoTJ3hh6bnYPkHR7Huq3LsKa3F67vK8Ny/izAhOwkFaXHo6DD4uqlERNTNk3dbWaOEPioRkSc5ulDW3mHwyIU0o1F06XVE5H9YbkLB7NV18/TkaXLNOu1JphMBf5x5VUo9PYtafU1NvmyyX/GH2c69MQFiIDB1hvNSYnH4fIPVCR3P1+mxYe85DMtN5/4jIgpw/tBHJSLyBEcThEaEh5pNBt7/eWfPjcs0Omw9UIqkw5Wo3VXKmsREQYBJYgVzlETy5ORp/pTA8seTBWfq6Sk10a1E/jbbuSe/w4HC1Bk+fL4eJyovW11Gq2tDfHQ461ESEQUBf+qjEhHJzdG5r1yTgZsGNbXWNeChej3e2HoGaw9pOEkoUYBjkljhpCSRPJVc8pcElr+dLHDSMs/w59nO+XnbN68wDdtP12BKbpLVkRPJsZE4V9uCBn5/iIiCgr/0UYmI5CZlIFnfycCHX5GB4Wlx2FFc0/O8FKZBTao+j3GSUKLAxySxn/BlB9gfOt/+dLLg6DYhpbdfqTjbeWAq0+iwo7gGP/5eHiAISFJFmH3OiaoIJKoiUK7V4dHJw/j9ISIKIoz5RBSM+p77ioBFmb3s5FgkpsUiWRWJL45X48/bSwAAf9lRKmkADQc1EQUvJokpoPjLj5U/lshQMnZkAlPf0eGzh6cgVBCwfE4+qhpa8O35ehSkDUCBCvj6Nx8icQq/P0REREQUHByV2Suu1uGz9io0R8b0PCZ1AA0HNREFL367ifrpdHHWV2eYbhNaMScfU/KSsGJOvl+URVAqpc527o1jKZD1HR1ertUjIzEGT318HF+VaPHg1Bwkx0Zg08kqjMoYiA2Lx/P7Q0SkQM78Ftpalr+nRES9TAMp1mwuxp7SWqzZXIyFr+1FmUYHoCtmVtTpAQC56ljMHp6C3O5+8p5SLQwSYuq8wjQkqdyvbUxE/sUvRhILgjANwCoAV6GrzacBrBZF8WNftosCi7cnPfOnEhn+YP5Vg3DmUhOOVzaitLuD5KuOjL9NoKdE/UeHl2p0eHBaNpJUETh0vgEPrP8GuepYjEuMxIj0ARiYzP1LRKQkzvwW2lqWv6dERJYcldkLCw1BtlqFVfNH4HQzcKqqETPy1XhwWjb07Z2Szj1Ng5q2HihBUmkMHp89DLPH5zEGEwU4xSeJBUG4CsAWAF8BeABAe/d/PxIE4UZRFD/zZfsoMPhy0jMmiN1nOomsaWrDvJFpuHJQHKout2JmforXOzL+PIGekli7zW31ptNYObcAlQ0tOHy+HpNzk3FDlgoDz0XYWRMREXmbM7+FtpbdsHg8Fq07wN9TIjs6DUaLerQU2CSV2QOQkRCNx7edQUVnWPdztUhSRWDD4vGS3ytHHYsl03Nh3D8YIdNzgTjGXqJA5w+/KHcAEAHMF0Xx36IobgZwF4BzAO72acsoYNi7GkvK1v92qz9vL8EvPznpkwQxwGNJTv1vc6vXd+Cvu8pw09XpuPOaISjX6rCl6BLq++1vIiLyLWd+C60tm6CKkLwOlqKgYFSm0eGVHSVY9OYBvLKjpKfMAAU+qWX2zmr1qLMSQ3cWa5x+z5AQwfmGEpFf8ockcQSADgAtpgdEUTQCaIJ/tJ8UztHVWCk1m8h3lJSU5bEkL2u1u/9012jcuXYfHvvbYXz4bSVe3noG/zhYgXItT46IiJTAmd9CW8tmJcXg0Ll6u+tgkoyClaN6tBT4HNUL7luTuD+ekxCRPf6QZH0LQCiAPwiCkCoIQqIgCMsBDAPwf75tGgUCpU56Ro4pLSnLY0l+ptrdb913DZbNzMPOYo3FRYGWDgO2FF3yUQuJiKgvZ34LbS1brtVjzNAEm+u4eLmFSTLyK3KOeFfSAAnyDUeToIeFhiAzMcbqa3lOQkT2KD46iKJ4EsAMAHcCqAZQi65J7G4TRfEr37WMAkkgz94ayLdhKjEpG8jHki+YRordv/4bvLTlO2QlxSAhJtxiuf3ltRwVQUSkEM78FlpbtkHfbncdnx6tYpKM/ILcI96VNkCCfMc0kGLDA+OxbKblhHLDUmKRyHMSInKSP0xcNwzARwC+AfAagE4APwDwD0EQvi+K4nYrr1kF4FlvtpP8m+lq7BcnqrGnVIvJuf4/g3awzAg+rzANb35Vbnay6MsOUCAeS315M75am8woSRWBlXML8NTHx82WnZCdxFERROTXAqn/6sxvob1lrT0+JDFG0qRNRL7micmMrU3sa8IRotYFUmy1xtZnnqCKwIb7xuPzs80BeU5CRJ7hVJJYEITfAXhLFMUSD7XHmucB6AEsEEXR0P3YFkEQsgD8AcDo/i8QRXEVukYb9xAEQfRoK8nvma7GLp2e4/cdLE90SpVKiUnZQDqW+vNmfLV1O2WZRodcdSxKu0fjRIeH4rorUz3RBCIirwm0/qszv4W2lrX1OJNk5A/slYVYNjPP5fUqbYCE0gVabLWl02BEWL/4l50ci2XZgwLynISIPMPZkcQ/B/BzQRD2oatW8D9EUWyUv1lmRgI41idBbPItgMc8/N4UhOz9gFr78VUiT3VKlUqpSVkltcXf2LudsqiqEQ9Oy8Jnx6pw7aDBWBiWiYTkwLr4QUQUKJz5LbS1bP/HmSQjpXNUFsKdPqsSB0iQ71i9ezTKfBmekxCRVM4miXMA3AfgHgCvA3hZEIR/AlgviuJWuRvXrRrA1YIghImi2Nnn8WsAVHroPW1rawPa2x0vF0yamrr2S1OTr1tinQztK9fqsKXoEvaV1WJiThKuuzIV2d5OSkncjk6DEd+eOAdVm+WMtodOnoNhtFoxHQWLpLubn5Xf3l4aEQFERvq6FYpi73bKgrQB+PJkDUZnxmPxhEEI/SbCyhqIiChQMUlGSufpshBKHSBB3mXr7tEP7ylEto/bRkT+yakksSiK5wD8BsBvBEGYAmARgIUAfiAIQiWADQDeFkXxjIxtfAXAPwB8IgjCWgAdAO4CcC2An8j4PtLs3Qvs3On1t1W0trau/QIoM9HlZvvqm9vx5cEKtHQYMApdtU++DA/F7eMykaDyYnJK4naEAVhaXodRpZajFybXJSNUu9tDDZSuvrkdZ2p0qKjTIzMxBsNSYrv2pQ+PJaNRREiI4NX37DFjRtcfmZmRr7Y6UiwvJRbrvirH6CHxPDEiIgpSTJKR0nljxDuP/eDW/+7RXHUsspNj8E15HZPEROQSlyeuE0XxawBfC4LwGICbADwI4CkAT3WXo1gH4D1RFNvcaaAoih8IgnAjgCcBrEfXYMEzAO4VRfFdd9btkkmTgLFjvf62imYa9fnYY0BcnNurk72kg5vt+2BXKf7UaXndQ5gxDEum57rZOCdI2A7TvlNrdfho3QHU9ek0JKoicNPi8YCPb8sv1+qwaN0B1CW2A4ndbYvsmlghO7K7PJhMx5LU9vh8lHgER8L2V6bR4cfvH8byOfmoamjBt+frUZA2AHkpsVi96TSSVBH4/qhBAAKupBwRETmBSTJSKo54J0/qW9IkISYcK+cWoKRGh1NVjbhQ34K65jbTqRYRkWQuJ4n7GAlgGoCxAAQA3wEYCOCvAH4tCMLtoijudecNRFH8DMBn7jZUFpGRyhwt62uRkV1JPTcSe1brKcnViXKxfZ0GI3ZebEVzZIzFc/+tasXiGJV3T05sbEf/ffc/I9Pw9uOzLDql2QrolH5+8BIqOsOAyN7w09wJfH62GcvGpcpyLElVptFh4bsneq7Ab6+sxNpDmoCc4M/ffHGiGqXaZjz18XGMGRKPx743DKerG/HZsYt4cFpOb3xQapkbIiIiCnoc8U6eEhYagok5SdhTWouVcwvw4ubinnOao0V6qI5cwPVaHbK9NPCGiAKDS0liQRAGoasu8X0AhgNoRldJiLe6RxhDEISp6EoUvwbgKllaSwHLVj0lXyfrPF1PTA729p3SOqUOJ/EYrfZqXeFgm+DPX/Q/Tg6db8AD679BrjoW8wpTFXVMExERETnCfgvJrUyjQ/rAKIwbmoCSGp3FOU1LhwFbii5hSfYgH7WQiPyRU79WgiDcKQjCFwDOA/h/AGoB/BBAmiiKD5oSxAAgiuJXAFYDuFLG9lKAspes84ROg1HysvMK05DUr/awkmbQdrTvlNQpNSXdrfF20t1RwtrgxDFC8rJ1nJRqdIiOCFPUMU1ERERE5G0nLzbi/QPn8aPv5eFUVaPVZfaX1/Kchoic4uyZ9vsARqArQTxMFMVrRVF8WxRFvY3lTwDwft1gUgSpiVhvJuvKNDq8sqMEi948gFd2lKBMo3P4GlM9sRVz8jElLwkr5uT7fISziT8mOpWSdFdSwposKeU4ISIiz3Lmwj2Rv+PxTu4ync/+7cB5jB6SgAZ9B67Jsl59eEJ2Es9piMgpzpabmAfgS1EUJc0UJIriAQAHnG4V+TVnawt7q6RDuda8/qwzJS2UWk/MH8ph9Gd3Eg8v15f1xqzT5Bqpk710GoyyFNcnIiLv8uhcFEQ+ZG0Sbh7vJAdrZQaTVBF45e4xeHffObNzmujwUFx3ZaqvmkpEfsqpc2tRFDd7qiEUGFytLeyNZN2Woktu159VYtLVHxOdSkm6W0tEzhmRyk67Qtg7TkwnW9+eOIel5XVQc2IOIiK/odS5KIjcYSsRzOOd5GKrzOC2U5fwm5tG4EhFA4qqGnHtoMFYGJaJhGQeX0TkHA7AIlm5OhGY1FGDrjIaRewrsxxtC3RPmKawEcLO8PS+8yQl7HNTIvKWq9Ox8zsNfvWvkxzhoTDWEsSmky1Vmx6jSrX4aN0BvP34LH5mRER+gBPHUqCxlQj+ZNlkHu8kC3tlBouqGjEqIx6LJg1F+sBohOqbgW8irC5LRGSP7zM0FDA6DUaUa3WYPTwFuf0SNVLq45qSdRseGI9lM/NkTfaEhAiYmJNk9TmllmVwhif3XTAo0+hw45+/xtP/PIE9pbVYs7kYC1/bK6lmNXmftZOtOg9OdElERPLxx/kUiByxlQg+UnGZxzvJwtF8KjdelY7MRJXfn9cSkW8xgpBsKur1SBsYDX27ATPy1XhhwUgkxIQDcC4R66kftuuuTA34ibDYKXCNvREepAymiV6YXCAi8m+cOJYCjb2+ya4zNTzeSRZlGh2uyhgY8OezRORbLDdBsqhvbseidQdQ0dl1SJmK6K+cW4AXNxcr4ocrO9l/yzKQ5zhKOvpzKZJA0L++3/yrBvndZI1ERGTOH+dTILLF3kTSWUmxPN7JbaZyJkZRxMq5BSip0eF0dSMmZCfh+6MG8XyWiGTDJDHJ4kyNDnWJ7UBk7yFV29yOyoYWfLJsMjITVT5sXS+lTJhGymGvY8+ko2/1re+Xq47F4fP12H66Br+5aYTFyVYiT7aIiPyGP8+nQGSNvUQwj3dyV9+7Hp/6+Dhy1bHISo5BcmwEjyMikhWTxOS2ToMRFXV6INHyucPn6/GTWcO83ygHmPijvjjCQ5m+OFENoyjihQUjUVKjw6mqRlydGY/L+g589Mgk/Od4NQ6dPIfJdcm4afF4ZLOTTETkN3jhngKJo0Qwj3dylbW7Hks1OpRqdGjtMOCOcZk8pohINkwSk9vCQkOQmRhj9TmOxCR/wBEeymPqEJtK1vSdLfyTw5X4x8MTsWxmHgyj1QjV7gaS+VkREfkj9hMpUEhJBPN4J2fxrkci8iZGFJLFsJRYJLKIPvkxU8d+wwPjsWxmHhPEPhYWGoLvjxqEkhqd1UkFN528BIAnW0RERKQs7JuQ3OYVpnHCOiLyCo4kVrhOgxFhftDRSFBFYMN94/H52WaOxCS/xo69clx7hRqfHauy+lzPpIJebhMREfkHo1HkaBgiCghS7nr0l7wBESkbk8QKVabR4YsT1fi6RIspef6RcM1OjsWy7EEeqbXFHz2i4DM4IQYTc5J4ex0REUlWptFh64FSJB2uRO2uUswez7uDiMj/2SpnYjVvEOXDhhKRX2OSWIHKNDosfG2vWQ3ON78qxwdLJ/lFJ1fOxI0/JsuJSD7fHzUIb+85azGp4Ix8tQ9bRURESmTqQ7fWNeChej3e2HoGaw9p/KYPTUTkSP8EsbW8wYf3FCLbVw0kIr/GYVgK9MWJaqs1OL84Ue2jFvmG6UdvzeZi7CmtxZrNxVj42l6UaXS+bhoReUmOOhYbFo/HQ9OyMSUvCYunZmP5nHz8+P3DjAVERGSGfWgiCia2Yt6Woks+ahER+TsmiRWm02DE1yVaq8/tKdXCYDB6uUW+w46+f+sMomOVPGtnsQbbT2sQFR6KncUaPPXxcZRqmxkLiIioB/vQ5CvO9HnZPya52It5+8trYTSKXm4REblLCb8RLDehMGGhIZiSlxz0NTgddfQ9UfeY5MESISQnUywo1ehQ2m/k8J5SLZaOVnPyOiIiYh+avM6ZPi/7xyQ3ezFvQnYSQrSCD1pFRK4w/UaUa3W49ooUXJ05EJmJKp+0hb0lBZpXmIYkVYTZY0mqCMwrTPNRi7zP9KNnDTv6ysUSISQ3xgIiIpJKrj60EkbykLI50+dl/5icJTUG2Yp5112Z6olmEZEHlGl0eOjtg0hURWBgdAT+duA8Nuw9h5MXL/ukPRxJrEA56lh8sHQSvjhRjT2lWkzODc6rzfMK0/DmV+WIj4lAdnIMyrV6NOjbgypZ7m/slQhZNjPPR60if2eKBf0nr+uKBV230nUajPxBIyIKcqY+9NYDJUgqjcHjs4dh9vg8yX1ojvYkqZzp81pbNj4mAicvNvL4IjPlWh005XV47Z2DGFs41GEMspU3yI5iqQkif/HFiWo8OD0HL24uNpuE8uNDlT6ZeJfn1AqVo47Fspl5Ximr0GkwIkyBo/FME1Z9caIah87VY97INHbWFYwlQshTTB3gfWW1uNjQgvT4aEzMSUKOOhYXz19C7YUG/K/EzjQREQW2HHUslkzPhXH/YIRMzwXipCeIF7621+wE7c2vyn1ygkbK5kyft/+yCTHhWDm3ACU1OvztwHmcr9Oz7+Lv2tqA9nbHyzlQrtXh4b/8FzcVV+F4bCX2l9Vh47YIbFg8HtnJdhLFUcCycaldJdhCQwCIQFNTV7uamtxul9vrkrMtSn5PpQn0fRAg29dpMKKqoga6qHC01jVABaAjNBztYeE+G2zHJLHCeTKppvTREmUaHRatO2DWWf/b/vPsrCsUawGSp9XrO3DofAOiI8LQ0mHA67tKse/oWdyn78C8wkF4fncZT+iJiMglvBuKpHKmz9t/2ZVzCyxGi7Hv4uf27gV27nR7NZryOtxUXIWxF08DADrCwrser9iG7OxEq68xGkWEhHTVHjabo6OtratdABAZ6V7D3F2XnG1R8nsqTaDvgwDZvjAA91Y34UTlZTxUrwcA7BsyEvuGjALgm8F2TBIHKX8YLcHOum3Ojv721mhx+2UBiFxjK14tn5OP/WV1GHW+Hh9tO4OV80fjqY+PM0YQEQWxMo0OWw+UIulwJWp3lUoqN8G7ochZzvR5TcsmqCJQUqPj+U2gmTQJGDvWrVV0Gox47Z2DOB5bCQBYP/ZG6COiAQDHcxMx9p5xZjGoXKvDlqJL2FdWi4k5SbjuylTz0cam0ZWPPQbExbnVNrfXJWdblPyeShPg+6Cz4XJXMtPD2+eNPIqqrhllByqwfs9ZAF0jiU18MdiOSeIgpfQELDvr1tU3t+ODXaXYebFV0uhvb48WZz1t8gRb8aqkRtfTIa7r/neuOjaoYwQRUTAzXVRsrWvAQ/V6vLH1DNYe0jgcBMG7ochZzvR5c9Sx+PRHU/DdpSa8sbvc6vrYd/FjkZFuj2QMAzC2cCj2l9WhIywc+ohoNEfGAADGjBiK0PiBPcuWaXRY+O6Jnr7x9spK63EuMrIreSZHAs3ddcnZFiW/p9IE4D4w5Te+PXEOSy82Q90mIDtd/u3zZh4lIy4ON4RHY+PpBkUMtmOSOAj5QwKWnXVL5VodvjxYgT91nkFzZIzD0d++Gi3uzXraFPjsxavT1Y0YmhRt9u+s5BiMGZLAY4+IKAiZLiqq+jwmdRAE74YiZ0np8/ZNNHx/1CBMzEnk+Q1ZNa8wDRu3RZg9Zi0GKX2wF5Gn9M1vqNr0GFWqxUfrDuDtx2fJmt+wl0cZkhjjkZHFI9IHKmawHX+JgpApAWuNkjoo8wrTkKRy/EMZLLYUXUJLh8HsMVOHwBp7HQhvUMpxRP7NXrwqSBuAc7UtZv9u0HcEbYwgIgpmjgZBGAxGu683jQxdMScfU/KSsGJOvqLKsJFy2UsQL3xtL9ZsLsae0lo8/c8TSB8YzfMbsso0afvk3GRMzE20GoPcjXNE/sxafqPOA/mNz45VWc2jfHyoEj//+Bhe2VGCMo1O1vcEei88bnhgPJbNdFwqy1OYxQlS/pCAZWe9V6fBiH1llqMOAOsdAnYgKJDYild5KbEo13b9QCeqIjAzX401t40KyhhBRBTs5BgEoZQTNAoM1hIav/v8FF6+82qe35BV2cmxGJ+diNfvGWc1BvnLYC8iuXkrv2Ev73K4oh71+g6s2VyMha/t9UiiGPD9YDuWmwhS/lI7lqULuoSFhmBiThL0Vp6z1iFguQ4KJNbi1Yx8dddkHbmJmFyXjPk/vAY5OWpfN5WIiHzIVDKita23x+TKIAj2k8hdthIa9foOvPrfUmx4YHzQn9+QbfaOC5bGoWDkTn7D2cnnRmUMtPo+BWkDsLNYAyCwS7z4TZJYEIRbAKwAcBWATgAnACwRRfGkTxvmx/wpAav09nnDdVem4svwULPH7HUI2IGgQGItXo1IH4j7C5MQqt0NqANnQgYiInKN6aLi1gMlSCqNweOzh2H2eI4IJu/jgA3yFH8Z7EXK5GzCVEms5TcS7eQ3XJl8Liw0BFPzkvHBwQsWeZS8lFis+6p38lFn5vPyp/3uF0liQRB+DOAPAP4XwC8BRAKYACDa3utIGnZS/EN2cixuH5cJYcYw/Leq1WGHgB0ICkT94xXjFxER9ZWjjsWS6bkw7h+MkOm5QBz7PeQbHLBBnuJPg71IGVxJmCpN3/zGoZPnMLkuGTctHo9sK9thb/I5R9udkRCNp+YV4FR1E05XN+LqzHhkJMRg9abTPcvkqmNx5zVDHH7//HG/Kz5JLAhCLoA1AH4iiuKf+zz1uY+aROQzCaoILJmei8UxKqdq67EDQURERMEkJETwdRMoyHHABnkaz+9ICncSpkpjym8YRqu77iZNtt5+azXhpZaIyEqOhVEE2g1GRIeHYGZ+Cpa88y3q9R1IiAnHyrkFKNPo8LcD53G+Tm8zrvvrfld8khjADwF0AHjD1w0hUgpHHYL+tzPI3YHwp9sliIiIiNh3IW8yHW8csEFEvuZOwlSpHNUgtjfJnZR4nKOORY46FgaDEaGhIT0X/LKTY/DLT05KSvz66373h1+qyQCKAdwtCEK5IAidgiCcEgThB75uGJFUnTLNtulImUaHV3aUYNGbB/DKjhLZZ9z09PqJiIiI5MS+C3mTreONCWLyJm+de5LyOUqYGgLwWDHVhO8rVx2L2cNT8D8jBzkVj03Lmi74lWv1NhO/ffnzfveHkcSDAKQDeAHAzwFUAFgM4H1BEDSiKG71ZeOI7PFmDRpP387gr7dLUOAzGkW/uOJJRESe12kw9pzglGt1WPjuCfZdyCvYVyZfM517fnviHJaW10Gt1SE7jpM7B7NgnUTTVBPeKIpYObcAJTU6nKpqhFbXhjKNTlJM7n8HkjMjlP15vyu3Zb1CAcQBeFgUxbe6k8J3ATiBrknsLAiCsEoQBLHvnxfbSwSgt6O4ZnMx9pTWYs3mYix8ba/HRrDYu53BH9ZP/kFJ8bVMo8Pru0rxz8OVeH1XKUeHEZFfU1J89Ud9R3C+vqsUl1vasaXoEvsu5DXsKytTsMTWvuee+8vqsKdUi0XrDrB/TJhXmIYkVYTZY9Ym0QykEeimmvD/94PReHFzMdZ9VY49pbX445YzWPjaXpy8eNnma23dEWJthLKJtcSv1P2uNP6QJDal3ntGDIuiKALYBqDQ2gtEUVwliqLQ988L7SQy482OoqdvZ/Dn2yVIXkqJr6aO8Mtbz6CiXo+Xt57x6EUYIiJPU0p89Uf9L8y/vPUM9pbWYl+Z5QgegH0Xco6UxAn7ysoVLLHV2rlnHS9SEHoTpivm5GNKXhJWzMk3u8MhUMsy5ahjcfTCZas5mU8OV6KirtniNY4G+jmT+HW03/tSUoLeH8pNnAQwAUD/YB4CIMr7zSFyTI5i6c7w9O0M/ny7BAWG/rf7mDrCqj7L+MNEAEREJD9ryZFLjW24KjMe2ystEyTsu5AUzpSNY1+ZfMnb557kf2xNoulMmRxvTgArx3vZ+14UVTXiSMVlZCaqzB53NNmcKfH7xYlq7CnVYnKu/d8GR5OXerM8qVT+kCT+F4AfArgOwMcAIAhCCIDvAfjGh+0isskXHUVT3Z2+QU3O2xk8vX4ia6z9cA5JjGFHmIiIANg+CazXt2N4WhySVHVe7bt48ySaPMeV+sLsKweptjagvd3xclI0NXWtr6nJqZeFAZiRHoWjRXoAQEx7C8I7OxDT3oJrB0UhVG85YtLTbZLt9f7ynkpjYx+IBiPQ5zdq64FStNY1mA28aW3TY+uBEiyZngugq77/lqJL2FdWi4k5SbjuylRkJ3smkSn5vSR8xv2/F32NGpCC/cfKccNQVc95Y6fBiG9PnIOqzXL5QyfPwTBajdDQEOREAcvGpWJp978B0eGxFmpjW+9bdwB13b8ZR4sqsHFbBN5eOhU5GUl21+dJ/pAk/jeA3QBeFwQhCb0T1w1HV+KYSJG83VF09qqW0tZP1J+tE7QNi8dj1vAUjtYhIiK7F+YvNbZ6re+ixNFA5DpHo8msYV85SO3dC+zcKc+62tq61gcAkZFOvXRhczvEIxVo6TAgvLMDYy+eRnR4KG6JOQ18E+F4BR5okyyvd4Uv3lNp+u2D+uZ2nKnRoaJOj8zEGAxLicXA6HAkHa7EQ/WWSdGk0hgY9w/G5ZYOfHmw67gaBUAP4MvwUNw+LhMJKjeOKyvqm9ulv1ef7TOGRyAkxHoVmb7fC5Po8FBM1iWjpd2A0NItPY+HAVhaXodRpZYXnifXJSNUu9vsMWuJX2doyutwq5X3OpqkQ85P7nZz7a5TfJJYFEVREIT5AP4fgOcBDABwFMANoihu92njiOzwRUfR0e0MSl8/UV+2TtA+OVyJ26/JRJKqFK19rvRytA4RUXCydmE+OjwU04apke2Fvosro05Judy5dZ995SA0aRIwdqw86zKNRnzsMSAuzqmXJgC4vnsU5tFTFZh46D/43lM/Q0KWm31jN9oky+td4Yv3VJo++6C8TcCidQdQl9gOJHY9nBgZgQ0PjEfthEt4Y+sZi5c/PnsYQqbn4oNdpfhTp+XzwoxhPSON5eLUezU1oV7fjk/GfB+7q1ttjjpOADC96jI+O1qF05cacUXKAAxWq/DU12fxf3ePBvotr9bq8FGf0b0AkKiKwE2Lx1ss645OgxGvvXMQ+xPrLJ4bF5WK+Qajz35DFJ8kBgBRFBsAPNz9R+Q3fNVR9PR7sdNLnuaohtTpqiZ8smwyvth7BkmlMXh89jDMHp/Hk3EioiDU/8L8tYMGY2FYJhL6nNB5su/iyqhTUi45ysaxrxxEIiPlHa0aGdmV2HQhuZkdF4cl2YNgmJKN0Je+BbLS5EmSutEmWV7vL++pNN374PPiS6joDAMie9N/zZ3A52ebMX/SMKw9pLG4+3n2+Dx0xsRg58VWNEfGWKz6v1WtWByjkhzrHJVi6jQYnXqvcq0OXx6rwZ9CLqI5MgbbKyux9pDG6sXZ4XFxiE1OwJGKy9h1pgaxiMCfHp6ObCvnjdlxcXj78VkWA/2sLeuOMABjC4die2WrxXPjC9J9+hviF0liIn/HjiKRc+ydoBWkDcCuMzW4oTANS6bnwrh/MEKm5wJxTBATEQUrswvz+mb3bq92AieMCkysL0z+jDGHTKT8Rtm7+9ndC2ZSSzE5e3FuS9ElsxISgP2Ls5mJKmQmqnBDYZrDdntroJ9Sf2eYJCYiv8SJYQKfrR/OvJRY1DW39/xo26pBRUREwcfbyRFfTFZMnsf6wiQnnreQr0j5jbKXFHUnkelsKSap79VpMGJfWS1GWXlPRxdnnflN9vTvt1J/Z5gkJiK/wolhgkeOOhYbFo/HJ4crUVTViIK0AchLicVfd5XhjfvG+bp5REREAJQ7Gojcw/rC5C6et5ASSP2Nshbn3ElkOluKSep7hYWGYGJOEiyn2/O/i7NK/J1hkpiI/AYnhgk+I9IHYkBUWE8Nqbrmdrxx3zh+3kREzmhrA9rbHS8XKJqaurbZNHGPh+VEAR/eU4gtRZewv7wWE7K7J9CJEr3WBtl4ed/5hJPb6O4M9gEpIkLeOrwBiOctpBTujlh1JZHZaTBC396JXHUsSjU6s+fsjfaV+l7XXZmKL8PNo7M/X5xVSoIYYJKYiPwIJ4YJTqYaUtcNT0FUBH+2iIictncvsHOnr1vhPW1tXdsMeCSRZTSKFqWOsgEsAfCgUUSIVgC+kf1tvcPD+04RgmEbPW3GjK4/sonnLaQkcoxYlfo60wj6w+cbMCNfjQenZWP1ptOo13cAkDba19Hz2cmxuH1cJoQZw/DfqlbFlGoIBDzbJiK/oISJYVhPzDdOXryML05U49C5eowZmoB5hWkYkT7Q180iIvIfkyYBY8f6uhXeYxoh+thjss5sX67VYUvRJewrq8XEnO7RwsnmJ6R+30vw0L5TlGDYRk+L8M7EkP5KCectRNZ4+rizNoI+SRWBlXML8NTHx2Ud7ZugisCS6blYHKPi90lGTBITkV/w5cQwrCfmOycvXsaidQcQHxOB7OQYfHG8Gn/bfx4bFo9nopiISKrIyOAbMRkZ2ZUAlCkJWKbRYeG7J3pOfLdXVmLtIU1g3jou875TpMhIdMaoEBbI20g+wwktKVjZGkF/rrYZz99SiIk5SbL/ZvL7JC/uTSKF6jQYfd0ExZlXmIYklfnIBU/XHjJdDV2zuRh7SmuxZnMxFr62F2X9aiuRZ+ws1mD5nHzMyFdD327AjHw1ls/Jx85ija+bRkREQcTerePkX8q1Ohwor8OSdw7ilR0l7NORR3jyvIXniaRE9kbQH6+8jDvGZQbeRdUAxJHERArDUau2uVt03xWsJ+Y7bR0GZCXF4Ff/Omlxy9JzN49AR4cB4T5uIxERBT7eOh44yjQ63LfuAG4t1WJ/Yh22V7ZyMjHyCE+ct/A8kZSMI+gDA5PERApidxbcKB83TiHkKLovFU8KfSsyPBSnqpqsJumLLjbhhpHpQKuPGkdEREGDJ76B44sT1ajjxX/yEjnPW+yeJzJRTAoxrzANb35Vbnb+5uk7f0le7NEQKQhvZZTOGydkppNCa9w9KeRtYo51Gow4dL7e6nOHK+pxsV7v5RYREVGw8kXJK5KXo4v/BvbNyEPkOG/heSL5A9MI+hVz8jElLwkr5uTzQoafYZKYSCHYcVUmuU8KyzQ6vLKjBIvePMA6eA7YS9JfOWgAHtt4BOVa7j8iIvI8nvj6P09e/CfyJJ4nkiuMRtEn72saQb/hgfFYNjOPv5N+huUmiBSCtzLKo9NgRJiM+0rOemK8Tcx5tm5ZylHH4o3d5dhSFIMlPmwfEREFD2+WvCLPmFeYho3bOCKc/AvPEz1DzvNGuc9B3VGm0WHrgVIkHa5E7a5SzB7v3UStaV/wuPRPTBITKYj9Gj6+uRLoLzw5kYNcJ4WcBM95OepY/P3hifjw2ws4XnkZBWkDkJcSi9WbTgMA9pfX4kGjyNtiiIjIa3ji679y1LHYsHg8NBXbcDw3EWNGDOXkX+QXWOtVPnKeNyptMkHToKTWugY8VK/HG1vPYO0hjVcGJSltX5BrmCQmUhC7o1abmnzdPMXy1ghdd2sQcxI81+SlxGFIYgzO1Oiws1iDdV+V9zw3ITsJIVrBh60jIiIif5KdHIvs7ESMvWccQuMH+ro5RJLIeXdjMJPzvFGJd4maBiWp+jzmjUFJStwX5BpmJIh8xNbEZazh4zx/mMiBdfDcMzEnCUfON6C0Tw3nJFUErrsy1YetIiIiIiWSMkEw+17kb3ie6D45zxuVdg7qbu1qdyZWV9q+INfxl5EUwZ2A5G+kTlwWjB1XV44Df5rIgTOju87ahEF/f3gispPZOSYiIqIunCCYgkEwnifKof95Y646FrOHpyBXHev0eaMSz0FdHZTkbtxU4r4g17HcBPlUsNWt8dZtGEoqnC+F6Tj49sQ5LC2vg1qrQ3ZcnKTX+tNEDrxNzD2m0RO3XJ2Ond9p8Kt/ncSM9CgsbG5Hgq8bR0RERD7F252JyB7TeeOpqkasnFuAkhodTlU1Yka+GjPz1U6dNyr1HHReYRq2n65BmqBCwsmuwUn2BiXJETeVui/INUwS+xl/S/7ZYysgfbJsMgYNjA6Y7ezL0xOX+WPSve9xoGrTY1SpFh+tO4C3H58lue3+NJEDZ0Z3T/+4cbRID/FIBa534sICERERBR5OEExEjswrTENqXCRe+OK0WR7ik8OVTl9QUuo56LVXqHGk6Dxy1Sr8ddE4DMpUI8vG3ZdyxU2l7gtyHpPEfsIfk3+O9A9ICTHhWD4nH38/eAGHztUHzHaaeHriMn8dPWHth6nOyR8mfxyhywSxa6wdLy0dBmwpuoQl2YN81CoiIiLyJWf72UajyLqLREEoRx2Lz45VyZIYVdo5qMXgq3P1+OijY3j78VlWl5czP6G0fUGuY5LYD/hr8s8eawFp5dwCvLi5OKC2sy9P34bhj6Mn5P5h4gjdwGbveNlfXovFBiM/eyIi8ilHd/0F0l2BSiK1n12m0WHrgVIkHa5E7a5SzB7Pyb/I9xgXvKfTYMS+Mss4Adg+/7T3+SjpHNTZwVdy5yeUtC/Idfzk/EAgzhTZv6h6XkosSmp0Abed/Xlq4jJ/LRbvanF9e/iDFLjsHS8TspP42RMRkc84mviHE6p5nqN+tmngzctbz6CiXo+Xt57Bwtf28rMgnympaTKLC+VaHovWuDPJff/XOnP+6Uzc9vV5iKv5AE/kJ3y9L8g9HEmscJ4uUeBLfevWTMxOxKmqRqvL+ft29hUiAE/NK8Cp6iacrm5EQdoADE+LQ4jg3nqlXgVU4lVqa/WLElm/iGywdrxEh4fiuitTfdgqIiIKBP37SVL7TY7u+gvEuwKVyNHtzqaBN6o+r1H6XXcUmMo0OlxsaMHjG4+YxYWNYZ34tI0TMpu4U3KzvrkdH+wqxc6LrRavlVI/19/itqujglkmgvpjkljhAnmmyBx1LD56ZBIu1LfgVFUjYqPCAnI7+/rP8Wqs2VyMXHUsspJjsLNYg3VflWPFnHy3O6f2fuyUXNO67w/ToZPnMLkuGTctHo9shbSPlCVEAF6/dyxKNTp8c64OOZEibg/LRIKNyRiIiIgc6dtPmjU8BRNzkrCzWCO53+So5Jc/lgTzV7Zud3Y08OaWq9ORnhDjrWZSIGlrA9rbHS/XrVyrw68+OYGRg+PRWtdgdtGitakFZfV1GNvU5F6bmpq62uXueuRYl4uvL9fqcN+6A6jrmay6Ahu3RWDD4vHIdtDvP3u2Gtv3l+EN/THoI6ItXpsTBXx4TyG2FF3C/vJaTMhOwnVXpiI7Suxp59YDpZafT5seWw+UYMn0XKe2xVtuyFJhY1gn6prbEdPegvDODgwOM+CGLJXd/Z8TBSwbl4qlo9XdcVOU59jxJDmPcaWJiAAiI3329kwS+4FAninSKKLnCuoLC0YiSRURkNsJmHdOSzU6lPa5XUWO0dK2rgKGCMCtryr7KqipQ28YrUaodjfAhB9ZcVarw8Gz9ThV3YRTVY0YlRGPrIEhENwciU9ERMGr/2ixG69Kx6J1ByT3mxwlHx+amh2wdwUqWf99am/gTUHaADy28QjW3DZKMX1j8iN79wI7d0peXFNeh9svt6DDIOKher3Zc+GdHYiuL4Xx5T8hJDrK9Ta1tXW1C3A/2eTuulx4/eWWdlRdbMSt5XUWz2kqtiE7O9Hu67XfVePKsydxf7sBHWHhVl+bDWAJgAeNIkK0AvBN7+uNRhFJhystPh8ASCqNgXH/YIS4eyuwG4xG0er7ZwP4tK0dZ+p0qNI0YFzLedzV/g0S3j4tab2hMrfTo+Q8xpVmxoyuPx9hktgPBPItAH1HVqzedBor5xagpEaH09WNAbWdgHdGhVsbPfHKjhK/Gb3CkySy50J9C1744rTZiXtmWCfea+lAvG+bRkREfqpvX9TRHBmuTPwTER7qsf6fEsuIKYW1fWMaeNPa1pv4SVJFIC8lFuu+Ksdnx6rw41nDJK2LqMekScDYsZIW7TQY8do7B1HT2I5pw5KxYe9Zs+dj2lswvu5rhDz+YyAuzvU2mUZXPvaYe+uRY11Ovr5cq8NrO0uhyWjD/mTLJPHx3ESMvWeczdjZaTDizTd2YURZLdaPvRH6iGi7r7W2lhAAtbtK8cbWMxbPPT57GEJ8NJK4XKvDlqJL2FdWi4k53aOf+w2uSgAwHoCh4TJC//KKPMeAEsl5jCtNRITjZTyISWI/EYgzRfYfeVGv78BTHx9HrjoW8wpTA2pbTbw1KrxvDWKOXqFA0Gkw4qsSrdUZe8/X6THYYPSvq99ERORz/ftJWUkxLs2R4ah/J3f/T8llxHzN3r7JUcfi7w9PxKe7i5FZGoNFk7IwJCsVqzd1jbLbX14LgyG35zPmfiZJIiMlj2QMAzC2cCjWbC7G3VmpiCpusJiXJTVkQFfSy93EV2SkPOuRY11OvP7zg5dwsM6AGfmDsL2y1eL5MSOGIjR+oM3XhwG4+spM6P8VDn1ENJojYyS/tq/Z4/Ow9pDGIm7PHp8HxHk/DpRpdFj47ome9myvrMTaQxqbd7mEAvIeA0oU6NvnI0wS+5lASujZGnlRqtEhOmJwQG2ribdHhQdyTWsKPscuXLb6+KWmNi+3hIiIAkH/flK5Vo8Z+WrZJ/6Rs//nb5MpeZOUfZOVpEKiKhxhIQJ2n9HixKGantePHDzQqXURucJ00aj/XbRjhyQgM9yAo3/6T9AOfjBduCvV6PDgtGyXS1Fed2Uqvgw334POXpjz1Hm7q3cmsLY9eYtfJokFQfgCwFwAvxZFcZWPm0NuCOR6y7Z4e1S4N/exKIo4deoULly4gM7OTudXoNcDZ84AmzcDMZw8xJqwsDBkZGRg+PDhEIKoGG9YaAgm5iRZPXHPiI/mBQ8KeAaDASdOnEB1dTUMBoOvmxN0QkJCkJycjKuvvhphYX7ZfSYb+vaT3ElMOOrfydX/Y6LANin7Jiw0BPmpA3DscivKtTogMgai0YDoxgqEX9Rh8+ZyAMBnRy/iwrELZuu6AODFv57D969K98r2BIvQ0FAMGjQIhYWFCAkJ/P6cKfn48eFKfHbsIqbmJePBqTn4v+1nUHzmIt4aEBW0/dq+F+76J9FHD0nAgtGDJSVps5Njcfu4TAgzhuG/Va1WE7ydBmPPe9oi53m7O3cmKO3uYGcS3W1tbThy5Ajq6uogiqJ8jWDeQJKwsDBkZmaioKBAcu7A73q5giDcDuBqX7eD5BHI9ZYdcTaQdxqMLn1hvbWPT548iT/+8Y+44oorkJOTgwhXaumEhADXXtv1344OWdsXKJqbm3H48GH84Q9/wE9+8hMUFhb6ukle8/1Rg/D2nrMWt+Vlh6jsvIrI/+3Zswd//etfMXLkSAwZMoRJSh8wGAzYv38/Xn31Vdx2222YN2+er5tEMunfT9K3d2LD4vHYWaxxqd/kqH/nbg1iJSUKlMSZfZOeEIWBw5LRMCkLW/Z+hZCzBzB14jikx4Wgo6MDRlHE8LQY/HSWZd3RmMhQtLe3B9WFek9raWnB1q1b8dJLL2HJkiWYOHGir5vkcTnqWNwxLgMX6vT48NtKlGqKAQCZqggMCwv882B7+l64M5WiHDl4AO4Yl4HMROl9/gRVBJZMz8XiGJVZXDyr1eFCfQu+KtHi2IXLmJiThO+PGmQ3xrsbV09evOzUhKj9KeXuYGcT3Rs3bsS2bdswbtw4qNVqhIbKOD6eeQNJmpubcejQIbz44ov42c9+hiuvvNLha/zqLEMQhAEAXgKwHMC7vm0NySUQ6y3LyRSMvz1xDkvL66DW6pDtZN0dT+/jS5cu4aWXXsLvf/97qNVq11fU2QlUVACZmQCTIHZptVo8/fTTeO6555Camurr5nhFjjoWGxaPxyeHK1FU1YiCtAEoUAFfvPBPTHfhe0HkD86cOYP3338fL7/8MuJ4jPtce3s7fvGLXyAxMRETJkzwdXNIJtb6SSPSB8rab5Jj8jOlJAqUyJl9MzQpFvUDo2HQliJdX4bfvv4a1ANViI7o7XteqNejoq7FYl2ZidHISOCoNU9oamrCk08+ieTkZOTlBf6o+MxEFX48a5jZQJ4bslRIePuw19qgxEkZbQ1wciZB3Fff736ZRodD5+otJsJ+e89Zj5WSqahrxieHK92+A8TXd2A7W4Jn06ZNKCkpwdq1az1zhwDzBk6pqanB008/jeeffx4pKSl2l1VWRHDsdwC+E0XxPV83hOTnqY6t6VYSf2QKxms2F2N/WR32lGqxaN0BlGl0Lq3PU/t427ZtWLBggUWC2CjnLSVkJjk5Gbfccgu2b9/u66Z41c5iDbaf1iAqPBQ7izVY9elJ1OvbselEta+bRuQRmzZtwn333ccEsUJERETgkUcewX/+8x9fN4U8oH8/SY5+U5lGh1d2lGDRmwfwyo4Sl/twJvMK05CkMr9bK9BLtUk1/6pBuPnqdOT2SVjY2jcJqgi0VRzFqid/giHqgWYJYtPrwkPNRwuHhwoW+57kExcXh/vuuw9ffvmlr5viNaYLVBseGI9lM/OQneydUcRyxyW59d8vciVv95XV4lR1k1miNVcdi9FD4rGvzPICkxyOVFzGyYu2J0Q1SMxVmJLnK+bkY0peElbMyfdqjXR75Xys+fzzz/GjH/0oKErI+IOUlBTcfPPN2LFjh8Nl/eYTEwRhHICHACzzdVsChT8nT6Uw/fgteecgDpTXddUd8zPWgnGdnWDsK8ePH8e4ceN6/t3S3okL9XqcqmrEhXo9WtpdqE9MDo0bNw7Hjh3zdTO8pu9kFttO1aC0T4f24Lk6XKzX+7B1RJ5x8uRJjB492tfNoD6ysrJw8eJFXzeD/EDfi/17SmuxZnMxFr62162EjK8TBUpVptHh06NVqGlqw7yRafjL3aPxy+8Pt7tvKquqkJ2dbfW56IgwjEgfgMzEaAyMDkNmYjRGpA+wSCaTvMaMGYPjx4/7uhle5827ADwRlzxFzv3SaTCisqEFp6q6ErYJMeF4YcFIzMhXQ99uwPk6PUprmmR7P9N7/ve7GgwfNMDq887eAeKp5Lkjjsr59E90d3Z2oq2tDfHx8V5oHUl1zTXXSIqvfvErJwhCKIDXAPxZFMWTvm6Pv3OnaLq/6Hs7hKpNj1GlWny07gDefnyW32yrP9Wda2lpgUrVdQtQS3snTl5sRIehaxRxY0snqi+3smPtASqVCnp98CRG7d1KekXKAOz8ToO7Jgz1QcuIPMdoNLIGscIIgsCRMSSJpyaZY6k2c9Zug05SRThMnoeEhtqtLRwdEYaMiDCI8SJrEHtJeHh4QE/OqoTyDv48+aU7+y8sNASD46PR2mHEpcY2LJ9zBX7xzxNmceODgxdkvegWFhqC7ORYJKoiXJoQ1RZvx31nSx21tbUhhpPJKY7U3IG/9Cp+BCANwCopCwuCsEoQBLHvn0db50f86cqhO3w5AtfZEdq2ljcFY2uUXHeutrm9J0Fs0mEQLT4Pcp8vTlh8HV/njEi1uNUzOjwUOWoV/nO8SvItW0T+gomJ4OHr+ErWuXrnnbMjr1yh1L6gtzl7G7SzGIf9mxJiq5zlHdy5G9gbcUlqO5wh1/6bnJuE669Mxe3jBuNoRYNH44bJvMI0/HVXGZbPycfiqdmYkpeEh6ZlY8Pi8X4zeA1wvtQR46bySP1MFD80RRCEZADPoWuyujBBEOL7PB3V/e9GURR7Io0oiqvQL6HMjnYXf75yKJWtH7/s5Fi0tHfCYDB6pFPt7AhtKctbK1CfqOC6c0ZRxOUW67OLNrZ0cCRGAPB1fM1LicPLd16NHcUanK5uxKgBKZisS8bSrd/hrtmFPGEmIr/l6/hK5ty9846TzLlPyqhBf7rzjnzD17HV2Qm/7K3H3buBfR2XXNkGufYfABhF4NH3DmH0kHjo262PWJc7buSoY/HGfePwxYlqNLa2485rhuDqzIEuT8TnK7YmFPSnRDdJo/gkMYAMAHEA1nb/9fVk998wACVebpffCZZOVP8fv/iYcMwanoq2Yck4dL4Br+0qkz2gOfvjJXX5vsH40MlzmFyXjJsWj0e2QoNxiCBgYHQ4GlssaxAPiA5ngphkkR4fjSMVDYiPCcfuM1pEnrqEkFRBsRdPiIjIv8iVlPD1bPT+yplEkq+TXkT2dBqMsgzSkjNR6qu4VK7VYeG7J5zeBjkHuZnWVa7VY0a+2mtxw1QiyFOD1byFpY6Cgz98siUAZlr5A4C3u/+/0jdN8y/+Wr7AFX1vh3hi9hXYU6LFhr1nPVZiw9nb3JxZ3hSMX79nHMZnJ3pt1ltXKXU26F//+tdIS0uDSqXCzTffjJqaGtnXIQiC1b8PPvhArs0gdH0n1tw2CmOGJCB1YAQm5yb73S1bRIHE3fh6//33W8TN5cuXmy1TVVWFH/zgB0hJSUFsbCxuvPFGVFRUWKzr7NmzuP/++22+l6PnpS5Dgc3V8gX9b6HmJHPOc6U0nrO3QfsTd+NrUVERbrvtNgwZMgSCIGD9+vUWy/zud7/DyJEjoVKpkJ6ejgcffBBarfWBRWRFWxvQ1GT2V15ehdf/cwR//vQwvj1xDqo2vcXfoZPnYGi4bPFaa+vbeqAErXUNZq9vrWvA1gMllq+3sx40NSEnSsSH9xTi6WmDMSsjCk9PG4wP7ylETpTo9Loc/XWatq+tDTsPljm9DZ0Nl+3uv6rzlyS3ue+6qi/UoEAFZIZ1mq0zM6wTN2SpXNpWKX+h+maPrVvOz02W7TAYgM5Oz/0ZDIDR6Nb7/PrZZ3vj6003oebiRadeX3TsGG679dbe+LpuncUyv3vuOYwsLOyNrz/8IbTV1fbXbfRt+UTFjyQWRVEHYGf/x7tHJJ4VRdHiOX/m6WL2wTKiwdQp31dWi4oLGoR0mN9OImeJDWdHaLs6ottfkvim2aBrm9vR2NKBAdHhSFJF+HTSurVr12L16tV45513kJWVhYcffhh33303tmzZIus6qqqqzF7z73//Gz/5yU8wd+5c2baFuvRckR+tRqh2N6DwiydEgUqO+AoAs2fPxjvvvNPzb9NkqCb33nsvdDodNm3ahPDwcDzxxBO4+eabcfDgQQiCgHfeeQdTp07tuWNFFEW89tprWLhwIZKTkx0+D0DSMhT4XOmn2Rv5ypFXznFl1GCg3gYtR3xtampCVlYWbr/9dpsXv/bu3Ysnn3wS48aNQ0NDA5YuXYo77rgD27Ztk2lLAtzevcDOnT3/rG9ux5cHK9DSYUBCTATuS1Zh1Pl6i5dNrktGqHY3jEYRISHdA2za2rrWBwCRkQAAo1FE0uFKPFRvOeFUUmkMjPsH977exMp6+soGsATAg0YRIVoB+MbO9jlYlzX1ze04U6NDRZ0eQ+PCMPLcSSQeqcRDTZZ3m9rcBnQlq5aW12FUqWVMHn0pAbu+eB/XX5mKhP6Dkay0uf+6ao98ir/kJaOuuR1aXRsyE2IwLCwWCW8flrSNiufC5yab9nbg4kXAysV82RiNQGMjcOEC4MJkwmvffx+r16zBO3/4A7IyMvDwM8/g7ttvx5Y+fVFHmkpLkZWYiNtXrsT9K1YAdXUW27x3xw48uXgxxo0ciYbGRiz9xS9wx4IF2Pbee7ZXHB8PJCQ4vU1yUXySOFjIUWNIikDtRFmTo47FkMQYLDlQglFWnperxIazt7kFw21xfWeD1ul0UEVF4Pe//z3effddlJWVYfbs2diwYQMGDBjglfa8+uqrePjhh7FgwQIAwEsvvYSpU6eipKQEeXnSLhRIWUdamvnFln/+85+47bbbEBcXJ+PWUF+B8H0hclVTUxMGDhzo9/EVACIjIy1iqIler8f27dvx2WefYcyYMT3vM2rUKHz77bcYN24ccnJycO+992LatGm4cOEC5s6di9GjRyM6OhoAHD4vdRkKfM7206TeBu7M75W7g0Y8PejEU9wpjSd3Mj5Q4uuECRMwYcIEAMAPf/hDq8t89tlnZv/+1a9+hVtvvRWXL1/GwIED3diCIDFpEjB2bM8/P9hVij91nun596r5I/DRtjOo63PxIztJhTm3j8LrZ7TYV1aLiTlJuO7KVGRHdpdKfuwxoPv8IQRA7a5SvLG1d50mj88ehpDpuZZtamqyWI81kr4pEtdlUq7VYdG6A6hLbAcSgZj2Fvy8thn19y/BG/uqLJa3uQ3d1FodPlp3wGz/JaoiMHjWMPzm05O4PGMYlvR/vY0291/X/wG4+sp4vHjrKAxKiHG4bX7Fyc9NVs3NwAsvAJmZVp9uamrCwKQk/P755/Hue++hrLwcs2fNwoa33pIeXw2GrgRxRgYQGup0E1/9xz/w8JIlWPDQQwCAlwYMwNQZM1DS1iY9vmZmYsKNNwIAfvjznwOJiRbb/NmXX5r9+1cGA269/XZcHjDAdnx1IektJ79NEouiGDDFTeWsMSRFMI1oCAsNwcScJFhed5U3IevsCG2ljOj29EmEIAg4cuQIRFHE559/jrfffhstLS248cYb8dJLL+FXv/qV3de/9957ePjhh60+N3ToUJw8edJhG9ra2nD8+HE8++yzPY9NmjQJ4eHhOHjwoKQfAVfWUVFRgc2bN2PHjh0O109E5IpAiK8mX3/9NVJTU5GQkIAbb7wRv/71rxET03XC1tHRAVEUEdlnJExUVBQA4PDhwxg3bhymTJmC7du347rrrsPXX3+NTz/9FDfccEPP8o6el7oMBQdn+mly1st0d9CItwaduEJKn1Nqgt7euuTq2wdSfHXWpUuXEB0dbRZzyY7IyJ6Rmp0GI3ZebEVzZG/C8fndF7By/micq23G8crLmJybjBn5atyz7kBP7NheWYm1hzT48J5CZEdGdiX1+iT2Zo/Pw9pDGouYNHt8HhBn4ztuZT1ubaPEdX1+8BIqOsOAyN5UU0WzAROuGIyo71qc2wYA2XFxeOvH38OH317A8crLKEgbgLyUWDy/6TSaI2Pw36pWLI5RWX73rbQ5Oy4Obz8+y2LA3CCFxEnZyXkMOCMkpCtxG2Y93XjkxImu+LppE97esKE3vv75z87FV1EE+sx55FR8PXECz65a1dPGSdOmdcXXI0eQV1AgbTv7s7PNJpe02q74qlI5XNZXlNmqICNn59IZgZ4gNrnuylR8GW5+dUnuhKyzI7R9PaLbmycRR44cQUREBP7+978jNTUVADBnzhwcOXLE4Wvnz5/fM/qhv/DwcEnvX1tbC6PRiOTkZCxfvhwff/wxTpw4gYSEBGg0Go+tY926dcjNzcX06dMlvQcRkbMCIb4CwA033IA77rgDGRkZOHr0KFasWIHKykq8//77AICBAwfiqquuwp/+9Cdcc801CA0NxXPPPYewsLCe99m3bx9WrlyJyZMnIzw8HC+//DL27NmDZ555BtHR0Q6fl7IOCh5S+2lyTgrt7qARZ17vzZHGzvY57SXo2X91Pr46S6fT4X//93+xbNmynotxJJ21Cx31+g489fFxPH9LIVZcn4/Q0BC8sqPE6vn/lqJLWGJlvb4+d5TKXkz82zfn8Y+HJ2LTyUtOb0NeShyGJMZA19aJtg4D/rq7HPX6DgDOD/wKpgFzSiVLfO3sBKqqgEGDepKtjK/yYJLYx+TsXJJ12cmxuH1cJoQZw/DfqlaP/ag6+4Pjqx8ob49cP3LkCG644YaeHwAAaGxslHQrSVxcnORSDUuXLsW7777b82+drmuCE1Hsum1LEASo1WoMGTIEYWFhPY9L4ew6jEYj3nzzTTzyyCOS34OIyFmBEF8B4Pbbb+/5f9PkSQsWLMDLL78MtVoNANiwYQPuuecexMfHIywsDD/60Y+gVqsR0n1L3nfffYf169cjJCQEq1atwltvvYVXX30Vzc3NiI6Odvi8lHVQcJHST5OzhJi7g0b6vj5XHYvs5BiUa/Vmr/f2SGNX+py2kmEhAnDrq+y/OhtfndHZ2Ym77roL6enp+N3vfuex9wl0ti50TMxJQmhoiN3z//3ltV11gq085w/JTXsxsXDwQOSmxGFZSpxL23BVZjzKtc04ebERM/LVeHBaNv66q8zlgV9K3YfBQJb42tnZNVI6M9PmiFzGV9fwm+FjpkBqTaDUp1WCBFUElkzPxYYHxmPZzDyPdoid/cy8/Rm7OmO3q44cOYJRo3qrQouiiOPHj/fUlbTnvffeQ2xsrNW/ESNGAACM3cH8N7/5DY4cOdLzZ5KcnIyQkBBoNBo8+eST2LlzJ8LCwtDQ0NCTfHDE2XVs2rQJFy9exH333Sdp/URErvB0fDXxZHy1Zmx3bcezZ8/2PDZq1CgcO3YMWq0WNTU1+N3vfgeNRoP09HQAwKJFi5CTk9OzvCAIePTRR3smnHP0vNRlKPg46qfNK0xDUr9Jk5y9Y83RoBGDwf5M56bXJ8SE44UFIzEjXw19uwEz8tXITo6BwWDsSdiu2VyMPaW1WLO5GAtf24syjU5yO53lap/TlAzr22//z3Hr69pXVotOB/vHFYEaX20xGo1YtGgRqqqq8OmnnyIiIsLxi8gq04WOFXPyMSUvCSvm5JtdzLB3/j8qIx5NbR1216/0/IC1mBgdHorrruxNCDq7DWWarjrHb+wux57SWqz7qhwvbi7Gn+4arbjR1OSYLPE1Ph6xhYVd/2V8lRVHEiuAUurTBgOl/6jKydqthN4eud7Z2YmTJ0+ajRLbtWsXqqurce+99zp8vb3b9QyigAv1elxu6cDA6HAkxSciJSXFYrnIyEgUFhZi9+7duOWWWwB0zeLc0dGBcePGWSyv1Wqh0+mQkZGBsO6rks6u44033sCcOXN6khdERHLzZHztf7teSkqKx+KrNadPnwYAZGVlWTyXmJgIoGtkscFgsCjpk5WVhfXr19tct6PnpS5DZCLHbeDujkg2vf7Gq9Lx4uZis9G2nxyOQEHaAK+Xt5Ojz9m3BnH/dSXEhGPl3AKcq23GojcPyDoyOpDjqzWiKOLBBx9EUVERtm/f7rWJ+QKZo1G/ts7/0wdGY+OBClyv1SFbhjqynQaj1xM+/WPitYMGY2FYJhKSXf9u2opfO4s1GJHOyRX9iWzxVUK5CcZX1zBJrAD+UmOI/IO9WwnlvC1SilOnTqGtrQ3vvfce5s2bh8bGRjz44IN45JFHzG4vscXW7Xot7Z04ebERdXUtAIDGlk5UX27FiPQBiI6wDGuPPPIIfvazn2Hq1KnIysrCE088gdmzZ1ud9GP58uV4++23UV5ebpagkLqO6upqfPbZZ9i4caPD7SMicpWn4quz3I2vOp0Ov/jFL7Bw4UIMHjwYp06dwo9//GPccccdZqM59u7di8bGRhQUFODw4cNYsWIF7r//fquJZCJvk+M2cHcHjcy/ahA27D1nMxFcrrU+YthT5e2k9Dml1ka2tq6VcwssEuJylZ8IlPgKAO3t7SgqKgLQNZrt/PnzOHLkCNLS0pCW1nVsLV26FNu2bcNnn32G9vZ2VFd3jfROTExU/Ig3pbP1vTKd/398uBKHz9f3TMb20r8OYWGHoas2cfYgl9+3XKuDprwOr71zEGMLh2LOiFTkpXhv8jKzmKhvBr5x/Thiec7AIlt8lVBuwh7GV9uYJFYIf6gxRMonpfabN0euHzlyBEOHDsWSJUswY8YMhISEYPHixfjtb39rsaxRtF5/y5ra5nZ0GMxrBnUYRNQ2tyPDSpJ46dKlqK6uxiOPPIKmpiZcf/31WLt2reTtMIqi5HW89dZbiI+Px/z58yWvn1zji9ERRErhTHz1JHfja2hoKI4dO4Z33nkHTU1NyMjIwK233opnn33WbLmWlhY8+uijqKioQEpKCh544AH85je/kXtziNziTh/e3UEjgwZG4+TFRqvP7SnV4s5rhuDDbystnnM0SMCdSe5s9Tln5Kvxyo4Sp2oj911XXkosSmp0HhsZHSjxFQAuXryI0aNH9/z72Wef7flbtWoVAOD1118HALPbvwFgx44dmDFjhlvbEAxc/Y4MSYxB9eUWRIWHYmexBuu+Koequ9TE/vJaLDYYXYopZRod7lt3ALeWarE/sQ7bK1vx5lflePnOq5EeH+3VgWhy5DWcGeTkzUk5yTWMr12UHF95fq0wTBCTO6TcSujNkeumekM/+tGP8KMf/cjqMi3tnahtbsfllg4kRIZAbRBhb15Soyjicov1Wl2NLR0Q40UIgmDx3KpVq3qCtT3r16/vuc24b9sGRofjyad/4XAdTz31FJ566imH70Ou6ztafkZ6FBY2tyPB140i8jIp8dVbXImvJtHR0di+fbvD137ve99DaWmpiy0kUj7Tb1u5Voc7rxmCqzMHIjNRJfn1jhIp44bG4+ar03G8shGl3XWI+w4S6J9cKdfq8PnBS25NcmetzzkjX40fv38YpdpmANJHAA9JjMFvbhqBQ+cbEB0egkPnG6wuJ8fIwkCJr0BX+RxHkzF5crKmQObuRJBhoSHITo7Fh5uLLZ6bkJ3k8jH8xYlq1Fk5H9xRrMGRigasuW2U392x7GiQU//P4oYsFbJ91Viyi/FV+ZgkJlIYV6+AOnMrjrdGrh85cgSTJk2y+bypbIRpVHBzswGirhWJHQZE27htJEQQMDA6HI0tnRbPDYgOt5ogdkX/tjkqaUGe12kw4nyd3my0/NEiPcQj8tVuI/IXjuIrEfmP/neCffhtJZJUEU6XTrA3cvfjwxdR09SGeSPTcOWgOFRdbsWsghQYRZiN6r0hS4X45nYsWncAFZ1d/R13SjkMSYzp6XOKANbuKutJEJtIGQEcFhqCs7V67CzWYFJuIgoHD/RY+TTGV3JEyt2bUlj7zvaf5M0Z9s4HT1c3Ij4m3GN1yD3J3iAna5/FxrBOfNrGQSRKxPiqfMx0ECmEHFejna037OmR60ePHsXDDz9s83lrZSOMooh6fTuioyNtvi5JFYHqy61mrw0PFSxm0nWHsyUtyHNM342z2makDoyyGC3fIkPtNiJ/4yi+EpH/kGtSOakjd00JaKMIi+TK3tQIPHapCXVJ7UBkb3/H2faYfruPVTTgvslZOHi2Dm0GIw47OQK47+AJU0Lt3X3n8cKCkUhSRbhUPs1RuSrGV3LEU99Zdyd5M50PHi2qsHiuIG0AdhZr0Nph8MsSl7YGOVn7LOqa23GmTofx3m4kOcT4qnzMdPgI6+VQX568Gu2pesNSaLXWr2QDjspGdGKQaL1sBABER4RhRPoA1Da3o7GlAwOiw5GkipBthK+rJS1Ifn2/G7OHp6CyocXqcu7UbiPyR/biKxH5D7knZeqfSHllR4nVkbv7ympRr++wSK4MjA7HhfoWIMn19vT97X5hwUg89rfDqG1uR646FjPy1Q4HNHQajKi63IJPj1ZZDJ4wJdR2Ftfg5TuvxqHzDdhfXiupfJopcf3tiXNYWl6HVtFgdTnGV7LHo99ZNyd5A7rOBzduM19HkioCeSmxWPdVORaMGezX/eX+NYhtfRYV9XqMNRgR6q2GkSSMr8rHJLGXuTta1F8xKW6fJ0eQKPUYs182IsxhEjY6IgwZEWEeSdh6q6QFOdb3u1Gu1ds8uXSndhsREZGvuHInmBShoSE2Eyi56lhEhoWgXKuzeO5cbQtSB1i/m0tqe0y/3XkpsSjXNvf8jpdqdHhwWrbVEcD/MzLN7DxpRPoA5Khjcaqq0WLwxLKZeTB0XxieOkwNgyFXUuJ6xYfHkBATjprGduwp1eI7VRta2jtZRoyc4snvrBxy1LHYsHg8as5vxfHcRGRlDUJeSixWbzrt08FDnmDvs8hMiOG5AZEL+K3xItNV9TWbi7GntBZrNhdj4Wt7cfLiZV83zWPKNDq8sqMEi948gFd2lKBMY9kZDXaOrkYbDEan1mfqPG94YDyWzcxTZILYJEkVgfBQ84RriCAgIUb6FXRPJWyttU3ukhZkX//vRqlGh7yUWIvPwJ3abURERL42rzDN4rdNSjKn00Ef0ZRAMUmICccLC0ZiRr4aHx2qRNrAaLywYCQSYnqnDC7X6jAkMQaJLrTH1CbTb3dWUgxOVJqf56zedBrL5+Rj6bU5mJKXhBVz8s1KX5jOk97YXY4XNxfj+QUjkauO7Rk8YdI3+SMlEXSxoQVXZ8ZD327AtGHJmDW8q9/Qf5AGkSOdBqPL31lvyU6OxYScJPx2fiFy1Sp8duwiHpyW4/AuVUcxRYmsfRaJqggMS1HuOTCRkvGyqRfZGi36yeFKDIgKc2oGY38gVwmFQKf0q9Ge1L9sRHxkBNRCFMLDfX9jkKdLWpBj1r4bqzedxsq5BahsaMHh8/Vu124jIiLyNWfvBHPmzsS+pchWzi3Ai5uLzfrmSaoIrJxbgKc+Pg6gK7kyoD0cGxaPx+dnm222x9Zdgn1/u8u1eswanmL2O16v78BTHx/Hk3Pz8eDUbMws6ErWvrKjxOp50tGKBtw2djDiYyKws7imZwSxM0pqmvD4xiO9k9626RFfokW6OpplxEiyvt+7WcNTsGHxeOws1ij67s1BCTG4a0gq7hiXafd74893O1uLnzdkqZDw9mFfN43ILyk/ixQg7I0WLapqxJGKwBtNbK+EAplT+tVoT4qOCENGQgyGDxqA9IHRFqN35fLrX/8aaWlpUKlUuPnmm1FTU+NU2zISYrD6hd/ZXYcgCFb/PvjgA49sUzDo/92o13fgxc3FuGNcBjY8MB4/nJKNBI7uJvIpV+Jrf//+978xduxYREVFITU1FU8//bTZ86tWrUJBQQFiYmKQmpqKu+++G1VVVRbrOXv2LO6//36b7+PoeanLEMlN6p1gtu5MtHW3nimB8ue7RqOyvsVq37yyoQULxw3Gijn52LB4POJjIpCdbL09Uu4SNP12l2p0yE5WWe3jqmMjMTSpa4CMvfOk45WXcfBcPV7cXIx7Jw51aRDE5pOXrE5629ppQFJspKITxO7G16KiItx2220YMmQIBEHA+vXrLZaRGl+DWf/v3XOfncKidQcw/6pBTt296auRuo4SxM7EFCXqHz+zOXiEJJCj/+poHffff79FbmD58uVybYJHMEnsJf1v9+qrIG0Adp2pcbqsgJLJXUIh0Jk68Cvm5JvdeucvV3Dl4MkO+tq1a7F69Wr85S9/we7du1FZWYm7777bqbZJWUdVVZXZ3+uvvw6VSoW5c+fKvUlBw9Z3w2AU8dquMix55yAOlNdZratIRJ7nbnwFgK1bt+LWW2/FzTffjMOHD2P79u249tprzZbJycnBX/7yF5w8eRKbNm3CxYsXsXDhwp7n33nnHZSXl/f8WxRFvPrqqz0TpDh6XuoyRJ5mK5ljSi65OghDFIFD5+utPnf4fD2ev2mk1eRK3/ZITSb1/e3eWVyD//vBaPx09jBMyUvC0mtz8PKdV2NcVkJPP9fRedJZrR61ze04dL7B7jZa09ZhsNlHaGk3ID463OpzSiBHfG1qakJWVhZefPFFREdHW13GUXwl29+7T49WSbpwoeQSjIE0sMsf7qQlZZAjvkpdx+zZs81yBM8++6xcm+ERvG/ai/re7mVimmm0rrk9oIKap0ooBLL+s1EHku+++w4///nP8dVXX6G5uRnDhw/HunXrcNVVV3nl/V999VU8/PDDWLBgAQDgpZdewtSpU1FSUoK8PGkTA0pZR1qa+cjvf/7zn7jtttsQFxcn49YEn/7fjTKNDre+2lXKRtWmx6hSLT5adwBvPz4rqC6sEAGBEV9/+9vf4t5778Uvf/nLnsdGjBhhtsyiRYvM/v3Tn/4UN910E1pbWxEVFYWcnBzce++9mDZtGi5cuIC5c+di9OjRPUkRR89LXYbI2/rfBp6VFIOEmHDU6zvMlttTqrXahzQlduNjImxO/jo5N1lSqS9nJlruP8Hc5LzknkEi1vq59s6T1n3VdfFmf3mtpEnqTNtt2m9jhibghQUjsXrTabP9FhsVhkg72x0I8XXChAmYMGECAOCHP/yh1WUcxddg52jwk6NzNyWXYJS0bV5uEwWHQIivUtcRGRlpkSdQssDKRCmcaabRh6ZlY0peEhZPzcbyOfn4666ygCwrEMwlFNwRaAlig8GA6667DikpKdi1axcOHjyIRYsWYcCAAQ5f+9577yE2NtbqX/8Egi1tbW04fvw4pk2b1vPYpEmTEB4ejoMHD3psHRUVFdi8ebPNDjk5z/TdsHaSWuenIx6I3BEI8bWjowN79uzB8OHDce211yI1NRWzZs3C0aNHbb6mrq4OGzZswIgRI3oSGFOmTMH27duxZ88e7Ny5E48//jh+//vfQ6VSSXpe6jJE3mRt5O6v/nUSK+cWWCxraxCG6TfT1uSvzkxIp2/vRK6VpJa9uwT7TzBnq5/b/84h03nS6k2nHW5jf/3325+3l+DFzcVm+y06PNTuRMmBEF9dYS2+Bjt7I92lHJNKHqnr7rYRuUKW+Bofj9jCwq7/Kjw/8PXXXyM1NRUFBQVYsWIF9Hq9pPfwFY4k9rIR6QMxICoMRyouY9eZGtQ1t+ON+8b5/CqiJzg7CQcFprNnz+L8+fN49NFHUVDQ1TkfPny4pNfOnz+/Z/RDf+Hh0m4PrK2thdFoRHJyMpYvX46PP/4YJ06cQEJCAjQajcfWsW7dOuTm5mL69OmS3oOkcXc0B1EgCYT4qtVq0dHRgdWrV+P555/HmDFj8OKLL2Lu3Lk4c+YMYmN7+wyfffYZ7rzzTjQ3N2PChAnYsmVLz3P79u3DypUrMXnyZISHh+Pll1/Gnj178MwzzyA6Otrh81LWQeRttpJLZRodctWxKO2+Zd1Worf/b6Zp8teSGh1OVzdK7pubRuUePt+AGflqPDgt22xUrlzJJNPo41uuTsdjG4/0jCC2t43W2NpvptrLw6KBhWGZ+Lbuks11BEJ8dYa9+Eq2R7o7Oib9od/q6rYRuUqW+NrZCVRVAYMGAWFdaU0l5gduuOEG3HHHHcjIyMDRo0exYsUKVFZW4v3335f0Pr7AJLEPZCaqkJmowg2FaT7/UfC0QC6hQNIMGTIE11xzDWbOnIk77rgD8+fPl1yjNy4uTnKphqVLl+Ldd9/t+bdO13XiJIoigK66wmq1GkOGDEFYWFjP41I4uw6j0Yg333wTjzzyiOT3IGlYyoaoVyDEV6Oxa/ThjTfeiAcffBBAV423xMREbN26FTfffHPPsjNnzsThw4dx/vx5/OpXv8Jjjz3WMzHod999h/Xr1yMkJASrVq3CW2+9hVdffRXNzc2Ijo52+LyUdRB5k6NJr5+YPQwbvzlvN9Hb/zezXt+Bpz4+jlx1LJ6YPQw3XpXusB3WbpVPUkVg5dwCPPXxcY8kk9ITYrDmtlEuDTSxt98On6/H+vuuQXirHvgmAqizvZ5AiK/OsBdfyfXBT/7Qb+XALvI2WeJrZycQGQlkZvYkiaXyZn7g9ttv7/n/kSNHQqVSYcGCBXj55ZehVqudare3+D4qBTEl/Ch4SzBtK5kLDw/Hnj17eq6W3XLLLVixYoWk1zpzu95vfvMbHDlypOfPJDk5GSEhIdBoNHjyySexc+dOhIWFoaGhQXJgdnYdpkk/7rvvPknrJ+dYK2WTyBEP1I+vZhD3Jmfjq7FPx1Up8TUxMRGCIOCKK67oeSwuLg7JycmorKw0W1alUmHYsGGYNWsWNm7ciA8//BAHDhwA0FVTMycnp2dZQRDw6KOPIjk5WdLzUpch8hZHt4HfeFU6NjwwHstm5tlN5lj7zWzQt2NE+gBJcdLWqNxztc14/pZCj9VVNQ00kbKNfTnab1JqLwOB0X91hr34Sl1cPSallmD0Zb/F1W0jcoUs8dWNchO+yA+YjB07FkDXaGql4khiIvK4sLAwzJkzB3PmzEFkZKTkWj/O3K6XkpKClJQUi+UiIyNRWFiI3bt345ZbbgEA7N27Fx0dHRg3bpzF8lqtFjqdDhkZGQjrvirp7DreeOMNzJkzB+npjkfokPNy1LH49EdT8N/vNNj2TQkm1yXjpsXjkc0Ord/qNBgRJtPFxP6TPAX6aBgp8bWlvRO1ze243NKBgdHhSFJFKCa+RkdHo6CgAGVlZT3L6fV61NbWYvDgwTa3OzS0K9HT3Nxs9nhWVhbWr19v83WOnpe6DJE3OLoNXMogDGujBGfkq7GjuAbbTtXYjZP2RuUer7yMFdfne3wgiCvrl+v2eX/vv7rKVnylLs4ek45G6vbtt8xIj8LC5nYkeKLhEnBgF3mL2/HVjXITvsgPmJw+3VVnPysrS1JbfYFJYiLymNbWVjzxxBO46667MGTIEBw/fhwbN240m8HeHmdu17PnkUcewc9+9jNMnToVWVlZeOKJJzB79myrM5cuX74cb7/9NsrLy82Ct9R1VFdX47PPPsPGjRvdbjdZ6tuRnpSThEevzUPjVx04UnQJs2PjFJ8MlDMZGgjkTuh6YgZxpX5mUuNrS3snTl5sRIehaxRxY0snqi+3YkT6AMmzN9vjTHz92fLl2GAlvj700EN45plnMGvWLIwePRpr1qxBYmIiZs2aBaDr9utnnnkGt9xyC7KysnDp0iX88pe/REZGBq655hqr7TKKIkIEwe3tI/IluW4D71v+7eLlFtz8yh5JcdKdW+V9GTvd3W+B1H9tb29HUVERgK7yPufPn8eRI0eQlpaGtLQ0l+IrOc9WCcb+/ZajRXqIRypwvVaHbBmOISKlkS2+ulFuAvBOfkCn0+EXv/gFFi5ciMGDB+PUqVP48Y9/jDvuuEOxpSYAJomJyIMuX76M8+fPY8GCBWhpaUFeXh5WrVqFhx9+2KvtWLp0Kaqrq/HII4+gqakJ119/PdauXeuRdbz11luIj4/H/Pnz5Wo+dbOWAPxHWCf+EheFH209g7WHNB677dUZ1k6MlTS6VSlJT08kdO3NIL5spnMJUSV9ZoBl0tNWfH1oyRKz19U2t/ckiE06DCJqm9uREeF+N1BKbOwZyazv+mxa2zvNnn/iiSdQV1eHxx9/HE1NTRgzZgy++OILqLonrQsLC8OFCxdwzz33QKPRID4+HpMnT8aXX35pNrGd2Xv1GTUdLcN2EvmKnPN7hIaG4NOjVU7FSWdH5Soldrqz3wKp/3rx4kWMHj2659/PPvtsz9+qVauciq/kvv7HorV+S0uHAVuKLmFJ9iBvNo3IKwIpvjpaR2hoKI4dO4Z33nkHTU1NyMjIwK233opnn31W7s2RFXvNROQxqamp+Pzzz33dDADAqlWrsGrVKofLrV+/3uZtxlLW8dRTT+Gpp55yvoEEtLUB7e02n956oBStdQ1Q9XmstakFlxt0uHJQCIrqGrD1QAmWTM/1fFutKNfqsKXoEvaV1WLuiDRMzUtGekIMyrU63LfuAOp6RolUYOO2CGxYPB7ZydZPwDyRyO3bvok5SbjuylSb7+8NVj/PNr3Ln2GnwYhvT5yDqk1v8dyhk+dgGK2WnChw5TPzlJaWNlysbcLllk4MjA5DQkwEosNDkZqUhM8//bR3uQ4D6vXtKK5s6FkuMiwUTc1tCDUaLNara26DGBsBOQbbrvrFL7DqF78wf7Czs6ddp6oa0WkQ8fTvXsLTv3sJ+lABLS1tiO6uDSoAeO7ZZ/Fcd6fZ2rZ89Pe/W3/zzt6Ec9/3AoAmfXvPqGkmisnfyXEbuL3yEXtKtV0J1X6POzMq1xMX/9zlyn4LpP5rVlaW3cmYoqKi8NFHH7nRQnKVve/j/vJaLDYYWf6BAk4gxVdH64iOjsb27dtdb6CPsMdMRETKsHcvsHOn1aeMRhFJhyvxUL15AjC8swOJ9aW4X9+OM5c7kFQaA+P+wQgJ8e5t5vXN7fjyYAVEAD/JS0bd3nbsbWrDsNRYtLQbcGuZ5a26moptyM5OtFjPmRodKur0yEyMwbCUWCT0m+zEnfa1dBgwCoAewJfhobh9XKbF+o1G0eP7z9bnCcDlzzAMwNLyOowqtTzhmlyXjFDtbsnr0pTX4VYr67H2mVkj1z6sb25HXVEJDOfOIxaAAUCdICAlLgrhob3r7zCIqGtqhVEULZYb3GHA5RbLiy8DOyMgdDQ4bIOIriSuqzpaO6G28v4dbQ2IjrLshtrblr7bbK19/d+rMTIWjUKsbKOmifydq+UjpI7KlfNuDqJAZ+/7OCE7iQliIvIJ9piJiEgZJk0Cumd87S8EQO2uUryx9YzZ4zHtLbiqZjfWD5+LoiYjHp89DCESR6HKOVr3g12l+FPnGayaPwKPbjvTNQI1FZiRr0ZLhwH7k+osXnM8NxFj7xnXcxJQrtVh0boDqEtsB7rzkImREdhwn/sjjk3t60+YMaxn1K43Rxrb+jwBOPUZ9qfW6vBRnxHAAJCoisCcRWOBQQMlraPTYMRr7xzE/kTHn1l/cu/DD3aV4vzBIlTFmdctExKikT4wuuffmsstqBRbLF4vJEQjURUBzcXe0bUAEBYqIHnQACC8/5jBXqbRvP1HMDvDKIqovKRDU1iHxXPN0eGIS42zGMlsb1v6bnP/9iWpIlGpbTZ7L2P3yhtbOiDGixBYo5jIrUndHNUgdjhKmUkvIjPWvo/R4aG47spUH7aKiIIZk8RERKQMkZFdfzbMHp+HtYc0Zh3pRFUEBrbHoqjJiKjEeMwenwfE2U/KyV0vsdNgxM6LrRiUmYLTzUBFZxgQ2fXzerpZwIz8Qdhe2WrxupzsQbhoDENmfFfBhc8PXjJ7LQA0dwKfn23Gsn516ZzZBlP7miNjLJ77b1UrFseocK5Oj4XvnujZt9srKz1e49na55mkipD0GdqSHReH1x6dgU8OV6KoqhEFaQOQlxKLH31agjfuGydpW8IAjC0cinPtYchOjkG5Vo9SjQ4AMGbEUITGW082l2l0su5D0+dmCAmFIcQ8OdvQZsSg0FAIggCjKKK+zWixTM9yiREYnpGA2uZ2NLZ0YICEOr0t7Z04eam5p5ZxQ1s7LjZ1SC7bYKoL3NphgComEg1tRotlYlWREMLN1+VwW7q32Vr76lqNiFdZf68B0eFMEBN1k2syvP7cmeSOKFj1/z5eO2gwFoZlIsGH5cCIKLjx15qIiBSt09CV9BmSGIMPlk7Cijn5mJKXhJ9edwVW3zoKZ7XNeHz2MEnJOFO9xDWbi7GntBZrNhdj4Wt7UdadBHSF6cQ4KykGp6oazZ4r1eiQlxKLpH4lHZJUEchRx+LTo1U922hvBJbB0Jv4cmYbTCONp+QlW1236cTd3i3CnmI6MTJ9nivm5Nv9DDsNlsk/a3YWa7D9tAZR4aHYWazBUx8fR6m22eq22FrnjHw1vleghr7dgBn5arywYCRyk1V2R9rJvQ/tfW59k54hgoCB0eF2l4uOCENGQgyGDxqAjIQYh4lee5PdOdLS3omTFxtRUdcCXasBcVFhiOtXViI8VLD4TkjdFlvta2rtRGxkmEVJClvvRRTMTOUjNjwwHstm5slyMbDTYMT8qwZZ/b2TMkqZKFj1/T4umZ4rS5kxIiJXMUlMREHh17/+NdLS0qBSqXDzzTejpqZG9nUUFRXhtttuw5AhQyAIgs0C9yRNmUaHdV+VYfPJarz4ZTEWvXkAX5yoxvyrBmHDA+Px41nDMCkvGf8zchCWTM+VdJLrqWTozVenIyYiDMMHDbB4bvWm0/jj7VdhyfQcTMlLwuKp2Vg+Jx+rN53uSQBLSeQ6sw1lGh1e2VGCRW8ewCs7SjAjX43cZJXZa0wn7s4kqN1hLSErJVHRf1vsJfRN21Kq0WHbqZqeEcD9t8XeOss0XWU/3thdjj2ltVj3VTle3FyMP9012uYx1tZhsLkPz9bq0N5hOXGcFPMK0yQlPZNUEZKWkzKa1iiKuNxiWR4C6C7bYGUCJLPYeMstqNVqkKNWIT4mHBcbWjAwOhzDB8UhURWOzMRoqyOS//3vf2Ps2LHIG5SI708Yjtde/K3VbTGKIp5c/hNMzkvG9i8+NVvHpcZWxLTV4Y+/fBwDo8OsvtfZs2dx//33290HUpYhCgRyjO7tG08/PVqFDYvH45ffHy7p4h855m7/ddWqVSgoKEBMTAxSU1Nx9913o6qqymI5UwyOiopCamoqnn76aYtlHMVGxlf3cLQ9kXd5I75KzQ8oKb4qPhIJgnCbIAgfCYJwThAEvSAIpwVB+J0gCOxtEJEka9euxerVq/GXv/wFu3fvRmVlJe6++27Z19HU1ISsrCy8+OKLiI6OtrEmksI0WjYmIgy/+tdJ/Hl7Sc+o2Ztf2YNzdb0TnkmdIMwTyVDTyfGKD48hLyUW3x9lOYoqRBAQGiJg26kas9Gt9foOswTwvMI0hyOwpGyDtZHGi9YdwJ/uGm111K4zCWpXSEny2noPe6OmrSWdpWyLo5HYtpLwO4s1NrftmU+OY8zQBLPnEmLC8cKCkUgdEI3713/jMMFtTY46FqMGxyMzMdpm0hMAoiPCMCJ9gMPlpJA6mtekb2z8765dqKysxPMrHkVFnR5Vl1vR2NKJC/UtKKnRITMxxupI5q1bt+LWW2/FzTffjMOHD2Pz1i24btYMq9uybetWlBWfsmjbF//8B2oqK1BRr0dUWCgK0uLw741vo7mxAQDwzjvvoLy8vGd5URTx6quvQqvt/T5JWYaIetn6vZmZnyLrKOVgJUf/NScnB3/5y19w8uRJbNq0CRcvXsTChQvNlukfg7dv345rr72253lHsZHxlYj8jbfiq6P8gBLjqz/UJF4O4DyApwFcADAawLMAZgiCME0URXmGOBGRxxgMBqxZswZ//etfceHCBQwdOhRPPPEEHnnkEa+8/6uvvoqHH34YCxYsAAC89NJLmDp1KkpKSpCXJ222bSnrmDBhAiZMmAAA+OEPf+iBLQkeX5yoRoIqAiU1OrszpXcajJJ/yOSul2g6OTa1b09pLf51uBIbFo/HzmKNWa3HEAFo0Ldj26neJGH/BLCUOpFVl1swIn2A1W0YPSQBNU2tdpOctmand2ciI2f30ZtflUseWWZrWz4+XInqyy3ITo612EeOtsXeSOyHp+dInnip/7a9sGAkklQRPf9eObcAL24udnnbTaIjQpGREGNz4jVX46tRFBHSb32mWsLR4aEIDxXMSjrYKtvQPza+sPpFzJ8zC+WlpcjIyulZrsMgoq65HTFWEte//e1vce+992L5k0+htrkdxpYOXJeTj/jocET2mSyvrq4Ojz76KD7+5FOMKrzSbB1Ds7Kw/EcPIf+q8Th7/jzmzZuH0aNH93TIc3JycO+992LatGm4cOEC5s6da/a81GWIqJe9eLpsprT+lZIFQv910aJFZv/+6U9/iptuugmtra2IiooC0BuDf/nLX/YsN2LEiJ7/dxQbGV+JyFnBEl8d5QeUGF/9IUl8oyiKfYfv/FcQBC2AdwBMBbDLN80iIqnuvPNOHD58GH/84x8xcuRIHD9+HGfPnnX4uvfeew8PP/yw1eeGDh2KkydPOlxHW1sbjh8/jmeffbbnsUmTJiE8PBwHDx6U9CMgxzpIOtNoWWs1fk32lGoxJDEG/9x1CkvL66DW6pAdF+dw3XImQ62dHJdqm20mY20lgE11gzsNxp7yC7Zmgf/0aBVy1LFmyUjTNuSpY/H/NhWjpqnNantN+2xE+gCLJKWnJjI6ebER8THmbZWaQLA3avrw+XpEhYfiw83FFolXe9viaCT20uk5ki4kdBqM2FdWa7Zdqzedxsq5BahsaEFNYysqG1pkTZ7YKhXhbHw1JYLfe+89/ObnP0HftRq7c8JpgzOw9+ARtHQYoG/rtDnZnbXYOPvaaQgLD8epE0fMksRAd7mKfsnujo4O7NmzB3Pm3oAp067F2dIzyLmiAD9++jlcWTjSbBTxI488gocffhgjRwwHACSqwjEwOgwxEWHIuX4mho8ajWX3LMDxQwfwr3/9C/NuuKEnET5lyhRs374d1113Hb7++mt8+umnuOGGG8zaJ2UZIuoiJZ76++3z/t5/7a+urg4bNmzAiBEjehIYphj8/e9/H9deey1Onz6NwsJC/PGPf8RVV10FwHFsZHwlImfJEl9FEejTp1RafJVCifFV8Unifglik2+7/zvYm20hIud98MEH+OSTT3D69Gnk5uYCALKzsyW9dv78+T1X3voLD7d+O3R/tbW1MBqNSE5OxvLly/Hxxx/jxIkTSEhIgEZjLbx4Zh0knWnE78eHKjEjX92TsMtVxyI7OQblWj2uHDQAL209g+oLdRhVqsVH6w7g7cdnOUxqypUMdeXkuH8C2FSq4OsSLcYMTcDwtDhUN7ZiZn6K1faY3vNUVSNWzi1ASY0Op6sbUZA2AGOHxmPzyUs4UdmIuYWpVpOcBWld+6xB3251NKujBLUzyjQ6fHGiGl+XaDEjX40Hp2Vj9abTqNd32N1Hfdkb+V2QNqCn/IO1xKutbZEymtzehYS+2zUqYyBeWDCyZ7vq9R146uPjWDhuMH47vxAPvH3Q6nbJmTxxNr6aJpXrMIgYO+06rP/XDoSFCrgiNQ51+nZUN7QCAELDw1GmaUZ0eCgyEqORHBtpdX22YmN8fDwa6iz3sbVyFVqtFh0dHfjDH9bgoZ88g/wRo/D+ulfw0x/ejr9vPYDagVHIiAjDO++8g7Nnz+L999/veW14aAhCBAEN+g5s2fkVXlm9CleNHY+Bqiis/sP/4tMvd2L5kz9HRvJAHD10ECtXrsTkyZMRHh6Ol19+GXv27MEzzzzTM9Ji3759DpchClamC5omct+dozSB0H81+eyzz3DnnXeiubkZEyZMwJYtW3qeM8Xg1atX4/nnn8eYMWPw4osvYu7cuThz5gxiY2MdxkYpsZPxlYhMZImvnZ1AVRUwaBAQ1pXWVFp8lUKJ8dVff71NRZIsi9IRkaK8+eabuOWWW3p+AJwRFxeHvLw8q39Dhw6VtA7TJEuCIECtVmPIkCEICwuzOvmSJ9dBzplXmIYGfTvyUmKRm6zCCwtGYka+Gvp2A75XoMas4amoa+4dMVvnxORzcszq7k4dX2t1cf+8vQS/+tdJxESEmdXHtfaepmTkzmJNT43jb8814ERlI0o1OoxIH2C1tnFeSixKNTqHE/XJkSDuu22mid9Wzi3oWUZqAsFWnWbTtpjYqilt7T0c1X42XUjoX785RIDZdr323zKL7QKArKRYREaEebTOs4mz8bW2ub2nhIQqNg4ZWTlIy8xGamYWEgcNQUZWDjKycjBocCYAoKXDgJrGVpuxzlZsFAD0m0fPZrkKo7Hrc5s+ey7m33Ev8guvwsrn/oCG+jp8s2cXGls6cP78efzsZz/Dm2++idDQ3vITMRGh0LV1oqXDgIqzpfjF//szHnv0UcQmpeKF1zciLjEFJZW1OHmxESeKTmH9+vVYunQpMjIysGnTJqSnp6O5ublnfd99953DZYiCjb268lJq6furQOi/msycOROHDx/G1q1bERoaiscee6znOVMMvvHGG/Hggw9izJgxWLt2LbRaLbZu3QrAcWyUEjsZX4nIRLb4mpWl6PgqhRLjq98liQVBGAzgNwA2iaJ4xMfNISIHjh07hnHjxrn02vfeew+xsbFW//rWSrMnOTkZISEh0Gg0ePLJJ7Fz506EhYWhoaEBarXaa+sg55gSdfr2Tvy/20bh0E8QxgAAFQxJREFUxc3FWPdVOfaU1uKN3eVY9t4hi+Scs5PPuZuoc+fk2FYdx5IaHeJjImwmcfu+Z6lGh22nanqS6aakadHFJvzy+1di6bW5mJKXhMVTs7F8Tj5Wbzrdsx5XJ+qTwt625XaXypCaQOifsP3R9/IstgVwLvFqKwnc92KBtQsJ/zluf7sA88/fG8kTZ+KrURRxuaWj59+b//UBZo0ailmjhmJoahLGXzG45993z53Ss5y10b8m9mLjyLxMSRPpJSYmQhAEFORf0fOYKjYO8QmJ0FZXYUB0OA4dOgStVouxY8ciKiqq5za++xfdi+eeWIzMxGjcedfdmDZ2BDqMxu67DwXces8PEZ+YhA6DiLm33IGcnN7yF4Ig4NFHH0Vycm8yf9GiRQ6XIQomjib6lBJP/VUg9F9NVCoVhg0bhlmzZmHjxo348MMPceDAAQC9MfiKK3pjcFxcHJKTk1FZWQnAcWyUEjsZX4nIRJb4Gh+P2MLCrv8qNL5KocT4qvhyE30JghAL4F8A2gHYnBVKEIRV6Jrcjoh8LDo6umeUgrPkuF0vMjIShYWF2L17N2655RYAwN69e9HR0WH1x0mr1UKn0yEjIwNh3beuOLuOQObN+JqjjkWOOhav7CixmZzLTu49EfX27a2ulq6wV6ridHUjspJj7Jas6PueVw7qqi9sSpomqSIwOCEaT/z9CGbmp+Cmq9Ox9r/lWPdVudl6PLWvHG3bE7OHWa2JbE/f0hEXL7fg5lf29JStAFxLvEotrdG3BrG97ZpXmIroiMFmn7+n6jz35Ux8DREEDIwOR2NLJwBg6qy5GHHVWABAWnwUEqLDcaZGh06DiNDu+Gpr9K+Jvdg4eeIEiwn3rMXX6OhoFBQU4NKF8z2T5bW26HG5oR5p6YOQpIrA7NmzUVRUZPbew4cPx4svvogFCxb0vI8IQKtOxy9W/9miraZ6yFlZWVi/fr3dfSVlGU9g/5WURsrEdHKWKlKSQOi/WmO6G8M0wswUg8vKynqW0ev1qK2txeDB5pUdHcVGpcZXxlYiZZElvrpRbsJb8dUZSoqvfpMkFgQhCsCnALIBTBNFscrWsqIorgKwqt/reV84kQ9cd911ePXVVzFx4kRkZGTg0KFDuHTpkqRbMeLi4hAnYTIyRx555BH87Gc/w9SpU5GVlYUnnngCs2fPtlqUfvny5Xj77bdRXl6OrKwsp9bR3t7ek8gwGo04f/48jhw5grS0NKSl+f+tl4D346uj5NzQpK46S4k+ur3VlZNjKbV2F4wZbHN9/ZOmnx6twpXpA1CQNgB5Kb0J4x3FNVg8NQsNevMTfE/eCuyoRuWNV6W7vO7Q0BBkJqpkTbzK8ZlNzk22+fl7OnnibHxNUkWg+nIrOgwiVLFxUMXGITxU6Bnlm5vXNaldY0uHzcnq+nMUG/uOQrYVXx966CE888wzmPm972HoFSPwl5f+iISEBCy69ftd7x8Ri4KCgv5vjfT0dGRkZPS8jwCYJcL7sjciWinYfyUlcbb2fiAliIHA6L/qdDo888wzuOWWW5CVlYVLly7hl7/8JTIyMnDNNdf0vNYUg2fNmoXRo0djzZo1SExMxKxZs9zeBiVgbCVSFlnia2cnEBkJZGb2JImd4a346o/5Ab9IEguCEA7gIwDjAMwSRbHIwUuISCF+//vfo6OjA3feeSeampowcuRIvPjii15tw9KlS1FdXY1HHnkETU1NuP7667F27VrZ13Hx4kWMHj2659/PPvtsz9+qVavk2JSgYy85N3pIAhoudSUKb1o8Htk+vL3V2ZNjW5Oj5aXE4pPDlZKSuKak6bKZebjl6nQ8tvGI2YjhJFUEMhKiPT6atT97E7/JwVej1uxtl6N2eKqdzsbX6IgwjEgfYDMRHB0RhoyIMLPRv47IEV+feOIJ1NXVYcXPfoqmpiaMGTMGmzdtQkpSglPrAcwT4SaORkQTkaVAn5jOkUDov4aFheHChQu45557oNFoEB8fj8mTJ+PLL79EbGxvP8AUgx9//PGeGPzFF1/IkugmIuovmOKrP+YHFJ8kFgQhBMB7AL4HYJ4oit/4uElE5ISBAwfir3/9q6+bgVWrVkkKxOvXr7d5m4ajdWRlZXEyOw+wlZxbMHowhoanIbR0C5DsX/UP+5ciGD0kAVcOikPV5VaX6jmmJ8RgzW2jLJLBWd37xZtJVW+UWQC8P2rNW9vlDFfiq5REsLMjbt2Nr4Ig4LnnnsNzzz0n+T1txVpHiXAiks7TF/2ULBD6r1FRUfjoo48cvtaVGExE5Kpgiq/+mB/whx7zKwAWAvgdgFZBECb2ee6CKIoXfNMsIiLyBrvJuaYmXzfPZf1HxBoMRrcSn45G2Hq7XnMg1qgMpO1SeukFd7gyIpqILCnx4hgRERF5jj8kied1//eZ7r++fo1+9YWIiCjwBFJyrj/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\n",
       "text/plain": [
        "<Figure size 1440x720 with 8 Axes>"
       ]
@@ -1169,9 +1255,44 @@
     "        ],\n",
     "        col_wrap=4,\n",
     "        height=5,\n",
+    "        sharey=False,\n",
+    "        sharex=False,\n",
     "    )\n",
     "    g.map(sns.scatterplot, \"x\", \"y\", s=50, alpha=1)\n",
     "    g.set_titles(row_template=\"{row_name}\", col_template=\"{col_name}\")\n",
+    "    g.set_xticklabels([])\n",
+    "    g.set_yticklabels([])\n",
+    "\n",
+    "    for ax_idx, ax in enumerate(g.axes):\n",
+    "        ax.set_xlabel(\"$x$\")\n",
+    "\n",
+    "        if ax_idx == 0:\n",
+    "            ax.text(\n",
+    "                -0.10,\n",
+    "                1.02,\n",
+    "                \"a)\",\n",
+    "                fontweight=\"semibold\",\n",
+    "                fontsize=20,\n",
+    "                transform=ax.transAxes,\n",
+    "                horizontalalignment=\"left\",\n",
+    "            )\n",
+    "        elif ax_idx == 4:\n",
+    "            ax.text(\n",
+    "                -0.10,\n",
+    "                1.02,\n",
+    "                \"b)\",\n",
+    "                fontweight=\"semibold\",\n",
+    "                fontsize=20,\n",
+    "                transform=ax.transAxes,\n",
+    "                horizontalalignment=\"left\",\n",
+    "            )\n",
+    "\n",
+    "        if ax_idx in (0, 4):\n",
+    "            ax.set_ylabel(\"$y$\")\n",
+    "            continue\n",
+    "\n",
+    "        sns.despine(ax=ax, left=True)\n",
+    "        ax.yaxis.set_tick_params(left=False)\n",
     "\n",
     "    mono = {\"family\": \"monospace\"}\n",
     "\n",
@@ -1184,8 +1305,9 @@
     "        rs, rs_p = spearmanr(x, y)\n",
     "\n",
     "        # ccc\n",
-    "        c, max_parts, parts = ccc(x, y, return_parts=True)\n",
-    "        c, c_p = ccc(x, y, pvalue_n_perms=10000)\n",
+    "        (c, c_p), max_parts, parts = ccc(\n",
+    "            x, y, return_parts=True, pvalue_n_perms=PVALUE_N_PERMS\n",
+    "        )\n",
     "\n",
     "        x_line_points, y_line_points = get_cm_line_points(x, y, max_parts, parts)\n",
     "        for yp in y_line_points:\n",
@@ -1213,8 +1335,10 @@
     "            linespacing=1.1,\n",
     "        )\n",
     "\n",
+    "    plt.subplots_adjust(hspace=0.2)\n",
+    "\n",
     "    plt.savefig(\n",
-    "        OUTPUT_FIGURE_DIR / \"relationships.svg\",\n",
+    "        OUTPUT_FIGURE_DIR / \"numerical_relationships.svg\",\n",
     "        # rasterized=True,\n",
     "        # dpi=300,\n",
     "        bbox_inches=\"tight\",\n",
@@ -1227,10 +1351,10 @@
    "id": "385c9fdb-823b-4eda-a551-dcdbe62e0943",
    "metadata": {
     "papermill": {
-     "duration": 0.008003,
-     "end_time": "2023-12-04T18:42:09.594882",
+     "duration": 0.011159,
+     "end_time": "2024-01-04T08:13:07.123639",
      "exception": false,
-     "start_time": "2023-12-04T18:42:09.586879",
+     "start_time": "2024-01-04T08:13:07.112480",
      "status": "completed"
     },
     "tags": []
@@ -1246,16 +1370,914 @@
     "1. With two internal clusters (Anscombe I, II and III) for each variable pair, CCC seems to capture linear relationships. However, two clusters also capture non-coexistence relationships."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5026f8e5-dc07-43ff-9596-76093dae779b",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006172,
+     "end_time": "2024-01-04T08:13:07.136794",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.130622",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Categorical datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2de70ce2-3d93-411c-8b3b-a61fb6b57c1a",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006188,
+     "end_time": "2024-01-04T08:13:07.149577",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.143389",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Independent Two-Categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a6811486-7838-4454-9441-97c1929808a3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.168691Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.168423Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.171405Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.170965Z"
+    },
+    "papermill": {
+     "duration": 0.017214,
+     "end_time": "2024-01-04T08:13:07.173257",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.156043",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rel_name = \"Two-Categorical I\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "68fe500f-22a3-4661-b727-a9372279ce53",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.193657Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.193111Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.198340Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.197786Z"
+    },
+    "papermill": {
+     "duration": 0.016171,
+     "end_time": "2024-01-04T08:13:07.199124",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.182953",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(4)\n",
+    "\n",
+    "x = np.random.choice([\"A\", \"B\", \"C\"], 100)\n",
+    "y = np.random.choice([\"Orange\", \"Blue\"], 100)\n",
+    "\n",
+    "cat_datasets_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"dataset\": rel_name,\n",
+    "        \"x\": x,\n",
+    "        \"y\": y,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "998b963a-fa66-4d45-a38e-536a39a32286",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006511,
+     "end_time": "2024-01-04T08:13:07.211942",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.205431",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Two-Categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "5f0b47d9-be00-4d30-beb2-667f5a12e847",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.225488Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.225237Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.228078Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.227597Z"
+    },
+    "papermill": {
+     "duration": 0.010618,
+     "end_time": "2024-01-04T08:13:07.228844",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.218226",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rel_name = \"Two-Categorical II\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "2b28f3fa-883d-45b8-82cb-6c766b2998a1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.242644Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.242241Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.251663Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.251160Z"
+    },
+    "papermill": {
+     "duration": 0.017316,
+     "end_time": "2024-01-04T08:13:07.252462",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.235146",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "x = np.random.choice([\"A\", \"B\", \"C\"], 100)\n",
+    "y = np.random.choice([\"Orange\", \"Blue\"], 100)\n",
+    "\n",
+    "_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"dataset\": rel_name,\n",
+    "        \"x\": x,\n",
+    "        \"y\": y,\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "_df.loc[_df[_df[\"x\"] == \"A\"].sample(frac=0.50).index, \"y\"] = \"Blue\"\n",
+    "_df.loc[_df[_df[\"x\"] == \"B\"].sample(frac=0.75).index, \"y\"] = \"Orange\"\n",
+    "\n",
+    "cat_datasets_df = cat_datasets_df[~cat_datasets_df[\"dataset\"].isin((rel_name,))]\n",
+    "cat_datasets_df = cat_datasets_df.append(\n",
+    "    _df,\n",
+    "    ignore_index=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "555497ee-42ee-45cf-acbc-5f9e2dbe95d0",
+   "metadata": {
+    "papermill": {
+     "duration": 0.007351,
+     "end_time": "2024-01-04T08:13:07.266416",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.259065",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Independent Two-Categorical-One-Numerical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1460089c-4615-48be-819a-e8c9087291fd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.280190Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.279799Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.282674Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.282209Z"
+    },
+    "papermill": {
+     "duration": 0.010715,
+     "end_time": "2024-01-04T08:13:07.283496",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.272781",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rel_name = \"Categorical-Numerical I\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d981cfbb-989a-4ea9-9be3-4f26f63f20ce",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.297872Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.297390Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.304697Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.304207Z"
+    },
+    "papermill": {
+     "duration": 0.015514,
+     "end_time": "2024-01-04T08:13:07.305522",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.290008",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(10)\n",
+    "\n",
+    "x = np.random.choice([\"A\", \"B\", \"C\"], 100)\n",
+    "y = np.random.rand(100)\n",
+    "y = minmax_scale(y, y_lim)\n",
+    "\n",
+    "cat_datasets_df = cat_datasets_df[~cat_datasets_df[\"dataset\"].isin((rel_name,))]\n",
+    "cat_datasets_df = cat_datasets_df.append(\n",
+    "    pd.DataFrame(\n",
+    "        {\n",
+    "            \"dataset\": rel_name,\n",
+    "            \"x\": x,\n",
+    "            \"y\": y,\n",
+    "        }\n",
+    "    ),\n",
+    "    ignore_index=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a54ea55-fbb0-45e5-877a-411cd0e7db8a",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006371,
+     "end_time": "2024-01-04T08:13:07.318775",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.312404",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Two-Categorical-One-Numerical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f102d770-4c21-40b2-8b5f-e47f93b8eebc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.332874Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.332548Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.335620Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.335147Z"
+    },
+    "papermill": {
+     "duration": 0.010963,
+     "end_time": "2024-01-04T08:13:07.336423",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.325460",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rel_name = \"Categorical-Numerical II\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "5e3ca2d6-d9af-4164-9636-d3116bec8122",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.350196Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.349865Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.362836Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.362261Z"
+    },
+    "papermill": {
+     "duration": 0.020718,
+     "end_time": "2024-01-04T08:13:07.363683",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.342965",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "x = np.random.choice([\"A\", \"B\", \"C\"], 100)\n",
+    "y = np.nan\n",
+    "\n",
+    "_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"dataset\": rel_name,\n",
+    "        \"x\": x,\n",
+    "        \"y\": y,\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "_idx = _df[_df[\"x\"] == \"A\"].index  # .sample(frac=0.50).index\n",
+    "_df.loc[_idx, \"y\"] = np.random.normal(0, 0.50, _idx.shape[0])\n",
+    "\n",
+    "_idx = _df[_df[\"x\"] == \"B\"].index  # sample(frac=0.75).index\n",
+    "_df.loc[_idx, \"y\"] = np.random.normal(1, 0.25, _idx.shape[0])\n",
+    "\n",
+    "_idx = _df[_df[\"x\"] == \"C\"].index  # sample(frac=0.75).index\n",
+    "_df.loc[_idx, \"y\"] = np.random.normal(1, 0.75, _idx.shape[0])\n",
+    "\n",
+    "_df[\"y\"] = minmax_scale(_df[\"y\"], y_lim)\n",
+    "\n",
+    "# _idx = _df[_df[\"x\"] == \"B\"].sample(frac=0.75).index\n",
+    "# _df.loc[_idx, \"y\"] = np.random.normal(0, 1, _idx.shape[0])\n",
+    "\n",
+    "cat_datasets_df = cat_datasets_df[~cat_datasets_df[\"dataset\"].isin((rel_name,))]\n",
+    "cat_datasets_df = cat_datasets_df.append(\n",
+    "    _df,\n",
+    "    ignore_index=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e772c48-9f23-4986-a1f1-d4dfe7347a34",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006672,
+     "end_time": "2024-01-04T08:13:07.376930",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.370258",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Create dataset dictionary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6a136ac-cde2-45ab-8513-9219adf0e25e",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006454,
+     "end_time": "2024-01-04T08:13:07.389775",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.383321",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Create a dictionary with easier access to datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "6f05696d-aca7-4325-9e67-35cc698dabbc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.403611Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.403365Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.409416Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.408935Z"
+    },
+    "papermill": {
+     "duration": 0.013946,
+     "end_time": "2024-01-04T08:13:07.410173",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.396227",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "categorical_datasets = {\n",
+    "    idx: df.drop(columns=\"dataset\") for idx, df in cat_datasets_df.groupby(\"dataset\")\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c8e5e42-b157-41b1-9fb3-89c8aede99b2",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006395,
+     "end_time": "2024-01-04T08:13:07.423046",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.416651",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "fb3a70a0-6325-486b-986b-b76c639c1298",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.436962Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.436586Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.445238Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.444759Z"
+    },
+    "papermill": {
+     "duration": 0.016448,
+     "end_time": "2024-01-04T08:13:07.446016",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.429568",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "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>dataset</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Two-Categorical I</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Orange</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Two-Categorical I</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Orange</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Two-Categorical I</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Blue</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Two-Categorical I</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Blue</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Two-Categorical I</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Blue</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>Categorical-Numerical II</td>\n",
+       "      <td>A</td>\n",
+       "      <td>7.029552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>Categorical-Numerical II</td>\n",
+       "      <td>C</td>\n",
+       "      <td>10.915389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>Categorical-Numerical II</td>\n",
+       "      <td>A</td>\n",
+       "      <td>8.293415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>Categorical-Numerical II</td>\n",
+       "      <td>B</td>\n",
+       "      <td>9.111649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>Categorical-Numerical II</td>\n",
+       "      <td>C</td>\n",
+       "      <td>8.893113</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>400 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      dataset  x          y\n",
+       "0           Two-Categorical I  C     Orange\n",
+       "1           Two-Categorical I  C     Orange\n",
+       "2           Two-Categorical I  B       Blue\n",
+       "3           Two-Categorical I  B       Blue\n",
+       "4           Two-Categorical I  A       Blue\n",
+       "..                        ... ..        ...\n",
+       "395  Categorical-Numerical II  A   7.029552\n",
+       "396  Categorical-Numerical II  C  10.915389\n",
+       "397  Categorical-Numerical II  A   8.293415\n",
+       "398  Categorical-Numerical II  B   9.111649\n",
+       "399  Categorical-Numerical II  C   8.893113\n",
+       "\n",
+       "[400 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_datasets_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "506ff63d-c1f8-43f0-9261-4b491077c64b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.460801Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.460307Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.465973Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.465504Z"
+    },
+    "papermill": {
+     "duration": 0.014039,
+     "end_time": "2024-01-04T08:13:07.466813",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.452774",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_cm_line_points(y, max_parts, parts):\n",
+    "    \"\"\"\n",
+    "    Similar to previous function definition of same name, but only takes one variable.\n",
+    "    \"\"\"\n",
+    "    y_max_part = parts[1][max_parts[1]]\n",
+    "    y_unique_k = {}\n",
+    "    for k in np.unique(y_max_part):\n",
+    "        data = y[y_max_part == k]\n",
+    "        y_unique_k[k] = data.min(), data.max()\n",
+    "    y_unique_k = sorted(y_unique_k.items(), key=lambda x: x[1][0])\n",
+    "\n",
+    "    # x_line_points, y_line_points = [], []\n",
+    "    y_line_points = []\n",
+    "\n",
+    "    for idx in range(len(y_unique_k) - 1):\n",
+    "        k, (k_min, k_max) = y_unique_k[idx]\n",
+    "        nk, (nk_min, nk_max) = y_unique_k[idx + 1]\n",
+    "\n",
+    "        y_line_points.append((k_max + nk_min) / 2.0)\n",
+    "\n",
+    "    return y_line_points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "6492c42b-41ed-4cc4-abd7-251642434de0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.481724Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.481356Z",
+     "iopub.status.idle": "2024-01-04T08:13:07.486323Z",
+     "shell.execute_reply": "2024-01-04T08:13:07.485839Z"
+    },
+    "papermill": {
+     "duration": 0.013412,
+     "end_time": "2024-01-04T08:13:07.487095",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.473683",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def plot_internal(x, y, **kwargs):\n",
+    "    x = pd.to_numeric(x, errors=\"ignore\")\n",
+    "    y = pd.to_numeric(y, errors=\"ignore\")\n",
+    "\n",
+    "    x_is_num = pd.api.types.is_numeric_dtype(x.dtype)\n",
+    "    y_is_num = pd.api.types.is_numeric_dtype(y.dtype)\n",
+    "\n",
+    "    if not x_is_num and not y_is_num:\n",
+    "        sns.countplot(x=x, hue=y, order=[\"A\", \"B\", \"C\"], hue_order=[\"Blue\", \"Orange\"])\n",
+    "    elif not x_is_num and y_is_num:\n",
+    "        sns.swarmplot(x=x, y=y, order=[\"A\", \"B\", \"C\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "8fb2bace-ba2f-4f61-b394-adf4b7cd8d71",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:07.502083Z",
+     "iopub.status.busy": "2024-01-04T08:13:07.501672Z",
+     "iopub.status.idle": "2024-01-04T08:13:10.740964Z",
+     "shell.execute_reply": "2024-01-04T08:13:10.740544Z"
+    },
+    "papermill": {
+     "duration": 3.247458,
+     "end_time": "2024-01-04T08:13:10.741751",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:07.494293",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with sns.plotting_context(\"paper\", font_scale=1.8):\n",
+    "    g = sns.FacetGrid(\n",
+    "        data=cat_datasets_df,\n",
+    "        col=\"dataset\",\n",
+    "        col_order=[\n",
+    "            \"Two-Categorical I\",\n",
+    "            \"Two-Categorical II\",\n",
+    "            \"Categorical-Numerical I\",\n",
+    "            \"Categorical-Numerical II\",\n",
+    "        ],\n",
+    "        col_wrap=4,\n",
+    "        height=5,\n",
+    "        legend_out=False,\n",
+    "        sharex=False,\n",
+    "        sharey=False,\n",
+    "    )\n",
+    "    g.map(plot_internal, \"x\", \"y\", s=1, alpha=1)\n",
+    "    g.set_titles(row_template=\"{row_name}\", col_template=\"{col_name}\")\n",
+    "    g.add_legend()\n",
+    "    # g.set_xticklabels([])\n",
+    "    g.set_yticklabels([])\n",
+    "\n",
+    "    for ax_idx, ax in enumerate(g.axes):\n",
+    "        if ax_idx in (1, 3):\n",
+    "            sns.despine(ax=ax, left=True)\n",
+    "            ax.yaxis.set_tick_params(left=False)\n",
+    "\n",
+    "        if ax_idx in (0,):\n",
+    "            ax.set(ylabel=\"$w$\", xlabel=\"$z$\")\n",
+    "\n",
+    "            ax.text(\n",
+    "                -0.10,\n",
+    "                1.02,\n",
+    "                \"c)\",\n",
+    "                fontweight=\"semibold\",\n",
+    "                fontsize=20,\n",
+    "                transform=ax.transAxes,\n",
+    "                horizontalalignment=\"left\",\n",
+    "            )\n",
+    "\n",
+    "        if ax_idx in (1,):\n",
+    "            ax.set(xlabel=\"$z$\")\n",
+    "\n",
+    "        if ax_idx in (2,):\n",
+    "            ax.set(ylabel=\"$y$\", xlabel=\"$z$\")\n",
+    "\n",
+    "            ax.text(\n",
+    "                -0.10,\n",
+    "                1.02,\n",
+    "                \"d)\",\n",
+    "                fontweight=\"semibold\",\n",
+    "                fontsize=20,\n",
+    "                transform=ax.transAxes,\n",
+    "                horizontalalignment=\"left\",\n",
+    "            )\n",
+    "\n",
+    "        if ax_idx in (3,):\n",
+    "            ax.set(xlabel=\"$z$\")\n",
+    "\n",
+    "    mono = {\"family\": \"monospace\"}\n",
+    "\n",
+    "    for ds, ax in g.axes_dict.items():\n",
+    "        df = categorical_datasets[ds]\n",
+    "        x, y = df[\"x\"], df[\"y\"]\n",
+    "        x = pd.to_numeric(x, errors=\"ignore\").to_numpy()\n",
+    "        y = pd.to_numeric(y, errors=\"ignore\").to_numpy()\n",
+    "\n",
+    "        x_is_num = pd.api.types.is_numeric_dtype(x.dtype)\n",
+    "        y_is_num = pd.api.types.is_numeric_dtype(y.dtype)\n",
+    "\n",
+    "        # ccc\n",
+    "        (c, c_p), max_parts, parts = ccc(\n",
+    "            x, y, return_parts=True, pvalue_n_perms=PVALUE_N_PERMS\n",
+    "        )\n",
+    "\n",
+    "        if y_is_num:\n",
+    "            y_line_points = get_cm_line_points(y, max_parts, parts)\n",
+    "            for yp in y_line_points:\n",
+    "                ax.hlines(y=yp, xmin=-0.5, xmax=20, color=\"r\", alpha=0.5)\n",
+    "\n",
+    "        # add text box for the statistics\n",
+    "        stats = f\"$\\it{{c}}$ ={c: .2f}{pvalue_to_star(c_p)}\"\n",
+    "        bbox = dict(boxstyle=\"round\", fc=\"white\", ec=\"black\", alpha=0.75)\n",
+    "        ax.text(\n",
+    "            0.69,\n",
+    "            0.07,\n",
+    "            stats,\n",
+    "            fontsize=14,\n",
+    "            fontdict=mono,\n",
+    "            bbox=bbox,\n",
+    "            transform=ax.transAxes,\n",
+    "            horizontalalignment=\"left\",\n",
+    "            linespacing=1.1,\n",
+    "        )\n",
+    "\n",
+    "    plt.savefig(\n",
+    "        OUTPUT_FIGURE_DIR / \"categorical_relationships.svg\",\n",
+    "        # rasterized=True,\n",
+    "        # dpi=300,\n",
+    "        bbox_inches=\"tight\",\n",
+    "        facecolor=\"white\",\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4b46cc0-86ac-4801-89e9-fd145ac0bdc1",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011166,
+     "end_time": "2024-01-04T08:13:10.760357",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:10.749191",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Create final figure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "57090b29-80c6-4ae5-8d2e-a1f256f8a58c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:10.784321Z",
+     "iopub.status.busy": "2024-01-04T08:13:10.784158Z",
+     "iopub.status.idle": "2024-01-04T08:13:10.793460Z",
+     "shell.execute_reply": "2024-01-04T08:13:10.793072Z"
+    },
+    "papermill": {
+     "duration": 0.024855,
+     "end_time": "2024-01-04T08:13:10.794268",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:10.769413",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from svgutils.compose import Figure, SVG, Panel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "e5a39467-e448-43a9-a9ba-9f947bfff1ed",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-04T08:13:10.809565Z",
+     "iopub.status.busy": "2024-01-04T08:13:10.809477Z",
+     "iopub.status.idle": "2024-01-04T08:13:10.817833Z",
+     "shell.execute_reply": "2024-01-04T08:13:10.817479Z"
+    },
+    "papermill": {
+     "duration": 0.016932,
+     "end_time": "2024-01-04T08:13:10.818624",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:10.801692",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "Figure(\n",
+    "    \"8.0cm\",\n",
+    "    \"6.5cm\",\n",
+    "    Panel(\n",
+    "        SVG(OUTPUT_FIGURE_DIR / \"numerical_relationships.svg\").scale(0.005),\n",
+    "    ),\n",
+    "    Panel(\n",
+    "        SVG(OUTPUT_FIGURE_DIR / \"categorical_relationships.svg\").scale(0.005),\n",
+    "    ).move(0, 3.45),\n",
+    ").save(OUTPUT_FIGURE_DIR / f\"relationships.svg\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25237272-7e8a-4e3b-8958-d1660ddcb6b0",
+   "metadata": {
+    "papermill": {
+     "duration": 0.007052,
+     "end_time": "2024-01-04T08:13:10.833316",
+     "exception": false,
+     "start_time": "2024-01-04T08:13:10.826264",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Now open the file, reside to fit drawing to page, and add a white rectangle to the background."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "1853d043-2f41-448a-8609-6c3eb8f83b7c",
    "metadata": {
     "papermill": {
-     "duration": 0.008003,
-     "end_time": "2023-12-04T18:42:09.611043",
+     "duration": 0.007065,
+     "end_time": "2024-01-04T08:13:10.847486",
      "exception": false,
-     "start_time": "2023-12-04T18:42:09.603040",
+     "start_time": "2024-01-04T08:13:10.840421",
      "status": "completed"
     },
     "tags": []
@@ -1288,14 +2310,14 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 27.127077,
-   "end_time": "2023-12-04T18:42:10.036390",
+   "duration": 31.557573,
+   "end_time": "2024-01-04T08:13:11.273592",
    "environment_variables": {},
    "exception": null,
    "input_path": "nbs/99_manuscript/intro/05-relationships_analysis.ipynb",
    "output_path": "nbs/99_manuscript/intro/05-relationships_analysis.run.ipynb",
    "parameters": {},
-   "start_time": "2023-12-04T18:41:42.909313",
+   "start_time": "2024-01-04T08:12:39.716019",
    "version": "2.3.3"
   },
   "toc-autonumbering": true
diff --git a/nbs/99_manuscript/intro/py/05-relationships_analysis.py b/nbs/99_manuscript/intro/py/05-relationships_analysis.py
index 5a5c9086..6e81db52 100644
--- a/nbs/99_manuscript/intro/py/05-relationships_analysis.py
+++ b/nbs/99_manuscript/intro/py/05-relationships_analysis.py
@@ -45,6 +45,9 @@
 # %% [markdown] tags=[]
 # # Settings
 
+# %% tags=[]
+PVALUE_N_PERMS = 10000
+
 # %% [markdown] tags=[]
 # # Paths
 
@@ -59,7 +62,7 @@
 display(OUTPUT_FIGURE_DIR)
 
 # %% [markdown] tags=[]
-# # Generate datasets
+# # Numerical datasets
 
 # %% [markdown] tags=[]
 # ## Anscombe dataset
@@ -220,9 +223,20 @@
     idx: df.drop(columns="dataset") for idx, df in datasets_df.groupby("dataset")
 }
 
+# %% [markdown] tags=[]
+# ## Create dataset dictionary
 
 # %% [markdown] tags=[]
-# # Plot
+# Create a dictionary with easier access to datasets
+
+# %% tags=[]
+datasets = {
+    idx: df.drop(columns="dataset") for idx, df in datasets_df.groupby("dataset")
+}
+
+
+# %% [markdown] tags=[]
+# ## Plot
 
 # %% tags=[]
 def get_cm_line_points(x, y, max_parts, parts):
@@ -302,9 +316,44 @@ def pvalue_to_star(pvalue):
         ],
         col_wrap=4,
         height=5,
+        sharey=False,
+        sharex=False,
     )
     g.map(sns.scatterplot, "x", "y", s=50, alpha=1)
     g.set_titles(row_template="{row_name}", col_template="{col_name}")
+    g.set_xticklabels([])
+    g.set_yticklabels([])
+
+    for ax_idx, ax in enumerate(g.axes):
+        ax.set_xlabel("$x$")
+
+        if ax_idx == 0:
+            ax.text(
+                -0.10,
+                1.02,
+                "a)",
+                fontweight="semibold",
+                fontsize=20,
+                transform=ax.transAxes,
+                horizontalalignment="left",
+            )
+        elif ax_idx == 4:
+            ax.text(
+                -0.10,
+                1.02,
+                "b)",
+                fontweight="semibold",
+                fontsize=20,
+                transform=ax.transAxes,
+                horizontalalignment="left",
+            )
+
+        if ax_idx in (0, 4):
+            ax.set_ylabel("$y$")
+            continue
+
+        sns.despine(ax=ax, left=True)
+        ax.yaxis.set_tick_params(left=False)
 
     mono = {"family": "monospace"}
 
@@ -317,8 +366,9 @@ def pvalue_to_star(pvalue):
         rs, rs_p = spearmanr(x, y)
 
         # ccc
-        c, max_parts, parts = ccc(x, y, return_parts=True)
-        c, c_p = ccc(x, y, pvalue_n_perms=10000)
+        (c, c_p), max_parts, parts = ccc(
+            x, y, return_parts=True, pvalue_n_perms=PVALUE_N_PERMS
+        )
 
         x_line_points, y_line_points = get_cm_line_points(x, y, max_parts, parts)
         for yp in y_line_points:
@@ -346,8 +396,10 @@ def pvalue_to_star(pvalue):
             linespacing=1.1,
         )
 
+    plt.subplots_adjust(hspace=0.2)
+
     plt.savefig(
-        OUTPUT_FIGURE_DIR / "relationships.svg",
+        OUTPUT_FIGURE_DIR / "numerical_relationships.svg",
         # rasterized=True,
         # dpi=300,
         bbox_inches="tight",
@@ -364,4 +416,302 @@ def pvalue_to_star(pvalue):
 # 1. When the number of internal clusters (separated by red lines) is higher, CCC is able to capture more complex relationships.
 # 1. With two internal clusters (Anscombe I, II and III) for each variable pair, CCC seems to capture linear relationships. However, two clusters also capture non-coexistence relationships.
 
+# %% [markdown] tags=[]
+# # Categorical datasets
+
+# %% [markdown] tags=[]
+# ## Independent Two-Categorical
+
+# %% tags=[]
+rel_name = "Two-Categorical I"
+
+# %% tags=[]
+np.random.seed(4)
+
+x = np.random.choice(["A", "B", "C"], 100)
+y = np.random.choice(["Orange", "Blue"], 100)
+
+cat_datasets_df = pd.DataFrame(
+    {
+        "dataset": rel_name,
+        "x": x,
+        "y": y,
+    }
+)
+
+# %% [markdown] tags=[]
+# ## Two-Categorical
+
+# %% tags=[]
+rel_name = "Two-Categorical II"
+
+# %% tags=[]
+np.random.seed(0)
+
+x = np.random.choice(["A", "B", "C"], 100)
+y = np.random.choice(["Orange", "Blue"], 100)
+
+_df = pd.DataFrame(
+    {
+        "dataset": rel_name,
+        "x": x,
+        "y": y,
+    }
+)
+
+_df.loc[_df[_df["x"] == "A"].sample(frac=0.50).index, "y"] = "Blue"
+_df.loc[_df[_df["x"] == "B"].sample(frac=0.75).index, "y"] = "Orange"
+
+cat_datasets_df = cat_datasets_df[~cat_datasets_df["dataset"].isin((rel_name,))]
+cat_datasets_df = cat_datasets_df.append(
+    _df,
+    ignore_index=True,
+)
+
+# %% [markdown] tags=[]
+# ## Independent Two-Categorical-One-Numerical
+
+# %% tags=[]
+rel_name = "Categorical-Numerical I"
+
+# %% tags=[]
+np.random.seed(10)
+
+x = np.random.choice(["A", "B", "C"], 100)
+y = np.random.rand(100)
+y = minmax_scale(y, y_lim)
+
+cat_datasets_df = cat_datasets_df[~cat_datasets_df["dataset"].isin((rel_name,))]
+cat_datasets_df = cat_datasets_df.append(
+    pd.DataFrame(
+        {
+            "dataset": rel_name,
+            "x": x,
+            "y": y,
+        }
+    ),
+    ignore_index=True,
+)
+
+# %% [markdown] tags=[]
+# ## Two-Categorical-One-Numerical
+
+# %% tags=[]
+rel_name = "Categorical-Numerical II"
+
+# %% tags=[]
+np.random.seed(0)
+
+x = np.random.choice(["A", "B", "C"], 100)
+y = np.nan
+
+_df = pd.DataFrame(
+    {
+        "dataset": rel_name,
+        "x": x,
+        "y": y,
+    }
+)
+
+_idx = _df[_df["x"] == "A"].index  # .sample(frac=0.50).index
+_df.loc[_idx, "y"] = np.random.normal(0, 0.50, _idx.shape[0])
+
+_idx = _df[_df["x"] == "B"].index  # sample(frac=0.75).index
+_df.loc[_idx, "y"] = np.random.normal(1, 0.25, _idx.shape[0])
+
+_idx = _df[_df["x"] == "C"].index  # sample(frac=0.75).index
+_df.loc[_idx, "y"] = np.random.normal(1, 0.75, _idx.shape[0])
+
+_df["y"] = minmax_scale(_df["y"], y_lim)
+
+# _idx = _df[_df["x"] == "B"].sample(frac=0.75).index
+# _df.loc[_idx, "y"] = np.random.normal(0, 1, _idx.shape[0])
+
+cat_datasets_df = cat_datasets_df[~cat_datasets_df["dataset"].isin((rel_name,))]
+cat_datasets_df = cat_datasets_df.append(
+    _df,
+    ignore_index=True,
+)
+
+# %% [markdown] tags=[]
+# ## Create dataset dictionary
+
+# %% [markdown] tags=[]
+# Create a dictionary with easier access to datasets
+
+# %% tags=[]
+categorical_datasets = {
+    idx: df.drop(columns="dataset") for idx, df in cat_datasets_df.groupby("dataset")
+}
+
+# %% [markdown] tags=[]
+# ## Plot
+
+# %% tags=[]
+cat_datasets_df
+
+
+# %% tags=[]
+def get_cm_line_points(y, max_parts, parts):
+    """
+    Similar to previous function definition of same name, but only takes one variable.
+    """
+    y_max_part = parts[1][max_parts[1]]
+    y_unique_k = {}
+    for k in np.unique(y_max_part):
+        data = y[y_max_part == k]
+        y_unique_k[k] = data.min(), data.max()
+    y_unique_k = sorted(y_unique_k.items(), key=lambda x: x[1][0])
+
+    # x_line_points, y_line_points = [], []
+    y_line_points = []
+
+    for idx in range(len(y_unique_k) - 1):
+        k, (k_min, k_max) = y_unique_k[idx]
+        nk, (nk_min, nk_max) = y_unique_k[idx + 1]
+
+        y_line_points.append((k_max + nk_min) / 2.0)
+
+    return y_line_points
+
+
+# %% tags=[]
+def plot_internal(x, y, **kwargs):
+    x = pd.to_numeric(x, errors="ignore")
+    y = pd.to_numeric(y, errors="ignore")
+
+    x_is_num = pd.api.types.is_numeric_dtype(x.dtype)
+    y_is_num = pd.api.types.is_numeric_dtype(y.dtype)
+
+    if not x_is_num and not y_is_num:
+        sns.countplot(x=x, hue=y, order=["A", "B", "C"], hue_order=["Blue", "Orange"])
+    elif not x_is_num and y_is_num:
+        sns.swarmplot(x=x, y=y, order=["A", "B", "C"])
+
+
+# %% tags=[]
+with sns.plotting_context("paper", font_scale=1.8):
+    g = sns.FacetGrid(
+        data=cat_datasets_df,
+        col="dataset",
+        col_order=[
+            "Two-Categorical I",
+            "Two-Categorical II",
+            "Categorical-Numerical I",
+            "Categorical-Numerical II",
+        ],
+        col_wrap=4,
+        height=5,
+        legend_out=False,
+        sharex=False,
+        sharey=False,
+    )
+    g.map(plot_internal, "x", "y", s=1, alpha=1)
+    g.set_titles(row_template="{row_name}", col_template="{col_name}")
+    g.add_legend()
+    # g.set_xticklabels([])
+    g.set_yticklabels([])
+
+    for ax_idx, ax in enumerate(g.axes):
+        if ax_idx in (1, 3):
+            sns.despine(ax=ax, left=True)
+            ax.yaxis.set_tick_params(left=False)
+
+        if ax_idx in (0,):
+            ax.set(ylabel="$w$", xlabel="$z$")
+
+            ax.text(
+                -0.10,
+                1.02,
+                "c)",
+                fontweight="semibold",
+                fontsize=20,
+                transform=ax.transAxes,
+                horizontalalignment="left",
+            )
+
+        if ax_idx in (1,):
+            ax.set(xlabel="$z$")
+
+        if ax_idx in (2,):
+            ax.set(ylabel="$y$", xlabel="$z$")
+
+            ax.text(
+                -0.10,
+                1.02,
+                "d)",
+                fontweight="semibold",
+                fontsize=20,
+                transform=ax.transAxes,
+                horizontalalignment="left",
+            )
+
+        if ax_idx in (3,):
+            ax.set(xlabel="$z$")
+
+    mono = {"family": "monospace"}
+
+    for ds, ax in g.axes_dict.items():
+        df = categorical_datasets[ds]
+        x, y = df["x"], df["y"]
+        x = pd.to_numeric(x, errors="ignore").to_numpy()
+        y = pd.to_numeric(y, errors="ignore").to_numpy()
+
+        x_is_num = pd.api.types.is_numeric_dtype(x.dtype)
+        y_is_num = pd.api.types.is_numeric_dtype(y.dtype)
+
+        # ccc
+        (c, c_p), max_parts, parts = ccc(
+            x, y, return_parts=True, pvalue_n_perms=PVALUE_N_PERMS
+        )
+
+        if y_is_num:
+            y_line_points = get_cm_line_points(y, max_parts, parts)
+            for yp in y_line_points:
+                ax.hlines(y=yp, xmin=-0.5, xmax=20, color="r", alpha=0.5)
+
+        # add text box for the statistics
+        stats = f"$\it{{c}}$ ={c: .2f}{pvalue_to_star(c_p)}"
+        bbox = dict(boxstyle="round", fc="white", ec="black", alpha=0.75)
+        ax.text(
+            0.69,
+            0.07,
+            stats,
+            fontsize=14,
+            fontdict=mono,
+            bbox=bbox,
+            transform=ax.transAxes,
+            horizontalalignment="left",
+            linespacing=1.1,
+        )
+
+    plt.savefig(
+        OUTPUT_FIGURE_DIR / "categorical_relationships.svg",
+        # rasterized=True,
+        # dpi=300,
+        bbox_inches="tight",
+        facecolor="white",
+    )
+
+# %% [markdown] tags=[]
+# # Create final figure
+
+# %% tags=[]
+from svgutils.compose import Figure, SVG, Panel
+
+# %% tags=[]
+Figure(
+    "8.0cm",
+    "6.5cm",
+    Panel(
+        SVG(OUTPUT_FIGURE_DIR / "numerical_relationships.svg").scale(0.005),
+    ),
+    Panel(
+        SVG(OUTPUT_FIGURE_DIR / "categorical_relationships.svg").scale(0.005),
+    ).move(0, 3.45),
+).save(OUTPUT_FIGURE_DIR / f"relationships.svg")
+
+# %% [markdown] tags=[]
+# Now open the file, reside to fit drawing to page, and add a white rectangle to the background.
+
 # %% tags=[]