diff --git a/CHANGELOG.md b/CHANGELOG.md
index 43359bdb..2fd5a899 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,11 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/)
 and this project adheres to [Semantic Versioning](http://semver.org/).
 
 
+## [Unreleased]
+
+### Added
+- Add ASCE 2023 INSPIRE workshop material [#303](https://github.com/IN-CORE/incore-docs/issues/303)
+
 ## [4.7.0] - 2023-11-08
 
 ### Added
diff --git a/manual_jb/content/_toc.yml b/manual_jb/content/_toc.yml
index 31dc6b9e..0f95e065 100644
--- a/manual_jb/content/_toc.yml
+++ b/manual_jb/content/_toc.yml
@@ -90,6 +90,14 @@ chapters:
         - file: workshops/20220131/session2/session2-hazard.ipynb
         - file: workshops/20220131/session3/session3-building_damage_analysis.ipynb
         - file: workshops/20220131/session4/session4-damage-analyses-lifelines.ipynb
+    - file: workshops/20231115/agenda
+      sections:
+        - file: workshops/20231115/session1/session1-jupyter-intro.ipynb
+        - file: workshops/20231115/session2/session2-remote-and-local-hazards.ipynb
+        - file: workshops/20231115/session2/session2-assignment.ipynb
+        - file: workshops/20231115/session2/session2-assignment-answers.ipynb
+        - file: workshops/20231115/session3/session3-bldg-dmg-recv.ipynb
+        - file: workshops/20231115/session4/session4-building-dmg-recovery-local-data.ipynb
 - file: notebooks
   sections:
     - file: notebooks/Galveston_testbed/Galveston_testbed.ipynb
diff --git a/manual_jb/content/workshops.md b/manual_jb/content/workshops.md
index 92fd3960..a5c734e0 100644
--- a/manual_jb/content/workshops.md
+++ b/manual_jb/content/workshops.md
@@ -23,4 +23,16 @@ Download the full directory of the files:
 
 Download the full directory of the files:
 
-[workshop_2022-01-31.zip](https://github.com/IN-CORE/incore-docs/blob/main/workshops/20220131/workshop_2022-01-31.zip)
\ No newline at end of file
+[workshop_2022-01-31.zip](https://github.com/IN-CORE/incore-docs/blob/main/workshops/20220131/workshop_2022-01-31.zip)
+
+## 2023-11-15 INSPIRE workshop
+
+[Agenda](workshops/20231115/agenda.md)
+* Session 1 Overview of IN-CORE & User Workshop Objectives
+* Session 2 **[Working with Remote and Local Hazards (Tornado and Earthquake)](workshops/20231115/session2/session2-remote-and-local-hazards.ipynb)**, [Assignments](workshops/20231115/session2/session2-assignment.ipynb), [Answers](workshops/20231115/session2/session2-assignment-answers.ipynb)
+* Session 3 **[Building Damage and Recovery Analyses with IN-CORE Web Services](workshops/20231115/session3/session3-bldg-dmg-recv.ipynb)**
+* Session 4 **[Building Damage and Recovery Analyses with Local Data](workshops/20231115/session4/session4-building-dmg-recovery-local-data.ipynb)**
+
+* Download the full directory of the files above:
+
+[workshop_2023-11-15.zip](https://github.com/IN-CORE/incore-docs/blob/main/workshops/20231115/workshop_2023-11-15.zip)
\ No newline at end of file
diff --git a/manual_jb/content/workshops/20210526/agenda.md b/manual_jb/content/workshops/20210526/agenda.md
index 3cf5a15d..14c1406f 100644
--- a/manual_jb/content/workshops/20210526/agenda.md
+++ b/manual_jb/content/workshops/20210526/agenda.md
@@ -38,8 +38,8 @@
     * [Session 4 assignment](session4/session4-assignment.ipynb)
     * [Session 4 assignment answers](session4/session4-assignment-answer.ipynb)
 5. Use Case - How to do research with IN-CORE
-    * Yousef Darestani Yousef Darestani *Galveston Island Socio-Physical Damage Analysis* (presentation starts at 3:33:45 of [Recording_1948x1096.mp4](http://resilience.colostate.edu/files/IN-CORE/GMT20210526-170314_Recording_1948x1096.mp4)
-    * Dylan Sanderson *A Spatially Explicit Decision Support Framework for Parcel- and Community-Level Resilience Assessment using Bayesian Networks* ([pdf](https://github.com/IN-CORE/incore-docs/blob/main/workshops/20210526/20210526_Sanderson_shareable.pdf
+    * Yousef Darestani **Galveston Island Socio-Physical Damage Analysis** (presentation starts at 3:33:45 of [Recording_1948x1096.mp4](http://resilience.colostate.edu/files/IN-CORE/GMT20210526-170314_Recording_1948x1096.mp4))
+    * Dylan Sanderson **A Spatially Explicit Decision Support Framework for Parcel- and Community-Level Resilience Assessment using Bayesian Networks** ([pdf](https://github.com/IN-CORE/incore-docs/blob/main/workshops/20210526/20210526_Sanderson_shareable.pdf))
 
 Download the full directory of the files above:
 
diff --git a/manual_jb/content/workshops/20220131/agenda.md b/manual_jb/content/workshops/20220131/agenda.md
index bf8bf29b..62622362 100644
--- a/manual_jb/content/workshops/20220131/agenda.md
+++ b/manual_jb/content/workshops/20220131/agenda.md
@@ -7,7 +7,7 @@
     * Introduction to IN-CORE Lab and Jupyter Notebook ([pdf](https://github.com/IN-CORE/incore-docs/blob/main/workshops/20220131/session1_intro.pdf))
     * Platform introduction ([pdf](https://github.com/IN-CORE/incore-docs/blob/main/workshops/20220131/intro_platform.pdf))
     * [Session 1 - Jupyter introduction](session1/session1-jupyter-intro.ipynb)
-3. Working with Hazards (Tornado and Earthquake
+3. Working with Hazards (Tornado and Earthquake)
     * [Session 2 - Working with Hazards (Tornado and Earthquake)](session2/session2-hazard.ipynb)
 4. Building Damage Analysis
     * [Session 3 - Building Damage Analysis](session3/session3-building_damage_analysis.ipynb)
diff --git a/manual_jb/content/workshops/20231115/agenda.md b/manual_jb/content/workshops/20231115/agenda.md
new file mode 100644
index 00000000..b9b71126
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/agenda.md
@@ -0,0 +1,19 @@
+# INSPIRE workshop November 15, 2023
+
+## Sessions
+
+1. Introduction
+    * Introduction to IN-CORE Lab and Jupyter Notebook ([pdf](https://github.com/IN-CORE/incore-docs/blob/main/workshops/20231115/session1/session1-intro.pdf))
+    * [Session 1: Introduction to Jupyter Notebook](session1/session1-jupyter-intro.ipynb)
+2. Working with Remote and Local Hazards (Tornado and Earthquake)
+    * [Session 2: How to Work with Remote and Local Hazards](session2/session2-remote-and-local-hazards.ipynb)
+    * [Session 2: Assignment](session2/session2-assignment.ipynb)
+    * [Session 2: Assignment answers](session2/session2-assignment-answers.ipynb)
+3. Building Damage and Recovery Analyses with IN-CORE Web Services
+    * [Session 3: Building Damage and Recovery Analyses with IN-CORE Web Services](session3/session3-bldg-dmg-recv.ipynb)
+4. Building Damage and Recovery Analyses with Local Data
+    * [Session 4: Building Damage and Recovery Analyses with Local Data](session4/session4-building-dmg-recovery-local-data.ipynb)
+
+Download the full directory of the files above:
+
+[workshop_2023-11-15.zip](https://github.com/IN-CORE/incore-docs/blob/main/workshops/20231115/workshop_2023-11-15.zip)
diff --git a/manual_jb/content/workshops/20231115/session1/jupyter-lab-leftpanel.png b/manual_jb/content/workshops/20231115/session1/jupyter-lab-leftpanel.png
new file mode 100644
index 00000000..649fea3a
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session1/jupyter-lab-leftpanel.png differ
diff --git a/manual_jb/content/workshops/20231115/session1/session1-intro.pdf b/manual_jb/content/workshops/20231115/session1/session1-intro.pdf
new file mode 100755
index 00000000..1b1eeb46
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session1/session1-intro.pdf differ
diff --git a/manual_jb/content/workshops/20231115/session1/session1-jupyter-intro.ipynb b/manual_jb/content/workshops/20231115/session1/session1-jupyter-intro.ipynb
new file mode 100644
index 00000000..aa54ce2e
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session1/session1-jupyter-intro.ipynb
@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2e1c6693",
+   "metadata": {},
+   "source": [
+    "# Session 1: Introduction to Jupyter Notebook\n",
+    "\n",
+    "Perform the following Jupyter notebook operations:\n",
+    "\n",
+    "- Run notebook\n",
+    "- Change a cell and re-run the cell\n",
+    "- Add a new line/cell to a notebook\n",
+    "- Download the workshop material with code cell\n",
+    "- Unzip the downloaded zip file with code cell\n",
+    "\n",
+    "Please follow the instruction at each cell.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d383d41e",
+   "metadata": {},
+   "source": [
+    "## 1. Run notebook\n",
+    "\n",
+    "Please run the code cell below.\n",
+    "\n",
+    "You can hit (Ctrl + Enter) to run current cell or (Shift + Enter) to run current cell and go to next cell.\n",
+    "Alternatively, you can click run cell in the toolbar above.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f670ab37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "message = \"Hello World\"\n",
+    "print(message)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6882dfb8",
+   "metadata": {},
+   "source": [
+    "## 2. Change cell and re-run the notebook\n",
+    "\n",
+    "Modify the message of the print statement below to say **\"Hello IN-CORE\"** and run the current cell.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "533d3de0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "message = \"Hello World\"\n",
+    "print(message)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b9aacb0",
+   "metadata": {},
+   "source": [
+    "## 3. Add a new line and execute it\n",
+    "\n",
+    "Add a new cell below to this notebook. On the new line, modify the **message** variable to print your name.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1789602d-bef0-4777-a6ac-b5aecfefcc90",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 4. Download a zip file of the workshop material\n",
+    "\n",
+    "URL: https://github.com/IN-CORE/incore-docs/raw/main/workshops/20231115/workshop_2023-11-15.zip\n",
+    "\n",
+    "Please run the following code to download a zip file. Please make sure that it appears in your file browser (left panel)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c64fa4e1-ddef-4bb1-ac56-1c9f409952d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import requests module\n",
+    "import requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fe9db06-e80c-4bcc-9d1e-448044367e4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set URL and get file contents\n",
+    "url = \"https://github.com/IN-CORE/incore-docs/raw/main/workshops/20231115/workshop_2023-11-15.zip\"\n",
+    "filename = \"incore-workshop.zip\"\n",
+    "headers = {\"user-agent\": \"Wget/1.16 (linux-gnu)\"}\n",
+    "r = requests.get(url, allow_redirects=True, stream=True, headers=headers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4f1e3b8-7e53-4dd8-bb58-f09660a81e97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write the contents to a file\n",
+    "open(filename, \"wb\").write(r.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9eadca4b-69d3-461f-a4d8-d51dde8168d7",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 5. Unzip the downloaded zip file\n",
+    "\n",
+    "Please run the following code to unzip the downloaded zip file. Please make sure that all the workshop materials appear in your file browser (left panel)\n",
+    "![jupyter-lab-leftpanel.png](jupyter-lab-leftpanel.png)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2f4aad6-d99c-46c8-b165-f043ee273f99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import zipfile module\n",
+    "import zipfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d05564b7-e13f-49da-acc5-80bc3bfecb78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract the zipfile\n",
+    "with zipfile.ZipFile(filename, \"r\") as zip_ref:\n",
+    "    zip_ref.extractall()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7390470e-6a85-49cb-b54d-cef005dc9591",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d9eeb0b-d255-46e8-a27b-a9104c30382b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/earthquake/eq-dataset-PGA.tif b/manual_jb/content/workshops/20231115/session2/data/hazard/earthquake/eq-dataset-PGA.tif
new file mode 100644
index 00000000..67824055
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/earthquake/eq-dataset-PGA.tif differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/earthquake/eq-dataset-SA.tif b/manual_jb/content/workshops/20231115/session2/data/hazard/earthquake/eq-dataset-SA.tif
new file mode 100644
index 00000000..a641cab0
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/earthquake/eq-dataset-SA.tif differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/earthquake/eq-dataset.json b/manual_jb/content/workshops/20231115/session2/data/hazard/earthquake/eq-dataset.json
new file mode 100644
index 00000000..4504830a
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session2/data/hazard/earthquake/eq-dataset.json
@@ -0,0 +1,31 @@
+{
+  "name": "Example - Memphis Deterministic EQ",
+  "description": "Memphis dataset based deterministic hazard - Pytest",
+  "eqType": "dataset",
+  "hazardDatasets": [
+    {
+      "hazardType": "deterministic",
+      "demandType": "SA",
+      "demandUnits": "g",
+      "period": "0.2",
+      "eqParameters": {
+        "srcLatitude": "35.927",
+        "srcLongitude": "-89.919",
+        "magnitude": "7.9",
+        "depth": "10.0"
+      }
+    },
+    {
+      "hazardType": "deterministic",
+      "demandType": "PGA",
+      "demandUnits": "g",
+      "period": "0.0",
+      "eqParameters": {
+        "srcLatitude": "35.927",
+        "srcLongitude": "-89.919",
+        "magnitude": "7.9",
+        "depth": "10.0"
+      }
+    }
+  ]
+}
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/flood/flood-WSE-50ft.tif b/manual_jb/content/workshops/20231115/session2/data/hazard/flood/flood-WSE-50ft.tif
new file mode 100644
index 00000000..3f85d539
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/flood/flood-WSE-50ft.tif differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/flood/flood-dataset.json b/manual_jb/content/workshops/20231115/session2/data/hazard/flood/flood-dataset.json
new file mode 100644
index 00000000..e8f0d5e8
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session2/data/hazard/flood/flood-dataset.json
@@ -0,0 +1,23 @@
+{
+  "name": "Example - Lumberton Deterministic Flood - riverine flooding",
+  "description": "Lumberton dataset based deterministic hazard - 2 datasets",
+  "floodType": "dataset",
+  "hazardDatasets": [
+    {
+      "hazardType": "deterministic",
+      "demandType": "inundationDepth",
+      "demandUnits": "ft",
+      "floodParameters": {
+        "model": "riverine flooding"
+      }
+    },
+    {
+      "hazardType": "deterministic",
+      "demandType": "waterSurfaceElevation",
+      "demandUnits": "ft",
+      "floodParameters": {
+        "model": "riverine flooding"
+      }
+    }
+  ]
+}
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/flood/flood-inundationDepth-50ft.tif b/manual_jb/content/workshops/20231115/session2/data/hazard/flood/flood-inundationDepth-50ft.tif
new file mode 100644
index 00000000..b0bd1597
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/flood/flood-inundationDepth-50ft.tif differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/Inundation_Raster.tif b/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/Inundation_Raster.tif
new file mode 100644
index 00000000..c8559768
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/Inundation_Raster.tif differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/Surge_Raster.tif b/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/Surge_Raster.tif
new file mode 100644
index 00000000..fbfccf7b
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/Surge_Raster.tif differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/Wave_Raster.tif b/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/Wave_Raster.tif
new file mode 100644
index 00000000..9c8b9584
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/Wave_Raster.tif differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/hurricane-dataset.json b/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/hurricane-dataset.json
new file mode 100644
index 00000000..1a759d97
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session2/data/hazard/hurricane/hurricane-dataset.json
@@ -0,0 +1,31 @@
+{
+  "name": "pytest - Galveston Deterministic Hurricane - Kriging ",
+  "description": "Galveston dataset based deterministic hazard - 3 datasets",
+  "hurricaneType": "dataset",
+  "hazardDatasets": [
+    {
+      "hazardType": "deterministic",
+      "demandType": "waveHeight",
+      "demandUnits": "m",
+      "hurricaneParameters": {
+        "model": "Kriging"
+      }
+    },
+    {
+      "hazardType": "deterministic",
+      "demandType": "surgeLevel",
+      "demandUnits": "m",
+      "hurricaneParameters": {
+        "model": "Kriging"
+      }
+    },
+    {
+      "hazardType": "deterministic",
+      "demandType": "inundationDuration",
+      "demandUnits": "hr",
+      "hurricaneParameters": {
+        "model": "Kriging"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.dbf b/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.dbf
new file mode 100644
index 00000000..e326eeab
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.dbf differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.prj b/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.prj
new file mode 100644
index 00000000..9e354778
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984", 6378137.0, 298.257223563]], PRIMEM["Greenwich", 0.0], UNIT["degree", 0.017453292519943295], AXIS["Longitude", EAST], AXIS["Latitude", NORTH]]
\ No newline at end of file
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.shp b/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.shp
new file mode 100644
index 00000000..a529f8a6
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.shp differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.shx b/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.shx
new file mode 100644
index 00000000..a6093723
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/joplin_path_wgs84.shx differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/tornado_dataset.json b/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/tornado_dataset.json
new file mode 100644
index 00000000..2d462d74
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session2/data/hazard/tornado/tornado_dataset.json
@@ -0,0 +1,7 @@
+{
+  "name": "Example - Joplin Tornado",
+  "description": "Joplin tornado hazard",
+  "tornadoType": "dataset",
+  "threshold": null,
+  "thresholdUnit": "mph"
+}
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/Tsu_100yr_Hmax.tif b/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/Tsu_100yr_Hmax.tif
new file mode 100644
index 00000000..2ea0525b
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/Tsu_100yr_Hmax.tif differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/Tsu_100yr_Mmax.tif b/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/Tsu_100yr_Mmax.tif
new file mode 100644
index 00000000..ecfc84a6
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/Tsu_100yr_Mmax.tif differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/Tsu_100yr_Vmax.tif b/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/Tsu_100yr_Vmax.tif
new file mode 100644
index 00000000..d6998804
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/Tsu_100yr_Vmax.tif differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/tsunami.json b/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/tsunami.json
new file mode 100644
index 00000000..c175ed28
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session2/data/hazard/tsunami/tsunami.json
@@ -0,0 +1,28 @@
+{
+  "name": "Example - Seaside Probabilistic Tsunami - 100 yr",
+  "description": "pytest - Seaside dataset based probabilistic tsunami hazard. This is just a test!",
+  "tsunamiType": "dataset",
+  "hazardDatasets": [
+    {
+      "hazardType": "probabilistic",
+      "demandType": "Vmax",
+      "demandUnits": "m/s",
+      "recurrenceInterval": "100",
+      "recurrenceUnit": "years"
+    },
+    {
+      "hazardType": "probabilistic",
+      "demandType": "Mmax",
+      "demandUnits": "m^3/s^2",
+      "recurrenceInterval": "100",
+      "recurrenceUnit": "years"
+    },
+    {
+      "hazardType": "probabilistic",
+      "demandType": "Hmax",
+      "demandUnits": "m",
+      "recurrenceInterval": "100",
+      "recurrenceUnit": "years"
+    }
+  ]
+}
diff --git a/manual_jb/content/workshops/20231115/session2/data/local_remote_hazard.png b/manual_jb/content/workshops/20231115/session2/data/local_remote_hazard.png
new file mode 100644
index 00000000..d59b83cd
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/local_remote_hazard.png differ
diff --git a/manual_jb/content/workshops/20231115/session2/data/pyincore-diagram.png b/manual_jb/content/workshops/20231115/session2/data/pyincore-diagram.png
new file mode 100644
index 00000000..c82de6d4
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session2/data/pyincore-diagram.png differ
diff --git a/manual_jb/content/workshops/20231115/session2/session2-assignment-answers.ipynb b/manual_jb/content/workshops/20231115/session2/session2-assignment-answers.ipynb
new file mode 100644
index 00000000..bae1c35d
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session2/session2-assignment-answers.ipynb
@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0694adca",
+   "metadata": {},
+   "source": [
+    "# Session 2: Assignment Answers\n",
+    "\n",
+    "Apply session 2 material to create a remote model-based Tornado and local dataset-based Tornado with the given information and get hazard values from created hazards\n",
+    "\n",
+    "**Agenda**\n",
+    "- [1. Create a model-based Tornado (Remote)](#1.-Create-a-model-based-Tornado-(Remote))\n",
+    "- [2. Create a dataset-based Tornado (Local)](#2.-Create-a-dataset-based-Tornado-(Local))\n",
+    "\n",
+    "Please follow the instruction at each cell."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeefddef",
+   "metadata": {},
+   "source": [
+    "## 1. Create a model-based Tornado (Remote)\n",
+    "\n",
+    "Using the data from section 2.1.2, slightly alter the values of the Centerville tornado and obtain a new set of\n",
+    "parameters that differ from the previously seen examples.\n",
+    "\n",
+    "Tornado model definition:\n",
+    "* Tornado Model: MeanWidthTornado\n",
+    "* EF Rating: EF3\n",
+    "* Start point of path: \n",
+    "    * Latitude:\n",
+    "    * Longitude: \n",
+    "* End point of path:\n",
+    "    * Latitude:\n",
+    "    * Longitude: \n",
+    "* Wind speed method: 1\n",
+    "* number of simulation: 1\n",
+    "* random seed: 2023\n",
+    "\n",
+    "After you have obtained them, proceed as shown during the session."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fa0b708f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Import modules\n",
+    "from pyincore import HazardService, IncoreClient\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "233a216e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connection successful to IN-CORE services. pyIncore version detected: 1.14.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Connect to IN-CORE serivce by creating IncoreClient\n",
+    "client = IncoreClient()\n",
+    "\n",
+    "# Create the HazardService object\n",
+    "hazardsvc = HazardService(client)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7eec0147",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: create a JSON (dictionary) definition of tornado model as given above\n",
+    "my_tornado_model = {\n",
+    "  \"name\": \"Centerville Model Tornado (modified)\",\n",
+    "  \"description\": \"Centerville mean width tornado hazard\",\n",
+    "  \"tornadoType\": \"model\",\n",
+    "  \"tornadoModel\" : \"MeanWidthTornado\",\n",
+    "      \"tornadoParameters\" : {\n",
+    "      \"efRating\" : \"EF3\",\n",
+    "      \"startLatitude\" : \"35.215\",\n",
+    "      \"startLongitude\" : \"-97.524\",\n",
+    "      \"randomSeed\" : \"2023\",\n",
+    "      \"endLatitude\" : [35.253],\n",
+    "      \"endLongitude\" : [-97.432],\n",
+    "      \"windSpeedMethod\" : \"1\",\n",
+    "      \"numSimulations\" : \"1\"\n",
+    "    }\n",
+    "}\n",
+    "tornado_model_json = json.dumps(my_tornado_model, indent=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3f771459",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"tornadoType\": \"model\",\n",
+      "    \"id\": \"654d5801b57f7b0a431fa51d\",\n",
+      "    \"name\": \"Centerville Model Tornado (modified)\",\n",
+      "    \"description\": \"Centerville mean width tornado hazard\",\n",
+      "    \"creator\": \"cnavarro\",\n",
+      "    \"owner\": \"cnavarro\",\n",
+      "    \"threshold\": null,\n",
+      "    \"thresholdUnit\": \"mph\",\n",
+      "    \"spaces\": [\n",
+      "        \"cnavarro\"\n",
+      "    ],\n",
+      "    \"date\": \"2023-11-09T22:06:56+0000\",\n",
+      "    \"tornadoModel\": \"MeanWidthTornado\",\n",
+      "    \"tornadoParameters\": {\n",
+      "        \"efRating\": \"EF3\",\n",
+      "        \"maxWindSpeed\": 250.0,\n",
+      "        \"startLatitude\": 35.215,\n",
+      "        \"startLongitude\": -97.524,\n",
+      "        \"randomSeed\": 2023,\n",
+      "        \"windSpeedMethod\": 1,\n",
+      "        \"numSimulations\": 1,\n",
+      "        \"endLatitude\": [\n",
+      "            35.253\n",
+      "        ],\n",
+      "        \"endLongitude\": [\n",
+      "            -97.432\n",
+      "        ]\n",
+      "    },\n",
+      "    \"tornadoWidth\": [\n",
+      "        413.78265910931174\n",
+      "    ],\n",
+      "    \"efBoxes\": [\n",
+      "        {\n",
+      "            \"efBoxWidths\": [\n",
+      "                0.0006292397330261612,\n",
+      "                0.0010052942480299617,\n",
+      "                0.0014930481239259802,\n",
+      "                0.0018616560148702996\n",
+      "            ]\n",
+      "        }\n",
+      "    ],\n",
+      "    \"datasetId\": \"654d580070bd6e5ce90503b9\"\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: create a tornado using the hazard service\n",
+    "model_response = hazardsvc.create_tornado_scenario(tornado_model_json)\n",
+    "print(json.dumps(model_response, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "26036930",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# TODO: acquire hazard id you created and visualize\n",
+    "tornado_model_id = model_response['id']\n",
+    "from pyincore_viz.geoutil import GeoUtil as viz\n",
+    "viz.plot_tornado(tornado_model_id, client, basemap=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c57650c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "    {\n",
+      "        \"hazardValues\": [\n",
+      "            82.54130976754479\n",
+      "        ],\n",
+      "        \"demands\": [\n",
+      "            \"Wind\"\n",
+      "        ],\n",
+      "        \"units\": [\n",
+      "            \"mph\"\n",
+      "        ],\n",
+      "        \"loc\": \"35.215, -97.521\"\n",
+      "    },\n",
+      "    {\n",
+      "        \"hazardValues\": [\n",
+      "            139.8882517057174\n",
+      "        ],\n",
+      "        \"demands\": [\n",
+      "            \"Wind\"\n",
+      "        ],\n",
+      "        \"units\": [\n",
+      "            \"mph\"\n",
+      "        ],\n",
+      "        \"loc\": \"35.233, -97.479\"\n",
+      "    }\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: Getting and printing hazard value from your tornado\n",
+    "points = [\n",
+    "    {\n",
+    "        \"demands\": [\"wind\"],\n",
+    "        \"units\": [\"mph\"],\n",
+    "        \"loc\": \"35.215, -97.521\"\n",
+    "    },\n",
+    "    {\n",
+    "        \"demands\": [\"wind\"],\n",
+    "        \"units\": [\"mph\"],\n",
+    "        \"loc\": \"35.233, -97.479\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "tornado_model_vals = hazardsvc.post_tornado_hazard_values(tornado_model_id, points)\n",
+    "print(json.dumps(tornado_model_vals, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cedb352",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 2. Create a dataset-based Tornado (Local)\n",
+    "\n",
+    "Now, using the dataset-based tornado definition seen in section 3.1.1, define a new local tornado hazard and get values for the same location with and without a seed parameter.\n",
+    "\n",
+    "Proceed as in the prior exercise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f1e12221-3a89-4fb4-8b08-424a84d672c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import modules\n",
+    "from pyincore import Tornado\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9b2ce2db-1cbc-4eb0-85f0-9e2b3a4d1548",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: indicate the path of the tornado dataset files\n",
+    "dir = \"data/hazard/tornado/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9cae0c55",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# TODO: create the tornado object with definition\n",
+    "tornado = Tornado.from_json_file(os.path.join(dir, \"tornado_dataset.json\"))\n",
+    "\n",
+    "# TODO: attach the shapefile representing the tornado\n",
+    "tornado.hazardDatasets[0].from_file((os.path.join(dir, \"joplin_path_wgs84.shp\")),\n",
+    "                                    data_type=\"incore:tornadoWindfield\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7f35f99e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'demands': ['wind'], 'units': ['mph'], 'loc': '-94.37, 37.04', 'hazardValues': [102.62435891472981]}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: Getting and printing hazard value from your tornado for specific seed value\n",
+    "payload = [\n",
+    "    {\n",
+    "        \"demands\": [\"wind\"],\n",
+    "        \"units\": [\"mph\"],\n",
+    "        \"loc\": \"-94.37, 37.04\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# get values\n",
+    "values = tornado.read_hazard_values(payload, seed=1234) # removing the seed will give random values\n",
+    "print(values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "73b060cf-a60b-4328-a4f1-61e0e9326096",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'demands': ['wind'], 'units': ['mph'], 'loc': '-94.37, 37.04', 'hazardValues': [99.78231243857864]}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: Getting and printing hazard values from your tornado for a random seed value\n",
+    "payload = [\n",
+    "    {\n",
+    "        \"demands\": [\"wind\"],\n",
+    "        \"units\": [\"mph\"],\n",
+    "        \"loc\": \"-94.37, 37.04\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# get values\n",
+    "values = tornado.read_hazard_values(payload) # removing the seed will give random values\n",
+    "print(values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "daf973c4-f3b1-491f-b6a9-caa8755d27ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
+       "&lt;html&gt;\n",
+       "&lt;head&gt;\n",
+       "    \n",
+       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
+       "    \n",
+       "        &lt;script&gt;\n",
+       "            L_NO_TOUCH = false;\n",
+       "            L_DISABLE_3D = false;\n",
+       "        &lt;/script&gt;\n",
+       "    \n",
+       "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
+       "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
+       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;script src=&quot;https://code.jquery.com/jquery-3.7.1.min.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
+       "    \n",
+       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
+       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
+       "            &lt;style&gt;\n",
+       "                #map_21c70f33ea6064add4b6ec6adb8f517b {\n",
+       "                    position: relative;\n",
+       "                    width: 100.0%;\n",
+       "                    height: 100.0%;\n",
+       "                    left: 0.0%;\n",
+       "                    top: 0.0%;\n",
+       "                }\n",
+       "                .leaflet-container { font-size: 1rem; }\n",
+       "            &lt;/style&gt;\n",
+       "        \n",
+       "    \n",
+       "                    &lt;style&gt;\n",
+       "                        .foliumtooltip {\n",
+       "                            \n",
+       "                        }\n",
+       "                       .foliumtooltip table{\n",
+       "                            margin: auto;\n",
+       "                        }\n",
+       "                        .foliumtooltip tr{\n",
+       "                            text-align: left;\n",
+       "                        }\n",
+       "                        .foliumtooltip th{\n",
+       "                            padding: 2px; padding-right: 8px;\n",
+       "                        }\n",
+       "                    &lt;/style&gt;\n",
+       "            \n",
+       "&lt;/head&gt;\n",
+       "&lt;body&gt;\n",
+       "    \n",
+       "    \n",
+       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_21c70f33ea6064add4b6ec6adb8f517b&quot; &gt;&lt;/div&gt;\n",
+       "        \n",
+       "&lt;/body&gt;\n",
+       "&lt;script&gt;\n",
+       "    \n",
+       "    \n",
+       "            var map_21c70f33ea6064add4b6ec6adb8f517b = L.map(\n",
+       "                &quot;map_21c70f33ea6064add4b6ec6adb8f517b&quot;,\n",
+       "                {\n",
+       "                    center: [37.03263349954294, -94.4129370000555],\n",
+       "                    crs: L.CRS.EPSG3857,\n",
+       "                    zoom: 10,\n",
+       "                    zoomControl: true,\n",
+       "                    preferCanvas: false,\n",
+       "                }\n",
+       "            );\n",
+       "            L.control.scale().addTo(map_21c70f33ea6064add4b6ec6adb8f517b);\n",
+       "\n",
+       "            \n",
+       "\n",
+       "        \n",
+       "    \n",
+       "            var tile_layer_aadaed9a658033f2279448abf5b16fb2 = L.tileLayer(\n",
+       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
+       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
+       "            );\n",
+       "        \n",
+       "    \n",
+       "                tile_layer_aadaed9a658033f2279448abf5b16fb2.addTo(map_21c70f33ea6064add4b6ec6adb8f517b);\n",
+       "    \n",
+       "            map_21c70f33ea6064add4b6ec6adb8f517b.fitBounds(\n",
+       "                [[36.9886069992473, -94.59310099988419], [37.07665999983858, -94.23277300022683]],\n",
+       "                {}\n",
+       "            );\n",
+       "        \n",
+       "    \n",
+       "        function geo_json_a436fe0ee96dba6f0065b0f14b14f8a8_styler(feature) {\n",
+       "            switch(feature.id) {\n",
+       "                default:\n",
+       "                    return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "            }\n",
+       "        }\n",
+       "        function geo_json_a436fe0ee96dba6f0065b0f14b14f8a8_highlighter(feature) {\n",
+       "            switch(feature.id) {\n",
+       "                default:\n",
+       "                    return {&quot;fillOpacity&quot;: 0.75};\n",
+       "            }\n",
+       "        }\n",
+       "        function geo_json_a436fe0ee96dba6f0065b0f14b14f8a8_pointToLayer(feature, latlng) {\n",
+       "            var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};\n",
+       "            \n",
+       "            let style = geo_json_a436fe0ee96dba6f0065b0f14b14f8a8_styler(feature)\n",
+       "            Object.assign(opts, style)\n",
+       "            \n",
+       "            return new L.CircleMarker(latlng, opts)\n",
+       "        }\n",
+       "\n",
+       "        function geo_json_a436fe0ee96dba6f0065b0f14b14f8a8_onEachFeature(feature, layer) {\n",
+       "            layer.on({\n",
+       "                mouseout: function(e) {\n",
+       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
+       "                        geo_json_a436fe0ee96dba6f0065b0f14b14f8a8.resetStyle(e.target);\n",
+       "                    }\n",
+       "                },\n",
+       "                mouseover: function(e) {\n",
+       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
+       "                        const highlightStyle = geo_json_a436fe0ee96dba6f0065b0f14b14f8a8_highlighter(e.target.feature)\n",
+       "                        e.target.setStyle(highlightStyle);\n",
+       "                    }\n",
+       "                },\n",
+       "            });\n",
+       "        };\n",
+       "        var geo_json_a436fe0ee96dba6f0065b0f14b14f8a8 = L.geoJson(null, {\n",
+       "                onEachFeature: geo_json_a436fe0ee96dba6f0065b0f14b14f8a8_onEachFeature,\n",
+       "            \n",
+       "                style: geo_json_a436fe0ee96dba6f0065b0f14b14f8a8_styler,\n",
+       "                pointToLayer: geo_json_a436fe0ee96dba6f0065b0f14b14f8a8_pointToLayer,\n",
+       "        });\n",
+       "\n",
+       "        function geo_json_a436fe0ee96dba6f0065b0f14b14f8a8_add (data) {\n",
+       "            geo_json_a436fe0ee96dba6f0065b0f14b14f8a8\n",
+       "                .addData(data);\n",
+       "        }\n",
+       "            geo_json_a436fe0ee96dba6f0065b0f14b14f8a8_add({&quot;bbox&quot;: [-94.59310099988419, 36.9886069992473, -94.23277300022683, 37.07665999983858], &quot;features&quot;: [{&quot;bbox&quot;: [-94.59310099988419, 36.9886069992473, -94.23277300022683, 37.07665999983858], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[-94.45507299970816, 37.075019999179425], [-94.44674600077443, 37.07375099951556], [-94.43764100004982, 37.071018999338555], [-94.42452300012341, 37.065730999778204], [-94.41307000071485, 37.0615420005093], [-94.40414699908156, 37.058972000369224], [-94.39244000044064, 37.05554600031785], [-94.37816300073622, 37.04962100001396], [-94.36895500102088, 37.04526600037609], [-94.327409999387, 37.03484399991653], [-94.30028399912209, 37.02549299964116], [-94.2832229997091, 37.01799799916941], [-94.26851799988349, 37.01292899988867], [-94.2516000008604, 37.00600500002511], [-94.23345499960213, 36.99483299977655], [-94.23277300022683, 36.99064400022906], [-94.23530599962335, 36.9886069992473], [-94.24082100022686, 36.990443999593275], [-94.24939899987947, 36.99448800022142], [-94.27194399976456, 37.00436299942263], [-94.29243100046523, 37.011072999448494], [-94.3060160004353, 37.01667099934518], [-94.32112800002317, 37.02228100035219], [-94.3384740002892, 37.02592099966067], [-94.37602199908974, 37.03327399940749], [-94.39135199950653, 37.03918700048313], [-94.40603099991809, 37.04410400034954], [-94.42085599997066, 37.04735500005905], [-94.44442699990014, 37.056635000333365], [-94.45298900024208, 37.059234000590514], [-94.46124599993188, 37.0615280007458], [-94.47189799979793, 37.062189999655644], [-94.47802699975941, 37.062194999495354], [-94.48718300014717, 37.060852000305104], [-94.49615500035604, 37.059325999882084], [-94.51059600065506, 37.05653499999559], [-94.51975700000071, 37.0553940006892], [-94.52899199877919, 37.054015999362385], [-94.53820800073072, 37.05352599946926], [-94.54547099983813, 37.05291700023787], [-94.55603000000178, 37.05224600047655], [-94.5674000008116, 37.05140999925105], [-94.5756230004914, 37.05114700001139], [-94.59234600000825, 37.05114699986677], [-94.59310099988419, 37.05189500001143], [-94.59289599979087, 37.05316199987041], [-94.55767800057176, 37.063049000519655], [-94.54974399897237, 37.06524699979396], [-94.54174799947975, 37.06787099987258], [-94.53289800006696, 37.06921399951546], [-94.52633700060987, 37.07026299955294], [-94.51792899997291, 37.07188400051544], [-94.50950999901895, 37.07382999944375], [-94.50071999977693, 37.07556000019346], [-94.48638900071002, 37.07665999983858], [-94.47747800052697, 37.07647700011718], [-94.46612499976972, 37.07556199989176], [-94.45507299970816, 37.075019999179425]], [[-94.57057275379107, 37.05350546132414], [-94.5703107561757, 37.05350339296727], [-94.57004881855295, 37.053508324497145], [-94.56978723212907, 37.053520248876325], [-94.56952628703678, 37.05353915563713], [-94.56926627125672, 37.05356502229919], [-94.56066900042168, 37.0540160002381], [-94.55551557333101, 37.05476523143361], [-94.5550515908286, 37.0548677514205], [-94.55458542197184, 37.054963694968706], [-94.55411721356681, 37.05505303022773], [-94.55364711135546, 37.05513573135803], [-94.55317526108202, 37.05521177252226], [-94.55270180956501, 37.055281129601426], [-94.55222690469701, 37.055343780194995], [-94.55175069437486, 37.055399705338026], [-94.55127332756905, 37.05544888778404], [-94.55079495111198, 37.055491313723145], [-94.55031571504895, 37.05552696762973], [-94.54983576942922, 37.05555583827223], [-94.54935526109297, 37.05557791871434], [-94.54887434223406, 37.05559319944504], [-94.54839316184014, 37.055601680398546], [-94.54791187103612, 37.05560335378571], [-94.54743061559846, 37.05559822126352], [-94.54694954880004, 37.055586284488356], [-94.54646881963032, 37.05556754597901], [-94.54598857922339, 37.05554201340589], [-94.54552829626617, 37.055556547286635], [-94.54506844460225, 37.05557813035811], [-94.54460919116485, 37.055606754837534], [-94.54415070502884, 37.05564241122735], [-94.54369315205481, 37.05568508574205], [-94.54323670024489, 37.05573476202288], [-94.54278151760312, 37.055791424572014], [-94.54232776677614, 37.05585505017108], [-94.54187561897942, 37.05592561474274], [-94.54142523365022, 37.05600309421635], [-94.54097678093639, 37.05608746280324], [-94.54053041920191, 37.056178683562926], [-94.5400863164569, 37.056276728995634], [-94.53561962888107, 37.05688059273921], [-94.53002286824106, 37.0575797747254], [-94.52849283769214, 37.057755627136274], [-94.52389696987818, 37.05805500507648], [-94.52187499080162, 37.05818666132856], [-94.51799381264843, 37.058643518241944], [-94.51410192325088, 37.059141564875866], [-94.51123000018362, 37.05950899924804], [-94.50858071443758, 37.05995084301944], [-94.50449396261783, 37.0606322980275], [-94.50019663396613, 37.06134870014538], [-94.49804700058692, 37.06170700028144], [-94.49422041353665, 37.06280021403425], [-94.49301440217114, 37.063144725913695], [-94.49266076486609, 37.063272893537935], [-94.49230378534413, 37.06339496492963], [-94.49194362647896, 37.063510885137276], [-94.49158045328983, 37.063620600069605], [-94.4912144318699, 37.06372406078712], [-94.49084573152878, 37.06382121835237], [-94.49047451943626, 37.063912030696244], [-94.4901009659784, 37.063996454035156], [-94.48972524261433, 37.06407445059587], [-94.48934752294781, 37.064145982607364], [-94.4889679773706, 37.064211021741926], [-94.48858678270372, 37.06426953366636], [-94.48820411255554, 37.06432149434913], [-94.48782014053648, 37.06436687804427], [-94.48743504561276, 37.06440566587507], [-94.48704900139671, 37.0644378381083], [-94.48666218685628, 37.06446338102174], [-94.4862747788176, 37.06448228347017], [-94.4858869530362, 37.06449453688552], [-94.48249453195204, 37.06481963068125], [-94.48033323392575, 37.06501015492617], [-94.47370549635532, 37.065201145763105], [-94.47296508705222, 37.065172382558934], [-94.47222514136742, 37.06513676112696], [-94.47148576124854, 37.06509428836686], [-94.47074704007494, 37.0650449686038], [-94.47000908300552, 37.064988809601445], [-94.46927198448792, 37.06492581826496], [-94.46853584539477, 37.06485600236182], [-94.46780076231255, 37.06477937137751], [-94.46706683825207, 37.064695936518284], [-94.4663341622992, 37.06460570813048], [-94.46560284067422, 37.06450869914272], [-94.46487296674323, 37.06440492162393], [-94.46414464136726, 37.06429438936419], [-94.46341796004893, 37.06417711872928], [-94.46269301721937, 37.0640531226537], [-94.46196991480203, 37.063922420084694], [-94.46124874722202, 37.063785027394566], [-94.4605296099718, 37.06364096353309], [-94.45981260175492, 37.063490246595016], [-94.4590978169859, 37.06333289896747], [-94.4583853490074, 37.06316893960607], [-94.45767529544098, 37.06299839176165], [-94.45696775069273, 37.06282127782772], [-94.45626281130774, 37.06263762020069], [-94.45556056847137, 37.062447443852], [-94.45486111764735, 37.06225077804836], [-94.45416455215906, 37.062047643474386], [-94.45347096532085, 37.061838070258254], [-94.4527804515166, 37.06162208509724], [-94.45209309655534, 37.06139971983702], [-94.4514090001692, 37.06117100032243], [-94.45082759681657, 37.06095401928498], [-94.44734491356272, 37.059654193967646], [-94.44365310551214, 37.05827615732499], [-94.442302649512, 37.05777203212693], [-94.44083217843315, 37.057223081244445], [-94.43929490113916, 37.0566491633308], [-94.43805727767953, 37.05631390734575], [-94.43667470690517, 37.05595248695736], [-94.43495559911763, 37.055503063188844], [-94.43394901977123, 37.05523990164122], [-94.43255967641859, 37.05487665188442], [-94.4305927845301, 37.05436236522819], [-94.43002257612245, 37.05421326448808], [-94.42834082128245, 37.053773493336415], [-94.4267787885952, 37.053365002437005], [-94.42540367158877, 37.05300537180356], [-94.42447330881726, 37.052762045331995], [-94.42226659483823, 37.05218486648019], [-94.42138940940202, 37.051968560415965], [-94.41973659512638, 37.05156601749809], [-94.41796736564878, 37.05113509184671], [-94.41591888334935, 37.05063611131395], [-94.4143326484366, 37.050249697339545], [-94.41297787421067, 37.04991965075658], [-94.4121400743793, 37.049715537990906], [-94.41032795336088, 37.04927402759933], [-94.40705699993777, 37.04847699925947], [-94.40502964516928, 37.04807027250818], [-94.40277331528617, 37.04761756374683], [-94.4000293017416, 37.047066940073165], [-94.39845000060805, 37.046749999216985], [-94.39632798254425, 37.0459922833954], [-94.39129800038486, 37.04419599898047], [-94.38594894279768, 37.043032246525655], [-94.38204700037946, 37.04222600019779], [-94.38190140439886, 37.04217402617592], [-94.3817524524435, 37.04212857191252], [-94.38160060040086, 37.04208977604058], [-94.38144630740041, 37.042057755740224], [-94.3812900454454, 37.042032610181835], [-94.38113229085825, 37.04201441365098], [-94.38097352427049, 37.042003222415104], [-94.3808142295566, 37.04199907214763], [-94.38065489062932, 37.04200197363304], [-94.38049599571507, 37.0420119179213], [-94.3803380266473, 37.04202887631695], [-94.38018146636772, 37.04205279695336], [-94.38002679035837, 37.04208360650071], [-94.37987446985439, 37.04212121016908], [-94.37972497183925, 37.04216549514162], [-94.37957874834747, 37.042216324555426], [-94.37943624716155, 37.04227354437859], [-94.37929790218125, 37.042336979967146], [-94.37916413448939, 37.0424064394991], [-94.37903535021874, 37.04248170882212], [-94.37891194482177, 37.04256256004099], [-94.37879429022863, 37.04264874721246], [-94.3786827498337, 37.04274000893542], [-94.3785776603012, 37.04283606318199], [-94.37847934332284, 37.04293662018423], [-94.37838809813614, 37.04304137384294], [-94.37830420259375, 37.04315000258681], [-94.37822791422163, 37.04326217709824], [-94.37815946059008, 37.04337755515314], [-94.37809905536936, 37.04349578507065], [-94.37804688012359, 37.04361650741083], [-94.37800309609142, 37.04373935326986], [-94.37796783239878, 37.04386394941781], [-94.3779412010546, 37.04398991488056], [-94.37792328195127, 37.044116866074226], [-94.37791412821632, 37.044244417668914], [-94.3779137694311, 37.04437217830034], [-94.37792220733184, 37.04449976000696], [-94.37793941367154, 37.04462677565276], [-94.37796533771886, 37.04475283636005], [-94.37799990089265, 37.04487755837065], [-94.37804299676667, 37.04500055961258], [-94.37809449426499, 37.045121470003465], [-94.378154237686, 37.04523991600075], [-94.37822204453161, 37.04535553862456], [-94.37829770765639, 37.045467988310065], [-94.37838099420752, 37.04557691804002], [-94.3784716552367, 37.04568199880468], [-94.37856941394037, 37.04578290757353], [-94.37867396671113, 37.04587933931302], [-94.37878500028114, 37.04597100013786], [-94.3820516343574, 37.047832107178], [-94.3844439999466, 37.049195000305836], [-94.38694999865717, 37.050453800637555], [-94.38833832419415, 37.051120235192215], [-94.39039515974194, 37.05207993351047], [-94.39070446348032, 37.05225509489355], [-94.39101882305796, 37.0524243754332], [-94.39133806616044, 37.05258767879943], [-94.39166201402804, 37.052744918957515], [-94.39199049003999, 37.05289600815944], [-94.39232331007175, 37.05304086036838], [-94.39266028998524, 37.05317939555734], [-94.39300124563032, 37.053311538850934], [-94.39334598428736, 37.053437213651065], [-94.39369431856873, 37.05355635624116], [-94.39404605359822, 37.05366889431653], [-94.39440099447498, 37.05377477016601], [-94.39475894629915, 37.053873922647135], [-94.39511971094367, 37.05396629834165], [-94.39548308706294, 37.05405184554727], [-94.3965947326925, 37.054286454879566], [-94.39770420996616, 37.0545275829152], [-94.39881145705726, 37.05477521249508], [-94.3999164185551, 37.05502933161765], [-94.40101903262187, 37.05528992741954], [-94.40211923956106, 37.055556985324685], [-94.40321698288608, 37.055830490761636], [-94.40431220289355, 37.05611043001661], [-94.4054048398803, 37.05639678680292], [-94.40649483627891, 37.05668954741244], [-94.40758213559181, 37.05698869642323], [-94.40866667382488, 37.057294215834474], [-94.40974839875797, 37.05760609023137], [-94.41082725067315, 37.05792430247855], [-94.41700484142808, 37.059623534748], [-94.41788300571584, 37.05985338111794], [-94.41875855426699, 37.06008955448091], [-94.41963141768123, 37.060332035105695], [-94.42050152333856, 37.060580806693984], [-94.42136880076328, 37.06083584780089], [-94.42223318054818, 37.06109713612578], [-94.42309459221092, 37.06136465022741], [-94.42395296740422, 37.061638372101], [-94.42480823457139, 37.06191827687491], [-94.4256603264344, 37.0622043413986], [-94.42650917249772, 37.0624965433795], [-94.42735470226225, 37.06279486052688], [-94.4281968505844, 37.06309926626323], [-94.43179550849567, 37.06428146668005], [-94.43229023820753, 37.06441852655555], [-94.43278226417777, 37.064561697531104], [-94.43327147193342, 37.0647109486759], [-94.43375774379128, 37.06486624219242], [-94.4342409609937, 37.06502754028428], [-94.43472101013434, 37.065194806018006], [-94.43519777566559, 37.06536799731061], [-94.43567114631767, 37.06554707551629], [-94.43614100653798, 37.06573199598014], [-94.43660724505435, 37.0659227140511], [-94.43706974951895, 37.06611918593718], [-94.43752841079619, 37.0663213644163], [-94.43798311867742, 37.06652919969207], [-94.4384337608077, 37.06674264196829], [-94.43888023553667, 37.066961644030556], [-94.44085397732053, 37.0678548167131], [-94.44123052103775, 37.06805195127805], [-94.44161152786303, 37.068243500871596], [-94.44199686818915, 37.068429403588695], [-94.44238640919372, 37.068609594090496], [-94.4427800223278, 37.068784015624786], [-94.44317757154494, 37.06895260542875], [-94.44357892400262, 37.069115307609096], [-94.44398394578376, 37.06927206541514], [-94.44439249653477, 37.069422829819494], [-94.44480443804491, 37.06956754578912], [-94.44521963316505, 37.06970616601803], [-94.44563794152728, 37.06983864577533], [-94.44605922062094, 37.06996493603921], [-94.44648332578357, 37.070084996371996], [-94.44691011663465, 37.07019878548133], [-94.44733944743189, 37.07030626636598], [-94.45089409302847, 37.07133089828993], [-94.45521267081111, 37.07220189359432], [-94.45648927732196, 37.072451734653434], [-94.46024271737917, 37.07291642701367], [-94.46474576664002, 37.073445871159116], [-94.46612500021442, 37.07360799991517], [-94.47067859768465, 37.07387480198344], [-94.4723932547323, 37.073975220038456], [-94.47628302330537, 37.07420293165627], [-94.47982138942868, 37.07432568189134], [-94.48349586180517, 37.07440115416429], [-94.48391715039196, 37.074427738489206], [-94.48433904167403, 37.07444722049615], [-94.48476135236095, 37.07445959243031], [-94.48518389380538, 37.074464849113745], [-94.48560648057267, 37.07446298794656], [-94.48602892508508, 37.07445400976444], [-94.48645103976455, 37.074437917980745], [-94.48687263917503, 37.0744147203033], [-94.48729353788076, 37.07438442701752], [-94.48771354723279, 37.07434705098602], [-94.48813248286739, 37.07430260936614], [-94.48855016256427, 37.07425112189289], [-94.48896639553487, 37.074192610019665], [-94.48938100277476, 37.07412710121177], [-94.48979379992538, 37.07405462465316], [-94.49732528167603, 37.073117358303534], [-94.49782502658138, 37.073059375953974], [-94.4983236054228, 37.0729952742166], [-94.49882090132485, 37.072925069412904], [-94.49931679848481, 37.072848777007955], [-94.49981118110144, 37.07276641676032], [-94.50030393337515, 37.07267800499708], [-94.50079493950824, 37.07258356491381], [-94.50128408477605, 37.07248311713286], [-94.50177125338489, 37.07237668657007], [-94.50225633382784, 37.072264296426304], [-94.50273921138688, 37.0721459733375], [-94.50615504660773, 37.07131351249618], [-94.50792598286947, 37.07089865117519], [-94.50964088541892, 37.07054930434507], [-94.51201240293864, 37.07006614988122], [-94.51362187281197, 37.06973822016496], [-94.51409899971438, 37.06964099966543], [-94.51535165221091, 37.069467543576884], [-94.5169627347172, 37.06924443649619], [-94.51917604023691, 37.068937892551574], [-94.52051940783161, 37.06875181505332], [-94.5224059652435, 37.068486124795434], [-94.52468189328623, 37.06814421575966], [-94.5264842831808, 37.06787341399194], [-94.52884778106328, 37.067518263623796], [-94.53091703400008, 37.06720728505878], [-94.53263937857801, 37.06694841336284], [-94.53381299984032, 37.06677200042869], [-94.53492580607258, 37.0665118076938], [-94.53699964472143, 37.06602687591904], [-94.53899950951931, 37.06555920338595], [-94.54003533578673, 37.065316956962576], [-94.54029894942451, 37.0652676016381], [-94.54056047058039, 37.06521155323499], [-94.54081963252561, 37.06514886850798], [-94.54107618031959, 37.06507961107663], [-94.54132984617587, 37.065003851433524], [-94.54158037730907, 37.064921665217994], [-94.54182752308479, 37.064833137512075], [-94.54207102965933, 37.06473835340077], [-94.54231064962852, 37.0646374142766], [-94.54254614416016, 37.06453041981407], [-94.54277727228823, 37.06441747655646], [-94.54300380269684, 37.064298701344875], [-94.54334381828592, 37.06411700005024], [-94.54368861142002, 37.063941173818186], [-94.5440380226093, 37.0637713076408], [-94.544391891292, 37.063607476212105], [-94.54475005691177, 37.06344975508572], [-94.54511235355989, 37.063298216385526], [-94.54547861640239, 37.06315293051989], [-94.54584867739419, 37.06301396360841], [-94.54622236527996, 37.06288137919869], [-94.54659950987812, 37.06275523826474], [-94.54697993779598, 37.0626355983503], [-94.54736347457276, 37.062522515284925], [-94.54774994574876, 37.06241604060849], [-94.54813917258046, 37.062316220714635], [-94.54853097632994, 37.06222310543252], [-94.54892517825705, 37.06213673601016], [-94.54932159855133, 37.06205715026489], [-94.54972005312106, 37.061984386018224], [-94.55012035894474, 37.06191847594345], [-94.55052233407423, 37.06185945271589], [-94.55354547249148, 37.06115775388162], [-94.55670199983248, 37.06042499913359], [-94.55958270085742, 37.05986578628501], [-94.56207838107419, 37.059381250170965], [-94.5642330779987, 37.05896286982289], [-94.5660517060391, 37.05860971007104], [-94.56751226888791, 37.058326060481036], [-94.56912089191374, 37.058013632674566], [-94.57139918760537, 37.05757110019221], [-94.57273300064239, 37.05731200029698], [-94.57286492847571, 37.057260769195686], [-94.57299289205069, 37.057203439414536], [-94.57311644815984, 37.05714020693741], [-94.57323517825209, 37.05707128833411], [-94.57334867450982, 37.05699691991035], [-94.5734565430603, 37.056917355989], [-94.57355842004263, 37.0568328714747], [-94.573653954465, 37.056743754139426], [-94.57374281891845, 37.05665030890709], [-94.57382471171684, 37.056552856135355], [-94.57389934511394, 37.05645172904772], [-94.57396646993813, 37.05634727715126], [-94.57402585309225, 37.056239855950466], [-94.5740772936241, 37.05612983380362], [-94.5741206130832, 37.05601758763701], [-94.57415566408712, 37.055903502081094], [-94.57418232710718, 37.05578796861392], [-94.57420050939523, 37.05567138384524], [-94.57421014712074, 37.05555414522316], [-94.57421121287084, 37.055436655321316], [-94.57420369957983, 37.0553193158404], [-94.57418763017024, 37.05520253103549], [-94.57416306396931, 37.05508669912885], [-94.574130084934, 37.05497221832559], [-94.57408880485565, 37.05485947908685], [-94.57403936550618, 37.054748869278455], [-94.57398193434804, 37.05464076816557], [-94.57391671095912, 37.05453554640883], [-94.573843919531, 37.05443356091919], [-94.57376380780745, 37.054335165158804], [-94.5736766481418, 37.05424069540731], [-94.57358274285455, 37.05415047419212], [-94.57348241138303, 37.05406481115491], [-94.57337599991615, 37.05398399961267], [-94.57313234378692, 37.05390678578153], [-94.5728856102096, 37.05383612150235], [-94.57263607764322, 37.053772089843235], [-94.57238402024429, 37.053714757568855], [-94.57212971966163, 37.05366419058314], [-94.57187345860247, 37.05362044449175], [-94.57161551976505, 37.053583568036075], [-94.57135619012104, 37.05355360137394], [-94.571095759849, 37.05353058037218], [-94.57083451804236, 37.053514527167486], [-94.57057275379107, 37.05350546132414]], [[-94.26806895663175, 37.00584262383328], [-94.26695600146019, 37.005399999312], [-94.26540299985814, 37.007416999611316], [-94.26578000021533, 37.00924999974212], [-94.27323662963038, 37.0128967567753], [-94.2791910005887, 37.015493000106396], [-94.28514199963848, 37.016965000014274], [-94.2875840002483, 37.01620699879407], [-94.28711821143956, 37.014892856868265], [-94.28711327453154, 37.014765614413164], [-94.2871000873686, 37.01463875284541], [-94.28707868543758, 37.014512611266056], [-94.28704912882787, 37.014387533968716], [-94.28701149155859, 37.01426385926211], [-94.28696588297288, 37.01414192380006], [-94.28691242206604, 37.01402205652743], [-94.28685125780947, 37.01390458300818], [-94.2867825498962, 37.01378982109944], [-94.28670649014722, 37.01367808098996], [-94.28662328217234, 37.01356966602228], [-94.28653315100762, 37.01346487099332], [-94.28643634419517, 37.01336397872341], [-94.28633312000204, 37.01326726261022], [-94.28622375919609, 37.01317498579181], [-94.28610855970635, 37.01308739684562], [-94.28598783232711, 37.01300473493092], [-94.28586190609138, 37.01292722207375], [-94.28573111861634, 37.01285507087473], [-94.28559582897859, 37.012788473374144], [-94.28545639841491, 37.01272761303395], [-94.28531320961962, 37.01267265361466], [-94.28516664961008, 37.012623742577674], [-94.28501711079011, 37.01258101366253], [-94.27902977857019, 37.010200931818964], [-94.26806895663175, 37.00584262383328]]], &quot;type&quot;: &quot;Polygon&quot;}, &quot;id&quot;: &quot;0&quot;, &quot;properties&quot;: {&quot;OBJECTID_1&quot;: 6, &quot;ef_rating&quot;: &quot;EF1&quot;, &quot;guid&quot;: &quot;962eb0fa-186f-4780-8cd1-d9ab89ce6075&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-94.53066207411212, 37.060820028255584, -94.46276899956395, 37.070983999603754], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[-94.526515456278, 37.06292453583781], [-94.5252080000577, 37.063048999765954], [-94.5242820577632, 37.06327129970328], [-94.52256484934126, 37.06368354330138], [-94.52090016988959, 37.06408314828569], [-94.51521482637881, 37.06520789434471], [-94.51104699960527, 37.065917999693674], [-94.50762899953355, 37.066772000297924], [-94.50494415571896, 37.067203683597775], [-94.50079299982949, 37.067870999960874], [-94.4976998499295, 37.06863204270058], [-94.4973897901727, 37.06873771733079], [-94.49707648003364, 37.06883704205179], [-94.49676011880835, 37.06892995504431], [-94.4964409122232, 37.06901639363284], [-94.49611906707875, 37.06909630372708], [-94.49579479232077, 37.06916963381383], [-94.49546829475568, 37.069236334098576], [-94.49513978761887, 37.069296364230745], [-94.4948094820058, 37.0693496838619], [-94.49447759222718, 37.069396260370866], [-94.49414433259557, 37.06943606113884], [-94.49380991742466, 37.0694690621325], [-94.49347456317163, 37.0694952418961], [-94.49313848736568, 37.069514582409525], [-94.49280190646523, 37.06952707252163], [-94.49246503692912, 37.069532702800366], [-94.49212809842925, 37.06953147154126], [-94.49179130956637, 37.06952337790048], [-94.49145488358472, 37.06950842790295], [-94.49111904122566, 37.069486630151445], [-94.49078400001571, 37.06945800011757], [-94.49031918908204, 37.06943835641856], [-94.48985402405488, 37.0694251877741], [-94.48938864299028, 37.06941849762569], [-94.4889231882287, 37.06941828855908], [-94.48845779675517, 37.06942456058671], [-94.48799261305312, 37.06943731114899], [-94.48752777625087, 37.06945653597126], [-94.48706342654809, 37.06948223163958], [-94.48659970200403, 37.06951438701694], [-94.48613674603364, 37.06955299526076], [-94.48567469562704, 37.06959804352156], [-94.48521369313123, 37.06964951809438], [-94.48475387661024, 37.06970740355915], [-94.48429538198756, 37.06977168278101], [-94.48044681222272, 37.070168862851304], [-94.4772702816995, 37.070454820349795], [-94.47347453014089, 37.07071216172804], [-94.46881100005282, 37.070983999603754], [-94.4679546527365, 37.070855076129895], [-94.46655549241942, 37.070781169376914], [-94.4647219995405, 37.07055699963812], [-94.46337900051235, 37.070067999999324], [-94.46276899956395, 37.06921400012377], [-94.46284047266676, 37.06901556449732], [-94.46293236347444, 37.068721510277214], [-94.46297835220803, 37.068482582053775], [-94.46304722598535, 37.068353941737165], [-94.46334250844907, 37.068398012981035], [-94.46367464360476, 37.068483074564575], [-94.4643012785305, 37.06864355580677], [-94.46557599974464, 37.06897000011935], [-94.46722719587584, 37.06912000401522], [-94.46826199978669, 37.06921399969865], [-94.46968640130014, 37.06912773141167], [-94.47189137228563, 37.068994153693325], [-94.47315137831806, 37.06891937148627], [-94.47540299995185, 37.06878699991103], [-94.47700701159646, 37.068557533102165], [-94.47882099993897, 37.068297999755316], [-94.48140662570502, 37.06805027308042], [-94.48426207077918, 37.067776627947396], [-94.48648257999335, 37.06756378179365], [-94.48668720969447, 37.06752896988102], [-94.48689342436096, 37.06750079606345], [-94.48710088878393, 37.06747930664579], [-94.48730926454466, 37.067464535918305], [-94.4875182153696, 37.0674565078739], [-94.48772739856099, 37.06745523620728], [-94.48793647677896, 37.06746072174129], [-94.48814510947147, 37.067472957575795], [-94.48835295608825, 37.06749192222162], [-94.48855968143485, 37.067517585609174], [-94.48876494603303, 37.06754990479591], [-94.48897774022797, 37.06758497182035], [-94.48919202006942, 37.067613641514065], [-94.48940748988747, 37.06763587264316], [-94.48962384436945, 37.06765163427488], [-94.48984078248466, 37.067660905778915], [-94.49005800320086, 37.067663672535325], [-94.49027520012837, 37.067659931942494], [-94.49049207116093, 37.06764968912624], [-94.49070831204949, 37.06763295693937], [-94.49092362175752, 37.06760976025358], [-94.49113769924878, 37.06758013080936], [-94.49216123886698, 37.06747247898567], [-94.493410237157, 37.06729916675023], [-94.49483925905393, 37.06705987515255], [-94.4968694075845, 37.066675201739145], [-94.49847399978408, 37.06634500011599], [-94.50022472162028, 37.06584240030567], [-94.50463899980342, 37.06457499996454], [-94.50695800053421, 37.06390400006841], [-94.50767283065649, 37.06378491418143], [-94.50806017703358, 37.06372242911975], [-94.50844917509825, 37.06366692943203], [-94.50883963002735, 37.063618442583085], [-94.50923134485724, 37.063576993465425], [-94.50962412155424, 37.06354260182421], [-94.51001776315687, 37.063515285690485], [-94.51041207056232, 37.06349505966469], [-94.51080684466768, 37.06348193148331], [-94.51120188851313, 37.0634759114602], [-94.51159699978254, 37.06347699961248], [-94.51348759904879, 37.06328788432388], [-94.5153534870625, 37.06310120978095], [-94.51783469718885, 37.062852928925615], [-94.51912195603232, 37.06272409738882], [-94.51945531024619, 37.06269651244027], [-94.5197876781754, 37.0626621227843], [-94.52011884338972, 37.06262094820726], [-94.5204485905339, 37.06257301793926], [-94.52077670425493, 37.062518362929346], [-94.52110297134475, 37.062457017561776], [-94.52142717752847, 37.06238902308988], [-94.52174911603134, 37.06231442248441], [-94.52206857365694, 37.062233264727794], [-94.52238534471016, 37.06214560309468], [-94.5226992213584, 37.06205149515382], [-94.52301000005701, 37.06195100019176], [-94.52330892548923, 37.06185915779319], [-94.52361085000017, 37.061773848465435], [-94.52391555197715, 37.061695135746476], [-94.52422280445391, 37.06162307716929], [-94.52453238582137, 37.061557726834515], [-94.52484406268994, 37.06149913198088], [-94.52515761023903, 37.0614473346976], [-94.52547279508182, 37.061402375361354], [-94.5257893859727, 37.06136428576753], [-94.52610714952436, 37.06133309342271], [-94.52642585235013, 37.061308823260966], [-94.52819800052647, 37.06109599973501], [-94.52929699974666, 37.06085200020376], [-94.5299509547823, 37.060820028255584], [-94.53043259521405, 37.06085666598906], [-94.53054730440176, 37.060948537292], [-94.53063908780501, 37.061058795420976], [-94.53066207411212, 37.06118745057693], [-94.53054754021768, 37.061536704800545], [-94.53019659752114, 37.06180540635716], [-94.5297822581756, 37.06205101585236], [-94.52896495903701, 37.06244188102363], [-94.52783466608162, 37.062785637470874], [-94.526515456278, 37.06292453583781]]], &quot;type&quot;: &quot;Polygon&quot;}, &quot;id&quot;: &quot;1&quot;, &quot;properties&quot;: {&quot;OBJECTID_1&quot;: 5, &quot;ef_rating&quot;: &quot;EF5&quot;, &quot;guid&quot;: &quot;abfbad65-cc4b-4632-962a-65f7e5a1aa33&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-94.55097034212706, 37.057651330390364, -94.44250290896092, 37.07227708125359], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[-94.46441700094525, 37.07116700014026], [-94.46227999947169, 37.070313000363214], [-94.46027488867583, 37.069727700812976], [-94.45785989042778, 37.06899834685766], [-94.45095769398849, 37.06687034227416], [-94.44591400033768, 37.06531499968896], [-94.44314044012981, 37.06406809730894], [-94.4426014303599, 37.063804036195016], [-94.44250290896092, 37.063519135691514], [-94.44256231738885, 37.06329761133741], [-94.44266120794917, 37.06309193181233], [-94.44285889144247, 37.06280717351548], [-94.44321437186866, 37.062759868599464], [-94.44377364617246, 37.062860750313654], [-94.444808000429, 37.06328200019485], [-94.44622985962096, 37.06375845990348], [-94.44939199992548, 37.06481800036726], [-94.45088949321931, 37.06514629141857], [-94.4546817674774, 37.065977562853426], [-94.45716937446066, 37.06652277410768], [-94.46034527942375, 37.06721875379613], [-94.46527100011899, 37.06829800036704], [-94.46741411040487, 37.06847690107901], [-94.46976540545867, 37.06867313555986], [-94.47005693426395, 37.06869876694047], [-94.4703492520444, 37.06871776811337], [-94.47064212415492, 37.068730126998204], [-94.4709353170205, 37.06873583237545], [-94.4712285927793, 37.06873487817678], [-94.47152171785021, 37.06872726777874], [-94.47181445543866, 37.06871300541783], [-94.47210657088988, 37.06869210305815], [-94.47239782954824, 37.068664576957744], [-94.47268799996972, 37.06863045110261], [-94.4729768464265, 37.06858975119658], [-94.4740892573416, 37.06847092765548], [-94.4757079995437, 37.06829799962879], [-94.4819950002941, 37.0671999997068], [-94.48510928608378, 37.066888003507906], [-94.48540133004276, 37.066849291653696], [-94.48569458497107, 37.066816995194344], [-94.48598883457663, 37.06679114156184], [-94.48628385400197, 37.06677174703097], [-94.48657942803038, 37.06675882702158], [-94.48687533073611, 37.06675239094609], [-94.48717134262087, 37.06675244478663], [-94.48746724204568, 37.06675898680231], [-94.48776280630062, 37.06677201353768], [-94.48992900006606, 37.06677200007163], [-94.4931144008116, 37.06660145655494], [-94.49754821635406, 37.065749314013985], [-94.4998831347786, 37.06515122243164], [-94.50343781413346, 37.064202101717825], [-94.50869861920421, 37.06279719570685], [-94.51078559513805, 37.06227085446304], [-94.51108395384779, 37.06217844467275], [-94.51138536201589, 37.06209263091773], [-94.51168959587697, 37.06201347671302], [-94.5119964241713, 37.061941042143246], [-94.51230561671261, 37.0618753812868], [-94.51261694010373, 37.06181654393307], [-94.51293016094907, 37.06176457300712], [-94.5132450426421, 37.06171950972006], [-94.51356134857696, 37.061681386702205], [-94.51387884000684, 37.061650232294916], [-94.51419727818578, 37.06162607140877], [-94.51451642436768, 37.06160892123188], [-94.51483603766471, 37.061598795521945], [-94.52014199983216, 37.06127899989214], [-94.52655000000982, 37.060425000425774], [-94.53063999966551, 37.060180999729894], [-94.53242703498535, 37.06026221058325], [-94.53601100036558, 37.06042499999929], [-94.53780740783964, 37.06024546241838], [-94.54028299968878, 37.059998000362455], [-94.54370099987571, 37.05908200110276], [-94.54699699987087, 37.058227999929], [-94.54943800018508, 37.05773900041498], [-94.55012091065412, 37.057651330390364], [-94.55055535577094, 37.05771444663645], [-94.55087135943063, 37.05782508951295], [-94.55097018334355, 37.057967473203796], [-94.55097034212706, 37.05820485140763], [-94.55072000031616, 37.05865499976329], [-94.54998799973512, 37.059081999917865], [-94.54946825925039, 37.059255307509304], [-94.54669199978122, 37.060181000072916], [-94.54544976261457, 37.06086368298754], [-94.54518912884453, 37.06099965529125], [-94.5449243548248, 37.0611303893762], [-94.54465560654927, 37.061255807045036], [-94.54438304787307, 37.06137582924392], [-94.54410684908432, 37.061490382067724], [-94.54382718154721, 37.06159939418709], [-94.54354421556097, 37.06170279856599], [-94.54325812892732, 37.06180053160029], [-94.54296909623962, 37.06189253226339], [-94.5426772985229, 37.06197874381949], [-94.54193099954286, 37.06219500036071], [-94.5382384436698, 37.06286596232381], [-94.53800382361614, 37.06291569519829], [-94.53776730232849, 37.06295926201399], [-94.5375291326038, 37.06299661976354], [-94.53728956831311, 37.063027728016046], [-94.53704886440315, 37.06305255235058], [-94.53680727689715, 37.06307106693111], [-94.536565065035, 37.06308325107257], [-94.53632248484878, 37.063089093534586], [-94.53607979665676, 37.063088586510794], [-94.5358372586398, 37.06308173163918], [-94.53559512791, 37.06306853571004], [-94.53535366479566, 37.063049013239784], [-94.53369099956471, 37.06304900013797], [-94.53031833069794, 37.06349640677233], [-94.528434694871, 37.06375243575794], [-94.526637297155, 37.064049324065564], [-94.52395566918786, 37.0644922116574], [-94.51898680314406, 37.065312677192594], [-94.51128440533957, 37.06671968675274], [-94.50796046266676, 37.06737988525111], [-94.50764419376054, 37.06743165952247], [-94.50732978450465, 37.06749025393137], [-94.50701746188793, 37.06755562813773], [-94.50670745397196, 37.06762773150384], [-94.50639998775009, 37.067706513394306], [-94.50609528593375, 37.06779191630947], [-94.5057935712373, 37.067883876743586], [-94.50549506316521, 37.06798232947637], [-94.50519997801189, 37.06808720156477], [-94.50490853314666, 37.068198417492525], [-94.50395821896308, 37.068521675240355], [-94.50250200038263, 37.06901700000814], [-94.49870375636802, 37.070051784468866], [-94.49810004695235, 37.07025151496825], [-94.49644290929578, 37.07079450603109], [-94.49623977378722, 37.07085145206627], [-94.49603423141632, 37.07090255764349], [-94.49582654787314, 37.070947757527186], [-94.49561698349395, 37.0709869950666], [-94.49540580611476, 37.071020219621175], [-94.49519328464406, 37.07104738827716], [-94.49497969013359, 37.07106846670598], [-94.4947652925646, 37.07108343002245], [-94.49455036620346, 37.071092257635115], [-94.49433518317427, 37.07109493839591], [-94.49412001774313, 37.07109146888378], [-94.49297100017357, 37.07105399979052], [-94.49245802365603, 37.070988420738985], [-94.4902959997845, 37.070712000321095], [-94.48819235825547, 37.070640797937266], [-94.48789935485898, 37.07061522986763], [-94.48760556113773, 37.07059636621042], [-94.48731121694762, 37.070584222435315], [-94.4870165600027, 37.07057880714748], [-94.48672182801701, 37.070580126379475], [-94.48642726513273, 37.07058817844204], [-94.486133106924, 37.07060295649732], [-94.48583959539317, 37.07062444856066], [-94.48554696611752, 37.07065263749904], [-94.48525546003226, 37.07068750017406], [-94.48496531164807, 37.070729008299004], [-94.48467675869168, 37.07077712843979], [-94.48439003567943, 37.070831822014206], [-94.48410537284674, 37.07089304271676], [-94.48382300578787, 37.07096074166977], [-94.48354315938951, 37.071034863988], [-94.48326606604051, 37.07111534963917], [-94.48168994101158, 37.07147423050714], [-94.48020413309409, 37.07184905229464], [-94.47996339375779, 37.07192139508911], [-94.47971976249364, 37.071987229096614], [-94.47947351145166, 37.07204647967259], [-94.47922491920966, 37.07209908075887], [-94.47897426648876, 37.072144973166424], [-94.47872183401078, 37.072184104575015], [-94.47846790678204, 37.07221643125069], [-94.47821276873738, 37.072241917186595], [-94.47795670702469, 37.0722605332454], [-94.47770000986179, 37.072272258875614], [-94.47744296760736, 37.07227708125359], [-94.47718586740521, 37.07227499356569], [-94.47692899960953, 37.072266000159686], [-94.47441801354799, 37.072115445228505], [-94.47164087881617, 37.071858565809094], [-94.46844499984196, 37.071838000938165], [-94.466247999879, 37.07165499996959], [-94.46441700094525, 37.07116700014026]], [[-94.47347453014089, 37.07071216172804], [-94.4772702816995, 37.070454820349795], [-94.48044681222272, 37.070168862851304], [-94.48429538198756, 37.06977168278101], [-94.48475387661024, 37.06970740355915], [-94.48521369313123, 37.06964951809438], [-94.48567469562704, 37.06959804352156], [-94.48613674603364, 37.06955299526076], [-94.48659970200403, 37.06951438701694], [-94.48706342654809, 37.06948223163958], [-94.48752777625087, 37.06945653597126], [-94.48799261305312, 37.06943731114899], [-94.48845779675517, 37.06942456058671], [-94.4889231882287, 37.06941828855908], [-94.48938864299028, 37.06941849762569], [-94.48985402405488, 37.0694251877741], [-94.49031918908204, 37.06943835641856], [-94.49078400001571, 37.06945800011757], [-94.49111904122566, 37.069486630151445], [-94.49145488358472, 37.06950842790295], [-94.49179130956637, 37.06952337790048], [-94.49212809842925, 37.06953147154126], [-94.49246503692912, 37.069532702800366], [-94.49280190646523, 37.06952707252163], [-94.49313848736568, 37.069514582409525], [-94.49347456317163, 37.0694952418961], [-94.49380991742466, 37.0694690621325], [-94.49414433259557, 37.06943606113884], [-94.49447759222718, 37.069396260370866], [-94.4948094820058, 37.0693496838619], [-94.49513978761887, 37.069296364230745], [-94.49546829475568, 37.069236334098576], [-94.49579479232077, 37.06916963381383], [-94.49611906707875, 37.06909630372708], [-94.4964409122232, 37.06901639363284], [-94.49676011880835, 37.06892995504431], [-94.49707648003364, 37.06883704205179], [-94.4973897901727, 37.06873771733079], [-94.4976998499295, 37.06863204270058], [-94.50079299982949, 37.067870999960874], [-94.50494415571896, 37.067203683597775], [-94.50762899953355, 37.066772000297924], [-94.51104699960527, 37.065917999693674], [-94.51521482637881, 37.06520789434471], [-94.52090016988959, 37.06408314828569], [-94.52256484934126, 37.06368354330138], [-94.5242820577632, 37.06327129970328], [-94.5252080000577, 37.063048999765954], [-94.526515456278, 37.06292453583781], [-94.52783466608162, 37.062785637470874], [-94.52896495903701, 37.06244188102363], [-94.5297822581756, 37.06205101585236], [-94.53019659752114, 37.06180540635716], [-94.53054754021768, 37.061536704800545], [-94.53066207411212, 37.06118745057693], [-94.53063908780501, 37.061058795420976], [-94.53054730440176, 37.060948537292], [-94.53043259521405, 37.06085666598906], [-94.5299509547823, 37.060820028255584], [-94.52929699974666, 37.06085200020376], [-94.52819800052647, 37.06109599973501], [-94.52642585235013, 37.061308823260966], [-94.52610714952436, 37.06133309342271], [-94.5257893859727, 37.06136428576753], [-94.52547279508182, 37.061402375361354], [-94.52515761023903, 37.0614473346976], [-94.52484406268994, 37.06149913198088], [-94.52453238582137, 37.061557726834515], [-94.52422280445391, 37.06162307716929], [-94.52391555197715, 37.061695135746476], [-94.52361085000017, 37.061773848465435], [-94.52330892548923, 37.06185915779319], [-94.52301000005701, 37.06195100019176], [-94.5226992213584, 37.06205149515382], [-94.52238534471016, 37.06214560309468], [-94.52206857365694, 37.062233264727794], [-94.52174911603134, 37.06231442248441], [-94.52142717752847, 37.06238902308988], [-94.52110297134475, 37.062457017561776], [-94.52077670425493, 37.062518362929346], [-94.5204485905339, 37.06257301793926], [-94.52011884338972, 37.06262094820726], [-94.5197876781754, 37.0626621227843], [-94.51945531024619, 37.06269651244027], [-94.51912195603232, 37.06272409738882], [-94.51783469718885, 37.062852928925615], [-94.5153534870625, 37.06310120978095], [-94.51348759904879, 37.06328788432388], [-94.51159699978254, 37.06347699961248], [-94.51120188851313, 37.0634759114602], [-94.51080684466768, 37.06348193148331], [-94.51041207056232, 37.06349505966469], [-94.51001776315687, 37.063515285690485], [-94.50962412155424, 37.06354260182421], [-94.50923134485724, 37.063576993465425], [-94.50883963002735, 37.063618442583085], [-94.50844917509825, 37.06366692943203], [-94.50806017703358, 37.06372242911975], [-94.50767283065649, 37.06378491418143], [-94.50695800053421, 37.06390400006841], [-94.50463899980342, 37.06457499996454], [-94.50022472162028, 37.06584240030567], [-94.49847399978408, 37.06634500011599], [-94.4968694075845, 37.066675201739145], [-94.49483925905393, 37.06705987515255], [-94.493410237157, 37.06729916675023], [-94.49216123886698, 37.06747247898567], [-94.49113769924878, 37.06758013080936], [-94.49092362175752, 37.06760976025358], [-94.49070831204949, 37.06763295693937], [-94.49049207116093, 37.06764968912624], [-94.49027520012837, 37.067659931942494], [-94.49005800320086, 37.067663672535325], [-94.48984078248466, 37.067660905778915], [-94.48962384436945, 37.06765163427488], [-94.48940748988747, 37.06763587264316], [-94.48919202006942, 37.067613641514065], [-94.48897774022797, 37.06758497182035], [-94.48876494603303, 37.06754990479591], [-94.48855968143485, 37.067517585609174], [-94.48835295608825, 37.06749192222162], [-94.48814510947147, 37.067472957575795], [-94.48793647677896, 37.06746072174129], [-94.48772739856099, 37.06745523620728], [-94.4875182153696, 37.0674565078739], [-94.48730926454466, 37.067464535918305], [-94.48710088878393, 37.06747930664579], [-94.48689342436096, 37.06750079606345], [-94.48668720969447, 37.06752896988102], [-94.48648257999335, 37.06756378179365], [-94.48426207077918, 37.067776627947396], [-94.48140662570502, 37.06805027308042], [-94.47882099993897, 37.068297999755316], [-94.47700701159646, 37.068557533102165], [-94.47540299995185, 37.06878699991103], [-94.47315137831806, 37.06891937148627], [-94.47189137228563, 37.068994153693325], [-94.46968640130014, 37.06912773141167], [-94.46826199978669, 37.06921399969865], [-94.46722719587584, 37.06912000401522], [-94.46557599974464, 37.06897000011935], [-94.4643012785305, 37.06864355580677], [-94.46367464360476, 37.068483074564575], [-94.46334250844907, 37.068398012981035], [-94.46304722598535, 37.068353941737165], [-94.46297835220803, 37.068482582053775], [-94.46293236347444, 37.068721510277214], [-94.46284047266676, 37.06901556449732], [-94.46276899956395, 37.06921400012377], [-94.46337900051235, 37.070067999999324], [-94.4647219995405, 37.07055699963812], [-94.46655549241942, 37.070781169376914], [-94.4679546527365, 37.070855076129895], [-94.46881100005282, 37.070983999603754], [-94.47347453014089, 37.07071216172804]]], &quot;type&quot;: &quot;Polygon&quot;}, &quot;id&quot;: &quot;2&quot;, &quot;properties&quot;: {&quot;OBJECTID_1&quot;: 4, &quot;ef_rating&quot;: &quot;EF4&quot;, &quot;guid&quot;: &quot;c4ef5a60-96fe-4f8e-a921-3f3493673a11&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-94.5562464609651, 37.05571734288637, -94.43647268924695, 37.07314233523648], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[-94.47476889519258, 37.07298499521892], [-94.474486999662, 37.07293700017867], [-94.47143599987272, 37.0729369999336], [-94.46801800044432, 37.07250999959023], [-94.46582000046492, 37.072265998891226], [-94.46474474813793, 37.07202700265956], [-94.46237372131675, 37.07149995331063], [-94.45627987392994, 37.07008558188314], [-94.4532190647198, 37.06932307743903], [-94.45013013868811, 37.06826505586099], [-94.44971854058525, 37.06813739020689], [-94.44930993518508, 37.068003693822156], [-94.44890446071044, 37.06786401228796], [-94.44850225323735, 37.06771839290291], [-94.44810344883768, 37.067566883825464], [-94.44770818036282, 37.0674095383641], [-94.44731658280496, 37.06724640639647], [-94.44692878686318, 37.06707754466629], [-94.44654492216156, 37.06690300991813], [-94.44616511939057, 37.06672285975666], [-94.44578950709062, 37.06653715693678], [-94.44541820951474, 37.06634596249594], [-94.44505135305135, 37.06614934090694], [-94.44468906194045, 37.06594735921762], [-94.44433145506207, 37.06574008447432], [-94.44397865664527, 37.06552758630176], [-94.44363078341443, 37.06530993775527], [-94.44328795209057, 37.06508721017428], [-94.44063790807823, 37.06370268459827], [-94.43708734923312, 37.06173440533335], [-94.43700251175538, 37.0617048253781], [-94.43692165208229, 37.06166879030502], [-94.43684553679286, 37.06162664042659], [-94.4367748810052, 37.06157877267871], [-94.43671035051959, 37.06152563890471], [-94.43665255539779, 37.06146773898486], [-94.43660203925373, 37.06140561997185], [-94.4365592803312, 37.06133986836603], [-94.43652467758763, 37.06127110238251], [-94.43649856247661, 37.06119997109883], [-94.43648117753575, 37.06112714585988], [-94.43647268924695, 37.06105331170125], [-94.43647317518621, 37.060979166483975], [-94.43648263153185, 37.06090540888174], [-94.43650096985631, 37.06083273408772], [-94.43652801713387, 37.06076182608966], [-94.43656351788577, 37.06069335509628], [-94.43660713633277, 37.06062796638043], [-94.43665846282462, 37.06056627684973], [-94.43671701277637, 37.06050886732128], [-94.43678223523956, 37.06045627909354], [-94.43685351290685, 37.06040900879679], [-94.4369301760406, 37.060367501534486], [-94.43701150033287, 37.060332148307616], [-94.43709671975965, 37.06030328258865], [-94.43718503086573, 37.06028117860768], [-94.4372756002692, 37.06026604105672], [-94.43736757429096, 37.060258015395114], [-94.43746008646042, 37.06025717583702], [-94.43755226393539, 37.06026353136332], [-94.4376432371432, 37.060277021434956], [-94.43773215048681, 37.060297519431934], [-94.43781816341516, 37.06032483179523], [-94.43790046434312, 37.060358700608944], [-94.43797828028661, 37.060398807038254], [-94.43870673530982, 37.06058611151702], [-94.4421345999366, 37.06149347931913], [-94.445897539288, 37.062423592765406], [-94.44848963818023, 37.06299116151262], [-94.44922703621886, 37.063122898257376], [-94.44996259360076, 37.0632610876691], [-94.45069622376019, 37.06340571172077], [-94.45142783905703, 37.063556754103715], [-94.45215735292088, 37.063714196795026], [-94.4528846755663, 37.0638780209142], [-94.45360972256158, 37.064048205868254], [-94.45433240518706, 37.06422473278113], [-94.45505264007775, 37.06440757934747], [-94.4586130002726, 37.06532700067215], [-94.46092728115579, 37.065751179420616], [-94.46562959642391, 37.06661735144664], [-94.46596010424088, 37.066698757450666], [-94.46629311677941, 37.066773322406604], [-94.46662841333416, 37.06684099816534], [-94.46696577105361, 37.06690173915457], [-94.46730496815243, 37.066955504954514], [-94.46764577855602, 37.06700226115502], [-94.46798797725722, 37.067041976781894], [-94.46833133817498, 37.067074623438266], [-94.46867563415124, 37.06710018302935], [-94.46902063481457, 37.067118634887166], [-94.46936611514347, 37.06712996950591], [-94.46971184476035, 37.06713417738106], [-94.47005759542567, 37.06713125845279], [-94.47040313675836, 37.06712121094676], [-94.47074824158652, 37.06710404425031], [-94.47109268166705, 37.06707976946976], [-94.47143622875605, 37.06704840200561], [-94.47701492571484, 37.06620873779864], [-94.48499352782869, 37.06500322354793], [-94.48803700036508, 37.06457499986845], [-94.49303030449174, 37.06366442915352], [-94.49774199957159, 37.06280500004619], [-94.50286464937409, 37.061993704117796], [-94.51007099956738, 37.06085199998825], [-94.51433144885517, 37.06049515546303], [-94.52223924969087, 37.059862782267274], [-94.52740500023313, 37.059509000388815], [-94.5311180462371, 37.05877225487204], [-94.53509499847011, 37.05798300039122], [-94.54013391730317, 37.05760812928345], [-94.54389862801105, 37.0573279107488], [-94.54435461843762, 37.05731076175652], [-94.5448101280562, 37.05728676347778], [-94.54526499396471, 37.05725592456352], [-94.54571906076109, 37.05721825795584], [-94.54617216554662, 37.057173774885406], [-94.54662414863868, 37.05712249001743], [-94.5470748535731, 37.05706442488481], [-94.54752411960135, 37.05699959759115], [-94.54797179133416, 37.056928030531886], [-94.54841771017348, 37.05684974953938], [-94.54886171645416, 37.0567647830236], [-94.54930365908088, 37.05667315939334], [-94.54974337946764, 37.05657491221253], [-94.55018072438526, 37.056470074186755], [-94.5506155395404, 37.05635868403241], [-94.55104767064284, 37.05624078046803], [-94.55147696876192, 37.05611640478677], [-94.55166851829686, 37.05604950778405], [-94.55186311297335, 37.05598852128264], [-94.55206047230496, 37.05593353379829], [-94.55226030723509, 37.05588462526888], [-94.55246232656265, 37.055841867910154], [-94.55266623479834, 37.05580532364162], [-94.55287173430716, 37.05577504580245], [-94.55307852744946, 37.055751078292225], [-94.55328631122643, 37.05573345643162], [-94.55349478263393, 37.05572220610186], [-94.55370363866135, 37.05571734288637], [-94.55391257308034, 37.05571887464719], [-94.55412128286756, 37.05572679808883], [-94.55432946285214, 37.055741102193956], [-94.55453680892737, 37.05576176736366], [-94.55474302018982, 37.05578876198298], [-94.55494779572986, 37.055822047572555], [-94.55506699697989, 37.05585615020322], [-94.55518304157053, 37.05589664698666], [-94.55529539606503, 37.055943351065835], [-94.55540354413942, 37.055996050685934], [-94.55550698871754, 37.05605450060989], [-94.55560525626393, 37.056118434991085], [-94.55569789142194, 37.0561875579339], [-94.55578446987043, 37.056261551212316], [-94.55586459618374, 37.056340076845586], [-94.55593789954489, 37.0564227728084], [-94.5560040433891, 37.05650925989246], [-94.55606272326035, 37.05659913913241], [-94.55611367110063, 37.05669199867006], [-94.55615665203548, 37.05678741118062], [-94.55619146759128, 37.056884939021025], [-94.55621795997897, 37.056984133369205], [-94.55623600460062, 37.057084537661005], [-94.55624551969179, 37.057185691876846], [-94.5562464609651, 37.05728712911112], [-94.55623882590172, 37.05738838501151], [-94.55622264732321, 37.05748899349048], [-94.55619800196517, 37.0575884935879], [-94.5561649997645, 37.057686424326086], [-94.55612379565207, 37.05778234015437], [-94.55607457776216, 37.05787579636317], [-94.55601757065853, 37.057966366249254], [-94.55595303854098, 37.05805363167285], [-94.55588127668562, 37.0581371933617], [-94.5558026167957, 37.05821666490079], [-94.55571741737076, 37.05829168303686], [-94.5556260711998, 37.05836190252533], [-94.55552900000961, 37.05842700042467], [-94.55326530566096, 37.05926297878352], [-94.55056359605915, 37.060222962708], [-94.54903818691686, 37.060640505805566], [-94.5454112335146, 37.06163320001156], [-94.54333543190292, 37.062292330835426], [-94.54158596791964, 37.06294523095137], [-94.53866165270632, 37.06398333717023], [-94.53564500024476, 37.06500199994478], [-94.53204327902485, 37.065670556169565], [-94.52945330794753, 37.06613109756486], [-94.52614625560143, 37.06649874668623], [-94.522149779138, 37.066942911810045], [-94.51983599998898, 37.06720000034292], [-94.51641193516865, 37.067798139749165], [-94.51230574239689, 37.0685612690676], [-94.50831935400359, 37.069404637495694], [-94.50474188402643, 37.07024761929879], [-94.5000715043853, 37.07139908215857], [-94.49650434192127, 37.072209150900456], [-94.4961762530111, 37.07229798833674], [-94.49584544722605, 37.07238010066905], [-94.49551213781011, 37.07245543810032], [-94.49517654229503, 37.07252394997745], [-94.49483887821508, 37.072585592516326], [-94.4944993652492, 37.07264032536855], [-94.49415822200729, 37.07268811247959], [-94.4938156713856, 37.072728922947604], [-94.4934719341394, 37.072762731881326], [-94.49312723316768, 37.072789515250435], [-94.49278179458358, 37.07280925761057], [-94.49243583914516, 37.07282194437798], [-94.49208959296622, 37.072827569555], [-94.4917432800187, 37.07282612628813], [-94.49139712534463, 37.07281761888473], [-94.49105135398591, 37.07280205079637], [-94.49070618991199, 37.07277943234382], [-94.49036185494873, 37.072749778141855], [-94.49001857520494, 37.07271310795795], [-94.4833980002212, 37.07226599965522], [-94.47961807890249, 37.07282164464948], [-94.47933998625545, 37.072882230163714], [-94.47905989071822, 37.072936567799836], [-94.47877800981506, 37.072984617241566], [-94.47849456428531, 37.07302633989189], [-94.4782097748687, 37.07306170230565], [-94.47792386337584, 37.073090677906634], [-94.4776370548308, 37.07311324441282], [-94.47734956997289, 37.0731293838352], [-94.47706163596769, 37.07313908333799], [-94.47677347462394, 37.07314233523648], [-94.47648531310467, 37.07313913785743], [-94.47619737642984, 37.07312949210434], [-94.47590988854523, 37.07311340746587], [-94.47562307125301, 37.07309089600727], [-94.4753371527802, 37.073061974089], [-94.47505235170999, 37.07302666493812], [-94.47476889519258, 37.07298499521892]], [[-94.47770000986179, 37.072272258875614], [-94.47795670702469, 37.0722605332454], [-94.47821276873738, 37.072241917186595], [-94.47846790678204, 37.07221643125069], [-94.47872183401078, 37.072184104575015], [-94.47897426648876, 37.072144973166424], [-94.47922491920966, 37.07209908075887], [-94.47947351145166, 37.07204647967259], [-94.47971976249364, 37.071987229096614], [-94.47996339375779, 37.07192139508911], [-94.48020413309409, 37.07184905229464], [-94.48168994101158, 37.07147423050714], [-94.48326606604051, 37.07111534963917], [-94.48354315938951, 37.071034863988], [-94.48382300578787, 37.07096074166977], [-94.48410537284674, 37.07089304271676], [-94.48439003567943, 37.070831822014206], [-94.48467675869168, 37.07077712843979], [-94.48496531164807, 37.070729008299004], [-94.48525546003226, 37.07068750017406], [-94.48554696611752, 37.07065263749904], [-94.48583959539317, 37.07062444856066], [-94.486133106924, 37.07060295649732], [-94.48642726513273, 37.07058817844204], [-94.48672182801701, 37.070580126379475], [-94.4870165600027, 37.07057880714748], [-94.48731121694762, 37.070584222435315], [-94.48760556113773, 37.07059636621042], [-94.48789935485898, 37.07061522986763], [-94.48819235825547, 37.070640797937266], [-94.4902959997845, 37.070712000321095], [-94.49245802365603, 37.070988420738985], [-94.49297100017357, 37.07105399979052], [-94.49412001774313, 37.07109146888378], [-94.49433518317427, 37.07109493839591], [-94.49455036620346, 37.071092257635115], [-94.4947652925646, 37.07108343002245], [-94.49497969013359, 37.07106846670598], [-94.49519328464406, 37.07104738827716], [-94.49540580611476, 37.071020219621175], [-94.49561698349395, 37.0709869950666], [-94.49582654787314, 37.070947757527186], [-94.49603423141632, 37.07090255764349], [-94.49623977378722, 37.07085145206627], [-94.49644290929578, 37.07079450603109], [-94.49810004695235, 37.07025151496825], [-94.49870375636802, 37.070051784468866], [-94.50250200038263, 37.06901700000814], [-94.50395821896308, 37.068521675240355], [-94.50490853314666, 37.068198417492525], [-94.50519997801189, 37.06808720156477], [-94.50549506316521, 37.06798232947637], [-94.5057935712373, 37.067883876743586], [-94.50609528593375, 37.06779191630947], [-94.50639998775009, 37.067706513394306], [-94.50670745397196, 37.06762773150384], [-94.50701746188793, 37.06755562813773], [-94.50732978450465, 37.06749025393137], [-94.50764419376054, 37.06743165952247], [-94.50796046266676, 37.06737988525111], [-94.51128440533957, 37.06671968675274], [-94.51898680314406, 37.065312677192594], [-94.52395566918786, 37.0644922116574], [-94.526637297155, 37.064049324065564], [-94.528434694871, 37.06375243575794], [-94.53031833069794, 37.06349640677233], [-94.53369099956471, 37.06304900013797], [-94.53535366479566, 37.063049013239784], [-94.53559512791, 37.06306853571004], [-94.5358372586398, 37.06308173163918], [-94.53607979665676, 37.063088586510794], [-94.53632248484878, 37.063089093534586], [-94.536565065035, 37.06308325107257], [-94.53680727689715, 37.06307106693111], [-94.53704886440315, 37.06305255235058], [-94.53728956831311, 37.063027728016046], [-94.5375291326038, 37.06299661976354], [-94.53776730232849, 37.06295926201399], [-94.53800382361614, 37.06291569519829], [-94.5382384436698, 37.06286596232381], [-94.54193099954286, 37.06219500036071], [-94.5426772985229, 37.06197874381949], [-94.54296909623962, 37.06189253226339], [-94.54325812892732, 37.06180053160029], [-94.54354421556097, 37.06170279856599], [-94.54382718154721, 37.06159939418709], [-94.54410684908432, 37.061490382067724], [-94.54438304787307, 37.06137582924392], [-94.54465560654927, 37.061255807045036], [-94.5449243548248, 37.0611303893762], [-94.54518912884453, 37.06099965529125], [-94.54544976261457, 37.06086368298754], [-94.54669199978122, 37.060181000072916], [-94.54946825925039, 37.059255307509304], [-94.54998799973512, 37.059081999917865], [-94.55072000031616, 37.05865499976329], [-94.55097034212706, 37.05820485140763], [-94.55097018334355, 37.057967473203796], [-94.55087135943063, 37.05782508951295], [-94.55055535577094, 37.05771444663645], [-94.55012091065412, 37.057651330390364], [-94.54943800018508, 37.05773900041498], [-94.54699699987087, 37.058227999929], [-94.54370099987571, 37.05908200110276], [-94.54028299968878, 37.059998000362455], [-94.53780740783964, 37.06024546241838], [-94.53601100036558, 37.06042499999929], [-94.53242703498535, 37.06026221058325], [-94.53063999966551, 37.060180999729894], [-94.52655000000982, 37.060425000425774], [-94.52014199983216, 37.06127899989214], [-94.51483603766471, 37.061598795521945], [-94.51451642436768, 37.06160892123188], [-94.51419727818578, 37.06162607140877], [-94.51387884000684, 37.061650232294916], [-94.51356134857696, 37.061681386702205], [-94.5132450426421, 37.06171950972006], [-94.51293016094907, 37.06176457300712], [-94.51261694010373, 37.06181654393307], [-94.51230561671261, 37.0618753812868], [-94.5119964241713, 37.061941042143246], [-94.51168959587697, 37.06201347671302], [-94.51138536201589, 37.06209263091773], [-94.51108395384779, 37.06217844467275], [-94.51078559513805, 37.06227085446304], [-94.50869861920421, 37.06279719570685], [-94.50343781413346, 37.064202101717825], [-94.4998831347786, 37.06515122243164], [-94.49754821635406, 37.065749314013985], [-94.4931144008116, 37.06660145655494], [-94.48992900006606, 37.06677200007163], [-94.48776280630062, 37.06677201353768], [-94.48746724204568, 37.06675898680231], [-94.48717134262087, 37.06675244478663], [-94.48687533073611, 37.06675239094609], [-94.48657942803038, 37.06675882702158], [-94.48628385400197, 37.06677174703097], [-94.48598883457663, 37.06679114156184], [-94.48569458497107, 37.066816995194344], [-94.48540133004276, 37.066849291653696], [-94.48510928608378, 37.066888003507906], [-94.4819950002941, 37.0671999997068], [-94.4757079995437, 37.06829799962879], [-94.4740892573416, 37.06847092765548], [-94.4729768464265, 37.06858975119658], [-94.47268799996972, 37.06863045110261], [-94.47239782954824, 37.068664576957744], [-94.47210657088988, 37.06869210305815], [-94.47181445543866, 37.06871300541783], [-94.47152171785021, 37.06872726777874], [-94.4712285927793, 37.06873487817678], [-94.4709353170205, 37.06873583237545], [-94.47064212415492, 37.068730126998204], [-94.4703492520444, 37.06871776811337], [-94.47005693426395, 37.06869876694047], [-94.46976540545867, 37.06867313555986], [-94.46741411040487, 37.06847690107901], [-94.46527100011899, 37.06829800036704], [-94.46034527942375, 37.06721875379613], [-94.45716937446066, 37.06652277410768], [-94.4546817674774, 37.065977562853426], [-94.45088949321931, 37.06514629141857], [-94.44939199992548, 37.06481800036726], [-94.44622985962096, 37.06375845990348], [-94.444808000429, 37.06328200019485], [-94.44377364617246, 37.062860750313654], [-94.44321437186866, 37.062759868599464], [-94.44285889144247, 37.06280717351548], [-94.44266120794917, 37.06309193181233], [-94.44256231738885, 37.06329761133741], [-94.44250290896092, 37.063519135691514], [-94.4426014303599, 37.063804036195016], [-94.44314044012981, 37.06406809730894], [-94.44591400033768, 37.06531499968896], [-94.45095769398849, 37.06687034227416], [-94.45785989042778, 37.06899834685766], [-94.46027488867583, 37.069727700812976], [-94.46227999947169, 37.070313000363214], [-94.46441700094525, 37.07116700014026], [-94.466247999879, 37.07165499996959], [-94.46844499984196, 37.071838000938165], [-94.47164087881617, 37.071858565809094], [-94.47441801354799, 37.072115445228505], [-94.47692899960953, 37.072266000159686], [-94.47718586740521, 37.07227499356569], [-94.47744296760736, 37.07227708125359], [-94.47770000986179, 37.072272258875614]]], &quot;type&quot;: &quot;Polygon&quot;}, &quot;id&quot;: &quot;3&quot;, &quot;properties&quot;: {&quot;OBJECTID_1&quot;: 3, &quot;ef_rating&quot;: &quot;EF3&quot;, &quot;guid&quot;: &quot;83e3b225-a060-4345-a09a-0492e2adae06&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-94.28758400024591, 37.00540000016822, -94.26540299985814, 37.016965000014274], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[-94.26540299985814, 37.007416999611316], [-94.26695600038734, 37.00540000016822], [-94.26806895662914, 37.0058426246916], [-94.2790297785677, 37.010200932677286], [-94.28501711078769, 37.01258101452086], [-94.28516664853733, 37.01262374343406], [-94.28531320961962, 37.01267265361466], [-94.28545639841491, 37.01272761303395], [-94.28559582790585, 37.01278847423053], [-94.28573111861634, 37.01285507087473], [-94.28586190501866, 37.012927222930145], [-94.28598783232471, 37.013004735789245], [-94.28610855970153, 37.01308739856226], [-94.28622375919369, 37.01317498665013], [-94.28633312000204, 37.01326726261022], [-94.28643634312483, 37.013363978721486], [-94.28653315100762, 37.01346487099332], [-94.28662328217234, 37.01356966602228], [-94.28670649014722, 37.01367808098996], [-94.2867825498962, 37.01378982109944], [-94.28685125673914, 37.01390458300626], [-94.28691242206604, 37.01402205652743], [-94.28696588297049, 37.01414192465838], [-94.28701149155378, 37.01426386097875], [-94.28704912775513, 37.014387534825126], [-94.28707868543758, 37.014512611266056], [-94.2871000873686, 37.01463875284541], [-94.28711327452915, 37.01476561527149], [-94.28711821143717, 37.01489285772659], [-94.28758400024591, 37.0162069996524], [-94.28514199963848, 37.016965000014274], [-94.27919099951833, 37.01549300010441], [-94.27323662962783, 37.01289675763362], [-94.26578000021533, 37.00924999974212], [-94.26540299985814, 37.007416999611316]]], &quot;type&quot;: &quot;Polygon&quot;}, &quot;id&quot;: &quot;4&quot;, &quot;properties&quot;: {&quot;OBJECTID_1&quot;: 2, &quot;ef_rating&quot;: &quot;EF2&quot;, &quot;guid&quot;: &quot;3ea8d328-0e41-4def-9183-364974dc3141&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-94.57421121287084, 37.04199907214763, -94.37791376942972, 37.074464849972074], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[-94.46474576663962, 37.07344587201744], [-94.46024271737917, 37.07291642701367], [-94.45648927732148, 37.072451735511756], [-94.45521267081111, 37.07220189359432], [-94.45089409195732, 37.07133089828949], [-94.44733944743189, 37.07030626636598], [-94.44691011663465, 37.07019878548133], [-94.44648332578357, 37.070084996371996], [-94.4460592195498, 37.06996493603872], [-94.44563794152728, 37.06983864577533], [-94.44521963316446, 37.06970616687636], [-94.44480443804429, 37.06956754664744], [-94.44439249653477, 37.069422829819494], [-94.44398394578313, 37.06927206627346], [-94.44357892400262, 37.069115307609096], [-94.44317757154494, 37.06895260542875], [-94.44278002125667, 37.06878401562426], [-94.44238640919306, 37.06860959494882], [-94.44199686711804, 37.06842940358817], [-94.44161152786303, 37.068243500871596], [-94.44123052103775, 37.06805195127805], [-94.44085397624876, 37.06785481757088], [-94.43888023553667, 37.066961644030556], [-94.43843376080702, 37.06674264282661], [-94.43798311653526, 37.06652919969095], [-94.43752841079619, 37.0663213644163], [-94.43706974951895, 37.06611918593718], [-94.43660724505435, 37.0659227140511], [-94.43614100653798, 37.06573199598014], [-94.43567114631767, 37.06554707551629], [-94.43519777566559, 37.06536799731061], [-94.43472101013434, 37.065194806018006], [-94.43424095992263, 37.06502754028368], [-94.43375774272022, 37.064866242191826], [-94.43327147193342, 37.0647109486759], [-94.43278226417702, 37.06456169838942], [-94.43229023820753, 37.06441852655555], [-94.43179550849567, 37.06428146668005], [-94.4281968505844, 37.06309926626323], [-94.42735470226225, 37.06279486052688], [-94.4265091714267, 37.06249654337884], [-94.42566032643356, 37.062204342256926], [-94.42480823457139, 37.06191827687491], [-94.42395296740422, 37.061638372101], [-94.42309459221005, 37.061364651085725], [-94.42223318054818, 37.06109713612578], [-94.42136880076328, 37.06083584780089], [-94.42050152226756, 37.06058080669327], [-94.41963141660932, 37.06033203596329], [-94.41875855426699, 37.06008955448091], [-94.41788300571584, 37.05985338111794], [-94.41700484142716, 37.05962353560632], [-94.41082725067315, 37.05792430247855], [-94.40974839875797, 37.05760609023137], [-94.40866667382488, 37.057294215834474], [-94.40758213452087, 37.0569886964224], [-94.40649483627891, 37.05668954741244], [-94.40540483987925, 37.05639678766125], [-94.40431220289355, 37.05611043001661], [-94.40321698288608, 37.055830490761636], [-94.40211923956106, 37.055556985324685], [-94.40101903262187, 37.05528992741954], [-94.3999164185551, 37.05502933161765], [-94.39881145705726, 37.05477521249508], [-94.39770420889525, 37.05452758291427], [-94.39659473269136, 37.05428645573789], [-94.39548308706294, 37.05405184554727], [-94.39511971094367, 37.05396629834165], [-94.39475894629797, 37.053873923505456], [-94.39440099447378, 37.05377477102432], [-94.39404605359701, 37.05366889517485], [-94.39369431749785, 37.05355635624019], [-94.39334598428616, 37.05343721450939], [-94.39300124455941, 37.05331153884997], [-94.39266028998524, 37.05317939555734], [-94.39232331007175, 37.05304086036838], [-94.39199049003999, 37.05289600815944], [-94.39166201402682, 37.05274491981586], [-94.39133806615922, 37.052587679657755], [-94.39101882305796, 37.0524243754332], [-94.39070446348032, 37.05225509489355], [-94.39039515974194, 37.05207993351047], [-94.38833832419415, 37.051120235192215], [-94.38694999865591, 37.05045380149588], [-94.3844439999466, 37.049195000305836], [-94.3820516343574, 37.047832107178], [-94.37878500028114, 37.04597100013786], [-94.37867396670977, 37.04587934017134], [-94.3785694117988, 37.04578290757134], [-94.3784716552367, 37.04568199880468], [-94.37838099420752, 37.04557691804002], [-94.37829770658561, 37.045467988308964], [-94.37822204453023, 37.0453555394829], [-94.378154237686, 37.04523991600075], [-94.37809449426499, 37.045121470003465], [-94.37804299569451, 37.04500056046981], [-94.37799990089265, 37.04487755837065], [-94.37796533771886, 37.04475283636005], [-94.37793941367154, 37.04462677565276], [-94.37792220733046, 37.04449976086528], [-94.37791376942972, 37.04437217915867], [-94.37791412821632, 37.044244417668914], [-94.37792328194989, 37.044116866932555], [-94.3779412010546, 37.04398991488056], [-94.37796783239878, 37.04386394941781], [-94.37800309502063, 37.043739353268755], [-94.37804688012221, 37.043616508269146], [-94.37809905536936, 37.04349578507065], [-94.37815946059008, 37.04337755515314], [-94.37822791315087, 37.04326217709714], [-94.37830420259375, 37.04315000258681], [-94.37838809813614, 37.04304137384294], [-94.37847934332146, 37.04293662104256], [-94.3785776603012, 37.04283606318199], [-94.3786827498337, 37.04274000893542], [-94.3787942902259, 37.04264874892911], [-94.37891194374966, 37.042562560898226], [-94.37903534914801, 37.042481708821036], [-94.37916413341864, 37.042406439498016], [-94.3792979011105, 37.04233697996605], [-94.37943624609079, 37.0422735443775], [-94.37957874834747, 37.042216324555426], [-94.37972497183925, 37.04216549514162], [-94.37987446985304, 37.04212121102741], [-94.38002679035837, 37.04208360650071], [-94.38018146636637, 37.042052797811685], [-94.38033802664594, 37.04202887717528], [-94.38049599571372, 37.04201191877962], [-94.38065489062932, 37.04200197363304], [-94.3808142295566, 37.04199907214763], [-94.38097352427049, 37.042003222415104], [-94.38113229085825, 37.04201441365098], [-94.3812900454454, 37.042032610181835], [-94.38144630739907, 37.04205775659855], [-94.38160059933013, 37.042089776039504], [-94.38175245244216, 37.04212857277085], [-94.38190140439754, 37.04217402703424], [-94.38204700037946, 37.04222600019779], [-94.38594894172564, 37.043032247382946], [-94.39129800038366, 37.04419599983879], [-94.39632798254308, 37.04599228425373], [-94.39844999953611, 37.04675000007439], [-94.4000293006708, 37.04706694007226], [-94.40277331528617, 37.04761756374683], [-94.40502964516713, 37.04807027422483], [-94.40705699993671, 37.0484770001178], [-94.41032795336088, 37.04927402759933], [-94.4121400743793, 37.049715537990906], [-94.41297787421067, 37.04991965075658], [-94.41433264629394, 37.05024969819632], [-94.4159188833484, 37.05063611217228], [-94.41796736564784, 37.05113509270503], [-94.41973659405461, 37.051566018355686], [-94.42138940940202, 37.051968560415965], [-94.42226659376648, 37.05218486733781], [-94.42447330881726, 37.052762045331995], [-94.42540367158877, 37.05300537180356], [-94.4267787875243, 37.053365002436344], [-94.42834082021155, 37.05377349333577], [-94.43002257612167, 37.054213265346405], [-94.43059278452934, 37.054362366086515], [-94.43255967534768, 37.054876651883816], [-94.43394901977123, 37.05523990164122], [-94.43495559911763, 37.055503063188844], [-94.43667470690517, 37.05595248695736], [-94.43805727767953, 37.05631390734575], [-94.43929489899728, 37.05664916332971], [-94.44083217843182, 37.05722308296109], [-94.44230264844037, 37.05777203298473], [-94.4436531055115, 37.05827615818331], [-94.44734491356212, 37.05965419482597], [-94.45082759574558, 37.06095401928454], [-94.4514090001692, 37.06117100032243], [-94.45209309655534, 37.06139971983702], [-94.45278045044506, 37.061622085955136], [-94.45347096532085, 37.061838070258254], [-94.45416455215906, 37.062047643474386], [-94.45486111764735, 37.06225077804836], [-94.45556056847087, 37.06244744471033], [-94.45626281130724, 37.06263762105901], [-94.45696775069273, 37.06282127782772], [-94.45767529544098, 37.06299839176165], [-94.45838534793589, 37.06316894046402], [-94.45909781591487, 37.0633328989671], [-94.45981260068342, 37.06349024745298], [-94.4605296099718, 37.06364096353309], [-94.46124874722202, 37.063785027394566], [-94.46196991480203, 37.063922420084694], [-94.46269301721894, 37.06405312351201], [-94.46341795897787, 37.064177118728956], [-94.46414464136726, 37.06429438936419], [-94.46487296674323, 37.06440492162393], [-94.46560284067422, 37.06450869914272], [-94.4663341622992, 37.06460570813048], [-94.467066837181, 37.064695936517985], [-94.46780076231255, 37.06477937137751], [-94.46853584539477, 37.06485600236182], [-94.46927198448792, 37.06492581826496], [-94.47000908300552, 37.064988809601445], [-94.47074704007494, 37.0650449686038], [-94.47148576017717, 37.06509428922492], [-94.47222514136742, 37.06513676112696], [-94.47296508598116, 37.06517238255869], [-94.47370549635502, 37.06520114662143], [-94.48033323285469, 37.06501015492599], [-94.48249453195204, 37.06481963068125], [-94.4858869530362, 37.06449453688552], [-94.48627477774639, 37.06448228432837], [-94.48666218685612, 37.06446338188006], [-94.48704900139671, 37.0644378381083], [-94.48743504561276, 37.06440566587507], [-94.48782014053648, 37.06436687804427], [-94.48820411255554, 37.06432149434913], [-94.48858678270372, 37.06426953366636], [-94.4889679773706, 37.064211021741926], [-94.4893475229477, 37.064145983465686], [-94.48972524261433, 37.06407445059587], [-94.4901009659784, 37.063996454035156], [-94.49047451943626, 37.063912030696244], [-94.49084573045764, 37.06382121921062], [-94.4912144318699, 37.06372406078712], [-94.49158045221878, 37.063620600069534], [-94.49194362647896, 37.063510885137276], [-94.49230378427309, 37.06339496492956], [-94.49266076486609, 37.063272893537935], [-94.49301440217106, 37.06314472677201], [-94.49422041353665, 37.06280021403425], [-94.49804699951592, 37.06170700028142], [-94.50019663396613, 37.06134870014538], [-94.50449396047583, 37.060632298027585], [-94.50858071443758, 37.05995084301944], [-94.51123000018373, 37.05950900010637], [-94.51410192217989, 37.05914156487599], [-94.51799381264843, 37.058643518241944], [-94.52187499080162, 37.05818666132856], [-94.52389696987818, 37.05805500507648], [-94.52849283769214, 37.057755627136274], [-94.53002286824106, 37.0575797747254], [-94.53561962888148, 37.05688059359754], [-94.5400863164569, 37.056276728995634], [-94.54053041920237, 37.05617868442127], [-94.54097678093639, 37.05608746280324], [-94.54142523365068, 37.05600309507467], [-94.54187561897989, 37.05592561560107], [-94.54232776677614, 37.05585505017108], [-94.54278151653219, 37.055791424572405], [-94.54323670024537, 37.05573476288121], [-94.54369315205481, 37.05568508574205], [-94.54415070502884, 37.05564241122735], [-94.54460919116536, 37.055606755695855], [-94.54506844460225, 37.05557813035811], [-94.54552829626617, 37.055556547286635], [-94.54598857922339, 37.05554201340589], [-94.54646881963032, 37.05556754597901], [-94.54694954880004, 37.055586284488356], [-94.54743061452753, 37.05559822126395], [-94.54791186996519, 37.05560335378615], [-94.54839316184014, 37.055601680398546], [-94.54887434223517, 37.05559320116169], [-94.54935526109352, 37.05557791957266], [-94.54983576835886, 37.055555839131], [-94.55031571504895, 37.05552696762973], [-94.55079495111198, 37.055491313723145], [-94.5512733264987, 37.05544888864282], [-94.55175069437486, 37.055399705338026], [-94.55222690469701, 37.055343780194995], [-94.55270180956501, 37.055281129601426], [-94.55317526108202, 37.05521177252226], [-94.55364711135546, 37.05513573135803], [-94.55411721356681, 37.05505303022773], [-94.55458542197184, 37.054963694968706], [-94.55505158868677, 37.0548677514215], [-94.5555155722601, 37.05476523143412], [-94.56066900042168, 37.0540160002381], [-94.56926627125672, 37.05356502229919], [-94.56952628596666, 37.05353915649608], [-94.56978723212987, 37.053520249734646], [-94.57004881855295, 37.053508324497145], [-94.5703107561757, 37.05350339296727], [-94.57057275272018, 37.053505461324775], [-94.57083451804236, 37.053514527167486], [-94.571095759849, 37.05353058037218], [-94.57135619012104, 37.05355360137394], [-94.57161551976505, 37.053583568036075], [-94.57187345753158, 37.0536204444924], [-94.57212971966163, 37.05366419058314], [-94.57238402024429, 37.053714757568855], [-94.57263607764322, 37.053772089843235], [-94.57288561021042, 37.05383612236068], [-94.57313234378692, 37.05390678578153], [-94.57337599991615, 37.05398399961267], [-94.57348241138303, 37.05406481115491], [-94.57358274285455, 37.05415047419212], [-94.5736766481418, 37.05424069540731], [-94.57376380673738, 37.05433516601779], [-94.57384391953184, 37.054433561777515], [-94.57391671095912, 37.05453554640883], [-94.57398193434886, 37.05464076902389], [-94.57403936336517, 37.05474887013811], [-94.57408880271466, 37.05485947994651], [-94.574130084934, 37.05497221832559], [-94.57416306396931, 37.05508669912885], [-94.57418763017024, 37.05520253103549], [-94.5742036985089, 37.05531931584107], [-94.57421121287084, 37.055436655321316], [-94.57421014712074, 37.05555414522316], [-94.5742005083243, 37.055671383845905], [-94.57418232603709, 37.05578796947291], [-94.57415566408712, 37.055903502081094], [-94.57412061308403, 37.05601758849534], [-94.57407729362492, 37.05612983466194], [-94.57402585309225, 37.056239855950466], [-94.57396646993895, 37.05634727800958], [-94.5738993451156, 37.056451730764365], [-94.5738247106459, 37.056552856136015], [-94.57374281891845, 37.05665030890709], [-94.573653954465, 37.056743754139426], [-94.57355842004263, 37.0568328714747], [-94.57345654306114, 37.05691735684732], [-94.57334867343887, 37.05699691991101], [-94.57323517825209, 37.05707128833411], [-94.57311644815984, 37.05714020693741], [-94.57299289097975, 37.0572034394152], [-94.57286492847571, 37.057260769195686], [-94.57273299957143, 37.057312000297635], [-94.57139918760537, 37.05757110019221], [-94.56912089191452, 37.05801363353289], [-94.56751226888791, 37.058326060481036], [-94.5660517060391, 37.05860971007104], [-94.5642330779987, 37.05896286982289], [-94.56207838107419, 37.059381250170965], [-94.55958270085742, 37.05986578628501], [-94.55670199983312, 37.06042499999192], [-94.55354547249208, 37.061157754739945], [-94.55052233407423, 37.06185945271589], [-94.5501203589453, 37.06191847680177], [-94.54972005205003, 37.06198438601867], [-94.54932159748031, 37.06205715026534], [-94.54892517825705, 37.06213673601016], [-94.54853097632994, 37.06222310543252], [-94.54813917258102, 37.062316221572964], [-94.54774994574876, 37.06241604060849], [-94.54736347457276, 37.062522515284925], [-94.54697993779598, 37.0626355983503], [-94.54659950987812, 37.06275523826474], [-94.54622236527996, 37.06288137919869], [-94.54584867739419, 37.06301396360841], [-94.54547861640239, 37.06315293051989], [-94.54511235355989, 37.063298216385526], [-94.54475005691177, 37.06344975508572], [-94.544391891292, 37.063607476212105], [-94.5440380226093, 37.0637713076408], [-94.54368861142052, 37.06394117467651], [-94.54334381828592, 37.06411700005024], [-94.54300380269684, 37.064298701344875], [-94.54277727228823, 37.06441747655646], [-94.54254614416016, 37.06453041981407], [-94.54231064962852, 37.0646374142766], [-94.54207102858872, 37.06473835425946], [-94.54182752308479, 37.064833137512075], [-94.54158037730954, 37.064921666076316], [-94.54132984617634, 37.065003852291845], [-94.54107617924853, 37.065079611076996], [-94.54081963252561, 37.06514886850798], [-94.54056046950933, 37.06521155323535], [-94.54029894835344, 37.06526760163846], [-94.54003533578673, 37.065316956962576], [-94.53899950951931, 37.06555920338595], [-94.53699964472186, 37.066026876777364], [-94.53492580607258, 37.0665118076938], [-94.53381299984032, 37.06677200042869], [-94.53263937857801, 37.06694841336284], [-94.53091703400008, 37.06720728505878], [-94.52884777999216, 37.06751826362405], [-94.5264842831811, 37.06787341485026], [-94.5246818932865, 37.068144216618], [-94.5224059652435, 37.068486124795434], [-94.52051940783161, 37.06875181505332], [-94.51917604023713, 37.06893789340989], [-94.5169627347172, 37.06924443649619], [-94.51535165113995, 37.06946754443537], [-94.51409899971438, 37.06964099966543], [-94.51362187066971, 37.069738220165206], [-94.51201240293878, 37.070066150739535], [-94.50964088541903, 37.0705493052034], [-94.50792598286955, 37.07089865203351], [-94.50615504553659, 37.07131351249624], [-94.50273921138692, 37.07214597419582], [-94.50225633382784, 37.072264296426304], [-94.50177125338489, 37.07237668657007], [-94.50128408370487, 37.07248311713287], [-94.50079493950824, 37.07258356491381], [-94.50030393337516, 37.07267800585541], [-94.49981118110144, 37.07276641676032], [-94.49931679848481, 37.07284877786627], [-94.49882090132485, 37.072925069412904], [-94.4983236054228, 37.0729952742166], [-94.49782502658138, 37.073059375953974], [-94.49732528167598, 37.073117359161856], [-94.48979379992538, 37.07405462465316], [-94.48938100277476, 37.07412710121177], [-94.48896639553476, 37.07419261087798], [-94.48855016149308, 37.074251121892786], [-94.48813248286739, 37.07430260936614], [-94.48771354723279, 37.07434705098602], [-94.48729353788076, 37.07438442701752], [-94.48687263917503, 37.0744147203033], [-94.48645103976455, 37.074437917980745], [-94.48602892508508, 37.07445400976444], [-94.48560648057267, 37.07446298794656], [-94.48518389380521, 37.074464849972074], [-94.48476135236078, 37.07445959328863], [-94.48433904167403, 37.07444722049615], [-94.48391715039196, 37.074427738489206], [-94.48349586180497, 37.07440115502261], [-94.47982138942868, 37.07432568189134], [-94.47628302223418, 37.07420293165606], [-94.47239325473197, 37.073975220896784], [-94.47067859768433, 37.073874802841765], [-94.46612500021442, 37.07360799991517], [-94.46474576663962, 37.07344587201744]], [[-94.47933998625545, 37.072882230163714], [-94.47961807890249, 37.07282164464948], [-94.4833980002212, 37.07226599965522], [-94.49001857520494, 37.07271310795795], [-94.49036185494873, 37.072749778141855], [-94.49070618991199, 37.07277943234382], [-94.49105135398591, 37.07280205079637], [-94.49139712534463, 37.07281761888473], [-94.4917432800187, 37.07282612628813], [-94.49208959296622, 37.072827569555], [-94.49243583914516, 37.07282194437798], [-94.49278179458358, 37.07280925761057], [-94.49312723316768, 37.072789515250435], [-94.4934719341394, 37.072762731881326], [-94.4938156713856, 37.072728922947604], [-94.49415822200729, 37.07268811247959], [-94.4944993652492, 37.07264032536855], [-94.49483887821508, 37.072585592516326], [-94.49517654229503, 37.07252394997745], [-94.49551213781011, 37.07245543810032], [-94.49584544722605, 37.07238010066905], [-94.4961762530111, 37.07229798833674], [-94.49650434192127, 37.072209150900456], [-94.5000715043853, 37.07139908215857], [-94.50474188402643, 37.07024761929879], [-94.50831935400359, 37.069404637495694], [-94.51230574239689, 37.0685612690676], [-94.51641193516865, 37.067798139749165], [-94.51983599998898, 37.06720000034292], [-94.522149779138, 37.066942911810045], [-94.52614625560143, 37.06649874668623], [-94.52945330794753, 37.06613109756486], [-94.53204327902485, 37.065670556169565], [-94.53564500024476, 37.06500199994478], [-94.53866165270632, 37.06398333717023], [-94.54158596791964, 37.06294523095137], [-94.54333543190292, 37.062292330835426], [-94.5454112335146, 37.06163320001156], [-94.54903818691686, 37.060640505805566], [-94.55056359605915, 37.060222962708], [-94.55326530566096, 37.05926297878352], [-94.55552900000961, 37.05842700042467], [-94.5556260711998, 37.05836190252533], [-94.55571741737076, 37.05829168303686], [-94.5558026167957, 37.05821666490079], [-94.55588127668562, 37.0581371933617], [-94.55595303854098, 37.05805363167285], [-94.55601757065853, 37.057966366249254], [-94.55607457776216, 37.05787579636317], [-94.55612379565207, 37.05778234015437], [-94.5561649997645, 37.057686424326086], [-94.55619800196517, 37.0575884935879], [-94.55622264732321, 37.05748899349048], [-94.55623882590172, 37.05738838501151], [-94.5562464609651, 37.05728712911112], [-94.55624551969179, 37.057185691876846], [-94.55623600460062, 37.057084537661005], [-94.55621795997897, 37.056984133369205], [-94.55619146759128, 37.056884939021025], [-94.55615665203548, 37.05678741118062], [-94.55611367110063, 37.05669199867006], [-94.55606272326035, 37.05659913913241], [-94.5560040433891, 37.05650925989246], [-94.55593789954489, 37.0564227728084], [-94.55586459618374, 37.056340076845586], [-94.55578446987043, 37.056261551212316], [-94.55569789142194, 37.0561875579339], [-94.55560525626393, 37.056118434991085], [-94.55550698871754, 37.05605450060989], [-94.55540354413942, 37.055996050685934], [-94.55529539606503, 37.055943351065835], [-94.55518304157053, 37.05589664698666], [-94.55506699697989, 37.05585615020322], [-94.55494779572986, 37.055822047572555], [-94.55474302018982, 37.05578876198298], [-94.55453680892737, 37.05576176736366], [-94.55432946285214, 37.055741102193956], [-94.55412128286756, 37.05572679808883], [-94.55391257308034, 37.05571887464719], [-94.55370363866135, 37.05571734288637], [-94.55349478263393, 37.05572220610186], [-94.55328631122643, 37.05573345643162], [-94.55307852744946, 37.055751078292225], [-94.55287173430716, 37.05577504580245], [-94.55266623479834, 37.05580532364162], [-94.55246232656265, 37.055841867910154], [-94.55226030723509, 37.05588462526888], [-94.55206047230496, 37.05593353379829], [-94.55186311297335, 37.05598852128264], [-94.55166851829686, 37.05604950778405], [-94.55147696876192, 37.05611640478677], [-94.55104767064284, 37.05624078046803], [-94.5506155395404, 37.05635868403241], [-94.55018072438526, 37.056470074186755], [-94.54974337946764, 37.05657491221253], [-94.54930365908088, 37.05667315939334], [-94.54886171645416, 37.0567647830236], [-94.54841771017348, 37.05684974953938], [-94.54797179133416, 37.056928030531886], [-94.54752411960135, 37.05699959759115], [-94.5470748535731, 37.05706442488481], [-94.54662414863868, 37.05712249001743], [-94.54617216554662, 37.057173774885406], [-94.54571906076109, 37.05721825795584], [-94.54526499396471, 37.05725592456352], [-94.5448101280562, 37.05728676347778], [-94.54435461843762, 37.05731076175652], [-94.54389862801105, 37.0573279107488], [-94.54013391730317, 37.05760812928345], [-94.53509499847011, 37.05798300039122], [-94.5311180462371, 37.05877225487204], [-94.52740500023313, 37.059509000388815], [-94.52223924969087, 37.059862782267274], [-94.51433144885517, 37.06049515546303], [-94.51007099956738, 37.06085199998825], [-94.50286464937409, 37.061993704117796], [-94.49774199957159, 37.06280500004619], [-94.49303030449174, 37.06366442915352], [-94.48803700036508, 37.06457499986845], [-94.48499352782869, 37.06500322354793], [-94.47701492571484, 37.06620873779864], [-94.47143622875605, 37.06704840200561], [-94.47109268166705, 37.06707976946976], [-94.47074824158652, 37.06710404425031], [-94.47040313675836, 37.06712121094676], [-94.47005759542567, 37.06713125845279], [-94.46971184476035, 37.06713417738106], [-94.46936611514347, 37.06712996950591], [-94.46902063481457, 37.067118634887166], [-94.46867563415124, 37.06710018302935], [-94.46833133817498, 37.067074623438266], [-94.46798797725722, 37.067041976781894], [-94.46764577855602, 37.06700226115502], [-94.46730496815243, 37.066955504954514], [-94.46696577105361, 37.06690173915457], [-94.46662841333416, 37.06684099816534], [-94.46629311677941, 37.066773322406604], [-94.46596010424088, 37.066698757450666], [-94.46562959642391, 37.06661735144664], [-94.46092728115579, 37.065751179420616], [-94.4586130002726, 37.06532700067215], [-94.45505264007775, 37.06440757934747], [-94.45433240518706, 37.06422473278113], [-94.45360972256158, 37.064048205868254], [-94.4528846755663, 37.0638780209142], [-94.45215735292088, 37.063714196795026], [-94.45142783905703, 37.063556754103715], [-94.45069622376019, 37.06340571172077], [-94.44996259360076, 37.0632610876691], [-94.44922703621886, 37.063122898257376], [-94.44848963818023, 37.06299116151262], [-94.445897539288, 37.062423592765406], [-94.4421345999366, 37.06149347931913], [-94.43870673530982, 37.06058611151702], [-94.43797828028661, 37.060398807038254], [-94.43790046434312, 37.060358700608944], [-94.43781816341516, 37.06032483179523], [-94.43773215048681, 37.060297519431934], [-94.4376432371432, 37.060277021434956], [-94.43755226393539, 37.06026353136332], [-94.43746008646042, 37.06025717583702], [-94.43736757429096, 37.060258015395114], [-94.4372756002692, 37.06026604105672], [-94.43718503086573, 37.06028117860768], [-94.43709671975965, 37.06030328258865], [-94.43701150033287, 37.060332148307616], [-94.4369301760406, 37.060367501534486], [-94.43685351290685, 37.06040900879679], [-94.43678223523956, 37.06045627909354], [-94.43671701277637, 37.06050886732128], [-94.43665846282462, 37.06056627684973], [-94.43660713633277, 37.06062796638043], [-94.43656351788577, 37.06069335509628], [-94.43652801713387, 37.06076182608966], [-94.43650096985631, 37.06083273408772], [-94.43648263153185, 37.06090540888174], [-94.43647317518621, 37.060979166483975], [-94.43647268924695, 37.06105331170125], [-94.43648117753575, 37.06112714585988], [-94.43649856247661, 37.06119997109883], [-94.43652467758763, 37.06127110238251], [-94.4365592803312, 37.06133986836603], [-94.43660203925373, 37.06140561997185], [-94.43665255539779, 37.06146773898486], [-94.43671035051959, 37.06152563890471], [-94.4367748810052, 37.06157877267871], [-94.43684553679286, 37.06162664042659], [-94.43692165208229, 37.06166879030502], [-94.43700251175538, 37.0617048253781], [-94.43708734923312, 37.06173440533335], [-94.44063790807823, 37.06370268459827], [-94.44328795209057, 37.06508721017428], [-94.44363078341443, 37.06530993775527], [-94.44397865664527, 37.06552758630176], [-94.44433145506207, 37.06574008447432], [-94.44468906194045, 37.06594735921762], [-94.44505135305135, 37.06614934090694], [-94.44541820951474, 37.06634596249594], [-94.44578950709062, 37.06653715693678], [-94.44616511939057, 37.06672285975666], [-94.44654492216156, 37.06690300991813], [-94.44692878686318, 37.06707754466629], [-94.44731658280496, 37.06724640639647], [-94.44770818036282, 37.0674095383641], [-94.44810344883768, 37.067566883825464], [-94.44850225323735, 37.06771839290291], [-94.44890446071044, 37.06786401228796], [-94.44930993518508, 37.068003693822156], [-94.44971854058525, 37.06813739020689], [-94.45013013868811, 37.06826505586099], [-94.4532190647198, 37.06932307743903], [-94.45627987392994, 37.07008558188314], [-94.46237372131675, 37.07149995331063], [-94.46474474813793, 37.07202700265956], [-94.46582000046492, 37.072265998891226], [-94.46801800044432, 37.07250999959023], [-94.47143599987272, 37.0729369999336], [-94.474486999662, 37.07293700017867], [-94.47476889519258, 37.07298499521892], [-94.47505235170999, 37.07302666493812], [-94.4753371527802, 37.073061974089], [-94.47562307125301, 37.07309089600727], [-94.47590988854523, 37.07311340746587], [-94.47619737642984, 37.07312949210434], [-94.47648531310467, 37.07313913785743], [-94.47677347462394, 37.07314233523648], [-94.47706163596769, 37.07313908333799], [-94.47734956997289, 37.0731293838352], [-94.4776370548308, 37.07311324441282], [-94.47792386337584, 37.073090677906634], [-94.4782097748687, 37.07306170230565], [-94.47849456428531, 37.07302633989189], [-94.47877800981506, 37.072984617241566], [-94.47905989071822, 37.072936567799836], [-94.47933998625545, 37.072882230163714]]], &quot;type&quot;: &quot;Polygon&quot;}, &quot;id&quot;: &quot;5&quot;, &quot;properties&quot;: {&quot;OBJECTID_1&quot;: 1, &quot;ef_rating&quot;: &quot;EF2&quot;, &quot;guid&quot;: &quot;46b8fe24-9a7e-4c8c-895e-bae325bfe342&quot;}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    geo_json_a436fe0ee96dba6f0065b0f14b14f8a8.bindTooltip(\n",
+       "    function(layer){\n",
+       "    let div = L.DomUtil.create(&#x27;div&#x27;);\n",
+       "    \n",
+       "    let handleObject = feature=&gt;typeof(feature)==&#x27;object&#x27; ? JSON.stringify(feature) : feature;\n",
+       "    let fields = [&quot;OBJECTID_1&quot;, &quot;ef_rating&quot;, &quot;guid&quot;];\n",
+       "    let aliases = [&quot;OBJECTID_1&quot;, &quot;ef_rating&quot;, &quot;guid&quot;];\n",
+       "    let table = &#x27;&lt;table&gt;&#x27; +\n",
+       "        String(\n",
+       "        fields.map(\n",
+       "        (v,i)=&gt;\n",
+       "        `&lt;tr&gt;\n",
+       "            &lt;th&gt;${aliases[i]}&lt;/th&gt;\n",
+       "            \n",
+       "            &lt;td&gt;${handleObject(layer.feature.properties[v])}&lt;/td&gt;\n",
+       "        &lt;/tr&gt;`).join(&#x27;&#x27;))\n",
+       "    +&#x27;&lt;/table&gt;&#x27;;\n",
+       "    div.innerHTML=table;\n",
+       "    \n",
+       "    return div\n",
+       "    }\n",
+       "    ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});\n",
+       "                     \n",
+       "    \n",
+       "                geo_json_a436fe0ee96dba6f0065b0f14b14f8a8.addTo(map_21c70f33ea6064add4b6ec6adb8f517b);\n",
+       "&lt;/script&gt;\n",
+       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7f5d8e8e94c0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: Visualize the tornado using GeoDataFrame.explore(), see section 3.2.1 for getting the geodataframe\n",
+    "tornado_gdf = tornado.hazardDatasets[0].dataset.get_dataframe_from_shapefile()\n",
+    "tornado_gdf.explore()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3741243a-a1d8-4096-8de3-cea343b1390b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/manual_jb/content/workshops/20231115/session2/session2-assignment.ipynb b/manual_jb/content/workshops/20231115/session2/session2-assignment.ipynb
new file mode 100644
index 00000000..005868ea
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session2/session2-assignment.ipynb
@@ -0,0 +1,216 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0694adca",
+   "metadata": {},
+   "source": [
+    "# Session 2: Assignment\n",
+    "\n",
+    "Apply session 2 material to create a remote model-based Tornado and local dataset-based Tornado with the given information and get hazard values from created hazards\n",
+    "\n",
+    "**Agenda**\n",
+    "- [1. Create a model-based Tornado (Remote)](#1.-Create-a-model-based-Tornado-(Remote))\n",
+    "- [2. Create a dataset-based Tornado (Local)](#2.-Create-a-dataset-based-Tornado-(Local))\n",
+    "\n",
+    "Please follow the instruction at each cell."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeefddef",
+   "metadata": {},
+   "source": [
+    "## 1. Create a model-based Tornado (Remote)\n",
+    "\n",
+    "Using the data from section 2.1.2, slightly alter the values of the Centerville tornado and obtain a new set of\n",
+    "parameters that differ from the previously seen examples.\n",
+    "\n",
+    "Tornado model definition:\n",
+    "* Tornado Model: MeanWidthTornado\n",
+    "* EF Rating: EF3\n",
+    "* Start point of path: \n",
+    "    * Latitude:\n",
+    "    * Longitude: \n",
+    "* End point of path:\n",
+    "    * Latitude:\n",
+    "    * Longitude: \n",
+    "* Wind speed method: 1\n",
+    "* number of simulation: 1\n",
+    "* random seed: 2023\n",
+    "\n",
+    "After you have obtained them, proceed as shown during the session."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa0b708f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Import modules\n",
+    "from pyincore import HazardService, IncoreClient\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "233a216e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Connect to IN-CORE serivce by creating IncoreClient\n",
+    "client = IncoreClient()\n",
+    "\n",
+    "# Create the HazardService object\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7eec0147",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: create a JSON (dictionary) definition of tornado model as given above\n",
+    "my_tornado_model = \"\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f771459",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: create a tornado using the hazard service\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26036930",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: acquire hazard id you created and visualize\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c57650c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Getting and printing hazard value from your tornado\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cedb352",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 2. Create a dataset-based Tornado (Local)\n",
+    "\n",
+    "Now, using the dataset-based tornado definition seen in section 3.1.1, define a new local tornado hazard and get values for the same location with and without a seed parameter.\n",
+    "\n",
+    "Proceed as in the prior exercise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1e12221-3a89-4fb4-8b08-424a84d672c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import modules\n",
+    "from pyincore import Tornado\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b2ce2db-1cbc-4eb0-85f0-9e2b3a4d1548",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: indicate the path of the tornado dataset files\n",
+    "dir = \"data/hazard/tornado/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cae0c55",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# TODO: create the tornado object with definition\n",
+    "\n",
+    "# TODO: attach the shapefile representing the tornado\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f35f99e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Getting and printing hazard value from your tornado for specific seed value\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73b060cf-a60b-4328-a4f1-61e0e9326096",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Getting and printing hazard values from your tornado for a random seed value\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "daf973c4-f3b1-491f-b6a9-caa8755d27ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Visualize the tornado using GeoDataFrame.explore(), see section 3.2.1 for getting the geodataframe\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/manual_jb/content/workshops/20231115/session2/session2-remote-and-local-hazards.ipynb b/manual_jb/content/workshops/20231115/session2/session2-remote-and-local-hazards.ipynb
new file mode 100644
index 00000000..a0e10c1b
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session2/session2-remote-and-local-hazards.ipynb
@@ -0,0 +1,1597 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5cc2d2b9",
+   "metadata": {},
+   "source": [
+    "# Session 2: How to Work with Remote and Local Hazards\n",
+    "\n",
+    "**Objective:**\n",
+    "- Learn about the basics of IN-CORE modules and objects.\n",
+    "- Learn how to create and use hazards by using pyIncore and IN-CORE web services and how to create and use local hazards.\n",
+    "\n",
+    "**Agenda**\n",
+    "- [1. Basics of IN-CORE modules](#1.-Basics-of-IN-CORE-modules)\n",
+    "- [2. Working with Existing Hazards (Tornado and Earthquake)](#2.-Working-with-Existing-Hazards-(Tornado-and-Earthquake))\n",
+    "    - [2.1 Tornado hazards](#2.1-Tornado-hazards)\n",
+    "        - [2.1.1 Working with an existing tornado](#2.1.1-Working-with-an-existing-tornado)\n",
+    "        - [2.1.2 Creating model-based tornado](#2.1.2-Creating-model-based-tornado)\n",
+    "        - [2.1.3 Creating a dataset-driven tornado](#2.1.3-Creating-a-dataset-driven-tornado)\n",
+    "    - [2.2 Earthquake hazards](#2.2.-Earthquake-hazards)\n",
+    "        - [2.2.1 Working with existing earthquake hazards](#2.2.1-Working-with-existing-earthquake-hazards)\n",
+    "        - [2.2.2 Creating model-based earthquakes](#2.2.2-Creating-model-based-earthquakes)\n",
+    "        - [2.2.3 Creating a dataset-driven earthquake (Probabilistic & Deterministic)](#2.2.3-Creating-a-dataset-driven-earthquake-(Probabilistic-&-Deterministic))\n",
+    "- [3. Working with Local Hazards](#3.-Working-with-Local-Hazards)\n",
+    "    - [3.1 Basic Operations for Local Hazards](#3.1-Basic-Operations-for-Local-Hazards)\n",
+    "        - [3.1.1 Tornadoes](#3.1.1-Tornadoes)\n",
+    "        - [3.1.2 Earthquakes](#3.1.2-Earthquakes)\n",
+    "        - [3.1.3 Tsunamis](#3.1.3-Tsunamis)\n",
+    "        - [3.1.4 Floods](#3.1.4-Floods)\n",
+    "        - [3.1.5 Hurricanes](#3.1.5-Hurricanes)\n",
+    "    - [3.2 Visualizations for Local Hazards](#3.2-Visualizations-for-Local-Hazards)\n",
+    "        - [3.2.1 Plotting a Tornado](#3.2.1-Plotting-a-tornado)\n",
+    "        - [3.2.2 Plotting an Earthquake](#3.2.2-Plotting-an-Earthqauke)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "41edbef0",
+   "metadata": {},
+   "source": [
+    "## 1. Basics of IN-CORE modules\n",
+    "\n",
+    "IN-CORE via ```pyincore``` provides a collection of Python classes that wrap the functionality for accessing the IN-CORE web services using Python methods. These classes provide access to Hazards, DFR3 curves, and Data. \n",
+    "\n",
+    "![pyincore-diagram.png](data/pyincore-diagram.png)\n",
+    "\n",
+    "To use IN-CORE in Jupyter Notebooks, we start by importing the modules we need.\n",
+    "\n",
+    "We will first focus on how to effectively use the following modules within the IN-CORE architecture to request information about various hazards:\n",
+    "\n",
+    "* **IncoreClient:** this entity provides access and authentication to IN-CORE services. To access the services, users\n",
+    "  require to have a valid *username* and *password*. Importing ```IncoreClient``` and creating a named instance of the\n",
+    "  client is the first step toward using IN-CORE by establishing a remote connection to various simulation and data\n",
+    "  access services. The variable containing that instance is used by various other services\n",
+    "  to validate the user's identity, as well as provide access to pieces of information required across tasks. *This step\n",
+    "  only needs to be done once.*\n",
+    "* **HazardService:** once the ```IncoreClient``` has been initiated, the ```HazardService``` module uses the information\n",
+    "  from that client to administer existing information about known or new hazards. ```HazardService``` can:\n",
+    "  * Retrieve existing metadata for various hazards\n",
+    "  * Search hazards per specific information\n",
+    "  * Request creation of a new hazard\n",
+    "  * Request deletion of a new hazard\n",
+    "\n",
+    "For more information on **pyincore** modules, see the documentation here: https://incore.ncsa.illinois.edu/doc/pyincore/index.html\n",
+    "\n",
+    "To begin, let's start by importing a few modules and creating our first IN-CORE objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96403a28",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T23:14:55.911896Z",
+     "start_time": "2023-11-08T23:14:54.936908Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Import modules\n",
+    "import os\n",
+    "import json\n",
+    "from pyincore import HazardService, IncoreClient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ad10ca3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T21:56:23.676069Z",
+     "start_time": "2023-11-08T21:56:15.382680Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Connect to IN-CORE serivce by creating IncoreClient\n",
+    "# This only needs to be done once as indicated above\n",
+    "client = IncoreClient()\n",
+    "\n",
+    "# Create a HazardService instance that depends on the client defined in the previous step\n",
+    "hazardsvc = HazardService(client)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eafe244b",
+   "metadata": {},
+   "source": [
+    "## 2. Working with Existing Hazards (Tornado and Earthquake)\n",
+    "### 2.1 Tornado hazards\n",
+    " \n",
+    "We will start by retrieving existing information about a specific tornado, and then using this information to create a slightly modified new tornado hazard scenario. These scenarios are representative of two of the most frequent tasks with IN-CORE: getting information from existing hazards and the creation of new hazards. We later also construct an example with a database-driven tornado.\n",
+    "\n",
+    "These two tasks alone can enable decision makers to systematically construct, test and refine scenarios, later to be easily shared and discussed across response teams using Jupyter notebooks such as this.\n",
+    "\n",
+    "\n",
+    "#### 2.1.1 Working with an existing tornado\n",
+    "\n",
+    "As with any given hazard, IN-CORE operates by utilizing hazard identification strings (*hazard id*) that uniquely\n",
+    "name existing scenarios and are associated with data and metadata useful for analysis and visualization purposes.\n",
+    "Metadata describe properties of the scenario, such as location and other details of the event.\n",
+    "\n",
+    "With this identifier, let's perform two tasks:\n",
+    "* Obtain metadata about the tornado\n",
+    "* Obtain hazard values, often used by decision makers during emergency planning and preparation\n",
+    "\n",
+    "IN-CORE uses the [JSON lightweight data-interchange format](https://www.json.org/json-en.html) to store and retrieve\n",
+    "hazard information across its services as flexible way to share information in a standardized manner. We will use\n",
+    "Python's ```json``` module to expand that information in a human-readable format."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6d0620f",
+   "metadata": {},
+   "source": [
+    "Let's start by using an identifier for one ficticious tornado in Memphis, TN stored as an IN-CORE dataset. We can find hazards on the IN-CORE service by using the hazard viewer here: https://incore.ncsa.illinois.edu/HazardViewer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa9f0fb6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-03T18:03:25.404198Z",
+     "start_time": "2023-11-03T18:03:25.396506Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Example tornado in Memphis, TN (It's an imaginary tornado)\n",
+    "example_tornado_id = \"60a44ae8605f0462bd4263ac\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3f04afc",
+   "metadata": {},
+   "source": [
+    "Now, using the ```HazardService``` module, we retrieve the metadata and display it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28be5e7c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-03T18:03:26.963369Z",
+     "start_time": "2023-11-03T18:03:26.837856Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tornado_model_metadata = hazardsvc.get_tornado_hazard_metadata(example_tornado_id)\n",
+    "print(json.dumps(tornado_model_metadata, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6a62a75",
+   "metadata": {},
+   "source": [
+    "Using ```pyIncore_viz```, we can now visualize the tornado hazard:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f80b39d1-c0b2-458d-9fe7-b83179f7ee49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Visualize the tornado using pyincore-viz\n",
+    "from pyincore_viz.geoutil import GeoUtil as viz\n",
+    "viz.plot_tornado(example_tornado_id, client, basemap=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07399611-207e-49d9-86a4-56b6d3399b46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interactive map to show the tornado path\n",
+    "# hovering mouse over the map shows lat/lon\n",
+    "import wsviz\n",
+    "wsviz.show_tornado(client, tornado_model_metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfa92f2f-bf9d-4960-beb4-14ecb8522677",
+   "metadata": {},
+   "source": [
+    "All geographic locations in IN-CORE are specified using the WGS84 coordinate system. Observe that the parameters of the\n",
+    "```json.dumps``` function are as follows:\n",
+    "\n",
+    "* the variable containing the resulting metadata\n",
+    "* the indentation used to display the information contained in the JSON format\n",
+    "* whether keys are sorted for convenient reading (no in our case)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "902d8a57",
+   "metadata": {},
+   "source": [
+    "We will discuss below the meaning of each element in this JSON output in the next section. Using the same service, we can obtain specific\n",
+    "data about the hazard, in this case, the hazard values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7a1e522",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-03T18:03:28.606220Z",
+     "start_time": "2023-11-03T18:03:28.545294Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Getting hazard value from your model\n",
+    "points = [\n",
+    "    {\n",
+    "        \"demands\": [\"wind\"],\n",
+    "        \"units\": [\"mph\"],\n",
+    "        \"loc\": \"35.134, -90.031\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "tornado_model_vals = hazardsvc.post_tornado_hazard_values(example_tornado_id, points)\n",
+    "print(json.dumps(tornado_model_vals, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fca6ac1-2817-4c70-adde-0baba677842b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### QUIZ: Please find the lat/lon of your location on the interactive map\n",
+    "### QUIZ: Then, acquire hazard value (wind speed) usin the code example above\n",
+    "\n",
+    "mypoints = [\n",
+    "]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f777368",
+   "metadata": {},
+   "source": [
+    "#### 2.1.2 Creating model-based tornado\n",
+    "\n",
+    "One of IN-CORE strengths is the ability to specify, execute and retrieve information about new hazard scenarios using\n",
+    "the same metadata standard.\n",
+    "\n",
+    "As promised above, here is a description of each metadata (JSON) element representing a tornado hazard:\n",
+    "\n",
+    "* **tornadoModel** (required) - the tornado model (Mean width will create a tornado using the mean width from historical data for the EF rating)\n",
+    "    * *Mean width tornado (MeanWidthTornado)* - start/end points with a width equal to the average for that EF rating looking at historical tornado data\n",
+    "    * *Random width tornado (RandomWidthTornado)* - start/end points, random width selected use the historical data to determine the max width for that tornado rating\n",
+    "    * *Mean Length/Width/angle (MeanLengthWidthAngleTornado)* - start point, length/width and angle are based on the historical average for those attributes based on EF selected\n",
+    "    * *Random length/width/angle (RandomLengthWidthAngleTornado)* - start point, random length/width/angle selected for the ef rating using historical data\n",
+    "    * *Random angle (RandomAngleTornado)* - start point, random angle selected based on historical data.\n",
+    "* **efRating (required)** - a value in the Enhanced Fujita (EF) scale intensity of the tornado (EF0 - EF5)\n",
+    "* **startLatitude/startLongitude (required)** - the starting location of the tornado\n",
+    "* **endLatitude/endLongitude (optional)**- depending on the model, an end latitude/longitude value. Some tornado models (e.g. mean length width, and angle) calcuate endLat and endLon, others (e.g. random angle) will generate multiple endpoints programmatically so the input must be passed as an array\n",
+    "* **windSpeedMethod(optional)** - computation of wind speed within an EF boundary, 0 indicates using linear interpolation, 1 indicates uniform random distribution. Default is Uniform random distribution.\n",
+    "\n",
+    "More information about the tornado models can be found here: https://ascelibrary.org/doi/10.1061/%28ASCE%29NH.1527-6996.0000138\n",
+    "\n",
+    "As you may already anticipate, the particular JSON elements vary per each hazard type. We call these *parameters*. Some\n",
+    "parameters are required (i.e. the analysis cannot be performed without them) and others are\n",
+    "\n",
+    "##### Random Seeds: about the computational reproducibility of hazard scenarios\n",
+    "\n",
+    "Ensuring reproducibility of hazard scenarios is paramount for the IN-CORE team due to its scientific and decision-making\n",
+    "consequences. Hence, care has been exercised to ensure that scenarios can be re-executed consistently in a standardized\n",
+    "and simple manner.\n",
+    "\n",
+    "Several models (such as this one) require generating pseudo-random numbers to simulate unexpected natural or human\n",
+    "effects, computational reproducibility of experiments can become somewhat challenging. IN-CORE harnesses the *random\n",
+    "seed*, a special number that determines the exact sequence random number generators (RNGs) produce to ensure\n",
+    "repeatability of that sequence independent of where the code is executed. The ```randomSeed``` parameter in IN-CORE\n",
+    "takes care of this aspect via an integer value that fixes the random number generating process regardless of when the\n",
+    "new analysis is requested or in which infrastructure it is executed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81f45c11",
+   "metadata": {},
+   "source": [
+    "##### Defining a new (slightly different) tornado\n",
+    "\n",
+    "With this information in hand, let's construct a slightly different version of the same tornado. Let us imagine a less\n",
+    "intense scenario with a longer trajectory due to conditions that preserve its energy for a longer time. While path\n",
+    "length have been reported to increase as their EF rating increases, the case we describe here is plausible (See\n",
+    "[On the Relationship of Tornado Path Length and Width to Intensity](https://journals.ametsoc.org/view/journals/wefo/19/2/1520-0434_2004_019_0310_otrotp_2_0_co_2.xml)).\n",
+    "\n",
+    "To do so, we will vary:\n",
+    "* the rating in the Enhanced Fujita (EF) rating from ```\"EF5\"``` to ```\"EF4\"```,\n",
+    "* the start and end points to have a longer trajectory,\n",
+    "* and the  random number seed to ensure the RNG starts at values different from those in the original scenario.\n",
+    "\n",
+    "The result of this process is\n",
+    "\n",
+    "```json\n",
+    "{\n",
+    "  \"name\": \"Centerville Model Tornado (modified)\",\n",
+    "  \"description\": \"Centerville mean width tornado hazard\",\n",
+    "  \"tornadoType\": \"model\",\n",
+    "  \"tornadoModel\" : \"MeanWidthTornado\",\n",
+    "      \"tornadoParameters\" : {\n",
+    "      \"efRating\" : \"EF4\",\n",
+    "      \"startLatitude\" : \"35.215\",\n",
+    "      \"startLongitude\" : \"-97.524\",\n",
+    "      \"randomSeed\" : \"3457\",\n",
+    "      \"endLatitude\" : [35.253],\n",
+    "      \"endLongitude\" : [-97.432],\n",
+    "      \"windSpeedMethod\" : \"1\",\n",
+    "      \"numSimulations\" : \"1\"\n",
+    "    }\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "We now proceed to create the model-based tornado with what we have learned so far. We take care to serialize the Python\n",
+    "dictionary structure into a JSON specification using ```json.dumps```:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e62595f6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-03T18:03:37.377534Z",
+     "start_time": "2023-11-03T18:03:37.371726Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a variable to store the new tornado hazard definition:\n",
+    "\n",
+    "tornado_model_metadata = {\n",
+    "  \"name\": \"Centerville Model Tornado (modified)\",\n",
+    "  \"description\": \"Centerville mean width tornado hazard\",\n",
+    "  \"tornadoType\": \"model\",\n",
+    "  \"tornadoModel\" : \"MeanWidthTornado\",\n",
+    "      \"tornadoParameters\" : {\n",
+    "      \"efRating\" : \"EF4\",\n",
+    "      \"startLatitude\" : \"35.215\",\n",
+    "      \"startLongitude\" : \"-97.524\",\n",
+    "      \"randomSeed\" : \"3457\",\n",
+    "      \"endLatitude\" : [35.253],\n",
+    "      \"endLongitude\" : [-97.432],\n",
+    "      \"windSpeedMethod\" : \"1\",\n",
+    "      \"numSimulations\" : \"1\"\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "tornado_model_json = json.dumps(tornado_model_metadata, indent=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbdad819",
+   "metadata": {},
+   "source": [
+    "Now, we create a tornado scenario, and retrieve the *model response* obtained from requesting IN-CORE to process it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c875fd95",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-03T18:03:40.490527Z",
+     "start_time": "2023-11-03T18:03:39.546252Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a tornado with the prior definition\n",
+    "model_response = hazardsvc.create_tornado_scenario(tornado_model_json)\n",
+    "print(json.dumps(model_response, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6b6d59f",
+   "metadata": {},
+   "source": [
+    "As with any analysis, we obtain the identifier of the specific response:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "399aa14f",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Acquire hazard id you created\n",
+    "tornado_model_id = model_response['id']\n",
+    "viz.plot_tornado(tornado_model_id, client, basemap=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "847120cc-e681-45df-ae72-04e6c39bdb6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dispaly the path on the interactive map\n",
+    "\n",
+    "wsviz.show_tornado(client, model_response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2d07852",
+   "metadata": {},
+   "source": [
+    "As with the first case, we obtain the hazard values for this tornado:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d1b4792",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Getting hazard value from your model based tornado\n",
+    "points = [\n",
+    "    {\n",
+    "        \"demands\": [\"wind\"],\n",
+    "        \"units\": [\"mph\"],\n",
+    "        \"loc\": \"35.215, -97.521\"\n",
+    "    },\n",
+    "    {\n",
+    "        \"demands\": [\"wind\"],\n",
+    "        \"units\": [\"mph\"],\n",
+    "        \"loc\": \"35.215, -97.519\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "tornado_model_vals = hazardsvc.post_tornado_hazard_values(tornado_model_id, points)\n",
+    "print(json.dumps(tornado_model_vals, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a6ad9ea",
+   "metadata": {},
+   "source": [
+    "#### 2.1.3 Creating a dataset-driven tornado\n",
+    "\n",
+    "If you have a deterministic tornado dataset available outside IN-CORE, you can use **pyincore** to create a tornado dataset on the serivce. In this section, we present an example of how to do this.\n",
+    "\n",
+    "The first step is to construct a hazard dataset description using JSON. In this example, we are creating a tornado dataset in Joplin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aadc8180",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tornado_dataset_data = {\n",
+    "  \"name\": \"Joplin Dataset Tornado - workshop\",\n",
+    "  \"description\": \"Joplin tornado hazard with shapefile\",\n",
+    "  \"tornadoType\": \"dataset\"\n",
+    "}\n",
+    "\n",
+    "tornado_dataset_json = json.dumps(tornado_dataset_data, indent=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6caee454",
+   "metadata": {},
+   "source": [
+    "We also specify where the dataset files can be located (in our case, they have been provided to you in the local\n",
+    "directory):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0b15df1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T23:15:11.804251Z",
+     "start_time": "2023-11-08T23:15:11.792344Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# zip file containing tornado path\n",
+    "file_paths = [\"data/hazard/tornado/joplin_path_wgs84.shp\",\n",
+    "              \"data/hazard/tornado/joplin_path_wgs84.dbf\",\n",
+    "              \"data/hazard/tornado/joplin_path_wgs84.prj\",\n",
+    "              \"data/hazard/tornado/joplin_path_wgs84.shx\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bdec1a5",
+   "metadata": {},
+   "source": [
+    "Using both the files and the JSON, we create a *scenario* tornado:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36e87805",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a tornado with definition and file\n",
+    "dataset_response = hazardsvc.create_tornado_scenario(tornado_dataset_json, file_paths)\n",
+    "print(json.dumps(dataset_response, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9798c6f3",
+   "metadata": {},
+   "source": [
+    "Similarly, we obtain the identifier of the output to the request (to ```HazardService```):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bee8a45d",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Display the path on the interactive map\n",
+    "\n",
+    "wsviz.show_tornado(client, dataset_response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f08693b",
+   "metadata": {},
+   "source": [
+    "Finally, we obtain the hazard values as usual."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d90910a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Getting hazard value from your dataset-based tornado\n",
+    "points = [\n",
+    "    {\n",
+    "        \"demands\": [\"wind\"],\n",
+    "        \"units\": [\"mph\"],\n",
+    "        \"loc\": \"37.066, -94.502\"\n",
+    "    },\n",
+    "    {\n",
+    "        \"demands\": [\"wind\"],\n",
+    "        \"units\": [\"mph\"],\n",
+    "        \"loc\": \"37.032, -94.348\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "tornado_dataset_id = dataset_response['id']\n",
+    "tornado_dataset_vals = hazardsvc.post_tornado_hazard_values(tornado_dataset_id, points)\n",
+    "print(json.dumps(tornado_dataset_vals, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2692b7a",
+   "metadata": {},
+   "source": [
+    "### 2.2. Earthquake hazards\n",
+    "\n",
+    "Similar to Tornado in the last section, we will now replicate these steps for a different type of hazard: earthquakes. We will look at both model-based and database driven hazard scenarios (deterministic and probabilistic)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c22b07b",
+   "metadata": {},
+   "source": [
+    "#### 2.2.1 Working with existing earthquake hazards\n",
+    "\n",
+    "For this case, we will obtain metadata and hazard values for a 7.9 earthquake in Memphis, TN. Since we already have started\n",
+    "the ```IncoreClient``` and ```HazardService objects```, we will not replicate the code here. Similar to Tornado, we can locate the hazard we want to work with by using the Hazard Viewer here: https://incore.ncsa.illinois.edu/HazardViewer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b300fa6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T21:57:07.606222Z",
+     "start_time": "2023-11-08T21:57:07.471058Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Example earthquake in Memphis, TN\n",
+    "example_earthquake_id = '5b902cb273c3371e1236b36b'\n",
+    "\n",
+    "# Obtain and display metadata\n",
+    "earthquake_model_metadata = hazardsvc.get_earthquake_hazard_metadata(example_earthquake_id)\n",
+    "print(json.dumps(earthquake_model_metadata, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b53e9c7-dba4-4159-8ce2-db5107089e4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "viz.plot_earthquake(example_earthquake_id, client)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4818bfea-96ef-42e1-8551-4ba7e2fdacdb",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Display histogram of EQ\n",
+    "wsviz.show_eq_hist(client, earthquake_model_metadata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46ea76b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get hazard values\n",
+    "points = [\n",
+    "    {\n",
+    "        \"demands\": [\"0.2 SA\"],\n",
+    "        \"units\": [\"g\"],\n",
+    "        \"loc\": \"35.07899, -90.0178\"\n",
+    "    },\n",
+    "    {\n",
+    "        \"demands\": [\"0.2 SA\", \"PGA\", \"0.8 SA\"],\n",
+    "        \"units\": [\"g\", \"g\", \"g\"],\n",
+    "        \"loc\": \"35.027, -90.077\"\n",
+    "    }\n",
+    "]\n",
+    "earthquake_model_vals = hazardsvc.post_earthquake_hazard_values(example_earthquake_id, points)\n",
+    "print(json.dumps(earthquake_model_vals, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a763e3bd",
+   "metadata": {},
+   "source": [
+    "#### 2.2.2 Creating model-based earthquakes\n",
+    "\n",
+    "Similar to the case of tornadoes, it is possible to create new model-based earthquakes through JSON parameters.\n",
+    "\n",
+    "\n",
+    "* *attenuations* - attenuation models capture how energy is lost as seismic waves propagate. At present, IN-CORE supports\n",
+    "the following ones:\n",
+    "  * AbrahamsonSilvaKamai2014\n",
+    "  * AtkinsonBoore1995\n",
+    "  * CampbellBozorgnia2014\n",
+    "  * ChiouYoungs2014\n",
+    "  * SadighChangEganMakdisiYoung1997\n",
+    "* *earthquake position (lat, lon), depth and magnitude* - a description of the location and geophysical properties of\n",
+    "the seismic event\n",
+    "* *demand type and demand units* - a description of the quantity of interest and its units.\n",
+    "\n",
+    "Following known steps for model-based tornados, we request computation of a new Memphis earthquake. In this case,\n",
+    "we decrease the depth to 7.0 km and decrease the intensity to 6.5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "deaa580e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the new earthquake\n",
+    "eq_model_metadata = {\n",
+    "  \"name\": \"Memphis EQ Model (modified)\",\n",
+    "  \"description\": \"Memphis model based hazard\",\n",
+    "  \"eqType\": \"model\",\n",
+    "  \"attenuations\" : {\n",
+    "    \"AtkinsonBoore1995\" : \"1.0\"\n",
+    "  },\n",
+    "  \"eqParameters\" : {\n",
+    "    \"srcLatitude\" : \"35.927\",\n",
+    "    \"srcLongitude\" : \"-89.919\",\n",
+    "    \"magnitude\" : \"6.5\",\n",
+    "    \"depth\" : \"7.0\"\n",
+    "  },\n",
+    "  \"visualizationParameters\" : {\n",
+    "    \"demandType\" : \"PGA\",\n",
+    "    \"demandUnits\" : \"g\",\n",
+    "    \"minX\" :\"-90.3099\",\n",
+    "    \"minY\" :\"34.9942\",\n",
+    "    \"maxX\" : \"-89.6231\",\n",
+    "    \"maxY\" : \"35.4129\",\n",
+    "    \"numPoints\" : \"1025\",\n",
+    "    \"amplifyHazard\": \"true\"\n",
+    "  }\n",
+    "}\n",
+    "\n",
+    "eq_model_json = json.dumps(eq_model_metadata, indent=4)\n",
+    "\n",
+    "# Create an earthquake with definition\n",
+    "eq_model_response = hazardsvc.create_earthquake(eq_model_json)\n",
+    "\n",
+    "print(json.dumps(eq_model_response, indent=4, sort_keys=False))\n",
+    "\n",
+    "# Acquire hazard id you created\n",
+    "eq_model_id = eq_model_response['id']\n",
+    "\n",
+    "# Plot the earthquake\n",
+    "viz.plot_earthquake(eq_model_id, client)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df1ccc7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Getting hazard value from your model based earthquake\n",
+    "\n",
+    "points = [\n",
+    "    {\n",
+    "        \"demands\": [\"0.2 SA\"],\n",
+    "        \"units\": [\"g\"],\n",
+    "        \"loc\": \"35.07899, -90.0178\"\n",
+    "    },\n",
+    "    {\n",
+    "        \"demands\": [\"0.2 SA\"],\n",
+    "        \"units\": [\"g\"],\n",
+    "        \"loc\": \"35.027, -90.077\"\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "eq_model_vals = hazardsvc.post_earthquake_hazard_values(eq_model_id, points)\n",
+    "print(json.dumps(eq_model_vals, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c65e04e9",
+   "metadata": {},
+   "source": [
+    "#### 2.2.3 Creating a dataset-driven earthquake (Probabilistic & Deterministic)\n",
+    "\n",
+    "Finally, we show how datasets can be used to create earthquake scenarios. In this case, both deterministic and probabilistic\n",
+    "alternatives are available. We concentrate here on a deterministic example.\n",
+    "\n",
+    "For the example below, the earthquake datasets are provided as TIFF files.\n",
+    "\n",
+    "As with the corresponding tornado example, specify the dataset(s) to be used:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88b4b09e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eq_dataset_data = {\n",
+    "  \"name\":\"Memphis Deterministic EQ\",\n",
+    "  \"description\":\"Memphis dataset based deterministic hazard\",\n",
+    "  \"eqType\":\"dataset\",\n",
+    "  \"hazardDatasets\":[\n",
+    "    {\n",
+    "      \"hazardType\":\"deterministic\",\n",
+    "      \"demandType\":\"SA\",\n",
+    "      \"demandUnits\":\"g\",\n",
+    "      \"period\":\"0.2\",\n",
+    "      \"eqParameters\":{\n",
+    "        \"srcLatitude\":\"35.927\",\n",
+    "        \"srcLongitude\":\"-89.919\",\n",
+    "        \"magnitude\":\"7.9\",\n",
+    "        \"depth\":\"10.0\"\n",
+    "      }\n",
+    "    },\n",
+    "    {\n",
+    "      \"hazardType\":\"deterministic\",\n",
+    "      \"demandType\":\"PGA\",\n",
+    "      \"demandUnits\":\"g\",\n",
+    "      \"period\":\"0.0\",\n",
+    "      \"eqParameters\":{\n",
+    "        \"srcLatitude\":\"35.927\",\n",
+    "        \"srcLongitude\":\"-89.919\",\n",
+    "        \"magnitude\":\"7.9\",\n",
+    "        \"depth\":\"10.0\"\n",
+    "      }\n",
+    "    }\n",
+    "  ]\n",
+    "}\n",
+    "\n",
+    "\n",
+    "eq_dataset_json = json.dumps(eq_dataset_data, indent=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85c423d2",
+   "metadata": {},
+   "source": [
+    "Similarly, specify where dataset files live:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "251b0291",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T23:15:30.257245Z",
+     "start_time": "2023-11-08T23:15:30.241403Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "file_paths = [\"data/hazard/earthquake/eq-dataset-SA.tif\", \"data/hazard/earthquake/eq-dataset-PGA.tif\"] \n",
+    "# order should be same as the hazardDatasets from above json\n",
+    "# eq-dataset-SA.tif represents 0.2 SA & eq-dataset-PGA.tif represents PGA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f908467",
+   "metadata": {},
+   "source": [
+    "And finally, we create the earthquake and obtain hazard values from it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c31bbb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an dataset-based earthquake with definition and files\n",
+    "eq_dataset_response = hazardsvc.create_earthquake(eq_dataset_json, file_paths)\n",
+    "print(json.dumps(eq_dataset_response, indent=4, sort_keys=False))\n",
+    "\n",
+    "# Acquire hazard id from recently created hazard\n",
+    "eq_dataset_id = eq_dataset_response['id']\n",
+    "viz.plot_earthquake(eq_dataset_id, client, \"0.2 SA\")\n",
+    "viz.plot_earthquake(eq_dataset_id, client, \"PGA\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c622013c",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Obtain hazard value from your dataset-based earthquakes\n",
+    "points = [\n",
+    "    {\n",
+    "        \"demands\": [\"0.2 SA\"],\n",
+    "        \"units\": [\"g\"],\n",
+    "        \"loc\": \"35.07899, -90.0178\"\n",
+    "    },\n",
+    "    {\n",
+    "        \"demands\": [\"PGA\"],\n",
+    "        \"units\": [\"g\"],\n",
+    "        \"loc\": \"35.027, -90.077\"\n",
+    "    },\n",
+    "]\n",
+    "eq_dataset_vals = hazardsvc.post_earthquake_hazard_values(eq_dataset_id, points)\n",
+    "print(json.dumps(eq_dataset_vals, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "089ac2c0",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "## 3. Working with Local Hazards\n",
+    "This section shows how to work with local hazards including tornadoes, earthquakes, tsunamis, floods, and hurricanes. It also has some example visualizations for tornadoes and earthquakes. \n",
+    "\n",
+    "**pyincore** has several local hazard modules that enable users to perform analyses, tests, and visualization using data on their local machine without relying on the **HazardService**. The available modules are:  Hurricane, Flood, Earthquake, Tornado, and Tsunamis. These modules allow a user to read local data into a hazard object and pass that object to **pyincore** analyses to obtain hazard information from the local data instead of the **HazardService**. We'll see more on this later in session 4.\n",
+    "\n",
+    "![local_remote_hazard.png](data/local_remote_hazard.png)\n",
+    "\n",
+    "To get started, we need to import the local hazard modules we'll be working with as shown below.\n",
+    "\n",
+    "**Note** - local hazards currently support dataset (file) based only"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "473d7b72",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T22:39:29.561948Z",
+     "start_time": "2023-11-08T22:39:29.553826Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# import local hazard modules\n",
+    "from pyincore import Hurricane, Flood, Earthquake, Tornado, Tsunami"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9fe045c-0ec0-440e-b63f-61146524645f",
+   "metadata": {},
+   "source": [
+    "### 3.1 Basic Operations for Local Hazards\n",
+    "Let's start with some basic operations for working with local hazards. Using the local hazard modules is straightforward. You can:\n",
+    "1) Create a hazard by providing a JSON file that describes the hazard (metadata).\n",
+    "2) Attach a dataset (file) to the hazard.\n",
+    "3) Get hazard values from hazard object.\n",
+    "\n",
+    "For datasets, tornadoes use a Shapefile, while other hazards require a TIF (Tagged Image File) format. Now, let's see how each type of hazard works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4cc11c1",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "#### 3.1.1 Tornadoes\n",
+    "**Inputs**: JSON representation of a dataset describing a tornado. Each available dataset in Shapefile format. For more information on the dataset type definition for a tornado, go here: https://incore.ncsa.illinois.edu/semantics/api/types/incore:tornadoWindfield"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37d904f5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T22:39:35.819562Z",
+     "start_time": "2023-11-08T22:39:35.817512Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# indicate the path to the tornado dataset files\n",
+    "dir = \"data/hazard/tornado/\"\n",
+    "\n",
+    "with open(os.path.join(dir, \"tornado_dataset.json\"), 'r') as file:\n",
+    "    tornado_dataset_json = file.read()\n",
+    "    print(json.dumps(json.loads(tornado_dataset_json), indent=4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08ba9ef2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T22:42:49.641932Z",
+     "start_time": "2023-11-08T22:42:49.597799Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# create the tornado object\n",
+    "tornado = Tornado.from_json_file(os.path.join(dir, \"tornado_dataset.json\"))\n",
+    "\n",
+    "# attach dataset from local file\n",
+    "tornado.hazardDatasets[0].from_file((os.path.join(dir, \"joplin_path_wgs84.shp\")),\n",
+    "                                    data_type=\"incore:tornadoWindfield\")\n",
+    "\n",
+    "payload = [\n",
+    "    {\n",
+    "        \"demands\": [\"wind\"],\n",
+    "        \"units\": [\"mph\"],\n",
+    "        \"loc\": \"-94.37, 37.04\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# get values\n",
+    "values = tornado.read_hazard_values(payload, seed=1234) # removing the seed will give random values\n",
+    "print(json.dumps(values, indent=4, sort_keys=False))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "367823db",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "#### 3.1.2 Earthquakes\n",
+    "**Inputs**: JSON representation of a dataset describing an earthquake. Each available dataset in TIF format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4170033e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T22:43:11.072727Z",
+     "start_time": "2023-11-08T22:43:11.063054Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# indicate the path\n",
+    "dir = 'data/hazard/earthquake/'\n",
+    "\n",
+    "earthquake_dataset_json = ''\n",
+    "with open(os.path.join(dir, \"eq-dataset.json\"), 'r') as file:\n",
+    "    earthquake_dataset_json = file.read()\n",
+    "    print(json.dumps(json.loads(earthquake_dataset_json), indent=4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "818054e3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T22:44:03.950701Z",
+     "start_time": "2023-11-08T22:44:03.913578Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# create the earthquake object\n",
+    "eq = Earthquake.from_json_file(os.path.join(dir, \"eq-dataset.json\"))\n",
+    "\n",
+    "# attach datasets from local files\n",
+    "eq.hazardDatasets[0].from_file((os.path.join(dir, \"eq-dataset-SA.tif\")),\n",
+    "                               data_type=\"ergo:probabilisticEarthquakeRaster\")\n",
+    "eq.hazardDatasets[1].from_file((os.path.join(dir, \"eq-dataset-PGA.tif\")),\n",
+    "                               data_type=\"ergo:probabilisticEarthquakeRaster\")\n",
+    "\n",
+    "payload = [\n",
+    "    {\n",
+    "        \"demands\": [\"PGA\", \"0.2 SA\"],\n",
+    "        \"units\": [\"g\", \"g\"],\n",
+    "        \"loc\": \"35.03,-89.93\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# get values\n",
+    "values = eq.read_hazard_values(payload)\n",
+    "print(json.dumps(values, indent=4, sort_keys=False))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bba7043",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "#### 3.1.3 Tsunamis\n",
+    "**Inputs**: JSON representation of a dataset describing a tsunami. Each available dataset in TIF format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf2367a4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T22:44:15.816620Z",
+     "start_time": "2023-11-08T22:44:15.807179Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# indicate the path\n",
+    "dir = 'data/hazard/tsunami/'\n",
+    "\n",
+    "with open(os.path.join(dir, \"tsunami.json\"), 'r') as file:\n",
+    "    tsunami_dataset_json = file.read()\n",
+    "    print(json.dumps(json.loads(tsunami_dataset_json), indent=4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29d3617a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T22:44:17.746084Z",
+     "start_time": "2023-11-08T22:44:17.729434Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# create the tsunami object\n",
+    "tsunami = Tsunami.from_json_file(os.path.join(dir, \"tsunami.json\"))\n",
+    "\n",
+    "# attach datasets from local files\n",
+    "tsunami.hazardDatasets[0].from_file((os.path.join(dir, \"Tsu_100yr_Vmax.tif\")),\n",
+    "                                    data_type=\"ncsa:probabilisticTsunamiRaster\")\n",
+    "tsunami.hazardDatasets[1].from_file((os.path.join(dir, \"Tsu_100yr_Mmax.tif\")),\n",
+    "                                    data_type=\"ncsa:probabilisticTsunamiRaster\")\n",
+    "tsunami.hazardDatasets[2].from_file((os.path.join(dir, \"Tsu_100yr_Hmax.tif\")),\n",
+    "                                    data_type=\"ncsa:probabilisticTsunamiRaster\")\n",
+    "\n",
+    "payload = [\n",
+    "    {\n",
+    "        \"demands\": [\"hmax\"],\n",
+    "        \"units\": [\"m\"],\n",
+    "        \"loc\": \"46.006,-123.935\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# get values\n",
+    "values = tsunami.read_hazard_values(payload)\n",
+    "print(json.dumps(values, indent=4, sort_keys=False))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70ba31d2",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "#### 3.1.4 Floods\n",
+    "**Inputs**: JSON representation of a dataset describing a flood. Each available dataset in TIF format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f62e11fc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T22:44:27.874635Z",
+     "start_time": "2023-11-08T22:44:27.865749Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# indicate the path\n",
+    "dir = 'data/hazard/flood/'\n",
+    "\n",
+    "with open(os.path.join(dir, \"flood-dataset.json\"), 'r') as file:\n",
+    "    flood_dataset_json = file.read()\n",
+    "    print(json.dumps(json.loads(flood_dataset_json), indent=4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffd8c327",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T22:44:30.806270Z",
+     "start_time": "2023-11-08T22:44:30.789261Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# create the flood object\n",
+    "flood = Flood.from_json_file(os.path.join(dir, \"flood-dataset.json\"))\n",
+    "\n",
+    "# attach datasets from local files\n",
+    "flood.hazardDatasets[0].from_file((os.path.join(dir, \"flood-inundationDepth-50ft.tif\")),\n",
+    "                                  data_type=\"ncsa:probabilisticFloodRaster\")\n",
+    "flood.hazardDatasets[1].from_file(os.path.join(dir, \"flood-WSE-50ft.tif\"),\n",
+    "                                  data_type=\"ncsa:probabilisticFloodRaster\")\n",
+    "\n",
+    "payload = [\n",
+    "    {\n",
+    "         \"demands\": [\"waterSurfaceElevation\"],\n",
+    "         \"units\": [\"m\"],\n",
+    "         \"loc\": \"34.60,-79.16\"\n",
+    "     }\n",
+    " ]\n",
+    "\n",
+    "# get values\n",
+    "values = flood.read_hazard_values(payload)\n",
+    "print(json.dumps(values, indent=4, sort_keys=False))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b53656f2",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "#### 3.1.5 Hurricanes\n",
+    "**Inputs**: JSON representation of a dataset describing a hurricane. Each available dataset in TIF format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77a4fa22",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T22:44:40.489756Z",
+     "start_time": "2023-11-08T22:44:40.480739Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# indicate the path\n",
+    "dir = 'data/hazard/hurricane/'\n",
+    "\n",
+    "with open(os.path.join(dir, \"hurricane-dataset.json\"), 'r') as file:\n",
+    "    hurricane_dataset_json = file.read()\n",
+    "    print(json.dumps(json.loads(hurricane_dataset_json), indent=4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d598f16e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-08T22:44:41.942725Z",
+     "start_time": "2023-11-08T22:44:41.924117Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# create the hurricane object\n",
+    "hurricane = Hurricane.from_json_file((os.path.join(dir, \"hurricane-dataset.json\")))\n",
+    "\n",
+    "# attach datasets from local files\n",
+    "hurricane.hazardDatasets[0].from_file((os.path.join(dir, \"Wave_Raster.tif\")),\n",
+    "                                      data_type=\"ncsa:deterministicHurricaneRaster\")\n",
+    "hurricane.hazardDatasets[1].from_file(os.path.join(dir, \"Surge_Raster.tif\"),\n",
+    "                                      data_type=\"ncsa:deterministicHurricaneRaster\")\n",
+    "hurricane.hazardDatasets[2].from_file(os.path.join(dir, \"Inundation_Raster.tif\"),\n",
+    "                                      data_type=\"ncsa:deterministicHurricaneRaster\")\n",
+    "\n",
+    "payload = [\n",
+    "        {\n",
+    "            \"demands\": [\"waveHeight\", \"surgeLevel\"],\n",
+    "            \"units\": [\"m\", \"m\"],\n",
+    "            \"loc\": \"29.22,-95.06\"\n",
+    "        },\n",
+    "        {\n",
+    "            \"demands\": [\"waveHeight\", \"surgeLevel\"],\n",
+    "            \"units\": [\"cm\", \"cm\"],\n",
+    "            \"loc\": \"29.23,-95.05\"\n",
+    "        },\n",
+    "        {\n",
+    "            \"demands\": [\"waveHeight\", \"inundationDuration\"],\n",
+    "            \"units\": [\"in\", \"hr\"],\n",
+    "            \"loc\": \"29.22,-95.06\"\n",
+    "        }\n",
+    "    ]\n",
+    "\n",
+    "# get values\n",
+    "values = hurricane.read_hazard_values(payload)\n",
+    "print(json.dumps(values, indent=4, sort_keys=False))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc7aa7b4-3e49-4997-955b-98aa8e717132",
+   "metadata": {},
+   "source": [
+    "### 3.2 Visualizations for Local Hazards\n",
+    "In this section, we provide examples to show how to plot local tornadoes and earthquakes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d14c8d9-d4f4-447d-a4b0-cbc00c545616",
+   "metadata": {},
+   "source": [
+    "#### 3.2.1 Plotting a tornado\n",
+    "In this example, we are using the identical tornado dataset discussed in Section 3.1.1. You will need to create a tornado object and attach the local shape file to the object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e25a10b5-2985-4ce4-b20e-5078d82f693b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# indicate the path\n",
+    "dir = \"data/hazard/tornado/\"\n",
+    "\n",
+    "# create the tornado object\n",
+    "tornado = Tornado.from_json_file(os.path.join(dir, \"tornado_dataset.json\"))\n",
+    "\n",
+    "# attach dataset from local file\n",
+    "tornado.hazardDatasets[0].from_file((os.path.join(dir, \"joplin_path_wgs84.shp\")),\n",
+    "                                    data_type=\"incore:tornadoWindfield\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c6d53b3-a0c2-4f4b-bc5e-3a5145e923fc",
+   "metadata": {},
+   "source": [
+    "Then, we can plot the local shape file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99558632-f055-49f0-87fe-1ea766d09015",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf = tornado.hazardDatasets[0].dataset.get_dataframe_from_shapefile()\n",
+    "id_field = tornado.EF_RATING_FIELD\n",
+    "\n",
+    "map = viz.plot_gdf_map(gdf, id_field)\n",
+    "map    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db502f38-db3b-4dee-a5c4-3fc58e16b70c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use GeoDataframe.explore() to interact with the map\n",
+    "gdf.explore(\n",
+    "    column=\"ef_rating\", \n",
+    "    tooltip=\"ef_rating\",  \n",
+    "    popup=True,  \n",
+    "    cmap=\"Set1\"  # use \"Set1\" matplotlib colormap)    \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2f09ac8-b4c3-4742-b2be-b6bd1cfcc199",
+   "metadata": {},
+   "source": [
+    "#### 3.2.2 Plotting an Earthqauke \n",
+    "In this example, we are using the identical earthquake dataset discussed in Section 3.1.2. Similary, you will need to create an earthquake object and attach the local tif files to the object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44f8c501-1755-4166-b750-ec87af77f90e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# indicate the path\n",
+    "dir = 'data/hazard/earthquake/'\n",
+    "\n",
+    "# create the earthquake object\n",
+    "eq = Earthquake.from_json_file(os.path.join(dir, \"eq-dataset.json\"))\n",
+    "\n",
+    "# attach datasets from local files\n",
+    "eq.hazardDatasets[0].from_file((os.path.join(dir, \"eq-dataset-SA.tif\")),\n",
+    "                               data_type=\"ergo:probabilisticEarthquakeRaster\")\n",
+    "eq.hazardDatasets[1].from_file((os.path.join(dir, \"eq-dataset-PGA.tif\")),\n",
+    "                               data_type=\"ergo:probabilisticEarthquakeRaster\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "342f317a-1e78-4286-935e-be150d6b8758",
+   "metadata": {},
+   "source": [
+    "Then, we can plot the local tif files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f30449d8-9e32-4134-8423-c3884e3436ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# display the earthquake\n",
+    "for earthquake in eq.hazardDatasets:\n",
+    "    demand_type = earthquake.demand_type\n",
+    "    period = earthquake.period\n",
+    "    title = \"Demand Type: \" + demand_type.upper() + \", Period: \" + str(period)\n",
+    "    raster_file_path = earthquake.dataset.local_file_path\n",
+    "\n",
+    "    viz.plot_raster_file_with_legend(raster_file_path, title)   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e936fcb1",
+   "metadata": {},
+   "source": [
+    "## Session review\n",
+    "\n",
+    "With the experience gained in this session, you should now be able to:\n",
+    "\n",
+    "* Initialize ```IncoreClient``` and ```HazardService``` objects to authenticate to and access IN-CORE\n",
+    "resources\n",
+    "* Understand how to use **pyincore** modules to create and use existing hazards in the IN-CORE web service as well as obtain hazard values\n",
+    "* Use **pyincore** local hazard modules to work local hazard data\n",
+    "* Visualize local and remote hazards using **pyincore-viz** and **GeoPandas**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbd1740e-eec8-4944-8ac2-680b24a5fe91",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "## Hands-on Assignment\n",
+    "\n",
+    "For the next part of this session, please open \"session2-assignment.ipynb\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4006d1c-91fc-4d2e-9b78-43063c19b8ad",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/manual_jb/content/workshops/20231115/session2/wsviz.py b/manual_jb/content/workshops/20231115/session2/wsviz.py
new file mode 100755
index 00000000..5aeca24c
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session2/wsviz.py
@@ -0,0 +1,35 @@
+from pyincore import Dataset, DataService, HazardService
+import geopandas as gpd
+import folium
+from folium.plugins import MousePosition
+from pathlib import Path
+import rasterio
+from rasterio.plot import show_hist
+
+def show_tornado(client, tornado_model_metadata):
+    tornado_dataset_id = tornado_model_metadata['datasetId']
+    tornado_dataset = Dataset.from_data_service(tornado_dataset_id, DataService(client))
+    tornado_gdf = gpd.read_file(tornado_dataset.local_file_path)
+    map = tornado_gdf.explore(
+        column="ef_rating", 
+        tooltip="ef_rating",  
+        popup=True,  
+        cmap="Set1"  # use "Set1" matplotlib colormap)
+    )
+    
+    MousePosition().add_to(map)
+    return map
+
+def show_tornado_by_id(client, id):
+    hazardsvc = HazardService(client)
+    metadata = hazardsvc.get_tornado_hazard_metadata(id)
+    return show_tornado(client, metadata)
+
+def show_eq_hist(client, eq_metadata):
+    eq_dataset_id=eq_metadata['rasterDataset']['datasetId']
+    eq_dataset = Dataset.from_data_service(eq_dataset_id, DataService(client))
+    raster_file_path = Path(eq_dataset.local_file_path).joinpath(eq_dataset.metadata['fileDescriptors'][0]['filename'])
+    src = rasterio.open(raster_file_path)
+    show_hist(
+        src, bins=50, lw=0.0, stacked=False, alpha=0.3,
+        histtype='stepfilled', title="Histogram")
\ No newline at end of file
diff --git a/manual_jb/content/workshops/20231115/session3/session3-bldg-dmg-recv.ipynb b/manual_jb/content/workshops/20231115/session3/session3-bldg-dmg-recv.ipynb
new file mode 100644
index 00000000..1e026228
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session3/session3-bldg-dmg-recv.ipynb
@@ -0,0 +1,1127 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 3: Building Damage and Recovery Analyses with IN-CORE Web Services\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Objective:**\n",
+    "- Learn how to execute the analysis\n",
+    "- Learn how to do building damamage analysis, Monte Carlo Simulation, and building recovery analysis\n",
+    "- Learn how to explore outputs of analysis\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Agenda**\n",
+    "- [1. Overview](#1.-Overview)\n",
+    "- [2. Explore Hazard, Data, and Fragility](#2.-Explore-Hazard,-Data,-and-Fragility)\n",
+    "- [3. Building Damage Analysis](#3.-Building-Damage-Analysis)\n",
+    "- [4. Monte Carlo Simulation (MCS)](#4.-Monte-Carlo-Simulation-(MCS))\n",
+    "- [5. Commercial Building Recovery Analysis](#5.-Commercial-Building-Recovery-Analysis)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Tornadoes occur at a high frequency in the United States compared with other natural hazards such as earthquakes and tsunamis but have a substantially smaller footprint. Even a single high-intensity tornado can result in high casualty rates and catastrophic economic losses and social consequences, particularly for small to medium communities.\n",
+    "\n",
+    "- The city of Joplin, Missouri, USA, was hit by an EF-5 tornado on May 22, 2011. The National Institute of Standards and Technology (NIST) conducted a technical investigation of this devastating event which can be found at: https://nvlpubs.nist.gov/nistpubs/NCSTAR/NIST.NCSTAR.3.pdf . The Center for Risk-Based Community Resilience Planning simulated this event for buildings and the electrical power network of Joplin in IN-CORE. This Juypter Notebook provides an example of how to use IN-CORE.\n",
+    " \n",
+    "- The initial damage estimation utilized the tornado path, and tornado fragility curves representative of a 19- archetype building dataset. Generic tornado paths are also available in IN-CORE, or a user defined tornado path is possible.\n",
+    "   \n",
+    "- This session demonstrates how users interact with the IN-CORE computational environment \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Explore Hazard, Data, and Fragility\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This section introduces the input for the infrastructure damage analysis including \n",
+    "- tornado path,\n",
+    "- building dataset, and\n",
+    "- building fragility curves for tornado."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1. Tornado Path\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A tornado path we will use is the 2011 Joplin tornado path. The tornado path represents the wind speed within the vortex (multi-vortex in the case of Joplin) that was estimated to have EF5 wind speeds of more than 200 mph, reducing to EF4 wind speeds as the areas move outward from the vortex, and eventually reaching EF1 zone. (Attary et al. 2018)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import modules\n",
+    "from pyincore import IncoreClient, HazardService\n",
+    "from pyincore import FragilityService, MappingSet, Dataset, DataService\n",
+    "from pyincore_viz.plotutil import PlotUtil as frag_plot\n",
+    "from pyincore_viz.geoutil import GeoUtil as viz\n",
+    "from pyincore.models.fragilitycurveset import FragilityCurveSet\n",
+    "import wsviz\n",
+    "\n",
+    "# Connect to IN-CORE serivce by creating IncoreClient\n",
+    "client = IncoreClient()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The 2011 Joplin Tornado is stored at IN-CORE Hazard service\n",
+    "\n",
+    "tornado_id = \"5d07cbeab9219c065b080930\"\n",
+    "viz.plot_tornado(tornado_id, client, basemap=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show the tornado path on interactive map\n",
+    "\n",
+    "wsviz.show_tornado_by_id(client, tornado_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Building Archetypes\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The 19 archetype buildings are used herein to represent the Joplin community. The selected building archetypes consider building characteristics such as footprint area, roof structure, number of stories, and so on. (Attary et al. 2018, Memari et al. 2018)\n",
+    "\n",
+    "\n",
+    "Joplin building archetypes\n",
+    "\n",
+    "| Archetype | Building Type               |\n",
+    "|-------  |-------------------------------|\n",
+    "|   1, 5  | Residential wood building     |\n",
+    "|     6   | Business and retail building  |\n",
+    "|     7   | Light industrial building     |\n",
+    "|     8   | Heavy industrial building     |\n",
+    "|     9   | Elementary/middle school      |\n",
+    "|    10   | High school                   |\n",
+    "|    11   | Fire/police station           |\n",
+    "|    12   | Hospital                      |\n",
+    "|    13   | Community center/church       |\n",
+    "|    14   | Government building           |\n",
+    "|    15   | Large big-box                 |\n",
+    "|    16   | Small big-box                 |\n",
+    "|    17   | Mobile home                   |\n",
+    "|    18   | Shopping center               |\n",
+    "|    19   | Office building               |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* **DataService:** once the ```IncoreClient``` has been initiated, the ```DataService``` module uses the information\n",
+    "  from that client to administer existing information about known or new dataset. ```DataService``` can:\n",
+    "  * Retrieve existing metadata for various dataset\n",
+    "  * Search datasets per specific information\n",
+    "  * Request creation of a new dataset\n",
+    "  * Request deletion of a new dataset\n",
+    " \n",
+    "* **Data Viewer:** https://incore.ncsa.illinois.edu/DataViewer\n",
+    "  * Let's browse the Joplin building inventory ```5dbc8478b9219c06dd242c0d``` on the Data Veiwer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the Joplin building dataset.\n",
+    "bldg_dataset_id = \"5dbc8478b9219c06dd242c0d\"\n",
+    "\n",
+    "bldg_dataset = Dataset.from_data_service(bldg_dataset_id, DataService(client))\n",
+    "\n",
+    "viz.plot_map(bldg_dataset, column=\"archetype\", category=\"True\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Display the buldiing inventory on the interactive map by using Geopandas\n",
+    "\n",
+    "bldg_gdf = bldg_dataset.get_dataframe_from_shapefile()\n",
+    "\n",
+    "bldg_gdf.explore(\n",
+    "        column=\"archetype\", # column for archetype info\n",
+    "        tooltip=\"archetype\", \n",
+    "        popup=True,  \n",
+    "        cmap=\"Set1\"  # use \"Set1\" matplotlib colormap)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3. Fragility Curves\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Damage analyses use fragility curve sets to calculate the limit state (LS) and damage stsate (DS). Mapping then determines which fragility curve set applying to which inventory. For example, a building that has certain number of stories could be mapped to a specific set of fragility curves.\r\n",
+    "\r\n",
+    "In IN-CORE, A user can both upload a set of fragility curves and mapping to the DFR3 services, or use local curve and mapping set object DFR3 stands for Damage, Functionality, Repair, Reconstruction, and Recovery.\n",
+    "\n",
+    "* **DFR3 Service** is the IN-CORE web service to manage curves (equations) of fragility, repair, damage, etc.s.\r\n",
+    "\r\n",
+    "This section briefly shows how to access fragility curves. A detailed instrcution of how to create your own fragility entities and mapping entities can be found in our Mannu[al: Toutorial - Create DFR3 Ob](https://incore.ncsa.illinois.edu/doc/incore/notebooks/create_dfr3_object/create_dfr3_object.html)\n",
+    "\n",
+    "Note that you will learn detail about this matter at Session 4.ject\r\n",
+    "\r\n",
+    "User can easily browsing and searching Fragility Curves and Mappings via DFR3 \n",
+    "\n",
+    "* **DFR3 Viewer:** https://incore.ncsa.illinois.edu/DFR3Viewer\n",
+    "\n",
+    "Let's browse the building fragility curve (tornado) ```5d8942dfb9219c068ea795ea``` on the DFR3 Veiwer.mvices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There 19 fragility curves set for these 19 building types in the four damage states, which covers the entire range of wind speeds associated with tornadoes (Attary et al. 2018, Memari et al. 2018). Below we selected 3 types to plot as examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using FragilityService to show the fragility curve\n",
+    "\n",
+    "fragility_service = FragilityService(client)\n",
+    "mapping_id = \"5d8942dfb9219c068ea795ea\"\n",
+    "mapping_set = MappingSet(fragility_service.get_mapping(mapping_id))\n",
+    "\n",
+    "# plot fragility for the first 3 archetypes using pyincore viz method\n",
+    "for mapping in mapping_set.mappings[:3]:\n",
+    "    fragility_id = mapping.entry[\"Non-Retrofit Fragility ID Code\"]\n",
+    "    fragility_set = FragilityCurveSet(fragility_service.get_dfr3_set(fragility_id))\n",
+    "    plt = frag_plot.get_fragility_plot(fragility_set, start=20, end=80)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Building Damage Analysis\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The models in this section implement the estimation of community-level damage to the buildings for the 2011 Joplin tornado.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Executing Building Damage Analysis\n",
+    "\n",
+    "This analysis computes building damage based on a particular hazard. Currently supported hazards are: earthquake, tsunami, tornado, hurricane and flood.\n",
+    "\n",
+    "The process for computing the structural damage is similar to other parts of the built environment.\n",
+    "\n",
+    "1. First, a fragility is obtained based on the hazard type and attributes of the building.\n",
+    "2. Based on the fragility, the hazard intensity at the location of the building is computed.\n",
+    "3. Using this information, the probability of exceeding each limit state is computed, along with the probability of damage state. \n",
+    "\n",
+    "The outputs of this analysis are CSV file with probabilities of damage and JSON file with information about hazard and fragilities.\n",
+    "\n",
+    "\n",
+    "You can Import the BuildingDamage Analysis from pyincore as following:\n",
+    "\n",
+    "`from pyincore.analyses.buildingdamage import BuildingDamage`\n",
+    "\n",
+    "\n",
+    "Input variables to the BuildingDamage Class are:\n",
+    "\n",
+    "| **key name**   | **type** | **name**    |\n",
+    "| -------------- | -------- | ----------- |\n",
+    "| result_name \\* | str      | Result name |\n",
+    "| hazard_type \\* | str      | Hazard type |\n",
+    "| hazard_id \\*   | str      | Hazard id   |\n",
+    "\n",
+    "\n",
+    "Input Datasets to BuildingDamage Class are:\n",
+    "\n",
+    "| **key name**        | **type**                                                                                                             | **name**         |\n",
+    "| ------------------- | -------------------------------------------------------------------------------------------------------------------- | ---------------- |\n",
+    "| buildings \\*     | [ergo:buildingInventoryVer4](https://incore.ncsa.illinois.edu/semantics/api/types/ergo:buildingInventoryVer4)<br>[ergo:buildingInventoryVer5](https://incore.ncsa.illinois.edu/semantics/api/types/ergo:buildingInventoryVer5)<br>[ergo:buildingInventoryVer6](https://incore.ncsa.illinois.edu/semantics/api/types/ergo:buildingInventoryVer6)<br>[ergo:buildingInventoryVer7](https://incore.ncsa.illinois.edu/semantics/api/types/ergo:buildingInventoryVer7) | Building dataset |\n",
+    "| dfr3_mapping_set \\* | incore:dfr3MappingSet                                                                                                | DFR3 Mapping Set |\n",
+    "\n",
+    "\n",
+    "Output Datasets of BuildingDamage class are:\n",
+    "\n",
+    "| **key name**     | **type**                        | **parent key** |\n",
+    "| ---------------- | ------------------------------- | -------------- |\n",
+    "| ds_result \\*     | ergo:buildingDamageVer5         | buildings      |\n",
+    "| damage_result \\* | incore:buildingDamageSupplement | buildings      |\n",
+    "> Note: \\* means it is a required input or an output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Details can be found: https://incore.ncsa.illinois.edu/doc/incore/analyses/building_dmg.html\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets explore both the Hazard and buildings on an interactive map\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lets get the tornado Dataset from service\n",
+    "\n",
+    "# tornado_id is defined in previous section\n",
+    "tornado_dataset_id = HazardService(client).get_tornado_hazard_metadata(tornado_id)[\"datasetId\"]\n",
+    "tornado_dataset = Dataset.from_data_service(tornado_dataset_id, DataService(client))\n",
+    "\n",
+    "# Reusing the merged building damage geodataframe bldg_dmg_gdf to visualize the tornado path and the damage values\n",
+    "viz.get_gdf_wms_map([tornado_dataset], [bldg_dataset])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.1. Setting up and Executing analysis\n",
+    "The following cell sets up the buiding damage analysis using the variables we defined previous sections:\n",
+    "* buiding inventory: **bldg_dataset**\n",
+    "* fragility mapping: **mapping_set**\n",
+    "* hazard_id: **tornado_id**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Please type in the variables and execute this cell\n",
+    "\n",
+    "# Import building damage module integrated into pyIncore.\n",
+    "from pyincore.analyses.buildingdamage import BuildingDamage\n",
+    "\n",
+    "bldg_dmg = BuildingDamage(client)\n",
+    "\n",
+    "# setting input data and parameters\n",
+    "bldg_dmg.set_input_dataset(\"buildings\", ) # needs a variable\n",
+    "bldg_dmg.set_input_dataset(\"dfr3_mapping_set\", ) # needs a variable\n",
+    "bldg_dmg.set_parameter(\"hazard_type\", \"tornado\")\n",
+    "bldg_dmg.set_parameter(\"hazard_id\", ) # needs a variable\n",
+    "bldg_dmg.set_parameter(\"num_cpu\", 4)  # Define the result name, etc., and choose the number of CPU locally\n",
+    "\n",
+    "# setting output info\n",
+    "result_name = \"Joplin_bldg_dmg_result\"\n",
+    "bldg_dmg.set_parameter(\"result_name\", result_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to run the model parallelly.\n",
+    "bldg_dmg.run_analysis()  # Run the building damage module to get building damage results for Joplin in a .csv file format."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.2. Exploring Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The building damage results herein are referred to fragilities at three damage states (moderate,\n",
+    "# heavy, complete) for 28152 buildings located in Joplin that fall within the tornado path or not.\n",
+    "building_dmg_result = bldg_dmg.get_output_dataset(\"ds_result\")\n",
+    "\n",
+    "# Convert dataset to Pandas DataFrame\n",
+    "df_bldg_dmg = building_dmg_result.get_dataframe_from_csv()\n",
+    "df_bldg_dmg.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.3. Joining dataset\n",
+    "Data preparation and data post-processing are common procedures. Prior to using pyIncore, users often encounter situation that they need to reshape their own dataset to make it compliant with the input dataset format of pyIncore.\n",
+    "\n",
+    "After acquiring outputs from pyIncore analyses, often time user would need to perform data aggregation to gain statitical insights. The below tutorial gives a few examples on how to join datasets and generate some basic visualizations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read building inventory as gdf\n",
+    "bldg_gdf = bldg_dataset.get_dataframe_from_shapefile()\n",
+    "\n",
+    "# you can choose columns to be merged\n",
+    "df_bldg_dmg_merged = bldg_gdf[['guid', 'archetype', 'geometry']].merge(df_bldg_dmg, on='guid')\n",
+    "df_bldg_dmg_merged.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.4. Show statistical summary on a column\n",
+    "\n",
+    "Try out different columns to see statistical info of other columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_bldg_dmg_merged[\"LS_0\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.5. Show table group by archetype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grouped_bldg_dmg = df_bldg_dmg_merged.groupby(by=['archetype'], as_index=True).agg({'DS_0': 'mean', 'DS_1':'mean', 'DS_2': 'mean', 'DS_3': 'mean', 'guid': 'count'})\n",
+    "grouped_bldg_dmg.rename(columns={'guid': 'total_count'}, inplace=True)\n",
+    "grouped_bldg_dmg.head(19)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Visualizing Building Damage Results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lets look at how many buildings were exposed to the Hazard\n",
+    "count = df_bldg_dmg_merged[\"haz_expose\"].value_counts()\n",
+    "plt = count.plot(kind=\"bar\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot Damage state by archetype\n",
+    "ax = grouped_bldg_dmg[[\"DS_0\", \"DS_1\", \"DS_2\", \"DS_3\"]].plot.barh(stacked=True)\n",
+    "ax.set_title(\"Stacked Bar Chart of Damage State Grouped by Archetype Type\", fontsize=12)\n",
+    "ax.set_xlabel(\"complete damage value\", fontsize=12)\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) #here is the magic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Now using Pyincore-viz, Vizualize Damage states**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyincore_viz.geoutil import GeoUtil as geoviz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the below cell, modify the string between DS_0 - DS_3 to see buildings in various damage states.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot a map with GeoDataframe\n",
+    "geoviz.plot_gdf_map(df_bldg_dmg_merged, \"DS_3\", basemap=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interactive Map\n",
+    "\n",
+    "df_bldg_dmg_merged.explore(\n",
+    "    column=\"DS_3\", \n",
+    "    tooltip=\"DS_3\", \n",
+    "    popup=True,  \n",
+    "    cmap=\"bwr\"  # use \"Set1\" matplotlib colormap)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Only the residential buildings**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "residential_archetypes = [1, 5]\n",
+    "mask = df_bldg_dmg_merged['archetype'].isin(residential_archetypes)\n",
+    "df_residential_bldg_dmg = df_bldg_dmg_merged.loc[mask]\n",
+    "geoviz.plot_gdf_map(df_residential_bldg_dmg, \"DS_3\", basemap=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_residential_bldg_dmg.explore(\n",
+    "    column=\"DS_3\", \n",
+    "    tooltip=\"DS_3\", \n",
+    "    popup=True,  \n",
+    "    cmap=\"bwr\"  # use \"Set1\" matplotlib colormap)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Only the commercial buildings**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "commercial_archetypes = [6, 7, 8, 15, 16, 18, 19]\n",
+    "mask = df_bldg_dmg_merged['archetype'].isin(commercial_archetypes)\n",
+    "df_commercial_bldg_dmg = df_bldg_dmg_merged.loc[mask]\n",
+    "geoviz.plot_gdf_map(df_commercial_bldg_dmg, \"DS_3\", basemap=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_commercial_bldg_dmg.explore(\n",
+    "    column=\"DS_3\", \n",
+    "    tooltip=\"DS_3\", \n",
+    "    popup=True,  \n",
+    "    cmap=\"bwr\"  # use \"Set1\" matplotlib colormap)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## TODO: Please create an interactive map of commercial buildings with column \"DS_1\" below\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Monte Carlo Simulation (MCS)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Researchers can use Monte Carlo Simulation to estimate the probability of each building being in a particular damage state. This example uses 500 iterations to determine the failure probability of buildings reaching damage state 2, damage state 3, and damage state 4. Users can run 10000 samples or even more for a more accurate Monte Carlo Simulation to determine the building failure probabilities. Note that this takes several minutes and we are working on developing a more efficient algorithm.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1. Monte Carlo Simulation with Building damages\n",
+    "\n",
+    "This analysis calculates a probability of failure using a stochastic process. Failure probability and Failure state are derived using the dictionary of failed damage states in the input infrastructure dataset. Failure probability is calculated from all stochastic runs, failure state shows all infrastructure standings as a string of failed (0) and not failed (1) states of each individual run.\n",
+    "\n",
+    "The output of this analysis are two CSV files;\n",
+    "\n",
+    "1. a failure proability base_name_failure_probability.csv with allocated house units and,\n",
+    "2. base_name_failure_state.csv.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can import the MCS class from pyincore as follows:\n",
+    "\n",
+    "`from pyincore.analyses.montecarlofailureprobability import MonteCarloFailureProbability`\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The input variables that MCS takes in are:\n",
+    "\n",
+    "| **key name**            | **type**  | **name**     |\n",
+    "| ----------------------- | --------- | ------------ |\n",
+    "| result_name \\*          | str       | Result name  |\n",
+    "| num_samples \\*          | int       | Samples      |\n",
+    "| damage_interval_keys \\* | list[str] | Damage keys  |\n",
+    "| failure_state_keys \\*   | list[str] | Failure keys |\n",
+    "\n",
+    "\n",
+    "Input Dataset to MCS Class is:\n",
+    "\n",
+    "| **key name** | **type**                                                                                                                                                                                                                                                                                                                                                                         | **name**              |\n",
+    "| ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------- |\n",
+    "| damage \\*    | ergo:buildingDamageVer4<br>ergo:buildingDamageVer5<br>ergo:bridgeDamage<br>ergo:bridgeDamageVer2<br>incore:epfDamage<br>incore:epfDamageVer2<br>ergo:nsBuildingInventoryDamage<br>ergo:nsBuildingInventoryDamageVer2<br>incore:pipelineDamage<br>incore:pipelineDamageVer2<br>ergo:roadDamageergo:roadDamageVer2<br>ergo:waterFacilityDamageVer4<br>ergo:waterFacilityDamageVer5 | Infrastructure damage |\n",
+    "\n",
+    "Schema of the dataset type can be found leveraging [Dataset Type Viewer](https://incore.ncsa.illinois.edu/SemanticViewer)\n",
+    "\n",
+    "Output Datasets of MCS class are:\n",
+    "\n",
+    "| **key name**            | **type**                  | **name** |\n",
+    "| ----------------------- | ------------------------- | -------- |\n",
+    "| failure_probability \\*  | incore:failureProbability | Results  |\n",
+    "| sample_failure_state \\* | incore:sampleFailureState | Results  |\n",
+    "\n",
+    "> Note: \\* means it is a required input or an output\n",
+    "\n",
+    "More details can be found [Monte Carlo failure probability analysis](https://incore.ncsa.illinois.edu/doc/incore/analyses/mc_failure_prob.html)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. MCS chaining with Joplin building damage\n",
+    "\n",
+    "**The MCS analysis will use the results of building damage analysis in previous section**\n",
+    "* building damage result: **building_dmg_result**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Please type in the variable and exeucte this cell\n",
+    "\n",
+    "# Import Monte Carlo failure probability module integrated into pyIncore.\n",
+    "from pyincore.analyses.montecarlofailureprobability import MonteCarloFailureProbability\n",
+    "\n",
+    "mc_bldg = MonteCarloFailureProbability(client)\n",
+    "\n",
+    "mc_bldg.set_input_dataset(\"damage\", )  # needs a varialbe\n",
+    "mc_bldg.set_parameter(\"num_samples\", 500)\n",
+    "mc_bldg.set_parameter(\"damage_interval_keys\", [\"DS_0\", \"DS_1\", \"DS_2\", \"DS_3\"])\n",
+    "mc_bldg.set_parameter(\"failure_state_keys\", [\"DS_1\", \"DS_2\", \"DS_3\"])\n",
+    "mc_bldg.set_parameter(\"num_cpu\", 8)\n",
+    "\n",
+    "# name of csv file with results\n",
+    "mc_bldg.set_parameter(\"result_name\", \"tornado_mc_failure_probability_buildings\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run the Monte Carlo Simulation module to obtain the building failure probabilities. The building failure\n",
+    "# probabilities herein only consider the physical damage without the interdependency.\n",
+    "mc_bldg.run_analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get buildings probability of non-functionality\n",
+    "building_failure_probability = mc_bldg.get_output_dataset(\"failure_probability\")\n",
+    "\n",
+    "df_bldg_fail = building_failure_probability.get_dataframe_from_csv()\n",
+    "df_bldg_fail.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get buildings failure states\n",
+    "building_failure_mcs_samples = mc_bldg.get_output_dataset(\"sample_failure_state\")\n",
+    "\n",
+    "bdmcs = building_failure_mcs_samples.get_dataframe_from_csv()\n",
+    "bdmcs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "building_damage_states_mcs_output = mc_bldg.get_output_dataset(\"sample_damage_states\")\n",
+    "building_damage_states_mcs_output.get_dataframe_from_csv().head()\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3. Visualize probability of non-functionality\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# getting geodataframe of building dataset and merge with output\n",
+    "bldg_gdf = bldg_dataset.get_dataframe_from_shapefile()\n",
+    "bldg_fail_gdf = bldg_gdf.merge(df_bldg_fail, on=\"guid\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "viz.plot_gdf_map(bldg_fail_gdf, column=\"failure_probability\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bldg_fail_gdf.explore(\n",
+    "    column=\"failure_probability\", \n",
+    "    tooltip=\"failure_probability\", \n",
+    "    popup=True,  \n",
+    "    cmap=\"bwr\"  # use \"Set1\" matplotlib colormap)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Commercial Building Recovery Analysis\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This analysis computes the recovery time needed for each commercial building from any damage states to receive the full restoration. Currently, supported hazards are tornadoes.\n",
+    "\n",
+    "The methodology incorporates the multi-layer Monte Carlo simulation approach and determines the two-step recovery time that includes delay and repair. The delay model was modified based on the REDi framework and calculated the end-result outcomes resulted from delay impeding factors such as post-disaster inspection, insurance claim, financing and government permit. The repair model followed the FEMA P-58 approach and was controlled by fragility functions.\n",
+    "\n",
+    "The outputs of this analysis is a CSV file with time-stepping recovery probabilities at the building level.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can import the Commercial Recovery Analysis from pyincore as follows:\n",
+    "\n",
+    "`from pyincore.analyses.commercialbuildingrecovery.commercialbuildingrecovery import CommercialBuildingRecovery`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The input variables for Commercial Recovery Analysis are:\n",
+    "\n",
+    "| **key name**   | **type** | **name**       |\n",
+    "| -------------- | -------- | -------------- |\n",
+    "| result_name \\* | str      | Result name    |\n",
+    "| num_samples \\* | int      | Samples number |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The input Datasets for Commercial Recovery Analysis are:\n",
+    "\n",
+    "| **key name**            | **type**                                                                                                             | **name**         |\n",
+    "| ----------------------- | -------------------------------------------------------------------------------------------------------------------- | ---------------- |\n",
+    "| buildings \\*            | ergo:buildingInventoryVer4<br>ergo:buildingInventoryVer5<br>ergo:buildingInventoryVer6<br>ergo:buildingInventoryVer7 | Building dataset |\n",
+    "| dfr3_mapping_set \\*     | incore:dfr3MappingSet                                                                                                | DFR3 Mapping Set |\n",
+    "| sample_damage_states \\* | incore:sampleDamageState                                                                                             | Damage states    |\n",
+    "| mcs_failure \\*          | incore:failureProbability                                                                                            | MCS failure      |\n",
+    "| delay_factors \\*        | incore:buildingRecoveryFactors                                                                                       | Delay factors    |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output dataset for Commercial Recovery Analysis are:\n",
+    "\n",
+    "| **key name**              | **type**                     | **name**  |\n",
+    "| ------------------------- | ---------------------------- | --------- |\n",
+    "| time_stepping_recovery \\* | incore:buildingRecovery      | Buildings |\n",
+    "| recovery \\*               | incore:buildingRecoveryTime  | buildings |\n",
+    "| total_delay \\*            | incore:buildingRecoveryDelay | buildings |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Note: \\* means it is a required input or an output\n",
+    "\n",
+    "\n",
+    "Details can be found at [Commercial Building Recovery](https://incore.ncsa.illinois.edu/doc/incore/analyses/commercial_building_recovery.html)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.1. Repair Curves and Mapping\n",
+    "\n",
+    "In order to perform recovery analysis, we need repair curves and mapping.\n",
+    "\n",
+    "* **DFR3 Viewer:** https://incore.ncsa.illinois.edu/DFR3Viewer\n",
+    "\n",
+    "Let's browse the building repair curve (tornado) ```60edfa3efc0f3a7af53a21b5``` on the DFR3 Veiwer.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyincore import RepairService\n",
+    "\n",
+    "# Repair mapping\n",
+    "repair_mapping_id = \"60edfa3efc0f3a7af53a21b5\"\n",
+    "# Create repair service\n",
+    "repair_service = RepairService(client)\n",
+    "repair_mapping_set = MappingSet(repair_service.get_mapping(repair_mapping_id))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.2. Setting up and Executing Analysis\n",
+    "\n",
+    "**The Commercial Building Recovery analysis will use the results of MCS analysis in previous section**\n",
+    "* building inventory: **bldg_dataset**\n",
+    "* DFR3 mapping: **repair_mapping_set**\n",
+    "* MCS result - sample damage state: **building_damage_states_mcs_output**\n",
+    "* MCS result - failure probability: **building_failure_probability**\n",
+    "* building damage result: **building_dmg_result**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyincore.analyses.commercialbuildingrecovery.commercialbuildingrecovery import CommercialBuildingRecovery\n",
+    "\n",
+    "# Create commercial recovery instance\n",
+    "com_recovery = CommercialBuildingRecovery(client)\n",
+    "com_recovery.set_input_dataset(\"buildings\", bldg_dataset)\n",
+    "com_recovery.set_input_dataset(\"dfr3_mapping_set\", repair_mapping_set)\n",
+    "\n",
+    "# using the building_damage_mcs_samples output dataset from the MCS analysis\n",
+    "com_recovery.set_input_dataset(\"sample_damage_states\", building_damage_states_mcs_output)\n",
+    "\n",
+    "# using the building_failure_probability output dataset from the MCS analysis\n",
+    "com_recovery.set_input_dataset(\"mcs_failure\", building_failure_probability)\n",
+    "delay_factors = \"64ee0bcd553ecf0768e21e55\"\n",
+    "com_recovery.load_remote_input_dataset(\"delay_factors\", delay_factors)\n",
+    "com_recovery.set_parameter(\"num_samples\", 50)\n",
+    "\n",
+    "# Specify the result name\n",
+    "result_name = \"joplin_commercial_test\"\n",
+    "com_recovery.set_parameter(\"result_name\", result_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run the analysis\n",
+    "com_recovery.run_analysis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.2 Visualizing Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve result dataset\n",
+    "com_result = com_recovery.get_output_dataset(\"time_stepping_recovery\")\n",
+    "\n",
+    "# Convert dataset to Pandas DataFrame\n",
+    "com_df = com_result.get_dataframe_from_csv()\n",
+    "\n",
+    "# Display top 5 rows of output data\n",
+    "com_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we need to first get archetypes from the building inventory dataset\n",
+    "# and merge with the recovery results to group by archetypes\n",
+    "bldg_gdf = bldg_dataset.get_dataframe_from_shapefile()\n",
+    "\n",
+    "# merge/join two dataframe\n",
+    "# you can choose columns to be merged\n",
+    "# we need guid to merge the two dataframes\n",
+    "# we can then drop it for the final dataframe\n",
+    "bldg_com_rec_gdf = bldg_gdf[[\"geometry\",\"guid\", \"archetype\"]].merge(com_df, on=\"guid\").drop([\"guid\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# visualizing the repair status at Quarter 6\n",
+    "bldg_com_rec_gdf.explore(\n",
+    "    column=\"quarter_6\", \n",
+    "    tooltip=\"quarter_6\", \n",
+    "    popup=True,  \n",
+    "    cmap=\"RdBu\"  # use \"Set1\" matplotlib colormap)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# visualizing the repair status at Quarter 10\n",
+    "\n",
+    "bldg_com_rec_gdf.explore(\n",
+    "    column=\"quarter_10\", \n",
+    "    tooltip=\"quarter_10\", \n",
+    "    popup=True,  \n",
+    "    cmap=\"RdBu\"  # use \"Set1\" matplotlib colormap)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Unique Archetypes\n",
+    "bldg_com_rec_gdf[\"archetype\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bldg_mean_com_rec_df = bldg_com_rec_gdf.drop([\"geometry\"], axis=1).groupby(\"archetype\").agg(\"mean\")\n",
+    "bldg_mean_com_rec_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the recovery curves\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "# we need to transpose the dataframe to change axes\n",
+    "bldg_mean_com_rec_df.T.plot(kind=\"line\", ax=ax)\n",
+    "ax.set_xlabel(\"Recovery Time (Quarters)\")\n",
+    "ax.set_ylabel(\"Recovery State\")  # 0 means damaged completely and 1 means fully recovered\n",
+    "ax.set_title(\"Joplin Commercial Building Recovery Curves by Archetype\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/manual_jb/content/workshops/20231115/session3/wsviz.py b/manual_jb/content/workshops/20231115/session3/wsviz.py
new file mode 100755
index 00000000..5aeca24c
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session3/wsviz.py
@@ -0,0 +1,35 @@
+from pyincore import Dataset, DataService, HazardService
+import geopandas as gpd
+import folium
+from folium.plugins import MousePosition
+from pathlib import Path
+import rasterio
+from rasterio.plot import show_hist
+
+def show_tornado(client, tornado_model_metadata):
+    tornado_dataset_id = tornado_model_metadata['datasetId']
+    tornado_dataset = Dataset.from_data_service(tornado_dataset_id, DataService(client))
+    tornado_gdf = gpd.read_file(tornado_dataset.local_file_path)
+    map = tornado_gdf.explore(
+        column="ef_rating", 
+        tooltip="ef_rating",  
+        popup=True,  
+        cmap="Set1"  # use "Set1" matplotlib colormap)
+    )
+    
+    MousePosition().add_to(map)
+    return map
+
+def show_tornado_by_id(client, id):
+    hazardsvc = HazardService(client)
+    metadata = hazardsvc.get_tornado_hazard_metadata(id)
+    return show_tornado(client, metadata)
+
+def show_eq_hist(client, eq_metadata):
+    eq_dataset_id=eq_metadata['rasterDataset']['datasetId']
+    eq_dataset = Dataset.from_data_service(eq_dataset_id, DataService(client))
+    raster_file_path = Path(eq_dataset.local_file_path).joinpath(eq_dataset.metadata['fileDescriptors'][0]['filename'])
+    src = rasterio.open(raster_file_path)
+    show_hist(
+        src, bins=50, lw=0.0, stacked=False, alpha=0.3,
+        histtype='stepfilled', title="Histogram")
\ No newline at end of file
diff --git a/manual_jb/content/workshops/20231115/session4/data/additional/Dataset1_REDi_business_new.csv b/manual_jb/content/workshops/20231115/session4/data/additional/Dataset1_REDi_business_new.csv
new file mode 100644
index 00000000..59340a3c
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session4/data/additional/Dataset1_REDi_business_new.csv
@@ -0,0 +1,5 @@
+Building_specific_conditions,Ins_med,Ins_sdv,Enmo_med,Enmo_sdv,Como_med,Como_sdv,Per_med,Per_sdv,Fin_med,Fin_sdv
+DS_0,0.00001,0,6,0.4,7,0.6,1,0.86,1,0.54
+DS_1,0.5,0.54,12,0.4,19,0.38,8,0.32,6,1.11
+DS_2,0.5,0.54,12,0.4,19,0.38,8,0.32,44.23,26.17
+DS_3,0.5,0.54,50,0.32,19,0.38,8,0.32,44.23,26.17
diff --git a/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.dbf b/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.dbf
new file mode 100644
index 00000000..04273a57
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.dbf differ
diff --git a/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.prj b/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.prj
new file mode 100644
index 00000000..f45cbadf
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
\ No newline at end of file
diff --git a/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.shp b/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.shp
new file mode 100644
index 00000000..b3a58c41
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.shp differ
diff --git a/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.shx b/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.shx
new file mode 100644
index 00000000..9d84e72b
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session4/data/building/joplin_commercial_bldg_v6_sample.shx differ
diff --git a/manual_jb/content/workshops/20231115/session4/data/dfr3/repair_archetype_6.json b/manual_jb/content/workshops/20231115/session4/data/dfr3/repair_archetype_6.json
new file mode 100644
index 00000000..b6bb5adb
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session4/data/dfr3/repair_archetype_6.json
@@ -0,0 +1,90 @@
+{
+    "description": "Mean structural repair cost probabilities for archetype T6",
+    "authors": [
+        "Maria Koliou",
+        "John W. van de Lindt"
+    ],
+    "paperReference": {
+        "name": "Development of Building Restoration Functions for Use inCommunity Recovery Planning to Tornadoes",
+        "doi": "https://doi.org/10.1061/(ASCE)NH.1527-6996.0000361",
+        "yearPublished": "2020"
+    },
+    "resultUnit": null,
+    "resultType": "Percent of Functionality",
+    "hazardType": "tornado",
+    "inventoryType": "building",
+    "creator": "vnarah2",
+    "owner": "vnarah2",
+    "curveParameters": [
+        {
+            "name": "repair_time",
+            "unit": "days",
+            "description": "Can be a numpy ndarray or a numeric value.",
+            "fullName": "repair_time",
+            "expression": null
+        }
+    ],
+    "timeUnits": "days",
+    "repairCurves": [
+        {
+            "description": "Slight",
+            "rules": [
+                {
+                    "condition": null,
+                    "expression": "scipy.stats.lognorm.cdf(repair_time, s=0.66, scale=numpy.exp(2.18))"
+                }
+            ],
+            "returnType": {
+                "type": "Percent of Functionality",
+                "unit": "",
+                "description": "PF_0"
+            },
+            "curveParameters": null
+        },
+        {
+            "description": "Moderate",
+            "rules": [
+                {
+                    "condition": null,
+                    "expression": "scipy.stats.lognorm.cdf(repair_time, s=0.61, scale=numpy.exp(3.78))"
+                }
+            ],
+            "returnType": {
+                "type": "Percent of Functionality",
+                "unit": "",
+                "description": "PF_1"
+            },
+            "curveParameters": null
+        },
+        {
+            "description": "Extensive",
+            "rules": [
+                {
+                    "condition": null,
+                    "expression": "scipy.stats.lognorm.cdf(repair_time, s=0.55, scale=numpy.exp(4.51))"
+                }
+            ],
+            "returnType": {
+                "type": "Percent of Functionality",
+                "unit": "",
+                "description": "PF_2"
+            },
+            "curveParameters": null
+        },
+        {
+            "description": "Complete",
+            "rules": [
+                {
+                    "condition": null,
+                    "expression": "scipy.stats.lognorm.cdf(repair_time, s=0.6, scale=numpy.exp(5.05))"
+                }
+            ],
+            "returnType": {
+                "type": "Percent of Functionality",
+                "unit": "",
+                "description": "PF_3"
+            },
+            "curveParameters": null
+        }
+    ]
+}
\ No newline at end of file
diff --git a/manual_jb/content/workshops/20231115/session4/data/dfr3/repair_archetype_7.json b/manual_jb/content/workshops/20231115/session4/data/dfr3/repair_archetype_7.json
new file mode 100644
index 00000000..8dc0ffea
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session4/data/dfr3/repair_archetype_7.json
@@ -0,0 +1,90 @@
+{
+    "description": "Mean structural repair cost probabilities for archetype T7",
+    "authors": [
+        "Maria Koliou",
+        "John W. van de Lindt"
+    ],
+    "paperReference": {
+        "name": "Development of Building Restoration Functions for Use inCommunity Recovery Planning to Tornadoes",
+        "doi": "https://doi.org/10.1061/(ASCE)NH.1527-6996.0000361",
+        "yearPublished": "2020"
+    },
+    "resultUnit": null,
+    "resultType": "Percent of Functionality",
+    "hazardType": "tornado",
+    "inventoryType": "building",
+    "creator": "vnarah2",
+    "owner": "vnarah2",
+    "curveParameters": [
+        {
+            "name": "repair_time",
+            "unit": "days",
+            "description": "Can be a numpy ndarray or a numeric value.",
+            "fullName": "repair_time",
+            "expression": null
+        }
+    ],
+    "timeUnits": "days",
+    "repairCurves": [
+        {
+            "description": "Slight",
+            "rules": [
+                {
+                    "condition": null,
+                    "expression": "scipy.stats.lognorm.cdf(repair_time, s=0.58, scale=numpy.exp(2.15))"
+                }
+            ],
+            "returnType": {
+                "type": "Percent of Functionality",
+                "unit": "",
+                "description": "PF_0"
+            },
+            "curveParameters": null
+        },
+        {
+            "description": "Moderate",
+            "rules": [
+                {
+                    "condition": null,
+                    "expression": "scipy.stats.lognorm.cdf(repair_time, s=0.54, scale=numpy.exp(3.28))"
+                }
+            ],
+            "returnType": {
+                "type": "Percent of Functionality",
+                "unit": "",
+                "description": "PF_1"
+            },
+            "curveParameters": null
+        },
+        {
+            "description": "Extensive",
+            "rules": [
+                {
+                    "condition": null,
+                    "expression": "scipy.stats.lognorm.cdf(repair_time, s=0.51, scale=numpy.exp(4.69))"
+                }
+            ],
+            "returnType": {
+                "type": "Percent of Functionality",
+                "unit": "",
+                "description": "PF_2"
+            },
+            "curveParameters": null
+        },
+        {
+            "description": "Complete",
+            "rules": [
+                {
+                    "condition": null,
+                    "expression": "scipy.stats.lognorm.cdf(repair_time, s=0.51, scale=numpy.exp(5.51))"
+                }
+            ],
+            "returnType": {
+                "type": "Percent of Functionality",
+                "unit": "",
+                "description": "PF_3"
+            },
+            "curveParameters": null
+        }
+    ]
+}
\ No newline at end of file
diff --git a/manual_jb/content/workshops/20231115/session4/data/joplin_commercial_bldg_v6_sample.csv b/manual_jb/content/workshops/20231115/session4/data/joplin_commercial_bldg_v6_sample.csv
new file mode 100644
index 00000000..38b659f0
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session4/data/joplin_commercial_bldg_v6_sample.csv
@@ -0,0 +1,16 @@
+X,Y,archetype,parid,struct_typ,year_built,no_stories,a_stories,b_stories,bsmt_type,sq_foot,gsq_foot,occ_type,occ_detail,major_occ,broad_occ,repl_cst,str_cst,nstra_cst,nstrd_cst,dgn_lvl,cont_val,efacility,dwell_unit,str_typ2,occ_typ2,guid,strctid,appr_bldg,appr_land,appr_tot,layer,path
+-94.5282394721245,37.0651714391093,"6",P00019501520030008000,,,,,,,,"5242",RES1,,,,,,,,,,,"1",,,ce7db703-eeff-40d5-bf27-3f2f6b462341,S0003287,,,,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.5221101182319,37.0633662436521,"6",P00019501510039005000,,"1968",,,,,,"1607",,,,,,,,,,,,"0",,,4daa5aad-89ee-44ac-9bf6-505eef74b0be,S0003237,24800,32950,57750,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.5328475925782,37.0588198745394,"6",P00019501640001101000,,"1977",,,,,,"11316",,,,,,,,,,,,"0",,,5c39bc92-5b8e-4b7f-aa75-e3a72d346965,S0028075,283940,99900,383840,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.4732421140909,37.067391341333,"7",P00020401800000014000,,"1956",,,,,,"1958",,,,,,,,,,,,"0",,,19c58735-f7a9-4d70-a9fc-e801ab09c590,S0026739,388230,378930,767160,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.5146165424894,37.0667993926503,"6",P00019501510020014000,,"1965",,,,,,"2620",,,,,,,,,,,,"0",,,2f903869-6d7d-4309-9ec6-b578137b39f8,S0002564,8500,43000,51500,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.4963449974136,37.0697176551812,"6",P00019101140025022000,,"1992",,,,,,"6318",,,,,,,,,,,,"0",,,7e2fe540-a21b-42e8-b385-ef6d4d1dbe1a,S0013295,219910,36080,255990,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.5391466614505,37.0890244046416,"7",P00019200440003008000,,"1966",,,,,,"5829",,,,,,,,,,,,"0",,,22405dda-61ce-40a6-bd66-36fdd45c6506,S0018079,61610,15300,76910,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.4187929658059,37.0532732034097,"7",P00045022000000007000,,,,,,,,"4215",RES1,,,,,,,,,,,"1",,,41c77ad5-c5a2-4b8b-804c-b5734eb9c2ca,S0025517,118928,,118928,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.4220728531525,37.0546993806359,"7",P00020501500000013000,,"1920",,,,,,"904",RES1,,,,,,,,,,,"1",,,ed0e9dbb-18f5-4aae-8c9f-3ebb12f7e62d,S0028449,41160,12150,53310,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.5140922897796,37.0638143944832,"6",P00019501510034002000,,"1930",,,,,,"1225",,,,,,,,,,,,"0",,,e2d50604-27f6-4b20-b83e-f5a9f0a89c5e,S0004738,36710,21500,58210,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.5338547500188,37.0628013599839,"6",P00019501610029003000,,"1930",,,,,,"1044",RES1,,,,,,,,,,,"1",,,9bb07af2-5299-4056-9da3-9620164ffc73,S0027265,28440,49840,78280,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.4781093123603,37.0726822722258,"6",P00019101240014001000,,"1965",,,,,,"2865",,,,,,,,,,,,"0",,,30456b6d-00d5-4674-8201-14f9cf960d47,S0010679,65740,223130,288870,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.4219912846723,37.0556873664352,"7",P00020501500000013000,,"1920",,,,,,"916",RES1,,,,,,,,,,,"1",,,ebe2c231-5290-443e-9de7-4c9a0e19b1db,S0028439,41160,12150,53310,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.5221363605728,37.0620566086301,"6",P00019501540017001000,,"2002",,,,,,"4721",,,,,,,,,,,,"0",,,dd5a7651-5fde-4e1c-bd30-15b818661c5d,S0003849,469770,44420,514190,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
+-94.5139189058078,37.0671493042074,"6",P00019501510018006000,,"1948",,,,,,"7056",,,,,,,,,,,,"0",,,a944cf06-7276-4192-a266-fcbd92647bfb,S0004342,108660,64500,173160,Selected features,"Point?crs=EPSG:4326&field=archetype:long(10,0)&field=parid:string(25,0)&field=struct_typ:string(8,0)&field=year_built:integer(4,0)&field=no_stories:integer(4,0)&field=a_stories:integer(4,0)&field=b_stories:integer(4,0)&field=bsmt_type:string(15,0)&field="
diff --git a/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.dbf b/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.dbf
new file mode 100644
index 00000000..e326eeab
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.dbf differ
diff --git a/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.prj b/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.prj
new file mode 100644
index 00000000..9e354778
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984", 6378137.0, 298.257223563]], PRIMEM["Greenwich", 0.0], UNIT["degree", 0.017453292519943295], AXIS["Longitude", EAST], AXIS["Latitude", NORTH]]
\ No newline at end of file
diff --git a/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.qpj b/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.qpj
new file mode 100644
index 00000000..5fbc831e
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.qpj
@@ -0,0 +1 @@
+GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]
diff --git a/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.shp b/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.shp
new file mode 100644
index 00000000..a529f8a6
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.shp differ
diff --git a/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.shx b/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.shx
new file mode 100644
index 00000000..a6093723
Binary files /dev/null and b/manual_jb/content/workshops/20231115/session4/data/tornado/joplin_path_wgs84.shx differ
diff --git a/manual_jb/content/workshops/20231115/session4/img/INSPIRE_Workshop_Session_4_mapping.svg b/manual_jb/content/workshops/20231115/session4/img/INSPIRE_Workshop_Session_4_mapping.svg
new file mode 100644
index 00000000..9f765d96
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session4/img/INSPIRE_Workshop_Session_4_mapping.svg
@@ -0,0 +1 @@
+<svg version="1.1" viewBox="0.0 0.0 960.0 540.0" fill="none" stroke="none" stroke-linecap="square" stroke-miterlimit="10" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><clipPath id="p.0"><path d="m0 0l960.0 0l0 540.0l-960.0 0l0 -540.0z" clip-rule="nonzero"/></clipPath><g clip-path="url(#p.0)"><path fill="#ffffff" d="m0 0l960.0 0l0 540.0l-960.0 0z" fill-rule="evenodd"/><path fill="#000000" fill-opacity="0.0" d="m93.31351 68.518936l144.96426 0l0 144.9602l-144.96426 0z" fill-rule="evenodd"/><g transform="matrix(0.28313333527559054 0.0 0.0 0.2831254161154856 93.31350657910762 68.51893580409447)"><clipPath id="p.1"><path d="m0 0l512.0 0l0 512.0l-512.0 0z" clip-rule="evenodd"/></clipPath><image clip-path="url(#p.1)" fill="#000" width="512.0" height="512.0" x="0.0" y="0.0" preserveAspectRatio="none" xlink:href="data:image/png;base64,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"/></g><path fill="#000000" fill-opacity="0.0" d="m93.31543 299.6127l144.96426 0l0 140.43353l-144.96426 0z" fill-rule="evenodd"/><g transform="matrix(0.28313333527559054 0.0 0.0 0.2831321474015748 93.31543014309709 299.6127010954856)"><clipPath id="p.2"><path d="m0 2.2737368E-13l512.0 0l0 496.0l-512.0 0z" clip-rule="evenodd"/></clipPath><image clip-path="url(#p.2)" fill="#000" width="512.0" height="496.0" x="0.0" y="0.0" preserveAspectRatio="none" xlink:href="data:image/png;base64,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"/></g><path fill="#000000" fill-opacity="0.0" d="m352.56564 289.1232l515.87366 0l0 183.38333l-515.87366 0z" fill-rule="evenodd"/><g transform="matrix(0.2518914567454068 0.0 0.0 0.2519001678215223 352.56565238015753 289.12320810839896)"><clipPath id="p.3"><path d="m-2.2737368E-13 0l2048.0 0l0 728.0l-2048.0 0z" clip-rule="evenodd"/></clipPath><image clip-path="url(#p.3)" fill="#000" width="2048.0" height="728.0" x="0.0" y="0.0" preserveAspectRatio="none" xlink:href="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACAAAAALYCAYAAADouhI8AACAAElEQVR4Xuzdidt931z/8d//1EAqGoRCiEwNQipTUb5lJqmUeS6pkFKJQomKb1IaRFEI+ZIpRcoc4vyux+56363P+q69zzn3fYb7vj+v53Xt6zOcffZZe633ek/rvdf+f6sQQgghhBBCCCGEEEIIIYQQQgghXHj+X/8fIYQQQgghhBBCCCGEEEIIIYQQQrh4pAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEEEIIIYQQQgghhBBCCCGEEEK4BKQAIIQQQgghhBBCCCGEEEIIIYQQQrgEpAAghBBCCCGEEEIIIYQQQgghhBBCuASkACCEq4z/+I//WD3zmc9cfd/3fd90/PM//3N/SjgDn/nMZ0769ud+7udWH/zgB/tTNuY973nP6n73u990rQc/+MGrj33sY/0p557nPve5q3vc4x6r7//+71+9+MUv7j/eiv/5n/+5QnZf+9rX9qdc9ZC/P/mTP+n/O1yFfOELX1jd+973nubKNddcs/rHf/zH/pTV5z73udUv/uIvTuf8wA/8wOpNb3rTyWfmGx3ERvj7rvjSl760es1rXnMyj+ugJ57xjGf0p4cQQgghhDDxG7/xG6t73etek+/4vOc9r/847IFHP/rRU38/8IEPXP3lX/5l//FB+PKXv7z6/Oc/P8W6n/zkJ1ef+tSnpr//93//9xRbhNOjP82lisne8pa39KdMvPCFL5xyOne/+92nv4cQwkVHnktOjE359Kc/PdkU9iaEy8ZVXQBgUltQe+Mb33i942//9m9Xb33rW1f/9E//tPrABz6w+uxnP3uhlMDb3/726T7+/u//fvXRj360/zhcxXzkIx9Z/cRP/MTqK77iK6aDnIfN4BD83d/93TS39JsgtIfjUH37Qz/0Q2cqsPiHf/iH1Td90zdN17rd7W63+td//df+lKNhcfG666470Znue8RP/uRPrr76q796dcMb3nD19Kc/vf94Kzhnrey+5CUv6U+ZoLe1yVj927/9W//xpeVDH/rQ1N+C8osG2bb4bNzOMmfC/0E/3eQmN5nmyrd/+7ev/vqv/7o/ZUqcPexhD5vO+dqv/drVn/7pn5589sUvfnH1ile8Ykr08IlG+u40SNL99m//9sk8/pqv+Zrpt7/+679+akvLxz/+8St8MzLy7//+71ecsw66iS9U16AXDuHP8b3a39X2bXW4Ptf3dY23ve1tkz8azo5Av2yFw79Pi++W3+3gZ20qY+KQ0n3HLPLTXvJZ9/DmN785CfUDoq/f9a53nfQ/PbeJDPkem3ka2Tst/L/3vve9J7/poOu2pW03Pff+97+/PyWcUzaJh7bhv/7rv6YFp5KFbe0cf6VkSbGjxawlFMOXfaZ/1/0eP8J1nc/X3vccC8s88YlPnHxHPuRP//RP9x8fhZJftpMe3hYyW9d4xzveMcn0eeK7v/u7p/7+1m/91tUf/uEf9h/vnU984hNT3/7ar/3a6md+5mdWD3jAA1Y/9mM/NsW9z372s6fid3PUws1p6X25TQ7n7xN+YRtLtAcdLC7gO7CfS35s2Wz6dRRHibf0a8Vmf/EXf9GfMvH4xz9+mntf+ZVfOf39tJx3eQ/XR1zbyp/5+J//+Z/9aYuQs/IdSobPAtusHf3cKFnnn7zzne+cHsg6q5+yS/g8bR+M5mT4X/hbpZf5a2Rol8gVezDlSU960vTAzEMf+tDVs571rEnv8nV3Telz477NOp11geqDyEs4LVd1AYDFpD/4gz84cXTag1PzDd/wDavb3va20xN0nmI9VNJ4F3zP93zPdB83v/nNVy9/+cv7j8NVTAoATo8g61u+5VtOFtRGizlXSwEAh+URj3jEyb3OOfDHKADwJLPP9d1v/dZv9R9fSsgZp5XteupTn9p/fO751V/91dWNb3zjadwe8pCH9B+HU3DWAgALS75zy1vecnoaRDLIws9Z6QsAHvvYx047ewi2XvWqV11xroQe/VHn0iO//Mu/vHGCz2+97nWvO5Etxzd+4zceZGFT8QQfrH73Rje60RRcbvPbf/M3fzPN6brGd33Xd63e/e5396eFUyBZWrbCITl6Wt73vvdNc6Su9ahHPWqaW5sgDiEbvkdmtpGPXSK5blGl5E2C9yz+S9gOOu2Hf/iHT2Tod37ndzaSBd+zQ1R9z5Oxu9DTS1g85dvVbzrYmm0SWZJqP/iDP3gibze4wQ1WT3jCE/rTwjnFQttNb3rTaezkSs4an7z+9a9f3frWt56uRx9uq3voW7bd98VLrreEHcm++Zu/eTr/677u66ak6hKeeOYLOV/ssctdkcL2nMcCgFvc4hZTe+QP6eFtIYPf9m3fNl1D4a0FovPEsQoA5F49cGCHML/d+sT9wee2c9lp4Qe2vtwmh5zrPnnlK1853Xf/u/qBrnTP3/md3znlnDzBb1F9VAgg1vI98+alL31p//HBCwAsYpVOJe/bLiSHw2NNoZVB8veyl71s4+IN5/Ft2dy6Bjt8FuQf6dx+fji+6qu+avJN+QR2VKWXt/Ut9sVv/uZvnrTTAxByG5v249WGfvne7/3eqa/kqv74j/+4P+XUkAd5sFYm66Bb5SfXFYhuS+WGyL64f1O+4zu+Y/qenNZIh4ewCSkAaAoATPxb3epWk5NlYjEa9ZkE9F3vetejbXm1LSkACHOkAOD0pADg/0gBwPlBMt7WjALy+973vheyKjQFALvnrAUA8ISGVwS4jgTNWRZJi74AYElH9gUADk/9bPqkqEUmCar2+8cqAHDoww9/+MP9qbO0+s6RAoDdsc8CAHPp937v9/rThpyXAgC7Idz5zne+Qt68xigchoteAOCg1zflr/7qr04WuxwpALhYnLcCAPANfF8y3VPCcw9tiCXoNnGJ8/25tI28+SRZzxcRV4jNwnFJAcDhOVYBAN3ymMc85sRP0s/iFvbicY973OpBD3rQtADOhvhcrua09AUA7vU2t7nN4mEXgn3SFwCI5xz6YZSv1v5rr732ev5DCgDCWekLAByPfOQjN34iW37q4Q9/+BXf32UBAB1ApsxL88PaTlswRG7lZs9SJLQL5Ca89rDtB69WOctrYy8z+yoAkMv+kR/5kem6ZOX+97//lKumB/m19UDw7//+7/dfPRMpAAjHJAUATQGAJxEkvzngAtHf/d3fnZy6SqA77nCHO1yILaWf85znTE8gcY7nFubC1cllKQCQvDdHf+EXfmFaPDwEHDPBjrn1lKc8ZRicXy0FABYGOR/6wvEv//Iv/SkTxygAIA/aJME3WvS8TOgTTzIpTOGsnkXe5pBEFfTZCefXf/3Xp9fi7Bo2l8Nt3NxPODu7KACA7W4tMAmc+RRnrVA/awEAWVeMOZfcb6EzJQLa7x+zAEBy99WvfnV/6hBtb3cucKQAYHdYxCxb4djm6eWevgDAIQm1SZLpPBQAWOBiz2tBrA7JCf20CfSNpPOv/MqvTP6RJ/aW4D/90R/90WRXPPmybsvuy85lKACQ0NzEPrgvMlJy70gBwMVCPPTzP//zk+6069QoHtqGXRQA1BN1krb8yTmd0iZ9HRbQfvzHf7w/7YQqsnWuuI5PFI7LZSwAsPhslyhzih3d9VOHZ2VdAQC9bit6xTee1hdznHWnDE+yK+Sv3T084PTnf/7nUy5WvsXORXSRXJYnkc3Tdb7HEn0BADtsodoW43OH4sl90hcA2NZcm+wQVvlqPkAVAoiZPAxgLFrE8WTrp37qp6Zr9By6AIDf3Mr7prtmnRfqFW3Pf/7zJxtIdjaJSy8yowIAsrmpvTZP28JPxy4LADzIaXHYvDQ//uzP/mz1ohe9aNrNWY6jflMucR9bu2+KeVs7ELX9+NrXvrY/Naz+N9dZa1sKmc5S5NUi5q44RH5FzM7mKEZ64QtfOMXE9JzCgE1im01JAUA4JikAaAoAGIM20WWiWyyVpFBNXudJSJ93BL2Ul4D8PL3vJhyfy1IAIHF8+9vffqrY2/f2awWdYU6ZW4LOUXL2aikAcO+CNX3hmHOMjlEAYAxK/226VfhFhYzc/e53nxxUCad9BJ+uyelkByVGTvOu33VwuGtuHTMou0zsqgDAXOf3SM6TAcm3s3DaAoCq4venwq9NkkUSZ+3TMY5jFAC0iTmLXJvoJcU21ea69xQA7A4yULbCcZZE9agAwPGjP/qja33w81AAQPfW4nO/va4E+CboQ0/gSky4n5G+aTHvFV7TKReluHqfXNQCgFZe6NZNYgpFhLX9f30/BQAXi03ioW3YRQGABZiSJwn/uWvwYdnSVs/Z5XHuCVTX9YSx8572tKdNdiMcl8tYACCOrjlFxvYRz52FdQUAfB2+vbhDPGEBZS43sCmeGC77Ri/QE3O6xm/pu7PYv74AYBN7tm/6AoD+/t03myzHUuewxXzLljZnM/JLD10AoN1sh/bIW583eV+HucpnIe9k065GF+0etqUtAGhjcvO+l8seMqc4qI/Jd1kAwE73u+z5XT4nPVK7hIjN95HL2gT9ZEeTakv1o35RSDJXuHi1U2tbZ9XxLXwHeRlj4aGxdv6KSWv9j3+4yxg1BQDhmKQAYKEAoHj7299+RaAo+AvhonJZCgBsx1PB9l3ucpf+46NxtRQAbMoxCgCuFgTz3p0ueCAf+3oqiUNcr77gBGdXmYvBrgoAQK/ZEYmsSdKvC/SXOE0BAP0h8KmFUgk6tmwdqradL9gqe3GMAoB2q3l/75/M6aHr6lVOAkRPNfh7CgDOJ3MFAJIH615Dcx4KAN7xjnecPP3vPZmVZKi5tklxhOTxve51r5PvSYYu8c53vvNkQYF+2mQ+X2YuagEA3WQreLbBooAnP9fxqle96mRBQwGjP1MAcHWziwIAT0wrDHcNO7C84Q1v6E+ZoHNvdrObncwZh9/21GAP/5fvJDmvXXbwueyLPBeBy1gAcN7ZpADAAl/NKU9Gn9UWWZCX43E9i3t8rX1yEQsACn5avZal+n8T3604dAHARcdCZL1WwbHpznQXmSoA8PCV3fUqbpDvXDfXFUxUjMDeVn5z3wUAhR2eKyaXG7FzxjEQ/8ulaYf8hteYlAzRsYnxD8fP/uzPnhQAKMpokXOvAgCvH9jlw0kpAAjHJAUAGxQAfOxjH5ueVKjzbFPUwwmg0CW0lt7fUrsKOM+xtL0XQ8oZE8BKID772c9eveAFL5iefH7LW96yuN2e7bhd31Ymqit7OI/uq9rhWpwW/+83bS3Fibc1p0SNe9qkitc13JOFKE8JenewCmDX83vrnANogyqv2s7Lli/aQUHazmepf+H7xsP3vYPVk4LGzIIx469tc87zpvi++9RX3oPIObeVrN+yKOl+9enS7+jP6n9tqr6xIGN7e23/pV/6pWns3cumTxzUUxnkUf+599e97nWTQ+TauywAcH+u6x44LGUc/Y5/247xWc961uyCoWDRVoySLuSNvNiuyTU9ETzCb/g9W8ZWoGPxs/rSYfu3fm7pF+2q3yNP5Er79LV+WPcUizaZU/Ubozmx7wKAmh/uQf9aACZ/2uW3l9pPvxmXkrk5zOMaV+0fyZ571551umybAgBj5N7ci+QCebAAaD4Yd7+5SQGASt9q++iJHvdn7lbb/d3/OZyvKpjeMv/0LX26ie4q/afv6G0yajt7RWTmpPtzfe1aNwaboJ/qHWJ0z9wYQNtq7tCFZJ9uJfuCVkmVPlFQ8m57w9p+UXJVAFh9R8/0dtO91j3WqyH0n+pZ42lc2bM2UG5lcy54c079ZhXDuC7bYts0uk6fS1ro2zkdsg3aWHKufVWZ7Xfpkte85jXTbzrYPG1p74vM+j96rfQN2WbHlmTKNSzs6Eu/63c8EW6+Syy6R+1ausYuCwAgWK4t6+YS65tw2gIAW1rWQrpkPHu+BFvnuwI479arBfVjFABYFKPD/Z1Op1eWEkW2lWTf3KcEUy1qbFIAQA+Ya+a1BCDbqq/838hmtXjaquYYuUU9VUWGvSeZ3iCP5K/VGeV78dOcR9bpCrpl9LRRoR98zv6+973vnfwVto294POSSTZg6Roov1f/1NNz5JsdJW+Kd1tfxNwpW+FYd/0l2gIA7zKtdwf6953udKfFgo/zUAAgiev3zRULz8a4Eh/k0JjOUU/XkLFaIHC4TvWtgzy5N+Pi32IZT/47V3KbvNa5dEL/TlHfo9P9v4IFC3Hkw9ziJ/u+31iScXPDWNVvoOKeVm7p+TqPPPU2boS5wC/0HXK8pJtHXNQCAHrN3ysRbB6M4s7CGFjoNz8ks+yy53tLBQD6ofx3/hkfhs1lD9kl/7cu5irIEHm1Tazx5jvRD/SOsdtkdxYyrw/4duZstYOu0Ua/saTfW8qn1yZ2XZvobf4EP0Ff9r5ZD/ktn6v8p4oFzUv+LLnWxjbehHbSl7ZU5WM4j+yZY8Zq7j7cY/0meV+ad5uwiwIA42J7a9dgO91H33f+zQf1ucWCWrRll0evn9JX+sQ5d77znaf8y64hc/qw9NIm85dcVDzKbhetPWUTyXXlkMgV282erpNz57i236h8k3a1vgCf9tprr51k6qzj32JOiFfMCb69tpvz+sZc8fk2BQDOJ+N0Op+yZFz+xv30MnJazloAYL6VDXHvo3YZ29Y2lawYT/pCzo5OYxsV4bm/TfRixafGm98mp2WcXbNiqaUCAP6l/rSle9ki+pyOLLvunrb1s9xjvcZL0Q69tE/Mw4taAIC2CJNtbfM3lX/mj45yyLssAKCH6veMIbvYt7uVd/pmnbxrdy/v/EDyTredVt7pFvkDuoyMVi6lzaX3T2X7TJx2zTXXnPQX+yFfUvLOFs/Zz/IhnC8GoJPktNiXTX2IPuZB5SbcCx+JjmYDjMUudHQVAFiAtONXPSAp1jaGS9APzmNr+awVEx+qAICOKh9DAYP+OTTGlJ9VOTX5F3at/s0vMd9Hc6GnnV/lc1dOjJ0n08afHicTrTy5vvP4XfqB/Pld8r+J/+FabLQ8gOu7hvsyFn5rXfvN3fJ3Kgdr3vk/tllc1+ejRvJ+Vvj+5UfIp9bOkuYKGyiuEZvQi7skBQDhmKQAYIMCAMa5nF6Ga5T4FlxR2pwhBnEOSrHeI+cYVZuDYjOxa8u50fGIRzyi/9oJleimYBjrHkpWAFzXkhTwm+6N89i/c9f7elxnyXHXnxxnDn8Zsjr0i0Sf/u6dqBYKlwPn3XyVPO0PAcAcDEh9v3+XaR13vOMdTxLbp0WiRPBZycv+0HbbD0oozjlwDHedT2b0nUM1Wi3a1CEZwvise0LK+DDmjFh//5wd17AI9KAHPejk/88S3BjLSjxaPBIscB4kYCqh7bAA0faDvxsDSSkLU/1Ye9pYApqz1n+PI9ueOzoESu3WTmRTYs5Cit/rt59y6B/vgBSszgUPxr2ehHad0RP5+ywA4FwrLvEkbvsuKwcHhg7znTnnTcKmZIsMzCGwqqDGXB69k4ouo4Pq9+eKPDYtACC7ZNMTu+WMtWPjt/S/LZXr/+cKAGqRUN+Nnr70W5IjdR1OK+fZfDVmvf6T1OGQLuk/MmP+0gu9/iNvd7vb3SbZ5STX50u2Yh1+T4BHDvQXR3lO1/h/gdfDH/7w6/Vte/RBk/7u32HeH+YuJ7mFPSk5k0jhUAuwJU9r7nGo23nmGvW+84c85CHN1f4Pfedz4/PIRz5yGjO/Raf3c7p0CJ051y+bYMwf9ahHTdek0yTXBCsC/f7d8tpAfup9kOysRQZ9UFu9VZ9ZIBRszSVhtVufeDpyNGau4akYgcNcUK/tNQ67KACQfKl79p05PbkO3ztNAQDfSSK4xpqtXGqDpJDz+FFskApu/z5GAYAFomqPQ9JsbpFM2yRRjbtFWAubmxQA+J6g+slPfvKkM3uZkfggc0tV7PSXc/W3Aig2nt9WT2DVQf70J3+LfXWQL0Uave9BD1vQn3tlg9/wOZ+pdED/W+YLPT3nP5oD5fe6d0lBiUU+T+tPtbqKPW13ZqC/T0tbAMC20ndVoGgc+USjhCuOXQBADsuvoW/4QOY6nVHt967vuYSOezd2/bj1h5iDTpTY7D/rD3LARrdY5LI9pjnQy5iDnmBn+StzepX/VLIsYcg2iMPIbauj2UJ2yN/pG1tDLs0b6DeyTl754HOyOsdFLgDgi5SvKl4c+YyFBZx73vOe07n0Od3o73MFAPrAd8xleqD3zxxsF99QPDMnpzCuZKh/92p70B1zaAu/nz3Rlt7vMPbiD/clWbnUFrDRFugVqI3svP8jm2KrJfkzV0rPiQNcVzzvu20s5rBjg8JPc5HeJLdin75fjaexmZNjPmXpOH0xioe2YRcFAMaHDq2Yksz0RcBiF/6jzz3xVXEMnULP9XOO3apCWzphXRx+Gui2yjMZx5Gv1mLcxHP8A/fa5nncL7/YTi7t6yt7GWALl4qGa44aY7qI/fJQAf3Zxuz+ru10wFKMtCnuzXwXr/R+jDGS3zFnjG3NmaUCANejF4xdHzeTeWPLx9pFwfBZCwDcV70fWyHhyGfg/ykqdI55Qs+Ye+Ij49qOjf5iU9bpRTIuPu3H1kEe6Uz+0lIBQBURLh38rW2f4OfTtv4FH3WUq90V/MCLXABQO+oYR4t/7Zysp9XNm9Hi0S4LAPiQYq/y38VyfazKH7NTi98i772uBjtQPht5Lnm3uMle9PIuh7RO3tlSi5Z8yZG8K5zwOxVTmNf96xTaYte5g13tdSKfk74X84jrRj6EuNGcE8PM3Qcfr9pAT8o16D+6mn5o7XnloNjYvj3b0hYAiDXFNnUP63Z/Kh1hrNmvWsw8VAEAmSz96nx+0KHhf5ALbSCvCj6MG3tX4yX/OpcfKMhF+eDsAL/HdeRFR7kpD7LK6YGt46OzfW3c4xD3udaSPTSPFcXwn3obTX7NV/N96Rr8kxoH/jQ9L+9R40h+6avCb1YOhx0n77vAfVT8a56za/qRzi3dZD6dJT8wIgUA4ZikAGBNAQDl9aIXvehEOTBaI4W2ywIARt31SglSQAIeCR4LfxxoCZd+q5KWbQsA/J77FPC7D0rQoorfLuPAwAgQRnBoGBb37nwBo3YKuLS9jBHDyzjNBZ2cQYpWPwpcGSzjwlhKZFhA4jDNYfGzFLZ78X39Vt93bWN41gIAyR1Gj6OorxkyvyGJY2zK8VIgMFcR2RYA6COyaPFI/1Ps7tP324S4ILd3oAtjIOivvmZUBAIcCe3jFFV728KFswQ3fQGAsZX8rvHTFw7/3y7CcX6r0EE/Mur6z3dti6QPfCbQa8fKNciIcwS6rWz6vzoEAKoEC/JmMUO7zGW/55xWPisI0IZeDxTHLADQRg6JwFRb3bOgSjsqqaHfJG18bxQgntcCADKtWrUqiek+Y6QNxse89X/uzT3Xb+6qAEAizIIkfakPFRnRf2SsFhrcgzbOIYlK/znPQe5LL2g3vaTtxq/Ga8lWrEMAaw65Dl1hzOcgg5XQIDecfu2iW80D/U5n90ETx5jeNKdKp/m+e6u55ruSgi1tAYDPyW3NV0kMfdtvI28MtikAIL8C6DbhRscZw9IL+lkAsS6QWqIvACBPglzXdrBJZJXMlA5RRCeAsAho3CVKjL3zKsHt0A9zT9ZK3rs3+tF5dIn54BD0lwwZt7n32O26AIDvI4ngXG077Q4Wpy0AkJzXvgrg/Tk3tuYHnem7ZN29H7MAQHW9OVrjYQznngpnV2rBQfJWP29SAGBBxiIY+TeW9KB7p2eMv74wx9iBuftvCwAkdxTpsXn+TQ/TI2S6ZN19CNwlCukFv22u8zPaBTL94InvEZJcxsr8Etxqt/lNP+mHWsQwbq4xSoi1BQDuXULhoQ996NQeusF1HXRGsc8CAD5OW6xSRbSjhdljFwAoZqoxl6DjL2mD/qtx1rdziXt2Q9KYfLQLTsa/9cskJvkW9JJ/k+nSY36bnqtz+en0Z4vkpXP9hgTTfe5znxO/0b+rr80RsdWo8KsvAOAnVmEaXapNdLTiU093lS2h98yvOfSXhC1d7zvkb1sucgGAOVxt1wcW8kdtMHfJOD1oPvMbqzBqrgCg/DWyaIwq5uIn0G1+v+TUv+cWaC1uWPh1Ll1o3MmOvvM985d/MqenwCdiL9llMkPGLU65hjaJrdwHeXbNpfjKfSlqqte76A9yJs6nx+m+KiihH+ni0YIg2gIAc8JiLFk2Fr7r3lrf2d+dYz6IE92Le9e3/Kh2Xs4lJ89jAQD44lVk675rF6rC4nItkokh3F/1izHrF6HYCeOtL+RNRnK9C9je8nVsCbw099lccqLN+qxts3EhR67D5vFHyRN7KhZpE+xzY4u2AMBCosPffY/Mmj9kpuYeuTnr09n0A71bhdjkgD6XW3EP/m5O0OGO0s9LBQAW+ipu5ruIaWq+8m38P9sjvzWXX9mUYxQAiCHkA81ZcmouGpuaA+6PrHg4Y4Q8DltTxQP0iLHX3/qKTqNHzI3SVaMCAE9gG5N2oVp/tD6AeKyPM9fBl1fMXT6StogFyNrSHDktF7kAQDxX85uv3+d/D1UAQHeXjiUD4r1R7vw0BQDk3aLzkrzTF3O5VzGjOKDiGHJlvH2HD+O3+JitvI8KAPgB5mFbTMjWtvLOJ+zthXZZBygfQmxG39FJ8jF0kft00FH6aDTertsWAJjD4kztMTba7l5KJzncpzzmyDfelLYAQBEGG1p+gHtZKvSm2yqWl1c8ZAGAe5arKjnRL0t5s33BzylfzBzRLnbHHK/FdG1cZ0v7AgD+Ya1N+PcoN0Wvsyl0N7+h/MM+N8V3VBQ3omw0v9dYaqtYrPKJZUf075J/0RYAWPhnw81pc0//mFvmT7GvAgCwn67L55ATIJ/a7z74xHbj2jUpAAjHJAUAMwUAFLK/M2yUKCUgoJrb+m2XBQCcA4G/a1HkFDVjxmmxGMoZYkDmFg6wbQEApS0QolAsKnnKUcKcgrWITilqj2BgFCBJQNVT5VVZXlu1SQq4Xjkq/hwF9pIhgt4K6Dx5IKnEWHEK9YvgaOnpjEqCCWBUcNYrEOr7+swCwsgR3QZywZH2p+BAYOU3JH1dv6peGVfJ9hFtAYAAi7PIaDOKnGWONwMsCV/OFaOnT0Z46rTO49haLONAcOi1z5N6tsAtZ7uOswQ3bQGAe+VckiFyYk6QU4azTxxzGskUwy/Y0Ab9R75rSy39Ryb1Z5vwJw+cvXJ0/TZnwf/VIQHSPi1DPs0DRt6fnD6LKhbrySfj2wbU2jDimAUA+kqAZIwlPiUOJTb0j8RMbaXlcJ8jJ/y8FgDQb7XbCWfV3HfvdLDPzFv6qk30OgQ8I7YtADBOAhOOp3lN59J/FvzpRm13Hvke6T+BmODROc71HX1WesEiA+decFlJDMeSrViH/hcouo7rzgW7MFcqoWvM6ArtMpfMA2NnsaV/wqy2z2anKmgyBvRTO9/6hHtbAGC+sEeCC06+5Lq+tbDbBqHbFABU0Exm6E6LBn6TjtPPxrCCQePB/pyWtgDAtWoO0tcCLrqC/rLgU0/MOtwrR52+FcSYq/qSXJSucT2LH32CAK4pySGpRv4lpehyhz43P8tWat+IXRcAwAJKFanp69NwlgIAcq4oqL47FwTq7yokY6f1/bELANi3CnrpAXI5Wsxuk33uY5MCgHqqUT/RY2ysfjXPzU/9RGbpT3qxLZBrqQIAfe58yQFzTAEdOWQDzbV2RwBPR5Np1+U/0pvkV9urkMH16J7Rk9nuj82S5OX3aLe+4rvVk7/8C9eRyB4VkLYFAOaFRIf2SExI9rmug09Y7LMAwHjQr/Xkmn7nS4/G7pgFAMajxpLv3tp6/VWL6vUU6AjXMGbGu5UL+rG1E/wl9+bwb8m4SmZItpCbOlesZRG+ha61uKlY2d8t7Onj8hvZ8Rp/8j/y89sCAPEV20mXmTt8Q4VXFkbJmPsqnWFOSbaM7D8syIkRy28dJSDX0RcA0JH+j31YOvjh9USR4xgFAOYOXV1tMK9G84n9qkUI/o8xrlh0rgDAvfDJxM71ui797b7pNp/RVa5BT4g5Rglu8lX+u7EiM6VnXIdu45/0C8YFua2ntPwOO+Sa5r628Pf8NntahTBioZG+grlm3pMZfgI/XuxCdzjoPn5++fZ849FTlGgLANgI+pjtoc8tbLo387N0JH1svjj4SnSze9e34k7/Lp1Lr404rwUA5l5tg61P6JNWHvwOOTBGPmN/Ss/pn77Qx7z3mRhhqTjkrOj7WgAwhnMLtu7FmJaPINfRyoR+43/yLcljbb9NV/L92dPytcnWaK6gCgCca774PePs98iu+cNe1XkOfsJZ7Bf9IH4yJyofRWfTE/Iril352uY7Ga7fnSsAoKfK35bLY3PY/Zqv4sPKicgBnlWGD10AoA/EAmRZbo3dM89dR2Ff2x65s9FYO7/ssHlB5/APyYy+og/okXahc1QAoD/5iO37rI2ftpRd1/aRD7gOxbJ0aY05+dA/fJL29Re7gN26iAUAxla8WOfw2/uY7xAFAOxn5UPMYX79XKH2tgUA9DRfZyTvZKH6iLyTz17e9RubXbG69onN2EdyxHZoE5+kXTgfFQDQpX677tVBN8mXlLzL17Zt8PvlN4nVxCdsCpnTR/wa842vU/Zc3NC/DgttAYBrmR9VxOohBffpXuR26uEa5/HLTzMHi74AgK4oP9kxt1gq3vU5/SYeJVuHLAAQ+7EtpUPo+7P0w2mQbyFvfF1zRyxd0I0VLzvkQZdoCwCMq+/yEc0Vcsg+y3vQ523xJ/tNRoyD9QW+hPPEPWxkXU++drRbBBllc8xFc1c8Zs6TX74Un6Byk353NKdRuRB5J/ZM2/lt2stHk8+rHQuwrwIAds7aSPWPcXFv8gfiwrl1v7PSFgCQgz6umztSABB2QQoAmgIAhlQQbZFMEkIihjPBqFiQEHj0zkSxywIARrGUNSU6ShCvY9sCAMqe8qW4W0fD/eqjckS0a+Rsc2AE7BxCSfre2WNEBCPO8VsMRm9YBF6V/NKP2z6lz5BXtSbnbl9KG8ZR+0bywMFjVBl4joZk0Ii2AKAq3TneHMbWuWdQOYB1bu+EQjtqIZ5RYMxG2zb6P85gBY+Os/RTWwBgzNwH+e7voUWQWI41B0DyrHeQBS01lha3enmC+6jrcHiWcE2JgHoHbY9F+/a9dRKvI45ZAFBJQgullUgvJLo5Auawcy1wjcb1vBYA0EXuz5yxmDQKdvSrYKsSvY65BYltCwC0zbjqw3b3B3LJyax+dU6/2A36r5KlHFRBV5+kpccFIBLIVVCwZCvWIcCpJDMndaSXwWEUoFbhBMd6W/RDyb3+nxvroi0AYBc5rGyLeTzSmdimAMBBVgQ8ghZ6qK7rT8lCCcEK9AS/pw302gKA0nGCSP5Aa5vJp3ZXP0vk0hGSEL3+siBZ92HhcxQg1bvVejsJ9+i6leyY2xVnHwUAgtKyVxYsTgPdddoCAPIsgK4FUwusPfqnnl523+bseSgAMB6SwiUjbGVvp/lmkqbkm79Ft68rAHAv5KHsocKRXt7JqsC6njrxG6O5WAUAJevmGL3bXk+bJJhq0cQ1yRl93O+eo101zsZqpD/1i3scLdhqo0RG3b/x7PsMbQGAgy4hAxLHvS4u9l0AALqyZICNYAP7dxceswDAgnclo4xPa3uNR/mKxpq9H+mqwnfb989K4CwhcVgJzJqnS1SCaRQTkZN2AVqiu5dFtAUA7smYsCv966ZgHMzXuqb7r3HtaZ+CMv/6a21CXwAgGcd2rzssRLfJ6mMVALBZZZO0p18ggjGvnVkqDlxXAKAvyZa+H82NSqrW/Utwj85zTi2wrtuqdgTbWIWU9CIfu5dFvyv+qSI19zR6r7x7qYVTfpyF014vgEzQq2XHzdFRbNwWAJBp/W+xs5+vrT7WNn6d/m/nnv6WxK9Eoz4b6dDzWgDAPpQdc+j/ar95UfkauofeNmbtLoX9Qwb89Brz3vbuEnOhFgD0a7s40OL+xPjuoXbRaPUNH8J4zhWeyDu1er2XkaLk0zlkhe8h1mnltPqz5hW57+fENuj7soX8afmCXpeyS/zvejjFuXMFAPqJbdNP4t/eL6KzLIi4hnPM8ZHu2JRDFwA49L3FP7at9aHqoZo6z2Jdf//0avmbzuEjjmIO7ZAHqPk+KgCA71p8qt+0IL0LW2T+GnP6ry0C0B65Crqjv7fTwpa1BQAKaeSb5o4+vtsH6woAyDF9UUUa7JR4qZ/b+y4A4EeJa+oz8jfK5xTbFgA4yLuFc3FBK+/kTP6ozhOf9jEFvSgHU7ELP6B/8AF8CjJV8j4qAID2Vp86LN73+qqFXW2LvNxDb1t9X2xigdp55iafpsf9lv+sr+lNMZB52upo12/jbe0d9fOm9AUA5rw8RukQcVePexJraSe7woc5RAGA3+WrGjv55up758wVwe8TskzOtYHP1Bb5kbnKr/icL9TLb0tbAFDxetmB1gaT79r5yrn8Tfl1fdLbl9oNzuE7/dwlc4pw9bXr8Vv7NpIHvrbfcc5IdlEFACW7ivvlaebsxa4LALRbH/Dv2vip5JHtklddms9nofICclvyqn1cN3dU3jcFAOEspACgKQAwCSk1k6qcFwFBBce9w9WyywIA1VhVnchYzS3sLLFtAQBlqjqvd0QggVqLPxw12+u1UI71hIIgem4BQbKknvK1tVifxFVVVgbBYWFnGxidWqjjtI3u+1AwkBXESoiODEhbAOCwyMRp6c8ld+1WrKppezg/lfzhUC1VLPuN2q3BsXTuOtoCAIeAvH/av0eg7lzzTOJ1NK+0sRagBT2jraa3KQBYhz4XrFdQICEw4pgFAJUQGj3ZD/OnHdfRYvt5LADgnNc9cojnXjMCc7za5djVKwDMVX07SiBYtK/koOv184Xs1OKU60g4zTmwzpVgqUBlyVasQ/BSOkHA3jvqBZ1uwbrudaQ/1qHdpy0A8Ke+HvVtyzYFAO7b1mkS0KO+1l6LllVIZ17PbZO/jrYAwCF4ndtCT+EX38F5bLcFqVEyjQzV9TzNMKpWX4cgr6q1/eaoPfsoABAUCfTNZ4nD03CWAgC0W+i59/6JTTaoPq/FuPNQAAA+Xy1y8Ffo7Rb+VW2dWYtU6woA2CFjaF6Q9bmtAyWVah5LII+SQO3CiXMkrEZ+oURBBaJ+02LTaI4bi7qe++rbvgnko57uJgd90gJtAYB+qC3rR/OiOEQBgDnI7pZvIbFgYbxt1zELAMq3pK+0s++vtlhp6ZUl2HcBwDrISSXPxBgjv7EtAHDwT+Z8KpDXdlG7fzoY5NH8qxjwNDKOvgDgtMexCgBAR/s/fUGX9IloCSuf0WcV/64rAFgHmeVz1ByzIDFahOSb1QK+WLOX9SWcW081mSu2Wh3pRZR9q8UYOreXMfOqbDP/ckn22bfy4ei/0Y5GbQGAOUCnjHwPCeeSE30h4TyyA+Zm+2T3qOjgvBYAGHvxf+kCcUothrvXWuDi19V9lY+nfy1qlGwYtyqaM8dHNm6X1G4Dfk8CvpcbiElKh0mazxUlzUEvtnq6z+kU7fgbX4vzI70iZqtFvNM+tFKUPeCj8SnmbKHxoWvK3x4VAPCtKqbgN40WfFyH3JUvw+eaK5zYhGMUAIgD5On6vvJvuag6j27tF6vZ8/bBmaV8o+uV3B26AADkis/W7hTqMM/1Gf0+FwdvA1vWFgAYS7qcrhsd7a5S+6IvAKBr2QXjxxdnz8re8EHM79GugPssAJAPlDuruWSxamRbWk5TAEDHmMsjeacz6jzt6uePeKZ2M5zLLRauVw+c7KIAwP+XXLFv/KMlH4INq/iRnPWFCm0BgIPtpMtG803Opuxh7bx0WvoCAJDP8gXMxb5f6Sd21H3TsfpiXwUA/KB6XaL+9hvaVvdfhXxz47Qv6K+2wM34t/Zde9r1H3OrL0ZsaQsAHOJruaV+XkCf1+/6UyHfyD/kC9T1xFB9foKfWrvasRt8wFE/mgd1H2R0RLve4xxrRKO2F7ssANDvfGayytciy7WToeuzL+LhvrgBiub5ZnyvueKGTai5fdojBQDhLKQAoCkAYCDudre7TUqtEgkUMCeHopJAHik67LIAgGGq7Xoc2jRKOi2xbQGAhblRoAmOQikqyrJfAPN529a5oN3/V9DJmesdPW1qq9Qstm2bDC3D5BCwSpSMnKF9wxkqY8sBGclNWwBgvJbeRSS4rYBntC2QxbN6iksF4FKf+d2qLHX047kNbQGAxIktp5Z+G1UswumfM+BkoyrXzSsBWc8uCwAgYVd9XAtNPccsANBvSwuF+t0Cac0fzknPeSwAsOBY15DkXApM6OxWdndVACBQ7BMjhYRIBSmu1+ts7a0kFJmY2/6s8NRbBaBLtmIdFrir/XavGD09VrSJMkGSex85tnPQX6ctAHD+KNDo2aYAgDyRvZFeLcyVdjFFYuA0tAUA+pDemftdQVb7qoW58wTD1S7zsA+WN0Gw1O6GMdK7+ygAMOfoSXp5pGM2oRZIqu1LOnJUAGCusnP1fXLRIvFa9y3BhPNSAMD+WnSttvdP+UnYmgful5xgXQGArSjr3ozzaLEG5KFspvNG/V4FAMbYItdcP2l3JVw89Td3Xuvn8IVGSfh1sP9tsmCU5G0LAMixflzHIQoAYNGtLcKS7Gj177EKACSua8GNXIy2nNbOmkvmIR9izqc+dgGAMah+pPtH99O/AmDdYg9ftN4P6bC40dszyS7b7fqcPM3p/XX0BQCuSc+tO8Q9VfzsOGYBgARi+aAWENv5LoFd86BdfD1rAQDMm9pZSbw0SqzzAcqfZsudt+niqfut+EBsKQm4BJ3cxvH+3aIwra4nJ7CEsWx3PxoVKLcFABZe5nSIPq/rKNguG9NDN7Q7z40WBs9rAQAspFYC2p+1ECShXU/Zt3O5XTiyoFPnl5+tby1m7hv6qBbQyE9fuCoGkl+oYj92eCTrS4hZqqDO0f9GUQUAVfAyt7DPryz/hC6YO28d7EfpjnpX9RJ8laUCgGuvvfbkHj0xPKfrxTO1CMlv7x9O2YZDFwC4/7lXnvi/tg8scLTXM05VcOKw8LsuLix7fYwCgEK7+RZVuFCH+WDOnNWPYMvaAoB1R++P74O+AICtc7Ah/LKaNw7+wJxe31cBgHFv4y6++Nx8a9m2AMD9shNz8m4BtdquQL29Hjnsn4Sfy3sXFVPsogBA35b/YSyXHnaB+26LsHp92BYAGAs5lLnf9t2K18RSZ5kjowIA/lH5wOxXv+uR+JecWuiv9Yx9FQA4yGU7JxxyWGR0KVe2T+jeypnqu74QHPys9kEui/pztAUAxp8v0F+v4GPX7gfyD3Pn0X312/KTfQx13XXXneQC6Zn+84JOrAc+jcvIH62YXl/YSWCuTcWuCgD0m3ih/Cy+i2Iq88k1K44wR3u/HXJg5JV8samnpXJDdJqipj6umztqHFMAEM5CCgCaAgALS7VlJCeYk1GTzUT3JwXbJ3+wywIAeEqzNWb+LmFgsVYSZZ2i3LYAYCmIZJAqqBkVAAhY6jqSUIyXBYX+kJSo6zAgoyQ1h4ATUYabIhaoWlg2NusCS21r+41RlFATIHHURkboLGgPZ8K4StLXe3Q4nmVENikAIDP9U4wtHPKlAgCfc65qYWyJfRUAMMhzwUJhXlW/MF4ciF5OHNpXzqRjtHB12gIAcm4ByZhJBllIs8Bd77Z2vfNaADD6vZZ2K0ZPBfScxwIAFZR1DeO+FDTuqwDAU/lzT/bQU5Xccr1aUCz8u+YmJ27uaZpiVwUA7ZMddMJSv5HBcsYd2qtYyxyi3+d0f0F/nbYAYO79sT3bFgCYr0v0u5OcZstftAUAktKjp++KtgDAotEc2xQAsFmSFOSQ7haQsGd8hDYBNrJt+ygAkIgqHW6xb/S76zhrAQDcf+m6tsjEPOBr8CHozGrfeSkAoMPogEpcsxHlH/Bx+Hn+X3Bcyat1BQB0UCWNLcjYVaK3qXVU0Cu4HT3NXQUA+ltQPkdbALBUCLJNAYB+4CNIlNFL7lNCTUK5feJtXQEAudgkOXCoAoBKxLcJXDq7/NljFABoU6tzbYs6R5sQ5k+PnizDoQoA9I84iE5U6MUvk0yt3UmqnaPkVFsAQH7X4bfap3YsHrZJZXqHLqKj+I9LT+ysoy8AsPX7JrLge62tO2YBALtb88A89KRR3YPkIb1H5uj/0nvbFACY536j998l4ct/nysAYOvo1XoSjI2wcM0vFn/N+YCwSFrfo4vW+XlkuU3et++O17Z2nG0xvQ4J9ZLtkb5tCwD4yHO0BQDir7mnVy96AQB93BZd1XjZHUc/mfttXOP8kh/9Ur9dO9Zpl+2aD0H5AOaPudzqAPGlJ/N8zu9ZejiktacW/d0TnWzutfH1ugIAYzz3ujXwIyq+OEsBgLlcbZJ7W8qJYF0BQL0P3TznK/e+UB10Qs2d/jU423LoAgDzZGTn0C+I9gUAfGU+Xn3u76P8Zst5KAAozAV+FB+7dLODLlq3E+USbFlbACDmow/njnV9tgv6AgDjLl5jS+UlxOfabLxH+cZiHwUAzq9XJfq3PMim/b9tAYC4ZM7/dN9sSLW9LwDgM8pB1efkfcnmY5cFAGLQ0jN2/VmXe2VzWxvW/35bAMBWiJXnYP8q77SPAgB4aIsNdcht1bw3T6sY0hhX/+yrAICfRCfQo+ZM5Qn0vXk08g0PATmufK7+6Z+uL+jMkhN6bW5Hv7YAgD4QO87RFgDIZ81B95W8jQoAyGytz7Cpc+s9fJQaDzmH0YNdVQDgvE2epDduuygA0O8KAl2HfLQ7evKP9Wn5gu6lLxIyPpXnW8qRrKNyQ2R/aex6as6kACCchRQAzBQAFAwXA1dBLsVnwvUGftcFABSOxQRBZykiB+MhOGTslozYIQsAfLeus+nhnkbJAv3K2ebUVGDn8HcKWxJl7kndQrGA4KgUtIMR5UhJwo4Cqm0hO4JT7ZF44/hyTBlhhrv97UMUALg3sqcNngZe4pgFAJyLNlDb9OgdX2xbACBIk4C0cOqJRUaXHGo3B6Qds4taAGCut9uO9pzHAgBBmu+TX7pxNFeK81gAQA5rbtJbS4u52FUBgDGp9nsirN8erkfykl2oBUAH+VdhTG5GBVmFMbloBQDsWS3cO9Y9ZTfHsQoA9Dnba7Ff4o4vUE996Cfj2PoGo4WifRQAGFttWOfrLLGLAgB2qxJHta06JFAruS0RWZyXAgC0i5B0RwXZ7kki3f3SW5VAWVcAIOlVC5SbHubzaLHyGAUAZJ1fRj5t46oP+HzaQM7Je+lYxyhBb76f1wIA1PaopR/cH1nFMQoAxDp86krmWMQWj4yO1laaO56KHbXxEAUAfCC2jz43/8mFvqTnStc5dlUAAPOt7svcbH0ACwD1NK052seQ23AZCgDYHDFzyZWkcMVcniL2f/Rwu3C5SQGA+zHHzA/JRTE5+2feGMdKbjrmCgAg+adArGL6OtwDH2TutSHmaNlb7Z9L3hbsaiUZHW2inF2v98A6+qflRvj98hstsPSkAOBKxGFkqeRQ/5tLFgD8m95pd90zXvXaoPKVzKv6PwWz63IPu4KvXkWWfOFWL9KZZJ0s8ofm2mRhiu6WL2BP9af5xZ6SkzYOPy8FAOZ2tYkNHclcy7oCgHYhc9ND4Xxvu7fhIhUA+HvF3w56aE5vFuepAADaS/7YzdYG0F3rYuI52LK2AOAsObJd0RcAsJnslTlDZviym/TzPgoAFOtUzEVv0RWbtAWHLABwb21OQP5onbzvsgBAvrhkVH8t5XwhT9++8pL/1HLeCgDMw4pj+MIKuiDvXnFwu+PdvgoA2KLaLVUfKrCsdtHvo6e69w2ZaHdx4juLj/pYyyFHWHPC/OLHjGTq0AUAVQxdn296iHlHBbPHKADQj3bvqnxCv/urz/lf5c/wk9qY3J98qvIr5/TjJqQAIByTFACsKQAAJ9miewUarXEpdl0AAMpOos1CB2NaAaFDkGzizzlZhywAaLcQ87n74xCtO0aOXqHiTbJU0N0uWFF4/n+UAC4oaMbGef2CF6NhEfIszg+5YSAkdxgH1/RUkAUfhkHAY0GlDMy+CwBcu8abjK570vWYBQCSSpV44IxaOOjlYnSMFhy3KQAwTyykSWiYRwyuxL8AgVMtYOVoV4LvMhQA0Bk957EAoLa7cs4oidNyHgsAbB1aMn3IAgCyWO2XZF/SpwVn3kKPQLvaUH1vnOaq9i9iAYA53wb76wqj5jhGAYD+Nhbmg+DI3BG4ekr3qU996tRXdFSbEBotFO2jAICuZIscdPNp2EUBgHuTeOJzsXuCZP/33Oc+d7L55Lu18+epAMBcpTvL3rDp5FXizL1adGgXyNYVAPATK8EhcUBeexvaH+bDyPYfowCALWEb6Bhy5V75FU9+8pOnOa8ooIJex8j/O+8FAHDvFpJr3PmNbPoxCgAUyrY7iBhv82h0tLGHw1wbbaO57wIA48MvoaeMsfEm62SHPfdn+cm7LACoguy6NlsJ/gh7UDqWLznnR2zCZSgAYLssKJa/YC4rvBVzOk8fiqfbp2rWFQDoZ7JkrpJFfhgfRlxEr7JJtjWtebVUAABxi8UuhcBiiLaQzsLR6ElvhcN1nrh0nS/uyWv2qa7rHgv5g9qG3rHJ60ostlUsQBf2pADgSsghH6N8SvfCP7AIWn3UttccKj+IzqOH5Weq+IysHQoJ6bIl+oPfVjkEeQ3/T27nfFG2hy7ie/CPzDu7ArH57KkFkSpscJyXAgCLSdUm+mxk51vWFQDU7g3mLT3U+0CjQ9+MbNumXKQCALpArFWfk/l1Y3feCgAK9ofMlDyYtxYeT4NrnfcCgE38ghFk3Pf102jx6LQFAPKhZR/9fdNF1kMXALQ5q0MXABjDWoSlL/u4v4dNorfr2n3+7LwVAGiv4kSf8UXkKegD+SmyIjfZrrEcogAAcuv1kJzP6Ymz6PjTwP/td+Hs46w23qq21v2MxusYBQB8jvqcTTWfehvaH+zOyEYdowDANeSE6x7aBy0KY8VPKnniR9VuiWKH8ivMp3U+yhIpAAjHJAUAGxQAwAJYJTRM+nY7P+yjAKCgcFQsSYZKkpVhYGA5OyMOWQDgHuo6kn99ccRp0R4JBokNznglISXIJcznnKzCPWqrRU9J83JOKU2LYByT02AhwcIuA+5aFhwULLSJP45eJXQPUQAgMUYu9BFna4ljFgBIitUC2Sbv0VximwIAY1ZzQoJCgCqoaMdFsqj6+KIWAPzmb/7midxJbvacxwKAWlwlvxY4lxIQ57EAQHKu5Ebwsy7w3VUBgCfQaqz14WjBaQS9agGavEsA1zXqOqNXCVzEAgCJrjZ4XkpgLkFWSkYPVQBA5gROAjbXE3BIDLZPtKiuNxZ1nVFCSNtrHHZVACCxrP/1xSZbF4/YRQEAjGn5XXaysHBT2+OyS22fnKcCAPOJHijdbmwEyNV2stPuyLGuAIAPVou5EkJnCUoPXQAg+DZvzH3JD/qdX9tW5UtKVLLAMbq/i1AAQObIRC2auV/FDYL4QxYAmPMWWsun5kfx7ZeOeuLa+Xy3URJ2nwUAPqvtUM0bCWg6sLVXxqD6cZcFAKAXtdH3JGzMT8nfiufMc9ce+fqbchkKAGCsyn+ku8VDFqLoabul8d1b1hUA8KlqbhkDvgId2PaNeVPyvK4AAL5Lr2obf6N0mYMt6W2yBGAVepKfuXcsF/1T/u09i4NafdYm0efg69bilu/2pADg+liMqSdT2RdyyUYYR3q3zQGQF3FA3S+bpNhSn/OL+pzPPjF3Kz6hc8k7Pccm1uKwRbfRLhRicn5y6Ws+hX5tc1u2461dSxznpQCg9Qm1e508rSsAqIVo/UAfnTbnsw0XqQCAvS4d7tjEbpzXAgCIS0t/kIs+V7YpbFkKAP73d+Zyem0BgJwjHVJ5SAdfbaSfeg5ZACA3VwVUDrnbdTphlwUA7rVyHOzl3NbuBVvd+oPt0/M4bwUAYKt8RgboXLaBP+X/FH62HKoAgG2Xw6yiN37mJu+c3yXih5ob/LQ+vuoP8tY+LDfKdR66AAB0YeU7xblnkaNjFACY77UTmYPNG+kA9+3+6l79Lp9fLFJ+kbzZ6LubkgKAcExSALBhAYBAt5xySpRT29IWAIwW3gpKxef1m5sUABSCP096cYSqCGBuEe+QBQAcoGqPJPW6pN+2SG5ZePWERyX3tGeUBB7h+8avElLaylE9TbKX083BqXYoyhgtmB2yAAD1/voKdJc4ZgEAKnDl1Ei4nJZtCgAkc5znyVAOa/9OH1z0AgD6jFNbc1GA1tMWACwt2lho9JRTjdM+CwAU49Q1tGluS0ucxwIAOr0SzwLZpSDM71o0LUf8LAUAkni1IKHfNgm2WwSPkryC5Fq0lmAY6YOLWACgP9ok++h955twjAIAY9DOU7ahtyHHKgCQ7GBnyPBpg49dFQDwTer+yC4fwUK49tFZbZ+dpwIASP7Y1cFn2ksPCoTdjydC2yT6ugIA8lRJFmM4t5PHJhy6AIAfVzZOYE32+7Hhq12GAgCwrXRDLaablwqVDlkAwI8peTFv6AXFM0uHnW5qMU3b+2Qk9lkAYE7wbfho+mv0ypp9FgDQNVWg41BsKRlkQdu/6Z7Rey634bIUALA7CpzKnxaT1Pw13r0fu64AoD43ryXvRls7b1sAUOhfyWIJyNJD/JB+wVObKyErAbfOn6CjS7YdrV/FLvFf6np8sCXcC/+ofPveDiIFANenfderw5iyjxKuve9iTOi5klnzUEGvPnf+6P73hbaQl1rwMw5klA72b3GsOGK00G7cyi/kKxij3nc8rwUAFs+qTe553fivKwCw619dTzw7t3C4Sy5SAQA9WrkRh/h8Lg4uznMBgLZX+8zjdQ/DzMGWpQDgf39nLqfXFgD4uzE2f6v/+YiKANb5RIcsACCbbZGXPMPIlyjkNyvHsosCAH1Ri/Duea5vC3LYFhH2eZfzWADABygfXMzKF5E7Ig/tK3dwqAIAyMvSb7UjsL6gGw8BeShfSrwg1u/jq/5gUys/wE8U8/SyeowCAJ+XHjK+fS5iG45RAOAaYre6R/HGKOdszMhU5V2Mgbi++pEckd+5ub4JKQAIxyQFABsWAHjvSQW6gux+0tlOsBJ6guqRw8s5FRC0W9JvUwAAbWZw6xqU0+i3DlkAQAFW8EcpC0zPUhU1wm8wiLe5zW1Ofqd3JtbBUNUYae9oMWQdnGPJ2+q3uad9D10AYHGhkkmC/t5otxi/MqKOfjy34TQFAJJXzjcWHPC5RPk6zN2SS8mnOfR9JbuXClQuegGAz9ok8Oj9zpKE5SRzSOew8FOJlX0XANQ7OR1kt94bNqIPiM5DAQD9W5/XIuVI/3nqi90Q+FUi9ywFAOaeHRNcR3A8esfWJtAV9fSwse6fzoM5VAsdkqL17uo5zkMBQBtcuu4osbAJhy4AYOPb1+qQzZE8HaMAQNtsXVxJ8euuu64/ZSN2VQBALmsLU+cIzNgVgXNfEHPeCgDoDYuGbZW/P80XdqZlXQEAH818qP5st5velkMXALRJf+M7Ks6j9y9LAQCMZyU9jT+9UjZh3wUApV8qGSQBssnv0Z8S6tVX7HDv1zqn7c91CTay0D5hteTflK9Bn80li/ZZAFDFv6VPzUfzzLjpS/PhtAtfxWUpAIAkZhWM6B/+JJ1isaC/p3UFAOXz8fMVwY84bQFAUVv2G09t7QtojW3pcnJjkWuJVvdrV59k9PRhxfHsFX9uDsnsdYWvKQC4PmTAU4dlYyt/YPF05MPyZ8iyc4xNxYOKCHpdt2/0N/+g+qJdXOPXjhbttdF9VexmAayXO5zXAgB2sdokJlnyRekfBVk1tqMCAH1Y9kD8ssmrNs7KRSoAYCPaxRB5kqWtsflLlf9YKgBgn+uaFrv2bYsKec2y7fqlf1BrU9iyFAD87+/M5fT6AgAY52uvvfZEz9ai2chfLw5ZAFDzoWItf87lXdky+ZnSL0sFALUluIPOmhsberFyEuzLujhNDFN94757XXAeCwD0cT3kyH7KKYjJxd58rJZDFgBATqB2qXAemZqTpV1inaB8Uzq2z03M4YGtkiu+dF90eowCAL5DxeLGlw7Z1tcujlEAQD7lwWotSD5zLo9Va4TlT/mz5pCd3+bWCzclBQDhmKQAYIMCAAGUhZYKBgVD/SIQo18KheM4Mq4cuHbx37FtAQAsQJQSUtU84pAFAGCYyqhSqJI/u4ahroo4hqw3TOsw3qXIOU3bFhCA4Zbsrn6bu0+LG+1vjYKqXRYAcH4q0GVMFKT0MHwWFiwa1Vg5lhZc13GaAgDzqZJGDD/Zm3OYl+DIV6DBuZqTX/ddAbVFsNFrM8iGBFo5+xbERhyzAKCe1JvDZzVPydwokPIESbVfYDFK1EgIlC5z7LsAQDvLMSVDxmHULosFEpKt7M4lpQ5ZAADFFnUtOpdMt4u25omEr/s0T2suLy2crUMfsQW1AHHa12m4TiXvzMtRgt0cqgCcHAryl9h3AYB79nTB6ClQ6Hv2reTYtn+n5dAFAIJ6RXT1uXvuA2eY7+075Ua+y64LANioSpAL3k6bFN9VAQD4HnWdOuiZvlp+kwIA32EfBJYO9n3OrmzCUgFAfV5JCwe5lrTqx3tdAYBx0E9l5/geAvnTcOgCADq8PrdYM1qw8J228GsUsF+kAgCYZ/W77cFfH8mmBTl22H35c8lfXEICpxaWjHFfbLIE3Vf+CFnt560xaAs1PAW+hD6zCOlcc3xp54raWUAMxZaOcC9lW8nLaDxPWwAASdTSue6/fG5JvdMW4LXsuwDAvCnd5hjFqZuyrgCA/FeBYh1s0ShmWlcAUElwf77mNa/pP558GDFP+e9i+dPobfrHuLKDFq566Ly6F4uPc/1Hh5XP4LDdfI8EdcUv7NFocQF0u8Xf0rUSwaPfvVoKAPRtK8OS66N4oWhftVOHBf1RH9KNdHd7rmPdQq6FGfa32mSebTJvl6hCrdJn7D755tOyt6Pr+z+/X+0mg6PFN7ajFvcdcz7foQsA2jjDPDRvej8O/CExQvWNY1QAAAtU7biPfIddcpEKAOCBiPo9fT7Sz1AkUnrYMVcAQAZrgdBBp87F1pviPjbR5+2rsOjCUT5K7Nr69yP5YsvOWgDgunagqN8yrpvcwxwXoQAAxkpbK0fNx7RYNupnHLIAAGxUFY075NBbHUl3VaxI11ZOYk7e3Zd4s64n37Q0zh6wqHPZ3373v0Ie0XytfNfILzqPBQBo82F1KJjsZXaTAgC5s9a28jlG9mWTAgDQh5UDZT/kMEf6yRpJ+7tyNkvjuoT54ffoarmoTfMmfOzyac01fnL73WMUAFTesdayzN11u3zMcYwCANAn9ToI82vuoS2QWfOwlWXrUGdZNyn2VQCgn8hr6yf3OaUQUgDQFAA88IEPnBSDAFnyggEReFOCbaBhUa93ZijNSgYJligzTgXnw6KxQIYiN9FbJ3pUACAYU7nKyRfsStBTTowUo207RdeiuOYWNg5dAKAvOfq14MKpsiAl2PBb2k8BcWr0Lce7V7gUloo3AQnDp4/ds/M4ARyNcmAkQXojKnnI0HIo9X3/fX3quxVAjxZM1tE7Ze7TmPh/Y8VYSvRLOFRfMDSck9752WUBANrtSSWWOJr6Wl9olwBecKRtlbRxnMWQnaYAACrxq0BCmzzRoq2trBs/bZfUHTkYEj+SInUfFq59x/cd1d/kpJKqxo3DpK+da8z8nXPNmNaYSWJoSy9jxywAcJiHFqvNyeor92E76VogESBZ6B/N53YXDedxHugoh8QIR6iqHGvrs30XAIBM15yis7TLmGuXAIm8CAA9xdEumM0lpQ5dAEBOJMvKWTcX6AZJKUnUCpTJqwRj6bG5QpNNsTBWgTGbM7oHesZCHrvS61V/Zx+qPRzLUeDk/mqRuAJ+41NzrQ/I9l0A4DBXLbyw0eyMe3L4e/t6EPI0F2RvwqELAOgtAXTpImMieUTX0i36XbBAJttCHU5+Pw67LgDwHtzaCWKUDNmUXRYAuFbpKgf9wPfodfcmBQB0YCVDHfTI0pOZ61hXAEC3tG2fe8pkXQEAjKMntOtJA3ZVsnPkg/ldMjhKAh26AIA/WXpT30searN20lcSevxZsl42gt7v7dtFKwAAvdLOYcdcAUCbwPEn+7Ut5oSYo2SS3zlXRDXC/Gi31Obz9/OMr1oyaDz0Q9mbXj/5THK4xpXsGfeRXakkmkPSk9/qXPPTPbDz7HP7xBa92fflWQoAXIu/W7/h8PcnPelJO0mw7LsAoH1FlGOTOTLHugIAcsGvaoveFR/3MoB1BQAV22i74hVyWP67+Nq72tm5NubiO/ayyccQa1TMZszIGf1ID1VSkI8/KoQ0FrUDDluprX7f9/W3P8UEdH35Mvpm9Jo4iFkqlregJ37SrvJl/N18bRf7+MKjsb0MBQD6vBbw3GsvU/Cb1SYHn2Bp7pnvFfM49DffrJcNkClj116fTC3F5uBbtHrcPBuN0baQyTbGdZhPbOQc7eujyKA5bp64t7Kn7JS5VrpAfmrU3kMXAIB/XbkOdk7eh/9jPpMvBUDkUj+wY6WL5woAtIXf516dK89HhtiNdv77t+v7zP+dloo7jIFCNX2+7mh3PTp0AYAcS73WyzlsKP+ZnvAZPUCPWwCVvyzfYW5BFGSs9ILraa++JmOO0dxbwpi4XzqO7ifPpXP5ff6unYrCy08kI32eFjU3HIoKR7EHvXPWAgDXaH17duMsscRFKQAo2LbyV81VuZ/R/R+6AAB0jDlW+o9ci2HsokZ/0RN0kH6omIJdGvlL5I9uLD1UT3iz3yXvLcaN3Pltc5feYl/ogPIhxPj0XsVX7NfITz+vBQD8+jaO1r+jmHWTAgB53nb9Rc5xtEvcpgUAZIS8lr7j68lF9PGkMWxturWWkT5ZRxvjm79zu+3M0RbRig1aX+QYBQDgC/KhSub5KPq/9HKbayALfNhRYf+xCgDMTfFixSbuw+ucjZXPzFGH+2Av5b1aWRDHjGLLbdlXAYB+FxNVex1nyYGGy0kKAJoCAI6hiSWQpahbR9HB2eGEjgLmSgy11+LwSS7WgiEHwpN97VatowKASmRRThSw5BLlyNByHCq5xiGa20rm0AUAkAC1hWL1mz8lbigi7ffuRsZbH7u3PqHAMFaVO+eb8yVhI5kl6VPOiwWIUXJGYtk5FLXvc/4s+vttY1POqvE9reHgPJTzWH3nepKYkr6eXtAGSdIK3Bkqv2/xpXUgdl0AwMHTlnJqyYm+JgttcYogUBBcvz03nptw2gIA33NPxrICc3NFMkeQZ6GPY1xtHz31w8FwThUSkDdjTl7IcytfFgRLLv2e6wqE9Lux5IBYrOc4+pwMcRLNzzaZccwCAP2r8MV9WFyWLHevFn1qcYhDwGEctQvaJjlYjhuHUJJHsOy+3Lv+kGRUaOScQxQAmAucqpJx5+tfbatEqvsm++0CxGgrVBy6AADsAnmq9raH+2E76GKV0BWo6JuzILiVhHZ99mY07hz4SmaoXOW4S0yRHQs4Jc90sv+bS86QgZpD5qR3kjmffu2fIt13AQA55TSz09pvyzlJV/qePjJOzvG5J8DOktA7dAEABGFlwx3suLnGdyBH+kngb37U7wmMJADahcddFgA4Tz+Yo/qVLjktuywAANmhPxz6fRS0nscCAEgsVNvp7lHbNykAAB+I3q7A1nhKivoN+o7/SeewI3y60VgfugCAT9Ruy2heuwc+lTnARrHLbFT5gP7Nd2+TPBexAID/Xon8OvZZAMBnMqY1vhKXfYJyCX3MBpc/SQ/1iUkJ2EpU0xWSugp72Qm+ZutPuR45MubO53fRReyxtrV2hX9R55EV+oxOZIckbf2W8beoWYsAxkERTBvbnKUAAJJBpVMdfP6Rf3QaLlMBAOhcc7n022hnMqwrAGj7XPvpQL6LOMbigc/oB+Na/ju/WKF5K2/l05JPciYm5R/QjxU7aYPYYG5Rmfzwo8wfdokNljx0b65Dn7iGQ78s9bHf0KbSa9pFpvkx4n32tvxy57Btc4nty1AAoPCxdAcbNrK92xYASOCKl+p8c55+GWGutVu+OtjKdeyrAEDbxbjttfkB/SJuC3vbxnh8ADrSfNEP5Ihc8gvK36bD6Jo+n3SMAgAxjXldNqb8ePOqik/dk/xB5XX831wBAMzZNg/CxxNjmP9kQVxqHvp/Nry3adtQuTJj5u/s+7qj3ar+0AUA8KQ63Vky40/jKMYoGRFTibdrwXGpAEAuS+6v5JZ88T3F4ub4trGDYr+yW2RAnE3nup4FEH83T0v324V0bjeh1nanAOD6i0e7KgAge+L1ihH4buS8H/tjFACIORQo9EX0DvejzXQmH77k3VjyJUeQD/5NXUMRgJiUfI6+w4Y5X/9VvlueX0ysGEuOq/yLephrlJ8/rwUA1hfEu+X3kY/RTjTHKAAAmah+c7AN5LCdU7sqAGA3S+fo99E4LmFdpNZ85BL4k5WjO1YBAPQ3H6Ku6ffFW2wpX5Rtlc8wb5wz8jOPVQAARTZihDZf69rmrfUGuRFxlDaSAzJY88fBdpjHc/nSTUgBQDgmKQBoCgD6g/GlmCzcM+iczKVtHjklFGIl5Ovwb99nNBklic36bFQAQPlwCPrXBdTBAZZwXdrC/hgFABQhoylByBlvg+g6KpEx2qKckycoE2y2Sao6GBEOoraOYKgoZcp09H0GShC5dK+bULsRMGzt7zAQHEqLQBIhqqjLIXFIWrVjtusCANg9gfPYy6B2apvEBIeqfZ/k3HhuwmkLAKAiUNLM3GDIRmNmzAWio/c1wkIaR72XNfLXJo+1k2Pdvn/doU8F0sbTmAkKakHUUU8SFccsACA/nNR2gbN+h66ipwQRfRKnx3jTU61T7aBvzC8LnJWY9P+HKADgeAvy6TXOcntv/q6vfde9rXsXKo5RAED/kQeL62RS8owelkQ1f+kDNkcQXLqdTTkLftOcF/Do49HTw/Q3h58tGM0x46Kd65KW5pAxaCvna3x6J3TfBQDulaxwml2/Ent1+Dcbo5BlFCxuwzEKAIyDOWdcWt2mr9liPoR5TB+5zxoPuql9z+kuCwDIGb2qD+YWcjZl1wUAZFPg6qCXRgno81oAwL5U2/kEo3ZtWgAA1+DbOb+fF3WQCbp+5H8eugCADmM7+G6930LfskN2+ZBAsDBQSWrzot0K/iIWAGgzGSkb79hnAYCkRyUqJVSX4pk5jEUlIBz9Nv/uidy0ye862Nm+GIt88M2qGLTOHS06+S3J/d734ddJ9pIRslSFnD73Z5vsPGsBgAWUWnyRGFJ8tslYb8JlKwDQBj5R6bc5W7yuAKASyhaSev+dTXJfdCSdVLGqw9/bnd74L65ROqQ9jCW5Jkftd3rYFrZXctP1+2u5DjtMb7GZo3FocW98QvNypK/9H3nlH412QisuQwEAv6KKfMQzI7YtAAB/qcbJdUd+UCHWbHcMmNtlsWVfBQDgz7Ux6ZwfUdAX7LrEddkLh3nDzliYteArFyUOrPlEzvrcwjEKAPgDZI9u6XNB/s6fstjvHL7KJgUA+oTf4Rzzv499HeRDP5vXo0XyTWn1z6aH+yiOUQBAVvn95nuvg/iBdKwiPnZmkwIA/W0hVvv73Ay5XNJjI+SszNv+Wu1BNvgBYpmRX1uUjnSkAOD6i0e7KgAAPWAhsOIE7adH2lzLMQoAoG3mhv7gc4kNjblYhqyTUdejz1xLPDX3mkX2h56uPEz9vr5RtN9j7OzayS7pk5EPwQ7S1e5nLjd1XgsA9Bs9U34fmRrJ67EKAOD8is31f//g1C4KAOSCyVMVJM/pyyXEU3LkJU9sRT1Nf8wCADi3imerSKE9yudQeNjnuHDMAgDQI171RbeMCoH86f/JE10i/1g7VDk88DqyH5uSAoBwTK7qAoAKNDyROTokURg6iXjGs09cjaBQJLk4UJ5aF3C4ju/7PcE+o1e/MXKEGUtBgMUtk9hCguQOZ0ECwyLP6HstnEXXF1CPFBRFasGt2iGgGBloaDOnzXnaNEoEtFA+Eh8W3iT2GFYLOvqCM2h7ck5fHyBzojhF2q5auPqQQ8/oWZwVxM210/hw1FVL+r7f830Og4VRyUP3eZaKrcI9XnvttSf3x4mUxJEcrQpXxplTqMpM9b0Fj7bvODHV/2RsKTjQn+TKuXMLrdA3ZFrfM8z6jtzod3LnNx0c3/rtsyQv9bndGGq+bGtkfF9CR+Kunk6UvCLz+lafafdo+6CCg6JvBLfuV19rS39ftZVP9YvznScZVHPb+GiLPiN/5k/r9Eme+T/367yRQ0iuq285KHMByCZwYi0qSoqae2TX/XIWJIJK9iQHydCmTy5Y6BLkuEdz05/mR80vsish4B7I0ijgohckkpZ0GQRNnDtjtBSY+11FMBwaes4YCZ78nbwav0po1G9aSB1hnH1OfgQQPe7P4mldh36eS1q5Tzqr+mLuPgvXMeb6kj4yHytB6R7p9Ar4RsUJ22I+Wwirwqq+feRRP3nywnn0kb6lGwXuEsfaOadXW9yX69QcEkiYp/2iHj1LX+uzTYMeeqC+M9rhBW0BANlwb+yY77ERZLlsBT25LjG8CcazbDZ9IeiZw286x7lzyQKw8SV75ln/VAQE/aq/jRl95H4FK2S7xtiY6Sv2jS7QP628a3v1KX01Cor5ApIMdX+juU6v0BESPsZ9FIBvg3ZvWgBAJ5T+kGg+LexB+UXm3cgPoGPo2Robfb2J3zeHuV++05LdXgf5KB3E/1tnU4wpX8R9sh/mqfnOVkjy80v4YCOfg+/pd/T30uuB2jk+VzSC1s+hC0ZPgmgvHUIXaSNZ57Owc1XQRma02f2QdffR2hOf1/iyZXNPgLW4/7IVjlFyfFP4KO6v+mNu2+8eY8lnrTaYCyPZNBbuq+6v33VlE9xf6ac5vbMOfVb36eAD9NC77DTbQPaMFx3V7wBQ0C/kWkEcOS1dJx5o8V2+MN+HTuTrSUDyfaq/+WTuzcKgzy3GtfNF4rP6+7T2txbpLSa0T2+eFfenALz61pwYyUKP75Uf7tBvI3uu/+scxyZzZA5jzMa5Dn1ylrlTsSg9P1fsS+4kuPlP5Ikf43x6rhLkdLyYXfxQPm0r42xb+bV8H3JW/i95Y+fnku0txoQc0R18b20Rt4gpyKP5QW9tMnbgx9Bl5JG/K2Yl33QcO2kng3W+DH1Q+tjcm8M8qfEXn8yNW8VLde7I5utv7SsZGMVDm8LOinNqkU+x1wi6opVhPsG6gn7yVfGzdi7ZT9cvO+JYKrYr9EPbpk396U3gs5Xtdywl5Av6QLvND3kQMk6W6D1zvuSSfynu5tORu96/UuzqN41x7+O3WDip3c3076ZyP0fNL+NQO0dqIxuiTeX/0hU1ruzCOoyt+a9fXEterXQJP9u8pTvXydMS5e9tc7Q6z32XDyrWGukj8ss3cg57PvLjUPmC+h25jtH14Fw6hA4qO0wH0Yv6tuwrO+1a7lMeYQ59KKajD/l09KN+JoMjXbKE+2PjjRsdq8DSwxUWgzzwQjbEpPTVyL9sKR1Z/TvSBXRf6+P0uaRNcA0xV12D3J4lltDXbWxy2jnGXvq+eTPKn7Azxrt+Zy6nR1fU3Ov1Rgu/uHSDgw1vZXBTeS+fzW/OyTvY3fqtJXkvjIl7VMQlD0S3lI/KLldRstwKeZ7D/NBv9Kl5U7kIeeIRxo9c6Q+5OHJNH5kj4njzTOy7ZEd8VvORr7P0EBndZ247l495FlstRnEdc2npQcR16Hf2wrXYmBGKMc37GlPxxkg/G7ea2+Rtk/sjb3Xdeu1owW9sf3e0ZrEOctr6Ekt57DnICTmua5Cn8ml9Vj44OzBXzAk+UMV+c0U90Mb6rXXrEqDLxR76nm/OVrMdfGpzgZ3i2450H9n3O+Ryk2L0NsZ3L2eJXwr6gX+tQEK8qKBbsZbiSOsR7sG8dh6Z0v/VP46RDt2U8hXI/lIOrKf8QXN+9Pv6mry27TxNrB8uN1d1AcC+YJw4LBwbzugo4bUplCvDLcDyJyfjtI7fMdBeRlVf6BNKdJP2U2DVh75PeW3Tj+33Ler6/pIjdVqMj/YZ55FT4l4FApyYbZ2Hs+BeGStt42Suc4LPA8aMjEt41HvxNh0z53FcjLfvzslZ2y+1oNzjHH1GfraRuX3g98kuJ6xPAPr3kuyto9VT/jzNNfaBMSm9Ye5umyw4z5BNyYpa+JxLdm+LOcNxVSnN+Z6b78aY3JdONme2HfdetxzSJvUFAIXfN0/YSX8eqj2HwFjSi+bCnA1xzz6fG/ezQM8IVDwdpRJawH/W/t2mACCcHuNk/MzT8kf3ISO7gnzTkUuyXD7Xpgvs4XgYI7JnvNiMJX/OZ/QYOV2yK61Mk5WR/Sp/cB9+twRzPSHsyZf3vve9/SlhT2zqv7OXS/67c/jU9Ayfwbnij9H11lE+FR/MnyN53JTyfbWLfPc+/2XB4rT+qv42RyVW6ylyfuwmC7phGf3K5tOpc7LEzppP7Opp5H/faJ/54B525duXrTH3S5eMFiiuRvQvHVt2eE5uNkW/ksHSj2exx/Q5u27h0IK4ohT2dxP/IoRtUQxUMardMUYF9D3lm9ItfIw5H6Sl5Np3zupDhHAs+BBlq8unvihoq3hRkTD/VDGNOGIuDxHCRScFACGEEMIB8GRWbXdny+NdFTdI2njqSALVVlwWyi8jcwUAYT+QK5XjFrxsVegpjF0EdX0BgAplTyOqCF966jyEEI6JJ749FWZrSE+IbJLgDSH8H5505FN4nY2neT1xZTvU2hlLkWwWQUIIIRwDsa9XObJHcip2UomvF0II4TKQAoAQQgjhjFi8tIXi6AkVC56enG7fK2h7uF0+seB3PZ3oqUTv3LqMpADgsEjC26LPe89sYbmrBEhfAGAxzZhKtFgcCCGE84YnRMqG01W72sEnhKsJT1OW7W/f8c4PsPhvnoUQQgi7xq4Rti2fe/2NXXhsCV7b/9/iFreYdqgJIYQQLgMpAAghhBDOyD3vec9p69K73OUu03vcvM/a+6+8A1FS88Y3vvEUTHrHqSeeNtlO7jQIar17+TKSAoDDYsHf+x29C25U2HJaFACYG576aw+7DHgPWwghnCfoP+/09SoUNoid36VODOFqgX9cuwo57njHO66uueaaqYj2rFuOhxBCCHMoZreof5Ob3GSyO8985jNXL37xi1cveclLVk95ylNWd73rXadcDj/vG77hG6aHNeLrhRBCuCykACCEEEI4IwoABIueYqonmtrjRje60erWt771lPx83/vet9eAcldPap83bL9s8V8RxaMf/ej+47AHdrlLRYt3sHrPWn94h1wIIZwnvNNSIZ/EMBv0spe9rD8lhLAh/ArvWHXsy8cIIYQQWv7wD/9wdbvb3W7KI/R5mnrAQHHA93zP96xe+tKX7uS1dyGEEMJ5IQUAIYQQwhl5wxveMD31711xP/ETP7F6wAMesLrvfe87VZg/5jGPmarM//RP/3T16U9/uv9q2JC//uu/Xj3hCU9YPelJT5qC+BBCCGHf2LHn13/91yf744mwT37yk/0pIYQQQgjhnMKXe+UrX7l6+tOfvnrEIx6xetCDHrS6//3vv/rhH/7h1UMf+tDVz//8z6+e//znTw9qhBBCCJeNFACEEEIIO+ILX/jC9LTgv/7rv05PNHvCyf+FEEIIIYQQQgghhMNjF0YPZHz0ox9dffCDH5wOhZ3ZkSaEEMJlJgUAIYQQQgghhBBCCCGEEEIIIYQQwiUgBQAhhBBCCCGEEEIIIYQQQgghhBDCJSAFACGEEEIIIYQQQgghhBBCCCGEEMIlIAUAIYQQQgghhBBCCCGEEEIIIYQQwiUgBQAhhBBCCCGEEC4dX/ziF1cf+MAHVm9729tW73jHO1af+cxn+lPCgfjUpz61ete73jWNxfve977Vl7/85ZPPPv3pT6/e/e53T5+1x3ve857V5z//+eYqIYQQQgghhBBC2IQUAIQQQgghhBBC2AgLt+1xGnZxjU34r//6r9XjHve41W1ve9vVne9852lRORyHN77xjat73OMe01g87GEPu2Jh/+///u9X9773vafP2uMBD3jA6kMf+lBzlRBCCCGEEEIIIWxCCgBCCCGEEEIIIazlne985+qJT3ziyfG0pz1t9da3vrU/bRFP5D/72c8+ucYzn/nM/pSd8R//8R+rBz3oQauv+IqvWH3d133d6s1vfnN/SjgQf/Znf7a6xS1uMY3Fve51r9V///d/n3z2pje9aXW3u91tddOb3nQ6bnCDG0znKdqwW0AIIYQQQgghhBC2IwUAIYQQQgghhBDW8sd//Merr/zKr5wWZx1f/dVfvXrGM54xbeG+Cbbkf9nLXra64Q1veHKNb/qmb+pP2xkpADg/LBUAfOITn5gKSewS4LjTne6UAoAQQgghhBBCCOEMpAAghBBCCCGEEMJa+gKAWsy97rrr+lOH2JL/wQ9+8BXfTwHA1cFSAUDPPe95zxQAhBBCCCGEEEIIZyAFACGEEEIIIYQQ1jIqALjRjW60ev3rX7/68pe/3J9+PSzm3vjGN04BwFVICgBCCCGEEEIIIYTDkQKAEEIIIYQQQghrqQIAh238qxjgCU94wkavAXjBC14wne/VAY4UAFw9pAAghBBCCCGEEEI4HCkACCGEEEIIIYSwlioA+Jqv+ZrVXe5yl9VNb3rTaaH2Dne4w+pDH/pQf/oVfOELX1h993d/9+qrvuqrVre//e1Xt771rTcuAPif//mf1Sc/+cnVRz/60dWHP/zh1b/9279N//7Sl77Un3oFSwUAdiz41Kc+tfrIRz4yHf6+yS4G7sPxxS9+8Yrz/ftjH/vY1D7t/PznP9986//w3Y9//ONTf/37v//76jOf+Ux/ylr0xyc+8Ymp3a7x2c9+dqO2Qzur/Uv953rtuZvgO9pifPRFu8ifAoAQQgghhBBCCOFwpAAghBBCCCGEEMJa2gKAH/uxH1v96I/+6LRQa1H/T/7kT/rTr+Atb3nLdN63fMu3rB796EdPxQBLBQAWpy2m/+Vf/uXqRS960epJT3rS6lGPetS0oP/whz98+vfLX/7y6Zw55goA/vM//3P16le/evWUpzxl9ZCHPGQ6nvzkJ6+uvfbaxYVpi/q/+qu/Oh2+7zra+Q//8A+rX/u1X5vu65prrpn+/I3f+I3Vv/zLv5wsslu0t5j927/926uf/umfnvrOfTzzmc+c2mWhfR1+73Wve93qhS984bTrwoMf/ODVIx/5yNWznvWs1e/8zu+s3v72ty8u6uMP/uAPpvb/1m/91uo973lP//EJ73//+1cveclLpnNdewkL/x/84Aen8dCWhz3sYVMfPPe5zz3ppxQAhBBCCCGEEEIIhyMFACGEEEIIIYQQ1tIWAPzsz/7s6nnPe970KgCLtT/+4z/en34FFqqd973f+72r3/u931tbAOAJ+Uc84hGr7/iO71jd4AY3mM7tj5vc5CZTUYDF5xGjAoB//ud/nhanb37zm1/vere61a1Wz3nOc2YX0T11X+fe5z73Wb3jHe9Y/f7v//7q+77v+05eaVCH31MkoW2u96Y3vWn1Iz/yI6sb3/jGV5ynP/XJa1/72v7nTvD9d7/73asnPvGJq1ve8pYnr15oD2Ny97vffVqs/9znPtdf4oR73OMe0/kW41/5ylf2H5+g0OC2t73tdK7dGpZ429veNvVzf2+Ob/7mb149/vGPXz3/+c9PAUAIIYQQQgghhHAgUgAQQgghhBBCCGEtbQGAxWhP51ugt1hrMd7W9yP8v88VC3j6/W//9m/XFgBYVL7ZzW42fed+97vf9IS+p+o9Qf/Yxz52up7vW2h/xjOeMdymvi8AeNnLXra6733vO+1EcKMb3WhaMH/gAx84vcKgigy+8Ru/cfVXf/VX/aUm2gKAe9/73tPv3u52t5v65Lu+67umwgLXc0/OcU39pAjA/SoSsJjuyf2HPvShJ69Q8P+KA+YKGRQa3P/+95/uwfl3utOdph0A7IygCMMOBnZW8Jk/H/e4x007DozYdQGA3Q/ce/Wpxf1f+IVfmNqmHT772q/92qm4gtykACCEEEIIIYQQQtg/KQAIIYQQQgghhLCWvgDA++c9+V+L4hZ9R3gq3ecWvG2z73UA6woA3vve965+6Zd+aSoE8D75T3/609MW/LbKtxBva/r63R/8wR9cfeQjH+kvcUUBgMX429zmNtOfflvxgvY7x1b43//93z/dm4XsxzzmMf2lJtoCAE+2ux8FA7bT9957n2vrL//yL588De9P17Z4r0DgXe9617QlvuONb3zj9HvOs9hu0b3nk5/85OqpT33qSYGCXQUsin/qU5+a+sPT/q5lMd8iu3MUR8y9kmGXBQC2/ldQ4RyL//rtAx/4wOqzn/3s1DZtV9SgUOLrv/7rT/ouBQAhhBBCCCGEEMJ+SQFACCGEEEIIIYS19AUAsBBfi7u3v/3tu2/87yJxLeje8Y53nBZ+NykAgO/OYdG7FpTvete7ToUCPW0BQB0WrEdb5L/gBS84ecL+bne7W//xRFsA4PC0vXfb90/bt/fn8IS/RfzRLgVeH+AcBQUvfelLr/jM/XvCXpFB9a9XGMyh8MDYGCPX1d6eXRYA/Pmf//n0dL9z7nKXu6w+/OEP96dMuO+Xv/zlJ7s2pAAghBBCCCGEEELYLykACCGEEEIIIYSwllEBwJvf/ObVd37nd54sdr/1rW+94jsWw29+85tPn9kavv5vkwKAddSW8l5D4In+nr4AwKK4p/5HvPrVrz5ZoHY/nmLvaQsAtNuCvSfde+wo8AM/8AMn5z7ykY+8XpFA4XUAzlFE8cIXvvCKz+x28PjHP376XL8/7WlPm578n8MuCO2rAF7xilf0p+ysAOBLX/rS9LoDOxjYneDnfu7n+lOuQKGE33S9FACEEEIIIYQQQgj7JQUAIYQQQgghhBDWMioA+MxnPjNtS19b2Xs3fWGR2PbvnhK3RbyFcZy2AMAT8Z4m9zoAOwDUE/u3u93tVm94wxv6068oALjhDW84vX5Am0a8/vWvP3nS/g53uMN0Xz1tAcC9733v2afxvb7Aawnq3KVF7Ic//OGzBQAWye93v/tNn+u/V73qVVd8PuKHfuiHTq73nOc8p/94ZwUAH//4x1d3utOdTn5r7pUDRQoAQgghhBBCCCGEw5ECgBBCCCGEEEIIaxkVAOBFL3rRydPztvm38A5P23sS3ncsTNeW/tsUAFiwv+6666YF5he/+MWr5z3veaunP/3pq8c97nHT1vqusUkBgGIBuxXMYTv7bQoA7nOf+5wUNPT0BQDvf//7+1NOWCoAsAtBLcLf7GY3mxbR1/GTP/mT0/n65rGPfez1XqOwqwKAd7/73VO/+9zY/93f/V1/yhWkACCEEEIIIYQQQjgcKQAIIYQQQgghhLCWuQIAi7QWiWtB33b6sGh/q1vdavr/17zmNSfnb1oA8M53vnPaQcBiu9cIeIrfQvktb3nLafFZW1zjshYA2O7fk/91j3/zN39zxecj9Ff97jXXXLP63Oc+d8XnuyoA8MoF4+Bz/eael0gBQAghhBBCCCGEcDhSABD+P3v3/ZXVma5x/K86v5yz1llnrdFEjaaYTMpkTJnJzOhE08wkMcEaexexd40lVjQRsGMFe68IovTy7rf3ch2erezI1sT2ovDy/bDuZYQHRBzDzFzXc28AAAAAeKTfKwCY59u3P8vePA9+zJgxdoBuzpjQ3gTI9wfqj1MAuHr1qh2im/c3v55Zhb9x40aVl5fr3Llzunjxov1ogfZwPBcLAOZRB//3f/9nv/3VV1+1f++PMnPmTOfXHTFihBKJRIe3Z6sAsH//fifQpwAAAAAAAEDXQgEAAAAAAPBIv1cAMMwN//bgedCgQSouLrbX/5ufm1D6fo8qADQ0NOizzz6z19ibX8+s/Ddhfjwe73DOhObmY+RqAcA8AuC9996z3/6nP/1JpaWlHd7+MO1FDFOcmDZtmvvNWSsAnDp1ytn6YB4BYP5M/wgFAAAAAAAAnh8KAAAAAACAR/qjAoC5ad5+W71Xr152iGsCdRPiX758ucPZRxUADhw44ITLb7/9tvvNjlwvAJiQ3KzxN283mxV27tzZ4e0P88EHHzih/KpVq9xvdgoA5pEKv/fxMpmMNm/ebP/ZmLMPKwCY3+PAgQOdz9087uGPUAAAAAAAAOD5oQAAAAAAAHikPyoAGOZ17aF3+wwePNgO4u/3qAKACa5NgG3ePn78ePebbSakNqG+OWMKACbAd+vuBYBkMqnly5fbJQpzZuzYsQ98Le9nvq7/+7//a5/t37+/Hbq7mT8P83ZT1ti0aZP7zfbjHI4fP+5sHjDzsAJAMBh0ygamnGA+N/O+bubPqbm5WXPmzLH/c2POUwAAAAAAAKBzUQAAAAAAADzSowoA169ft1fPtwfH//Vf/2WH2u6w91EFgGXLljm3+82N9UAg0OHtJogvKyuzg2dzxtwsf9ht9u5eADDMx3r//fftM3379rW3I5higFtra6tGjRplf01MYcB83Gg06j6mCRMmOKG9CeXNYwbamX82mxRMAN/+cczZhxUAjAULFjh/BuZxAeXl5R0e02D+3CsqKvTjjz/qv//7v+3/PJizFAAAAAAAAOhcFAAAAAAAAI/0qAKAue390UcfOcG3CY7PnTvnPvbIAsC+ffvsG+zm7SY0LigosMP7ixcv2gH4rFmz9NJLLzkr6k24bJ53b1mW/Tm0y4UCgAnKN27c6DxewdzM/+WXX3Tt2jU1NjaqpqZGJ06c0JQpU5wz5mvnfuxCu19//dUJ4s3jFdavX2/f+C8tLdWiRYvs3/uf/vQnO4Tv3bu38+f4MLW1tXrttdecP6dBgwZp7dq19scqKSnR4sWL7Y0DZpuD+fNu//pSAAAAAAAAoHNRAAAAAAAAPNKjCgCGCe9NGG3GBNcmlHd7VAHA3GY3wXj7NgHz65ng26yc79evnx2WjxgxQtu2bdP//M//2GfM67///nt7C0F7CSAXCgBGU1OTfdvebAAwZ00w/8knn9i/t88++8wO8tu/Dh9++KEdvt9fhLhfS0uLHcC3f24m7De/31deecUO8U2JYPbs2fbjAcytfnPm9woAhtm80F7WMGM+D/Nz83HNf1bMhgDz57JhwwZ7U4M5QwEAAAAAAIDORQEAAAAAAPBIj1MAMCvgfT6fPWal/MOC6EcVAAwTok+aNMlZMW/GrKQfOHCg1qxZo/r6eiUSCY0bN85ZVW8+N3M7vn1Ffq4UAAzz9dy9e7cd/Juvf/vHNmOCe7MRwWxBuHr1qtLptPvdHeZtZiuDKQ7c/7U1Y4oEprRhSgIHDx58rAKA+TMwj2Mwn5f745kSgNnWYLYUHDp0iAIAAAAAAADPCQUAAAAAAMAjmVDchNkm0PV6ve43PzYT/pr19eZjmTXyD2OKA4FAQFeuXNGOHTvsG+QmwG9oaOgQHpvPw6zA37Jli06ePGmH9O2lg1QqZYfZ7b/OH4XO0WjUPmPOml/jYcUFE56bt5tpbm62w++HMQUE8/b2s+2FhIcxJYX2z8/8fv+I+ThmO8Lt27ftQN0UBrZv324/GqGurk6hUOihn7eb+X2Yr4v5epmv67p16+yPZzYNtH+NTHnDlCzM52Z+/CPm45nPyzyWwDxiwBQ0ioqK7M/TfE6G+XjmczQfz/w6f/R5UgAAAAAAAODZUAAAAAAAAABdAgUAAAAAAACeDQUAAAAAAADwQpjtB+bRA0uWLLGnf//+FAAAAAAAAHgGFAAAAAAAAMALUV5ertdff90O/e8fCgAAAAAAADwdCgAAAAAAAOCFqKio0PTp0/X99993mPz8fLW0tLiPAwAAAACAR6AAAAAAAAAAXohEIiGfzyfLsjqM3+9XKpVyHwcAAAAAAI9AAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAQAAAAAAAAAAAAAAgBxAAaCTpVIpBQIB96sBAAAAAAAAAAAAAMgqCgBZlslk5Pf7derUKa1bt04//vij1qxZY78eAAAAAAAAAAAAAIDOQgEgy0z4P3XqVA0ZMkRDhw7V4MGDtXDhQgoAAAAAAAAAAAAAAIBORQEgy2KxmM6cOaOqqiqdPn1aeXl5FAAAAAAAAAAAAAAAAJ2OAkAnunjx4gsvAKTTaUWjUYZhGIZhGIZhGIZhGIZhGIZhGIZhusEkEgl35Ac8NgoAnagrFADMRoL6+nqGYRiGYRiGYRiGYRiGYRiGYRiGYbrB+Hw+d+QHPDYKAJ2oKxQAUqmUwuEwwzAM042nwRPUsRuWfj7RrHXHma46a4/Va+nOM5q7arumzJyn8eMnMQyTAzNh4uS2v9PTNH3eDM1aMkv5q+do3oa5WrBtnpbsXKBluxZqxb7FWlm6RKuPLNVP5cu09uRyrT+7QhsurNTGK6u06cZqbalao623f1Jh3VrtaF7PMAzDMAzDMAzDMEwOz69VWx/4/3mfZOLxuDvyAx4bBYBO1BUKAACA7sl822gOprTvekRLyvyastfSyKJW/bCT6Urz7dY7Gjpvvz78YbHe/ORbDXj3X+r72l/U++VX9ac/9WEYpgtM75f6qu+rA9T/ndf0+ocD9dY/39I7n72j9795Tx+Ofl9/m/CB/jnzYw2Z/3cNW/4PfbFusL7e+m99WzRUeQc/18hDX2jU0S81pvwrjT05XOPOfK0fz3+tCRf/o4lXvtGka99q8o22qfhOUyq/09SqEZpaPULTbn+v6TVtU/uDZtT/oJmNbdOUp1kteZrtGckwDMMwDMMwDMMwTI7OlMoftObMz+7/yxd4bigAdCIKAACAJ2W+XVS1JPXzmaAm7bU0psSjvIcEz08yI4s9GrfLo8l7PJq531L+Qa/mH/Zp6TG/Vh0PaN0pvzadDajwfFA7L4W062pYB25EdPhmVOW3Yjp9J6YLdTFdbYirojmhak9Sdb6UmoIpeSNp+aI9ay5cr9aqDYX6/JvR6j9wkF5+5a27gX+vvg8Ej9maXm0fu3//N/Tmm+/qL3/5UB9//A8NGTJUn38+XN9++71Gjhxj31SeNm2W5s6dr8WLl2rlyjXasGGjtm4tVFFRsfbu3a/S0kM6dOiwDh8+oiNHjuro0WM6dqxc5eXHdfz4CZ04cVInT57SqVOndebMGZ09e07nzp3X+fMXdOHCxbb/bnNJly9f1pUrV3X16lVdu3Zd16/f0I0bFaqouKmbNytVVVWlW7eqVV19W7dv39GdOzWqqalVXV29mpubGeapp6GpQTcar+l8/WmdqDuqI7X7tb+mSMV3tml79QZturVS66oWa0XlXC2qnKb8ih815caIDuH8lKrvNPXWvYD+TntA/71m1LlC+mYT1D/4P+BzdfI9o7XAGq9F3kla7J2spd7pWuGbqVW+2Vrjm6t1/vna4F+kn/1LtNm/TFv9K7Tdv1q/BNbq1+B6FQd/1q7gZu0JbtXecKEOhH/VwXCRDkdK7s0uHYns1tHobh2L7lVZdJ895dH9Oh49oJPR0rY5qJOxgzoVPdQ2h3U6eqRtjuqMPcd0NnZM52PlbXNcF+w5oYuxk7pkJn5Kl+OndSV+xp6r8bO6Fj9nz/X4Bd2IX1RF/FLbXO6Cc0U326YycVVViWv23EpcV3Xihm4nKnTHTPJm21SqJlnVNrdUl6y2pz55u23uqCFZo8ZkbdvUqSnZ9u/atmlJNtjTmmpsmyZ57GmRlWqVr238KUuBtI9hGIZhGIZhGIbphPEnfArHo+7/6xd4bigAdCIKAACAx5Fu+xYRiWfsoN2E8+4A30xeUatGFbdqwWGvDlZEVF4V1anbJpiP62pDQpXNSd2xEqr3J9UaSikQTSuayNgfG08nnU4rmUzK5/OrrKxcs2fP1fvvf2CH8e6A/v7p3bufXn65vwYMGKghQ4ZpzJhxmjRpqmbOnKMFCxZr+fJVWrfuZ23ZUqidO4u1b98BHTlyzA7dL126ops3b9qBeUtLi4LBIP8dAjkn47yk772k7r5kkvKnLTvgvBw7rfLIPjtU3uhfoiXeqZrjGfVAcP08x/z6HWe0HZx3nDHOzH1gxt4d67dZYE3Q0rbf20rvbK31FRBU1PoAAIAASURBVGi9f5E2+5er0L9GvwTWqTi4UXtC21Qa+kWHwyUqi+7RiehBnYkdtUPwK7Ezuha/oMr4Vd1O3FBt29euIXlHjam2f4ekGmSlm+VNexRIexVKBxRJhxTLRJTIJNx/LAAAAAAAAMgRFAA6EQUAAMDvMcF8MJbWHW/SvnE/fb/3gZv+JvQfv9ujOQe9+uViSHespPvDIEvM9+l4PCGfz6f6+nr7pvvatRv0zTff6/XX33og5L//Zn7fvgM0cOA7+utfP9KwYV+2fd9frKNHy2RZXvcvA/QIJtJPZhKKZaIKp0MKpv3ypS1ZqWb7pnJl/LJ9e/toZLd2hbZoS2C5Vvpm2SG5O3TP1phwfq41RgXWOM2zfrSD94XWRC3yTtZi7xS7YLDMO13LvTO0ou1zWembrdW+fPv2+1rfPG3yL9WOwE8qaft894d26Eh4l05EDti30s3t86vxc/YtcxPEVycqVJussoP45lS9POnmtt+/x/46mAA+kYm1fQ9Iub9sAAAAAAAAQFZQAOhEFAAAAG6xZEYN/qTO1ca0/nTQDvjdt/1HFrVqVqlXP50K6FhVVJ5Qyn40ALLLfG82gX9V1S177f22bYWaPHmaPv74n/YtfnfYf/8Nf7OK/x//GKLvvvtBBQXztWvXbnvtfSpFqIfcZwL+eCZm3yg3wbZZMW7C7juJSnuN+aXYaZ2MHrJXwJsb7Fv8y+0w3QTuz3qLf541zg7tza15E9a3B/UmpF/vX6Cf/Yu1KbBMWwIrtC2wWtsDP+nX4AbtDm7R/vAvOhLZpfLIAXu9/IXoCV2Jn1NF4vK90L5aTal6WekWO6yPZiL2VgIAAAAAAACgO6EA0IkoAAAADPMtwKzkv9oYt2/7Lzrqs0N+d/BvygBLy/zadS2s600JRRJ878i2aDRqP5vePPd+8+atmjkzX1988bUd6P/ean/z+ldfHWgH/qNHj9OiRUu0c2dJ2/f5y3aBgO/xyFWJTFzeVKv9nHHzTPJrsfM6Fy3X8cgBHQoXa3doq3YE19qBuwnhzU16s/reHdo/ycy3xts38X/yzdVG/1IVBtaoKLhRe0Pb7RX4JyOH7Oe+X42dk3mmu/m8zLPRzXPQm1N19pYBv1l3nwnaGwhS3LQHAAAAAABAD0MBIMsSiYQuX76s8nITLGzW119/rYkTJ6qsrEzHjx/XrVu33O8CAMhhTYGkjlRFtfZUQDP2WxrlCv7Nmv/pba/fdiGkM7UxNfpT9uMBkD1mFf+pU6e1ceNmTZs2U8OHf6tBgz5Wnz4DHgj77w/9zUr/kSPHasWKVdq7d58uXLioxsYmbvkjZ4XTATvsvxI7q7LwXu0KbdbWwEqt8y+wb9ovsaaowBr7QGj/uDPHM9reAmA+lrmpvyP4k10iOBQu0YnIQfuxAOZ59lWJ6/az7M36fFNAMJsGzMYBAAAAAAAAAI9GASDLgsGgCgoK9J///EdffvmlBg8erE8//dT++YgRI1RYWOh+FwBAjkmmM7rZnNC280HNPeR96Jp/M/OP+HT4ZkQ13qSCsTTBf5aYMt7Nm5XaseNXe6X/p59+bgf+r7325h+u9n/ttbf01VffaOnS5Tp69Jhu3KiwA/9IJMItf+ScRNtLS6pR1xMXdCy8x36+/Qb/Qq3yzdES71QVWOOeal3/Amui1vjmaqt/hXaFtuhwpERnokd0LX5OtxM3793Ur3du6ofTd2/qJzNJZcTfMwAAAAAAAOBZUQDIsnQ6rUAgIMuyHhiv16twOOx+FwBAjgjHMzp9J6bFZX5N2O154La/mTElHq07HdDNloRC8bRdFsCzMwF9RUWFCgoW6J///FRvvvmO+vd/Qy+99MoDQf/98+GHf9esWfkqLT2kurp6+f0BRaMx+/s5kEsi6bBuJyp0MnpQvwTX2bfwF3kna4E1wb7V/zhhvzlj3me9b4H9MUojO3UqdljX4udVl7zl3NaPpEOKZiL2rX3zGIGUkkqLv1MAAAAAAADA80ABAACAp2Si+1Ra8kXS2n01rKn7rAcC/7y2MUWAafst+4w3zPr4znDhwiX17fvwlf5mnf9LL/VTnz799eab7yovb7S2b/9FjY2N7g8DdFvm9rwJ2VNtLyZ0N7f7zUp9s2J/pW/WYwf8+Z7RmusZY5cC1vrn62CkSNdiF+0b++bjAgAAAAAAAOjaKAAAAPCEEqmMvJG0rjcl9POZoH2r3x38m9eZQsDy436drYkpEuemf2eprKzUW2+92yHw79fvtbbXvacPPvibhg//VqtWrdGZM+cUDIbc7w50OybqN7frQ+mgfCmPGpO1uho/p0PhYm3xr9BCa9ID4b575nhGa571oxZ7p2iFd6bW+uarJLhZZ6NHVZOssm/wAwAAAAAAAOh+KAAAAPAYTHxvVvbf8iR06GZEi4/6NKrYddu/qFWT9lpafMyv4ithVbUmlObZ8Z3q9u07Gjr0cyf8N8H/Z599pSVLlttr/evrG9zvAnQr5ma/uXnvT3vVlKrX7cRNXYmd0ZHwbnsN/2pvvn1b3x3wu2euNUZLvFP1k69AmwPLVRzapPLIflXEL6k11ahUhu0kAAAAAAAAQC6gAAAAwB8w+X1TIKXTNTEVXghqVqlXI4s6Bv/m53MOerXlfFCn7sTUGkorTe7f6RoaGjRu3ES9/HJ/O/zv3bufvvrqG924UeE+CnQr0UxYjala3Yxf1rlomQ6HS7QztEHr/Yu02Dv5gXD/YTPfGq81vrnaFlilPaFtdth/JXZWdclqBdM++2EBAAAAAAAAAHIPBQAAAH5HNJHRocqIlpX7NXHPw9f8rzzu15HKqH3bPxRL25sC0Pl8Pr8WLVqiAQMGOrf/Bw36WCdOnFQ6nXYfB7o8s9bfm2rVpdgpFQc3aoN/kZZ7Z6jAGvdAuO+eOZ5RWuqdps3+Zdob2qaT0cO6Hr+g2sQtWakWxTIRe5MAAAAAAAAAgNxHAQAAgN9x+k5MU/ZaDwT/5nXbL4RU0ZyQFU4pSd78XMVicRUW7tCbb77rhP99+gzQgQMHlUwm3ceBLq3t3yCqTlzX3lCh1vrmabE12Q703SG/e8ztflMUOBEtVWXimpqSdXaBIJwJ2h8TAAAAAAAAQM9EAQAAgIeIJTP66VRAefdC/7yiVs0+6NWxqqi94j+e5D7ti2Bu95eVndC77w5ywn8zGzZsJPxHtxJIe3U8WqqffAVaYE1QvmfMAyF/+5h1/pv8S3U4UqIb8UvypzyKZMKKZ2JKtr3wbyMAAAAAAAAA7SgAAADwEOZ2/+xSr3Prf82JgPsIXoDq6tv66KNPnOC/V6++mjJluvsY0OWk217MKv6biSvaHlyjub8T+Od7RqvAGqvtgdU6FytTU7JWyUzC/eEAAAAAAAAA4KEoAAAA4JJKS3uuhTW6+Lfb/9ca4+5jeM4sy9K33/7ghP+9e/dr+/n38vn87qNAl2CC+2Dar/rEbR0O79JK7+yHrvefa43VUu9UbfQvsVf6e9Me94cCAAAAAAAAgMdCAQAAAJemQErLy/3O7X+zCSCVZsX2ixQIBDVz5pwON/+HDv1CN25UKJPhzwZdh7npb0L/2uQtnY4e0bbAShVY4x4I/U0RYIl3qr3avzS8U7cS1xXLRN0fDgAAAAAAAACeCAUAAADuY3L+c3VxTdjtcQoAZVUR9zE8R9FoVCtXrrZD//YCwN///i8dO1auZDLpPg68EIlMXHXJap2Nlak4tEnLfTMfett/vjVeG/yLdSD0q67EzspKtSojSiwAAAAAAAAAsoMCAAAA9wnFMyq8GHLC/8l7LPkjafcxPCepVEpbtxbqjTf+7IT/77zzvnbs+NUuBgAvmpVq0cXYSe0KbdE6/3w74HeH/qYIsNw7U3tChbocO63GVK1dGAAAAAAAAACAbKMAAADAfep8SU3bbzkFgO0XgkqR/78wBw4c1KBBf3Nu/7/yyutauHCJQqGQ+yjw3JhV/VWJa9ob2qa1/vlabE1+6G3/udYYbQus0vnocTWl6hXOhOxHBAAAAAAAAABAZ6EAAADAPcm0dKQyqrx74f/oYo+qPQn3MTwnV65c1bBhXzrhv/nxhx9GE/7jhQmkLZVH9+sn3zwtsCYo3zP6gdDfzArvLB0N71ZrqtEuC6TaXgAAAAAAAADgeaAAAADAPeF4RguP+Jzb/ytP+OWPclv3RWhsbNL48ZOctf9mPvroH/L7/e6jQKfItL2klVI8E9WN+CVt9a/83cA/3zPGLgTsCP6kysQ11vsDAAAAAAAAeGEoAAAAcE9la1J5RXfD/5FtP5bdiiqZzriPoZP5/QEtWrRUvXv3c8L/99//SDU1te6jQNYlMwkF0j7dSVTqULhYy70zHrrev8Aap6XeadroX6KTkUOy0q3uDwUAAAAAAAAAzx0FAAAA7ll/OuDc/s8/6NUdK+k+gk6WSqW0Zcs29ev3mhP+v/feByovP+4+CmRNuu3FhP41iSqdih7WlsAKzbPGPxD6myKACf03B5brcLhEtxMVSnLbHwAAAAAAAEAXQgEAAIA2nlBa40ruhv95bbPjYkjhOOv/n7f9+w9o4MB3nPDf/PPWrdsVjxOyIvvMiv+mZJ1OR4+oKPSzlnunPxD6m5lvjbdv+h8MF+la/Lx8aY9dGgAAAAAAAACAroYCAAAAbXZfCzu3/yfvtXSuLiaW/z9fp0+f1V/+8oET/vfv/4b9KACfz+c+CjyzaCasc7FyO9g36/zdof8cz2it9M3W3lChrsbOqSlVrwS3/QEAAAAAAAB0cRQAAAA9XjSZ0dT9llMAWHHcL08o5T6GTlRVVa0hQ4Y54f9LL72iiROnqLGxSZkMVQxkV1OyVjsCa7XIO1mzPaM6BP/zPOO0PbBGF2Mn1ZysVzgTUoY6EAAAAAAAAIBuggIAAKDHO1sT06jiu+H/2BKP9lwLi8z5+bEsr374YZR69+5nh/+9evXV558PV3X1bcJ/ZJW5wX8svFcLrYn2Df/7g/+l3mk6GtkjT6pJ8UzUfjwAAAAAAAAAAHQ3FAAAAD1aOiMtL/cp797t/1mlXlW3JtzH0EkikYjmzl2gPn0GOLf/33//I128eMl9FHhqqbaX6kSF1vgKXGv+R2me9aP2hbcrkgm53w0AAAAAAAAAuh0KAACAHu1Wa1LT9t1d/59X1KqfTgXsUgA6XzQa1aZNW/TGG2874f9rr72loqIS91HgqaSUVEuqUaXhX7XAmtAh/J9v/ahN/mW6Gb9snwMAAAAAAACAXEABAADQY5mgv/hqWGNKPHYBYNwuj87VxtzH0AmSyaRKSw/pr3/9yAn/+/V7TatW/cTaf2RFMO3XxdhJrfcvtG/633/r32wCKIvskz9tud8NAAAAAAAAALo1CgAAgB6rJZTS4jK/Hf6byT/oVSiedh9DJ7hy5ar+/e/PnPD/5Zf7Kz9/vhIJHr+AZ5PKJFUVv6aS0GbNt8Z3uPW/0JqovaFC3UlUKi3+rgMAAAAAAADIPRQAAAA91snbUU3cc/f2v5l918LuI+gElmXp66+/c8J/M5MmTVVzc4v7KPBEWlONOhwu1krvrA7Bf75ntLYEVuhq/JyiGf6eAwAAAAAAAMhdFAAAAD1SKJbRlvNB5RXdDf9HFnlkRbgR3NlisZhGjhyj3r37OeH/8OHfqKLiJqv/8dTimbguxk5pg3+RCqxxHcL/pd5pKo8ckJVq4dY/AAAAAAAAgJxHAQAA0CNVtSY056DXuf3/85mAiJ87lwn4Z86c0yH8//DDv6us7LjSaYJZPI2MvKlmFQU32Ov+53hGPXDrvz55x34sAAAAAAAAAAD0BBQAAAA9TjItHa6KOuG/2QJQ3UpA2JlMwL91a6EGDHjDCf8HDnxHhYU7CP/xxDJtL8lMQhdix7XYmtzhxr8pASywJupstEzpTMr9rgAAAAAAAACQ0ygAAAB6HE84peXlfqcAsPCIT8k09/87SyqV0rFj5Ro06GMn/O/f/w0tWbLMfiQA8CRimahqk7e0yb/sgeB/sXeKioI/y5e23O8GAAAAAAAAAD0CBQAAQI9iHjN/rSmusSX3bv+3TdmtqMj/O4e53X/58hV9/vlw9erV1w7/zY+TJ0+T3+93Hwd+V0pJNafqdTSyW4u8HW/9m/X/Zt3/1dhZezMAAAAAAAAAAPRUFAAAAD1KPJlR4YWQc/t/xgGvGgOsCe8Mpmxx506NRo8ep5deesW5/T98+Leqr29wHwd+Vygd0IXYCW30L9Fcz9gOt/5X+eaoPLJfnlSzMuJxEgAAAAAAAAB6NgoAAIAepTWc1uS9llMA+OVSSJEE1/87g98f0KxZ+erb91Un/P/73/+lysoq91Hgd91O3LTX+pv1/vff+s+3xmh3aIvuJCuVyMTd7wYAAAAAAAAAPRIFAABAj3K4MuqE/+N3eXS5IW7fVEd2Zdq+qGvXrlf//m844f8777yv06fPuo8CDxXJhHQgvFPLvTOV7xndIfz/yVegm/HLCqeDbSf5CwwAAAAAAAAA7SgAAAB6lOn7f7v9v+qEXy0h1v93hvLyEx1u/r/yyuvas2efksmk+yjQQSaTVkXistb4CpTvGdMh+J9vjdfhcImCaZ8yBP8AAAAAAAAA8AAKAACAHuN6Y8IJ/0cWeXTgZkRpMsSsMjf/b9yo6BD+9+kzQKtWrVE4HHYfBxwZpeVLe7Q3XKgCa1yH4H+uZ4w2+peoJlFpnwMAAAAAAAAAPBwFAABAj2CC/tUnAk4BYO4hr6paE+5jeEbV1bf1t7/9q0P4P3nyNDU1NbmPAo5QOqArsdNa45vbIfif4xmlVb7ZOhbZq0jGrPsHAAAAAAAAAPwRCgAAgB6h3p/SmBLPvdv/rdp0NqhIguv/2VRXV6+8vDF66aVX7PDf/Dh8+Lf2RgDgYRKZmG4nbqo4tNG+5X9/+L/EO1XFwY2qS1a73w0AAAAAAAAA8DsoAAAAeoTiK2E7+DcFgEl7LJ28E3UfwTNobfWooGC++vd/w7n9//HH/9CJEyfdRwF7jX9zql5HI3u0zDu9Q/BfYI3VtsAqXYqdtgsCAAAAAAAAAIDHRwEAAJDzvJG05h32Oev/5x/x2a9DdoTDYW3cuEVvvvmOE/6/8srrKi09pFQq5T6OHi6Rietq7Jw2B5bZYf/94f9y3wyVR/arNdWktPg7CgAAAAAAAABPigIAACDnnboT08Q9lh3+jy72aNfVsDJs/88KE/AfPnxU7777Vyf879Wrr375ZaeSyaT7OHq4RCahC7GT9q3/OZ5RTvCf7xmjHcF1qkneUpxb/wAAAAAAAADw1CgAAAByWjwlrTsdUN699f8TdntU5yOYzpZbt6r1179+5IT/ZhYvXkr4jwekldLl2BkttCZ2uPW/yJrU9nqz7j+ujGjmAAAAAAAAAMCzoAAAAMhplS0JzTnoddb/Ly/zuY/gKZnV/0OGDHOC/969++mHH0bL7/e7j6KHM+v8r8bPae59K//zPaNVFNygaCbkPg4AAAAAAAAAeEoUAAAAOSuVlvbdCNtr/034n9c2Vxri7mN4CplMRuPHT+oQ/n/xxXBVV992H0UPZ272X46fUb41xgn/51k/andoqyJpwn8AAAAAAAAAyCYKAACAnNUcTGnlcb9z+3/GAa8SaVaMZ0NTU5MT/vfq1VeffDJYx4+fUDqddh9FDxbLRHQmelQF1jgn/J9vjde+0HYF0l73cQAAAAAAAADAM6IAAADISZmMdKE+rol7LKcAcPhmxH0MT2nnzmKnAPDaa29p3boNisVi7mPowaKZsE5GDmmRNalD+L8/tEO+lMd9HAAAAAAAAACQBRQAAAA5KRzPaMelkBP+T9ztkTeSch/DU8rLG+0UAMzt/0uXLruPoAcz4f+JSKkWW1Oc8L/AGqsD4V+5+Q8AAAAAAAAAnYgCAAAgJzX4k5p+4Lfb/9vOB5ViO31WtLS06P33P3QKAN9994PC4bD7GHqoWCaq45HSDjf/zZjwP5wJuY8DAAAAAAAAALKIAgAAIOeYoL/sVlR598L/kUUeVbYmlHEfxFMpLT2k11//sx3+v/LK61q2bKX7CHqoZCapk9HDmmuN7RD+H4nsantbwn0cAAAAAAAAAJBlFAAAADknksho8VG/c/t/RblfvgjX/7Mhk8koP3+e+vQZYBcA/vzn93TixEn3MfRAqbYXE/7fH/ybIkBZdJ/S4u8fAAAAAAAAADwPFAAAADmn2pNQXlH77f9WHa2MKJHi/n82+Hw+ffHF1876/08+GaxQiPX/PV0sE1F5ZH+H8H+BNVFl0f2KZ6Lu4wAAAAAAAACATkIBAACQczaeDTq3/2eXeu1CALLj5MnT+uCDv9vhf69efTVt2kz3EfQwoUxAxyJ7lO8Z7YT/i72TVRbZp3Am5D4OAAAAAAAAAOhEFAAAADnFG05r3C6PHf7ntU3hxZBCcdaPZ8v69T9rwICBTgHg4MFD7iPoQQJpnw5FSjTfGu+E/wutSfY2gHA66D4OAAAAAAAAAOhkFAAAADll//WIc/t/0l5LZ2piYvl/dvj9AU2aNNUO/k0BoH//N+xHAqBnCqb9Ohguslf9t4f/86wfdSJ6UJE0N/8BAAAAAAAA4EWgAAAAyBnxZEbT91tOAWBZuV8twZT7GJ7S1avXNHTo53b4b2bEiJHuI+ghzO3+A+FfOtz8N3M2WqZ4JuY+DgAAAAAAAAB4TigAAAByxvnauEYX313/P6a4VSVXQspw/T9r9uzZpzfeeNspABQX73IfQQ+QyMS1N1SofM8YJ/jP94zWhdhJpUXhBgAAAAAAAABeJAoAAICckM5IK0/4lXfv9v/MA5aqWhLuY3hKkUhUS5eucML/l19+ReFw2H0MOS6ZSWh3aEuHW//zrPG6GDupDA/bAAAAAAAAAIAXjgIAACAn3PYkNX2/1w7/84patfqkX8m0+xSeVm1tnUaMyHMKAF999Y37CHJcMO3X7tDmDuH/Yu8UnY+W28UAAAAAAAAAAMCLRwEAANDtmdv/u6+FNbbk7vp/8+Ppmqj7GJ7BuXPn9fbb7zsFgJ9/3uQ+ghxmpVpVEtykOZ5RTvi/zDtdZ6Nlimf4uwYAAAAAAAAAXQUFAABAt+cJp7S03G+H/2ZmHbAUjHH9P1sSiYS2b/9FvXr1tcP/fv1e07Vr193HkKM8qWY7/C+wxjnh/1LvNJ2PlStG+A8AAAAAAAAAXQoFAABAt3e6JqZJey2nALD7Ssh9BM/A7/drwoTJzu3/Tz/9XB6P5T6GHGTC/+LgRs2zfnTC/4XeiboYO6V4JuY+DgAAAAAAAAB4wSgAAAC6tXA8o20XQsoruhv+j2z70Ypw+z+bGhoa9fbbf3EKAIsWLVUkEnEfQ47xpS39EljX4eb/vLZ/vh6/oGQm4T4OAAAAAAAAAOgCKAAAALq1ak9Scw/5nNv/60753UfwjI4dK3fW//fu3U9Hjx5TJpNxH0MOiWUiKvSv1hzP6A7hf3WyQhlRsAEAAAAAAACArooCAACg20qlMzpaFVHevfDfbAGobOFmcjaZoH/OnALn9v8nnwxRRcVN9zHkkGgmos3+5U7wb2aRNUkViSvKiOIHAAAAAAAAAHRlFAAAAN2WWfW/8kTAuf0/77BXiRQBZTYlEkl98MHfnALA1Kkz5PF43MeQI7wpT4fwf45nlFZ4Z9lr/1NKuo8DAAAAAAAAALoYCgAAgG7JbKC/0ZzQ2F0epwBwpCqiNPl/Vpnb/u3r//v0GaCtWwuVTKbcx9DNmZv9zcn6e2v/Rznh/2rfHF2Nn1NSbNYAAAAAAAAAgO6AAgAAoFsyN/13XAw54f/0/Zbq/dxQzralS1c4t/8HDfpYx4+fdB9BN2fC/4ZkjQoDqzXXM8YJ/1f55uha/DzhPwAAAAAAAAB0IxQAAADdkjeS0uS9llMA2H4xqHA87T6GZ5DJZDRo0G/r/0eMGKna2jr3MXRjJvxvTNZqmwn/rbHO6v9l3um6mbiiJGv/AQAAAAAAAKBboQAAAOiWjt2KOuH/+F0eXaqP2Y8FQPZUVVU56/9feukVzZu3UIkEgXAuaUk1alNgmXPz38wi72TdSVYqLR71AAAAAAAAAADdDQUAAEC3NOPAb7f/V5b71RwkrMy2n3/e5Nz+//Of/6KSkt3uI+jGAmm/1vnn2+v+28P/Jd6pakrV2ZsBAAAAAAAAAADdDwUAAEC3c7M56YT/I4tate9GWKk0gWU2pdu+nsOHf+cUAAYPHqqKikr3MXRDJtwPpL1a45vrBP+mBLDCO0v1yduE/wAAAAAAAADQjVEAAAB0KybnX3vK7xQA5hz06mZLwn0Mz6i2tk7vvvtXO/zv3bufRo8ep0SCr3N3l1FaTck6rfEVOOF/vme01vrmqTpR0fbWtPtdAAAAAAAAAADdCAUAAEC30hRIadwujx3+5xW16uczAYXjhJbZVlRUotdee9MuAPTv/4bWr//ZfQTdjAn3axO3tMG/0Fn7b8L/9W0/vxW/1vZWHqMBAAAAAAAAAN0dBQAAQLey62pYo4ru3v6fsMfS8eqo+wieUTqd1tSpM/Tyy/3tAsDbb/9FFy9ech9DN2LC/9uJCm30L7ZD//a1/+t8C3Qrfp2b/wAAAAAAAACQIygAAAC6DX80rXlHfM76/4LDXllhbi1nW0NDo4YN+9IO/80MHjxU0ShFi+4q0/ZSm6zWhvvCfzNr/fNUk6gi/AcAAAAAAACAHEIBAADQbZypiWniHssO/0cVt6rockiZjPsUntWRI0f1/vsf2uF/r159NH/+IvcRdCOhdEAloU0dwv9VvjlqSTXa5QAAAAAAAAAAQO6gAAAA6BYSqYw2nAko797t/x93eVRjcfu/MyxfvlJ9+gy4VwDoq1OnzriPoJtIK6WLsVMdwv/Vvnz50l73UQAAAAAAAABADqAAAADoFm61JjT3kNdZ/7/4mM99BFng8Xg0duwEZ/3/gAEDlUpRtOiuWlNNWmxNvu/m/2w1J+vdxwAAAAAAAAAAOYICAACgy0ulpdKKsMaWeJwCwMX6uPsYsuDChQv65z//7RQAJk2a6j6CbiKaCWuzf6kT/i+0JupS7JRSSrqPAgAAAAAAAAByBAUAAECX1xpKafVJvxP+T99vKZ7i2eXZlk6nVVRUrFdffdMpAJSVlbuPoRtIZZI6Et7lhP9zrbHaGypUKO13HwUAAAAAAAAA5BAKAACALi2TkS41xDRpz2+3/0srIu5jyIJAIKh58xY64f8bb7ytcJivdXd0PXZRC6wJ9woAo7TBv0gNyRr3MQAAAAAAAABAjqEAAADo0sKJtH69HHLC//G7PfKE0+5jyILq6tv68sv/OAWAUaPGuY+gG2hONdiB/xzPKLsAsMQ7RZdjp5VWyn0UAAAAAAAAAJBjKAAAALq0xkBSsw5YTgFg87mgkmnW/3eG06fPaMCAgU4BYOfOYvcRdHGxTFR7QoWa6xlrh//5njHaE9ymRCbuPgoAAAAAAAAAyEEUAAAAXZbJ+U9WR5V3L/zPK2pVRUtcxP/ZF4vFtWHDRif8f+WV11VTU+s+hi4s0/ZyIXZCi7yT7q3+H6mV3tmKpIPuowAAAAAAAACAHEUBAADQZUWTGS0r8zm3/5eW+eWNsP6/M3i9PuXljXYKAP/5z4i213ndx9BFmfC/PnlH6/0LnfC/wBqn2mS1+ygAAAAAAAAAIIdRAAAAdFl3vEn71r8J/0e2/XjwZlSJFPf/O8OdOzV68813nALATz+tUywWcx9DFxVOB7U3vN0J/+d6xuh45ID7GAAAAAAAAAAgx1EAAAB0WVvOBpzb/zNLvaryJNxHkCV79+53wv8BAwbqxImTymQoW3QHaaV0PnZC86wfnQLAjuBPimWi7qMAAAAAAAAAgBxHAQAA0CX5ImmN3+VxCgBbzgcVjLH+v7NMmDDFKQAMG/aFqqpuuU6gq2pM1mqlb7YT/q/xzVVd8rb9WAAAAAAAAAAAQM9CAQAA0CUdrIg44f/EPZZO3YmJC+mdI5FI6K233nUKAHPmFMjn87uPoQsyt/y3BlY44f9Ca6LOx8qVzLAtAwAAAAAAAAB6IgoAAIAuJ5HK2Cv/2wsAS4751RxIuY8hS86cOatevfra4X+/fq9px45flU6zbaE7OBD6Vfme0Xb4P8czSntChQplgu5jAAAAAAAAAIAeggIAAKDLuVgX05iSu+v/RxW3auflEMvMO9HMmXOc2/9///u/dPbsOfcRdEGViSuaZ/3o3P5f71+ohlSN+xgAAAAAAAAAoAehAAAA6FLSGWntSb/yiu7e/p9+wKubLawz7yypVEpvvPFnpwAwevQ4NTU1u4+hi/GmPVrrn++E/4usSTobLXMfAwAAAAAAAAD0MBQAAABdSo03qZkH7q7/NyWAVSf89iMB0DkuXbrshP99+gzQ0qUrlMnw9e7KYpmI9oW3a6411g7/zSMA9oS2KZGJu48CAAAAAAAAAHoYCgAAgC7D3P7fdz2icffW/5vHAJy8HXMfQxYtW7bSKQC8994glZYedB9BF5JSUpdip7TUO825/b/Ov0C+tMd9FAAAAAAAAADQA1EAAAB0Gb5IWiuO++3w38zMA5YC0bT7GLIkHk9o2LAvnQLA0KFfqLa2zn0MXUSm7aU+WaMN/kVO+L/AmqBbievuowAAAAAAAACAHooCAACgy6hqTSj/0N31/2Z2Xgq5jyCLrl+v0Ntvv2+H/y+99IomTZqqZDLpPoYuIpwO2qv+8z1jnALAyeghpUVJBgAAAAAAAABwFwUAAECXca42pil7LacAcKmOZ5p3ps2bt2rAgIF2AeDVV9/U9u073EfQRZiQ36z+n2f96IT/O4JrFc2E3UcBAAAAAAAAAD0YBQAAQJdxuCqiMSV3w38zTcGU+wiyJJPJaPTocerVq69dAHjnnb+qsrLKfQxdhD/l0SJrkhP+L/fOUFOKxzUAAAAAAAAAADqiAAAA6BJSaankatgJ/0cVtyqTcZ9CtlRX39aQIcPs8N/M0KFf2qUAdD3JTEIbfUud8N9sATgfO6GUeFwDAAAAAAAAAKAjCgAAgC4hGEtr09mgUwCYe8jnPoIs2rt3v33rv70AsHr1WvcRdAGptpfyyH4n/J/rGaPdoS0Kpvn7AQAAAAAAAAB4EAUAAECX0BxMacVxv1MA2Hw25D6CLEkmk1q8eKn69BngFACuXbvuPoYuoDJxVQusCXb4P8czSut8C1SbqHYfAwAAAAAAAADARgEAANAl3LaS9q3/9gLA4ZtR9xFkSUNDo/LyRjvh//vvf2iXAtC1tKaatNG/xA7+TQFgkXeyLtir/1PuowAAAAAAAAAA2CgAAAC6hGtNcU3cYzkFgBttP0fnOHXqjD766B9OAaCgYIH7CF6wWCaqA+FfVWCNs8P/fM9o7QltVTQTcR8FAAAAAAAAAMDxXAoA8XhcdXV1unTpkk6fPu28PpFI6ObNm6qsrFQmk7nvPQAAPYn5FnC6Jqa8orvhvxlfhFvOnSGdTquwcEeH9f8XL15yH8MLlGl7uRw7oyXeKXb4b2alb5b8aa/7KAAAAAAAAAAAHXRqAcAE/8ePH9e4ceP06aef6t///re++eYb5+13n0G8WJMnT7bLAACAnimeymj/jYgT/k/daymWpBjWGSzLq5kz5zjh/1tvvUsJr4tpStbpZ/9iJ/w3U5e85T4GAAAAAAAAAMADOq0AYAL9gwcP6quvvtKXX36pH374QZ9//rm+/vrrDue2bNlivy4azf6zns3n4PV6nS0Dfr//iZ5xbAKRSCQin8/3u+P+mKlU6oEz7RMIBO776ACAdqF4WtvOB50CwLIyv10KQPbdvFmpIUOGOQWAqVNnuI/gBYpkwioN/+oE/2b1/9HIbvcxAAAAAAAAAAAeqtMKAA0NDZo0aZJmzJiha9eu2cH4vHnzHigAHD582C4J1NbWdnj9swqFQiopKdH3339vjykgTJgwQeXl5fZmgsdhAvv169fbBYbfm1GjRqmiosJ5n/r6evv15vc0fPjwDmO+HgCAB3kjaa047ncKADsuhJRMUwDINlNsO3asTH37vmqH/7169VVp6UH3Mbwg6baXK7Ezmmf96BQAtgZWKJbJfkkSAAAAAAAAAJCbOq0AUF1dre+++0779+93Vgv/UQHg/hD9WZmywa+//qphw4bZAf7Fixd19uxZLVq0SKNHj9a5c+cea92xuf1/6NAhLV++/IFZsGCB/fsbOXKkvV2gnSkyDBkyRFOmTNGGDRs6jPmcAAAPagqkNKvU6xQAjlZGRP6ffWbbzooVq+5b//+e6usb3MfwgjQn67TKO9sJ/1f6Zqs2eUsZ8ZcBAAAAAAAAAPB4OrUAYG7dl5aWOq9zFwBisZjWrl1rPxrArMjPltu3b9u/9rRp0+xNAO1u3bql8ePH21sJHudRAKYkYM6ZjQH3j/m8r1y5orFjx2rdunX2YwDamQLAZ599puLi4vs+EgDgj9R4kxpb4nEKANeb4nqMnhaekGVZ+vLL/zgFgDFjfmz7/vvb9zC8OPFMTNsDa5zwf4E1QWeiR5XIPN7WIgAAAAAAAAAAjE4rADQ1NdlBu7kp39jYaIfp9xcAzC1Ec/vfrOefPn26EomE6yM8vcLCQg0dOtS+vX8/E+avXLnS3gzwLBsHwuGwVqxYocmTJ+vmzZsdtglQAACAJ2P+FXqpPuaE/+N2eVTne3RJC0+uquqW+vd/wykAbN1amNXvv3g65ob/sche5XtG2+H/HM8olQQ3K5jOXjkSAAAAAAAAANAzdFoBwITtJuD/9ttvNW7cOPumv1m/b8LxoqIi5efna/jw4XYh4PLly4+1kv9xmF93/vz5+te//mWXENxKSkqeOaA/deqUvf5/z549D2wSoAAAAE8mlc7oUEXEKQDMOehVSyjlPoYsKCzc4YT/Awe+ozNnzrmP4AWoTFzVImuyc/t/rX8eq/8BAAAAAAAAAE+l0woARiQSsYPwr776yg7kzfzzn//U4MGD7X82t//Ly8uzevvQPEpg5syZ9sc3WwbcTCnhyy+/1OrVq91veixm/b/5vGfPnm1vAnAzBQDza48YMUILFy7Ujh071Nra6j723JjPt6GhgWEYpstOTV2D1pa3OAWApYdbVHH7wXPMs89XX33jFAA+++xLnT595oEzzPOdqsabWtNa4IT/81vHq7SpWPUN9Q+cZRiGYRiGYRiGYRiGYRimZ0w2H52OnqdTCwDtampqtH37dvsRABMmTLCDcXMT3zwaINvMX4opU6bYt/AfViw4ceKEvXnABPhPKpVKad26dXah4ezZs+4320zYP3fuXPuxBnl5eXbZwHwuv/zyy0M/n85mCgBmEwLDMExXnZr6Js0rvRv+m/n5ZItu1z14jnm2uXPnjvr0GWCH/7169dWsWXN069atB84xz29qG2u0s+VnzfWMcVb/b/OsVn1T3QNnGYZhGIZhGIZhGIZhGIbpORMIBNyRH/DYnksB4HkyfymmTp2qoUOHPjRwNxsHTAGgoKDA/aZHunTpkoYMGWIXGEwZ4FFM+H7+/Hm79GA2H5SWlrqPAECPF46nNWmP5RQADldGlEix+jzbSksPObf/X331Te3atcd9BM9RWildjZ/VMu905/b/al++rHSL+ygAAAAAAAAAAI+t0woA6XRawWDQXpNv/vlhksmkfWPesixlMtkJe8yvaW73m8D9YSv6TQj/xRdfaP369e43/SGzamPGjBn2bX5zY/JJXLhwwX4swNixYx9aSgCAnqwlmHLC/1HFrTpfF+PJ551gzJjxTgFgyJBhunTpsvsInpNM20tzql6bA8t+W/1vjVdF/JL7KAAAAAAAAAAAT6TTCgBer1fbtm3T4cOHFY1G3W+2mRvyO3bs0IYNG+wyQDaYm/lLliyxA/fbt2+732w/imDYsGHav3+/+02/y5QTdu/erc8//1yLFy9+4rKC+f0PHjxY33zzjZqbm91vBoAe7XJD3CkATNlrqaKZolS2BYMhvf76n50CwPjxk9q+T/MMqRclkg5pX2h7h9X/RyO7ldHDC5MAAAAAAAAAADyuTisA1NbWKi8vzy4B/F64bzYDbNy4USNGjFA8Hne/+ant27fPvuVfXFzc4fWmcLBgwQI7yDePCnhcDQ0NmjZtmv79738/8e1/wzynwxQSvv32W3vbAQDgNwduRJwCwPzDPtV4H/49A0+vvPyEE/737/+61qxZ+8RlNmRHuu3lSuyMFlgTnNv/hYHVCqV5phcAAAAAAAAA4Nl1WgHABOXmxvujbtqbt3/11VdPFMg/isfj0ahRo+wCggnf250/f95+ndkQYDYFtGtpadGePXu0efPmhwb0R48e1ddff63p06f/bpnhj+zatcsuAJgSwe89DgEAeqoNp4NOAWD1Cb9aQ7/9+xnZMWfOPKcAMGjQxyorK3cfwXPiS3m03DvDCf+XeqepJnnLfiwAAAAA/p+9+3CL8kr7OP5Hbeom7ya70agxppnEuNlkU7H3LgjYuwiIvTfsaGyIitixKxYUFUF6mcr0dr+eY3hWHjUUGaR8P1znIsxzzwzFOFzev3MfAAAAAMCriloAoLCwUDf2GwsAZGZm6h35t2+37lnEFy5c0Dv2582bp/9bfR5Tp06VKVOmyOPHjxvUFhQUyPTp03WT3rzDXwUF1JQCde3AgQON7phUIYOtW7fq57x586akp6frr099LubnBQCIpOTYjABAxo06cfkJSrWmQCAgP//8uxEAGDFijNTU1JrL0EZ2OlYbzX91BMA17zkJRZofLgQAAAAAAAAA4EWiFgBQRwCoZvumTZsa7MJ/lsvlkhUrVujmuNq139ouXryoPwe1e3/cuHGycOFCefDgwXNNfBVWSE5O1kcRlJSUNLhms9n016Cu3b9/v8G1Fzl//rx+rjFjxuilpiAsXbr0uccFAIiEwiJxh542/+OerGP33BL+65wVmun69evyzTf9dfO/R49PZcGCJHMJ2oAa/X/Je8po/idbEuRw3Q5xhm3mUgAAAAAAAAAAWixqAQA1Sl8190eNGiX79++XsrIyHQTwer36vfp4z549+vrs2bP1DsVoUCP7VfO9oqKiReP7m0t9Heo4AxUWePjwof4+MPYfAF6s3BE0dv9PO2KR80Vecwle0caNm6V37y90AOCLL76RrKxj5hK0geLAfVlunfVn8z9eNtmX6NsAAAAAAAAAAGhNUQsAqNH5V65c0TvnBw0apEfxb968WTIyMvT7uXPnypAhQ/R1tTvRvCsfAND5XS/xGQGA+dlWuVXuN5fgFXg8HomNjZdu3XrpAMC///2jlJdXmMsQZbZwrexyrtGNfxUAUEEANfo/HAmZSwEAAAAAAAAAeCVRCwAoasT/6dOnZebMmToEEBMTYyzV/F+wYIHk5uaKz+cz3xUA0AUcyXcbAYAlp+1SZInONJiu6u7de/L774N181+tSZOmELhrY76IV065D0uadbpu/qdYEuRI3S7xRtzmUgAAAAAAAAAAXllUAwCK3++X4uJiOX/+vD4KYOvWrXLgwAG5ePGiPgagLcbyAwDapw0XHEYAYE2uQ2pd7IhuTQcOHJK+fb8zAgB79uw1lyCKIk/e8v3XZY1tvm7+q7XevlgsoWpzKQAAAAAAAAAArSLqAYB66kgAtdPf7Xbr9+Fw2FwCAOhC1Eb0ecetRgBg6xWn+ILsTm8tgUBAUlOXSvfunxgBAMb/t63qYIXsdKw2Rv+r3f/FgQIdDAAAAAAAAAAAIBraLAAAAMCz7J6wTD9i0c3/KYdqZe9Nl7kEr6C4+LGMHz/JaP7/+usAcwmiqH70f/3Of7XOeI6YywAAAAAAAAAAaFVRDQCo8f42m00ePnwoN27ckGvXrr10MREAALqWhzUBmZr5NACgggCnHnjMJXgF587lyvff/9cIAGzcuNlcgihRO/wL/XdliXWq0fzf4Vgl/ojPXAoAAAAAAAAAQKuKWgBANf9VY3/69OkyYMAAiYmJ+culjgUAAHQdF4p9knD4aQBg7jGr5JX5zSVoIfUavH37LmP8v3pfWPjIVIVoUbv/9zg3GM3/NbYF8jj4kNH/AAAAAAAAAICoi1oAoKqqSubPny8jRoyQhQsXypo1a2TdunUvXeqsYgBA13HotlviD9XqAMCiEzZ5bA2aS9BCNTW1MmvWXGP3/08//SaRCM3ntnLPn2c0/1OtUyXXm83ufwAAAAAAAABAm4haAODRo0e6+Z+WliZFRUXi8XjE7/e/dAEAupaNF50Sd/BpACD1lF0cXhrUreXevQL5+ecYIwCwdOlycwmiRO3+32xfYgQAtjlWSGWw1FwGAAAAAAAAAEBURC0AUFJSIrGxsZKenq6b/wAA1PMGIpJ2xq6b/2qtOu+QMP3/VhEOh+XkyVPSs2cfIwBw+fIVcxmi5Kr3rCRb4nXzP806XS54ciQYYcoRAAAAAAAAAKBtRC0AYLVaZcWKFZKUlCTl5eXmywCALqzKGZLFOTbd/J9yqFb23HCZS9BCbrdbli5dYTT/f/jhF6moqDSXIQrqwnZZa1to7P7f7lgpNaEKcxkAAAAAAAAAAFETtQCAOmv4zp07kpiYKBkZGUwBAAAY7lUFZN5xqw4AJBy2yKkHvEa0ltpaiwwcONQIAMydu0CcTqe5DFFwxp0lqZZE3fxfap0huZ4T5hIAAAAAAAAAAKIqagEAv98vxcXFsnv3bhk1apRs27ZNCgoK5MGDBy9camQxAKBruFDklZlZFh0AmJppkdsVPnMJWki91nbr1ks3/9X7Q4cOSzAYNJehlVlCVbLJnmLs/t/qWKYnAgAAAAAAAAAA0JaiFgAoKyvTu/9jYmKatHw+mj8A0FVk5rslMfNpAGD6EYtUOkLmErTQ5s1bjd3//fp9Lzdv3jKXoJUFIwE57T4iadZpuvmvpgBc9Z4zlwEAAAAAAAAAEHVRCwBYrVbZuXOnrFy5skkrEAiYHwIA0AmFwiI7rtVJ3MFaHQCYfcwq/mDEXIYWGjZspBEAiItLlPLycnMJWllZsEi22NP+t/vfvlQ8EZe5DAAAAAAAAACAqItaAECN9He5XPrc4aasSITmDwB0BQ5vWNZfdOjmv1rLzzAmvbVUV1dLr159jPH/69ZtFK+XCTvR5It45Yz7iCyxTtXN/2RLvBT4mboAAAAAAAAAAHg9ohYAAADgRcrsQVl+1m4EADJu1JlL0EJ//HHQ2P3/9df9JTs7x1yCVqZ2/2+ypxi7//c410swEjSXAQAAAAAAAADQJl57AKCiokLWrVsnHo/HfAkA0Andq/JLUo7NCACce+g1l6CFxowZbwQAhg8fLQUF980laEVq9/9pd6YkWxJ08z/lyfvSwCNzGQAAAAAAAAAAbaZNAgChUEjq6urE4XA0WKWlpbJ582YZNmyYFBQUmO8GAOiELj/2yqyjFiMA8KAmYC5BCzidddK9+ydGAGDevIWE66KsOlQhK61zjN3/h107JBDxm8sAAAAAAAAAAGgzUQsAhMNhKSwslCVLlsjQoUMlJibmhWvgwIEybtw4sds5AxoAOrvIk3XivkemHHra/FfL6Q2by9ACx45lS7duvXTz/7PP+sqOHbvMJWhFoUhQTrj3G83/5dZZUhp8JE//lAMAAAAAAAAA8HpELQBgtVpl2bJlMmTIEImNjZVZs2bJhAkTZPjw4TJ79my94uLi9Lp8+bIODAAAOjdfMCL7b7mN5v+MLKu5BC00a9Y8Y/f/Tz/9JteuXTeXoBVVB8slzTpdN/+TLfFy3P2HeCIucxkAAAAAAAAAAG0qagGA4uJiGTt2rCQnJ0teXp5UVVXJnj17ZOrUqVJTU6PXjRs3JCkpSS5cuEAAAAC6AJsnLOlX6owAwNpch7kELaCm6Kimv2r+qykAY8ZMeHIb39toCUtI9tdtNXb/r7MnSVHgvkTY/Q8AAAAAAAAAeM2iFgBQ4/9Hjx4tx44dk0jk6T+Inz17Vk8BcLme7pALBoOSnp4uc+bMEZ/P9+zdAQCdUIUjJKvOOYwAwMHbbnMJWuDcufPSt+93OgDQq9dnsnTpCnMJWtGjQIGkWqbq5n+KJVGy3fvFG+HPMgAAAAAAAADg9YtaAKCoqEgmT54sOTk5xm35+fkyYsQIuXv3rnHbwYMHZeTIkVJSUmLcBgDonB7WBmTRCZsRALj8mPBXa1i+fJVu/KsAwJdffitnzpw1l6CVBCI+2eFcbez+32hPkaJAgbkMAAAAAAAAAIDXImoBgIqKCpk5c6bs27dPQqGQvk3t8h83bpysX79evF6v2Gw2WbJkiQwYMEAePnxoegQAQGdzq8IvUzMtRgCgxBY0l6CZnE6nTJgwWY/+VwGA77//rzgcjP+Pltu+K7LUOkM3/1OtiXLcvU+CEf4cAwAAAAAAAADah6gFAOrq6mTz5s2yePFiHQaot3PnTt3wHzVqlF4xMTEyduxYcbsZnQsAnVk4LHKhyGc0/6cctognwJnpr+r69Tz55ZcY3fxXa/r02eYStBJfxCPbHSuM3f/rbIukIvjYXAYAAAAAAAAAwGsTtQBAOByWsrIyuXz5stjtduN2j8cja9as0ZMARo8eLXFxcXLp0iVdDwDovLzBiBzJdxsBgJSTNvEFCQC8qt27M+SLL742AgBZWcfMJWgFkSdv1725ssI6Wzf/ky0Jcsy111wGAAAAAAAAAMBrFbUAwF/x+/3y4MEDuXbtmlRWVtL8B4AuwOkNy46rdUYAYOtlpwRCBABehQrVLViQZDT/e/XqIzU1NeYytAJb2CJ7nOuN3f+rbPPEEqoylwEAAAAAAAAA8Fq9lgAAAKDrsbhDsuKs3QgAqGkAwTABgFehwnQjR441AgAjRowxl6AVhCQo17znZbl1lhEAOOdh0gIAAAAAAAAAoP0hAAAAaBMVzqDMPW41AgBXHvuE/v+rOXnylHz9dX8jALB37x/mErQCtdN/j3Od0fzfYFss7nCduQwAAAAAAAAAgNeuVQIAVVVVcvjwYdm2bVuLVzAYND8sAKATKbIEZMqhp81/tQprA+YSNIPX65ONGzdL9+6f6Oa/el9TU2suwysKRYJyw3tB0qzTjADALd9lcxkAAAAAAAAAAO1CqwQA7t27JzNmzJDBgwe3ePl8PvPDAgA6CbXT/2qJ12j+zz1mlUpHyFyGZqioqJT4+KnG7v+hQ0dKOBw2l+EV2cMW2epYZjT/d9hXiifsMpcBAAAAAAAAANAutEoAoKamRo4fPy4ZGRkN1qZNm2TSpEmSlJQku3fvbnBt7dq1MnHiRFmxYoX+mAkAANB5BcMRybrrNgIAK87axeImAPAq7tzJl/79fzQCABs2bJZIhDMVWlP4ydt17zmj+Z9siZd7/pv6dgAAAAAAAAAA2qNWCQC8iNrRf+TIEVm0aJE8fvzYfFkCgYBkZmbK/Pnz5f79+zQtAKATC4Qikn7FaQQAtl+tE6ePJmpLhUIhOXr0uNH8//jj3nL79h1zGV6RK+yUNbb5RgBgr3OjOMI2cxkAAAAAAAAAAO1G1AIAFRUVMnv2bDlw4IBuVLyIzWaT5ORkmTt3rvj9fvNlAEAn4QtGJPWUzQgAZOa7xRsg+NVSLpdLFixIMgIAMTGDpbq62lyGV3TOc8xo/i+3zpI7vmvs/gcAAAAAAAAAtGtRCwAUFRXp8f8nT540XzKoXf9bt26V4cOH07gAgE7M5Q/LtCMWIwCQW+SVEH3UFlNH7/z4469GACAlJU2HAtB6rKFqWWGbbYz+3+/cKvaQxVwGAAAAAAAAAEC7ErUAgBr7P3nyZNm7d+9Ld/cHg0HZsGGDDBo0SB49emS+DADoJEptQaP5Py3TIrcrXvy6gKa5cuWadO/+iW7+9+jxqRw9mv3SaTtovoiEJcu1x9j9v8o2T275rujbAQAAAAAAAABoz6IWALBYLLJkyRKJi4uT48ePS11dXYPrbrdbTp06JbGxsTJs2DBxOp0NrgMAOo8rj31GACDphE0KawPmEjTDihWrjN3/P/30m9y5k28uwSsoDRbKStscY/f/H84t4grzewoAAAAAAAAAoP2LWgAgHA7LjRs3ZMqUKTJixAjd6J87d66kpaXJokWLJCEhQUaOHCkDBgyQjIwMdi4CQCd2+LbbCACsOueQCgd/578K1fSvDwBMnz5bqqo4Rqe1hCIhOVC3TZItCToAsNI2V/L9N0TNBQAAAAAAAAAAoL2LWgBAUSGAO3fuyJw5c2TgwIESExPTYA0fPly2b9/OucUA0MmtzXUaAYD0K3Vi8zBKvaVKSkqN5r9a6enbCdG1ooeBO7LWtuDP8f/xstuxVsIRvr8AAAAAAAAAgI4hqgGAesFgUPLz8+XEiROye/duveP/9OnTUl5eLpEIO+oAoLObc8xqBAAO3HKJL8jf/S21ZUu60fzv3/9HOXPmnLkELeSLeCTTtUOP/VcBgKXW6VIUKDCXAQAAAAAAAADQbrVJAAAA0HV5/GGj+T/lUK3k3PcI2a+WUZN1Bg4cagQAxo6dKEVFxeYytEDkyVuB/6astS38c/f/FPnDuUXfDgAAAAAAAABAR9EmAQA1mtjhcEhlZaWUlpY2uL22tlYsFguTAACgkyqsCRgBgFlHLXLlsddcgiYqKyuXjz/urZv/3bt/IklJKeL18v1sDXVhh2TW7TJ2/y+3zpSqYJm5DAAAAAAAAACAdi2qAQA1+v/Ro0eyb98+WblypSxevFjS0tKM64FAQF9LT0/XtQCAzif3kdcIACTl2OReld9cgibau/cP6datlw4AfPXVt09eQ/ebS9AC4SdvD/y3ZZVtrrH7P8d1wFwGAAAAAAAAAEC7F7UAgBpTfPv2bZkzZ44MHTpUBg8eLIMGDZKxY8caNWrX/5YtW2TSpEni99MQAoDOaG+eywgALDtrlzI7ga+WmjZtpjH+/7ffBkp+/l1zCVrAHa6Tg3Xbjeb/att8sYaqzWUAAAAAAAAAALR7UQsAqNH+S5Yskbi4OMnOztbj/5OSkhoEABR1bfTo0VJTU9PgdgBA57DijN0IAKy/6BCHN2wuQROooNwvv8QYAYAxYyaIz+czl6GZIk/eCgN3ZYl1qhEAyPVkSzBCUAUAAAAAAAAA0PFELQBQXFwskydPlszMTD0NQFHj/80BgIsXL+rbbt261eB2AEDHFwpHZEaWxQgA7LjmlEjEXIWmePiwUPr3/0E3/3v0+FSSklLMJWiBYCQgOx1rjOb/RnuK1IQqzWUAAAAAAAAAAHQIUQsAFBYWysSJE+Xs2bPGbeYAgAoGHDhwQEaMGCFVVVXG7QCAzqHaGZJpR54GABIPWyQz32UuQROdOJEjX33VTwcA+vT5SjIy9plL0AIPA3eM5n+qJVEue0/pUAAAAAAAAAAAAB1R1AIA5eXlMnPmTNm5c6e4XE8bPs8GAILBoA4JzJ07V2JjY/VoYwBA55JfEZCpmU8DADOzLJJb5DWXoInWr98kvXt/oQMAffv2k2vXrptL0EzeiFs22VONAMB2x0qpCpaZywAAAAAAAAAA6DCiFgBQTf/du3dLfHy8fn/t2jXd7B81apTcvXtXTpw4IYsWLdIfZ2VlGccEAAA6j9MPvZJw+GkAYP5xq9yrIuzVEqFQSGbMmC3du3+iAwDqKACLxWouQzNd9p42mv9LrTPkgieH3f8AAAAAAAAAgA4tagEARU0BWLNmjW7yq+MA1Kj/QYMGydSpU2XkyJEyevRoSU9PF7vdbr4rAKATyLjhkimHanUAYHGOTSqdIXMJmqC21vLktXScbv6rNXz4aIlEIuYyNIMtVCsb7MkNdv/XhirNZQAAAAAAAAAAdChRDQCoXf21tbVy6tQpPf5fTQNQYQAVAFi1apXk5uaKw+GgiQEAndTqXIdu/quVdtou3gB/37fErVu35bffBhoBgNTUNHMJmum0O1NSrVON3f8XPSclLEwjAgAAAAAAAAB0bFENANQLBoP6SAC1099isej3brdbjzQGAHROTl9YUk/bdfM/7lCtbLzkMJegiY4dOy79+v3HCAAcOZJlLkEzVAXLZLN9ibH7X/23K8yfTwAAAAAAAABAx9cmAQAAQNdTYg3KohM2HQCIP1Qrh267zSVoos2bt8onn3xuBAAKCx+ZKtBUkSdvp9Tuf8vT3f+plkS57jtnLgMAAAAAAAAAoEOKegBAHQOgdvtbrVaprq7W69lrahqAzWbjGAAA6GRulvtl7jGrDgAkHLbIhSKfuQRN4PP5JCkpxWj+f/rpl0zQeQWlwSLZ4lhq7P7faE+RQMRvLgMAAAAAAAAAoEOKagDA6/XKnTt3ZMuWLTJr1iwZP368TJ482biumhrr1q2TlJQUCQQCz9wTANDRnXnolelHLDoAMDXTIg9r+Hu+JcrKyp+8dsYbAYCxYyeaS9BE/ohPTruPSIolUTf/1fsC/01zGQAAAAAAAAAAHVbUAgCqoZ+bmytxcXG68T9nzhwZM2aMjB07tkHdjh07ZMKECTosAADoPPbfckn84VodAFBBALsnbC5BE9y8eUsGDBhiBADWrdtgLkGTRKQsWCSb7KnG7v89znUSijBNAQAAAAAAAADQeUQtAFBTUyNJSUkyb948OX/+vBQVFcnChQufCwCcOnVKRo8eLeXl5Q1uBwB0XP5gRLZecermv1rzs60S5qSXFsnJOSV9+35nBAAuXrxsLkET+CJeOe3OlBRLgm7+p1mnS3HggbkMAAAAAAAAAIAOLWoBANXwnzhxomRlZRlnFaelpT0XADhz5owOANy7d6/B7QCAjsviCsmaXIcRAFh/wWEuQROEw2HJyNgn3bt/opv/6n1dXZ25DE1QGSyVVbb5xu7/TNcu8UeYPgQAAAAAAAAA6FyiFgB49OiRxMbGyunTp43bzAGASCQiu3btkqFDh4rVajVuBwB0bMXWoKSdthsBgMw7LnMJmsDpdD557Vxu7P7/+effdSgAzROWkBx37TWa/ytss6UkWCgRYSwFAAAAAAAAAKBziVoAoKKiQmbPni1bt24Vl8ulm/3PBgDUx/n5+ZKYmCjx8fHi9/tNjwAA6KhuV/hlQbbVCABce+wzl6AJyssrJDY23ggAzJw5V79+onmsoRpj9L9a2a4/xBNxm8sAAAAAAAAAAOjwohYA8Hq9cvjwYRkzZoysWLFCLl26JPPnz5dRo0ZJQUGBZGRk6CMCRowYoa/R0ACAziO3yCvTj1iMAEC5PWguQRMUFNzXu/7rAwA7duzi9bKZQk/e/qjbYjT/19oWyKMAxw4BAAAAAAAAADqnqAUAFIvFIjt27NCN/oEDB0pMTIxeAwYMkEGDBklcXJwcPHhQ3G524QFAZxGOiBy955G4P5v/8YdrxR+iad1cqtF/+fJV6d37C93879atl1y9es1chkYUBe5LsiVeN/9TLImS7d7P7n8AAAAAAAAAQKcV1QCAosb/qx3+6iiA5cuXy+LFi/VEABUMyMvLE5+PsdAA0Jm4/RHJyHMZu/+TcmzmEjRBMBiUAwcOGrv/v/rqWyktLTOX4S/4Ih7Z7Vhr7P7fYE+WokCBuQwAAAAAAAAA0AGVlZVJUVGRPH78WJxOp/nyC4XDYbl//76cP39esrOz5cyZM7pn/ejRI3E4HObyZlMb39Xjnz17Vs6dOycPHjzQk/PbUtQDAPVUo99qtUp5ebnYbDYJBALmEgBAJ1DrCsumS04jAJB+pWkvumhI/ZKQmppmBABGjhwrtbW15jL8hdu+K7LUOl03/1MtUyXb9YcEIn5zGQAAAAAAAACgA5o2bZqeOj927FjJyckxX35OdXW1rF27Vk+s//rrr6V3797y5Zdfyg8//KAfJz093XyXZqmsrJSUlBT9+Opx+/btq/974cKF+rnbSpsFAF4kFArpNIaaEgAA6BxKbUFZdsZuBABO3GPcekvY7XYZM2aCEQBISkqRuro6cxlewhV2yG7HOmP8/1rbQqkMlpjLAAAAAAAAAAAdlGri/+1vf5N//vOfsn37dvPlBtSmu/nz58uHH36o7/P222/r+//888/Ss2dPeeuttyQxMdF8tyZTG/jGjBkj77//vv58RowYIUOHDpV3331X/v73v8vEiROlpKRt/o06agEAlWJIS0uT3bt3v7TBr25fs2aNpKamMhEAADqJ+9UBmXfcagQA7lSw47olqqtr5Kuv+hkBgN27M8Tv53vZVFe952S5dZZu/idbEuSE+4CEn7wBAAAAAAAAADqH5gQATp8+LZ9//rnR/D927Jhu2qsJ9lVVVfoogOPHj5vv1mTJyck6RPCPf/xDVq5cqSfiq41+S5cu1c/53nvvyeLFi/URBNEWtQCASjDExsbK3r179U7/l9mzZ49MmDBBHxEAAOj4bpT5Je7Q0+a/WrV10X8x64xu3843mv89enwqubkXzCV4CVu4VvY41+vmv1rLrDPEEbabywAAAAAAAAAAHVhzAgCbN282dv9/++23EolEzCUtZrFY9C5/9di//vqrFBUVGddUn7z+edVRA7dv337mntERtQDAo0ePZNy4cZKdnW2+1IBKUowcOVKfiQAA6NiCoYicfug1mv8zj1rFG2i9F9GuJCPjDyMA8N///ip37uSbS/ACYQnJNe85WWqdYQQAcj1//bsIAAAAAAAAAKDjaU4AID09Xf71r3/p+nfeeUeuX7/eahvUV69erR/3zTfflMmTJ0swGGxwffz48fq6Oh5g48aNDa5FQ1QDAGPHjtXjE/4qQXHo0CEZPny4FBQUmC8BADoYTyAi+2+5jADAynMO8QVf/hqAl5s7d4ERAJgwIbbNzgbq6CyhKtnjXGc0/9fbksQb8ZjLAAAAAAAAAAAdXHMCAFevXtU7/1W9Wl9++aXs3LlT/9u7uWHfXGraff2Y/5SUFPNlIyCg1qJFi6J+3G/UAgBlZWWSkJCgzzhQYw9eRJ2rkJaWJkOHDtXnKwAAOja7NyybLzmNAMCe63USCBEAaImff/7dCAAkJaXos4Lw14JP3q77ciXNOk03/5MtCXLTd8lcBgAAAAAAAABoonJHUE4/9LTZelgbMH8KL9WcAIBquqvd9x999JHRjP/HP/4hY8aMkaysLHE6nea7NNl//vMf4/FUs99MBQ3qnzMxMTHqffGoBQBUo2Lt2rV6vL86U6GwsNBIM6hxCg8ePNDfZHVdJR0Cgab/MAEA7VONKySpp2xGAODkfa+EwuYqNKaurk569PhUN/+7d+8l6enbXzmB2BXYQxZJdywzdv/vcKwSd7jOXAYAAAAAAAAAaKLzRf879rct1sE7LvOn8FLNCQAo6t/eMzIypF+/fkZD/o033pDPPvtMFi5c+NJN7X8lHA7LJ598oh/rww8/1H1xMzURv/75xo0bJ+Xl5eaSVhW1AID6YtXZCXFxcXqHv5oGMH/+fElNTdXv4+Pj9e2TJk2S/Pz8vzwmAADQMagk4PQjFuOF+lZ5QPjrvfmuXr1m7P7/6qtv5ejR4+YSmEQkLFe8ZyTFkqCb/6mWqZLvu/bkVhIoAAAAAAAAANBSnSkAoKhN6Xfv3pV58+bp+9U35tX4fjXKv7mb8VSPu2/fvvoxPvjgA1m/fr25RPbu3Ws8T2xsrNTU1JhLWlXUAgCK+gZeuXJFpk6dKgMGDJCYmJgG7+fOnSu3b99u9jcSAND+qEb/vaqAxP35Ip1w2CIlNv5+b4lNm7YaAYDffhso165dN5fAxBOpk9W2+cbu/311m8Qeju4YJQAAAAAAAADo7DpbAKCey+WS3Nxc+eWXX4zm/Ntvvy0HDx40lzZKbXpX93///fdl6dKl5suyadMm4zlUf9zj8ZhLWlVUAwD11DgFFQTYt2+frFu3Tvbv3y+3bt2K+hcHAGg74YjIucL//SKQlGOTqrqQuQxNMHFirBEAGD9+sjx+XGIugUmuN9to/i+1zpB833WJCOMnAAAAAAAAAKCzepUAQL28vDz54YcfGuzQb645c+bo+77zzjsye/Zs82U9HV9df/PNN2XZsmVRn4zfJgEAAEDnFwxHZP/NOiMAsO6CQ2wexq83VygUki+++NoIAMyZM1/cbre5DM/wR7yy0jbbCABkODeINVRtLgMAAAAAAAAAdCLNCQC8rOmuNrKrI+vrAwDqGIDmunDhgm7+q/sPGTJELBaLcU1Nwv/mm2/0tc8++0yys7OfuWd0RC0AEA6H9REATaHOOXjZNx0A0DH4QxFZf8FhBAAybtSJy08AoLmKix/Lxx/31s3/nj37yJo168wlMLnsPWU0/9Os0+Wi56SEhT97AAAAAAAAANCZ1QcAPvjgA72zvri4+LlVVlamN9k9evRIrl+/LuXl5fpjtRlPNf8vX74sv/76q36cN954o9EgwYuoPvfgwYP1Y3z++edy9OhR3fhX/fLTp0/rx33rrbd00MBms5nv3uqiFgCw2+1y9uxZ/U1UX9yLqG/s/fv3ZdWqVU0OCwAA2idfMCLzj1uNAEB2gUeHAtA8x45lS/fun+gAwDff9JfDh4+YS/AMd7hONttTjQDAVscyqQyWmssAAAAAAAAAAJ1MfQDg7bff1rvshw8f/tyaOnWqbvyvXbtWvvvuOxk/frwkJyfLmjVrZMGCBfLTTz/J3//+d/04v/zyi1RUVJifpkmuXLki3377rR7zrx5ny5YtsmnTJv05qgBA//795cyZM+a7RUXUAgCq8a/OM0hJSdHfVHOD3+v1Sm5urj4HQSUifD5fg+sAgI5Fjfufcuhp81+9v1LiE4a7NN/y5SulW7deOgDw88+/y40beeYSPCPPe1GWWqfr5v8S61TJcR+U4JM3AAAAAAAAAEDnVh8A+KvVp08fOX78uG7Gq6MCzNfV+uijjyQ2Nlby8lr+7/FqQ/ypU6fk999/18cBvP/++/q92vmvJgycP3/+uX55tEQtAKAa+mqkQXx8vEyePFkyMjLE4/Hoa2qcwh9//CETJ07UyYvdu3fraQAAgI7rQXXQ2P0/66hV7lb5zSVogjFjJujmv1pDh46U6uoacwn+5I14JMO5QZIt8ToAsMa2QEqDj8xlAAAAAAAAAIBO6NatW3Lp0qW/XKqpb7VapaqqSq5evSpZWVl6d/6SJUtk+fLluk998eJFff1lU+2bSo39LyoqkpycHD1xYPPmzXpivjp+4FUfuzmiFgBQ1Bf58OFDWbx4sW70z5w5U3+T1RkMQ4cO1QEA9UXXBwMAAB3X2UKvEQBIPWWTYiu7sJvLZrPLjz/+qpv/agpAXFxim/5S0NHc89+U1bb5uvmvQgD767ZKWPh+AQAAAAAAAABeTG1K9/v9elq9WqqfHWnlccbq3/XVZnn1PK392E0R1QBAPfUFqvTEoEGDJCYmRgYMGCCzZs2Se/fuvZYvGgDQ+jJu1BkBgHUXnFJdx2SX5srLuyn9+n2vAwC9en0mq1atNZfgT4GIX47U7ZbFf+7+T7VMleLAfXMZAAAAAAAAAABNpnrX6hj7ESNGNLrU5veysjLzQ7x2bRIAcLlccvLkSRk2bJgOAKgjAW7evMnYfwDoRJaethsBgF3X66TOx07s5tq//5B88cXXOgDw+edfy9Gjx80l+JNq9m+0p+jmv1rbHCskIoQKAQAAAAAAAAAtp3bvHz9+XL788stG15AhQ/R4//YmqgEA1eBXqYetW7fqIwAmTZokKSkpMn78eElKStIhADUdAADQsYXCEZmaaTECAJn5bgnS/2+2tLRl0qtXHx0A+Prr/nL//gNzCeTp7v+z7ixJsSTo5r96Xxi4ay4DAAAAAAAAAKDZ3G63lJaWNroqKyslEAiY7/7aRS0AoL5Y1eBPTk7WO/8XLFggly9f1tMAjh49KtOmTZPExETJysoSh8NhvjsAoAOpdYWM5n9ipkXOFHrNJWiECsTFxsZLt269dADghx9+EY/HYy7DE1WhMkl3LGuw+z8UYaoQAAAAAAAAAABRCwDU1NTo5r86/2D9+vVSVFSkz0xQ/H6/3Lp1S5YsWSITJkyQtWvXSjAYND0CAKCjuFPhNwIA845b5UYp012aq6SkVIYNG6Wb/2pNnhxvLsETwUhQrnnPS6p1qm7+J1vi5a7/hrkMAAAAAAAAAIAuKWoBADX6f/HixXLo0CGxWq1G87+eOh6gqqpKdu7cKaNHj+YoAADowE4UeIwAQMopuxTWtr+RN+3dpUuX5b///c0IAGzYsNlcgiecYbvscKz63+5/+zJxhZ3mMgAAAAAAAAAAuqSoBQDU2QjFxcWNnnugGv8XL17UgQAAQMe07YrTCACsOueQaid/pzfX/v0H5auv+hkBgPPnL5hLurzIk7cC/01JsSQYAYAbvlwJCX/eAAAAAAAAAABQWjUAoHb5m3f6NyYcDjP+HwA6uMUnrUYAYPNlp3gCzXstgMiqVWuke/dPjACAxWIxl3R5qtGf4VhvNP832VOkJlRhLgMAAAAAAAAAoMtqtQCAy+WSmzdv6tH/zZGTkyPJycmNTgoAALRP3kBEZmRZdPM/7lCt7MlzmUvQCLvdLrNnzzOa/99++29zCZ4oDxZLsiVeN/+TLQly1pMlvojXXAYAAAAAAAAA6AJUf9rpdEpdXV2Te81qM7v6N/nKykopLS2ViooKqa2t1Y/j9/vN5S2mpt+rz0s9j9oQ35ZaLQDw8OFDiY2NlT179pgvSVFRkeTl5b3wi9uxY4cMGTJEHwUAAOh4HlsDMu3I0wCAen+iwG0uQSMKCx/JmDETjADAjBmzzSVdXlhC8kfdZmP3/zr7IikK3DeXAQAAAAAAAAC6iPXr18vChQslLS1Nrl27Zr78HNWQz83NldmzZ8ugQYPk3//+t/z2228yceJEWbBggZw8edJ8l2ZRj6/64pcvX5YDBw7I4sWLZeDAgfr2ttQmAYANGzbI+PHjX9jkJwAAAB3btRKfTM18GgCYfcyqP0bzXLlyVX766TcjAJCRsc9c0uVVhIolxZr45+7/eMly7RFPhGkTAAAAAAAAANBVff311/K3v/1N/vnPf8r27dvNlxtQu/u3bdsmvXv31vd56623pGfPnvLFF1/Ihx9+KG+++aYkJiaa79ZkagLB0qVLZdiwYdKnTx9555139POo5XA4zOVRRQAAAPBKjt31SMLhpwGABdlWeVTbtDE7+J+jR489+YXgSyMAcO8eO9sbishh1w5j9/9q23y5679uLgIAAAAAAAAAdCHNCQDcuHFDvvvuO3njjTd083/JkiV6x7+aCJCVlSUrVqzQfeuW8nq98s0338ivv/4qo0eP1uGC+gCAOl6gLREAAAC8km1XnTLlUK0OACSftInV/fxxL3g5n88vW7akS7duvXTzv0+fr/QvCvifylCprLLN+zMAEC97nBvEFW7bX5gAAAAAAAAAAO1LcwIAqif90Ucf6frPP//8ubH8brf7lXbqh8NhuXv3rhQXF8uFCxfkxx9/JADwomsAgPYtFI7I8rN23fxXa9mT/35yE5rBYrHK/PmLjN3/I0aMkVAoZC7r0rLd+yXV8nT8/zLrTLnmPS9qKgAAAAAAAAAAoOtqTgBg48aNetS/qn///felqqrKXNJqVBDg559/7jwBgF27dukzFJ5da9eu1QEA9cWZr6WnpxMAAIAOyuIKScopm27+qykAO682TMyhcY8fl8jo0eOMAEBq6jKdFMRTtaFK2WJPM8b/b7SniCvCnzMAAAAAAAAA6OqaEwA4ceKEfPrpp7peHQPwww8/SF5engSDQXPpK+t0AYBBgwbJ0KFDG6yBAwfKgAEDdKP/RddiYmIIAABAB/SwJiALsq06ABB/uFay73nMJWjEnTv58t13/zECAJmZWRKJsLtdiUhYLnlO6l3/qvmfbEmQ855scxkAAAAAAAAAIErsdoc8ePCwzVZNTa35U3ip5gQA1NG706dPl/fee89ozHfr1k2Sk5OfPO8DCQQC5ru0WKcJAJSUlMjixYslISGhRUtNAwAAdCxXSnwy++jTAEBipkVulPF3eXOoRv/Zs+ele/dPdPO/Z89P5d69AnNZl2UPWyTDucHY/b/SNkffBgAAAAAAAABoG3v3/mFsYGuLtXTpCvOn8FLNCQAotbW1Mnv2bOnRo4fRnH/zzTflv//9rxw4cEDcbrf5Li3SaQIAHo9HpyPu3LnTohUOM+4YADqa7AKPTM206ADAtCMWKbO1/qiczuzpUTjbjV9sfvjhF6moqDSXdUlq9/9t31VZaZ1jBABy3AfNZQAAAAAAAACAKOpMAQBFNfn379+vJ9e///77DaYBrFmzplV61p0mAAAA6FrUlPqMvDqZcqhWBwBmZFnEF2R0fXPU1blk9ux5xi82cXEJYrVazWVdUl3YIZmuXUbzXx0DwO5/AAAAAAAAAGhbnS0AoKjpvOp4+/Xr1xuPodYHH3wgFy9eNJc3GwEAAECH5PaHZdNlp27+q5VyymYuQSOsVpsMGDDE+MVm5co1rTZiqGOLSHHgvqyyzTUCAMfc+8xFAAAAAAAAAIAoq621SF7ezTZb5eUV5k/hpVoaAKinJtxnZmZK3759jWa9OiLgVREAAAB0SFXOkKw6ZzcCADuv1plL0IjKyirp2bOPEQA4evR4q4wX6uh8Ea/keA7KYku8bv6nWhOlOtT0X/oAAAAAAAAAAJ3fqwYAFIfDIRMnTjSa9aNGjTKXNBsBAABAh/SwJiApJ21GAODkA6+5BI3Izb1gNP+/+OIbuXLlqrmkS7KEqmXlM7v/M107dSgAAAAAAAAAAIB6zwYAtm7dKoFA4LkVDAb1xjuv16t3/Nd/rKj3FRUVuulf36xfuXKl6VmazxwAUCGDtkQAAADQItdLfTL3mNUIANyrDJhL0IgNGzYZAYCBA4dKQcF9c0mXE3nydt6bbTT/l1qnS6H/rr4dAAAAAAAAAIB69QGAd999V2JiYmTBggXPrVWrVklBQYGsW7dOfv/9d1m2bJnk5OTIjRs39Pj/8ePHy//93//px+nRo4eubQkVJlCN/5s3b8qBAwekX79+RgAgNzdX364eu64u+tOUCQC8gN/v5wxmAGjEmYceSTz8tPmvls0TMpegEbGx8UYAIDFxulRUVJpLuhx3pE7W2ZOMAMB+5xZxhG3mMgAAAAAAAABAF1cfAPir1adPHzl+/LisX79ePvjgA+P2N954w/jv999/X77//ns5fPiw+SmaTE0Y6Nmz53PP/+zq27evnDt3znzXVtcqAQA1LqGoqEhqa2slEum4O/RsNpv+pu/evVvS09PlxIkTUlJS0qyvSaU21B+iF622+IECQFsIhCJyON9tNP9nHLFIM/6qhDxNA3777fdGAGDFitWEz5646j33zO7/GZLnuyghIVwCAAAAAAAAAGhIjf1PS0v7y7Vx40Z5+PCh3vG/evVqmTdvnkyZMkWP/R87dqxMmzZN1q5dq3foh0It/7dodbSAChmYn//ZpT7f4uJi811bXasEANQ3TX3Sly9fNs5MUF+kCgS4XC5TdftktVpl27ZtEhcXp7+WNWvW6B+4+u9Hjx41OQRQVlYmQ4cOleHDh+s/OM+umTNnmssBoENyesOy81qdEQBYedZuLkEjyssrpFu3Xrr536vXZ5KRsa/JrzWdlTfikXW2/+3+3+lcI9WhCnMZAAAAAAAAAADNpv4NXm1sVz1stQlc9XXtdrvua3cmrRIAuHjxokycOFHu3LljNC+qq6v1GQqnTp0yVbc/Ks2hRjqMGTNGjh49qn/o6od95coVmTp1qmzYsEEHBJqitLRUBwC2bNmigxHPrsePH5vLAaBDqq4LyboLDiMAsC+vY4S92pPjx08Yu//79//hyevlGXNJl5Pnv2g0/5dYp8o5zzEJSef6xQsAAAAAAAAA0H6pXvft27clJSWl0bVp0yapqakxP8Rr1yoBADXaXo1IyMvLMwIAKjGRmJgoBw4cMFW3P6ppv3DhQklKShKLxWLcrs5q2Lt3rw4GqHBDU6jHUrv/X+WMCABo74qtQUk5aTcCAOcLveYSNCI1dakRABg8ePiT15l8c0mXEowEZZNjiREA2GxfImXBInMZAAAAAAAAAABRo6bd79+/X959991GV79+/eT+/fvmh3jtWiUAcOvWLYmPj5fk5GR58OCB3kGfn5+vz0/YtWuXbqo3tl7n2OPc3FwZNmyY/mH6fL4G1y5duiSjR4+WjIwMHQhoDAEAAF3BvSq/zMqyGAGAwtqAuQSNGDRomBEAiItL1JNzurJ8/3VZZp2pm/8plgTJcu2RiDw9VggAAAAAAAAAgLbicDikoKCg0aWOkTf3ltuDVgkAuN1u2bx5s4wYMUJiYmJatF7XN0ed6XDkyBH9OagggEp1POvu3buSkJAgK1euFKfT2eDai6gAgAoT7Ny5UzdzbDZbpzs3AkDXpvJaV0t8RvM//lCt1Plo1DaHOmOoZ88+uvnfrVsvSUpK6dKvFYGIX/bVbZJkS7wOAKy2zZOiQIG5DAAAAAAAAAAANKJVAgCK2vV/8OBBWbp0qSxatEhmzZqlG+GTJ0+WBQsWNLoCgdeze1Q1YXbv3i0DBw6Ua9eumS/Lw4cPZebMmfpzVM38xpSXl8uoUaNk0qRJMnv2bP39OHnypFRWVppLAaBD8gcjkl3gMQIASSds4g28vikuHdHNm7fk44976wBAnz5fydat280lXcrDQL6stS38c/x/vOx1bpJg5PX8XgAAAAAAAAAAQEfWagGAemonvxrpr44FUEcA7NixQ++Eb2y9riMA6urqJD09XYYMGSJ5eXnmy1JUVCRz5syRGTNm6K+rMS6XS3JycuTYsWP62ICUlBQZOXKkpKamvpbxzmpHqfoaWSwWq7VWlbVOdlyxGQGAteesYrE/X8d6+dq+fad07/6JDgD06/cfycw88lxNV1nWOotkOfZIqiXRGP+fZ7v8XB2LxWKxWCwWi8VisVgsFovFYnWV9bomp6NzaPUAQD3VCL906ZIUFxebL7UragLArl279ASA69evmy8bEwDmz5/fpAkAzwqFQnrn/4YNG/QRA5s2bTKXRJ36C0JNJWCxWKzWWveKKmT5qWojALDzYpU8Ln2+jvXyNXXqTD36XwUAfvrpNzl37vxzNV1l3ai8LOtrk//c/T9FNtWmyuOK4ufqWCwWi8VisVgsFovFYrFYLBarqyy73W5u+QFNFrUAgBIOh1/bzv6mUjvkMzMzdYP+woULz32+d+/elYSEBFmxYoU4nc4G15qqtLRUBgwYILGxseJ2u82Xo0r9DPx+P4vFYrXaKrN6ZeFxixEAOPfQJV7f83WsFy+v1/vkNWGIbv6rNWLEmCe/zDmeq+sKy+13y9m6Y5Ly5+5/te56bzxXx2KxWCwWi8VisVgsFovFYrFYXWmpTcZAS0U1AFBPNb1v3Lghhw8fli1btsjBgwf1xy1tqLe2c+fOydChQ/Xnp/6netbly5dl9OjRsnPnTj0toCXU/6RqwsD48eOlrKzMfBkAOpRSW1CmHHra/FfrQU1A2nfUq30pLS2T//znJ938//jj3jJnznxzSZdRG6qUnY7VRvN/oz1ZwsIvtgAAAAAAAAAAtFRUAwCq8Z2XlyczZszQO+DVUo3w+v+eOnWqXL16VQKBgPmubUodUzBv3jxZsmRJg5EaKgzwxx9/yIgRI+TmzZvGdAD1Xn3Oaqkd9o2pqqrSX++kSZP00QgA0FGpvwbzyv1G83/mUYuUO4LmMvyFc+dy5Ztv+usAQO/eX0h6+nZzSZcQfvJ223dVUiwJuvmfbImXPN9FcxkAAAAAAAAAAC+UnZ0t+/bt05u8CwsLzZefo/q6aoP6tm3bJD4+XgYPHiwTJ07UPeK9e/fK/fv3zXdpEtU7VkfJHz16VB8rrzaXDxo0SKZMmaKPiq+trW3TqQ5RCwCob6Bq/qtv3pgxY2T69OmSmpoqa9eu1d9EFQpQt48dO7ZBc/11UI189YdDfS5qGoD6watG/a1bt/TnvXLlSqmpqTHq6+rqZPXq1fpYgPz8/Gce6em0A/VDVPdXEwMsFov+waoAwPr16xvUAkBHEwqL5Nz3GAGApaftUutquxetzmDbth3Sp89XOgDw5ZffyPnzueaSLsEVdsreuo3G7v/19sVPbnOYywAAAAAAAAAAeKGvv/5a/va3v8k///lP2b79rzfbqQa86gN/8803+j5vvPGGvP/++/Lhhx/Ku+++qz9OTEw0361JVO9YPdabb74pH3zwgXTv3l2/Vx+r51If79q1Sx8R3BaiFgBQO+nXrFmjkw0nTpzQX/iz1MenTp2ShIQEnYR43VMA1C79devWSVxcnGzcuFEnP2bOnCkLFy6Uu3fvNtjprxIcEyZM0OmN3NyGjZuLFy/KokWLZOvWrbJ7925JSkrSo/9V+EGFAQCgIwuGIrLrep0RANhy2SkOb+OTUPCUCrvNn79IevTorQMA3377b6moqDSXdQmPAvdkiWWasfv/gjdHgpHX+7sAAAAAAAAAAKDjaE4A4NGjR/LLL7/oRr9qzKvj4ZctW6b7wgsWLJDhw4fL8uXLzXdrErW5vF+/fhIbG6s3hh84cEA/rtp8/s477+jPsVevXnLy5EnzXaMiagGA0tJSnZJQTXCfz2e+rAWDQdmzZ4+MHDlSN9VfNxUCUKMZ1E59tcN///798uDBg+fG/Kt0xpEjR/Q4iZKSkgbX1GgI9QNV0wHU5IAtW7boH2Z7+PoA4FX5QxFZcdZuBAAO3naJJ/D6Jrh0NA6HU8aPn6yb/2rFxAyWkBqr0MVEJCz767Y8s/s/SSqCDV9PAQAAAAAAAAD4K80JAKgR/x9//LGu79Gjh5SXlxvX1Oa96upqfWx8S6hj5S9duqQnxT9LbQ6v/xzffvttvfnc3HeOhqgFAFSKQp2ZoHb5/xW1g16lHwoKCsyXXgv1A1YpDTXBoCVnMagfmhr/r36gVqtVHwPwOo83AIDW5A1GZEaWxQgAnCv0SijM33FNVVBwXwYOHGoEAObOXWAu6RIqQ6WSZp1u7P4/6T4k3ojHXAYAAAAAAAAAwEs1JwCwadMmPe5f1Xfr1q3NNm/Hx8fr51Rr3LhxOiwQbVELAKiExOTJk+XYsWPmSw3k5OTIqFGjpKyszHwJANDOVDtDEvdn8z8xs1byyl884QUvdurUGfn++/8aAYCMjH3mki7hkGu7sft/rW2hPPTnS0QIkgAAAAAAAAAAmq45AQA12b1nz566Xh0BoI6xb+mO/6ZSm8SHDBliTACYMWNGx54AUFlZKXPnzpWUlBT93y+idtmnpaXpAIAaqw8AaN9ulfuN3f/zs61yv4Yz25tjx45d0qfPV0YA4M6dfHNJp1cbrpIUS4IRAMis2ymesMtcBgAAAAAAAADAX2pOAED1pQcPHixvvPGGvs9bb70lffv21cfZq+nu0XD16lX517/+pZ9PhQ+ys7PNJVERtQBAIBCQo0eP6lSDOs/g9OnT8vjxYz1OobS0VM6ePSvz5s3T1/ft29eicfsAgLZ17J7HCAAsP2uXUnvQXIKXCAZDsmTJMqP5//HHvcXv71oBCrXLP9v1h9H8X2mbIzd9l81lAAAAAAAAAIB24ro31/g33bZY6sjYpmpOAEBRPeoff/xR78avH8uvggBqs7o6rr61duernf9VVVXy+++/68DB3//+d1m8eHGbHRsftQCAUltbK5s3b5bRo0fLgAEDJCYmxljqY3X7ypUr2+yMBQDAq9ly2WkEADZfcorFTXirqaqqqiUxcboRABg8eLi5pNOzhWpltW2+/iUu2RIvu5xrxRG2mssAAAAAAAAAAO1EZwoAKGoSwIIFC6R3794NggB9+vTRG9pftUmvQgQqaKCOGHj33Xf157Zw4ULx+/3m0qiJagBAqaurk1OnTulGv/riZs2apb+pK1askBMnTojT6TTfBQDQTi3IthkBgH0368QbeLUXwq7k7t17MnToSCMAoKYBdCVq9/85zzFZYp2mf4lbap0uF7055jIAAAAAAAAAQDvS2QIAimrynz9/XiZNmiQff/yxEQLo0aOHPHr0yFzeZKr5/+DBA4mPj5f33ntPvvzyS71ZvrUmCzRV1AMA9bxer1RXV0tRUZEeeeDxeF45QQEAaDu+QETiDj1t/k958v54gVvC/DXeZOfO5cp33/1gBABOnOhazW9buFa2OVYYv8RttKfo2wAAAAAAAAAA7VdFsETOe4632SoKFJg/hZdqaQBAUX1qNc1+9erV0rNnTyMEsGTJEnNpk6jHKywslHHjxumd///+978lKytLgsG2P0q5zQIAAICOrcQWNHb/z8iyyIUir7kEL6Fe+A8ePPzkl4hPdfO/W7de+kiAriMi17znZLl1lm7+q/H/pz2ZeioAAAAAAAAAAAAt8SoBgHqVlZUycuRIIwAwduxYc0mTWCwW3fxXxwp89913cvz4cfH5fOayNkEAAADQJFeLfUYAYOEJm9ypaLvzajo6t9stq1evM3b/9+//owQCAXNZp+UM22R/3Rbd+FcBgCWWaWILsfsfAAAAAAAAANByzQkAqDH8L5pOb7VadeO+PgCwaNEic0mj1OR7FRx455135P/+7/9k1apVr635rxAAAAA0yeE7LiMAsPS0XUqsbT+2pqNSR+BMmzbTCABMmZIooVDIXNZp3fPnySrbXGP8/zFXBrv/AQAAAAAAAACvpD4A8N5778n48eNl27Ztz639+/dLSUmJpKeny6RJk+TgwYP6Y3V8/f3792XhwoXy0Ucf6cd588035cqVK+anaZQKH7z11lv6MVQA4KeffpLRo0c/t9atW9cmRwIQAAAANMn6XIcRAFj75L9t7rC5BC9RWPhIBg4cYgQANmzYpNOGXYEn4pLjrn1G8z/FkiC2sNVcBgAAAAAAAABAs9QHAP5q9enTR4/jX7NmjfzjH/8wbn/jjTeM/1aNfxUiUDUtsXbt2uee90VLTQnw+6M/XZkAAACgUeGIyPxsqxEASL/ilEDX2cD+yvLybsrnn39tBADOnDn3wlFDnY3a5V8SKJS1tkVGAOBQ3V+PYQIAAAAAAAAAoCkmTpwoP/74418utfP+0qVLcuTIERk8eLD0799f+vbtK5999pl8/vnn8u2338qoUaPk2LFjLT66V00ZMD/vi1ZKSkqLn6M5CAAAABpldYdlRpZFN//jD9fK/lsucwleQo36P3Ys22j+f/rpl1JUVGQu65QCEb+c9Rwzmv9LrTOkJFhoLgMAAAAAAAAAIOrUxjy73S6FhYVy7do1uXnzppSVlYnP5zOXdmhRCwCohofH4zHfDADogO7XBGTakacBABUEOFvoNZfgJdQ5QqtXrzUCAIMGDZOqqipzWadkCVXLBttiIwCwv26LeCP8bgAAAAAAAAAAaJ9USKC0tFQyMzMbXadPnxan02l+iNcuagGA2tpa2blzp2RlZUl5eXmXOesYADqjC0VeScx8GgCYe9wqtyuif0ZNZ6Fe/GNjE4wAwOzZ83TCsLNT4/8ve89IsiVeN//TrNPknj9PwsLvAwAAAAAAAACA9kn1tFV/u1evXo2u33//XU8TaG+iFgCoqKiQWbNmyciRIyUhIUE2bdok+fn5XeLMYwDobA7eduvR/yoAsOiETcrsQXMJXsJiscj33//XCABs2ZKupwJ0diEJynpbkrH7f49zvdjCteYyAAAAAAAAAADaDdXLfvz4sRw8eLDRlZOTIw6Hw/wQr13UAgAqHaFCABkZGTJ+/HgZNGiQDBs2TGbOnClnzpwRt9ttvgsAoJ1af9EhcQefBgBST9nE7SfM1VRFRcXSrVsv3fzv3v0TOXXqdJcIw932XzF2/6dYEuSq95yEJWQuAwAAAAAAAACgXVH/hq+Ou29sqX54e/z3/qgFAJ6lmv3qDIQZM2boEIAKA6hQwO7du6WsrEx8Pl+7/OYAAEQ8gYgsPWPXzX8VAliT2/7SbO3ZkSNHjd3//fv/KHl5N80lnY5q9K+zLzR2/29zrJDKUIm5DAAAAAAAAAAAtLI2CQDUU03+O3fuyIYNGyQuLk6GDBkiI0aMkBUrVsjVq1elqqqqS4xFBoCOpMIRksUnbToAMOVQrfyR5zKX4C+kpKQZAYBRo8bJo0dF5pJO554/7//Zu++/qM78/eP/nDGWxCRms9nsN/WzidEosUeNiliwF4oC9l5ijQ0Eu2BDUeyiUpU+Z3ov5/3lnOi9eouGTRiYwddzHvdjnDmXo5hd+OF93fdRw/98I0vKA6USM7ltBAAAAAAAAAAAydanBQBLJBKRhoYG2b9/v0yZMkVGjRolP/74o4wePVoWLVokR48elYcPH0owGNR/KwCgHzxsi8iyM067ADCnxJBLdXx//l+MGzdBFQCWLFkuDoehRwaUqBmRA97NqgCw1Z0rjdHHegwAAAAAAAAAACRBnxQArJ3/hmFIVVWVPfhfunSpvfN/3Lhxkp2dLbt375Z9+/bZv7ZOBViyZIk8fsywAABSwZX6kGSXGXYBIOuEITXtET2CN4hGo/LhhyPt4f+gQUNl69btEolE9diAUhd5KEXORfbwP8/IkhP+/RIxw3oMAAAAAAAAAAAkQVILANZu/9raWjly5Ijk5ubKjBkz7J3+EydOtI/9v3DhgtTX19u7/cPhsNTV1cnp06dl27Zt8uDBA/3jAAD9oORBwN75bxUA5pUaYvgTegRvUFPzSO3+HzHiUykuPqFHBpSYGZUS3z7JM+bYBYB1rsXyJHpPjwEAAAAAAAAAgCRJWgHA6XTK9u3bZebMmZKRkWEf85+ZmWmXAaxBv9vtlljs9fsBW7slrWuhUEi/BADoY/GEKXtv+mTWcYddAMg+6ZSut9BDBw4cUgWAb775j1RWXtMjA0pT7Ilsdeeo4//3eTfYtwQAAAAAAAAAAAB9I2kFgJaWFpk7d659pH9+fr7cunVLAoFAt0N/AEBq8oQSsrXSYw//rbX+skeP4C3mz1+kCgATJ06R2to6PTJgJLoe5wLH1e7/XCNTHkSq9RgAAAAAAAAAAEiipBUAXC6XfcS/VQQwTbaLAkA6euaKSWGFWxUAjt/16xG8gfWz79///loVAObNWygez8AtULTFn8oez1q1+3+Ta4UkzLgeAwAAAAAAAAAASZS0AkAkEpH6+npxOBz6pVf4fD5paGjgZAAASEEP2yOy8qxLFQAqG8N6BG/Q0dEp77//gT38Hzx4uBQUrNUjA0a861EVqpDVzrlq9//t8MC+3QEAAAAAAAAAAKkoaQWA5uZmycrKkmPHjumXXnHlyhWZMmWKnQcApJbrTSHJLjNUAaDJSVmrpy5evGwP/q0CwCef/FN+//2IHhkwnPFOOejdonb/b3HlSMSkLAIAAAAAAAAAQF/r1wJANBqVgwcPSkZGhtTU1OiXAQD9yLp7y7knQcks/mP4P6vrORTlli49tXXrDnnvvWF2AeCrr76TysqBuSM+IXF5GLklBc4FqgBgnQYAAAAAAAAAAAD6Xq8XANxut5w4cUL27t1r7+zPycmxX+vr6NGjsnnzZpk+fbpdAOjs7NQ/CgDQj8IxU47d9avd/8tPu/QI3mLWrCwZNGioXQAYPXqcPHs2ME+6CSb8csy3+6Xd/6vEGX/77X8AAAAAAAAAAEBy9HoBoKOjQ8aPH28P9UePHi1jx461X+vrxfUxY8bI/v37JRbjWGkASCWuYEJ2V3lVAWB7pUeP4A38/oD85z+j7eG/VQL45ZcZA/bn3NNYvaxxzlcFgEvBkxI1I3oMAAAAAAAAAAD0gV4vAEQiEXtnf0FBgUyYMEEWLFhgv9bX1q1b5cCBA3Lv3j0JhUL6xwAA+lmrJy7rLnlUAaDsQUCP4A1qah7Jv//9tV0AGDLkQ8nLW6NHBgSz61HqO6CG/xtcy6Ql1qjHAAAAAAAAAABAH+n1AsALra2tsmTJEiktLdUvAQDSQJ0jKivPulQBoPpZWI/gDU6ePC0jR35uFwA+/HCkHD16XI8MCJ64U/KMLFUAOO0/LEHTr8cAAAAAAAAAAEAfSVoBwDrq2OFwiN/PIAAA0tG91ojMLTVUAaDFPTCPsE+GjRs3y/DhH9kFAKsIcOfOXT0yIJwJHFHD/3WuxfIockePAAAAAAAAAACAPpS0AgAAIH3FEyJXG0Jq+D+/1BB/xNRj6IZVgJs7d4EMGjTULgB88cVX4vF49Fja8yScUuBc8LwAkClHfbu63nPpMQAAAAAAAAAA0Id6rQDgcrmkoqJCTp8+bb/2+Xxy6dIlKSsr69GKx+PaJwIA+ksoasqJBwFVACgsd9vv4c+1t7fLxIlT7eG/tSZOnKJHBoQLwRK1+7/ItVBuhi+LKfxvBAAAAAAAAACA/tRrBYC6ujrJysqSSZMm2a9bW1tl4cKFkpGR0aMVDnNvaQBIFb5wQn674VMFgL1dv47EGe72xK1bt+X770erAkBR0Xo9kvZcCYdsdeeoAsBvnnXiTHTqMQAAAAAAAAAA0Md6rQDgcDikuLhYDhw4YL+2jjsuLS2Vffv29WhZRyYDAFKDM5CQogq3KgCcrglKLEEBoCdKS0/KP//5b1UAOHPmnB5Je5Wh87LGOd8e/lvPl4OnJNH1AAAAAAAAAAAA/avXCgCmadrH+L8Y5L/8uicLAJA62r1xWXzSUAWAm8/Cwvy/Z7Zs2S7vv/+BKgA8e9asR9KaN+GRvZ71avf/RtcKMRIdegwAAAAAAAAAAPSDXisAAAAGjkZnVA3/Zxc7ul5T1OoJvz8gy5fnqOH/hx9+okfSmtn1uBOqlHXOxaoAcDpwRI8BAAAAAAAAAIB+QgEAAPAKa6d/VVNIFQBWnHFKqyeux9CNp0+fyYwZs1QB4NdfM/VIWvMnvHLct0cN/9c450lHvEWPAQAAAAAAAACAftIrBQCv1yv379+Xqqqqv7wSCe4dDACpIN717bj0QUAVADZc9ogR4Ht0T9y6dUd++GGMKgDs3Llbj6Qta/f/48hd2ehargoAx3wD5+sDAAAAAAAAAGAg6JUCwKNHj2Tx4sUyYcKEv7zC4bD+sQCAfhCNm7LrulcVAPZX+8QXpgDQE+fOXZBPPvlMFQBu3qzWI2kraPrlTOCI5BqZL+3+b9ZjAAAAAAAAAACgH/VKAaClpUUOHTokmzdv/ssrFuP+0gCQCiJxU/LOu1QB4NTDgIRjph6Dxvo5tm/fQXnvvWH28H/o0BH2CTkDRXOsQTa5Vqjd/9atAKxTAQAAAAAAAAAAQOrolQJAPB4Xv99vDzr+6jJNhggAkAoCkYTMKflj+G+ta40hSfAt+k95PB7Jz1+jdv+PGvXTgCm3Rc2IXAqeUrv/85xZ8jRWq8cAAAAAAAAAAEA/65UCAABg4HjqjKnhf3aZIfdbI3oE3bBOw5k+fZYqACxdusIuyA0EnoRLNrqWq93/R7w7JJjw6zEAAAAAAAAAANDPKAAAAF5xvTGsCgC5511Sb0T1CLrx6NFj+frr/1MFgIMHD0sikdBjaelm6JIa/ucbWVITuS0JGRhfGwAAAAAAAAAAA0mvFQDC4bA8e/ZMGhoa7NfRaFSam5ulvr6+R4tbAABAajh2N6AKABsve6TNOzB2sSeT9TPs+vUqGTx4uD38f++9YVJdfUsGwo+2iBmWbe48VQA44NkszninHgMAAAAAAAAAACmg1woA1hA/Oztbpk2bZr9ubW2VpUuXyqRJk3q0rAIBAKD/bbrsUQWA3254xRNip/efiUZj8vvvR9Tu/88++7c8ffpMj6Wle+Ebavi/xjlPqkIVEjNjegwAAAAAAAAAAKSAXisAWAP/rVu3SkFBgf3aMAzZtWuX5Ofn92hZJwYAAPrf/kk9kAAAbqZJREFUwjJDFQCO3/NLND4AtrEnWTAYlFWr8lQBYMqU6dLZ6dBjacfa/b/dna8KAHs8RdIae6rHAAAAAAAAAABAiui1AoA1wHc4HNLe3m6/jsVidgnAet2TxS0AAKD/Wbv9Xwz/55Q45NyToB5BNzwej0yYMEUVAHJy8sXr9eqxtPMwcktyjUx7+L/aOVfKAyckakb0GAAAAAAAAAAASBG9VgAAAKS/xx1RVQBYdNIpVU3cnqUnHA5DPvnkM1UAOHDgkEQi6X2yjXXM/17verX7f5s7V55Ga/UYAAAAAAAAAABIIX1eAIjH4/pbAIAUcbEupAoAq8655FFHeg+x+8rdu3fV8H/o0BFy6dIVPZJ2aqMPZK1rkT38zzPmSIl/rySEn+EAAAAAAAAAAKSypBYArGF/W1ubHDp0SObNmyfjxo2T0aNHy4wZM6SgoECqqqrE7/dz/D8ApIhDt3yqAFB00S2tbga+PbF//yFVAPj22x/k3r37eiStxCUmx3171PH/61yL7UIAAAAAAAAAAABIbUkrAFjD/3v37sn8+fPtof/EiRNl+vTpMnPmTJk2bZpMmDDBLgSsW7fOLgkAAPpfUYVbFQC2VnrEF07oEXRj4cKlqgAwffpMaWp6qkfSSmP0sWxx56jj//d7N0nM5DQIAAAAAAAAAABSXdIKAE6nU4qKiiQjI0Py8/Pl9OnTUl1dLffv37d3/p88eVLWrFljFwN27NghsVhM/wgAQB+KxExZeNJpD/9nFTtkX7VPEhzQ0iPffPMfVQBYtSpf3G6PHkkbUTMi5wPFku+caw//rVMAHkXv6jEAAAAAAAAAAJCCklYAaGpqkl9++UVycnLk2bNnrx3zb712OByyfv16uwTg8aTvsAQABoJWT0wWlBl2ASDrhCGlDwN6BN1wu90yePBwe/j/3nvDZNeuPRKLpe+tE1pjTbLbU6R2/+92F0jEjOgxAAAAAAAAAACQgpJWAGhoaJBJkybJ/v37JRJ58+CgoqLCzj158kS/BADoQ/daIjKv9I8CQPZJp1Q2hvUIulFZeU3t/h858nM5efK0HkkbMTMm10Plsto5TxUAaiK39RgAAAAAAAAAAEhRSSsAtLe3y9KlS2Xz5s1v3d1//vx5uwDQ2dmpXwIA9KHzT4Iyp+SPAsDS00553ME933tiy5ZtqgDw/fej5ebNaj2SNpzxDjng3aSG/zs8qyVm8r8DAAAAAAAAAADSRdIKAOFwWIqLi2Xu3LlSU1OjX7bFYjHZtm2bLFy48K2nBAAAku9gtV9mFzvsAsCqcy7p9KXvMfZ9aerUGaoAYP366dNneiQtJLoeDyO3Jc/IUgWA2+FKPQYAAAAAAAAAAFJYrxQA4vG4+P1+e6f/y8s6BWD37t2Sl5cnTU1Nr12/ePGiZGVlyY0bN+zPAAD0D9MUWX/ZYw//rbWmwi2xhKnHoLF+dn300aeqALBo0VIJBoN6LC0EEn454tuhhv+b3SvFl3jzCT4AAAAAAAAAACD19EoB4PHjx7JixQqZOnXqa8s63n/MmDH2s34tIyNDxo4dK/Pnz+cWAADQjzyhhKwud9vD/1nFDtl+ncFvT9TW1smQIR/Yw/+hQ0fIxo1b9EjaaIrWymrnXHv4n2tkytXgWYly/D8AAAAAAAAAAGmlVwoADQ0NsmHDBlm8ePFfXh0dHfrHAgD6SKMzJivPuewCQGaxQ0ofBPQIulFWdkoGDx5uFwA+++wLOXGiVI+kCVOO+Xar3f9b3DnSGmvSQwAAAAAAAAAAIMX1SgEgFApJc3OzXQT4qysaZZchAPSX280RWXLaaRcA5pQYUtUU0iPoxpo1RfLee8PsAsC3334vt27d0SNpoSPeLGuc89Xu/3OB4xIyKYEAAAAAAAAAAJBueqUAAABIbxdqgzK/1LALAFknDGkwKGX1xMSJU2XQoCF2AWDs2PHicDj0SFoo8f+mdv9vdq2U2sh9MbseAAAAAAAAAAAgvfR7AcDtdsuZM2ckFovplwAAfeTwHb/MLnbYBYB5pYYEIgk9Ao3DYdi7/q3h/6BBQ2XWrDmSSKTfv1tnvEVWO+eqAkCxb68EEn49BgAAAAAAAAAA0kC/FgCsQcnZs2dl8uTJ9m0AAAB9LxwzZfcNrz38t9aKc049gm7cvFkt//rXl3YBYOjQEbJhwyY9khZO+w6r4f865xK5G67SIwAAAAAAAAAAIE0krQBgmqYYhiElJSWyfPlymT17tsycOfOVNWnSJBk9erRkZGRIS0uL/hEAgD7g8Mdl0xWPKgDsvO7VI+jG4cPH5OOPP7MLACNGfCqnTp3RIynPGe+QDa5lzwsAmbLfu0l8Cf77AwAAAAAAAACQrpJWAPD5fLJz5057wD9hwgSZMWOG/Tx27Fj59ddf7WXt/LfW8ePHJR6P6x8BAOgDDUZUVpe7VAHgdE1Aj6Abq1cXypAhH9oFgE8//Vxqa+v0SEpLdD0uBU/Jauc8uwBQ4Fwg10MVegwAAAAAAAAAAKSRpBUAnj59ag/5FyxYIKWlpXLr1i3ZsmWLZGZmyp07d+xVVlYmq1atkgcPHtgnBgAA+t7dlogsO+NUBQDrNd4uFArJrFlZ9vDfWl9//X8SDof1WEpzxjtlj2etOv5/uztPPAlu/wAAAAAAAAAAQDpLWgGgrq5OJk6caN8C4MXu/gsXLthH/78YklgDlM2bN0tBQYFEo9GXfzsAoI9cbgjJ3FJDFQAcPk5k+TONjU2SkTFRFQBmz87SIynN7HrcCF2SIudCVQC4GDypxwAAAAAAAAAAQJpJWgGgvr5epk+fLufPn1fv3b59274NQENDg3qvuLjYvg1AR0eHeg8A0DfiCZGTNQGZ9Xz4P/eEYb+Ht7t69Zp8881/VAFg+/adeiSluRNOOeLbKblGpjr+35tw6TEAAAAAAAAAAJBmklYAePbsmWRlZdknALw43t/r9doFgMOHD9vvxWIx+7YAY8aMkZqaGu0TAADJ5g0lZN9Nn9r9v6bcrUfQjSNHjsknn/xTFQAqK6/pkZT2MHJL1roWqd3/5wPH9QgAAAAAAAAAAEhDSSsAuFwuWb9+vaxdu1YMw1DvW6/Hjx8veXl5snz5chk7dqy93G6GTgDQ19q8cdlw2aMKAAerfXoEmkQi0fXzbZO8994wVQDw+/16LGX5Ez456T+khv95RpaEzPT5+wMAAAAAAAAAgDdLWgEgHo/LnTt35NChQ9LW1qbeb29vl+zsbMnIyLAH/9aJAMeOHbPzAIC+9bgjKktOOVUB4EZTWI9A43S6ZNGipWr4/9VX3+mRFGbK01idrHctVQWA0/7DeggAAAAAAAAAAKSppBUA3sQ6+r+zs1POnDlj3wrg5s2bEg4zcAKAvpYwRa41hWTW8+H/nBKHOPwJPQZNbW2dTJ78iyoALF68TI+krIgZlguBEjX8L3RmS1vsmR4DAAAAAAAAAABpqs8LAACA1BCMmnL0bkDt/l9zwSWhrvfwdtevV8nXX/+fKgAcOXJUj6QsR7xdNrlWqALACd9+CZlBPQYAAAAAAAAAANJUnxQAQqGQtLS0yKNHj+TevXvS2Ngobrfbvo8yAKB/OIMJWXfJowoAh2/7JBqnAPA21ik2ZWWnZPjwj1QBoLa2Vo+lrKuhcy/t/l8ojyN3JSH8LAYAAAAAAAAAYKBIagEgGo3aA/9NmzbJ4sWLJSsrSzIzMyU7O1tycnLk2LFj0tHRYQ9UAAB9q9kTk/mlhioAVDWF7dsC4M2sW9bs2LFLDf8/+eSfEgymxw76iBmSja7lqgBwxLtD3AmnHgMAAAAAAAAAAGksaQUAa3d/dXW1PfAfM2aMjBo1SkaPHi0///yz/Pjjj/ayfr18+XJpa2vTfzsAIIms3tXNZ2E1/J9bakijM6bHoDEMpyxZskIVACZN+kVisfT4d6sKXVTD/wLnfLkVvsrufwAAAAAAAAAABpikFQA6Oztl1apV9tC/qKjIPiI5EonYgxJrt2RNTY2sXbtWMjIyZM2aNWkzQAGAgSCeEDlyx68KAGsvucXhj+sxaJqansr48ZNVAWDNmiKJx1P/3y1hxmWtc6EqAOz3bpTOeKseAwAAAAAAAAAAaS5pBYDGxkaZOnWqFBYWisPh0C/bQqGQbN261S4BeDwe/TIAIEmicVNWX3CrAsDh237xRzj//888ePBQPvvsC1UAKC09lRa3sbkdvqqG/6ud8+RSoOvvze5/AAAAAAAAAAAGnKQVABoaGmTSpEmyf/9+e+f/m1RUVNi5x48f65cAAEniDiYks9iwh/+zix1yqT5knwqAN7MG/ZcuXVbD/yFDPpSHD2v0WMoJm0HZ5SlUBYAd7tXSEmvUYwAAAAAAAAAAYABIWgGgo6NDVqxYIRs3bhSXy6VfVs6fPy9TpkyR9vZ2/RIAIEluPgur3f+LTxnyoP3NRS38wSqz7dy5WxUAvv32B2lubtFjKed++IYUPT/+P9/IktP+wxI3ue0OAAAAAAAAAAADUdIKAOFwWM6ePSvLli2TGzdudHuPZL/fL5s2bZL8/HyJRqP6ZQBAkhyo9qsCwLpLbmlxMxD+M35/QBYsWKQKAJmZc8UwnHospYTMgBz17ZJcY45dAFjnWiJPY3V6DAAAAAAAAAAADBC9UgAIBAJSX18vDx8+fGVVVVXJ+vXrZeXKlXLt2rXXrh8/flyysrKkvLy824IAAKD3WbesX3rKqQoA+6t94gtz/v+fcbvd8v33o1UBYP36TfbPv1T2JHJPNrlWqOP/j3p3SUwoewAAAAAAAAAAMFD1SgHgyZMn9nH/v/zyy2trwoQJMmbMGJk8efJr18aPH29fmzZtmrS1tekfCwBIgg5fXGY9H/5nFhty+lHQLgXg7Vpb22TIkA9UAaCs7JQkEqlbnIiYYTkdOCJ5z3f/W6sx+kSPAQAAAAAAAACAAaRXCgCPHj2SRYsW2QP9v7qam5v1jwUAJMHVhrDa/b/0tFNuN0f0CLpRUXFJDf9Hjvxcrl+v0iMp5Vm0Tna4V6vh/07Pmq53aXoAAAAAAAAAADCQ9UoBAACQPvZU+VQBoLDCLc1ujoTviY0bt6gCwE8//SyPHj3WIykjZkblSvCs5BqZ9vDfeq6L1ugxAAAAAAAAAAAwwFAAAIB3SCRmyrIzTlUA2HHNK6Eou8J74pdfflUFgLlzF0hbW7seSRmd8TbZ41mrdv/v8hSKye5/AAAAAAAAAAAGvD4rAEQiEXE6ndLS0mI/h8NhMbnpNAD0qSZnVOaXGfbwf06JIcX3/XoE3YjHE/ax/y8KAEVF6yUYDOqxlBDvetwOV0qeMcce/lvP9yM39RgAAAAAAAAAABiAkl4ACAQCcu3aNdm1a5cUFRVJTk6OFBYWys6dO+XKlSvi9zN8AoC+cqkuJFkn/igALDrplOpnYT2Cbjx9+kwN/4cN+0gOHjycsiU2f8Irv3nWq93/ezxF4k249RgAAAAAAAAAABiAkloAsHb6HzhwQKZPny6jR4+WUaNGqWW9njZtmuzYsUM8Ho/+WwEASfDbDZ/MLv7j+P+VZ13S5onpEXSjtLRMFQD+/e+vpby8Qo+kjEeRu5JvZNnD/1wjU26GLknM5L8zAAAAAAAAAADvgqQVAKLRqJw7d07Gjx8v8+fPl7KyMrl//740Njbaz9brBQsWSEZGhpSWlkoikdA/AgDQi3zhhBRWuO3hv7XWXnJLnG+9PbJqVZ4qAIwdO14ePHioR1LGXu9/d//vdBdIW6xZjwAAAAAAAAAAgAEqaQWA9vZ2WbZsmaxatUqam5tfOyrZet3Z2Wlfnzx5skQikVeuAwB6V11nVFacddnDf+sUgON3uQVLT/3ww0+qADBrVlbXzy+HHkkJLbFGNfy31sXgSYma/HwFAAAAAAAAAOBdkbQCQFNTk8ycOdPe6f82p0+flgkTJkhbW5t+CQDQiy7XhSS7zLALAHNKDLndzGC4J7xerwwb9pE9/B80aKh9GkAqnlqT6Hoc9GxVw/+t7hxpiD7SYwAAAAAAAAAAYABLWgGgrq5Opk2bJuXl5fqlV1RWVsqUKVPk4cPUPU4ZANKdddT/odt+e+e/VQCYV2qIM5B6Q+xUdONGtQwZ8qFdABgx4lPZs2evHkkJzbFGyTUy7eF/njFHSv0HJWgG9BgAAAAAAAAAABjAklYAePr0qcyZM0d+//33Nx7vH4/H5dixY/YJANbtAAAAyeEKJmRLpcce/lsr/4JLXr0xC95k374DMnjwcLsA8MUXX8mFCxV6pN/Fzbic8O1Tu/83uJbJw8gtPQYAAAAAAAAAAAa4pBUAnE6nrF27VubOnSsXL16UQODVXYjW6+vXr0t2drYsXLjwjSUBAMDfV+eI2kP/FwWA4nt+PYI3WLRoqbz33jC7APD996OltrZWj/S7lliTbHQtt4f/1ikAh7xbJZDw6TEAAAAAAAAAADDAJa0AYO3ur6qqkpkzZ8r06dMlJydHNm/eLL/99pts3bpVcnNz5ddff7WP/7eKAKbJXlQASJZrTSFZUGaoAkBNO6WrnohGozJmTIY9/LfW+PGTJRgM6rF+ZUpCzgWOS75zrl0AKHRmy63wVT0GAAAAAAAAAADeAUkrAFjC4bBUVlbapwCMHj1axowZI2PHjrWff/zxR/sWAdbpAFYOAJAc0bgppQ8DMuv58H9OiSGROKWrnmhsbJSvvvrOHv5btwFYsmS5Hul37fFm2e0pVMf/b3fnSdB89dQdAAAAAAAAAADwbkhqAeAFn89n7/I/ePCgrF+/Xvbv3y/Xrl0Tt9vNzn8ASDJnICG7qrxq9/+6ix49gjeoqLgon332b7sAMHz4x/Lbb/v0SL+7HDwjq5/v/rfW9VC5HgEAAAAAAAAAAO+IpBUArFsAWIN/a3c/Q34A6D9NzpjkX3CpAsCpmtQ6wj5VWT+7Nm/eag/+rQLAxx//Q65fr9Jj/cqId8hB7xY1/C90LpRQgt3/AAAAAAAAAAC8q5JWAGhra5PCwkKpqKiQWCymXwYA9AGrfnWvNSLzThiqAPCkM6rH0A232yPTps20h//W+vLLb8XlcumxfpPoetwJX5NCZ7YqAFwOntZjAAAAAAAAAADgHZK0AsDTp09l5syZsmvXLgkE2I0IAP0hGjfl/JOgGv4vPOkUbzihx9CNqqob8t13P6gCwKJFy/RIv/Im3FLs+00N/9c7l4ovwe0dAAAAAAAAAAB4lyWtAODxeGTr1q2yZMkSqa+v5zYAANAPfOGE7KnyqgLAtkqvhGN8P/4ziYQpe/bslQ8++EQVAC5fvqrH+o3Z9aiL1sha5yJVALgQKNFjAAAAAAAAAADgHZO0AkAikZDGxkbZuHGjrFu3TlpbW/ulBGD9mW632y4k/NU/3/pafD6fNDc3i8PhkHg8rkcAICU5/HFZddapCgBnHgUllvhr3wvfJQ6HIdnZS9Tw/9NPP5dgMKjH+k3YDMkp/+9q+G8VAdpjzXoMAAAAAAAAAAC8Y5JWALAG5Zs3b5bZs2dLRkaGZGZmyooVK2TlypXdrmi09+9J/fDhQ1m+fLn8+uuvMmvWLLuIYN2aoKdFgHA4LNevX5f8/Hz7dgbTpk2TGTNmyKJFi+TatWsSi8X03yIdHR32qQfdrfXr1+txAEiqOkdMZhf/Mfyf1fX8qCMiPfsO+G67deuOfP/9aFUAWLkyV4/0q85YmxQ6s1UBwCoDhM3UKSgAAAAAAAAAAID+kbQCgLVbfu7cuTJq1KgeLWvY3pvu3btnFw+sAkJxcbH8/vvvdhEgOztb2tvb9Xi3rGG+NfyfN2+e/fvLy8ulsLBQfvrpJ5k8ebKcP39e/y3y7Nkz++uxMj///PMry/ocAOgr1kb/C09Cavf/8jNOafW8XlzCq6zj/48fL5HBg4erAsDt23f0WL86FyxWw/8C5wJpiD7SIwAAAAAAAAAA4B2UtAJAIBCQmzdvysWLF3u0evNYfa/Xa5cPpk6dKp2dner9qqoqewf/9u3b7WP9/0woFJKWlpbXygk1NTV2uSA3N/e1MoFVALCG/SUl3IsZQP+yjvrfec2rCgA7r3vFHfzz733vOqfTKcuXr1TD/y+++EoikYge6zfBhP+V3f/H/b+JN+HSYwAAAAAAAAAA4B2UtAJAf7J25k+cOFEOHz78yvvWQL+goMC+ZhjGK9f+Fx6PR+bPn2+fJlBbW/vKNQoAAFJFKGbKklNOVQA4WROQcIwbAPyZJ09q5bvvflAFgDVrinq1pPZ3VYTK1PDfKgLcDV8Xkxs7AAAAAAAAAAAASUIBwNotb+2Qt3b/37p1S+rr6yUY7Lv7Els7+zds2CCjR49+bThvsY7yHzduXLfH9/eUVR6YPn26LF++3B74v4wCAIBU8dQVU8P/uScMufksLCZz4reyBv1nzpyV994bZg//hwz5UCorr3X9u6XGP5y103+da4kqABzybhFnvEOPAQAAAAAAAACAd1SvFgCsnfH79u2z73U/bdo0+7j9xYsXy8GDB8XhcOjxpPD7/fbR/KNGjbJ/rTt37pxMmDBB9uzZo1/qsStXrsiYMWNk27Zt9qkCL7MKAD/99JPk5+fLhQsX5Pbt293+PQAg2c4+CqoCQO55l9Q5onoEGquwtmzZf4////HHn6SlpVWP9ZuboUuSa2Sq3f+VwfOSEG7rAAAAAAAAAAAA/tCrBYCysjLJyMiQH3/8USZPniy//PKLPSi3dsSvX79eotHkD586Ojpk6dKl9i7/7u7ZbA3vrb/bmjVr9Es90tTUJLNnz5bMzEy5f/++flna2tpk1qxZMnPmTPuUAOs5Ly9Pqqur9SgAJNWGS25VANhW6RWHP3WOsU9VhuGUzz//f6oAUFCwVny+1ChxuROG7HYXqd3/O91rpDOeOuUEAAAAAAAAAADQ/3qtAGAd/b9kyRJ75/3x48els7PT3vV/48YNWbBggYwdO9a+JUCytba22qcOWKWD7goHlZWVdgEgJydHv/SnrM/OysqSqVOn2l9Ld/eEtt6zbhFgfe1WWeDYsWMyceJEmTRpkjx+/FiPJ51VgrBKESwW691arW0dklnyx/DfWnuvdUhza/trOdarq7i4RAYNGqqO/z9y5Khd7NJz/bFOGYdltTHXHv7nG1lS3LlP2tpT4+/GYrFYLBaLxWKxWCwWi8VisVis3lter1cf+QE91msFAGvgPWfOHHtnvO7mzZv2aQArVqzQL/U6l8slK1eutE8h6O4EgPLycnsgv2XLFv3SW73Y+W/dPqC+vl6//FZnzpyx/z4LFy7ULyWdVcxoaWlhsVjv2Lr6sE0N/+edcMiJW+2vZVivr1mzstTu/1GjxsilS5dfy/THutN2Q7Y4ctTu/7WOxfK49cFrORaLxWKxWCwWi8VisVgsFovFYqX/crvd+sgP6LFeKwA0NjbaR99v3rxZv2Tvil+0aJG9cz7ZrF3/1vH+1kkE1ikEutLSUhk/fry9M78nEomENDQ02KcbWF9fc3OzHvlTVkvHKgBYtwTg/7AA+kLZw4AqAOScc8mjjtdPRMGrQqGQfPjhSHv4P2jQEMnJye/6nu3RY30uYkbkXOC45BlZ9vA/18iUa6FyPQYAAAAAAAAAANB7BYDa2lr7fvf79+/XL9mKiopk9OjR3e7K723W32HcuHFy9erVV963ighbt261r92/f/+Va29iDfzz8/Ptkw0ePHigX+4Raxe+9bVbpyBYR0kDQDKZpsjai25VANhw2SPOwOu3LMGrzp69oHb/jxgxUo4ePa5H+pzZ9aiLPpSt7v/u/t/uWS1RM6xHAQAAAAAAAAAAer8AsHbtWrl9+/ZrKycnx94Ff+XKldeuWcvaad9bnjx5IjNmzJC8vDx7+P6CdYx/dna2fRqBdVLAC4FAwP49d+/elWAw+Mr7hw4dsk8MsE4O+KvlBevrs772efPmvfL3AYBkcAcTsuiU0x7+zy52yL5qn8StVgDeavbs/x7/P2bMuK6fCff0SJ/zJ3xy0n/I3vVvDf9XO+dJfbRGjwEAAAAAAAAAANh6vQAwYcIE+1lfP//8s30svzWY169Z668O17tjDff37dtn/5nWUf/19fVSU1Nj357A+rOsEoL50jDMur569WqZPXu2PH36VL1v3dbAes86MWDTpk1y9OhR+/NerAsXLohhGCpvefbsmVy7ds3+TOv0AOvX1u0DrBMAzp0790oWAJLhXktE5pcadgHAer7w5L/FJnSvo6NTPvnkn8+P/x8q2dmLxefz6bE+leh6PIrclbXORWr3/wn/PkmYvVeYAwAAAAAAAAAAA0uvFQCswXlubq5kZWX9pdWbBQCL2+2WvXv3ypQpU+yd93PmzLHXmTNnXtnlb3n06JGdsQoK1uD+5fczMjLs3ftjx461iwAvr8WLF9snB7ysoqJCpk+fbn+eddqA9Wvr67P+XOv+0gCQbCceBGROyR8FgKWnnfK4478nnqB7JSWl8v77H9gFgI8++kfXz4/9rxTF+kPQ9Mt+zyY1/F/vWiqtsf+W1AAAAAAAAAAAAHS9VgCIxWLi8XjE5XL9pZWMQYs1cG9paZGrV6/KjRs3pL29vduigXVigLWT37r+8q0BrF93dHTY73e3nE7nK3mLVTyorq62d/tbJwTcv39fHA6H/e8DAMmW6PpeuumqR2Ydd9gFgNzzLvGHe//760Aza9Yce+e/VQD49tsf5N69+3qkz90OV0qeMUcVAC4HT0vUfP1nGAAAAAAAAAAAwAu9VgAAAPS/dl9C8i647eG/VQLYWenRI9A8e9Ys33zzH3X8/9Sp0/u9tBU3o7LW9d+j/3e5C6Uj1qLHAAAAAAAAAAAAXkEBAAAGkDstEfvYf6sAkFnskAtPXr3lCV537FixfPLJP+0CwLBhH8mePXv1SJ9KdD3OBYrV8H+1c55UhS5K3IzrUQAAAAAAAAAAgFdQAACAAcI66P9kTUDmnjDsAsCcEkMajP7dyZ7qrNu4LF68TAYPHm4XAEaO/FweP36ix/pUS6zJHvpbw/9cI1MOebeKEW/XYwAAAAAAAAAAAK+hAAAAA0Q4ZsqeG157+G+thScNCUetWgDepK6uXsaNm2AP/63188+TJB7vv532YTMkR327JMfItAsA61yL5Xa4Uky73gEAAAAAAAAAAPB2FAAAYIBo9cRl3SW3KgBsq/ToEWhKS8vks8++UAWAvXv365E+dS9cJUXOhc93/8+RI94dEkj49BgAAAAAAAAAAEC3KAAAwABxrzUiy884VQHgUl1Ij+AlwWBQ1qwpksGDh9nDf+s2AK2tbXqsz7gThhz0brGP/bcKAEXORVIfrdFjAAAAAAAAAAAAb0QBAAAGAOuA+Iq6kGSWGKoA0OHtv6Ps00FdXb1MnjxN7f6fMGGymGb/HLVvHfF/PVQuBc4F9vDfWqW+/RIX/hsCAAAAAAAAAICeowAAAANAIGLK4Tt+NfxfeNLQI9CUl1+UkSM/VwWAI0eO65E+0x5vlr2e9Wr4n29kid/06jEAAAAAAAAAAIC3ogAAAANAuy8um654VAFg7w2Gx28TCoVk+/adavg/fPhHYhj9U5qImhG5EjwrecYce/hv3QLgWui8HgMAAAAAAAAAAPhTFAAAYACodURl2WmnKgBcawzpEbykublFpk6drgoAv/6aKdFoVI8lnXX0f3OsUba589Tu/12eAomYET0KAAAAAAAAAADwpygAAECaS5giVU1hmV38x/A/s8SQVg/3jn8T0zSluvqWfPzxZ6oAcOxYsSQSCT2adBEzLGf8R9Xwf41zvtREbotVDQAAAAAAAAAAAPhfUQAAgDQXippy/F5A7f7PO++WQIQB8ptEIhHZuXOPGv5/9tm/5cmTWj3WJ5qij6XIuUgVAIp9v0nQ9OsxAAAAAAAAAACAHqEAAABpzhVMyIbLHlUAOHTLL5EYBYA38Xp9Mn78ZFUAyMqaL4Zh6LGks47/3+/ZqIb/G13LpT5S0/Vu359EAAAAAAAAAAAABgYKAACQ5tq8cZlXaqgCQGVjWOLMkN+ovr5B3n//A1UA+P33IxKNRvVY0t2LVEmeMcce/ucamXI+UCxhM6THAAAAAAAAAAAAeowCAACkMdMUud0cVsP/rBOGNDpjegwv2b59pxr+f/HFV3LzZrUeSbpwIiBrXzr6f4d7tTyN1ukxAAAAAAAAAACA/wkFAABIY9ZO/+P3/KoAUFjhlk5fXI/huUQiIaNG/aQKAJmZ86SlpVWPJVWi61EeOGHv+reG/6ud86QiUCpx4b8bAAAAAAAAAAD4eygAAEAaiyVMKSh3qwLAwVs+8YU5//9NGhoaZdCgofbw37oNwPbtu/r8+P/mWKNscC1XR//v8ayVznjflhAAAAAAAAAAAMDARAEAANKYO5iQOSWGPfyfVeyQirqQXQpA9zZs2KR2/3/55bdy8eIlPZJUITMopb4Dkm9k2QWANc75UhWqEFP4bwYAAAAAAAAAAP4+CgAAkMZuN0fU7v9Fp5xyvy2iR/BcJBKRb7/9QRUApk+fKa2tbXosqR5F7sjG57v/rbXPs0ECCZ8eAwAAAAAAAAAA+EsoAABAGjt0y6cKAEUXPfLMHdMjeO727TsybNhH9vB/6NARUli4TuLxuB5LGm/CJcW+3yTXmKMKAA3Rx3oMAAAAAAAAAADgL6MAAABpbPEppyoA7L3pE284oUfw3Nq162Xw4OF2AeBf//pSzp27oEeSJtH1uBeukkJnthr+W2UA630AAAAAAAAAAIDeQgEAANKU4Y+r4f/sYoecrAlyJ/k3CIfDMnr0OHX8/5gxGeJwOPRY0rgSDjng3aSG/6ud8yWQ8OoxAAAAAAAAAACAv4UCAACkqeuNIVUAWHLKKTefhfUInrt27bq9698a/r///geyYkWOHkmaeNfjVviK5BqZ9vDfugXAleBZMalrAAAAAAAAAACAXkYBAADSlHXk/4sCwJpytzx1xfQIupimKRs3bpHhwz+2CwAjRoyU8vKLeixpnPFO2e7OU7v/d7oLxB136jEAAAAAAAAAAIC/jQIAAKShSNyUleecqgCwrdIrwSg7yrvjdLpkxoxZ6vj/r776Ttxutx5LmvOBYjX8L3Rmy+3wNYmblDUAAAAAAAAAAEDvowAAAGnomSsm2WWGPfyfU2LI8Xt+PYLnrOP/v/32B1UAWL58lR5JmpZYkz30f1EAOObbJZ4Eu/8BAAAAAAAAAEByUAAAgDR0tSEkc0/8UQBYeNIpVU/DegRdEomE7Nq1R4YN+0gVAK5fr9JjSREzY7Lfu1EN/ze4lsmDcLUkuh4AAAAAAAAAAADJQAEAANLQ/mqfzC7+4/j/FWdd0uKO6xF06ezslPnzF6nh/8iR/5RYrG+O378brpJ8Z5Y9/M81MqXEt09CZkCPAQAAAAAAAAAA9BoKAACQZkJRU4ouuu3hv7XWVLgkbpp6DF2qq2/L//3fKFUAyM8v0CNJETD9st2dr3b/b3avlPpojR4DAAAAAAAAAADoVRQAACDN1BtRWXXOZQ//rVMADt/26RF0icfjcvTocXn//Q/s4f977w2TO3fu6rFel5C4XA6ekTXO+fbwP8/IkpP+Q2Jy9D8AAAAAAAAAAEgyCgAAkGauNoRk4UnDLgBkljik+llYj6CLYThlyZLlavf/d999L6FQSI/1utb4U9npKVC7/7e4c6Qj1qrHAAAAAAAAAAAAeh0FAABII3FT5Ohdv2QW/3H8f9YJQwx/XI+hy5MntfL11/+nCgBFReslFovpsV4VNoNyPlAsq51zVQHgWuiCHgMAAAAAAAAAAEgKCgAAkEbcwYRsu+a1h//Wyj3vkoSpp2Ad/3/y5Gn72H9r+D9kyIdy/XqVmGYy/7FMaYg+snf8vxj+73YXStAM6EEAAAAAAAAAAICkoAAAAGmk3ojK6gtuVQCwTgPA64LBkCxe/N/j/3/66WdpaWnRY70qkPDJSf8hyTXm2MP/vK7nxthjPQYAAAAAAAAAAJA0FAAAII3ceBqWhWWGKgA8aI3qEXRxudzy6aefv3L8v9+fvLKE2fWoiz6UAucCtfu/xL9P4pLcWw4AAAAAAAAAAAC8jAIAAKSJeMKUUzUBNfyfXeyQSCyZR9qnrwsXytXwf+jQEV2vK/RIrwqZATno3aKG/4XObGmPJffEAQAAAAAAAAAAAB0FAABIE+5gQvbc8KoCQGGFW4/gublzs1UBICNjojx+/ESP9KoHkWo1/LeO/r8YKJOIGdZjAAAAAAAAAAAASUUBAADSxDNXTFaXu1QBoPRBQI+gi8fjlREjPrWH/4MGDZXc3NX2e8niT3hliytHFQB2eQqlPdasxwAAAAAAAAAAAJKOAgAApIkHbRGZX2qoAsCjjogeQZeTJ0+r3f8ff/wPOXasWEwzObdKMLseFwLFavhf4Fwg10PlEjOjehQAAAAAAAAAACDpKAAAQBqIxk0prw2p4f/CMsO+JQBe98svv6oCwE8//Sz37z/QI72mOdYgRc6FqgBwwLtZjHiHHgMAAAAAAAAAAOgTFAAAIA34wwnZe8OnCgCbr3okGE3OrvZ01tbWJh999A91/P/ChUskEAjqsV4RMyNy2LdTco1Me/i/1rVYboWvSkIoZgAAAAAAAAAAgP5BAQAA0oARiEvOOZcqAJx8GLBPBcCrjh07LkOGfPj8+P/PZP/+Q3qk1zwIV8s652J7+G+VAA56N0vIDOgxAAAAAAAAAACAPkMBAADSQJMzJrOK/xj+W+the0QY/7/KNE2ZMWO2vfPfKgB8++0P8vBhjR7rFX7TZx/3/+Lof+s2ALXR5N1qAAAAAAAAAAAAoCcoAABAikuYIhV1ITX8X3baKc3umB575zU2Nsk33/xHHf8/ffosicXieuxvS0hcboQu2kP/FwWAYt8eMalkAAAAAAAAAACAfkYBAABSXDwhsrvKqwoA2yu94gxwn3ndkSPH5JNP/mkXAIYN+0j27t2vR3pFR7xF9nk3quH/WudiccRb9RgAAAAAAAAAAECfowAAACkuEjdlySmnKgCUPQxIOMZu85eFw2FZvHiZDB483C4AWEWA+voGPfa3RcywXA2dk9XOeaoAcDV4To8BAAAAAAAAAAD0CwoAAJDimt1xmfV8+J91wpCqp2Exmf+/4smTWhk3boI9/LfWxIlTJR7v7eP/TWmNPZVt7lw1/N/tLpSg6deDAAAAAAAAAAAA/YICAACkuAtPQmr3/6pzLqntjOqRd96JE6Xyj3/8SxUADh78XY/8bVEzIucCxyTXyLSH/3nOLHkUvi2m0MYAAAAAAAAAAACpgQIAAKS4TZc9qgCw5apHOn29vbM9vYVCYSkoWCuDBg21h//vvTdMDMPQY39bW/yZrHHOV7v/j/v3iD/h1WMAAAAAAAAAAAD9hgIAAKSwuCkyu/iP4b+1jtzxSyTGjvOX1dbWyaRJv6jd/1OmTNMjf5u1y3+/Z5Ma/q91LpaG6CM9BgAAAAAAAAAA0K8oAABACqtzRNXwf+4JQ8prg3rknWaaply4UC4jR36uCgDFxSf02N/2MFKthv95xhw54z8qwYRfjwEAAAAAAAAAAPQrCgAAkMJO1wRUAWDlWZc8bI/okXdaIBCQTZu2qOH/Rx/9Qzo7HXrsb/ElPLLZvVIVALa786Up+kSPAQAAAAAAAAAA9DsKAACQoqyD/jdc9qgCwLpLbnH443rsndbc3PLK8f+ZmXMlEum9kkSi61ERLLN3/VvD/zXO+VIRKJWo2Xt/BgAAAAAAAAAAQG+hAAAAKcoTSsiSU057+D+r2CG7b/gklrBqAbBYx//fvHlLRoz4VBUATpwok0QioUf/suZYo2xx5ajd/zs9BdIZb9NjAAAAAAAAAAAAKYECAACkqAdtUZlfZtgFgHmlhpx9HNQj77RIJCrbtu1Qw//PP/9/Ultbp8f+srZYsxzybpU8I+v57v95UhWqsE8FAAAAAAAAAAAASEUUAAAgRZ16GJQ5JX8UAKyTAGraOHb+ZT6fX8aOHa8KAPPmZYthGHrsL3EmOuWIb6c6+t9au9yFEjFDehQAAAAAAAAAACBlUAAAgBRkmiJbKj0y67jDLgCsOueybwmA/2psbJRBg4baw3/r+fDhoxKLxfTY/8yX8Eip76Aa/Ftrg2upuBMOPQoAAAAAAAAAAJBSKAAAQArq9MVldbnbHv5bJYDNV7165J23Zcs2tfv/yy+/lZs3b+mR/1kg4ZNz/mOSa2Q+H/5nyhZ3jrTHW/UoAAAAAAAAAABAyqEAAAAp6F5rRJadcdoFgNnFDjn7KKhH3mnxeFz+858fVQEgK2u+tLa26bH/ScgMSnnwhOS+dOz/dne+NEWfiNn1AAAAAAAAAAAASHUUAAAgBZ15FJS5Jwy7ADCnxJBaR1SPvNMePXqsjv8fOvRD2bFjl0Sjf/3fKGgGpDxwQvK04f/jyD2Jy9+/rQAAAAAAAAAAAEBfoAAAACkmHDNlX7XPPvrfKgAsKDMkFGUH+suKitar3f9fffV/cunSZT3SY2Fr53/ghKx2zlPDf+vY/weRaomaf71UAAAAAAAAAAAA0NcoAABAimn3xmXDZY89/LfWlqsePfJOi0Si8tVX36kCwPTpM6WtrV2P9UjEDMvF4EkpdGar4f9611K5E74mUTOixwEAAAAAAAAAAFIaBQAASDEP26Ky8qxLFQAqnoT0yDvtxo2bMnz4x8+P/x9hnwaQSCT02J+KmTG5FrwgRc6Favi/xjlfboYucew/AAAAAAAAAABISxQAACDFXKwLSWaJoQoALW6G0S8rKCiSwYOH2wWAf/3rKykvv6hH/pQ14L8bvv7K8D/PmCNXgmf1KAAAAAAAAAAAQNqgAAAAKSQYNeXoXb8a/i866RTT1FPvLp/PL6NHj1PH/48dO14cDkOPvVW861ETuWMf9f9i+F/gnC8VgVI9CgAAAAAAAAAAkFYoAABACun0xWXLFY8qAOyu8umRd9qVK5Xyr399aQ//33//A8nJydcjf8KUJ5H7ssm9Qg3/C53ZUh44YRcDAAAAAAAAAAAA0hkFAABIIXWOqCw97VQFgCsNYT3yzjJNU9av3yjDhn1kFwA+/HCkXL58RY+9VW30gWx2rXxp5/8COe8/LkHTr0cBAAAAAAAAAADSDgUAAEgRCVOk6mlYZhf/Mfy3nls87Ep/obPTIdOnz5JBg/44/v/LL78Vn6/nJyTURR7IVneuGv6vcc6X0/4j4k249CgAAAAAAAAAAEBaogAAACkiFDOl5L5f7f7POecSfyShx95ZV69WyjfffG8P/631vxz/3xB9JDvdayTHyLSH//nOLCnx7RNP3Clm1wMAAAAAAAAAAGAgoAAAACnCE0rI5iseVQDYX+2TcIzhtMU6/n/nzt0yZMiHqgBQXX1bj3WrJdYkezxrJff58N96PujdIr6ER48CAAAAAAAAAACkNQoAAJAi2n1xmV9mqALAlfqQxK37AkA6Ojpk3ryFavg/cuTnding7UxxxNvkkHeLOvbfWrvcBeI3vXoYAAAAAAAAAAAg7VEAAIAUYI2y77VG1PA/q8SQeiOqx95Z1dW35LvvflAFgMLCdXrkFdax/s54pxz37VGDf2vn/y73GvEknHocAAAAAAAAAABgQKAAAAApwNroX3LPrwoABeVuaffG9dg7KRaLyZEjx+T99z+wh//W87179/XYK7wJl5T6D6hj/3O6nn/zrLNPBAAAAAAAAAAAABioKAAAQAqIJ+T/t3cf7k1def7H/7idzCaTTDKTmdnZnWd+O5kKIQRIgJAQY6oh9N5C6ARISIAQOoQeeje92JhiMNhy75K+Pz6HuVrpWC4Sble8X/vcZ4J0JZc9Pjr3fD/nXFt6rCoRAPjuYp3VNMX8015JFRUVNmXKtMTq/3/+832rr2/wT0uoi9fa3rrNNrdibGLl/7fVy+xR6z23MwAAAAAAAAAAAECuIgAAAP1AXXPcxu+ucMX/vOfHkbsN1qptAWB37ty1P/3pL4kAwFdfrbSWltbUk/6tIV5ne93K/xfFfx0bqpfavZbbFjN2VAAAAAAAAAAAALmNAAAA9APXHzcnVv9P3ldhV0qb/VNeSdFo1Pbu3WevvfYrV/x//fVf28WLlywebxuOaIo32P66LTa/Ynyi+L+2ar7dbblu0Xj6wAAAAAAAAAAAAEAuIQAAAP3AtsK6RABg8c9V9qCSgrU0NjZaQcEXidX/H374kZWWPvZPs5Z4sx2q32ELIxMTxf+vKqfbjeZLFjNupQAAAAAAAAAAAF4NBAAAoB+YebAyEQDYeL7WqhspWktlZaX95je/TwQAli1bbnV1dSnnqPh/suGALUoq/i+OFNilxlMp5wEAAAAAAAAAAOQ6AgAA0MdU7A+K/2N2ltueG/UWa7vD/SvpwIGDieL/W2/91o4ePZay/b+K/xcaT9jSyikpxf+zDUeS3gUAAAAAAAAAAODVQAAAAPrYhQdNiQDAlP0RO/f833hhzJhxiQDA0KEf2507dxPPRa3VChvP2LLKaYni/5LIZDvdcNjiRoICAAAAAAAAAAC8eggAAEAf23yxNhEAWHCkyu5HWv1TXkmRSMTefvtdV/z/xS/esPnzF1ltbW3i+WtN52155YyU4v+phoMuGAAAAAAAAAAAAPAqIgAAAH2oNRa3uYcrEwGAVaerrb6Z1euya9eexOr/d9/9o+3cuTux/f/1pospxX9t+3+8Yb81xuu9dwEAAAAAAAAAAHh1EAAAgD5UWtVqk/dFXPF/7K5y+7Gwjs3r/+3jjz9JBAA++GCI3bhx0z1+q/lySvF/YWSiHa7fafWx/9sdAAAAAAAAAAAA4FVEAAAA+tDpkkabsKfCBQAK9lbYmef/hllp6WN7553fJbb/LyiYak1NTVbcctNWVc1OFP/nV4yzPXWbrSFW578FAAAAAAAAAADAK4cAAAD0oS2X6ix/54vt/6cfiNjDSu5fLz/8sM3eeOPXLgDw29/+wb7fvMVKW+/Z+upFieL/vIqxtq1mnTXEKf4DAAAAAAAAAAAIAQAA6CONLXH78niVK/7rWHi0ylqi3ACgtbXV8vLG2WuvveECAH/569/tyLWDtql6eaL4P7ci3zbXrLTaWJX/cgAAAAAAAAAAgFcWAQAA6CMlkVabc6jSFf/zdpbblkvcw16Kiortvff+4Yr/r/3yVzZswjD7vnKVK/oHxf/va1ZadSzivxQAAAAAAAAAAOCVRgAAAPrImZJGm7wv4gIAY3aW27n7Tf4pr6StW7e5bf/d9v9/edemnclPKf5vqVltFdEy/2UAAAAAAAAAAACvPAIAANAHYnGzHVfrLH/Xi+3/83dVWHldzD/tlfP48WO3/f8vf/mmvfVf79jn+4fanPIXxX8dKv6XtT60uHGrBAAAAAAAAAAAAB8BAADoAzWNMVt3tsYV/3XMPhhxoYBXVUtLi/388wkbMWKUvfXWb+zN371teQeG2uxnLwr/OjZVf2WlrSUWM4ISAAAAAAAAAAAA6RAAAIA+UBJptYVHqxIBgB8u1/qnvDJqa2tt7twF9tZbv7XXXvuVvf72r+2z3R/a7Kd5ieL/uqoFVtJyh5X/AAAAAAAAAAAAHSAAAAB94OLDJpuyryIRALhS2uyfktPi8bg1NTXZ0aPH7A9/+B/7j1+8br9841f2+q/ftEGL/m4zHoxOFP9XVs2yOy1X/LcAAAAAAAAAAACAhwAAAPSyaCxuB27VW96/i//5u8qtoeXVWdne0NBg16/fsIKCL+w/X3/L3vz92/ZfH/7BPv7mfZta9Fmi8K/jq8rpdr3pgv8WAAAAAAAAAAAASIMAAAD0surGmG26UJtY/b/waKV/Sk6KRqN2//4DW79+o/31H/+0d//6O/vL+D/bqO0f2vSkFf+Jlf+Vs+1K01m2/QcAAAAAAAAAAOgiAgAA0IvicbOSSKstPFqVCADsulrnn5ZzGhsbbf/+AzZm/Dj7fx//2QYu/JvlHRpmsx5/nlL0n1cx1tZUzbX99VvtTvM1/20AAAAAAAAAAADQAQIAANBL6prjdrK40VacrLZxuysSAYAbT1r8U3PKjRs3bObs2TZs+hD7aONAm3B2uM0s9Qv/4+zr6kV2suGAPWi9a43xev9tAAAAAAAAAAAA0AkCAADQw7Tl/+HbDbbgaJUV7K2wvH8X/nVMev7vyoaY/5KcUFNTY19vXG9jVnxm+UeG25Sbo2zWk7w2W/1/U/2lFTadsUj0mTXHm/y3AQAAAAAAAAAAQBcRAACAHlJZH7N9N+pt+oGIjd31fyv+k4+91+utJZpb97iPx+N26folm751sk29Otqt9p9dnlr011b/W6pX2f2WO67oH7PcDEEAAAAAAAAAAAD0JgIAANCNWmNxV/hXYf+L/ZE2Bf8xO8tt4t4KW3262u48a7FoDtW9W+Ot9qy+zDYWrrZpN0e3Wemvov/SyBTbUbvRSltLLGZR/y0AAAAAAAAAAADwEggAAMBLisfN6ppj9qCy1XZerbOCfW1X+2sHgJkHK23j+ZqcKvzHLWb1sVp73PLA9lf8YHPuj21b+H86ztZUzbP9NVvtSfShew0AAAAAAAAAAAC6HwEAAMhSLG5W2RCzK4+bbfOlWivY13bF/4Q9Fbb4WJULBhSXtz5/TW5s96/V+5WxCrvVXGh76zbbwmcTUwv/2vL/4Vjb8GypHXi6w8pbn/hvAQAAAAAAAAAAgG5GAAAAslBRF7VT95ps/bkam9JO4X/lqWo7cqfBHlaq8O+/QzjFn/9fWfSRnWv82bbVrLMlkSkphf/Zz8bYtNuj7csrs+zg/d32pK7UvQYAAAAAAAAAAAA9jwAAAGSgrCZqB2412PKT1TY5TeFfW/1/fbbGzj9osqe1UWvNkcp/LB61h63FdqR+p22sXmqLIwVttvqffP0TG7NlhG0++a0VPb5jzS3N/tsAAAAAAAAAAACgBxEAAIBOaNf+x1WttvNKnc07XOlW9/uF/3G7K+zb87V291mLVTfGcmbFf0u8xYpabtj22o22snKWLYiM97b6z7cJ50bYgFnv2djZY+3qrStWW1vrvw0AAAAAAAAAAAB6AQEAAGiHCv9ltVHbfKnWpuyrsPydqUV/HRP3VNj3F2uttLrVWqK5s9l9a7zFbjUX2qbqr2xhZKLNrcj3tvrPs/Gnhttfxv7Z/uevf7adu3dZQ0Pj899ZrvwGAAAAAAAAAAAAwocAAAAkUf26ORq3JzVR23Sh1q3s94v++bvKbcr+iG25VGdPqqP+W4RW1FqtMd5gl5tO29qqeW22+J/9NM9mPBhtY44Msz+P+pO9/dvf2aRJX9ijR6UU/gEAAAAAAAAAAPoBAgA9KBqNWktLC4UxIASiMbOqxpjdKGu2DedrbEw7q/3nHam0Xdfq7Wlt+Ar/sef/1xJvdkX+uliNVccqLRJ9ZhXRp/a49b6dbDxoKypntin8z3z0uRVcGWkjNw+yPw79o7311m9s8OBhtnv3XmtoaPC/DAAAAAAAAAAAAPoIAYAe0NjYaDdv3rSDBw/a7t277dSpU1ZaWuqfBqAfUOG/rCZqZ+832bozNTZhT+qK/7znx+R9EVtxqtqO3GmwSH3/LfzH4lFrijdYbazaItGn9iT60B62FFtRyw272VzoVvafajhoh+p32O66TbalZpWtr1psa6rm29LIlNRt/svH2PSSzyz/2Ec2eNk/7PcDfm+/eO0Ne++9f9jSpV/ZvXsl/pcHAAAAAAAAAABAHyMA0M2am5vt0KFDVlBQYFOnTrV58+ZZfn6+zZkzx4qKivzTAfSRWNzsQWWrHb7dYCtOVrcp/OvQNv/fnK+1M/ebrKI+5l7Tl7RFf3281q3afxx9YCUtt+1m02VX2D/deNiO1u+xn+p+sJ2139iWmtW2sXqpramca19WTrUFkfFtVva3d0y7N9pGbR9sf5/6v/abv/zWXvvlG/b227+zL76YbqdOnXY7mwAAAAAAAAAAAKD/IQDQza5evWp5eXm2fPlyV/B/8uSJXbx40UaMGGGzZ8+2yspK/yUAelE0Frfiihb78WqdLTpWZeN3ty38T90fsW2FdXajrMWqGnqv8K8t+rV6/2m01BX3bzRdtAuNx+14w37bX7fVdtRutK01a2xT9XJbX62V+3NteeUMWxyZbPMrxrUp5Gd6TLs92oZ9PcD+NPK/7df//Y5b8f/aa7+yIUM+tl279lhFRYX/LQMAAAAAAAAAAKAfIQDQzRYvXuxW/N+4ccPi8RdVw1gsZjt27LChQ4fa4cOHvVcA6A2tMbO7z1rs2/O1NutQuY3bU2Zjdj22vN2lz49HNmbPQ5t++JFtv1lmNyMV9qQpYlWxCndUxp5ZReyplUef2LPoY1egf9njcet9u9Z0zo7W7bHdNZvs++qVrqi/tmq+raqaY8srZ9qyyqm2JDLZFkYm2ryKsW0K9pkes8vG2MyiPJt2Kc+mHM+zSbs/t/HfjbbPV46yv+W9Z+/+7V1745237D/+43V3vPvuH23ZsuVWXHzPWlpa/V8pAAAAAAAAAAAA+hkCAN3o2bNn9v7777uV/roVQLJIJGIDBw50twRoampKea4/O1100RZemWWLygtscYSDI9zHgooCm/N0ks1OOSYmjjnPj/nlk2xRJN0x0RXiu/PQtvzzKsbZXFfcz29TsM/0mFn6uRUUfmJ5h4bZiE2DbNCSv9vfJv2v/c+wP9rbf3rHXv/1m/b6W2/af7757+ONF8cv//NNt9o/KPz/4hdv2AcfDLGLFy+57f6DMBMAAAAAAAAAAAD6NwIA3ejcuXMuALB27Vr/KWfkyJE2efJkKysr85/qMarbtUZjWR97Hm63+Y8KbOGzyRwcHH18zC8tsOmXx9rEA6Nt9KbhNmzJEBswaYD970fv2e///Cd7+50/dMuxdt1Ga2hoatMfcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHD0/KHbGQPZIgDQjfbu3WsffPCBfffdd/5TztixY23cuHFWVFTkP9Vjqmqb7ey1ZxwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHCE47pXW+iU/oMsIAHSj77//3gYPHmzbtm3zn3KmTJliY8aMsWvXrvlP9Zj6xlYrflST9XHlTsR1NHcfVLd5joOjPx+XblW4tus/zsHR348LN8ppuxyhPM5df2bnn7df/3EOjv5+qM+9eLOizeMcHP35KHr4ou0W3o60eY6Doz8ft+9Xu7Z79S5tlyNcx817Va7tXi+ubPMcB0d/PtRm1XZvllS1eY6Doz8fV+6+qEvcoS7BEbLj8u0XdQlds/nPZXqUVzb6JT+gywgAdKPt27e7AMCWLVv8p5zx48e7XQBu3brlP9VvlTyudZ1Vc0vMfwro14KJJSBsggscIGxU/Ff4Cggb9bk3iqv8h4F+LRqNJyaVgDBpaGx1bfdhWZ3/FNCvVf57h82nESbiES5lkQbXdrVLLBAmD57Uubbb2BT1nwL6tbsPXoS1Y2zfjz5GAKAbnTx50gYNGmTr16/3n3JGjRplkyZNstLSUv+pfosAAMKKAADCigAAwooAAMKKAADCiAAAwooAAMKKAADCigAAwooAAMKKAAD6CwIA3aikpMQGDBhgixYtev7HnVowr6urc+GAGTNmuP8OCwIACCsCAAgrAgAIKwIACCsCAAgjAgAIKwIACCsCAAgrAgAIKwIACCsCAOgvCAB0o3g87lb4FxQU2OPHj1OeO3HihLs9wNatW915YUEAAGFFAABhRQAAYUUAAGFFAABhRAAAYUUAAGFFAABhRQAAYUUAAGFFAAD9BQGAbnb06FEbPny47dixwxobG62lpcUqKystPz/fRo8ebY8ePfJf0q8RAEBYEQBAWBEAQFgRAEBYEQBAGBEAQFgRAEBYEQBAWBEAQFgRAEBYEQBAf0EAoJtpe//Vq1fbmDFjbM2aNfbjjz/a1KlTbdSoUXbs2DH/9H6PAADCigAAwooAAMKKAADCigAAwogAAMKKAADCigAAwooAAMKKAADCigAA+gsCAD0gEonY9u3bbeHChTZnzhxbsWKFnT171j8tFAgAIKwIACCsCAAgrAgAIKwIACCMCAAgrAgAIKwIACCsCAAgrAgAIKwIAKC/IADQQ2KxmFVXV1t5ebm7DUBYEQBAWBEAQFgRAEBYEQBAWBEAQBgRAEBYEQBAWBEAQFgRAEBYEQBAWBEAQH9BAAAdIgCAsCIAgLAiAICwIgCAsCIAgDAiAICwIgCAsCIAgLAiAICwIgCAsCIAgP6CAAA6RAAAYUUAAGFFAABhRQAAYUUAAGFEAABhRQAAYUUAAGFFAABhRQAAYUUAAP0FAQB0iAAAwooAAMKKAADCigAAwooAAMKIAADCigAAwooAAMKKAADCigAAwooAAPoLAgDoEAEAhBUBAIQVAQCEFQEAhBUBAIQRAQCEFQEAhBUBAIQVAQCEFQEAhBUBAPQXBADQIQIACCsCAAgrAgAIKwIACCsCAAgjAgAIKwIACCsCAAgrAgAIKwIACCsCAOgvCACgQwQAEFYEABBWBAAQVgQAEFYEABBGBAAQVgQAEFYEABBWBAAQVgQAEFYEANBfEABAhwgAIKwIACCsCAAgrAgAIKwIACCMCAAgrAgAIKwIACCsCAAgrAgAIKwIAKC/IACADtU/vziPVDfRWSF0autbXNsFwqb6+UU5bRdhVFnTzKQSQkl9bk1di/8w0K/F4y/abl1Dq/8U0K8pvKK229BE20W4tLTGXNttaqYQhXBRm1XbVRsGwkShQbXdKHUJhExtw4u6hK7ZgL5EAAAdamxstCdPnlhrKxfn6F9isZhVV1dbSUmJPX361KJRLsKR2+rr6+3x48eu7QMAOtbS0mJ1dXVd6jPVv2Yy3m1qarKHDx+6rwF0N7Uvtd14J7NFtbW1rh1qbNDcTPgKfU9zB2q7PUH9c2lpqfsaQHdT+6qsrPQfBvoltdeKigq7d++ea7edjRcCNTU19uzZsy7PnWl8rLk2xrvoLmpLaoPFxcVWVVXVadtVW1Ubv3//vpWXl3fpug7oCWqLQb8biUS63I9mStd3mfTTQFcQAEBaKqxu3rzZRo4caYMHD7bhw4fbhg0b6IDQ5zTp8/PPP1tBQYENGTLEPvjgA9dGR48ebcePH087ea8B46xZs+zzzz9vc0yfPt3Kysr8lwA94vr16zZz5sw27VDH6tWr3UWNT499/fXXrh9WWx8xYoRt3bqV/hi9Rv3umjVr2rRZ/0ieONVF+qJFi9qco0OP63mgu2ki6cSJE7Z8+XI3Lvjmm286LEbp4n3evHn24Ycfuv41Ly/Pjhw50u5Ep97ru+++c/2wzv/444/d+LihocE/FegyTX7qs15tb+nSpTZq1Cjbvn172naoc0+ePGlTp051bTA41N4PHDiQtjh6+/ZtmzNnTpu+WIf+VhR+AbKhiXgFUNT2FixY4PrGgwcP+qclqC36bTA4vvzyS/90p/X5tZ362Y8++igxDtY8heYrgGyp7aqwqX53yZIlbt5r/fr1/mkJ7V2/Bce0adNcUSCgRQqTJ09uc56OhQsXdjg2Adqjfk/jgzFjxrj+MJgL09zYtWvX2i2mqq1/9dVXiX70008/7bCvVqhQfxfDhg1LjDGOHj2adlwCdIUKmt9//7199tlnibar+VzN0969e9c/3bVBjS0mTpzortOS533Pnz+ftq3fuHGjTX8bHBrvAtnQ/MKOHTtc2wvartqkrsU0v+uHUvRvXav5bVCH5ho0l5uOwgUrVqxw8wtBP/3TTz/5pwFZIQCANnQxootsXVxrwl9FVf2vOqBVq1a5VSlAX9HFiyZHZ8+ebXv27LHTp0+7dqn2qg9KDRL9D2B9kOrDdujQoYkP3eAgAIDedOnSJdcG1VaT26EOBQB0YZRMxSkNAj/55BPbuHFjorCltvztt9+y4g+9QgUltTv1s/6htvz+++/bwIEDXXsNFBUVuYlPTRz5/e7ixYvdxCjQ3TSBrzapwNSgQYNceEop+nT02a8Ld527bds2Fy5UOEXtWmMJv3/VqikVB3T+unXr7MyZM268rK+n/lvPA9nQtVXQnwZ96g8//JB2ol1jXBXz1b/u3LnTTTBpfKBJKbV5tV0/IHj16lXLz89PO/bQGIMAALKl4r/arvpFFZY0Fti/f79/WoLa4r/+9a+0bXHZsmX+6S5cpes8FQnWrl3rxsHqf9XXq/+l30W2NA6dMmVKot9V29WcV3vU5/pj4KDtDxgwwE3UJxex7ty5495X42C/rWusQQAA2VDI5IsvvnDFeRXwNXZVoCSY5yosLPRfkgi7qvCq8JRCLwpsqc1rPs2fO1O/qr8NtW8VbHW+vobassYYQDbUNhVU0bhTYZJjx4658awKqXpcfWYytUMFq+bOnWv79u1zbT0IsagtajzghwA016b+WP2y3+/q6wLZULBEbVF1iEOHDrm2p8/x4PNdIYDktqg+Vf2z+ljN4/ptMV0AQOEu9csaS2ixgf5G9DV0TajrPb+fBjJFAAAp1GnpQ1MDSA32glUkelyrqPRhq44I6Ctqk5q09yfmb9265dqtBoh+QT8IAKgQ4E+KAr0pCABoQOhfsPjUVnVhpH5XF+dBm9frNEGlweHZs2e9VwE9QwUqbQPpHzdv3rRJkya5C/jkYlUQANiyZUvaFalAT9DkkHaXUF+rPrK9AEBra6srmurC/dy5c4nHtQpbq1BVLH3w4EHicV10K3CoopMu2oM2rTavFQF6XONjLs6RDX3eq5Cv7c31ua9J9/YCABoDqHClNpxMK//Gjh3rggB+YSkIAOzdu5dxMLqVxgEKQ+naSxOUuhbrLACgyUy1xc6oravwpIl8TYYG1P/q70PF1QsXLtDvIiuabFdBSv2uPr81HugoAKB254+BdWi8q9fOnz8/ZTegIACQ3HaBlxXcssr/LFcbVrFJgVR/wZb6ZvWjmn8I+kudM2PGDDd+TS68qt/dtGmTK6Lq/IBWwAYFL82tAZlSn6vFLsmf2Rrnqn2q7frjXo1zdS3mj3cvX77s2qfar/4ekgW1jMOHD6c8DrwMtV31e8n9rtqqVudroawWZiW3xSAAoOK/wgFdoflezftqDJ3cT2uOTWMJ7aAFvAwCAEihjkbpTk0gKeWUTFv76sNUK/f8D1qgr6l9ansopZV1IZ6MAAD6i0wCACpaaacLpfyLi4tTntPkvy7AtQqK4ir6isYCmtgMxgzJbZoAAPqSikwdBQCClX/qX/2JUhWcdKGt1SbBmEGrUDSpqtS+AofJNDmlfl0rS7h/MF6WVpV0FABoj8a6miRScdVvhwQA0Bs0edmdAQD1uwpkqT/2d8jSmGP8+PHuecYYeFna8bKzAEA6wQ5ZfhFVCACgNykUoL5VRfrkHdnURjV21Y5X/q0G1R9rK2st9ArGBjpHtxfQOMQfHytkq78TBQSA7qICqdqu5rXSXbP51Fa145UWIPg7WBEAQG+6cuWK21lFOwMoJBXINACgflr9rnbC0I7HyfT6YFdDruHwMggAIIU6Hm3Bo3ucJXdgos5Gk6Uqsqa7Rw/Ql/RBqYtvTX5qO8pkBADQX2QSANAFjRKlmtz0L4bUP6svVvL54cOHKc8BvUEXNhcvXnQT8LoXpT8BTwAAfamzAIAm+zW5qYl5f2WJdrVQu07e1eLRo0cu6KKt+dLtMqQJ1wkTJrQJIAKZyjYAoCCKJvjV7v02TwAAvaG7AwAqoGqCP90qv2BLa62W4jYAeFnZBgD0umCnNh8BAPSmoIiqW6Yk70ShVaNaXa1brPi7pajAr9urqI8N5n6125XatG4x4NNYWAUtHUB3CHbAUtvVnIG/y2s6ug7T+bqVqz/eJQCA3qK5XO3GqrGDbkmVvPtapgEAjRfUT2uxrd9P629C/bRqcX6NDsgEAQCk0BZoui+fJjj9CSJ1REo2qXilSX+gP9EgTwNBffj6A8cgAKCC6Y8//ui2V/WTdUBvCAIAs2bNSty711/VFFARSv2x0tD+QFAXO9pqUgUnnQf0NvWrukjRBLzGDr4gAKCLFRWytDVluvOAntBZAECT9QpYaes+f7yrUJV2BtCKqWD1k3Zh0QX+ypUr21x8q/gU3JNaXxd4GdkEANSGtXOFXqedKPxxcBAA0CS/AlsqWvlBFuBldTUAEGzdu2vXLnf7AH/HioDmGzRm1j1//SCh+mYFZPVefvAbyFQ2AQAVQ1WA0jg4ueAaCAIAWtGnWwWpb1e/21kAHMhUcHtAtWGFq5LbmK6/glX+6Wj1qoJWwYKC3bt3uwVhmzdv9s40V+DS2FqFqnRtHsiUwn2qMWj8qnmxzqht61bFWhGtW7n5/anm2vScbgurtqzbvBESRE9Qf6g2qP5VY4jk+YQgAKC+VGNY7Sp4/vz5NtdnAe2uEqzyT0e3d9PiBN3mEMgWAQCk0BYmupDWBbVPnZgmOPXhrA4K6C+0I4Um3ttbfaeJJa3k0+SnUni6B5r+W2EAv7AK9CQlQDVRpMKS+lK1RU0MaeLe32ZPF0G6wNY9pXy6WNJgUhftBLLQ27RiWqErtT9NLPkFVNFqVI0ltJ2Z+mdNgmoFtYIvTBqhp3UUANDnvlaZaLyrCXl/HKCAoHbC0kV7UHTSdtM6X+/n319d7Vkr/HThrvtRAy8jmwCAxsEaS6i/VSjQnxDVCkDd4i3ojzX2UCj2wIEDbcYeQLa6EgBQWxw3blxiHKxDO1doFZU/ltDfgtrq+vXr20yaqo0rkKWg7LVr11KeAzKVaQBAfbOKUGqf/m2BApqonzZtWkq/q0Krxs9+oAV4GSo0qe9V0VMB7WQa7yrwum3btpTHAxo76PosWFCgeQcVtNLt0qLxskJZGg8T6kZ30LxAELDWfdY7c+rUKXe+iqH+9v+i6zWNMdRONb5Q36ugVmFhoX8qkDWNV1UT0/yWAiz+gi71ldpNRX2rivc6T21Rt3dNtyOA+mf1u+mCV6JFNRo3M97FyyAAgBSauFTBSYUlnzoxTXzq4kUT/kB/oLSy2qQKUboA9yc9RY/pQl2HJuo1yaRBoXYMSLdlH9BT1I+qeKqJTE26qy9V21UwRRc0ye1XO1Wojaa7z54CALrnpApcFJzQ2zS5pLangpK/Gjqgtqy2rn5XbV2pZ02C6qJdq/78oivQnToKAOiiXRP3Khzp4txvi7qIVwBABX1N0qst64Jb52uXIX8rao0r9H7qr9XOgZeRaQAgCKxoXKvxgF9ElaA/DsYe+hrB5KjGIenGzkCmuhIACK7J1BY1flCRSX21tpy+d+9eyrkaB2vSVCusOgoAXL58OeU5IFOZBgBUTNKYdu3ate2GWpPnHxQcVLFAxQD9jWis4I89gGxohbPGq9pxzS9CiQr6CrRq9590VFhSWw4KSxrn6v20Q5ZPbToIALASFS9L/a6Kntp9uL2dgJKpjWq8oKO9nX/UrwZjDM1XqN1rrKuxBDvAortonkHjVi0yVBgq3XVUclssLy93cwWqtSmA7fefek5/C1u3bk15PKD+XX2vFuwC2SIAgBQlJSXuQlqT+n4npg5M945SsUoDTaAvqT0qqaxEndJw/odoZ9TW9QGs9pzuYgnoDWrH2qpMA0jdQzq5LWqAp/5YBSy/P9ZEki6WlHBOlyIFeorarCZIlWLuylZ9yRTYClb7qQ8GekpHAQBRCEUTnFqJ50/Ca1tftVGND4LV0dpdSBOoq1evbrOVpN5fX0eTS1yY42VlEgDQmEGTQhoLaDcgf6zQHp2nVVJqs9qVKN0qKiBTXQkA+NQWVWjSOFhtMZnmG9QPq9Dv71ShCVXNS2icHGxdDWQrkwCAgitqk6NGjXI7WnSV2rratMYm2hnA300IyIRCfQrwaSGMQoDpxrqinX5UWNJOVekEt8kMrstUMNV4V2MQn8Iu6pM1h9be1wM6o7Gtwn9ql1o93VlfqGCrAq7awVW3aOtKWCCgfle7vqpNa64NeBlqiwrwDRkyxN2ONdNQiQr8WjCgW7IkX7MdOnQocVuLdFTvUICwuLjYfwroMgIASKEPX3VISjL5K5w0QarBpSaZuOc0+pI+eDVxqW30NPGZ6QdvQINITbIyYY++pASztiZLvvgWFaHUH+t+vv5WkdoiTRdAKlL5K6aAnqS+Vxco6cYJndH5ChhqsknBF6CndBYA0G1XVHDSpJC/Ylr37VV/rP41WHWqW1oocLhkyRKX4k+miSgVorSCKpNiAJBOVwMAGjuojWob1GxuBaR2PGvWLPd62i26QzYBANGEpiY21e6Tabyh9pnuHusaBysIq62tu7JtMNCRrgYANF7QzkEKT+nWFOnGFx1Rv61+V+OPTIpYQDKNTdVmNe7UuNQPSCXTbhWaT1CA1Q8Jqj2rLWosEdw6QDuv6G9B5/s056ZdC9XP++8FdIXmtBT6UzvSHJe/u49PQRft8qOdKjT3kM28r8a4WrigA8iWrsn0+a8+s6u7Vvg0n6Dgqh980bxFsBO337cG/bRu1+LPQQCZIACAFCryq2NRYdUvKqmD0oW57lvi31sK6C36QFTbnDFjhmunuu9ptjTBr9Q0W6ijL2nlnSaDFK5KTnVqQlMTn3rOv8+eJpDUdlV0am8LdqC76aJdq5aUoj948KD/dKc0ga+JKq0eYat09KTOAgDBPSIVSPEnn7SzhSamdPsVTTyJxr06V2NkP32v/nnChAluJUBZWVnKc0CmuhIACIr3Ggdkuyub2rTuF6wCAsFudIdsAwDaxU39sVZUJU98aocLFf81ZvDHutqVRf2xArR+OADIVFcDAJrwV7+pQpL6Xn+ivjMqXqmIpXF0JBLxnwY6pWKQQtQKTakI1dlOluo7NVbQmMEfD2tOTeEA3VYwGG9oMYLGz3p/PyCrNq9ClK7lgEzpmkp9rdqXCp2dLSRQ/6rPei160fyDrr8y7XNF70FwBS9D7UY1Ay0G0KLYbOthmsNVAEC7USSHV4N+Wu3c76fVJ6ufVmjAn7MAMkEAACnUsWn7EXU+SuYFE5+iyX5dmO/YsYMPTvQZbb2rrXE0gFObTG6jmdDqal1860PcL64CvUV9qQpVuijxi0ca4Km/1RaTKkglX4Tv3LnT9dPqr+mP0Vu0W4q269MK/mxSz+pr1ecqxa+LcaCndBYA0KSTVp5oEj858Bps568++datW4n+VWMNjTl0vm4bEEyUql/WfX3VH+/evbvdgi3QVZ0FAPSYtvLVTiy6/3Q2YwC9Ru1bfyMKdjMORnfIJgCgtqhtrNXmNamaTAsTNN5VYVZFg4D+BvQ1gnGwX6QCMtXVAIDuQa1dJ1R4zXQlqtq6Crfaylfj4HRjE6AzandqfxqPamW0fxurdDRW0PWXCljJ41rdykK7WSQHCdWfaoGBVqMm32ZQQSv9fWj+jF2DkA0VMlXg1NjTX2yYjtrcqlWrXOgk2896tXfdbkBjE4W3gGxowZbaj+phun7KlsbJKuZrByG/jqHH9LeRHC7UOep31X61+wDwMggAoA2tKlHaXqurtUWqJuk12anOSMlRVjehL2nFv1aCqAil5Jw+KJMPTcD7SWgVqjRRpIKAVpnoQ1UTnkrf6T48/ocv0FO0tbSKRVp9qhDKqVOn3OolTXzqwiZ5sl8DP20Tpd0udLGkEEBxcbELZ6k/Vvo+2/QpkA3dryzYnqwzKihpUl/9ru7Pq0lPjS00wap+17+tBdAd1L9qzKq2qgkj9Z/79u1zj/mTTZrI1+pnjW3VPjW+UNFVARe93r8npVL7mnTV+FhtW+MJ/a92atGKPu5DjWwFQRK1U012BveWVOhEbSy5bWkCVaulNYZVu/PHwTr8VVUaO2hbX03ma+xx9uxZV2xVAVV/H6woQbbU1tS21HYXL17siqNffvml+7fCLJo0TaZ+V/MLmkBVW9Z56lNVhEp3ayC9Xtds6qtVpFVb1t+FdnHTmIJ5CWRLBXjNCQT9rtqurrf0b/XH/me6rss2b97swlf6386KURpTqP/WNZ/eS9d86nc1NtHXYP4BmVKxX5/fKsJr7kCFev/zX23LH7+q31Sfqb5Ur1cBX7fA0lhDIQB/FxWdr7kGrbzWjm06X+NjLUpQMaqztg/41N9pDktzuGpH6nP9tqs+Mnl+QO1QbVRzB9qFzT9/165dbXYHUihGYxK1WV336WtqNwuNd1+mcItXm+Zp9dmt9qg+0G+LGtcm96O6rtIcmMbBasc6VKdQ29dtiNPtvKZ5X42HNXdx5swZ14a1GEztvys7ZgCdIQCANjSwVAelSU4NLFVs1UW5OiJdhGez0gToLpq81EXPgAEDXHpOF+HJx5QpU9rcFkCDP31wakWf0s9q17qoUQKPD1L0Jl2EqE/V4E9FJrVFXZRo0JiuLeoCWxNHmjBS0l+v1eBT4RcFXeiP0VsUTlH/qqKTLqw7o3tSa5VT0O+q/eq/1e9yv170lKCP1AW6xgmaaNLnvx7ThXcyTUZpV4ugfeq1aqMbNmxw4Sq/f9X4WAUrFZ10fjCe0ISqwgBdWYUFpKP792pcqnYajHFVjNK/g10nAiqgqu2pL043Dtbhby2tIpReo7FH0N7VP6uQ6hcKgExobkDtSm1VbTa57apor0nMZNu3b3fPqb2rLWqOQWMLrWxKt+OF+mEFChWW1bnqp9Xvqhig6zv6XWRLn9sq+Cf3u+o/9W+1TU34J1Nb08p99Z/JO1K0R69XOw/6XbVbhbf0OP0usqF5Ac0Z6PO/vbkwBbH8+0TrdYWFhYn2G4w3Vq9enfaaTG1di8DUXoN5MxVQNT72xxdAV2icq9CJ2q4WE/jtVse6devcbq8BjXc7autqz/4OVtohS2MFtdngmlDjB81LMF5AtlSIV5sLxgn+oYVZyWEUBQy//fZb12+qHerQKn4tOlC7TheiUvvUcxoTJ/fTCsuk66eBTBEAQFq62NZFuNLK+rBUoYmUMvoDfTAqUdfeoXbrT9rrMU3aK+WviShd0KjYyiAQvU3tTgErJZw12al2qbRoR20x6I91rlL4uvCmP0ZfUDtUP9tRew3oQl+Tq1ppovauYBb9LnqaPyZIPtJdbAf9q/pljXfVv6Y7L6Dz1f9qwkl9uFaf6t/+uAPIlN9e22u76kODvri9w2+PGmeoWKo2q0P/TX+M7qC25re/4FA79duY2p12w9K4QOMDrXhS+/TbrE/9rPpbXctpNxb6Xbys4PPfb7fBkW4soPPTtet0tJI16HfV3vXfKvx35bVAezqbC2uvb9RjapOaB1MYQMWqdG08oPN1Laf5B62qVjC2o/OBzqj9+O21o7bbWVtPN++rPlYrp9Xnat5Mu16pHfvnAZnItO3qv9XHavGidnFVH6rArH+eL+ini4uLXT+tnYzpd9FdCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAAAAAAAAAAJADCAAAAAAAANAPNDc3W0VFhVVWVvpPdVlLS4v/UL8XjUYtFov5DwMAAAAAgCwQAAAAAAAAoAONjY329OlTq6qq8p/qVnfu3LGVK1faxo0b/ae6pLS01M6ePes/3O/dvXvXioqKXBAAAAAAAAC8HAIAAAAAAAB04OrVq7ZgwQLbtm2b/1S3OnfunI0aNcomTJjgP9Wp4uJimz9/vu3cudN/qt87ceKELVmyxK5du2bxeNx/GgAAAAAAZIAAAAAAAAAAHTh27JgNGjTIFi5c6D/VrbINAGhngkWLFtnMmTNf6vYBfUXf84oVK2zatGlWXV3tPw0AAAAAADJAAAAAAAAAgA7U19e77fUrKir8p7pVNgGAWCxmO3bssJEjR7pdAMKqpKTE8vLybO3atf5TAAAAAAAgAwQAAAAAAADoB7IJAKjoP3nyZLeCvqmpyX86VL7++mt7//33Qx1kAAAAAACgrxEAAAAAAADkpNu3b9usWbNs//791tDQkHi8tbXVzpw547ac1+r52traxHNaUX/lyhWbO3euXb161T126dIlmz59un333XeJ8+TGjRvuvKD4fvHiRVu8eLFbja/V7AcPHrSWlpaU14h2FNi9e7d98cUX9vHHH9uUKVNs37597vxPPvmkywGAeDzuXjNs2DA7f/68+96TnTp1yt0WQLcw0M+or6mf49NPP7WvvvrKioqK3Gu0Bf+mTZts/Pjx7r2WLl3qVuTr/QPa/WD16tXuuH//vvt6Om/48OHuZ9Xry8rK2nwPdXV1dvjwYfczfvTRR+58fR39vD79Pj/44AMXBAAAAAAAANkhAAAAAAAAyElaSa5is4rgT548STxeU1NjX375pf3rX/+ygoICe/ToUeI5FefXr19vEydOdAECOXr0qA0YMMDmzZuXOE/Onj1rQ4cOdQVwFbn1v3qdVuSrsK/XHD9+POU1VVVVtmbNGhs8eLAriq9cudJ9L/o+9L1++OGHXQ4A6OdYtWqVjRgxwhXlfSr4a0W9CuoKKXz++ecu9KDvUV9fP49CDrNnz7b8/HwXDtD/6jUq7ut7DTx+/NgmTZpk48aNsw0bNrj30L/1mjFjxtigQYNs4cKFKb9nBS0UvtDPpJ9NP+vy5cttxowZ9uOPPybOC+h3rwDCZ5995oIDAAAAAAAgcwQAAAAAAAA5KRKJuJXuKsbfvHkzsaK9tLTUFcNVmFYB/9q1a4nnnj596orcKlRXV1e7xzoLAAwZMsQVuHfu3OnCBOXl5bZx40a3mn3q1KmJ87U6/sCBA+77UdFdq+olGo26sIKK7irMdzUA8PDhQ1dMV+AgufAeCAIA2lVAIYjCwkJXWL9z5477vvTc2LFjXQDh1q1brgB/4cIFV9BXqEDvHwgCAPp5tXPBTz/95B5rbGx0IQK9n753/XzBrgfadUGBAxX0dXuD4Hes3RIUXkhH3492CtD3AwAAAAAAMkcAAAAAAACQk7QCfdeuXW51ugrT+reK0KdPn3bb4KsIr2Lznj17XNFaz2m7fxXo9VhQsO4sABAUvpubmxPP6XYAek7F94C22l+0aJENHDgwcXuBZPq+MrkFwN27d13wQLc5ePbsmf90IgCg4vy9e/cSj+t2ANqFQDsgLFmyJOW12sZ//vz57nXJrwkCAKNGjXK7GiTf2kC/V92KQIGH5J0DFADQ70w/k34/Kvx3RoEG/d50iwYAAAAAAJA5AgAAAAAAgJyle9Wr2K/CtFa4axW+tqLXKnYV9rWVvYrywXPaLl87AOh+9IHOAgDaTcAvbl+/ft1tZ68wQaCoqMht9a/Hk7fXD2iVvArsXQ0A6Gtodf2CBQsSuwkkCwIAmzdvTinY63vVDgUKAPz888/u5w7offS70s+bLgCgQ7sV+C5fvmwjR450tzUIAgXa2eDQoUMuIDF69Ghbt25dyk4M6eg2AtqZQYEBAAAAAACQOQIAAAAAAICcpS35tf29itNaga9CuIrsCgHoFgEq/mu7++TntAJeq9cDnQUAFBjwKUDgBwBUJM/Ly3OHVuH7Mg0AqJiu7frnzp3rbjvgCwIA27Ztc6v0A8kBgFOnTnVLAEA/r74XbeGffDsC3XJAxXwFFbRDgHYD0G0Zgtsr+PSz6JYKx44d858CAAAAAABdQAAAAAAAAJCzVPhevXq1K2gXFhbatWvX3Lb/KurLd999524RcOXKFfecis/bt29PeY/uCgDotgDaLUCr4bsjAKAC/eTJk23atGn29OlT/+leDQDo96civ74XP4ygFf+lpaVuBwD9fvXe2gkh+esGtIOAfm8KSwAAAAAAgMwRAAAAAAAA5LSffvrJFf1V9FYYQEV4FbRFBXCtSv/222/drgAqwN+6dSvl9d0VALh9+7a75YC2uPe37FeRXN+LdiroagCgrKzM7VagVfcqsPt6KwCg7/3MmTPud7Fs2bJ2V/eLblug8/S7Sb7Nguj70O9/+PDh9vDhw5TnAAAAAABA1xAAAAAAAADkNBXe8/Pz3Rb1KrCrkB/ch16FcxXcVcRXsX7q1KnW3Nyc8vruCgA8e/bMFez1Xnv37rVoNOoeV3H+/v37rniubfK7GgDQ97llyxa3ql63Awh+pkBPBAB0+4Lz58+nvJ92M9iwYYMNHjzYbfevWymIvh/dAiD5+9LPrNX/CkEcPnw48bjo1gH6+adPn552dwAAAAAAANA5AgAAAAAAgJxWX19vs2fPdkXtgQMHuiJ1QIXshQsXusd1qFju664AgArh+/fvd7sRKIiwb98+t3J+z549tmjRIrf9vYr5XQ0AyIULF9wtBXbt2pVSlJeeCACoQD9jxgw7dOiQ2ylB4Qp97U8//dT9jh89epR4TWNjo7vFwvHjx11AQeceOXLEFf/1PSe/vxw8eNB9vzoHAAAAAABkhwAAAAAAACDnbdq0yRWvtUpdW+cn27Fjh3t80KBBVlJSkvKcdFcAQCorK92qfYUAFDjQdvfa9l4Fee0KoP/OJABQVVXldg7Qqnp/6/2eCADo9gkrVqxwX0+3M9BjI0aMsDlz5tjVq1dTvo6CF9OmTXPFfv1MOl+/E/23ivzBDgiiXQRmzZrlnvN/DgAAAAAA0HUEAAAAAAAAOe/p06dWWFjoDn97eRW9g+f8VfTB85cuXbKioqKUx1V8v3Llilvd7lNBW8+pKO7Tc9euXXNb4GvVu8ICCgbo0OPp3q892lVAr9ctDn744YeU5/QzX7582W2tn7wNv35+rdTXz6Svmfyctu9X4V/PNTQ0JB4PAgA6rl+/bnfv3rUTJ0640IJ2IdDzyQV90dfRrQ30Ozh27Jjb8UCv0fsHtwkIzvvpp59c+EH/PwAAAAAAANkjAAAAAAAAQB9Q2CBd4CBTKryrgK7dBFSM7wnJAYDi4mL3mAr3KuQnBwjao+9R5/rhC1HoQe+rnQqSgwEAAAAAACBzBAAAAAAAAAg5FeF//vlnW7lypf9Ut0gXAOgu+/fvdzsJNDY2+k8BAAAAAIAMEQAAAAAAACBH6LYEPaGsrMymTZvmDm3h3510q4Hm5mb/YQAAAAAAkAUCAAAAAAAAoEN1dXV24sQJd/RUyAAAAAAAALw8AgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOQAAgAAAAAAAAAAAAAAAOSA/w9EMUfC09CCKgAAAABJRU5ErkJggg=="/></g><path fill="#000000" fill-opacity="0.0" d="m352.56567 71.17508l515.8737 0l0 179.34926l-515.8737 0z" fill-rule="evenodd"/><g transform="matrix(0.2518914567454068 0.0 0.0 0.25189502036745404 352.5656721780576 71.1750771414698)"><clipPath id="p.4"><path d="m-2.2737368E-13 5.684342E-14l2048.0 0l0 712.0l-2048.0 0z" clip-rule="evenodd"/></clipPath><image clip-path="url(#p.4)" fill="#000" width="2048.0" height="712.0" x="0.0" y="0.0" preserveAspectRatio="none" xlink:href="data:image/png;base64,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"/></g><path fill="#000000" fill-opacity="0.0" d="m261.11453 166.66833l65.4281 0" fill-rule="evenodd"/><path stroke="#595959" stroke-width="3.0" stroke-linejoin="round" stroke-linecap="butt" d="m261.11453 166.66833l47.4281 0" fill-rule="evenodd"/><path fill="#595959" stroke="#595959" stroke-width="3.0" stroke-linecap="butt" d="m308.54263 171.62354l13.614288 -4.9552l-13.614288 -4.9552z" fill-rule="evenodd"/><path fill="#000000" fill-opacity="0.0" d="m258.63977 380.8143l65.4281 0" fill-rule="evenodd"/><path stroke="#595959" stroke-width="3.0" stroke-linejoin="round" stroke-linecap="butt" d="m258.63977 380.8143l47.42813 0" fill-rule="evenodd"/><path fill="#595959" stroke="#595959" stroke-width="3.0" stroke-linecap="butt" d="m306.0679 385.7695l13.614288 -4.9552l-13.614288 -4.9552z" fill-rule="evenodd"/><path fill="#000000" fill-opacity="0.0" d="m82.1811 217.54593l157.92126 0l0 41.7323l-157.92126 0z" fill-rule="evenodd"/><path fill="#595959" d="m129.10756 238.06593l3.296875 -8.59375l1.21875 0l3.515625 8.59375l-1.28125 0l-1.015625 -2.609375l-3.578125 0l-0.953125 2.609375l-1.203125 0zm2.484375 -3.53125l2.90625 0l-0.890625 -2.375q-0.421875 -1.078125 -0.609375 -1.78125q-0.171875 0.828125 -0.46875 1.640625l-0.9375 2.515625zm6.3164062 3.53125l0 -6.21875l0.953125 0l0 0.9375q0.359375 -0.65625 0.65625 -0.859375q0.3125 -0.21875 0.6875 -0.21875q0.53125 0 1.078125 0.328125l-0.359375 0.984375q-0.390625 -0.234375 -0.765625 -0.234375q-0.359375 0 -0.640625 0.21875q-0.265625 0.203125 -0.375 0.578125q-0.1875 0.5625 -0.1875 1.21875l0 3.265625l-1.046875 0zm8.074219 -2.28125l1.03125 0.140625q-0.171875 1.0625 -0.875 1.671875q-0.703125 0.609375 -1.71875 0.609375q-1.28125 0 -2.0625 -0.828125q-0.765625 -0.84375 -0.765625 -2.40625q0 -1.0 0.328125 -1.75q0.34375 -0.765625 1.015625 -1.140625q0.6875 -0.375 1.5 -0.375q1.0 0 1.640625 0.515625q0.65625 0.5 0.84375 1.453125l-1.03125 0.15625q-0.140625 -0.625 -0.515625 -0.9375q-0.375 -0.328125 -0.90625 -0.328125q-0.796875 0 -1.296875 0.578125q-0.5 0.5625 -0.5 1.796875q0 1.265625 0.484375 1.828125q0.484375 0.5625 1.25 0.5625q0.625 0 1.03125 -0.375q0.421875 -0.375 0.546875 -1.171875zm1.9375 2.28125l0 -8.59375l1.046875 0l0 3.078125q0.734375 -0.84375 1.859375 -0.84375q0.703125 0 1.203125 0.28125q0.515625 0.265625 0.734375 0.75q0.21875 0.46875 0.21875 1.390625l0 3.9375l-1.046875 0l0 -3.9375q0 -0.796875 -0.34375 -1.15625q-0.34375 -0.359375 -0.96875 -0.359375q-0.46875 0 -0.890625 0.25q-0.40625 0.234375 -0.59375 0.65625q-0.171875 0.40625 -0.171875 1.140625l0 3.40625l-1.046875 0zm10.923828 -2.0l1.09375 0.125q-0.25 0.953125 -0.953125 1.484375q-0.703125 0.53125 -1.78125 0.53125q-1.359375 0 -2.171875 -0.84375q-0.796875 -0.84375 -0.796875 -2.359375q0 -1.5625 0.8125 -2.421875q0.8125 -0.875 2.09375 -0.875q1.25 0 2.03125 0.84375q0.796875 0.84375 0.796875 2.390625q0 0.09375 0 0.28125l-4.640625 0q0.0625 1.03125 0.578125 1.578125q0.515625 0.53125 1.296875 0.53125q0.578125 0 0.984375 -0.296875q0.421875 -0.3125 0.65625 -0.96875zm-3.453125 -1.703125l3.46875 0q-0.0625 -0.796875 -0.390625 -1.1875q-0.515625 -0.609375 -1.3125 -0.609375q-0.734375 0 -1.234375 0.484375q-0.484375 0.484375 -0.53125 1.3125zm8.173828 2.765625l0.15625 0.921875q-0.453125 0.09375 -0.796875 0.09375q-0.578125 0 -0.890625 -0.171875q-0.3125 -0.1875 -0.453125 -0.484375q-0.125 -0.296875 -0.125 -1.25l0 -3.578125l-0.765625 0l0 -0.8125l0.765625 0l0 -1.546875l1.046875 -0.625l0 2.171875l1.0625 0l0 0.8125l-1.0625 0l0 3.640625q0 0.453125 0.046875 0.578125q0.0625 0.125 0.1875 0.203125q0.125 0.078125 0.359375 0.078125q0.1875 0 0.46875 -0.03125zm0.9902344 3.328125l-0.125 -0.984375q0.34375 0.09375 0.609375 0.09375q0.34375 0 0.546875 -0.125q0.21875 -0.109375 0.359375 -0.3125q0.09375 -0.171875 0.328125 -0.796875q0.015625 -0.078125 0.09375 -0.25l-2.375 -6.234375l1.140625 0l1.296875 3.59375q0.25 0.6875 0.453125 1.453125q0.1875 -0.734375 0.4375 -1.421875l1.328125 -3.625l1.046875 0l-2.359375 6.328125q-0.390625 1.015625 -0.59375 1.40625q-0.28125 0.53125 -0.65625 0.765625q-0.359375 0.25 -0.859375 0.25q-0.296875 0 -0.671875 -0.140625zm6.046875 0l0 -8.609375l0.953125 0l0 0.796875q0.34375 -0.46875 0.765625 -0.703125q0.4375 -0.234375 1.046875 -0.234375q0.796875 0 1.40625 0.40625q0.609375 0.40625 0.90625 1.15625q0.3125 0.75 0.3125 1.640625q0 0.953125 -0.34375 1.71875q-0.328125 0.765625 -0.984375 1.171875q-0.65625 0.40625 -1.375 0.40625q-0.53125 0 -0.953125 -0.21875q-0.421875 -0.234375 -0.6875 -0.5625l0 3.03125l-1.046875 0zm0.953125 -5.46875q0 1.203125 0.484375 1.78125q0.484375 0.5625 1.171875 0.5625q0.703125 0 1.203125 -0.59375q0.5 -0.59375 0.5 -1.84375q0 -1.1875 -0.484375 -1.765625q-0.484375 -0.59375 -1.171875 -0.59375q-0.671875 0 -1.1875 0.625q-0.515625 0.625 -0.515625 1.828125zm9.970703 1.078125l1.09375 0.125q-0.25 0.953125 -0.953125 1.484375q-0.703125 0.53125 -1.78125 0.53125q-1.359375 0 -2.171875 -0.84375q-0.796875 -0.84375 -0.796875 -2.359375q0 -1.5625 0.8125 -2.421875q0.8125 -0.875 2.09375 -0.875q1.25 0 2.03125 0.84375q0.796875 0.84375 0.796875 2.390625q0 0.09375 0 0.28125l-4.640625 0q0.0625 1.03125 0.578125 1.578125q0.515625 0.53125 1.296875 0.53125q0.578125 0 0.984375 -0.296875q0.421875 -0.3125 0.65625 -0.96875zm-3.453125 -1.703125l3.46875 0q-0.0625 -0.796875 -0.390625 -1.1875q-0.515625 -0.609375 -1.3125 -0.609375q-0.734375 0 -1.234375 0.484375q-0.484375 0.484375 -0.53125 1.3125zm8.9765625 -3.765625l0 -1.015625l5.5625 0l0 0.828125q-0.8125 0.875 -1.625 2.328125q-0.796875 1.4375 -1.234375 2.96875q-0.328125 1.078125 -0.40625 2.359375l-1.09375 0q0.015625 -1.015625 0.390625 -2.453125q0.390625 -1.4375 1.09375 -2.765625q0.71875 -1.328125 1.53125 -2.25l-4.21875 0z" fill-rule="nonzero"/><path fill="#595959" d="m122.99329 252.46593l0 -8.59375l1.140625 0l0 7.578125l4.2343826 0l0 1.015625l-5.3750076 0zm6.5957108 -7.375l0 -1.21875l1.0625 0l0 1.21875l-1.0625 0zm0 7.375l0 -6.21875l1.0625 0l0 6.21875l-1.0625 0zm2.4628906 0.515625l1.03125 0.15625q0.0625 0.46875 0.359375 0.6875q0.390625 0.296875 1.0625 0.296875q0.734375 0 1.125 -0.296875q0.40625 -0.296875 0.546875 -0.8125q0.09375 -0.328125 0.078125 -1.359375q-0.6875 0.8125 -1.71875 0.8125q-1.28125 0 -1.984375 -0.921875q-0.703125 -0.9375 -0.703125 -2.21875q0 -0.890625 0.3125 -1.640625q0.328125 -0.765625 0.9375 -1.171875q0.609375 -0.40625 1.4375 -0.40625q1.109375 0 1.828125 0.890625l0 -0.75l0.96875 0l0 5.375q0 1.453125 -0.296875 2.0625q-0.296875 0.609375 -0.9375 0.953125q-0.640625 0.359375 -1.578125 0.359375q-1.109375 0 -1.796875 -0.5q-0.6875 -0.5 -0.671875 -1.515625zm0.875 -3.734375q0 1.21875 0.484375 1.78125q0.484375 0.5625 1.21875 0.5625q0.734375 0 1.21875 -0.5625q0.5 -0.5625 0.5 -1.75q0 -1.140625 -0.515625 -1.71875q-0.5 -0.578125 -1.21875 -0.578125q-0.703125 0 -1.203125 0.578125q-0.484375 0.5625 -0.484375 1.6875zm6.001953 3.21875l0 -8.59375l1.046875 0l0 3.078125q0.734375 -0.84375 1.859375 -0.84375q0.703125 0 1.203125 0.28125q0.515625 0.265625 0.734375 0.75q0.21875 0.46875 0.21875 1.390625l0 3.9375l-1.046875 0l0 -3.9375q0 -0.796875 -0.34375 -1.15625q-0.34375 -0.359375 -0.96875 -0.359375q-0.46875 0 -0.890625 0.25q-0.40625 0.234375 -0.59375 0.65625q-0.171875 0.40625 -0.171875 1.140625l0 3.40625l-1.046875 0zm8.970703 -0.9375l0.15625 0.921875q-0.453125 0.09375 -0.796875 0.09375q-0.578125 0 -0.890625 -0.171875q-0.3125 -0.1875 -0.453125 -0.484375q-0.125 -0.296875 -0.125 -1.25l0 -3.578125l-0.765625 0l0 -0.8125l0.765625 0l0 -1.546875l1.046875 -0.625l0 2.171875l1.0625 0l0 0.8125l-1.0625 0l0 3.640625q0 0.453125 0.046875 0.578125q0.0625 0.125 0.1875 0.203125q0.125 0.078125 0.359375 0.078125q0.1875 0 0.46875 -0.03125zm4.6992188 0.9375l0 -8.59375l1.125 0l0 8.59375l-1.125 0zm3.0058594 0l0 -6.21875l0.9375 0l0 0.875q0.6875 -1.015625 1.984375 -1.015625q0.5625 0 1.03125 0.203125q0.484375 0.203125 0.71875 0.53125q0.234375 0.328125 0.328125 0.765625q0.046875 0.296875 0.046875 1.03125l0 3.828125l-1.046875 0l0 -3.78125q0 -0.65625 -0.125 -0.96875q-0.125 -0.3125 -0.4375 -0.5q-0.3125 -0.203125 -0.734375 -0.203125q-0.671875 0 -1.171875 0.4375q-0.484375 0.421875 -0.484375 1.609375l0 3.40625l-1.046875 0zm10.705078 0l0 -0.78125q-0.59375 0.921875 -1.734375 0.921875q-0.75 0 -1.375 -0.40625q-0.625 -0.421875 -0.96875 -1.15625q-0.34375 -0.734375 -0.34375 -1.6875q0 -0.921875 0.3125 -1.6875q0.3125 -0.765625 0.9375 -1.15625q0.625 -0.40625 1.390625 -0.40625q0.5625 0 1.0 0.234375q0.4375 0.234375 0.71875 0.609375l0 -3.078125l1.046875 0l0 8.59375l-0.984375 0zm-3.328125 -3.109375q0 1.203125 0.5 1.796875q0.5 0.578125 1.1875 0.578125q0.6875 0 1.171875 -0.5625q0.484375 -0.5625 0.484375 -1.71875q0 -1.28125 -0.5 -1.875q-0.484375 -0.59375 -1.203125 -0.59375q-0.703125 0 -1.171875 0.578125q-0.46875 0.5625 -0.46875 1.796875zm10.048828 3.109375l0 -0.921875q-0.734375 1.0625 -1.984375 1.0625q-0.546875 0 -1.03125 -0.203125q-0.46875 -0.21875 -0.703125 -0.53125q-0.234375 -0.328125 -0.328125 -0.796875q-0.0625 -0.296875 -0.0625 -0.984375l0 -3.84375l1.0625 0l0 3.453125q0 0.8125 0.0625 1.109375q0.09375 0.40625 0.40625 0.65625q0.328125 0.234375 0.8125 0.234375q0.46875 0 0.875 -0.234375q0.421875 -0.25 0.59375 -0.671875q0.1875 -0.421875 0.1875 -1.21875l0 -3.328125l1.046875 0l0 6.21875l-0.9375 0zm2.1738281 -1.859375l1.03125 -0.15625q0.09375 0.625 0.484375 0.953125q0.40625 0.328125 1.140625 0.328125q0.71875 0 1.0625 -0.28125q0.359375 -0.296875 0.359375 -0.703125q0 -0.359375 -0.3125 -0.5625q-0.21875 -0.140625 -1.078125 -0.359375q-1.15625 -0.296875 -1.609375 -0.5q-0.4375 -0.21875 -0.671875 -0.59375q-0.234375 -0.375 -0.234375 -0.84375q0 -0.40625 0.1875 -0.765625q0.1875 -0.359375 0.515625 -0.59375q0.25 -0.171875 0.671875 -0.296875q0.421875 -0.125 0.921875 -0.125q0.71875 0 1.265625 0.21875q0.5625 0.203125 0.828125 0.5625q0.265625 0.359375 0.359375 0.953125l-1.03125 0.140625q-0.0625 -0.46875 -0.40625 -0.734375q-0.328125 -0.28125 -0.953125 -0.28125q-0.71875 0 -1.03125 0.25q-0.3125 0.234375 -0.3125 0.5625q0 0.203125 0.125 0.359375q0.140625 0.171875 0.40625 0.28125q0.15625 0.0625 0.9375 0.265625q1.125 0.3125 1.5625 0.5q0.4375 0.1875 0.6875 0.546875q0.25 0.359375 0.25 0.90625q0 0.53125 -0.3125 1.0q-0.296875 0.453125 -0.875 0.71875q-0.578125 0.25 -1.3125 0.25q-1.21875 0 -1.859375 -0.5q-0.625 -0.515625 -0.796875 -1.5zm8.71875 0.921875l0.15625 0.921875q-0.453125 0.09375 -0.796875 0.09375q-0.578125 0 -0.890625 -0.171875q-0.3125 -0.1875 -0.453125 -0.484375q-0.125 -0.296875 -0.125 -1.25l0 -3.578125l-0.765625 0l0 -0.8125l0.765625 0l0 -1.546875l1.046875 -0.625l0 2.171875l1.0625 0l0 0.8125l-1.0625 0l0 3.640625q0 0.453125 0.046875 0.578125q0.0625 0.125 0.1875 0.203125q0.125 0.078125 0.359375 0.078125q0.1875 0 0.46875 -0.03125zm1.0214844 0.9375l0 -6.21875l0.953125 0l0 0.9375q0.359375 -0.65625 0.65625 -0.859375q0.3125 -0.21875 0.6875 -0.21875q0.53125 0 1.078125 0.328125l-0.359375 0.984375q-0.390625 -0.234375 -0.765625 -0.234375q-0.359375 0 -0.640625 0.21875q-0.265625 0.203125 -0.375 0.578125q-0.1875 0.5625 -0.1875 1.21875l0 3.265625l-1.046875 0zm4.0117188 -7.375l0 -1.21875l1.0625 0l0 1.21875l-1.0625 0zm0 7.375l0 -6.21875l1.0625 0l0 6.21875l-1.0625 0zm6.7285156 -0.765625q-0.59375 0.5 -1.140625 0.703125q-0.53125 0.203125 -1.15625 0.203125q-1.03125 0 -1.578125 -0.5q-0.546875 -0.5 -0.546875 -1.28125q0 -0.453125 0.203125 -0.828125q0.203125 -0.390625 0.546875 -0.609375q0.34375 -0.234375 0.765625 -0.34375q0.296875 -0.09375 0.9375 -0.171875q1.265625 -0.140625 1.875 -0.359375q0 -0.21875 0 -0.265625q0 -0.65625 -0.296875 -0.921875q-0.40625 -0.34375 -1.203125 -0.34375q-0.734375 0 -1.09375 0.265625q-0.359375 0.25 -0.53125 0.90625l-1.03125 -0.140625q0.140625 -0.65625 0.46875 -1.0625q0.328125 -0.40625 0.9375 -0.625q0.609375 -0.21875 1.40625 -0.21875q0.796875 0 1.296875 0.1875q0.5 0.1875 0.734375 0.46875q0.234375 0.28125 0.328125 0.71875q0.046875 0.265625 0.046875 0.96875l0 1.40625q0 1.46875 0.0625 1.859375q0.078125 0.390625 0.28125 0.75l-1.109375 0q-0.15625 -0.328125 -0.203125 -0.765625zm-0.09375 -2.359375q-0.578125 0.234375 -1.71875 0.40625q-0.65625 0.09375 -0.921875 0.21875q-0.265625 0.109375 -0.421875 0.328125q-0.140625 0.21875 -0.140625 0.5q0 0.421875 0.3125 0.703125q0.328125 0.28125 0.9375 0.28125q0.609375 0 1.078125 -0.265625q0.484375 -0.265625 0.703125 -0.734375q0.171875 -0.359375 0.171875 -1.046875l0 -0.390625zm2.6738281 3.125l0 -8.59375l1.0625 0l0 8.59375l-1.0625 0z" fill-rule="nonzero"/><path fill="#000000" fill-opacity="0.0" d="m82.1811 443.0l157.92126 0l0 41.73227l-157.92126 0z" fill-rule="evenodd"/><path fill="#595959" d="m129.10756 463.52l3.296875 -8.59375l1.21875 0l3.515625 8.59375l-1.28125 0l-1.015625 -2.609375l-3.578125 0l-0.953125 2.609375l-1.203125 0zm2.484375 -3.53125l2.90625 0l-0.890625 -2.375q-0.421875 -1.078125 -0.609375 -1.78125q-0.171875 0.828125 -0.46875 1.640625l-0.9375 2.515625zm6.3164062 3.53125l0 -6.21875l0.953125 0l0 0.9375q0.359375 -0.65625 0.65625 -0.859375q0.3125 -0.21875 0.6875 -0.21875q0.53125 0 1.078125 0.328125l-0.359375 0.984375q-0.390625 -0.234375 -0.765625 -0.234375q-0.359375 0 -0.640625 0.21875q-0.265625 0.203125 -0.375 0.578125q-0.1875 0.5625 -0.1875 1.21875l0 3.265625l-1.046875 0zm8.074219 -2.28125l1.03125 0.140625q-0.171875 1.0625 -0.875 1.671875q-0.703125 0.609375 -1.71875 0.609375q-1.28125 0 -2.0625 -0.828125q-0.765625 -0.84375 -0.765625 -2.40625q0 -1.0 0.328125 -1.75q0.34375 -0.765625 1.015625 -1.140625q0.6875 -0.375 1.5 -0.375q1.0 0 1.640625 0.515625q0.65625 0.5 0.84375 1.453125l-1.03125 0.15625q-0.140625 -0.625 -0.515625 -0.9375q-0.375 -0.328125 -0.90625 -0.328125q-0.796875 0 -1.296875 0.578125q-0.5 0.5625 -0.5 1.796875q0 1.265625 0.484375 1.828125q0.484375 0.5625 1.25 0.5625q0.625 0 1.03125 -0.375q0.421875 -0.375 0.546875 -1.171875zm1.9375 2.28125l0 -8.59375l1.046875 0l0 3.078125q0.734375 -0.84375 1.859375 -0.84375q0.703125 0 1.203125 0.28125q0.515625 0.265625 0.734375 0.75q0.21875 0.46875 0.21875 1.390625l0 3.9375l-1.046875 0l0 -3.9375q0 -0.796875 -0.34375 -1.15625q-0.34375 -0.359375 -0.96875 -0.359375q-0.46875 0 -0.890625 0.25q-0.40625 0.234375 -0.59375 0.65625q-0.171875 0.40625 -0.171875 1.140625l0 3.40625l-1.046875 0zm10.923828 -2.0l1.09375 0.125q-0.25 0.953125 -0.953125 1.484375q-0.703125 0.53125 -1.78125 0.53125q-1.359375 0 -2.171875 -0.84375q-0.796875 -0.84375 -0.796875 -2.359375q0 -1.5625 0.8125 -2.421875q0.8125 -0.875 2.09375 -0.875q1.25 0 2.03125 0.84375q0.796875 0.84375 0.796875 2.390625q0 0.09375 0 0.28125l-4.640625 0q0.0625 1.03125 0.578125 1.578125q0.515625 0.53125 1.296875 0.53125q0.578125 0 0.984375 -0.296875q0.421875 -0.3125 0.65625 -0.96875zm-3.453125 -1.703125l3.46875 0q-0.0625 -0.796875 -0.390625 -1.1875q-0.515625 -0.609375 -1.3125 -0.609375q-0.734375 0 -1.234375 0.484375q-0.484375 0.484375 -0.53125 1.3125zm8.173828 2.765625l0.15625 0.921875q-0.453125 0.09375 -0.796875 0.09375q-0.578125 0 -0.890625 -0.171875q-0.3125 -0.1875 -0.453125 -0.484375q-0.125 -0.296875 -0.125 -1.25l0 -3.578125l-0.765625 0l0 -0.8125l0.765625 0l0 -1.546875l1.046875 -0.625l0 2.171875l1.0625 0l0 0.8125l-1.0625 0l0 3.640625q0 0.453125 0.046875 0.578125q0.0625 0.125 0.1875 0.203125q0.125 0.078125 0.359375 0.078125q0.1875 0 0.46875 -0.03125zm0.9902344 3.328125l-0.125 -0.984375q0.34375 0.09375 0.609375 0.09375q0.34375 0 0.546875 -0.125q0.21875 -0.109375 0.359375 -0.3125q0.09375 -0.171875 0.328125 -0.796875q0.015625 -0.078125 0.09375 -0.25l-2.375 -6.234375l1.140625 0l1.296875 3.59375q0.25 0.6875 0.453125 1.453125q0.1875 -0.734375 0.4375 -1.421875l1.328125 -3.625l1.046875 0l-2.359375 6.328125q-0.390625 1.015625 -0.59375 1.40625q-0.28125 0.53125 -0.65625 0.765625q-0.359375 0.25 -0.859375 0.25q-0.296875 0 -0.671875 -0.140625zm6.046875 0l0 -8.609375l0.953125 0l0 0.796875q0.34375 -0.46875 0.765625 -0.703125q0.4375 -0.234375 1.046875 -0.234375q0.796875 0 1.40625 0.40625q0.609375 0.40625 0.90625 1.15625q0.3125 0.75 0.3125 1.640625q0 0.953125 -0.34375 1.71875q-0.328125 0.765625 -0.984375 1.171875q-0.65625 0.40625 -1.375 0.40625q-0.53125 0 -0.953125 -0.21875q-0.421875 -0.234375 -0.6875 -0.5625l0 3.03125l-1.046875 0zm0.953125 -5.46875q0 1.203125 0.484375 1.78125q0.484375 0.5625 1.171875 0.5625q0.703125 0 1.203125 -0.59375q0.5 -0.59375 0.5 -1.84375q0 -1.1875 -0.484375 -1.765625q-0.484375 -0.59375 -1.171875 -0.59375q-0.671875 0 -1.1875 0.625q-0.515625 0.625 -0.515625 1.828125zm9.970703 1.078125l1.09375 0.125q-0.25 0.953125 -0.953125 1.484375q-0.703125 0.53125 -1.78125 0.53125q-1.359375 0 -2.171875 -0.84375q-0.796875 -0.84375 -0.796875 -2.359375q0 -1.5625 0.8125 -2.421875q0.8125 -0.875 2.09375 -0.875q1.25 0 2.03125 0.84375q0.796875 0.84375 0.796875 2.390625q0 0.09375 0 0.28125l-4.640625 0q0.0625 1.03125 0.578125 1.578125q0.515625 0.53125 1.296875 0.53125q0.578125 0 0.984375 -0.296875q0.421875 -0.3125 0.65625 -0.96875zm-3.453125 -1.703125l3.46875 0q-0.0625 -0.796875 -0.390625 -1.1875q-0.515625 -0.609375 -1.3125 -0.609375q-0.734375 0 -1.234375 0.484375q-0.484375 0.484375 -0.53125 1.3125zm14.3828125 -2.78125l-1.046875 0.078125q-0.140625 -0.625 -0.390625 -0.90625q-0.4375 -0.453125 -1.0625 -0.453125q-0.5 0 -0.890625 0.28125q-0.484375 0.375 -0.78125 1.078125q-0.28125 0.6875 -0.296875 1.96875q0.375 -0.578125 0.921875 -0.859375q0.5625 -0.28125 1.171875 -0.28125q1.046875 0 1.78125 0.78125q0.75 0.78125 0.75 2.0q0 0.8125 -0.359375 1.515625q-0.34375 0.6875 -0.953125 1.0625q-0.609375 0.359375 -1.375 0.359375q-1.328125 0 -2.15625 -0.96875q-0.828125 -0.96875 -0.828125 -3.1875q0 -2.5 0.921875 -3.625q0.796875 -0.984375 2.15625 -0.984375q1.015625 0 1.65625 0.578125q0.65625 0.5625 0.78125 1.5625zm-4.296875 3.703125q0 0.546875 0.21875 1.046875q0.234375 0.484375 0.640625 0.75q0.421875 0.265625 0.890625 0.265625q0.65625 0 1.140625 -0.53125q0.484375 -0.546875 0.484375 -1.46875q0 -0.890625 -0.484375 -1.40625q-0.46875 -0.515625 -1.1875 -0.515625q-0.71875 0 -1.21875 0.515625q-0.484375 0.515625 -0.484375 1.34375z" fill-rule="nonzero"/><path fill="#595959" d="m108.98646 477.92l0 -8.59375l3.234375 0q0.984375 0 1.578125 0.265625q0.59375 0.265625 0.921875 0.8125q0.34375 0.53125 0.34375 1.125q0 0.546875 -0.296875 1.03125q-0.296875 0.484375 -0.90625 0.78125q0.78125 0.234375 1.1875 0.796875q0.421875 0.546875 0.421875 1.296875q0 0.59375 -0.25 1.125q-0.25 0.515625 -0.625 0.796875q-0.375 0.28125 -0.953125 0.421875q-0.5625 0.140625 -1.375 0.140625l-3.28125 0zm1.140625 -4.984375l1.859375 0q0.75 0 1.078125 -0.09375q0.4375 -0.125 0.65625 -0.421875q0.21875 -0.3125 0.21875 -0.765625q0 -0.421875 -0.203125 -0.75q-0.203125 -0.328125 -0.59375 -0.4375q-0.375 -0.125 -1.296875 -0.125l-1.71875 0l0 2.59375zm0 3.96875l2.140625 0q0.546875 0 0.765625 -0.046875q0.40625 -0.0625 0.65625 -0.21875q0.265625 -0.171875 0.4375 -0.484375q0.171875 -0.3125 0.171875 -0.71875q0 -0.484375 -0.25 -0.84375q-0.234375 -0.359375 -0.671875 -0.5q-0.4375 -0.140625 -1.265625 -0.140625l-1.984375 0l0 2.953125zm10.863281 1.015625l0 -0.921875q-0.734375 1.0625 -1.984375 1.0625q-0.546875 0 -1.03125 -0.203125q-0.46875 -0.21875 -0.703125 -0.53125q-0.234375 -0.328125 -0.328125 -0.796875q-0.0625 -0.296875 -0.0625 -0.984375l0 -3.84375l1.0625 0l0 3.453125q0 0.8125 0.0625 1.109375q0.09375 0.40625 0.40625 0.65625q0.328125 0.234375 0.8125 0.234375q0.46875 0 0.875 -0.234375q0.421875 -0.25 0.59375 -0.671875q0.1875 -0.421875 0.1875 -1.21875l0 -3.328125l1.046875 0l0 6.21875l-0.9375 0zm2.1738281 -1.859375l1.03125 -0.15625q0.09375 0.625 0.484375 0.953125q0.40625 0.328125 1.140625 0.328125q0.71875 0 1.0625 -0.28125q0.359375 -0.296875 0.359375 -0.703125q0 -0.359375 -0.3125 -0.5625q-0.21875 -0.140625 -1.078125 -0.359375q-1.15625 -0.296875 -1.609375 -0.5q-0.4375 -0.21875 -0.671875 -0.59375q-0.234375 -0.375 -0.234375 -0.84375q0 -0.40625 0.1875 -0.765625q0.1875 -0.359375 0.515625 -0.59375q0.25 -0.171875 0.671875 -0.296875q0.421875 -0.125 0.921875 -0.125q0.71875 0 1.265625 0.21875q0.5625 0.203125 0.828125 0.5625q0.265625 0.359375 0.35938263 0.953125l-1.0312576 0.140625q-0.0625 -0.46875 -0.40625 -0.734375q-0.328125 -0.28125 -0.953125 -0.28125q-0.71875 0 -1.03125 0.25q-0.3125 0.234375 -0.3125 0.5625q0 0.203125 0.125 0.359375q0.140625 0.171875 0.40625 0.28125q0.15625 0.0625 0.9375 0.265625q1.125 0.3125 1.5625 0.5q0.4375 0.1875 0.6875076 0.546875q0.25 0.359375 0.25 0.90625q0 0.53125 -0.3125 1.0q-0.29688263 0.453125 -0.8750076 0.71875q-0.578125 0.25 -1.3125 0.25q-1.21875 0 -1.859375 -0.5q-0.625 -0.515625 -0.796875 -1.5zm6.4218826 -5.515625l0 -1.21875l1.0625 0l0 1.21875l-1.0625 0zm0 7.375l0 -6.21875l1.0625 0l0 6.21875l-1.0625 0zm2.6660156 0l0 -6.21875l0.9375 0l0 0.875q0.6875 -1.015625 1.984375 -1.015625q0.5625 0 1.03125 0.203125q0.484375 0.203125 0.71875 0.53125q0.234375 0.328125 0.328125 0.765625q0.046875 0.296875 0.046875 1.03125l0 3.828125l-1.046875 0l0 -3.78125q0 -0.65625 -0.125 -0.96875q-0.125 -0.3125 -0.4375 -0.5q-0.3125 -0.203125 -0.734375 -0.203125q-0.671875 0 -1.171875 0.4375q-0.484375 0.421875 -0.484375 1.609375l0 3.40625l-1.046875 0zm10.923828 -2.0l1.09375 0.125q-0.25 0.953125 -0.953125 1.484375q-0.703125 0.53125 -1.78125 0.53125q-1.359375 0 -2.171875 -0.84375q-0.796875 -0.84375 -0.796875 -2.359375q0 -1.5625 0.8125 -2.421875q0.8125 -0.875 2.09375 -0.875q1.25 0 2.03125 0.84375q0.796875 0.84375 0.796875 2.390625q0 0.09375 0 0.28125l-4.640625 0q0.0625 1.03125 0.578125 1.578125q0.515625 0.53125 1.296875 0.53125q0.578125 0 0.984375 -0.296875q0.421875 -0.3125 0.65625 -0.96875zm-3.453125 -1.703125l3.46875 0q-0.0625 -0.796875 -0.390625 -1.1875q-0.515625 -0.609375 -1.3125 -0.609375q-0.734375 0 -1.234375 0.484375q-0.484375 0.484375 -0.53125 1.3125zm5.455078 1.84375l1.03125 -0.15625q0.09375 0.625 0.484375 0.953125q0.40625 0.328125 1.140625 0.328125q0.71875 0 1.0625 -0.28125q0.359375 -0.296875 0.359375 -0.703125q0 -0.359375 -0.3125 -0.5625q-0.21875 -0.140625 -1.078125 -0.359375q-1.15625 -0.296875 -1.609375 -0.5q-0.4375 -0.21875 -0.671875 -0.59375q-0.234375 -0.375 -0.234375 -0.84375q0 -0.40625 0.1875 -0.765625q0.1875 -0.359375 0.515625 -0.59375q0.25 -0.171875 0.671875 -0.296875q0.421875 -0.125 0.921875 -0.125q0.71875 0 1.265625 0.21875q0.5625 0.203125 0.828125 0.5625q0.265625 0.359375 0.359375 0.953125l-1.03125 0.140625q-0.0625 -0.46875 -0.40625 -0.734375q-0.328125 -0.28125 -0.953125 -0.28125q-0.71875 0 -1.03125 0.25q-0.3125 0.234375 -0.3125 0.5625q0 0.203125 0.125 0.359375q0.140625 0.171875 0.40625 0.28125q0.15625 0.0625 0.9375 0.265625q1.125 0.3125 1.5625 0.5q0.4375 0.1875 0.6875 0.546875q0.25 0.359375 0.25 0.90625q0 0.53125 -0.3125 1.0q-0.296875 0.453125 -0.875 0.71875q-0.578125 0.25 -1.3125 0.25q-1.21875 0 -1.859375 -0.5q-0.625 -0.515625 -0.796875 -1.5zm6.0 0l1.03125 -0.15625q0.09375 0.625 0.484375 0.953125q0.40625 0.328125 1.140625 0.328125q0.71875 0 1.0625 -0.28125q0.359375 -0.296875 0.359375 -0.703125q0 -0.359375 -0.3125 -0.5625q-0.21875 -0.140625 -1.078125 -0.359375q-1.15625 -0.296875 -1.609375 -0.5q-0.4375 -0.21875 -0.671875 -0.59375q-0.234375 -0.375 -0.234375 -0.84375q0 -0.40625 0.1875 -0.765625q0.1875 -0.359375 0.515625 -0.59375q0.25 -0.171875 0.671875 -0.296875q0.421875 -0.125 0.921875 -0.125q0.71875 0 1.265625 0.21875q0.5625 0.203125 0.828125 0.5625q0.265625 0.359375 0.359375 0.953125l-1.03125 0.140625q-0.0625 -0.46875 -0.40625 -0.734375q-0.328125 -0.28125 -0.953125 -0.28125q-0.71875 0 -1.03125 0.25q-0.3125 0.234375 -0.3125 0.5625q0 0.203125 0.125 0.359375q0.140625 0.171875 0.40625 0.28125q0.15625 0.0625 0.9375 0.265625q1.125 0.3125 1.5625 0.5q0.4375 0.1875 0.6875 0.546875q0.25 0.359375 0.25 0.90625q0 0.53125 -0.3125 1.0q-0.296875 0.453125 -0.875 0.71875q-0.578125 0.25 -1.3125 0.25q-1.21875 0 -1.859375 -0.5q-0.625 -0.515625 -0.796875 -1.5zm13.818359 1.09375q-0.59375 0.5 -1.140625 0.703125q-0.53125 0.203125 -1.15625 0.203125q-1.03125 0 -1.578125 -0.5q-0.546875 -0.5 -0.546875 -1.28125q0 -0.453125 0.203125 -0.828125q0.203125 -0.390625 0.546875 -0.609375q0.34375 -0.234375 0.765625 -0.34375q0.296875 -0.09375 0.9375 -0.171875q1.265625 -0.140625 1.875 -0.359375q0 -0.21875 0 -0.265625q0 -0.65625 -0.296875 -0.921875q-0.40625 -0.34375 -1.203125 -0.34375q-0.734375 0 -1.09375 0.265625q-0.359375 0.25 -0.53125 0.90625l-1.03125 -0.140625q0.140625 -0.65625 0.46875 -1.0625q0.328125 -0.40625 0.9375 -0.625q0.609375 -0.21875 1.40625 -0.21875q0.796875 0 1.296875 0.1875q0.5 0.1875 0.734375 0.46875q0.234375 0.28125 0.328125 0.71875q0.046875 0.265625 0.046875 0.96875l0 1.40625q0 1.46875 0.0625 1.859375q0.078125 0.390625 0.28125 0.75l-1.109375 0q-0.15625 -0.328125 -0.203125 -0.765625zm-0.09375 -2.359375q-0.578125 0.234375 -1.71875 0.40625q-0.65625 0.09375 -0.921875 0.21875q-0.265625 0.109375 -0.421875 0.328125q-0.140625 0.21875 -0.140625 0.5q0 0.421875 0.3125 0.703125q0.328125 0.28125 0.9375 0.28125q0.609375 0 1.078125 -0.265625q0.484375 -0.265625 0.703125 -0.734375q0.171875 -0.359375 0.171875 -1.046875l0 -0.390625zm2.7050781 3.125l0 -6.21875l0.9375 0l0 0.875q0.6875 -1.015625 1.984375 -1.015625q0.5625 0 1.03125 0.203125q0.484375 0.203125 0.71875 0.53125q0.234375 0.328125 0.328125 0.765625q0.046875 0.296875 0.046875 1.03125l0 3.828125l-1.046875 0l0 -3.78125q0 -0.65625 -0.125 -0.96875q-0.125 -0.3125 -0.4375 -0.5q-0.3125 -0.203125 -0.734375 -0.203125q-0.671875 0 -1.171875 0.4375q-0.484375 0.421875 -0.484375 1.609375l0 3.40625l-1.046875 0zm10.705078 0l0 -0.78125q-0.59375 0.921875 -1.734375 0.921875q-0.75 0 -1.375 -0.40625q-0.625 -0.421875 -0.96875 -1.15625q-0.34375 -0.734375 -0.34375 -1.6875q0 -0.921875 0.3125 -1.6875q0.3125 -0.765625 0.9375 -1.15625q0.625 -0.40625 1.390625 -0.40625q0.5625 0 1.0 0.234375q0.4375 0.234375 0.71875 0.609375l0 -3.078125l1.046875 0l0 8.59375l-0.984375 0zm-3.328125 -3.109375q0 1.203125 0.5 1.796875q0.5 0.578125 1.1875 0.578125q0.6875 0 1.171875 -0.5625q0.484375 -0.5625 0.484375 -1.71875q0 -1.28125 -0.5 -1.875q-0.484375 -0.59375 -1.203125 -0.59375q-0.703125 0 -1.171875 0.578125q-0.46875 0.5625 -0.46875 1.796875zm9.4453125 3.109375l0 -8.59375l3.8125 0q1.15625 0 1.75 0.234375q0.59375 0.234375 0.953125 0.828125q0.359375 0.578125 0.359375 1.28125q0 0.921875 -0.59375 1.546875q-0.59375 0.625 -1.828125 0.796875q0.453125 0.21875 0.6875 0.421875q0.5 0.453125 0.9375 1.140625l1.5 2.34375l-1.4375 0l-1.125 -1.78125q-0.5 -0.78125 -0.828125 -1.1875q-0.3125 -0.40625 -0.578125 -0.5625q-0.25 -0.171875 -0.515625 -0.25q-0.1875 -0.03125 -0.625 -0.03125l-1.328125 0l0 3.8125l-1.140625 0zm1.140625 -4.796875l2.453125 0q0.765625 0 1.203125 -0.15625q0.453125 -0.171875 0.671875 -0.515625q0.234375 -0.359375 0.234375 -0.78125q0 -0.609375 -0.453125 -1.0q-0.4375 -0.390625 -1.390625 -0.390625l-2.71875 0l0 2.84375zm11.634766 2.796875l1.09375 0.125q-0.25 0.953125 -0.953125 1.484375q-0.703125 0.53125 -1.78125 0.53125q-1.359375 0 -2.171875 -0.84375q-0.796875 -0.84375 -0.796875 -2.359375q0 -1.5625 0.8125 -2.421875q0.8125 -0.875 2.09375 -0.875q1.25 0 2.03125 0.84375q0.796875 0.84375 0.796875 2.390625q0 0.09375 0 0.28125l-4.640625 0q0.0625 1.03125 0.578125 1.578125q0.515625 0.53125 1.296875 0.53125q0.578125 0 0.984375 -0.296875q0.421875 -0.3125 0.65625 -0.96875zm-3.453125 -1.703125l3.46875 0q-0.0625 -0.796875 -0.390625 -1.1875q-0.515625 -0.609375 -1.3125 -0.609375q-0.734375 0 -1.234375 0.484375q-0.484375 0.484375 -0.53125 1.3125zm8.173828 2.765625l0.15625 0.921875q-0.453125 0.09375 -0.796875 0.09375q-0.578125 0 -0.890625 -0.171875q-0.3125 -0.1875 -0.453125 -0.484375q-0.125 -0.296875 -0.125 -1.25l0 -3.578125l-0.765625 0l0 -0.8125l0.765625 0l0 -1.546875l1.046875 -0.625l0 2.171875l1.0625 0l0 0.8125l-1.0625 0l0 3.640625q0 0.453125 0.046875 0.578125q0.0625 0.125 0.1875 0.203125q0.125 0.078125 0.359375 0.078125q0.1875 0 0.46875 -0.03125zm5.0996094 0.171875q-0.59375 0.5 -1.140625 0.703125q-0.53125 0.203125 -1.15625 0.203125q-1.03125 0 -1.578125 -0.5q-0.546875 -0.5 -0.546875 -1.28125q0 -0.453125 0.203125 -0.828125q0.203125 -0.390625 0.546875 -0.609375q0.34375 -0.234375 0.765625 -0.34375q0.296875 -0.09375 0.9375 -0.171875q1.265625 -0.140625 1.875 -0.359375q0 -0.21875 0 -0.265625q0 -0.65625 -0.296875 -0.921875q-0.40625 -0.34375 -1.203125 -0.34375q-0.734375 0 -1.09375 0.265625q-0.359375 0.25 -0.53125 0.90625l-1.03125 -0.140625q0.140625 -0.65625 0.46875 -1.0625q0.328125 -0.40625 0.9375 -0.625q0.609375 -0.21875 1.40625 -0.21875q0.796875 0 1.296875 0.1875q0.5 0.1875 0.734375 0.46875q0.234375 0.28125 0.328125 0.71875q0.046875 0.265625 0.046875 0.96875l0 1.40625q0 1.46875 0.0625 1.859375q0.078125 0.390625 0.28125 0.75l-1.109375 0q-0.15625 -0.328125 -0.203125 -0.765625zm-0.09375 -2.359375q-0.578125 0.234375 -1.71875 0.40625q-0.65625 0.09375 -0.921875 0.21875q-0.265625 0.109375 -0.421875 0.328125q-0.140625 0.21875 -0.140625 0.5q0 0.421875 0.3125 0.703125q0.328125 0.28125 0.9375 0.28125q0.609375 0 1.078125 -0.265625q0.484375 -0.265625 0.703125 -0.734375q0.171875 -0.359375 0.171875 -1.046875l0 -0.390625zm2.7050781 -4.25l0 -1.21875l1.0625 0l0 1.21875l-1.0625 0zm0 7.375l0 -6.21875l1.0625 0l0 6.21875l-1.0625 0zm2.6347656 0l0 -8.59375l1.0625 0l0 8.59375l-1.0625 0z" fill-rule="nonzero"/></g></svg>
\ No newline at end of file
diff --git a/manual_jb/content/workshops/20231115/session4/session4-building-dmg-recovery-local-data.ipynb b/manual_jb/content/workshops/20231115/session4/session4-building-dmg-recovery-local-data.ipynb
new file mode 100644
index 00000000..e134808a
--- /dev/null
+++ b/manual_jb/content/workshops/20231115/session4/session4-building-dmg-recovery-local-data.ipynb
@@ -0,0 +1,1564 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "86a09fc7-8415-4548-a7eb-68eec87dd08c",
+   "metadata": {},
+   "source": [
+    "# Session 4: Building Damage and Recovery Analyses with Local Data\n",
+    "\n",
+    "**Objective:**\n",
+    "- Gain an in-depth understanding of the damage analysis mechanism, fragility curves, and mapping by creating and using local objects.\n",
+    "- Conduct an analysis similar to that in session 3, but with the advantage of utilizing local objects. Some of the benefits includes:\n",
+    "    1. **Flexibility**: Local datasets allow for easy experimentation and testing with different fragilities, hazards, datasets during analysis development. It's ideal for rapid prototyping, quick data swapping, and parameter testing.\n",
+    "    2. **Custom Preprocessing**: You have full control over data preprocessing to suit your analysis needs.\n",
+    "    3. **Data Privacy**: If it is sensitive data.\n",
+    "\n",
+    "Local objects provide control, flexibility, and security in data analysis, especially during development and testing phases. \n",
+    "\n",
+    "**Agenda**\n",
+    "- [1. Introduction to Local Objects](#1.-Introduction-to-Local-Objects)\n",
+    "- [2. Creating Tornado Hazard from Shapefile](#2.-Creating-Tornado-Hazard-from-Shapefile)\n",
+    "    - [2.1. Step 1: Describe the tornado](#2.1.-Step-1:-Describe-the-tornado)\n",
+    "    - [2.2. Step 2: Attach hazard dataset from local file](#2.2.-Step-2:-Attach-hazard-dataset-from-local-file)\n",
+    "    - [2.3. Step 3: Visualize the created tornado](#2.3.-Step-3:-Visualize-the-created-tornado)\n",
+    "- [3. Creating Building Dataset from given CSV file](#3.-Creating-Building-Dataset-from-given-CSV-file)\n",
+    "    - [3.1 Building Inventory Explained](#3.1-Building-Inventory-Explained)\n",
+    "    - [3.2 Create Joplin Commercial Building Dataset from tabular data (csv)](#3.2-Create-Joplin-Commercial-Building-Dataset-from-tabular-data-(csv))\n",
+    "        - [3.2.1. Step 1: Convert CSV file to ESRI Shapefile](#3.2.1.-Step-1:-Convert-CSV-file-to-ESRI-Shapefile)\n",
+    "        - [3.2.2. Step 2: Add GUID](#3.2.2.-Step-2:-Add-GUID)\n",
+    "        - [3.2.3. Step 3: Create a local dataset](#3.2.3.-Step-3:-Create-a-local-dataset-for-IN-CORE)\n",
+    "        - [3.2.4. Step 4: Visualize created building dataset](#3.2.4.-Step-4:-Visualize-created-building-dataset)\n",
+    "- [4. Creating local DFR3 objects](#4.-Creating-local-DFR3-objects)\n",
+    "    - [4.1. Create Fragility Objectcs](#4.1.-Create-Fragility-Objectcs)\n",
+    "        - [4.1.1. Step 1: Define each fragility curve](#4.1.1.-Step-1:-Define-each-fragility-curve)\n",
+    "        - [4.1.2. Step 2: Define Curve Paramters](#4.1.2.-Step-2:-Define-Curve-Paramters)\n",
+    "        - [4.1.3. Step 3: Piece together complete fragility curve set](#4.1.3.-Step-3:-Piece-together-the-complete-fragility-curve-set)\n",
+    "        - [4.1.4. Step 4: Create fragility curve set object](#4.1.4.-Step-4:-Create-fragility-curve-set-object)\n",
+    "        - [4.1.5. Alternative: Construct fragility curves from string](#4.1.5.-Alternative:-Construct-fragility-curves-from-string)\n",
+    "        - [4.1.6. Visualize fragility curves](#4.1.6.-Visualize-fragility-curves)\n",
+    "        - [4.1.7. Calculate limit state from local fragility](#4.1.7.-Calculate-limit-state-from-local-fragility)\n",
+    "        - [4.1.8. Derive damage state from limit state](#4.1.8.-Derivie-damage-state-from-limit-state)\n",
+    "    - [4.2. Create Fragility Mapping](#4.2.-Create-Fragility-Mapping)\n",
+    "        - [4.2.1. Step 1: Establish mapping rules](#4.2.1.-Step-1:-Establish-mapping-rules)\n",
+    "        - [4.2.2. Step 2: Add metadata](#4.2.2.-Step-2:-Add-metadata)\n",
+    "        - [4.2.3. Step 3: Create MappingSet Object](#4.2.3.-Step-3:-Create-MappingSet-Object)\n",
+    "- [5. Building Damage Analysis](#5.-Building-Damage-Analysis)\n",
+    "    - [5.1. Explore building damage resutls](#5.1.-Explore-building-damage-resutls)\n",
+    "        - [5.1.1. Joining dataset](#5.1.1.-Joining-dataset)\n",
+    "        - [5.1.2. Show statistical summary on a column](#5.1.2.-Show-statistical-summary-on-a-column)\n",
+    "        - [5.1.3. Sort table](#5.1.3.-Show-table-sorted-by-LS_0-(decending)-and-archetype-(ascending))\n",
+    "        - [5.1.4. Group by](#5.1.4.-Show-table-group-by-archetype)\n",
+    "    - [5.2. Visualization building damage](#5.2.-Visualization-building-damage)\n",
+    "- [6. Monte Carlo Simulation](#6.-Monte-Carlo-Simulation)\n",
+    "    - [6.1. Get buildings non-functional probabilities](#6.1.-Get-buildings-non-functional-probabilities)\n",
+    "    - [6.2. Get buildings sample damage states](#6.2.-Get-buildings-sample-damage-states)\n",
+    "    - [6.3. Visualize probability of non-functionality](#6.3.-Visualize-probability-of-non-functionality)\n",
+    "- [7. Commercial Building Recovery Analysis](#7.-Commercial-Building-Recovery-Analysis)\n",
+    "    - [7.1. Step 1: Prepare repair curve sets and repair curve mappings](#7.1.-Step-1:-Prepare-repair-curve-sets-and-repair-curve-mappings)\n",
+    "    - [7.2. Step 2: Chain with MCS output](#7.2.-Step-2:-Chain-with-MCS-output)\n",
+    "    - [7.3. Step 3: Prepare additional input datasets](#7.3.-Step-3:-Prepare-additional-input-datasets)\n",
+    "    - [7.4. Step 4: Run commercial recovery analysis](#7.4.-Step-4:-Run-commercial-recovery-analysis)\n",
+    "    - [7.5. Visualize recovery grouped by archetypes](#7.5.-Visualize-recovery-grouped-by-archetypes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8188f7a6-4a28-454c-a12e-94025435f8eb",
+   "metadata": {},
+   "source": [
+    "## 1. Introduction to Local Objects\n",
+    "\n",
+    "**Hazard**\n",
+    "In pyIncore, any analysis can utilize either a Hazard Object, or a hazard identifier/hazard type from incore-services as input for analysis. Currently, we support a range of hazards, including earthquakes, floods, hurricanes, tornadoes, and tsunamis. This tutorial provides examples of how to create a pyIncore Hazard Object for Tornadoes using local shapefiles.\n",
+    "\n",
+    "> To see more examples of [create and use local hazard](https://incore.ncsa.illinois.edu/doc/incore/notebooks/create_local_hazard/create_local_hazard.html)\n",
+    "\n",
+    "**Dataset**\n",
+    "For any analysis in pyIncore, the default input is a Dataset Object. This tutorial introduces users to the fundamental concept of creating and using Dataset Objects, which involves loading data from local files. We also demonstrate essential preprocessing, postprocessing, and visualization steps.\n",
+    "\n",
+    "> To see more examples of [create and use local dataset](https://incore.ncsa.illinois.edu/doc/incore/notebooks/create_dataset/create_dataset.html)\n",
+    "\n",
+    "\n",
+    "**DFR3 Curves and DFR3 Mapping**\n",
+    "DFR3 stands for Damage, Functionality, Repair, Restoration, Recovery (DFR3). For example, Damage curves (aka Fragility Curves) are commonly used in disaster models to define the probability of exceeding a given damage state as a function of environmental change. DFR3 mapping sets serve the purpose of establishing a connection between an item within a given inventory and corresponding DFR3 curves. For example, a particular set of fragility curves should be applied to a building based on its specific characteristics. This tutorial hands-on tutorial demonstrates how to create DFR3 and Mapping Objects for fragility and repair curves, along with guidance on their local utilization within pyIncore.\n",
+    "\n",
+    "> To see more examples of [create and use local DFR3 Object](https://incore.ncsa.illinois.edu/doc/incore/notebooks/create_dfr3_object/create_dfr3_object.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "737530f9-52e9-4516-9756-6f9a06a43ba0",
+   "metadata": {},
+   "source": [
+    "## 2. Creating Tornado Hazard from Shapefile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16d2eb06-59a4-403a-b378-726217cb3f49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyincore import Tornado"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f32cc8f1-72a5-4cae-b478-f966b10f8e6d",
+   "metadata": {},
+   "source": [
+    "### 2.1. Step 1: Describe the tornado"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "157c2c01-339c-45a2-928e-c555463891fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tornado_metadata = {\n",
+    "  \"name\": \"Joplin Hindcast Tornado\",\n",
+    "  \"description\": \"Joplin tornado hazard\",\n",
+    "  \"tornadoType\": \"dataset\",\n",
+    "  \"threshold\": None,\n",
+    "  \"thresholdUnit\": \"mph\"\n",
+    "}\n",
+    "\n",
+    "# create the tornado object\n",
+    "tornado = Tornado(tornado_metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c23cd222-d6fc-4493-a6a2-5a7838107b11",
+   "metadata": {},
+   "source": [
+    "### 2.2. Step 2: Attach hazard dataset from local file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57498b42-7d95-4b31-bca1-06e06674c0f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tornado.hazardDatasets[0].from_file(\"data/tornado/joplin_path_wgs84.shp\", data_type=\"incore:tornadoWindfield\")\n",
+    "tornado"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09abc8bf-70ae-4d61-8c75-75f4dad6a7db",
+   "metadata": {},
+   "source": [
+    "### 2.3. Step 3: Visualize the created tornado"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecfbd110-1081-4e1b-8e9c-b48501a974fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyincore_viz.geoutil import GeoUtil as viz\n",
+    "import geopandas as gpd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4291e395-904d-44aa-9ae1-9629d20f6ce3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read tornado shapefile into geopandas dataframe. \n",
+    "# Explanation about geopandas see below section.\n",
+    "tornado_dataset = tornado.hazardDatasets[0].dataset\n",
+    "tornado_gdf = tornado_dataset.get_dataframe_from_shapefile()\n",
+    "\n",
+    "##### pyincore-viz method rendering static Map with colors and labels\n",
+    "viz.plot_gdf_map(tornado_gdf, 'ef_rating', basemap=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d44cc20-5e10-4e8c-bd68-3b401229954a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# interactive map leveraging geopandas dataframe explore\n",
+    "tornado_gdf.explore(\n",
+    "    column=\"ef_rating\",  # make choropleth based on \"BoroName\" column\n",
+    "    tooltip=\"ef_rating\",  # show \"BoroName\" value in tooltip (on hover)\n",
+    "    popup=True,  # show all values in popup (on click)\n",
+    "    cmap=\"Set1\"  # use \"Set1\" matplotlib colormap)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "034a3c0f-2d78-45bd-ab48-972126f71bfc",
+   "metadata": {},
+   "source": [
+    "## 3. Creating Building Dataset from given CSV file\n",
+    "\n",
+    "There are many ways to create building inventory dataset; for example 1) using GIS software (e.g. ArcGIS, QGIS, etc), 2) using python code. In this exerercise, we will create a buiding inventory dataset in ESRI Shapefile from CSV file by using **GeoPandas library**.\n",
+    "\n",
+    "GeoPandas is an open source project to make working with geospatial data in python easier. GeoPandas extends the datatypes used by pandas to allow spatial operations on geometric types. For more information, please follow [GeoPandas's user guide](https://geopandas.org/en/stable/docs/user_guide.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bddd1cb2-0d56-4274-a5a3-8377076f6014",
+   "metadata": {},
+   "source": [
+    "### 3.1 Building Inventory Explained\n",
+    "\n",
+    "The schema (columns) of building Inventory has been going through several iteration (v5, v6, v7), and here is a list of supported column names in version 6. Having a fully populated Building Inventory, with no additional columns, will hold all the data needed to perform all analyses that are compatible with that version of the Building Inventory. Here are some of the definition. For the full schema please visit [buildingInventoryVer6](https://incore.ncsa.illinois.edu/semantics/api/types/ergo:buildingInventoryVer6).\n",
+    "\n",
+    "|column name|description|type|\n",
+    "|-----------|-----------|----|\n",
+    "|GUID|added by IN-CORE|string|\n",
+    "|ARCHETYPE| building structure archetype|integer|\n",
+    "|OCC_TYPE|Broad HAZUS Occupancy Category (e.g. RES3 - multi-family residential)|string|\n",
+    "|APPR_BLDG|Appraised value for the building|double|\n",
+    "|...|...|...|...\n",
+    "\n",
+    "\n",
+    "> If you would like to find other dataset types schema, you can explore our [Dataset Type Viewer](https://incore.ncsa.illinois.edu/SemanticViewer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bdca37d-11c1-4f22-8590-e7cd85dae509",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas as gpd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78080297-3ace-4526-a782-74a8278c04ef",
+   "metadata": {},
+   "source": [
+    "### 3.2 Create Joplin Commercial Building Dataset from tabular data (csv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f270f627-a36b-4d30-bbe9-af046264099d",
+   "metadata": {},
+   "source": [
+    "#### 3.2.1. Step 1: Convert CSV file to ESRI Shapefile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b96ffcd-7448-4f73-bbbf-fb86405316d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load to Pandas Dataframe\n",
+    "df = pd.read_csv('data/joplin_commercial_bldg_v6_sample.csv')\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc0f37b0-5b99-4506-9d4e-60716a301bae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Turn Pandas Dataframe into Geopandas dataframe\n",
+    "gdf = gpd.GeoDataFrame(df, crs='epsg:4326', geometry=gpd.points_from_xy(df.X, df.Y))\n",
+    "gdf.to_file(\"joplin_commercial_bldg_v6_sample.shp\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "228ed817-14e7-4f3c-9277-199db5dec952",
+   "metadata": {},
+   "source": [
+    "#### 3.2.2. Step 2: Add GUID\n",
+    "**Each built-in infrastructure needs a GUID.** A GUID is an acronyom that stands for Globally Unique Identifier, they are also referred to as UUIDs or Universaly Unique Identifiers. Technically they are 128-bit unique reference numbers used in computing which are highly unlikely to repeat when generated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1a759e6-db4b-4d83-93da-0d15bd373005",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pyincore has utility methods help user easily add GUID\n",
+    "from pyincore import GeoUtil\n",
+    "\n",
+    "GeoUtil.add_guid(\"joplin_commercial_bldg_v6_sample.shp\", \"joplin_commercial_bldg_v6_sample_w_guid.shp\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23a2a653-ca5d-46a9-8b84-69a84682bb52",
+   "metadata": {},
+   "source": [
+    "#### 3.2.3. Step 3: Create a local dataset for IN-CORE\n",
+    "The following code create a local Dataset object with ESRI shapefile for buiding inventory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5f51e8e-074b-4e31-aa24-9c23d38564b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use the method \"from_file\" in Dataset class to construct dataset object from local file\n",
+    "from pyincore import Dataset\n",
+    "\n",
+    "buildings = Dataset.from_file(\"joplin_commercial_bldg_v6_sample_w_guid.shp\", data_type=\"ergo:buildingInventoryVer6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b1d7fc8-d650-4367-a487-c1de2fd50220",
+   "metadata": {},
+   "source": [
+    "#### 2.3.4. Step 4: Visualize created building dataset\n",
+    "For your reference, Details about Joplin building archetypes see below table\n",
+    "| Archetypes | Building Type                 |\n",
+    "|------------|-------------------------------|\n",
+    "|   1, 5  | Residential wood building     |\n",
+    "|     **6**   |  **Business and retail building**  |\n",
+    "|     **7**   | **Light industrial building**     |\n",
+    "|     8   | Heavy industrial building     |\n",
+    "|     9   | Elementary/middle school      |\n",
+    "|    10   | High school                   |\n",
+    "|    11   | Fire/police station           |\n",
+    "|    12   | Hospital                      |\n",
+    "|    13   | Community center/church       |\n",
+    "|    14   | Government building           |\n",
+    "|    15   | Large big-box                 |\n",
+    "|    16   | Small big-box                 |\n",
+    "|    17   | Mobile home                   |\n",
+    "|    18   | Shopping center               |\n",
+    "|    19   | Office building               |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c6e20c6-7751-4c10-be91-e38475f44b03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "viz.plot_map(buildings, column=\"archetype\", category=\"True\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37e0f05a-8b70-4493-a219-b31f6a6b578a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# interactive map leveraging geopands explore\n",
+    "buildings.get_dataframe_from_shapefile().explore(\n",
+    "    marker_kwds={\"radius\": 10},\n",
+    "    column=\"archetype\",  # make choropleth based on \"BoroName\" column\n",
+    "    tooltip=\"archetype\",  # show \"BoroName\" value in tooltip (on hover)\n",
+    "    popup=True,  # show all values in popup (on click)\n",
+    "    cmap=\"Set1\",  # use \"Set1\" matplotlib colormap)\n",
+    "    categorical=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ecc0f3c-33a6-4eeb-9cf8-a1c02d292b95",
+   "metadata": {},
+   "source": [
+    "#### 2.3.5. Overlay tornado on building"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a7db8b8-77a3-4cf6-bb3f-5b2b78504d4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tornado_dataset = tornado.hazardDatasets[0].dataset\n",
+    "\n",
+    "# fill in appropriate metadata\n",
+    "buildings.metadata = {\"title\": \"Joplin Commercial Buiding Sampled\"}\n",
+    "tornado_dataset.metadata = {\"title\": \"Joplin Hindcast Tornado\"}\n",
+    "\n",
+    "# plot interactive map\n",
+    "viz.get_gdf_map([buildings, tornado_dataset])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55512735-51d9-4c87-8dd5-10bbf7576e7f",
+   "metadata": {},
+   "source": [
+    "## 4. Creating local DFR3 objects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fb9fcf1-2ee2-4f74-9ab6-5f5046d696de",
+   "metadata": {},
+   "source": [
+    "### 4.1. Create Fragility Objectcs\n",
+    "In this section we are going to explore the creation of fragility curve sets used by the pyIncore in Building Damage analyses. We provide examples of getting the curves into your project as well as basic use of pyIncore's functions to print and visualize various attributes and variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad7b54d0-a648-4081-a953-b3debcb82081",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyincore import FragilityService, FragilityCurveSet, RepairService, RepairCurveSet, Mapping, MappingSet\n",
+    "import copy\n",
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58a898ee-46f2-408a-ba1a-01bb6f4d31d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fragility_curve_template = {\n",
+    "    \"description\": \"\",\n",
+    "    \"rules\": [],\n",
+    "    \"returnType\": {\n",
+    "        \"type\": \"\",\n",
+    "        \"unit\": \"\",\n",
+    "        \"description\": \"\",\n",
+    "    },\n",
+    "    \"curveParameters\": None\n",
+    "}\n",
+    "\n",
+    "curve_rule_template =  {\n",
+    "    \"condition\": [],\n",
+    "    \"expression\": \"\"\n",
+    "}\n",
+    "\n",
+    "curve_parameter_template = {\n",
+    "    \"name\": \"\",\n",
+    "    \"unit\": \"\",\n",
+    "    \"description\": \"\",\n",
+    "    \"fullName\": \"\",\n",
+    "    \"expression\": None\n",
+    "}\n",
+    "\n",
+    "fragility_definition_template = {\n",
+    "    \"description\": \"\",\n",
+    "    \"authors\": [],\n",
+    "    \"paperReference\": None,\n",
+    "    \"resultUnit\": None,\n",
+    "    \"resultType\": \"Limit State\",\n",
+    "    \"hazardType\": \"\",\n",
+    "    \"inventoryType\": \"\",\n",
+    "    \"curveParameters\": [],\n",
+    "    \"demandTypes\": [],\n",
+    "    \"demandUnits\": [],\n",
+    "    \"fragilityCurves\": []\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddabad43-7ced-4f1c-a4f1-48dbf825a121",
+   "metadata": {},
+   "source": [
+    "#### 4.1.1. Step 1: Define Curve Paramters\n",
+    "For every parameter mentioned in the equation, it is essential to provide a precise definition.\n",
+    "For example:\n",
+    "> scipy.stats.norm.cdf((math.log(**wind**) - (4.075))/(0.21))\n",
+    "\n",
+    "Note that in the equation, we haven't adequately defined the term **\"wind.\"** \n",
+    "\n",
+    "\n",
+    "**Table 1. Default Curve Parameters**\n",
+    "| Parameter name | unit | description | expression |\n",
+    "|---|---|---|---|\n",
+    "|wind | mps | Wind value from hazard service | N/A |\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6f5c153-4455-4098-8af5-03e9cb591ade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "curve_parameter = copy.deepcopy(curve_parameter_template)\n",
+    "curve_parameter[\"name\"] = \"wind\"\n",
+    "curve_parameter[\"unit\"] = \"mps\"\n",
+    "curve_parameter[\"description\"] = \"wind value from hazard servic\"\n",
+    "curve_parameter[\"fullName\"] = \"wind\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41122bba-75e7-403b-bab4-3014cbfb00dd",
+   "metadata": {},
+   "source": [
+    "#### 4.1.2. Step 2: Define each fragility curve\n",
+    "\n",
+    "For given fragility curveset\n",
+    "\n",
+    "**Table 2. Curve Information**\n",
+    "|Limit State| condition|expression|Description |\n",
+    "|---|---|---|---|\n",
+    "|0|wind > 0|scipy.stats.norm.cdf((math.log(wind) - (3.625))/(0.11))|legacy - StandardFragilityCurve - Moderate|\n",
+    "|1|wind > 0|scipy.stats.norm.cdf((math.log(wind) - (3.895))/(0.11))|legacy - StandardFragilityCurve - Extensive|\n",
+    "|2|wind > 0|scipy.stats.norm.cdf((math.log(wind) - (4.075))/(0.21))|legacy - StandardFragilityCurve - Complete|\n",
+    "\n",
+    "> Caution: It is crucial to verify the validity and safety of your equation. Our platform supports Scipy, Numpy, and Math libraries for this purpose.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e00e41a3-2204-4855-8d2c-ca79c6d78d1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# instantiate a copy from template\n",
+    "LS_0_curve_rule = copy.deepcopy(curve_rule_template)\n",
+    "\n",
+    "# add condition\n",
+    "LS_0_curve_rule[\"condition\"] = [\"wind > 0\"]\n",
+    "\n",
+    "# add equation\n",
+    "LS_0_curve_rule[\"expression\"] = \"scipy.stats.norm.cdf((math.log(wind) - (3.625))/(0.11))\"\n",
+    "\n",
+    "# add metadata\n",
+    "LS_0_curve = copy.deepcopy(fragility_curve_template)\n",
+    "LS_0_curve[\"description\"] = \"legacy - StandardFragilityCurve - Moderate\"\n",
+    "LS_0_curve[\"returnType\"][\"type\"] = \"Limit State\"\n",
+    "LS_0_curve[\"returnType\"][\"unit\"] = \"\"\n",
+    "LS_0_curve[\"returnType\"][\"description\"] = \"LS_0\"\n",
+    "\n",
+    "# append the aforementioned rule\n",
+    "LS_0_curve[\"rules\"].append(LS_0_curve_rule)\n",
+    "pprint(LS_0_curve)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd3ea18d-7650-40ce-b575-a348492ffa0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Similarly construct LS_1\n",
+    "LS_1_curve_rule = copy.deepcopy(curve_rule_template)\n",
+    "LS_1_curve_rule[\"condition\"] = [\"wind > 0\"]\n",
+    "LS_1_curve_rule[\"expression\"] = \"scipy.stats.norm.cdf((math.log(wind) - (3.895))/(0.11))\"\n",
+    "\n",
+    "LS_1_curve = copy.deepcopy(fragility_curve_template)\n",
+    "LS_1_curve[\"description\"] = \"legacy - StandardFragilityCurve - Extensive\"\n",
+    "LS_1_curve[\"returnType\"][\"type\"] = \"Limit State\"\n",
+    "LS_1_curve[\"returnType\"][\"unit\"] = \"\"\n",
+    "LS_1_curve[\"returnType\"][\"description\"] = \"LS_1\"\n",
+    "\n",
+    "# append the aforementioned rule\n",
+    "LS_1_curve[\"rules\"].append(LS_1_curve_rule)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc27e272-e1cd-4ec9-b6db-8372dbf7980d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# QUIZ 1: Define limit state 2 curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9bd3842-cec1-4e61-8c2c-7ddccf7a8c58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Similarly construct LS_2\n",
+    "LS_2_curve_rule = copy.deepcopy(curve_rule_template)\n",
+    "LS_2_curve_rule[\"condition\"] = [\"wind > 0\"]\n",
+    "LS_2_curve_rule[\"expression\"] = \"scipy.stats.norm.cdf((math.log(wind) - (4.075))/(0.21))\"\n",
+    "\n",
+    "LS_2_curve = copy.deepcopy(fragility_curve_template)\n",
+    "LS_2_curve[\"description\"] = \"legacy - StandardFragilityCurve - Complete\"\n",
+    "LS_2_curve[\"returnType\"][\"type\"] = \"Limit State\"\n",
+    "LS_2_curve[\"returnType\"][\"unit\"] = \"\"\n",
+    "LS_2_curve[\"returnType\"][\"description\"] = \"LS_2\"\n",
+    "\n",
+    "# append the aforementioned rule\n",
+    "LS_2_curve[\"rules\"].append(LS_2_curve_rule)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7392d74c-8979-489f-ac39-218a1a13ef4d",
+   "metadata": {},
+   "source": [
+    "#### 4.1.3. Step 3: Piece together the complete fragility curve set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "180e36e5-cb1c-4893-ba77-b6a0d056ea78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fragility_definition_archetype_6 = copy.deepcopy(fragility_definition_template)\n",
+    "fragility_definition_archetype_6[\"description\"] = \"Business and retail building (strip mall)\"\n",
+    "fragility_definition_archetype_6[\"authors\"].append(\"M. Memari N. Attary H. Masoomi J.W. van de Lindt  S.F. Pilkington M.R. Ameri & H. Mahmoud\")\n",
+    "fragility_definition_archetype_6[\"hazardType\"] = \"tornado\"\n",
+    "fragility_definition_archetype_6[\"inventoryType\"] = \"building\"\n",
+    "fragility_definition_archetype_6[\"demandTypes\"].append(\"wind\")\n",
+    "fragility_definition_archetype_6[\"demandUnits\"].append(\"mps\")\n",
+    "\n",
+    "# fill in the curves\n",
+    "fragility_definition_archetype_6[\"fragilityCurves\"].append(LS_0_curve)\n",
+    "fragility_definition_archetype_6[\"fragilityCurves\"].append(LS_1_curve)              \n",
+    "fragility_definition_archetype_6[\"fragilityCurves\"].append(LS_2_curve)\n",
+    "\n",
+    "# fill in the curve parameters\n",
+    "fragility_definition_archetype_6[\"curveParameters\"].append(curve_parameter)\n",
+    "pprint(fragility_definition_archetype_6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dccdde5d-c9b0-4a20-8a32-d828ae425688",
+   "metadata": {},
+   "source": [
+    "#### 4.1.4. Step 4: Create fragility curve set object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "321b3fd4-88cc-4778-a5df-204b4746663c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fragility_archetype_6 = FragilityCurveSet(fragility_definition_archetype_6)\n",
+    "fragility_archetype_6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "107059f8-c5a8-46de-8772-f31eef46f8ff",
+   "metadata": {},
+   "source": [
+    "#### 4.1.5. Alternative: Construct fragility curves from string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "498cf910-9ce7-4a5a-8517-c5f3376293b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fragiity definition as string\n",
+    "fragility_definition_archetype_7 = \"\"\"{\n",
+    "    \"description\": \"Light industrial\",\n",
+    "    \"authors\": [\n",
+    "        \"M. Memari N. Attary H. Masoomi J.W. van de Lindt  S.F. Pilkington M.R. Ameri & H. Mahmoud\"\n",
+    "    ],\n",
+    "    \"paperReference\": null,\n",
+    "    \"resultUnit\": null,\n",
+    "    \"resultType\": \"Limit State\",\n",
+    "    \"hazardType\": \"tornado\",\n",
+    "    \"inventoryType\": \"building\",\n",
+    "    \"creator\": \"incore\",\n",
+    "    \"owner\": \"incore\",\n",
+    "    \"curveParameters\": [\n",
+    "        {\n",
+    "            \"name\": \"wind\",\n",
+    "            \"unit\": \"mps\",\n",
+    "            \"description\": \"wind value from hazard service\",\n",
+    "            \"fullName\": \"wind\",\n",
+    "            \"expression\": null\n",
+    "        }\n",
+    "    ],\n",
+    "    \"demandTypes\": [\n",
+    "        \"wind\"\n",
+    "    ],\n",
+    "    \"demandUnits\": [\n",
+    "        \"mps\"\n",
+    "    ],\n",
+    "    \"fragilityCurves\": [\n",
+    "        {\n",
+    "            \"description\": \"legacy - StandardFragilityCurve - Moderate\",\n",
+    "            \"rules\": [\n",
+    "                {\n",
+    "                    \"condition\": [\n",
+    "                        \"wind > 0\"\n",
+    "                    ],\n",
+    "                    \"expression\": \"scipy.stats.norm.cdf((math.log(wind) - (3.695))/(0.1))\"\n",
+    "                }\n",
+    "            ],\n",
+    "            \"returnType\": {\n",
+    "                \"type\": \"Limit State\",\n",
+    "                \"unit\": \"\",\n",
+    "                \"description\": \"LS_0\"\n",
+    "            },\n",
+    "            \"curveParameters\": null\n",
+    "        },\n",
+    "        {\n",
+    "            \"description\": \"legacy - StandardFragilityCurve - Extensive\",\n",
+    "            \"rules\": [\n",
+    "                {\n",
+    "                    \"condition\": [\n",
+    "                        \"wind > 0\"\n",
+    "                    ],\n",
+    "                    \"expression\": \"scipy.stats.norm.cdf((math.log(wind) - (3.785))/(0.1))\"\n",
+    "                }\n",
+    "            ],\n",
+    "            \"returnType\": {\n",
+    "                \"type\": \"Limit State\",\n",
+    "                \"unit\": \"\",\n",
+    "                \"description\": \"LS_1\"\n",
+    "            },\n",
+    "            \"curveParameters\": null\n",
+    "        },\n",
+    "        {\n",
+    "            \"description\": \"legacy - StandardFragilityCurve - Complete\",\n",
+    "            \"rules\": [\n",
+    "                {\n",
+    "                    \"condition\": [\n",
+    "                        \"wind > 0\"\n",
+    "                    ],\n",
+    "                    \"expression\": \"scipy.stats.norm.cdf((math.log(wind) - (3.865))/(0.1))\"\n",
+    "                }\n",
+    "            ],\n",
+    "            \"returnType\": {\n",
+    "                \"type\": \"Limit State\",\n",
+    "                \"unit\": \"\",\n",
+    "                \"description\": \"LS_2\"\n",
+    "            },\n",
+    "            \"curveParameters\": null\n",
+    "        }\n",
+    "    ]\n",
+    "}\"\"\"\n",
+    "\n",
+    "# create fragility curve set object\n",
+    "fragility_archetype_7 = FragilityCurveSet.from_json_str(fragility_definition_archetype_7)\n",
+    "fragility_archetype_7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "452135e1-44dc-48f3-bf0b-03c383f3e4c1",
+   "metadata": {},
+   "source": [
+    "#### 4.1.6. Visualize fragility curves\n",
+    "Use pyincore-viz library to visualize fragility curve set. Details can be found on [pyincore-viz's Documentation](https://incore.ncsa.illinois.edu/doc/pyincore_viz/modules.html#plotutil)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "375e5b3b-6f03-4b3c-8a56-0256578ee47f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyincore_viz.plotutil import PlotUtil as plot\n",
+    "plt = plot.get_fragility_plot(fragility_archetype_6, start=0, end=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45dc7083-e00c-46da-ae3a-ed6c4ffc3f36",
+   "metadata": {},
+   "source": [
+    "#### 4.1.7. Calculate limit state from local fragility"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb4b24bd-ca1b-411b-850e-c5dc0c9753d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hazard_value = {\"wind\":40}\n",
+    "limit_states = fragility_archetype_6.calculate_limit_state(hazard_value)\n",
+    "limit_states"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fed8040d-d318-4c0c-bdf4-fd37eb237184",
+   "metadata": {},
+   "source": [
+    "#### 4.1.8. Derivie damage state from limit state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d104f8a-ec82-499a-84ef-19852eb76cb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fragility_archetype_6._3ls_to_4ds(limit_states)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "164e1d2b-2448-4c8c-bc03-23a82688e018",
+   "metadata": {},
+   "source": [
+    "### 4.2. Create Fragility Mapping\n",
+    "\n",
+    "DFR3 mapping sets serve the purpose of establishing a connection between an item within a given inventory and corresponding DFR3 curves. For example, a particular set of fragility curves should be applied to a building based on its specific characteristics. \n",
+    "\n",
+    "![Local Image](./img/INSPIRE_Workshop_Session_4_mapping.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "360926ae-67c2-40ef-9e16-93ebdacfe216",
+   "metadata": {},
+   "source": [
+    "#### 4.2.1. Step 1: Establish mapping rules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f0b5c9f-a0c4-4c1e-868d-c8c6338bb6fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fragility_entry_archetype_6 = {\"Non-Retrofit Fragility ID Code\": fragility_archetype_6}\n",
+    "fragility_rules_archetype_6 = {\"OR\":[\"int archetype EQUALS 6\"]}\n",
+    "fragility_mapping_archetype_6 = Mapping(fragility_entry_archetype_6, fragility_rules_archetype_6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7941193d-1646-4603-b835-5e41077aa75e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Quiz: write rules for archetype 7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39292189-a16b-4f53-8066-99dd6fc30646",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fragility_entry_archetype_7 = {\"Non-Retrofit Fragility ID Code\": fragility_archetype_7}\n",
+    "fragility_rules_archetype_7 = {\"OR\":[\"int archetype EQUALS 7\"]}\n",
+    "fragility_mapping_archetype_7 = Mapping(fragility_entry_archetype_7, fragility_rules_archetype_7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ecdbfc6-e4a6-45d6-aa66-e892b77fe878",
+   "metadata": {},
+   "source": [
+    "#### 4.2.2 Step 2: Add metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3695fb8-5372-45c8-a14d-c07d8d0c5097",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fragility_mapping_set_definition = {\n",
+    "    \"id\": \"N/A\",\n",
+    "    \"name\": \"local joplin tornado fragility mapping object\",\n",
+    "    \"hazardType\": \"tornado\",\n",
+    "    \"inventoryType\": \"building\",\n",
+    "    'mappings': [\n",
+    "        fragility_mapping_archetype_6,\n",
+    "        fragility_mapping_archetype_7,\n",
+    "    ],\n",
+    "    \"mappingType\": \"fragility\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea116785-1f19-491e-adbd-ee94f34a2519",
+   "metadata": {},
+   "source": [
+    "#### 4.2.3. Step 3: Create MappingSet Object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7900e475-f17b-409b-8d82-10ac0ff39e80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fragility_mapping_set = MappingSet(fragility_mapping_set_definition)\n",
+    "fragility_mapping_set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d5c8e05-51a4-497f-83da-448399173e83",
+   "metadata": {},
+   "source": [
+    "## 5. Building Damage Analysis\n",
+    "This analysis computes building damage based on a particular hazard. In this secion, you will perform building damage anlayis for tornado in this tutorial.\n",
+    "\n",
+    "The process for computing the structural damage is similar to other parts of the built environment. First, a fragility is obtained based on the hazard type and attributes of the building. Based on the fragility, the hazard intensity at the location of the building is computed. Using this information, the probability of exceeding each limit state is computed, along with the probability of damage. For the case of an earthquake hazard, soil information can be used to modify the damage probabilities to include damage due to liquefaction.\n",
+    "\n",
+    "The outputs of this analysis are CSV file with probabilities of damage and JSON file with information about hazard and fragilities. The detail information about the analysis in our manual: [Building damage analysis](https://incore.ncsa.illinois.edu/doc/incore/analyses/building_dmg.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55c62677-bff0-4766-a546-30e1a6465177",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyincore.analyses.buildingdamage import BuildingDamage\n",
+    "from pyincore import IncoreClient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02b73849-82c2-4901-82ea-70171e319081",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = IncoreClient(offline=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67f6a7dc-73f9-4e80-bd3b-aeabba495d7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create building damage\n",
+    "bldg_dmg = BuildingDamage(client)\n",
+    "\n",
+    "# Load input dataset\n",
+    "bldg_dmg.set_input_dataset(\"buildings\", buildings)\n",
+    "\n",
+    "# Load fragility mapping\n",
+    "fragility_service = FragilityService(client)\n",
+    "bldg_dmg.set_input_dataset(\"dfr3_mapping_set\", fragility_mapping_set)\n",
+    "\n",
+    "# Set hazard\n",
+    "bldg_dmg.set_input_hazard(\"hazard\", tornado)\n",
+    "\n",
+    "# Set analysis parameters\n",
+    "bldg_dmg.set_parameter(\"result_name\", \"joplin_tornado_commerical_bldg_dmg\")\n",
+    "bldg_dmg.set_parameter(\"num_cpu\", 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac8619b2-34ad-4395-9bcd-9fc5ad23e917",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run building damage analysis\n",
+    "bldg_dmg.run_analysis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efd15a50-a691-4294-a5c3-078838221a88",
+   "metadata": {},
+   "source": [
+    "### 5.1. Explore building damage resutls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91d8654d-bace-4bb1-b678-8378f4b9747e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# display building damage\n",
+    "bldg_dmg_df = bldg_dmg.get_output_dataset(\"ds_result\").get_dataframe_from_csv()\n",
+    "bldg_dmg_df.head(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d12a9663-e12e-4bc9-8f9b-d79a682d111f",
+   "metadata": {},
+   "source": [
+    "#### 5.1.1. Joining dataset\n",
+    "Data preparation and data post-processing are common procedures. Prior to using pyIncore, users often encounter situation that they need to reshape their own dataset to make it compliant with the input dataset format of pyIncore.\n",
+    "\n",
+    "After acquiring outputs from pyIncore analyses, often time user would need to perform data aggregation to gain statitical insights. The below tutorial gives a few examples on how to join datasets and generate some basic visualizations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec957dab-d476-4b9e-84f6-de0b1f24afcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# getting geodataframe of building dataset \n",
+    "bldg_gdf = buildings.get_dataframe_from_shapefile()\n",
+    "\n",
+    "# merge/join two dataframe\n",
+    "# you can choose columns to be merged\n",
+    "bldg_gdf_merged = bldg_gdf[['guid', 'archetype', 'geometry']].merge(bldg_dmg_df, on='guid')\n",
+    "bldg_gdf_merged.head(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e8d1886-ead6-434c-b3a8-547df9f39eaa",
+   "metadata": {},
+   "source": [
+    "#### 5.1.2. Show statistical summary on a column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5861c5c8-fe0a-4cf8-9ecc-e758432fc0fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bldg_gdf_merged[\"LS_0\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33bd5a88-1005-48f4-b86f-dfac62c34b80",
+   "metadata": {},
+   "source": [
+    "#### 5.1.3. Show table sorted by LS_0 (decending) and archetype (ascending)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03e16a0c-de33-4e30-b46e-10c195705f97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bldg_gdf_merged.sort_values(['LS_0', 'archetype'], ascending=[0,1]).head(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17baa505-9e01-489c-af88-63bc5524e5e8",
+   "metadata": {},
+   "source": [
+    "#### 5.1.4. Show table group by archetype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0e579c5-cf8d-4bf0-a03a-b8cd8e4054dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grouped_bldg_dmg = bldg_gdf_merged.groupby(by=['archetype'], as_index=True).agg({'DS_0': 'mean', 'DS_1':'mean', 'DS_2': 'mean', 'DS_3': 'mean', 'guid': 'count'})\n",
+    "grouped_bldg_dmg.rename(columns={'guid': 'total_count'}, inplace=True)\n",
+    "grouped_bldg_dmg.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2d7d9d3-5efe-49c2-9d83-66b7a85af452",
+   "metadata": {},
+   "source": [
+    "### 5.2. Visualization building damage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3127456-c055-4921-b860-0ea03fe27b97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hazard Exposure\n",
+    "count = bldg_gdf_merged['haz_expose'].value_counts()\n",
+    "plt = count.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c7161d0-3c15-440c-95ad-d67899fdd794",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot Damage state by archetype\n",
+    "ax = grouped_bldg_dmg[[\"DS_0\", \"DS_1\", \"DS_2\", \"DS_3\"]].plot.barh(stacked=True)\n",
+    "ax.set_title(\"Stacked Bar Chart of Damage State Grouped by Archetype Type\", fontsize=12)\n",
+    "ax.set_xlabel(\"complete damage value\", fontsize=12)\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) #here is the magic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efb338a7-ab58-4218-a40e-ddf7d64f4fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot a map with GeoDataframe\n",
+    "viz.plot_gdf_map(bldg_gdf_merged, \"DS_3\", basemap=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5eafbede-14ed-4382-bb5d-cf457e4672d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# leveraging geopandas explore\n",
+    "bldg_gdf_merged.explore(\n",
+    "    marker_kwds={\"radius\": 10},\n",
+    "    column=\"DS_3\",  # make choropleth based on \"BoroName\" column\n",
+    "    tooltip=\"DS_3\",  # show \"BoroName\" value in tooltip (on hover)\n",
+    "    popup=True,  # show all values in popup (on click)\n",
+    "    cmap=\"Set1\",  # use \"Set1\" matplotlib colormap)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1244b15-6f05-42f9-902a-322278bcfa7a",
+   "metadata": {},
+   "source": [
+    "## 6. Monte Carlo Simulation\n",
+    "\n",
+    "Researchers can use Monte Carlo Simulation to estimate the probability of each building being in a particular damage state. This example uses 500 iterations to determine the failure probability of buildings reaching damage state 2, damage state 3, and damage state 4. Users can run 10000 samples or even more for a more accurate Monte Carlo Simulation to determine the building failure probabilities. Note that this takes several minutes and we are working on developing a more efficient algorithm. \n",
+    "\n",
+    "For more details, please refer to [Monte Carlo Simulation Analysis Documentation](https://incore.ncsa.illinois.edu/doc/incore/analyses/mc_failure_prob.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad9da172-d0fc-4f63-ab71-9d588c49abd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyincore.analyses.montecarlofailureprobability import MonteCarloFailureProbability "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e5b314b-d084-40ce-8349-864de0902799",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mc_bldg = MonteCarloFailureProbability(client)\n",
+    "\n",
+    "# get bldg dmg result from previous step of building damage\n",
+    "bldg_dmg_result = bldg_dmg.get_output_dataset(\"ds_result\")\n",
+    "mc_bldg.set_input_dataset(\"damage\", bldg_dmg_result)\n",
+    "\n",
+    "# set input parameters\n",
+    "mc_bldg.set_parameter(\"num_cpu\", 8)\n",
+    "mc_bldg.set_parameter(\"num_samples\", 500)\n",
+    "mc_bldg.set_parameter(\"damage_interval_keys\", [\"DS_0\", \"DS_1\", \"DS_2\", \"DS_3\"])\n",
+    "mc_bldg.set_parameter(\"failure_state_keys\", [\"DS_1\", \"DS_2\", \"DS_3\"])\n",
+    "\n",
+    "mc_bldg.set_parameter(\"result_name\", \"joplin_tornado_mc_failure_probability_buildings\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96a6370a-eed9-4738-bbcf-7e53e11128f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run the Monte Carlo Simulation module to obtain the building failure probabilities. The building failure   \n",
+    "# probabilities herein only consider the physical damage without the interdependency.\n",
+    "mc_bldg.run_analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6e16463-db5f-449b-ba91-0b2491d2d4b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# list all the outputs\n",
+    "mc_bldg.get_output_datasets()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "220aed80-9525-4d6f-addd-09979e080770",
+   "metadata": {},
+   "source": [
+    "### 6.1. Get buildings non-functional probabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e7f59d8-b42f-4dd7-abde-ae9b97f80f4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bldg_failure_probability = mc_bldg.get_output_dataset('failure_probability')\n",
+    "bldg_failure_probability_df = bldg_failure_probability.get_dataframe_from_csv()\n",
+    "bldg_failure_probability_df.head(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e821ad8-dae4-428d-a240-d736b8ad5131",
+   "metadata": {},
+   "source": [
+    "### 6.2. Get buildings sample damage states"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66eeb33f-42ef-4b49-b2d3-be9b0c0ac47c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bldg_damage_mcs_samples = mc_bldg.get_output_dataset('sample_damage_states')\n",
+    "bldg_damage_mcs_samples_df = bldg_damage_mcs_samples.get_dataframe_from_csv()\n",
+    "bldg_damage_mcs_samples_df.head(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "263511c4-b78a-44f0-87d8-c45a0fa804a1",
+   "metadata": {},
+   "source": [
+    "### 6.3. Visualize probability of non-functionality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28bdc948-b939-45e4-9cf9-04eb60f53959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# getting geodataframe of building dataset and merge with output\n",
+    "bldg_gdf = buildings.get_dataframe_from_shapefile()\n",
+    "bldg_failure_probability_gdf = bldg_gdf.merge(bldg_failure_probability_df, on=\"guid\")\n",
+    "\n",
+    "viz.plot_gdf_map(bldg_failure_probability_gdf, column=\"failure_probability\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e3d44db-aa05-4d54-8171-72999cbd2338",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bldg_failure_probability_gdf.explore(\n",
+    "    marker_kwds={\"radius\": 10},\n",
+    "    column=\"failure_probability\",  # make choropleth based on \"BoroName\" column\n",
+    "    tooltip=\"failure_probability\",  # show \"BoroName\" value in tooltip (on hover)\n",
+    "    popup=True,  # show all values in popup (on click)\n",
+    "    cmap=\"Set1\",  # use \"Set1\" matplotlib colormap)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53484008-6927-4847-bfc3-2651da03c48b",
+   "metadata": {},
+   "source": [
+    "## 7. Commercial Building Recovery Analysis\n",
+    "\n",
+    "Commercial building recovery analysis computes the recovery time needed for each commercial building from any damage states to receive the full restoration. Currently, supported hazards are tornadoes. The methodology incorporates the multi-layer Monte Carlo simulation approach and determines the two-step recovery time that includes delay and repair. The delay model was modified based on the REDi framework and calculated the end-result outcomes resulted from delay impeding factors such as post-disaster inspection, insurance claim, financing and government permit. The repair model followed the FEMA P-58 approach and was controlled by fragility functions. \n",
+    "The outputs of this analysis is a CSV file with time-stepping recovery probabilities at the building level.\n",
+    "\n",
+    "To read more about this analysis, please visit [Commercial Building Recovery Documentation](https://incore.ncsa.illinois.edu/doc/incore/analyses/commercial_building_recovery.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a16b77a9-04b6-4c09-af4e-41f11aee132e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyincore.analyses.commercialbuildingrecovery.commercialbuildingrecovery import CommercialBuildingRecovery\n",
+    "\n",
+    "commercial_recovery = CommercialBuildingRecovery(client)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49f2ccf3-92cf-4b09-ab9f-fbbcf159dca0",
+   "metadata": {},
+   "source": [
+    "### 7.1. Step 1: Prepare repair curve sets and repair curve mappings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87334910-edd1-4b9d-8433-a2b53408562f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# repair curve set\n",
+    "repair_archetype_6 = RepairCurveSet.from_json_file(\"data/dfr3/repair_archetype_6.json\")\n",
+    "repair_archetype_7 = RepairCurveSet.from_json_file(\"data/dfr3/repair_archetype_7.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83463638-66a6-4af5-b3b1-b48ffcd5f684",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define mapping rules\n",
+    "repair_entry_archetype_6 = {\"Repair ID Code\": repair_archetype_6}\n",
+    "repair_rules_archetype_6 = {\"OR\":[\"int archetype EQUALS 6\"]}\n",
+    "repair_mapping_archetype_6 = Mapping(repair_entry_archetype_6, repair_rules_archetype_6)\n",
+    "\n",
+    "repair_entry_archetype_7 = {\"Repair ID Code\": repair_archetype_7}\n",
+    "repair_rules_archetype_7 = {\"OR\":[\"int archetype EQUALS 7\"]}\n",
+    "repair_mapping_archetype_7 = Mapping(repair_entry_archetype_7, repair_rules_archetype_7)\n",
+    "\n",
+    "# construct mapping\n",
+    "repair_mapping_set_definition = {\n",
+    "    \"id\": \"N/A\",\n",
+    "    \"name\": \"Building Mean structural repair cost mapping for archetypes 6 & 7\",\n",
+    "    \"hazardType\": \"tornado\",\n",
+    "    \"inventoryType\": \"building\",\n",
+    "    'mappings': [\n",
+    "        repair_mapping_archetype_6,\n",
+    "        repair_mapping_archetype_7,\n",
+    "    ],\n",
+    "    \"mappingType\": \"repair\"\n",
+    "}\n",
+    "repair_mapping_set = MappingSet(repair_mapping_set_definition)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1792d818-55ed-4ce1-8cc8-e6c7ae3726e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set recovery mapping\n",
+    "commercial_recovery.set_input_dataset('dfr3_mapping_set', repair_mapping_set)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0a6017a-6db5-4864-8ef0-6860e27cfb68",
+   "metadata": {},
+   "source": [
+    "### 7.2. Step 2: Chain with MCS output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31cfb090-378d-4561-a133-126711dadf92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "commercial_recovery.set_input_dataset(\"sample_damage_states\", bldg_damage_mcs_samples)\n",
+    "commercial_recovery.set_input_dataset(\"mcs_failure\", bldg_failure_probability)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ad6f9dd-b6b1-4628-894f-76e202a75188",
+   "metadata": {},
+   "source": [
+    "### 7.3. Step 3: Prepare additional input datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0dae0f53-131d-4c3d-ae79-f63214f05ce8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# delay factor\n",
+    "delay_factors = Dataset.from_file(\"data/additional/Dataset1_REDi_business_new.csv\", data_type=\"incore:buildingRecoveryFactors\")\n",
+    "commercial_recovery.set_input_dataset(\"delay_factors\", delay_factors)\n",
+    "\n",
+    "# building inventory\n",
+    "commercial_recovery.set_input_dataset(\"buildings\", buildings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecae1f29-cc3c-47f5-a2a5-8319d56ec7f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set other parameters\n",
+    "commercial_recovery.set_parameter(\"result_name\", \"joplin_tornado_commercial_bldg_recovery\")\n",
+    "commercial_recovery.set_parameter(\"num_samples\", 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9989ad9-6e4a-4efa-abe7-5259d95a266b",
+   "metadata": {},
+   "source": [
+    "### 7.4. Step 4: Run commercial recovery analysis "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c8772df-fed7-41b0-bf61-789fd7c90fab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "commercial_recovery.run_analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be880a94-9345-4d83-876b-0de33fff258e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "commercial_recovery.get_output_datasets()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86c8c96a-ad60-4fe6-8d0a-9e590deced86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_stepping_recovery = commercial_recovery.get_output_dataset(\"time_stepping_recovery\")\n",
+    "time_stepping_recovery_df = time_stepping_recovery.get_dataframe_from_csv()\n",
+    "time_stepping_recovery_df.head(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "859b0036-4f2d-4704-a834-ec95654bbaaa",
+   "metadata": {},
+   "source": [
+    "### 7.5. Visualize recovery grouped by archetypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3f802f8-0025-46c8-aaa8-4067f2b8ade1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# merging with building inventory\n",
+    "bldg_gdf = buildings.get_dataframe_from_shapefile()\n",
+    "time_stepping_recovery_df = bldg_gdf[[\"guid\", \"archetype\"]].merge(time_stepping_recovery_df, on=\"guid\").drop([\"guid\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd6254b0-f695-44ce-9fc5-d51df1a8dfcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_stepping_recovery_grouped = time_stepping_recovery_df.groupby(\"archetype\").agg(\"mean\")\n",
+    "time_stepping_recovery_grouped.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d1f03c9-e750-4ed9-99b6-2fd4e2f0211b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = time_stepping_recovery_grouped.copy(deep=True) \n",
+    "df = df.T\n",
+    "ax = df.plot(kind='line', figsize=(12, 3))\n",
+    "ax.set_xlabel('Quarter')\n",
+    "ax.set_ylabel('Recovery Rate')\n",
+    "ax.set_title('Joplin Commercial Building Recovery Curves by Archetype')\n",
+    "ax.legend(title='archetype')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workshops/20231115/jupyter-lab-leftpanel.png b/workshops/20231115/jupyter-lab-leftpanel.png
new file mode 100644
index 00000000..649fea3a
Binary files /dev/null and b/workshops/20231115/jupyter-lab-leftpanel.png differ
diff --git a/workshops/20231115/session1-intro.pdf b/workshops/20231115/session1-intro.pdf
new file mode 100755
index 00000000..1b1eeb46
Binary files /dev/null and b/workshops/20231115/session1-intro.pdf differ
diff --git a/workshops/20231115/session1-jupyter-intro.ipynb b/workshops/20231115/session1-jupyter-intro.ipynb
new file mode 100644
index 00000000..aa54ce2e
--- /dev/null
+++ b/workshops/20231115/session1-jupyter-intro.ipynb
@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2e1c6693",
+   "metadata": {},
+   "source": [
+    "# Session 1: Introduction to Jupyter Notebook\n",
+    "\n",
+    "Perform the following Jupyter notebook operations:\n",
+    "\n",
+    "- Run notebook\n",
+    "- Change a cell and re-run the cell\n",
+    "- Add a new line/cell to a notebook\n",
+    "- Download the workshop material with code cell\n",
+    "- Unzip the downloaded zip file with code cell\n",
+    "\n",
+    "Please follow the instruction at each cell.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d383d41e",
+   "metadata": {},
+   "source": [
+    "## 1. Run notebook\n",
+    "\n",
+    "Please run the code cell below.\n",
+    "\n",
+    "You can hit (Ctrl + Enter) to run current cell or (Shift + Enter) to run current cell and go to next cell.\n",
+    "Alternatively, you can click run cell in the toolbar above.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f670ab37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "message = \"Hello World\"\n",
+    "print(message)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6882dfb8",
+   "metadata": {},
+   "source": [
+    "## 2. Change cell and re-run the notebook\n",
+    "\n",
+    "Modify the message of the print statement below to say **\"Hello IN-CORE\"** and run the current cell.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "533d3de0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "message = \"Hello World\"\n",
+    "print(message)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b9aacb0",
+   "metadata": {},
+   "source": [
+    "## 3. Add a new line and execute it\n",
+    "\n",
+    "Add a new cell below to this notebook. On the new line, modify the **message** variable to print your name.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1789602d-bef0-4777-a6ac-b5aecfefcc90",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 4. Download a zip file of the workshop material\n",
+    "\n",
+    "URL: https://github.com/IN-CORE/incore-docs/raw/main/workshops/20231115/workshop_2023-11-15.zip\n",
+    "\n",
+    "Please run the following code to download a zip file. Please make sure that it appears in your file browser (left panel)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c64fa4e1-ddef-4bb1-ac56-1c9f409952d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import requests module\n",
+    "import requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fe9db06-e80c-4bcc-9d1e-448044367e4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set URL and get file contents\n",
+    "url = \"https://github.com/IN-CORE/incore-docs/raw/main/workshops/20231115/workshop_2023-11-15.zip\"\n",
+    "filename = \"incore-workshop.zip\"\n",
+    "headers = {\"user-agent\": \"Wget/1.16 (linux-gnu)\"}\n",
+    "r = requests.get(url, allow_redirects=True, stream=True, headers=headers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4f1e3b8-7e53-4dd8-bb58-f09660a81e97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write the contents to a file\n",
+    "open(filename, \"wb\").write(r.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9eadca4b-69d3-461f-a4d8-d51dde8168d7",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 5. Unzip the downloaded zip file\n",
+    "\n",
+    "Please run the following code to unzip the downloaded zip file. Please make sure that all the workshop materials appear in your file browser (left panel)\n",
+    "![jupyter-lab-leftpanel.png](jupyter-lab-leftpanel.png)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2f4aad6-d99c-46c8-b165-f043ee273f99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import zipfile module\n",
+    "import zipfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d05564b7-e13f-49da-acc5-80bc3bfecb78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract the zipfile\n",
+    "with zipfile.ZipFile(filename, \"r\") as zip_ref:\n",
+    "    zip_ref.extractall()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7390470e-6a85-49cb-b54d-cef005dc9591",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d9eeb0b-d255-46e8-a27b-a9104c30382b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workshops/20231115/workshop-2023-11-15.zip b/workshops/20231115/workshop-2023-11-15.zip
new file mode 100644
index 00000000..231c32e6
Binary files /dev/null and b/workshops/20231115/workshop-2023-11-15.zip differ